The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R in the UK Biobank population

Nathalie Chami; Michael Preuss; Ryan W Walker; Arden Moscati; Ruth J F Loos

doi:10.1371/journal.pmed.1003196

. 2020 Jul 21;17(7):e1003196. doi: 10.1371/journal.pmed.1003196

The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R in the UK Biobank population

Nathalie Chami ^1,², Michael Preuss ^1,², Ryan W Walker ³, Arden Moscati ¹, Ruth J F Loos ^1,^2,^3,^*

Editor: Karine Clément⁴

PMCID: PMC7373259 PMID: 32692746

Abstract

Background

Melanocortin 4 receptor (MC4R) deficiency, caused by mutations in MC4R, is the most common cause of monogenic forms of obesity. However, these mutations have often been identified in small-scale, case-focused studies. Here, we assess the penetrance of previously reported MC4R mutations at a population level. Furthermore, we examine why some carriers of pathogenic mutations remain of normal weight, to gain insight into the mechanisms that control body weight.

Methods and findings

We identified 59 known obesity-increasing mutations in MC4R from the Human Gene Mutation Database (HGMD) and Clinvar. We assessed their penetrance and effect on obesity (body mass index [BMI] ≥ 30 kg/m²) in >450,000 individuals (age 40–69 years) of the UK Biobank, a population-based cohort study. Of these 59 mutations, only 11 had moderate-to-high penetrance and increased the odds of obesity by more than 2-fold.

We subsequently focused on these 11 mutations and examined differences between carriers of normal weight and carriers with obesity. Twenty-eight of the 182 carriers of these 11 mutations were of normal weight. Body composition of carriers of normal weight was similar to noncarriers of normal weight, whereas among individuals with obesity, carriers had a somewhat higher BMI than noncarriers (1.44 ± 0.07 standard deviation scores [SDSs] ± standard error [SE] versus 1.29 ± 0.001, P = 0.03), because of greater lean mass (1.44 ± 0.09 versus 1.15 ± 0.002, P = 0.002). Carriers of normal weight more often reported that, already at age 10 years, their body size was below average or average (72%) compared with carriers with obesity (48%) (P = 0.01).

To assess the polygenic contribution to body weight in carriers of normal weight and carriers with obesity, we calculated a genome-wide polygenic risk score for BMI (PRS_BMI). The PRS_BMI of carriers of normal weight (PRS_BMI = -0.64 ± 0.18) was significantly lower than of carriers with obesity (0.40 ± 0.11; P = 1.7 × 10⁻⁶), and tended to be lower than that of noncarriers of normal weight (−0.29 ± 0.003; P = 0.05). Among carriers, those with a low PRS_BMI (bottom quartile) have an approximately 5-kg/m² lower BMI (approximately 14 kg of body weight for a 1.7-m-tall person) than those with a high PRS (top quartile).

Because the UK Biobank population is healthier than the general population in the United Kingdom, penetrance may have been somewhat underestimated.

Conclusions

We showed that large-scale data are needed to validate the impact of mutations observed in small-scale and case-focused studies. Furthermore, we observed that despite the key role of MC4R in obesity, the effects of pathogenic MC4R mutations may be countered, at least in part, by a low polygenic risk potentially representing other innate mechanisms implicated in body weight regulation.

Ruth F Loos and colleagues investigate the penetrance of previously reported MC4R mutations at a population level using UK Biobank data.

Author summary

Why was this study done?

Obesity is a major risk factor for type 2 diabetes, cardiovascular disease, chronic kidney disease, and many cancers.
The melanocortin 4 receptor (MC4R) plays an important role in regulating energy balance and satiety. Mutations in MC4R, although rare (<1% of the population), represent the commonest cause of extreme early onset obesity.
Mutations in MC4R have been identified predominantly in small-scale studies of individuals with obesity. The mutations’ impact on obesity risk in the general population remains to be studied. Furthermore, it is not clear why some carriers of MC4R mutations maintain a normal weight, even when the mutation was shown to increase risk of obesity.

What did the researchers do and find?

For 59 mutations previously reported to possibly cause obesity, we determined how many individuals, of a large-scale, population-based cohort (N > 450,000), carried the obesity-increasing allele and how many of these carriers had obesity (i.e., penetrance of mutation).

For 11 of these mutations, the penetrance of obesity was high.
Of the 182 individuals who carried at least one of these 11 mutations, 154 (85%) individuals had obesity/overweight, whereas 28 (15%) individuals were of normal weight.
We observed that, compared with carriers who had obesity, the 28 carriers of normal weight have other inherited genetic variants that overall predispose them to a lower body weight, which may offset the risk caused by the MC4R mutation they carry.

What do these findings mean?

Our findings show that large-scale population data are needed to more accurately assess the impact of MC4R mutations on extreme early onset obesity.
We show that the obesity-increasing effect of MC4R mutations may be mitigated by a low overall genetic susceptibility to obesity.
These findings show that body weight is the result of an intricate interplay between rare mutations that have a large impact on obesity, as well as an overall genetic susceptibility determined by common genetic variants each with small effects.

Introduction

Obesity is a major risk factor for leading causes of mortality, including type 2 diabetes, cardiovascular disease, chronic kidney disease, and many cancers [1]. To date, more than 650 million adults worldwide suffer from obesity (body mass index [BMI] ≥ 30 kg/m²), a tripling over the past 4 decades [2]. Obesity is the result of an intricate interplay between genetic susceptibility and an obesogenic environment. In a fraction of cases, obesity is caused by mutations in a single gene, resulting in severe early onset obesity. In these instances, environment is believed to play only a minimal role. Many of the pathogenic mutations that cause severe early onset obesity affect genes and proteins in the leptin-melanocortin pathways (e.g., LEP, LEPR, POMC, PCSK1, and MC4R) [3,4].

Mutations in the melanocortin 4 receptor (MC4R) represent the commonest cause of severe early onset obesity [5]. It has been estimated that up to 5% of patients with severe childhood obesity carry pathogenic mutations that cause MC4R deficiency [6,7]. Patients with MC4R deficiency exhibit hyperphagia from an early age [6,7]. Besides increased fat mass, they also have more lean mass, greater bone-mineral density, and are taller compared with non-MC4R deficient individuals with obesity [8]. The severity of the clinical phenotype varies, depending on the functional implications of the mutation on the receptor [6].

In a recent large-scale genetic association study, a well-known nonsense mutation (p.Tyr35Ter, rs13447324) in MC4R was associated with approximately 7-kg higher body weight in carriers (approximately 1 in 5,000 people) [9]. This mutation has been repeatedly found to be the cause of severe early onset obesity [5,6,10–13]. In-depth functional analyses show that Tyr35Ter results in a complete loss-of-function (LoF) of MC4R [12,14–17]. Nevertheless, of the 30 mutation carriers identified in approximately 120,000 participants of the UK Biobank, 6 (20%) were of normal weight [9]. This observation supports the notion that penetrance of pathogenic MC4R mutations is incomplete and that genetic and/or nongenetic factors may affect the clinical outcome. Owing to the fact that MC4R mutations have typically been identified through small-scale and case-focused studies, the estimates of penetrance and representation of clinical phenotypes are likely to be biased because of ascertainment. With the availability of large-scale unselected population datasets with extensive genotype and phenotype data, it is now possible to assess the penetrance at the population level.

Here, we first assess the impact of pathogenic MC4R mutations, previously implicated in severe and early onset obesity, in the UK Biobank, a large-scale population-based cohort of approximately 500,000 individuals living in the United Kingdom. Next, we examine why some individuals who carry these MC4R mutations are able to remain of normal weight. These observations may provide new insights into the mechanisms that control body weight.

Methods

Study population

All analyses are based on data from the UK Biobank, a prospective cohort study with extensive genetic and phenotypic data collected in approximately 500,000 individuals, aged between 40–69 years. Participants were enrolled from April 2007 to July 2010 at one of 21 assessment centers across the UK to provide baseline information, physical measures, and biological samples according to standardized procedures [18–21]. Questionnaires were used to collect health and lifestyle data [22]. Study design, protocols, sample handling and quality control have been described in detail elsewhere [18–21].

We restricted analyses to individuals of European ancestry (N = 453,800) (S1 Text). Women who were pregnant at the time of recruitment (N = 119) and individuals with poor-quality samples based on metrics for heterozygosity or missingness (N = 421) were excluded. Individuals (N = 274) who underwent weight loss surgery before recruitment were considered as “individuals with obesity” for the penetrance calculation. They were removed for the subsequent analyses leaving 452,986 individuals (207,350 men and 245,636 women) of European ancestry in the study.

The UK Biobank received ethical approval from the North West–Haydock Research Ethics Committee (REC reference 11/NW/0382). The current study was conducted under UK Biobank application 1251. Appropriate informed consent was obtained from the participants.

Phenotype data

All phenotypic data used for analyses were collected at the baseline visit. We provide a brief description here; more details can be found in S1 Text and elsewhere [18–21]. BMI, calculated as weight (kg) divided by height squared (m²), was used to categorize individuals with underweight (BMI < 18.5 kg/m²), normal weight (18.5 kg/m² ≤ BMI < 25 kg/m²), overweight (25 kg/m² ≤ BMI < 30 kg/m²), or obesity (BMI ≥ 30 kg/m²). Waist-to-hip ratio (WHR) was calculated by dividing waist over hip circumference. Body composition (fat mass and fat-free mass) and basal metabolic rate was assessed by bioimpedance. Body fat percentage (BF%) was calculated as fat mass (kg) divided by weight (kg) times 100. Fat-free mass index (FFMI) was calculated as fat-free mass (kg) divided by height squared (m²) to assess relative leanness [23].

Birth weight (kg), participants’ “comparative body size at age 10” and “comparative height at age 10”, and age at menarche (years) for women were obtained through self-report. Daily physical activity was assessed using the International Physical Activity Questionnaire (IPAQ), which allows the categorization of individuals into 3 groups of physical activity levels (low, moderate, and high). In addition to IPAQ, we also used the summed MET-minute/week for all activities as a continuous trait. Quantitative information on diet was not available for sufficient participants (in particular, mutation carriers) to allow analyses. The Townsend Deprivation Index (TDI) was used as a proxy of socioeconomic status; a negative value represents high socioeconomic position [24]. Descriptive characteristics are presented in S1 Table.

Genotype data

The majority (approximately 450,000) of UK Biobank participants were genotyped using the UK Biobank Axiom Array, a custom-designed array that includes 821,000 genetic markers. A subset of participants (approximately 50,000) was genotyped using the UK BiLEVE Array, which was designed first, with the aim to study the genetics of lung health and disease, and has >95% markers in common with the UK Biobank Axiom Array. Variant- and sample-based quality control was performed by the UK Biobank; including testing for batch, plate, and array effects, Hardy-Weinberg equilibrium, and discordance across control replicates. Poor-quality samples with high missingness rate and heterozygosity were identified using high-quality markers from both arrays [18]. Because imputation quality of rare variants (minor allele frequency [MAF] ≤ 0.1%) is generally low, only genotyped variants were analyzed.

Genotyped data were used to identify individuals of European ancestry, as described in S1 Text.

Mutations in MC4R reported to play a role in obesity

We used the Human Gene Mutation Database (HGMD) [25] and Clinvar (a clinical genetic database) [26] databases to collate all mutations (MAF ≤ 0.1%) in MC4R that have been reported previously as having a role in obesity.

The HGMD, a database of mutations that have been reported to be associated with inherited diseases, reported 150 mutations in MC4R (accessed November 2018). Of these 150 mutations, 74 had been genotyped in the UK Biobank, of which we excluded 4 mutations because of low call rate (<90%) and 1 because of high MAF (MAF = 1.9%). Thus, a total of 69 mutations reported by HGMD were retained. In addition to HGMD, ClinVar reported 51 mutations in MC4R with a role in obesity, of which 20 were genotyped in the UK Biobank, all of which were also listed in HGMD.

Quality control of MC4R variants

We assessed the quality of the genotype data for each of the 69 mutations following the procedures proposed by UK Biobank Access Team [27]. They made the following recommendations, based on quality control procedures reported by others [18,28,29]

Specifically, we examined the individual cluster plots for each of the 69 variants and assessed the concordance and discordance between the genotyped and sequenced data available in approximately 10% of the UK Biobank participants of European ancestry (N = approximately 46,000) (S1 Text). As such, we identified 10 mutations of low quality (cluster plots were of “poor” or “intermediate” quality) (S1 Text, S2 Table) that were removed from downstream analyses. We also “flagged” 20 additional variants that were not fully concordant between the genotyped and the sequenced data. Given that the sequenced subset consists of only approximately 10% of the full population analyzed, we chose to not remove these mutations from our analyses (S1 Text, S2 Table). Removing the high-impact variants that were flagged in a sensitivity analysis does not impact our main conclusions (S1 Text, S1 and S3 Tables, S1 and S2 Figs).

Taken together, 59 MC4R mutations that had previously been reported to play a role in obesity were included in our analyses.

Assessing the impact of identified MC4R mutations in obesity among UK Biobank participants

Although HGMD and ClinVar are valuable collections of reported mutations, they do not intend to validate their functional implications or role in disease. Furthermore, the studies that have identified MC4R mutations are typically small and case focused. To confirm the mutations’ impact on obesity, we first determined their penetrance and effect among the 452,198 individuals of the UK Biobank (Fig 1).

Fig 1 — HGMD, Human Gene Mutation Database; MAF, minor allele frequency; MC4R, melanocortin 4 receptor; OR, odds ratio.

For each mutation, we calculated the penetrance of obesity as the percentage of individuals with obesity among carriers of a given mutation (= [# carriers with obesity/total # of carriers] × 100). In addition, we calculated the odds of mutation carriers to have obesity compared with them being of normal weight and the odds of noncarriers to have obesity compared with them being of normal weight to derive odds ratios (ORs) for each mutation. Because there are mutations for which there were no carriers of normal weight, we added a constant (“+1”) to the number of carriers with obesity (numerator) and to the number of carriers of normal weight (denominator) to allow deriving “proxy” ORs. These ORs are only used to identify “high-impact” mutations and not to quantify the exact effect of the mutation on obesity as such. The combination of penetrance and effect (OR) allows the identification of mutations that likely have the biggest impact on obesity risk (Fig 2, S1 Text).

Fig 2 — The red dotted lines denote the thresholds of penetrance (≥30%) and effect (OR ≥ 2) that determine impact of mutations. The amino acid change for the top-ranking mutations are labeled. The effect is the odds of mutation carriers to have obesity compared with the odds of them being of normal weight. MAF, minor allele frequency; OR, odds ratio.

The mean penetrance and OR across the 11 high-impact mutations was calculated using individual level data, rather than the average across the 11 values.

Polygenic risk score for BMI

A polygenic risk score (PRS) assesses a person’s overall genetic susceptibility to a certain trait or disease. We calculated a PRS for BMI (PRS_BMI) using the software PRSice (https://www.prsice.info) [30] and summary statistics from the most recent genome-wide association study (GWAS) meta-analyses for BMI [31] that does not include data from the UK Biobank. Our PRS_BMI included 351,597 variants and was, as expected, significantly associated with BMI (1.29 kg/m² per PRS-standard deviation [PRS-SD], P < 2×10⁻¹⁶). More details are available in S1 Text.

Statistical analyses

We created sex-specific residuals for all continuous outcomes, adjusted for age and the first 10 genetic principle components (PCs) using linear regression, followed by inverse normal transformation. As such, transformed traits are on the same scale with a mean of 0 and a standard deviation (SD) of 1, which allows direct comparison of effects sizes across traits. In a secondary analysis, we additionally adjusted for physical activity (MET), smoking behavior, and TDI to compare PRS_BMI.

We used Welch’s t-test for continuous traits and the Fisher’s Exact and Cochrane Armitage Trend tests for discrete traits to compare differences between (1) carriers and noncarriers within each BMI category (normal weight and obesity) and between (2) individuals of normal weight and with obesity among carriers and noncarriers.

To quantify the effect of carrier status and polygenic risk on BMI and obesity risk, we assessed the difference between individuals with a low polygenic risk (lowest quartile of PRS) and a high polygenic risk (highest quartile) among MC4R mutation carriers and noncarriers. Individuals with a low PRS who were noncarriers were considered to be at the lowest genetic risk and served as the “reference group.” This reference group was compared with (1) MC4R mutation carriers with a low polygenic risk, (2) noncarriers with a high polygenic risk, and (3) MC4R mutation carriers with a high polygenic risk (highest genetic risk). We used logistic regression to calculate OR for obesity comparing each group to the reference group. All analyses were performed using R (https://www.r-project.org) and PLINK v.1.9 (https://www.cog-genomics.org/plink2) [32].

This study is reported as per the Strengthening The Reporting of OBservational Studies in Epidemiology (STROBE) guideline (S1 Checklist).

Results

MC4R mutations reported to cause obesity have variable penetrance and effect on obesity

We studied 69 mutations, previously reported to cause obesity in small-scale and case-focused studies, in 452,128 individuals of the UK Biobank (Table 1, S1 Table). Of these, 59 mutations passed quality control criteria (Methods, S2 Table, S1 Text). Their penetrance ranged from 0% for 13 mutations to 100% for 2 mutations, with an average of 25.3% across all 59 mutations (Fig 2, S4 Table). Their effect (OR) on obesity risk ranged from 0.22 (protective) to 9.43 (risk), with an average OR of 1.22. For 6 mutations (MAF of approximately 0.0001%), we observed only 1 carrier, and for 3 mutations (MAF > 0.01%), there were more than 100 carriers.

Table 1. Comparison between carriers and noncarriers of normal weight and with obesity (in adjusted standardized scores).

		Carriers				Noncarriers
	BMI category	N	Mean ± SE		P value (normal weight versus obesity)	N	Mean ± SE		P value (normal weight versus obesity)	P value (carrier versus noncarrier)
Age	Normal weight	28	−0.14 ± 0.15		0.40	147,530	−0.10 ± 0.003		<2×10⁻¹⁶	0.80
	Obesity	76	0.02 ± 0.11			109,178	0.04 ± 0.003			0.90
BMI	Normal weight	28	−0.98 ± 0.12		<2×10⁻¹⁶	147,530	−1.04 ± 0.001		<2×10⁻¹⁶	0.60
	Obesity	76	1.44 ± 0.07			109,178	1.29 ± 0.001			0.03
WHR	Normal weight	28	−0.46 ± 0.18		2.3×10⁻⁶	147,514	−0.59 ± 0.002		<2×10⁻¹⁶	0.50
	Obesity	76	0.64 ± 0.10			109,116	0.73 ± 0.003			0.40
Body fat percentage	Normal weight	27	−0.8 ± 0.13		2.6×10⁻¹⁶	145,396	−0.88 ± 0.002		<2×10⁻¹⁶	0.50
	Obesity	74	1.10 ± 0.10			107,316	1.13 ± 0.002			0.80
FFMI	Normal weight	27	−0.86 ± 0.16		1.5×10⁻¹⁵	145,599	−0.87 ± 0.002		<2×10⁻¹⁶	0.91
	Obesity	74	1.44 ± 0.09			107,273	1.15 ± 0.002			0.001
Height	Normal weight	28	0.33 ± 0.21		0.30	147,530	0.11 ± 0.003		<2×10⁻¹⁶	0.30
	Obesity	76	0.07 ± 0.11			109,178	−0.13 ± 0.003			0.09
Birth weight	Normal weight	22	−0.23 ± 0.19		0.06	87,942	−0.02 ± 0.003		<2×10⁻¹⁶	0.30
	Obesity	40	0.23 ± 0.13			61,372	0.04 ± 0.004			0.20
Age at menarche	Normal weight	19	−0.05 ± 0.23		0.94	96,306	0.12 ± 0.003		<2×10⁻¹⁶	0.50
	Obesity	43	−0.05 ± 0.16			56,385	−0.20 ± 0.004			0.30
Townsend Deprivation Index	Normal weight	28	−0.18 ± 0.20		0.05	147,371	−0.06 ± 0.003		<2×10⁻¹⁶	0.60
	Obesity	76	0.28 ± 0.11			109,033	0.14 ± 0.003			0.20
Physical activity (MET)	Normal weight	23	0.22 ± 0.19		0.07	121,666	0.12 ± 0.003		<2×10⁻¹⁶	0.60
	Obesity	61	−0.21 ± 0.14			85,012	−0.20 ± 0.004			0.91
PRS_BMI	Normal weight	28	−0.64 ± 0.18		1.7×10⁻⁶	147,554	−0.29 ± 0.003		<2×10⁻¹⁶	0.05
	Obesity	76	0.40 ± 0.11			109,216	0.37 ± 0.003			0.76
	BMI category	N_total	N	%	P value (normal weight versus obesity)	N_total	N	%	P value (normal weight versus obesity)	P value (carrier versus noncarrier)
Sex (men)	Normal weight	28	9	32%	0.50	147,545	51,181	35%	<2×10⁻¹⁶	0.80
	Obesity	76	33	43%		109,200	52,769	48%		0.40
Current smokers	Normal weight	27	6	22%	0.55	147,082	16,477	11%	<2×10⁻¹⁶	0.11
	Obesity	76	11	14%		108,621	10,491	10%		0.20
Physical activity (IPAQ)	Normal weight	23			0.09	121,666			<2×10⁻¹⁶	0.2
	Low		1	4%			17,410	14%
	Moderate		10	43%			49,777	41%
	High		12	52%			54,479	45%
	Obesity	61				85,012				0.45
	Low		19	31%			22,287	26%
	Moderate		14	23%			34,184	40%
	High		28	46%			28,541	34%
Comparative body size at age 10 years	Normal weight	28			0.01	145,241			<2×10⁻¹⁶	0.05
	Below average		9	32%			57,971	40%
	Average		12	43%			73,899	51%
	Above average		7	25%			13,371	9%
	Obesity	75				107,073				1.0×10⁻⁴
	Below average		13	17%			26,759	25%
	Average		22	29%			51,128	48%
	Above average		40	53%			29,186	27%
Comparative height at age 10 years	Normal weight	28			0.30	145,288			1.6×10⁻⁸	0.20
	Below average		2	7%			30,541	21%
	Average		18	64%			77,041	53%
	Above average		8	29%			37,706	26%
	Obesity	76				107,060				0.98
	Below average		15	20%			20,737	19%
	Average		41	54%			58,656	55%
	Above average		20	26%			27,667	26%

Open in a new tab

Data for continuous traits are expressed in SD scores (i.e., we calculated residuals after adjusting for age and the first 10 principal components in men and women, followed by inverse normal transformation to a distribution with mean of 0 and SD of 1).

BMI, body mass index; FFMI, fat-free mass index; IPAQ, International Physical Activity Questionnaire; MET, metabolic equivalent minutes; PRS_BMI, polygenic risk score for BMI; SE, standard error; WHR, waist-to-hip ratio.

Eleven MC4R mutations with high impact on obesity

The combination of penetrance and effect allows identifying the mutations with the biggest impact on obesity. Given that the prevalence of obesity among noncarriers in the UK Biobank is 24%, mutations for which ≥30% of carriers had obesity were considered moderately-to-highly penetrant. As the ratio of obesity over normal weight among noncarriers in the UK Biobank is 0.74 (i.e., more normal weight than obesity), mutations that increase the odds of obesity by 2-fold or more (compared with normal weight) were considered to have a moderate-to-high risk.

Eleven mutations met both penetrance (≥30%) and effect (OR ≥ 2) thresholds and were considered for further analyses (Fig 2; S4 Table). Their average penetrance (42%) and effect on obesity (OR = 3.49) were substantially higher than that of the remaining 48 mutations (26%; OR = 1.04). For 10 (91%) of the 11 mutations, there was evidence that the mutation impaired MC4R function and/or led to reduced activity, based on functional characterization (S4 and S5 Tables). In contrast, functional evidence was reported for only 20 (42%) of the remaining 48 mutations (P = 0.006).

Carriers of high-impact MC4R mutations have higher BMI and have more often obesity

There were 183 individuals (0.004% or 1 in 2,471 individuals) who carried one of the 11 mutations. No individuals carried more than 1 mutation, and all were heterozygous carriers. One carrier had undergone weight loss surgery and was removed from further analyses, leaving 182 carriers. Because we restricted our analyses to high-impact mutations, the average age- and sex-adjusted BMI of carriers (mean ± SD = 29.9 ± 5.1 kg/m²; or 0.51 ± 0.08 SD scores [SDSs]) was substantially higher than that of noncarriers (27.4 ± 4.7 kg/m²; −0.0002 ± 0.001 SDS). Furthermore, although most carriers suffered from obesity (N_OB = 76, 42%) or overweight (N_OW = 78; 43%), 28 (15%) carriers were of normal weight, defying their genetic risk. We next investigated what sets these 28 carriers of normal weight apart from carriers with obesity and from noncarriers of normal weight.

Body composition in MC4R mutations carriers compared to noncarriers

Body composition of individuals of normal weight did not differ between carriers and noncarriers (Table 1; S6 Table). However, among individuals with obesity, carriers had a significantly higher BMI (0.7 kg/m² equivalent to 2 kg for a 1.7-m-tall person, P = 0.03) than noncarriers, which may be driven by a higher FFMI (0.7 kg/m², P = 0.001). Carriers with obesity tended to be somewhat taller than noncarriers, but this difference did not reach significance (1.8 cm, P = 0.09) (Table 1; S6 Table).

Normal-weight MC4R mutation carriers compared to carriers with obesity

Although at birth, carriers of normal weight already tended (P = 0.05) to be lighter than carriers with obesity, the difference in body size became more apparent by age 10 (P = 0.01). Carriers of normal weight reported more often (75%) to be below average or of average body size at age 10 (compared with peers) than carriers with obesity (46%) (Table 1), suggesting that carriers of normal weight may have been able to resist weight gain already at a young age.

We next examined the role of innate (genetic) and environmental/lifestyle (nongenetic) factors to determine which compensatory mechanisms contribute to the ability of some carriers to remain of normal weight.

Low polygenic susceptibility protects MC4R mutation carriers from obesity

We assessed people’s overall genetic susceptibility to obesity using a polygenic risk score (PRS_BMI) based on the BMI association of common variants across the genome. We found that the PRS_BMI of normal-weight carriers was more than 1 SDS (corresponding to 1.34 kg/m² or approximately 4 kg in body weight for a 1.7-m-tall person) lower than that of carriers with obesity (P < 1.7×10⁻⁶) (Table 1, Fig 3). This difference was only slightly attenuated after additionally adjusting for physical activity (metabolic equivalent minutes [MET]), smoking behavior, and TDI (S7 Table). Even among individuals of normal weight, carriers had a 0.35 SDS (0.45 kg/m² or approximately 1.3 kg in body weight) lower PRS_BMI than noncarriers (P = 0.05) (Table 1, Fig 3, S3 Fig). These observations suggest that carriers of normal weight offset their increased obesity risk caused by MC4R mutations, at least in part, because of a low polygenic risk.

Fig 3 — Mean standardized PRS_BMI (y-axis) for carriers and noncarriers of normal weight and with obesity, respectively. MC4R, melanocortin 4 receptor; PRS_BMI, polygenic risk score for BMI; SE, standard error.

Carriers of normal weight tended to be exposed to a healthier environment: less deprivation (P = 0.05) and more physical activity (P = 0.07) compared to carriers with obesity (Table 1). However, because these data were collected cross-sectionally, we cannot determine whether the healthier environment is the cause or consequence of being normal weight.

Polygenic risk impacts the obesity-increasing effect of MC4R mutations

We next assessed the extent to which people’s polygenic risk (PRS_BMI) affects BMI and obesity risk among carriers and noncarriers (Table 2, Fig 4). Compared with noncarriers in the bottom PRS_BMI quartile (lowest risk), carriers in the top PRS_BMI quartile (highest risk) have a 9.7-fold increased odds of having obesity (95% CI, 5.6–16.9). The odds of having obesity for noncarriers in the top PRS_BMI quartile (medium risk) is 3.7 (95% CI, 3.6–3.7), and for carriers in the bottom PRS_BMI quartile (medium risk) is 2.12-fold higher (95% CI, 1.1–4.3), compared with noncarriers in the bottom PRS_BM (the lowest risk) (Table 2). Furthermore, the average BMI difference between the lowest risk (bottom PRS_BMI, noncarriers) and the highest risk group (top PRS_BMI, carriers) was 6.4 kg/m² (or 18.5 kg in body weight for a 1.7-m-tall person, or an approximately 25% difference) (Fig 4). Carrier status was associated with a 1.7 kg/m² (or 4.9 kg, 6.3%) higher BMI among the low PRS_BMI group and with a 3.2 kg/m² (or 9.2 kg, 11%) higher BMI among the high PRS_BMI group.

Table 2. Risk of obesity among MC4R carriers and noncarriers with high and low polygenic risk.

PRS_BMI	MC4R carrier status	Risk group	N	Risk of obesity OR (95% CI)	P value
Low (bottom quartile)	noncarrier	Reference	113,661	1
Low (bottom quartile)	carrier	Medium	44	2.2 (1.1–4.3)	0.028
High (top quartile)	noncarrier	Medium	113,260	3.7 (3.6–3.7)	2.0×10⁻¹⁶
High (top quartile)	carrier	High	52	9.7 (5.6–16.9)	1.2×10⁻¹⁵

Open in a new tab

MC4R, melanocortin 4 receptor gene; OR, odds ratio; PRS_BMI, polygenic risk score for body mass index.

Fig 4 — The box represents the median and interquartile range; the red dot is the mean. Low/high PRS_BMI: bottom/top quartile, high PRS_BMI. *MC4R*, melanocortin 4 receptor gene; PRS_BMI, polygenic risk score for body mass index.

Discussion

In contrast to previous, mainly small-scale, often case-focused studies that reported mutations in MC4R claimed to cause severe early onset obesity, we leveraged data from over 450,000 individuals and conducted one of the largest studies to validate MC4R mutations to date. Of the 59 MC4R mutations available in the UK Biobank, only 11 had an impact on obesity risk. Although carriers of these 11 MC4R mutations (N = 182) were overall more likely to have obesity, 15% (N = 28) were of normal weight. In these normal-weight carriers, the obesity-increasing effects of the MC4R mutations are offset through—at least in part—a significantly lower polygenic risk compared with the carriers with obesity. In addition, compared with noncarriers with a low polygenic risk, mutation carriers with a low polygenic risk have a 2-fold increased risk of obesity and weigh approximately 6% more, whereas carriers with a high polygenic risk have a 9-fold increased risk and weigh approximately 25% more. Taken together, a low polygenic susceptibility to obesity seems to attenuate the impact of pathogenic mutations in MC4R.

Studies reporting on MC4R mutations that cause severe and early onset obesity have typically been small and case-focused studies and may therefore have overestimated their impact. With the availability of large-scale population studies, such as the UK Biobank, it has become possible to assess these mutations’ impact on obesity at a population level. So far, one other study has examined the impact of MC4R variants on obesity in the full UK Biobank population using genotype data [33]. In their study, 61 MC4R variants (MAF < 2%) were functionally characterized; 47 variants resulted in LoF, 9 in gain-of-function (GoF), and 5 had no clear functional impact. All of the high-impact mutations identified in the current study that overlapped with the 61 variants were among the ones with the largest impact on MC4R function, based on their low-to-no β-arrestin recruitment [33]. In a second study, MC4R was sequenced in a subset of 6,547 UK Biobank participants, identifying 23 protein-altering mutations of which 54 carriers had higher BMI than the noncarriers, a difference that was even more pronounced when only the pathogenic mutations were considered [34], in line with our observations. However, neither study examined the penetrance of the observed MC4R mutations, nor did they examine the role of the PRS among carriers.

Consistent with reports on putative causal variants for other diseases [28,35–37], the majority (84%) of mutations previously claimed to cause severe and early onset obesity do not show convincing penetrance or association with obesity in approximately 450,000 individuals of the UK Biobank. For just 11 (19%) of the 59 mutations, the prevalence of obesity among mutation carriers (42%) was substantially higher than among noncarriers (25%) and mutations carriers had a 3.5-fold higher odds of suffering from obesity than noncarriers. Obesity risk for the remaining 48 MC4R mutations was not different between carriers and noncarriers, even though for more than a third of these 48 mutations, in vitro analyses have shown evidence of functional implications (S4 and S5 Tables). In fact, for 25 of these 48 mutations, the minor alleles were even more often observed in individuals who did not have obesity and thus associated with lower risk of obesity. Thus, our results suggest that current variant annotations and in vitro functional analyses provide only partial information on the impact of mutations. We show that many of the MC4R mutations previously reported to cause severe early onset obesity may not be as pathogenic as previously thought.

We focused our subsequent analyses on 11 MC4R mutations that did have an impact on obesity in the approximately 450,000 UK Biobank individuals of European ancestry. Despite the fact that carriers of these 11 mutations were at a substantially higher risk of obesity than noncarriers and that all but one of these mutations had been shown to have functional implications, 28 mutation carriers were of normal weight. Understanding why some mutation carriers remain of normal weight can help elucidate protective mechanisms and provide new targets for treatment and prevention, as has been shown for other disease outcomes [38–41].

MC4R mutations affect body weight from an early age onward [6,42,43]. Nevertheless, based on self-reported life-course data, the carriers of normal weight seem to have been of normal weight from a young age, and likewise, the carriers with obesity seem to have been larger than average since childhood. This suggests that the genetic and/or nongenetic mechanisms that counteract the obesity-increasing effects of MC4R mutations in carriers of normal weight act throughout the life course. In adulthood, body composition of normal weight carriers was the same as for normal-weight noncarriers. However, carriers with obesity had a higher BMI (likely because of more lean mass) than noncarriers with obesity, consistent with was has been observed for MC4R mutations carriers before [6].

Most interestingly, we show that some carriers seem able to counteract the obesity-increasing effects of the MC4R mutations they carried and remain of normal weight because—at least in part—of their substantially lower polygenic susceptibility (>1 SD in PRS_BMI) than carriers with obesity. The normal-weight carriers’ polygenic susceptibility was even lower than that of normal-weight noncarriers, suggesting that an “overcompensation” was needed for them to be of normal weight. Furthermore, the impact of the obesity-increasing MC4R mutations is substantially attenuated in individuals with a low polygenic susceptibility compared with those with a high polygenic susceptibility. For example, the difference in BMI between carriers and noncarriers is 1.7 kg/m² (or 4.9 kg, 6%) among individuals with a low polygenic susceptibility and almost double (3.2 kg/m² or 9.2 kg, 11%) among those with a high polygenic susceptibility. Carriers with a low polygenic risk were even leaner (1.5 kg/m² or 4.3 kg, 6%) than noncarriers with a high polygenic risk, whereas the difference was largest (6.4 kg/m² or 18.5 kg, 25%) between noncarriers with low polygenic risk and carriers with a high polygenic risk. These observations illustrate that both MC4R mutations and polygenic susceptibility contribute to people’s body weight and that one can attenuate or exacerbate the other.

However, differences in polygenic susceptibility do not fully explain the difference in BMI between carriers of normal weight and carriers with obesity; other genetic and nongenetic mechanisms are likely implicated. Recent studies have speculated that variable penetrance of functional mutations may be due to the fact that in heterozygous carriers, gene function is “rescued” by the “healthy” allele [44,45]. Determining whether this is the case in carriers of normal weight and not in carriers with obesity would require in vitro functional follow up. Besides genetic factors, nongenetic factors may also partially explain why some carriers are able to remain of normal weight, as weight loss surgery [46–48], lifestyle interventions [49,50], and weight loss medication [51] have been shown to affect the weight of MC4R mutation carriers to some extent. We found that carriers of normal weight, compared with carriers with obesity, reported less material deprivation, a known contributor to obesity risk [52]. Because the data on deprivation were collected cross-sectionally, we were not able determine whether less deprivation is causally related to lower body weight.

An accurate estimation of penetrance and effect of mutations on disease is important for clinical genetic testing. A correct diagnosis, specifically in children with severe and early onset obesity, allows implementation of tailored treatment and care. We acknowledge that the UK Biobank population is healthier and less deprived than the general UK population [53,54]; i.e., as individuals with obesity may have been less inclined to participate, penetrance and effect may have been somewhat underestimated. Furthermore, only genotype-array data were available for all participants [18], which captured roughly half of all MC4R mutations previously reported to cause severe early onset obesity. Although the accuracy of genotype data for very rare mutations has been a concern [28], we implemented stringent quality control measures to remove low-quality mutations. Sensitivity analyses in which we excluded additional mutations with potential quality concerns continue to support our primary findings. The exome sequencing dataset currently constitutes approximately 10% of the total UK Biobank population and is still too small to examine the rare (MAF < 0.1%) mutations of interest [55].

Taken together, our findings suggest that, in addition to existing case-focused studies and in vitro functional analyses, large-scale population data are required to more accurately assess the impact of MC4R mutations (and potentially others) on severe early onset obesity. In addition, we show that the impact of even highly penetrant MC4R mutations may be at least partially mitigated by a low polygenic susceptibility to obesity and potentially healthier environments and behaviors, blurring the lines between monogenic and polygenic forms of obesity.

Supporting information

S1 Text. Supplementary methods.

(PDF)

Click here for additional data file.^{(86.7KB, pdf)}

S1 Fig. Sensitivity analysis: Standardized PRS_BMI values in carriers and noncarriers of normal weight versus obesity after excluding rs1367004987, Affx-89021050, and rs775382722 from the high-impact variants.

PRS_BMI, polygenic risk score for BMI.

(TIF)

Click here for additional data file.^{(278.8KB, tif)}

S2 Fig. Sensitivity analysis: BMI among carriers and noncarriers in the top and bottom quartiles of PRS_BMI after excluding rs1367004987, Affx-89021050, and rs775382722 from the high-impact variants.

BMI, body mass index; PRS_BMI, polygenic risk score for BMI.

(TIF)

Click here for additional data file.^{(181.4KB, tif)}

S3 Fig. Density plots of PRS_BMI for MC4R mutation carriers and noncarriers with obesity and of normal weight, respectively.

MC4R, melanocortin 4 receptor gene; PRS_BMI, polygenic risk score for BMI.

(TIF)

Click here for additional data file.^{(327.5KB, tif)}

S1 Table. Descriptive characteristics of 451,508 individuals of European Ancestry from the UK Biobank.

(XLSX)

Click here for additional data file.^{(14KB, xlsx)}

S2 Table. Quality of 69 genotyped mutations in MC4R in UK Biobank participants of European ancestry.

The sequencing dataset is based on the European subset only because of allele frequency differences for some variants between the European and other populations, such as the African American population. It is also based on the same subset that was analyzed in our analyses of the genotyping data. *Refers to the number of individuals who were carriers in the genotyped dataset but noncarriers in the sequencing dataset. **Refers to the number of individuals who were carriers in the sequenced dataset but noncarriers in the genotyping dataset. *** FFP = high (>25%) false positive proportion; NFP = high (>25%) false negative proportion. MC4R, melanocortin 4 receptor gene.

(XLSX)

Click here for additional data file.^{(25.9KB, xlsx)}

S3 Table. Sensitivity analysis: Comparison between carriers and noncarriers of normal weight versus obesity after removing rs1367004987, rs775382722, and Affx-89021050.

Values are expressed in SD scores (i.e., we calculated residuals after adjusting for age and the first 10 PC in men and women, followed by inverse normal transformation to a distribution with mean of 0 and SD of 1). Cochran–Armitage test for trend was used to compare carriers and noncarriers as well as individuals of normal weight and with obesity for IPAQ, comparative body size at age 10 years and comparative height at age 10 years. P values are reported for adjusted means for age, sex, and 10 PCs. IPAQ, International Physical Activity Questionnaire; MET, metabolic equivalent minutes; PC, principle component; PRS_BMI, standardized scores of the polygenic risk score of BMI with mean 0 and SD of 1; SD, standard deviation.

(XLSX)

Click here for additional data file.^{(17.4KB, xlsx)}

S4 Table. The number of carriers for each mutation stratified by BMI category and their impact on obesity.

*Because there are mutations for which there were no normal-weight carriers, we added a constant (“+1”) to the number of carriers with obesity (numerator) and to the number of carriers of normal weight (denominator) to allow deriving “proxy” ORs. **Functional evidence refers to existing literature that demonstrated that the mutation leads to lower expression, impaired signaling or loss of function. "Ambiguous" refers to mutations that had conflicting reports of either supporting or refuting downstream effects or for which the loss of function effects of the mutation were not clear. BMI, body mass index; OR, odds ratio.

(XLSX)

Click here for additional data file.^{(22.1KB, xlsx)}

S5 Table. Annotation of the 59 MC4R mutations.

Functional evidence refers to existing literature that demonstrated that the mutation leads to lower expression, impaired signaling or loss of function. "Ambiguous" refers to mutations that had conflicting reports of either supporting or refuting downstream effects or for which the loss of function effects of the mutation were not clear. MC4R, melanocortin 4 receptor gene.

(XLSX)

Click here for additional data file.^{(21.5KB, xlsx)}

S6 Table. Comparison between carriers and noncarriers of normal weight versus obesity.

Values for anthropometry and lifestyle factors are inferred from the standardized scores (Table 1) that were adjusted for age, PCs in men and women separately). PC, principle component.

(XLSX)

Click here for additional data file.^{(13.6KB, xlsx)}

S7 Table. Comparison of PRS_BMI between carriers and noncarriers stratified by BMI category (in adjusted standardized scores).

P values are reported for adjusted means. Model 1 is the adjusted mean of the PRS after including age, sex, and the first 10 principal components in the model. Model 2 is the adjusted mean of the PRS after adding in addition to the covariates in model 1, MET scores, current smoking, and the Townsend Deprivation Index. BMI, body mass index; PRS_BMI, polygenic risk score for BMI with mean 0 and SD of 1.

(XLSX)

Click here for additional data file.^{(10KB, xlsx)}

S1 STROBE Checklist. STROBE, Strengthening The Reporting of OBservational Studies in Epidemiology.

(DOCX)

Click here for additional data file.^{(32KB, docx)}

Abbreviations

BF%: body fat percentage
BMI: body mass index
FFMI: fat-free mass index
GoF: gain-of-function
GWAS: genome-wide association study
HGMD: Human Gene Mutation Database
IPAQ: International Physical Activity Questionnaire
LoF: loss-of-function
MAF: minor allele frequency
MC4R: melanocortin 4 receptor
MET: metabolic equivalent minute
OR: odds ratio
PC: principle component
PRS: polygenic risk score
PRS_BMI: polygenic risk score for BMI
PRS-SD: polygenic risk score standard deviation
SD: standard deviation
SDS: standard deviation score
SE: standard error
STROBE: Strengthening The Reporting of OBservational Studies in Epidemiology
TDI: Townsend Deprivation Index
WHR: waist-to-hip ratio

Data Availability

Data of the UK Biobank can be obtained directly from the UK Biobank (http://biobank.ndph.ox.ac.uk). Details on the application process are described here https://www.ukbiobank.ac.uk/researchers/.

Funding Statement

This research was supported by the National Institutes of Health (R01DK110113; R01DK124097) and by an Alliance Award of the University of Copenhagen (Denmark), NNF Center for Basic Metabolic Research. NC is supported by a grant from the Canadian Institutes of Health Research (CIHR Fellowship). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Collaborators GBDO, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med. 2017; 377(1):13–27. 10.1056/NEJMoa1614362 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Collaboration NCDRF. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387(10026):1377–96. 10.1016/S0140-6736(16)30054-X [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Farooqi IS, O’Rahilly S. The Genetics of Obesity in Humans In: Feingold KR, Anawalt B, Boyce A, Chrousos G, Dungan K, Grossman A, et al. , editors. Endotext. South Dartmouth (MA): 2000. [Google Scholar]
4.van der Klaauw AA, Farooqi IS. The hunger genes: pathways to obesity. Cell. 2015;161(1):119–32. 10.1016/j.cell.2015.03.008 [DOI] [PubMed] [Google Scholar]
5.Farooqi IS, Yeo GS, Keogh JM, Aminian S, Jebb SA, Butler G, et al. Dominant and recessive inheritance of morbid obesity associated with melanocortin 4 receptor deficiency. J Clin Invest. 2000;106(2):271–9. 10.1172/JCI9397 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Farooqi IS, Keogh JM, Yeo GS, Lank EJ, Cheetham T, O’Rahilly S. Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. N Engl J Med. 2003;348(12):1085–95. 10.1056/NEJMoa022050 [DOI] [PubMed] [Google Scholar]
7.Stutzmann F, Tan K, Vatin V, Dina C, Jouret B, Tichet J, et al. Prevalence of melanocortin-4 receptor deficiency in Europeans and their age-dependent penetrance in multigenerational pedigrees. Diabetes. 2008;57(9):2511–8. 10.2337/db08-0153 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Martinelli CE, Keogh JM, Greenfield JR, Henning E, van der Klaauw AA, Blackwood A, et al. Obesity due to melanocortin 4 receptor (MC4R) deficiency is associated with increased linear growth and final height, fasting hyperinsulinemia, and incompletely suppressed growth hormone secretion. J Clin Endocrinol Metab. 2011;96(1):E181–8. 10.1210/jc.2010-1369 [DOI] [PubMed] [Google Scholar]
9.Turcot V, Lu Y, Highland HM, Schurmann C, Justice AE, Fine RS, et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet. 2018;50(1):26–41. 10.1038/s41588-017-0011-x [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Hinney A, Schmidt A, Nottebom K, Heibult O, Becker I, Ziegler A, et al. Several mutations in the melanocortin-4 receptor gene including a nonsense and a frameshift mutation associated with dominantly inherited obesity in humans. J Clin Endocrinol Metab. 1999;84(4):1483–6. 10.1210/jcem.84.4.5728 [DOI] [PubMed] [Google Scholar]
11.Kleinendorst L, Massink MPG, Cooiman MI, Savas M, van der Baan-Slootweg OH, Roelants RJ, et al. Genetic obesity: next-generation sequencing results of 1230 patients with obesity. J Med Genet. 2018;55(9):578–86. 10.1136/jmedgenet-2018-105315 [DOI] [PubMed] [Google Scholar]
12.Larsen LH, Echwald SM, Sorensen TI, Andersen T, Wulff BS, Pedersen O. Prevalence of mutations and functional analyses of melanocortin 4 receptor variants identified among 750 men with juvenile-onset obesity. J Clin Endocrinol Metab. 2005;90(1):219–24. 10.1210/jc.2004-0497 [DOI] [PubMed] [Google Scholar]
13.Hinney A, Hohmann S, Geller F, Vogel C, Hess C, Wermter AK, et al. Melanocortin-4 receptor gene: case-control study and transmission disequilibrium test confirm that functionally relevant mutations are compatible with a major gene effect for extreme obesity. J Clin Endocrinol Metab. 2003;88(9):4258–67. 10.1210/jc.2003-030233 [DOI] [PubMed] [Google Scholar]
14.Brumm H, Muhlhaus J, Bolze F, Scherag S, Hinney A, Hebebrand J, et al. Rescue of melanocortin 4 receptor (MC4R) nonsense mutations by aminoglycoside-mediated read-through. Obesity. 2012;20(5):1074–81. 10.1038/oby.2011.202 [DOI] [PubMed] [Google Scholar]
15.Lubrano-Berthelier C, Dubern B, Lacorte JM, Picard F, Shapiro A, Zhang S, et al. Melanocortin 4 receptor mutations in a large cohort of severely obese adults: prevalence, functional classification, genotype-phenotype relationship, and lack of association with binge eating. J Clin Endocrinol Metab. 2006;91(5):1811–8. 10.1210/jc.2005-1411 [DOI] [PubMed] [Google Scholar]
16.Calton MA, Ersoy BA, Zhang S, Kane JP, Malloy MJ, Pullinger CR, et al. Association of functionally significant Melanocortin-4 but not Melanocortin-3 receptor mutations with severe adult obesity in a large North American case-control study. Hum Mol Genet. 2009;18(6):1140–7. 10.1093/hmg/ddn431 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Xiang Z, Litherland SA, Sorensen NB, Proneth B, Wood MS, Shaw AM, et al. Pharmacological characterization of 40 human melanocortin-4 receptor polymorphisms with the endogenous proopiomelanocortin-derived agonists and the agouti-related protein (AGRP) antagonist. Biochemistry. 2006;45(23):7277–88. 10.1021/bi0600300 [DOI] [PubMed] [Google Scholar]
18.Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–9. 10.1038/s41586-018-0579-z [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Elliott P, Peakman TC, Biobank UK. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int J Epidemiol. 2008;37(2):234–44. 10.1093/ije/dym276 [DOI] [PubMed] [Google Scholar]
20.Collins R. What makes UK Biobank special? Lancet. 2012;379(9882):1173–4. [DOI] [PubMed] [Google Scholar]
21.Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779 10.1371/journal.pmed.1001779 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.UK Biobank. UK Biobank touchscreen questionnaire 2018 [Internet]. 2018 [cited 2020 May] https://www.ukbiobank.ac.uk/wp-content/uploads/2011/06/Touch_screen_questionnaire.pdf
23.VanItallie TB, Yang MU, Heymsfield SB, Funk RC, Boileau RA. Height-normalized indices of the body’s fat-free mass and fat mass: potentially useful indicators of nutritional status. Am J Clin Nutr. 1990;52(6):953–9. 10.1093/ajcn/52.6.953 [DOI] [PubMed] [Google Scholar]
24.Townsend P. Deprivation. Journal of Social Policy. 1987;16:125–46. [Google Scholar]
25.Krawczak M, Ball EV, Fenton I, Stenson PD, Abeysinghe S, Thomas N, et al. Human gene mutation database-a biomedical information and research resource. Hum Mutat. 2000;15(1):45–51. [DOI] [PubMed] [Google Scholar]
26.Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42:D980–5. 10.1093/nar/gkt1113 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.UK Biobank and Access Team UB. UK Biobank—Access Update—Reliability of genotype data for rare and very rare variants [Internet]. 2019 [cited NNNN].
28.Wright CF, West B, Tuke M, Jones SE, Patel K, Laver TW, et al. Assessing the Pathogenicity, Penetrance, and Expressivity of Putative Disease-Causing Variants in a Population Setting. Am J Hum Genet. 2019;104(2):275–86. 10.1016/j.ajhg.2018.12.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Weedon MN, Jackson L, Harrison JW, Ruth KS, Tyrrell J, Hattersley AT, Wright CF. Assessing the analytical validity of SNP-chips for detecting very rare pathogenic variants: implications for direct-to-consumer genetic testing. bioRxiv 696799 [Preprint]. 2019 [cited 2020 May]. 10.1101/696799 [DOI]
30.Euesden J, Lewis CM, O’Reilly PF. PRSice: Polygenic Risk Score software. Bioinformatics. 2015;31(9):1466–8. 10.1093/bioinformatics/btu848 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197–206. 10.1038/nature14177 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75. 10.1086/519795 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Lotta LA, Mokrosinski J, Mendes de Oliveira E, Li C, Sharp SJ, Luan J, et al. Human Gain-of-Function MC4R Variants Show Signaling Bias and Protect against Obesity. Cell. 2019;177(3):597–607 e9. 10.1016/j.cell.2019.03.044 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Khera AV, Chaffin M, Wade KH, Zahid S, Brancale J, Xia R, et al. Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood. Cell. 2019;177(3):587–96 e9. 10.1016/j.cell.2019.03.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Walsh R, Thomson KL, Ware JS, Funke BH, Woodley J, McGuire KJ, et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet Med. 2017;19(2):192–203. 10.1038/gim.2016.90 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Bick AG, Flannick J, Ito K, Cheng S, Vasan RS, Parfenov MG, et al. Burden of rare sarcomere gene variants in the Framingham and Jackson Heart Study cohorts. Am J Hum Genet. 2012;91(3):513–9. 10.1016/j.ajhg.2012.07.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Minikel EV, Vallabh SM, Lek M, Estrada K, Samocha KE, Sathirapongsasuti JF, et al. Quantifying prion disease penetrance using large population control cohorts. Sci Transl Med. 2016;8(322):322ra9 10.1126/scitranslmed.aad5169 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Knoblauch H, Muller-Myhsok B, Busjahn A, Ben Avi L, Bahring S, Baron H, et al. A cholesterol-lowering gene maps to chromosome 13q. Am J Hum Genet. 2000;66(1):157–66. 10.1086/302704 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Oprea GE, Krober S, McWhorter ML, Rossoll W, Muller S, Krawczak M, et al. Plastin 3 is a protective modifier of autosomal recessive spinal muscular atrophy. Science. 2008;320(5875):524–7. 10.1126/science.1155085 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Castel SE, Cervera A, Mohammadi P, Aguet F, Reverter F, Wolman A, et al. Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk. Nat Genet. 2018;50(9):1327–34. 10.1038/s41588-018-0192-y [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Chen R, Shi L, Hakenberg J, Naughton B, Sklar P, Zhang J, et al. Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat Biotechnol. 2016;34(5):531–8. 10.1038/nbt.3514 [DOI] [PubMed] [Google Scholar]
42.Vaisse C, Clement K, Guy-Grand B, Froguel P. A frameshift mutation in human MC4R is associated with a dominant form of obesity. Nat Genet. 1998;20(2):113–4. 10.1038/2407 [DOI] [PubMed] [Google Scholar]
43.Yeo GS, Farooqi IS, Aminian S, Halsall DJ, Stanhope RG, O’Rahilly S. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat Genet. 1998;20(2):111–2. 10.1038/2404 [DOI] [PubMed] [Google Scholar]
44.Crowley JJ, Zhabotynsky V, Sun W, Huang S, Pakatci IK, Kim Y, et al. Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance. Nat Genet. 2015;47(4):353–60. 10.1038/ng.3222 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Matharu N, Rattanasopha S, Tamura S, Maliskova L, Wang Y, Bernard A, et al. CRISPR-mediated activation of a promoter or enhancer rescues obesity caused by haploinsufficiency. Science. 2019;363(6424): eaau0629 10.1126/science.aau0629 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Hatoum IJ, Stylopoulos N, Vanhoose AM, Boyd KL, Yin DP, Ellacott KL, et al. Melanocortin-4 receptor signaling is required for weight loss after gastric bypass surgery. J Clin Endocrinol Metab. 2012;97(6):E1023–31. 10.1210/jc.2011-3432 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Jelin EB, Daggag H, Speer AL, Hameed N, Lessan N, Barakat M, et al. Melanocortin-4 receptor signaling is not required for short-term weight loss after sleeve gastrectomy in pediatric patients. Int J Obes. 2016;40(3):550–3. [DOI] [PubMed] [Google Scholar]
48.Censani M, Conroy R, Deng L, Oberfield SE, McMahon DJ, Zitsman JL, et al. Weight loss after bariatric surgery in morbidly obese adolescents with MC4R mutations. Obesity. 2014;22(1):225–31. 10.1002/oby.20511 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Hainerova I, Larsen LH, Holst B, Finkova M, Hainer V, Lebl J, et al. Melanocortin 4 receptor mutations in obese Czech children: studies of prevalence, phenotype development, weight reduction response, and functional analysis. J Clin Endocrinol Metab. 2007;92(9):3689–96. 10.1210/jc.2007-0352 [DOI] [PubMed] [Google Scholar]
50.Reinehr T, Hebebrand J, Friedel S, Toschke AM, Brumm H, Biebermann H, et al. Lifestyle intervention in obese children with variations in the melanocortin 4 receptor gene. Obesity. 2009;17(2):382–9. 10.1038/oby.2008.422 [DOI] [PubMed] [Google Scholar]
51.Iepsen EW, Zhang J, Thomsen HS, Hansen EL, Hollensted M, Madsbad S, et al. Patients with Obesity Caused by Melanocortin-4 Receptor Mutations Can Be Treated with a Glucagon-like Peptide-1 Receptor Agonist. Cell Metab. 2018(1);28:23–32 e3. 10.1016/j.cmet.2018.05.008 [DOI] [PubMed] [Google Scholar]
52.Stafford M, Brunner EJ, Head J, Ross NA. Deprivation and the development of obesity a multilevel, longitudinal study in England. Am J Prev Med. 2010;39(2):130–9. 10.1016/j.amepre.2010.03.021 [DOI] [PubMed] [Google Scholar]
53.Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am J Epidemiol. 2017;186(9):1026–34. 10.1093/aje/kwx246 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Weng SF, Vaz L, Qureshi N, Kai J. Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches. PLoS ONE. 2019;14(3):e0214365 10.1371/journal.pone.0214365 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.van Hout CV. Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank. biorxiv 572347 [Preprint]. 2019 [cited 2020 May]. 10.1101/572347 [DOI] [PMC free article] [PubMed]

PLoS Med. doi: 10.1371/journal.pmed.1003196.r001

Decision Letter 0

Adya Misra

7 Feb 2020

Dear Dr Loos,

Thank you for submitting your manuscript entitled "Low polygenic risk attenuates the obesity-increasing effects of pathogenic mutations in MC4R" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff [as well as by an academic editor with relevant expertise] and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by .

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Adya Misra, PhD,

Senior Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1003196.r002

Decision Letter 1

Adya Misra

9 Apr 2020

Dear Dr. Loos,

Thank you very much for submitting your manuscript "Low polygenic risk attenuates the obesity-increasing effects of pathogenic mutations in MC4R" (PMEDICINE-D-20-00082R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by Apr 30 2020 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Title-Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon).

Abstract

Background- is it monogenic forms of obesity? Please specify this point if so

Background-please clearly highlight the aim of your study

Format-Please structure your abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions).

Please combine the Methods and Findings sections into one section, “Methods and findings”.

Please include the study design, population and setting, number of participants, years during which the study took place, length of follow up, and main outcome measures

Please provide 95% confidence intervals along with p values

Please clarify this sentence “Normal weight carriers more often reported that, already at age 10y, they were thinner/average (72%) compared to obese carriers (48%) (P=0.02)”. From the language it appears as though the weight was self reported which should be clarified in the methods section of the abstract. Also it is not clear if the measure is BMI or body composition, Please revise as needed.

Abstract Conclusions:

* Please address the study implications without overreaching what can be concluded from the data; the phrase "In this study, we observed ..." may be useful.

* Please interpret the study based on the results presented in the abstract, emphasizing what is new without overstating your conclusions.

* Please avoid vague statements such as "these results have major implications for policy/clinical care". Mention only specific implications substantiated by the results.

* Please avoid assertions of primacy ("We report for the first time....")

Author summary

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

Introduction

References must be in Vancouver style and provided within square brackets please.

Paragraph 3 on page 4- I assume this is still MC4R mutations so it might be better to mention this in the first two sentences

Please move methods to just after Introduction on page 5

Results

Please simplify this sentence: “For 10 (91%) of the 11 mutations, there was evidence that the mutation impaired MC4R function and/or led to reduced activity, based on functional characterization (Tables S3 and S4), which was significantly more often than for the remaining 48 mutations (P = 0.0006) for which we found evidence for only 17 (33%)”. You may consider splitting up the sentence as it is currently hard to follow.

Please report p values of up to two decimal places

Please provide “(no individuals carried more than one mutation)” as a separate sentence.

Page 7 please introduce FFMI on first view

Discussion

Please rephrase “beating their genetic odds” on page 8. The same goes for “counteract the obesity-increasing effects of MC4R mutations”.

Recommend revising instances “normal phenotype” to non-obese or similar, to avoid any stigmatising labels.

Please avoid assertions of primacy such as “we show for the first time” by adding “to our knowledge”

You mention sensitivity analyses here but not in the results? Please provide these as SI files as needed to support your findings

Limitations of the UK biobank cohort and your methodology (specifically self reported weight) more generally must be outlined in further detail.

Page 22- please revise the last sentence containing multiple instances of (R,2013)

Please ensure that the study is reported according to a appropriate reporting guideline (GRIPS? Or STROBE), and include the completed checklist as Supporting Information. When completing the checklist, please use section and paragraph numbers, rather than page numbers. Please add the following statement, or similar, to the Methods: "This study is reported as per the xxxx guideline (S1 Checklist)."

Please report your study according to the relevant guideline, which can be found here: http://www.equator-network.org/

Comments from the reviewers:

Reviewer #1: The authors report the findings investigating the effect of having MC4R mutations on differences in BMI (based on BMI) on between carriers and non-carriers in the UK Bio Bank. They found that 11 MC4R mutations have a high penetrance and that low polygenic risk score is protective of having increased risk of obesity in carriers of MC4R mutations. This study was similar to a recent study (the authors have cited as well) in Cell (https://www.sciencedirect.com/science/article/pii/S0092867419302909?via%3Dihub) using UK Biobank (though that previous study did extend their study to validate in four other datasets as well as explore long-term effects on cardio-metabolic conditions). The genetic risk score from this study mirrored similar size and composition of the previous (> 300,000 SNPs), but with similar findings. This current study does explore BMI between carrier and non-carriers specifically and the results offer some insight in the implications of the protective effects of having low polygenic risk score.

The statistical methods are sound, constructions of the polygenic risk score followed conventional design using linear additive models, with appropriate quality control and principal components to account for European ancestry. The the environmental variables also seem to provide some insight into the potential effect modification.

One aspect is that I am not sure the second aim of the study was fully answered on why some individuals who carry these mutations are able to remain of normal weight - largely the results simply report the observed differences between groups and does not investigate the underlying effect modification between environmental factors in each group. For instance, on page 7, the authors state that environmental/lifestyle (non-genetic factors were examined). They have provided some interesting results in on Table 1 comparing lifestyle factors stratifying by carriers and non-carriers. What I find interesting is that there is some definite effect modification by lifestyle factors here. For instance, physical activity (MET and IPAQ) are not different between obese and normal weight carriers but is significantly different in normal and obese non-carriers. Also other demographic attributes like sex and height look to have important differences in association between normal and obese in carriers vs non-carriers. Could the authors elaborate further on whether environmental factors looks like they might have a pretty strong modifying effect - potentially even more so than genetic susceptibility and whether there were any interactions that could be explore in the analysis between gene and environment.

Some other minor comments:

Selection of 11 mutations: Authors should rationalise why >= 30% penetrance and >= 2 OR was used to define high impact on obesity

Top of Page 8: Physical activity according was not significant here (P = 0.08) in carriers. I think one aspect that has not been mentioned in the text is the impact of the other environmental risk factors which did seem to have different associations between carriers and non-carriers (smoking, physical activity).

Reviewer #2: Chami et al manuscript Low polygenic risk attenuates the obesity-increasing effects of pathogenic mutations in MC4R

The authors have leveraged the large-scale dataset from UKB to understand the relevance of heterozygous mutations in MC4R to the risk of obesity. This is an important area of interest and large population studies such as UKB may provide information on the penetrance and pathogenicity in the relatively healthy populations while compared to the possible ascertainment bias in the small-scale disease focused population.

Reviewer comments:

1) The most important limitation of this study is the use of genotype data to assess rare variants without confirmation of the variants. The authors report the use of the tool Evoker (Morris et al 2010) to ascertain the quality of the variants resulting in 59 variants of "good" quality. Evoker is being extended here from its original use for common variants. Based on the use of Evoker lite, Wright et al 2019 have noted that variants below MAF 0.001% are not reliably genotyped with the false positive rates ~60% in data from UKB, while those with MAF > 0.005% was ~20%. It is to be noted that 29 (out of 69) variants have MAF <= 0.001% including some from the 11 considered high penetrance and 59 variants have MAF <= 0.005%. Lotta et al 2019 used the same method for validation of the variants published in their paper, and the authors need to provide further evidence that these variants are true positives before perpetuating this method for ascertaining rare variants further. There is some evidence in the literature of the false-positive results of rare variants in Mendelian genes ascertained from genotyping data.

In their own comparison of the subset of the sequencing data with the corresponding genotype data, the authors postulate that 28 of the variants were deemed to be of low quality. While this reviewer acknowledges the limitation of scaling the results from analyses of ~10% of the data to the entire cohort. Most studies using exome data also validate the identified variants by one or other method prior to publication. Given the substantial implications of the conclusions of this manuscript, is it prudent to wait till the sequencing data for the entire UKB becomes available prior to making the assertion? If indeed a large number of variants are found to be false positive, would the conclusions still hold? The stochastic nature of such false positive findings makes it difficult to identify true positive amongst the variants noted in this study. It is possible that the conclusions of the study will hold after the due diligence of validation of the variants in which case, this publication will be applauded for accurate paradigm shifting conclusions. The influence of polygenic risk on BMI is not to be underestimated, especially when considering a similar effect in individuals who are not carriers of variants in MC4R. It is just not clear if the low polygenic risk is "protective" if the validity of the variants in MC4R is not established unequivocally.

2) This manuscript focuses on individuals with overweight/obesity, while completely ignoring the principal of "people first language". People first language has been widely recognized as important for use in academic publications relevant to individuals with obesity (Kyle et al 2014, PMID 24616446, Wittert et al, PMID 26373880 and several others). The authors need to review their language throughout the manuscript, e.g. "normal weight carriers" will be carriers with normal weight, "obese carriers" will be carriers with obesity. "mutation carriers" should be carriers with mutations, "non-MC4R deficient individuals" should be individuals without mutations in MC4R etc. It is critical for scientists to remember that people are more important than pathology, always.

3) In the phenotype review, the authors have included the assessment of anthropometric parameters, but not the diagnoses codes or health status of the individuals. While the population enrolled in UK Biobank is expected to be healthier, for this manuscript, it will be important for the authors to review the ICD codes for the individuals under study to ensure that the "normal" weight was not due to an underlying illness? Further, as the authors are well aware that environmental influences far exceed indices of socioeconomic status and physical activity. How would the authors account for educational status as a measure of the socioeconomic awareness, smoking status and dietary habits in their modeling? Given the extensive phenotype data ascertained by UKB, it is naïve to include only age, sex and PCs derived from genetic data in the models. Additional phenotype data ought to be considered, at minimum for exploration or for sensitivity analysis.

4) Page 6: "For 10 (91%) of the 11 mutations, there was evidence that the mutation impaired MC4R function and/or led to reduced activity, based on functional characterization (Tables S3

and S4), which was significantly more often than for the remaining 48 mutations (P = 0.0006) for which we found evidence for only 17 (33%)."

in table S3, there are 20 variants that have been reported to have functional effect while the text mentions 17. Which one is correct?

5) Authors need to discuss the limitations of using recall data from 10 years of age for adults recruited at 40-60 years of age, especially with reference to a phenotype such as height and weight and be conservative in their conclusions from such data in the abstract.

6) Page 7: "These observations suggest that normal weight carriers are able to overcome their increased obesity risk due to MC4R mutations, at least in part, thanks to a low polygenic risk."

Scientific articles should not be using language that glorifies one weight category over the other.

There are a few other instances where colloquial language is used: "the extent to which people's polygenic risk (PRSBMI) affects BMI and obesity risk among carriers and non-carriers"

Reviewer #3: SUMMARY

Alterations in the DNA sequence coding for MC4R is considered the most common form of monogenic obesity, but evidences have been accumulated that the penetrance of the different MC4R isoforms is variable. Aim of the present study is to examine in a large population based study (>450000 subjects of European Ancestry from UK biobank) why some carriers of pathogenic mutations remain of normal weight, with the ultimate goal of acquiring novel knowledge on mechanisms underlying body weight control.

To this end they put in place the following experimental design.

1. Selection of MC4R high impact variants based on 2 stringent criteria: 1. Penetrance ≥30% of carriers are obese. 2. Obesity risk (OR) defined as follows : provided that in non carriers the ratio obese/normal weight is 0.74, mutations for which this ratio is ≥2. This approach led to the identification of 11 mutations which met both criteria. For 91% of them functional studies indicating an impaired MC4R function were available. The other 48 mutations present in the literature because associated to obesity or known to alter MC4R functions were discarded from further analysis.

2. Individuals (182) carrying one of the high impact MC4R variants are then stratified according to BMI. 29 of them show normal body weight (NW), while 75 are obese. Non-carriers of high impact mutations are similarly stratified. Pair comparisons were then performed at different levels: normal weight vs obese or carrier versus non carriers. Parameters taken into account are physical parameters, history of body weight, life style, socio-economic conditions. Results indicate that NW carriers were protected against obesity already at age 10, and show better socioeconomic conditions compared to obese carriers.

3. When polygenic risk susceptibility score (PRS) for obesity is taken into account the carriers with normal weight show a much lower PRS, compared with obese carriers and also a lower PRS when compared to normal weight non carriers, indicating that normal weight carriers defy their obesity risk with a very low PRS.

Their conclusion is that large scale population studies are very important to accurately assess the impact of MC4R mutations on severe obesity and that the line between polygenic and monogenic forms of obesity is not that sharp.

GENERAL COMMENT

The genetics of obesity field has been crowded for the past 25 years with case control studies based on small cohorts, which in most cases reported a modest difference between normal weight and obese subjects for a given mutation. This is particularly true in the case of MC4R. Furthermore, criteria on subjects stratification has varied. Overall this has produced a huge set of not organized, not reliable data, and confusing information, which led many scholars in the field to open their papers with a generic sentence stating that monogenic forms of obesity represent 5% of the total obese population and that MC4R variants account for most of these forms.

Chami and colleagues provide a very timely and important piece of research, which I welcome as an unmet need and a new starting point. Rigorous methods, solid evidences and brilliant discussion characterize this paper.

Below a few suggestions to make it more clear and usable from a large audience

DETAILED COMMENTS

- This study is not easy to read for clinicians and biologist unless they have a strong background in modern genetics and statistics. My recommendation is to provide both graphics and explanations to better guide the reader throughout the experimental design. A scheme putting into evidence the main questions addressed as well as the relative answers should be included.

- The 11 mutations with high impact are selected based on penetrance (>30%) and obesity risk (increase>2 fold). More in deep and detailed explanations should be provided to explain these criteria which may sound arbitrary otherwise.

- The last column of table 1 as well as of the supplementary tables indicate the P value when carriers and non carriers are compared. The sample size of these groups differ by various order of magnitude. Is it right to compare groups with so different sample size , i.e 29 versus > 100000? Provide explanations

Reviewer #4: The MC4R is a central player in the leptin-melanocortin pathway, which plays a critical role in the brain control of food intake and body-weight. Genetic disruption of the pathway is known to result in severe obesity. Mutations in the MC4R are no exception, and have been known, since 1998, to be strongly associated with increased body-weight. What is interesting is that while non-synonymous mutations in this receptor are relatively common (compared to super rare conditions), questions remain about its penetrance woth regards to increased body-weight. In this manuscript, Chami and colleagues study the impact of non-synonymous variations in the MC4R gene on body-weight within UK Biobank. They note that about 25% of people carrying loss of function MC4R variants are not obese, and report that their polygenic risk score with regards to BMI can mitigate against carrying an MC4R mutation. This is an important study which tackles the nuance of what was previously thought to be a 'monogenic' cause of obesity. I've got a few issues to raise:

1. The authors do acknowledge that UK Biobank represents a healthier slice of society. Given that deprivation is inversely correlated with health, how does the deprivation index of UK biobank compare to other large population cohort studies?

2. While the carriers with a lower polygenic risk score have their MC4R genetic risk mitigated, does the specific SNP (rs571312) near MC4R play any increase or decreased role in this mitigation?

3. I apologise if this is an ignorant question, but in table 1, what exactly are adjusted standardized scores?

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Jul 21;17(7):e1003196. doi: 10.1371/journal.pmed.1003196.r003

Author response to Decision Letter 1

30 Apr 2020

Attachment

Submitted filename: PMEDICINE-D-20-00082R1 - RESPONSE TO EDITOR AND REVIEWERS.pdf

Click here for additional data file.^{(241.2KB, pdf)}

PLoS Med. doi: 10.1371/journal.pmed.1003196.r004

Decision Letter 2

Adya Misra

28 May 2020

Dear Dr. Loos,

Thank you very much for re-submitting your manuscript "The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R" (PMEDICINE-D-20-00082R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by xxx reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript by 2nd June 2020. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Jun 02 2020 11:59PM.

Sincerely,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

Title- please include a study descriptor or perhaps add “a UK Biobank cohort study”

Abstract- please include a limitation of your study design/methodology in the last sentence of the 2methods and findings” section

Some of the author summary requires revision for clarity, for example this sentence is a bit unclear “We observed that, compared to carriers with obesity, carriers of normal weight have a lower

overall genetic susceptibility..”. Please revise this as needed

“To date, more than 650 million adults worldwide are obese” should be revised to “suffer from obesity” or similar. Usage of “obese” throughout should be revised in this way throughout the submission please. There are several instances in the methods/results and I would appreciate these are revised- for instance on page 10, 12, 14. Please check Fig 1 for similar language which must be revised.

Is there anyway we can substitute healthy weight for normal weight? I am concerned that normal weight adds an element of stigma that can be avoided. I think it is fine to use normal weight when describing various weights in relation to BMI in the methods but not to describe individuals. Please revise this throughout the manuscript. There is also the phrase “average weight”, which should probably be revised? (Page 14)

Tables need revision to remove stigmatising language as well. It is very unusual to see words like “Normal versus obese” in scientific articles. In addition, please revise the use of “plumper” when looking at comparative body size. Can you possibly rephrase to “lower than average”, “average” and “greater than average” in this table? I would suggest the same for heights.

Please revise “The odds of having obesity for non-carriers” for grammar as well as using non stigmatising language

Please ensure bibliography is in Vancouver style

Comments from Reviewers:

Reviewer #1: The authors have done a thorough job responding to comments and revising their manuscript.

I felt that Figure 1 was particularly helpful in understanding their study design and analysis. Pleased to recommend this for acceptance now.

Reviewer #2: The authors have addressed many of the concerns raised by the editors and reviewers.

The reviewer appreciates the repetition of the explanation from text and acknowledges that this study is important to be entered into the literature to allow future studies on the WES data to look at this question again.

There are some minor concerns that can be addressed easily by the authors:

The "people first language" is significantly improved and now it is up to the editors to take care of the rest.

Page 8: "BMI, calculated as weight (kg) divided by height (m2) squared,.." This comes across a bit odd: (m2) squared will be m4?. It seems like the authors are trying to account for the units, but the calculation really is height in meters squared? Similar issue in the definition of FFMI.

Page 17 (Discussion):

"Our data suggests….." The data belongs to the UKB. The authors should note this as "Our analysis suggests….

Next, the statement that a "large number of MC4R mutations……." can be more appropriately restated as "many of the mutations….." It will be prudent to avoid hyperbolic claims, as has already been pointed out by the editor.

Page 19 (Discussion):

"In addition,we show that the impact of even highly penetrant MC4R mutations can be mitigated by a low". Please rephrase it to account for the limitations of the study, similar to what has already been done in the previous sections:

" …….may be at least partially mitigated……"

Reviewer #3: The authors satisfactorily addressed all issues raised during the revision process and greatly improved the quality of the manuscript

Reviewer #4: The authors have responded to all of my concerns.

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Jul 21;17(7):e1003196. doi: 10.1371/journal.pmed.1003196.r005

Author response to Decision Letter 2

6 Jun 2020

Attachment

Submitted filename: PMEDICINE-D-20-00082R3 - RESPONSE TO PRODUCTION TEAM - FINAL.pdf

Click here for additional data file.^{(29.4KB, pdf)}

PLoS Med. doi: 10.1371/journal.pmed.1003196.r006

Decision Letter 3

Adya Misra

16 Jun 2020

Dear Dr. Loos,

On behalf of my colleagues and the academic editor, Dr. Karine Clément, I am delighted to inform you that your manuscript entitled "The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R" (PMEDICINE-D-20-00082R3) has been accepted for publication in PLOS Medicine.

PRODUCTION PROCESS

Before publication you will see the copyedited word document (in around 1-2 weeks from now) and a PDF galley proof shortly after that. The copyeditor will be in touch shortly before sending you the copyedited Word document. We will make some revisions at the copyediting stage to conform to our general style, and for clarification. When you receive this version you should check and revise it very carefully, including figures, tables, references, and supporting information, because corrections at the next stage (proofs) will be strictly limited to (1) errors in author names or affiliations, (2) errors of scientific fact that would cause misunderstandings to readers, and (3) printer's (introduced) errors.

If you are likely to be away when either this document or the proof is sent, please ensure we have contact information of a second person, as we will need you to respond quickly at each point.

PRESS

A selection of our articles each week are press released by the journal. You will be contacted nearer the time if we are press releasing your article in order to approve the content and check the contact information for journalists is correct. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact.

PROFILE INFORMATION

Now that your manuscript has been accepted, please log into EM and update your profile. Go to https://www.editorialmanager.com/pmedicine, log in, and click on the "Update My Information" link at the top of the page. Please update your user information to ensure an efficient production and billing process.

Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Text. Supplementary methods.

(PDF)

Click here for additional data file.^{(86.7KB, pdf)}

PRS_BMI, polygenic risk score for BMI.

(TIF)

Click here for additional data file.^{(278.8KB, tif)}

S2 Fig. Sensitivity analysis: BMI among carriers and noncarriers in the top and bottom quartiles of PRS_BMI after excluding rs1367004987, Affx-89021050, and rs775382722 from the high-impact variants.

BMI, body mass index; PRS_BMI, polygenic risk score for BMI.

(TIF)

Click here for additional data file.^{(181.4KB, tif)}

S3 Fig. Density plots of PRS_BMI for MC4R mutation carriers and noncarriers with obesity and of normal weight, respectively.

MC4R, melanocortin 4 receptor gene; PRS_BMI, polygenic risk score for BMI.

(TIF)

Click here for additional data file.^{(327.5KB, tif)}

S1 Table. Descriptive characteristics of 451,508 individuals of European Ancestry from the UK Biobank.

(XLSX)

Click here for additional data file.^{(14KB, xlsx)}

S2 Table. Quality of 69 genotyped mutations in MC4R in UK Biobank participants of European ancestry.

(XLSX)

Click here for additional data file.^{(25.9KB, xlsx)}

S3 Table. Sensitivity analysis: Comparison between carriers and noncarriers of normal weight versus obesity after removing rs1367004987, rs775382722, and Affx-89021050.

(XLSX)

Click here for additional data file.^{(17.4KB, xlsx)}

S4 Table. The number of carriers for each mutation stratified by BMI category and their impact on obesity.

(XLSX)

Click here for additional data file.^{(22.1KB, xlsx)}

S5 Table. Annotation of the 59 MC4R mutations.

(XLSX)

Click here for additional data file.^{(21.5KB, xlsx)}

S6 Table. Comparison between carriers and noncarriers of normal weight versus obesity.

Values for anthropometry and lifestyle factors are inferred from the standardized scores (Table 1) that were adjusted for age, PCs in men and women separately). PC, principle component.

(XLSX)

Click here for additional data file.^{(13.6KB, xlsx)}

S7 Table. Comparison of PRS_BMI between carriers and noncarriers stratified by BMI category (in adjusted standardized scores).

(XLSX)

Click here for additional data file.^{(10KB, xlsx)}

S1 STROBE Checklist. STROBE, Strengthening The Reporting of OBservational Studies in Epidemiology.

(DOCX)

Click here for additional data file.^{(32KB, docx)}

Attachment

Submitted filename: PMEDICINE-D-20-00082R1 - RESPONSE TO EDITOR AND REVIEWERS.pdf

Click here for additional data file.^{(241.2KB, pdf)}

Attachment

Submitted filename: PMEDICINE-D-20-00082R3 - RESPONSE TO PRODUCTION TEAM - FINAL.pdf

Click here for additional data file.^{(29.4KB, pdf)}

Data Availability Statement

Data of the UK Biobank can be obtained directly from the UK Biobank (http://biobank.ndph.ox.ac.uk). Details on the application process are described here https://www.ukbiobank.ac.uk/researchers/.

[pmed.1003196.ref001] 1.Collaborators GBDO, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med. 2017; 377(1):13–27. 10.1056/NEJMoa1614362 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref002] 2.Collaboration NCDRF. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387(10026):1377–96. 10.1016/S0140-6736(16)30054-X [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref003] 3.Farooqi IS, O’Rahilly S. The Genetics of Obesity in Humans In: Feingold KR, Anawalt B, Boyce A, Chrousos G, Dungan K, Grossman A, et al. , editors. Endotext. South Dartmouth (MA): 2000. [Google Scholar]

[pmed.1003196.ref004] 4.van der Klaauw AA, Farooqi IS. The hunger genes: pathways to obesity. Cell. 2015;161(1):119–32. 10.1016/j.cell.2015.03.008 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref005] 5.Farooqi IS, Yeo GS, Keogh JM, Aminian S, Jebb SA, Butler G, et al. Dominant and recessive inheritance of morbid obesity associated with melanocortin 4 receptor deficiency. J Clin Invest. 2000;106(2):271–9. 10.1172/JCI9397 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref006] 6.Farooqi IS, Keogh JM, Yeo GS, Lank EJ, Cheetham T, O’Rahilly S. Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. N Engl J Med. 2003;348(12):1085–95. 10.1056/NEJMoa022050 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref007] 7.Stutzmann F, Tan K, Vatin V, Dina C, Jouret B, Tichet J, et al. Prevalence of melanocortin-4 receptor deficiency in Europeans and their age-dependent penetrance in multigenerational pedigrees. Diabetes. 2008;57(9):2511–8. 10.2337/db08-0153 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref008] 8.Martinelli CE, Keogh JM, Greenfield JR, Henning E, van der Klaauw AA, Blackwood A, et al. Obesity due to melanocortin 4 receptor (MC4R) deficiency is associated with increased linear growth and final height, fasting hyperinsulinemia, and incompletely suppressed growth hormone secretion. J Clin Endocrinol Metab. 2011;96(1):E181–8. 10.1210/jc.2010-1369 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref009] 9.Turcot V, Lu Y, Highland HM, Schurmann C, Justice AE, Fine RS, et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet. 2018;50(1):26–41. 10.1038/s41588-017-0011-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref010] 10.Hinney A, Schmidt A, Nottebom K, Heibult O, Becker I, Ziegler A, et al. Several mutations in the melanocortin-4 receptor gene including a nonsense and a frameshift mutation associated with dominantly inherited obesity in humans. J Clin Endocrinol Metab. 1999;84(4):1483–6. 10.1210/jcem.84.4.5728 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref011] 11.Kleinendorst L, Massink MPG, Cooiman MI, Savas M, van der Baan-Slootweg OH, Roelants RJ, et al. Genetic obesity: next-generation sequencing results of 1230 patients with obesity. J Med Genet. 2018;55(9):578–86. 10.1136/jmedgenet-2018-105315 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref012] 12.Larsen LH, Echwald SM, Sorensen TI, Andersen T, Wulff BS, Pedersen O. Prevalence of mutations and functional analyses of melanocortin 4 receptor variants identified among 750 men with juvenile-onset obesity. J Clin Endocrinol Metab. 2005;90(1):219–24. 10.1210/jc.2004-0497 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref013] 13.Hinney A, Hohmann S, Geller F, Vogel C, Hess C, Wermter AK, et al. Melanocortin-4 receptor gene: case-control study and transmission disequilibrium test confirm that functionally relevant mutations are compatible with a major gene effect for extreme obesity. J Clin Endocrinol Metab. 2003;88(9):4258–67. 10.1210/jc.2003-030233 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref014] 14.Brumm H, Muhlhaus J, Bolze F, Scherag S, Hinney A, Hebebrand J, et al. Rescue of melanocortin 4 receptor (MC4R) nonsense mutations by aminoglycoside-mediated read-through. Obesity. 2012;20(5):1074–81. 10.1038/oby.2011.202 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref015] 15.Lubrano-Berthelier C, Dubern B, Lacorte JM, Picard F, Shapiro A, Zhang S, et al. Melanocortin 4 receptor mutations in a large cohort of severely obese adults: prevalence, functional classification, genotype-phenotype relationship, and lack of association with binge eating. J Clin Endocrinol Metab. 2006;91(5):1811–8. 10.1210/jc.2005-1411 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref016] 16.Calton MA, Ersoy BA, Zhang S, Kane JP, Malloy MJ, Pullinger CR, et al. Association of functionally significant Melanocortin-4 but not Melanocortin-3 receptor mutations with severe adult obesity in a large North American case-control study. Hum Mol Genet. 2009;18(6):1140–7. 10.1093/hmg/ddn431 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref017] 17.Xiang Z, Litherland SA, Sorensen NB, Proneth B, Wood MS, Shaw AM, et al. Pharmacological characterization of 40 human melanocortin-4 receptor polymorphisms with the endogenous proopiomelanocortin-derived agonists and the agouti-related protein (AGRP) antagonist. Biochemistry. 2006;45(23):7277–88. 10.1021/bi0600300 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref018] 18.Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–9. 10.1038/s41586-018-0579-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref019] 19.Elliott P, Peakman TC, Biobank UK. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int J Epidemiol. 2008;37(2):234–44. 10.1093/ije/dym276 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref020] 20.Collins R. What makes UK Biobank special? Lancet. 2012;379(9882):1173–4. [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref021] 21.Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779 10.1371/journal.pmed.1001779 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref022] 22.UK Biobank. UK Biobank touchscreen questionnaire 2018 [Internet]. 2018 [cited 2020 May] https://www.ukbiobank.ac.uk/wp-content/uploads/2011/06/Touch_screen_questionnaire.pdf

[pmed.1003196.ref023] 23.VanItallie TB, Yang MU, Heymsfield SB, Funk RC, Boileau RA. Height-normalized indices of the body’s fat-free mass and fat mass: potentially useful indicators of nutritional status. Am J Clin Nutr. 1990;52(6):953–9. 10.1093/ajcn/52.6.953 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref024] 24.Townsend P. Deprivation. Journal of Social Policy. 1987;16:125–46. [Google Scholar]

[pmed.1003196.ref025] 25.Krawczak M, Ball EV, Fenton I, Stenson PD, Abeysinghe S, Thomas N, et al. Human gene mutation database-a biomedical information and research resource. Hum Mutat. 2000;15(1):45–51. [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref026] 26.Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42:D980–5. 10.1093/nar/gkt1113 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref027] 27.UK Biobank and Access Team UB. UK Biobank—Access Update—Reliability of genotype data for rare and very rare variants [Internet]. 2019 [cited NNNN].

[pmed.1003196.ref028] 28.Wright CF, West B, Tuke M, Jones SE, Patel K, Laver TW, et al. Assessing the Pathogenicity, Penetrance, and Expressivity of Putative Disease-Causing Variants in a Population Setting. Am J Hum Genet. 2019;104(2):275–86. 10.1016/j.ajhg.2018.12.015 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref029] 29.Weedon MN, Jackson L, Harrison JW, Ruth KS, Tyrrell J, Hattersley AT, Wright CF. Assessing the analytical validity of SNP-chips for detecting very rare pathogenic variants: implications for direct-to-consumer genetic testing. bioRxiv 696799 [Preprint]. 2019 [cited 2020 May]. 10.1101/696799 [DOI]

[pmed.1003196.ref030] 30.Euesden J, Lewis CM, O’Reilly PF. PRSice: Polygenic Risk Score software. Bioinformatics. 2015;31(9):1466–8. 10.1093/bioinformatics/btu848 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref031] 31.Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197–206. 10.1038/nature14177 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref032] 32.Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75. 10.1086/519795 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref033] 33.Lotta LA, Mokrosinski J, Mendes de Oliveira E, Li C, Sharp SJ, Luan J, et al. Human Gain-of-Function MC4R Variants Show Signaling Bias and Protect against Obesity. Cell. 2019;177(3):597–607 e9. 10.1016/j.cell.2019.03.044 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref034] 34.Khera AV, Chaffin M, Wade KH, Zahid S, Brancale J, Xia R, et al. Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood. Cell. 2019;177(3):587–96 e9. 10.1016/j.cell.2019.03.028 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref035] 35.Walsh R, Thomson KL, Ware JS, Funke BH, Woodley J, McGuire KJ, et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet Med. 2017;19(2):192–203. 10.1038/gim.2016.90 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref036] 36.Bick AG, Flannick J, Ito K, Cheng S, Vasan RS, Parfenov MG, et al. Burden of rare sarcomere gene variants in the Framingham and Jackson Heart Study cohorts. Am J Hum Genet. 2012;91(3):513–9. 10.1016/j.ajhg.2012.07.017 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref037] 37.Minikel EV, Vallabh SM, Lek M, Estrada K, Samocha KE, Sathirapongsasuti JF, et al. Quantifying prion disease penetrance using large population control cohorts. Sci Transl Med. 2016;8(322):322ra9 10.1126/scitranslmed.aad5169 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref038] 38.Knoblauch H, Muller-Myhsok B, Busjahn A, Ben Avi L, Bahring S, Baron H, et al. A cholesterol-lowering gene maps to chromosome 13q. Am J Hum Genet. 2000;66(1):157–66. 10.1086/302704 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref039] 39.Oprea GE, Krober S, McWhorter ML, Rossoll W, Muller S, Krawczak M, et al. Plastin 3 is a protective modifier of autosomal recessive spinal muscular atrophy. Science. 2008;320(5875):524–7. 10.1126/science.1155085 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref040] 40.Castel SE, Cervera A, Mohammadi P, Aguet F, Reverter F, Wolman A, et al. Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk. Nat Genet. 2018;50(9):1327–34. 10.1038/s41588-018-0192-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref041] 41.Chen R, Shi L, Hakenberg J, Naughton B, Sklar P, Zhang J, et al. Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat Biotechnol. 2016;34(5):531–8. 10.1038/nbt.3514 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref042] 42.Vaisse C, Clement K, Guy-Grand B, Froguel P. A frameshift mutation in human MC4R is associated with a dominant form of obesity. Nat Genet. 1998;20(2):113–4. 10.1038/2407 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref043] 43.Yeo GS, Farooqi IS, Aminian S, Halsall DJ, Stanhope RG, O’Rahilly S. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat Genet. 1998;20(2):111–2. 10.1038/2404 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref044] 44.Crowley JJ, Zhabotynsky V, Sun W, Huang S, Pakatci IK, Kim Y, et al. Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance. Nat Genet. 2015;47(4):353–60. 10.1038/ng.3222 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref045] 45.Matharu N, Rattanasopha S, Tamura S, Maliskova L, Wang Y, Bernard A, et al. CRISPR-mediated activation of a promoter or enhancer rescues obesity caused by haploinsufficiency. Science. 2019;363(6424): eaau0629 10.1126/science.aau0629 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref046] 46.Hatoum IJ, Stylopoulos N, Vanhoose AM, Boyd KL, Yin DP, Ellacott KL, et al. Melanocortin-4 receptor signaling is required for weight loss after gastric bypass surgery. J Clin Endocrinol Metab. 2012;97(6):E1023–31. 10.1210/jc.2011-3432 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref047] 47.Jelin EB, Daggag H, Speer AL, Hameed N, Lessan N, Barakat M, et al. Melanocortin-4 receptor signaling is not required for short-term weight loss after sleeve gastrectomy in pediatric patients. Int J Obes. 2016;40(3):550–3. [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref048] 48.Censani M, Conroy R, Deng L, Oberfield SE, McMahon DJ, Zitsman JL, et al. Weight loss after bariatric surgery in morbidly obese adolescents with MC4R mutations. Obesity. 2014;22(1):225–31. 10.1002/oby.20511 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref049] 49.Hainerova I, Larsen LH, Holst B, Finkova M, Hainer V, Lebl J, et al. Melanocortin 4 receptor mutations in obese Czech children: studies of prevalence, phenotype development, weight reduction response, and functional analysis. J Clin Endocrinol Metab. 2007;92(9):3689–96. 10.1210/jc.2007-0352 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref050] 50.Reinehr T, Hebebrand J, Friedel S, Toschke AM, Brumm H, Biebermann H, et al. Lifestyle intervention in obese children with variations in the melanocortin 4 receptor gene. Obesity. 2009;17(2):382–9. 10.1038/oby.2008.422 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref051] 51.Iepsen EW, Zhang J, Thomsen HS, Hansen EL, Hollensted M, Madsbad S, et al. Patients with Obesity Caused by Melanocortin-4 Receptor Mutations Can Be Treated with a Glucagon-like Peptide-1 Receptor Agonist. Cell Metab. 2018(1);28:23–32 e3. 10.1016/j.cmet.2018.05.008 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref052] 52.Stafford M, Brunner EJ, Head J, Ross NA. Deprivation and the development of obesity a multilevel, longitudinal study in England. Am J Prev Med. 2010;39(2):130–9. 10.1016/j.amepre.2010.03.021 [DOI] [PubMed] [Google Scholar]

[pmed.1003196.ref053] 53.Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am J Epidemiol. 2017;186(9):1026–34. 10.1093/aje/kwx246 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref054] 54.Weng SF, Vaz L, Qureshi N, Kai J. Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches. PLoS ONE. 2019;14(3):e0214365 10.1371/journal.pone.0214365 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003196.ref055] 55.van Hout CV. Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank. biorxiv 572347 [Preprint]. 2019 [cited 2020 May]. 10.1101/572347 [DOI] [PMC free article] [PubMed]

PERMALINK

The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R in the UK Biobank population

Nathalie Chami

Michael Preuss

Ryan W Walker

Arden Moscati

Ruth J F Loos

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Study population

Phenotype data

Genotype data

Mutations in MC4R reported to play a role in obesity

Quality control of MC4R variants

Assessing the impact of identified MC4R mutations in obesity among UK Biobank participants

Fig 1. Flow chart of the study design.

Fig 2. Penetrance and effect on obesity (OR) of 59 MC4R mutations previously reported to cause obesity.

Polygenic risk score for BMI

Statistical analyses

Results

MC4R mutations reported to cause obesity have variable penetrance and effect on obesity

Table 1. Comparison between carriers and noncarriers of normal weight and with obesity (in adjusted standardized scores).

Eleven MC4R mutations with high impact on obesity

Carriers of high-impact MC4R mutations have higher BMI and have more often obesity

Body composition in MC4R mutations carriers compared to noncarriers

Normal-weight MC4R mutation carriers compared to carriers with obesity

Low polygenic susceptibility protects MC4R mutation carriers from obesity

Fig 3. Polygenic risk (PRSBMI) in MC4R carriers and noncarriers.

Polygenic risk impacts the obesity-increasing effect of MC4R mutations

Table 2. Risk of obesity among MC4R carriers and noncarriers with high and low polygenic risk.

Fig 4. BMI among MC4R carriers and noncarriers in the top and bottom PRSBMI quartiles.

Discussion

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Adya Misra

Roles

Decision Letter 1

Adya Misra

Roles

Author response to Decision Letter 1

Decision Letter 2

Adya Misra

Roles

Author response to Decision Letter 2

Decision Letter 3

Adya Misra

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Fig 3. Polygenic risk (PRS_BMI) in MC4R carriers and noncarriers.

Fig 4. BMI among MC4R carriers and noncarriers in the top and bottom PRS_BMI quartiles.