Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals

Kevin T Nead; Aihua Li; Mackenzie R Wehner; Binod Neupane; Stefan Gustafsson; Adam Butterworth; James C Engert; A Darlene Davis; Robert A Hegele; Ruby Miller; Marcel den Hoed; Kay-Tee Khaw; Tuomas O Kilpeläinen; Nick Wareham; Todd L Edwards; Göran Hallmans; Tibor V Varga; Sharon L R Kardia; Jennifer A Smith; Wei Zhao; Jessica D Faul; David Weir; Jie Mi; Bo Xi; Samuel Canizales Quinteros; Cyrus Cooper; Avan Aihie Sayer; Karen Jameson; Anders Grøntved; Myriam Fornage; Stephen Sidney; Craig L Hanis; Heather M Highland; Hans-Ulrich Häring; Martin Heni; Jessica Lasky-Su; Scott T Weiss; Glenn S Gerhard; Christopher Still; Melkaey M Melka; Zdenka Pausova; Tomáš Paus; Struan F A Grant; Hakon Hakonarson; R Arlen Price; Kai Wang; Andre Scherag; Johannes Hebebrand; Anke Hinney; BioBank Japan, AGEN-BMI, GIANT Consortium; Paul W Franks; Timothy M Frayling; Mark I McCarthy; Joel N Hirschhorn; Ruth J Loos; Erik Ingelsson; Hertzel C Gerstein; Salim Yusuf; Joseph Beyene; Sonia S Anand; David Meyre

doi:10.1093/hmg/ddv097

. 2015 Mar 17;24(12):3582–3594. doi: 10.1093/hmg/ddv097

Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals

Kevin T Nead ^1,², Aihua Li ³, Mackenzie R Wehner ², Binod Neupane ³, Stefan Gustafsson ⁴⁸, Adam Butterworth ¹, James C Engert ⁵⁰, A Darlene Davis ⁴, Robert A Hegele ^5,⁶, Ruby Miller ⁴, Marcel den Hoed ^7,⁸, Kay-Tee Khaw ¹, Tuomas O Kilpeläinen ^7,⁹, Nick Wareham ⁷, Todd L Edwards ¹⁰, Göran Hallmans ¹¹, Tibor V Varga ¹², Sharon L R Kardia ¹³, Jennifer A Smith ¹³, Wei Zhao ¹³, Jessica D Faul ¹⁴, David Weir ¹⁴, Jie Mi ¹⁵, Bo Xi ¹⁶, Samuel Canizales Quinteros ¹⁷, Cyrus Cooper ^18,^19,²⁰, Avan Aihie Sayer ¹⁸, Karen Jameson ¹⁸, Anders Grøntved ²¹, Myriam Fornage ²², Stephen Sidney ²³, Craig L Hanis ²⁴, Heather M Highland ²⁴, Hans-Ulrich Häring ^25,²⁶, Martin Heni ^25,²⁶, Jessica Lasky-Su ^27,²⁸, Scott T Weiss ^27,²⁸, Glenn S Gerhard ²⁹, Christopher Still ³⁰, Melkaey M Melka ³¹, Zdenka Pausova ³¹, Tomáš Paus ³², Struan F A Grant ^33,³⁴, Hakon Hakonarson ^33,³⁴, R Arlen Price ³⁵, Kai Wang ^22,³⁶, Andre Scherag ³⁷, Johannes Hebebrand ³⁸, Anke Hinney ³⁸; BioBank Japan, AGEN-BMI, GIANT Consortium⁴⁹, Paul W Franks ^10,^11,³⁹, Timothy M Frayling ⁴⁰, Mark I McCarthy ⁴¹, Joel N Hirschhorn ^42,^43,⁴⁴, Ruth J Loos ^7,⁴⁵, Erik Ingelsson ⁴⁸, Hertzel C Gerstein ^3,^46,⁴⁷, Salim Yusuf ^3,^46,⁴⁷, Joseph Beyene ³, Sonia S Anand ^3,^46,⁴⁷, David Meyre ^3,^47,^48,^*

¹Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK,

²Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA,

³Department of Clinical Epidemiology and Biostatistics,

⁴Department of Medicine,

⁵Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada, L8S 4L8,

⁶Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala SE 751 05, Sweden,

⁷The Research Institute of the McGill University Health Centre, McGill University, Montreal, QC, Canada, H3H 2R9,

⁸Six Nations Health Services, Ohsweken, Canada, N0A 1M0,

⁹Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, London, ON, Canada, N6A 5K8,

¹⁰Department of Medicine, University of Western Ontario, London, ON, Canada, N6A 3K7,

¹¹MRC Epidemiology Unit, University of Cambridge, Cambridge, UK,

¹²Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden,

¹³The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Copenhagen 2100, Denmark,

¹⁴Center for Human Genetics Research, Vanderbilt Epidemiology Center, Department of Medicine, Vanderbilt University, Nashville, TN 37235, USA,

¹⁵Department of Public Health and Clinical Medicine, Umeå University, Umeå 901 87, Sweden,

¹⁶Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö 205 02, Sweden,

¹⁷Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, USA,

¹⁸Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA,

¹⁹Department of Epidemiology, Capital Institute of Pediatrics, Beijing 100020, China,

²⁰Department of Maternal and Child Health Care, School of Public Health, Shandong University, Jinan 250100, China,

²¹Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, Universidad Nacional Autónoma de México, Instituto Nacional de Medicina Genómica, Mexico City, Mexico,

²²MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK,

²³National Institute for Health Research Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton General Hospital, Southampton SO16 6YD, UK,

²⁴National Institute for Health Research Biomedical Research Unit, University of Oxford, Oxford OX3 7LE, UK,

²⁵Department of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense DK-5230, Denmark,

²⁶University of Texas Health Science Center at Houston Institute of Molecular Medicine and Division of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, Houston, TX 77030, USA,

²⁷Division of Research, Kaiser Permanente of Northern California, Oakland, CA 94612, USA,

²⁸The Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX 77030, USA,

²⁹Internal Medicine IV (Endocrinology, Diabetology, Angiology, Nephrology, and Clinical Chemistry), University Hospital of Tuebingen, Tübingen 72076, Germany,

³⁰German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany,

³¹The Department of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,

³²Center for Genomic Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA,

³³Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Department of Pathology and Laboratory Medicine, Pennsylvania State University, Hershey, PA 17033, USA,

³⁴Geisinger Obesity Institute, Danville, PA 17822, USA,

³⁵The Hospital for Sick Children, Department of Physiology, University of Toronto, Toronto, ON, Canada, M5G 1X,

³⁶Rotman Research Institute, University of Toronto, Toronto, Canada, M6A 2E1,

³⁷Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA,

³⁸Department of Pediatrics and,

³⁹Center for Neurobiology and Behavior, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA,

⁴⁰Zilkha Neurogenetic Institute, Department of Psychiatry and Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90089, USA,

⁴¹Clinical Epidemiology, Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena 07740, Germany,

⁴²Department of Child and Adolescent Psychiatry, University of Duisburg-Essen, Essen 45141, Germany,

⁴³Department of Nutrition, Harvard School of Public Health, Boston, MA 02115, USA,

⁴⁴Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter EX2 4TH, UK,

⁴⁵Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 9DU, UK,

⁴⁶Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA,

⁴⁷Divisions of Genetics and Endocrinology, Children's Hospital, Boston, MA 02115, USA,

⁴⁸Department of Genetics, Harvard Medical School, Boston, MA 02115, USA,

⁴⁹The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA, and

⁵⁰Population Health Research Institute, McMaster University, and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON, Canada, L8L 2X

To whom correspondence should be addressed. Tel: +1 9055274322; Fax: +1 9052965806; Email: meyred@mcmaster.ca

PMCID: PMC4498155 PMID: 25784503

Abstract

Polymorphisms rs6232 and rs6234/rs6235 in PCSK1 have been associated with extreme obesity [e.g. body mass index (BMI) ≥ 40 kg/m²], but their contribution to common obesity (BMI ≥ 30 kg/m²) and BMI variation in a multi-ethnic context is unclear. To fill this gap, we collected phenotypic and genetic data in up to 331 175 individuals from diverse ethnic groups. This process involved a systematic review of the literature in PubMed, Web of Science, Embase and the NIH GWAS catalog complemented by data extraction from pre-existing GWAS or custom-arrays in consortia and single studies. We employed recently developed global meta-analytic random-effects methods to calculate summary odds ratios (OR) and 95% confidence intervals (CIs) or beta estimates and standard errors (SE) for the obesity status and BMI analyses, respectively. Significant associations were found with binary obesity status for rs6232 (OR = 1.15, 95% CI 1.06–1.24, P = 6.08 × 10⁻⁶) and rs6234/rs6235 (OR = 1.07, 95% CI 1.04–1.10, P = 3.00 × 10⁻⁷). Similarly, significant associations were found with continuous BMI for rs6232 (β = 0.03, 95% CI 0.00–0.07; P = 0.047) and rs6234/rs6235 (β = 0.02, 95% CI 0.00–0.03; P = 5.57 × 10⁻⁴). Ethnicity, age and study ascertainment significantly modulated the association of PCSK1 polymorphisms with obesity. In summary, we demonstrate evidence that common gene variation in PCSK1 contributes to BMI variation and susceptibility to common obesity in the largest known meta-analysis published to date in genetic epidemiology.

Introduction

The prevalence of obesity has reached epidemic proportions throughout the world (1). In addition to being the main risk predictor for the rapid increase of type 2 diabetes (T2D) (2), obesity also significantly increases the global disease burden of cardiovascular disease and cancer (3,4). Rising rates of childhood obesity, combined with an increasing prevalence of obesity in aging adult populations, suggest that the impact of this disease on human health will continue to grow in the future (5). Therefore, there is an urgent need to improve understanding of the etiology of obesity to help curb the obesity epidemic (6).

Genetic factors have been shown to play a substantial role in the etiology of obesity (7) and accordingly research has focused on identifying specific underlying genetic determinants of body weight regulation. Candidate-gene, gene-centric and genome-wide association (GWAS) studies have identified 42 loci with single-nucleotide polymorphisms (SNPs) that significantly associate (P < 5 × 10⁻⁸) with body mass index (BMI, as a continuous variable) (8–17). Additionally, case–control candidate gene and GWAS approaches have been used to examine the genetics of childhood and adult obesity (as a binary variable) (13,18–28). These studies have identified 46 loci with alleles associated with obesity at the genome-wide significance level. The majority of alleles (N = 24) influence both BMI variation and the risk for obesity, but 18 and 22 loci have been shown to contribute more specifically to BMI variation and the genetic risk for obesity, respectively (13,18–27). These data indicate that the genetic architecture of BMI variation and obesity may not be totally overlapping. Obesity may not only represents the extreme of the phenotypic spectrum of BMI (18), but perhaps a partially distinct inherited condition (29).

The PCSK1 gene may be illustrative of this paradigm. Mutations in PCSK1 lead to PC1/3 enzyme deficiency in neuroendocrine cells, which is characterized by monogenic obesity in mice and humans resulting from the abnormal maturation of hormones involved in energy and glucose metabolism (30–32). In a positional candidate-gene study, Benzinou et al. showed convincing evidence for the association of coding variants rs6232 and rs6234/rs6235 (pooled given perfect linkage disequilibrium between the two SNPs among diverse ethnic backgrounds) with childhood and adult severe obesity (27). The candidacy of these common variants is further strengthened in that they have been shown to reduce PC1/3 enzymatic activity through altered protein secretion, biosynthesis and catalytic activity (27,30,33,34) and determine glucose-stimulated proinsulin conversion (35,36), which is also a characteristic of complete human PCSK1 deficiency (31).

However, replication of the association of PCSK1 variants with obesity has provided conflicting results (27,37–45) with the lack of statistical power and genetic/phenotypic/ethnic heterogeneity being likely contributors to this variability. Additionally, conflicting evidence for the association of PCSK1 variants with BMI variation has been observed in individual studies (35,37–40,43,44,46,47) and only nominal evidence of association with BMI variation has been found for rs6232 and rs6235 in large GWAS meta-analyses (12,36). To give a more conclusive answer regarding whether PCSK1 variants differ in their contribution to extreme obesity (e.g. BMI ≥ 40 kg/m²), common obesity (BMI ≥ 30 kg/m²) and continuous BMI variation, we have systematically collected data from the literature and unpublished sources to perform a meta-analysis of the association of variants rs6232 and rs6234/rs6235 with quantitative BMI variation and common obesity risk in up to 331 175 subjects from diverse ethnic groups.

Results

Study selection

Results of the systematic search and data collection are presented in Figure 1. In total, 85 unique records were screened by title and abstract and 40 records were reviewed in full text, of which 10 were excluded. Reasons for exclusion after full text review included overlap with larger studies from the literature review, family-based studies (where clustering was not accounted for in the analysis), candidate-gene studies not examining the variants of interest, neither obesity nor BMI variation having been evaluated, lack of response from the authors and overlap with larger datasets. In total, 30 records were included from the literature, consortia, the Database of Genotypes and Phenotypes (dbGap), direct collaboration and novel contributions by the authors, as detailed in Supplementary Material, Table S1. Of these, 19 contained data on rs6232 and 28 contained data on rs6234/rs6235. Details and characteristics of all data sources included in the analysis can be found in Supplementary Material, Table S1. The minor allele frequency for each variant for rs6232, rs6234 and rs6235 are provided for the EpiDream cohort and from the 1000 Genomes Project in Supplementary Material, Tables S2 and S3, respectively.

Study quality

Study characteristics, genotyping and analysis methods of the included studies are described in the Supplementary Material, Table S1. As many studies were not initially designed to evaluate obese cases compared with non-obese controls, population structure was variable with many studies containing few obese cases and therefore likely underpowered to evaluate genetic associations with obesity. Hardy–Weinberg equilibrium was either reported or obtained via correspondence with P > 0.05 for all studies included. Similarly, all studies included were found to have SNP-wise call rates of >95% and all study estimates were adjusted for age and/or sex as covariates.

Binary obesity analysis

The results for the meta-analyses of rs6232 and rs6234/rs6235 with obesity are presented in Figure 2 using both classical random-effects meta-analytic techniques and a global random-effects meta-analytic method, designed to detect genetic associations among multi-cohort studies with high heterogeneity (48). The combined analysis of 131 284 individuals demonstrated a significant association of the G allele of rs6232 with obesity status [odds ratio (OR) = 1.15; 95% confidence interval (CI), 1.06–1.24] with a global method P-value (P_G) = 6.08 × 10⁻⁶ and a classical random-effects method P-value (P_C) = 4.38 × 10⁻⁴. Similarly, the analysis of 239 581 individuals demonstrated an OR of 1.07 (95% CI 1.04–1.10; P_G = 3.00 × 10⁻⁷; P_C = 2.75 × 10⁻⁵) for the association of rs6234/rs6235 with obesity status. Low-to-moderate between-study heterogeneity was observed for the association of both rs6232 (I² = 43%; 95% CI, 9–64%, P = 0.011) and rs6234/rs6235 (I² = 68%; 95% CI, 54–77%, P = 2.18 × 10⁻⁹) with binary obesity. Finally, exclusion of the initial PCSK1 discovery cohort (27) consisting of 1045 obese French adults and 1265 non-obese controls did not significantly impact the significance of our analysis for rs6232 (OR = 1.13; 95% CI 1.05–1.22; P_G = 8.31 × 10⁻⁵; P_C = 0.002) or rs6234/rs6235 (OR = 1.06; 95% CI 1.03–1.09; P_G = 4.45 × 10⁻⁵; P_C = 2.05 × 10⁻⁴).

Continuous BMI variation analysis

The results for the meta-analysis of rs6232 and rs6234/rs6235 with BMI variation are presented in Figure 3. In the analysis of 202 803 individuals, the average BMI increase for each G allele at rs6232 was 0.03 (95% CI 0.00–0.07; P_G = 0.047; P_C = 0.123). In the analysis of rs6234/rs6235, each effect-allele conferred a 0.02 (95% CI 0.00–0.03; P_G = 5.57 × 10⁻⁴; P_C = 0.008) unit increase in BMI among 331 175 individuals. The between-study heterogeneity was non-significant for the associations of rs6232 (I² = 30%; 95% CI, 0–59%, P = 0.102) and rs6234/rs6235 (I² = 22%; 95% CI, 0–50%, P = 0.137).

Heterogeneity and subgroup analysis

As low-to-moderate between-study heterogeneity was observed for the association of both rs6232 and rs6234/rs6235 with binary obesity, causes of heterogeneity were explored through pre-specified subgroup analyses (Table 1). Stratification by ethnicity, cohort size (≤1000 or >1000) and study ascertainment did not have any significant impact on the association of rs6232 with obesity. Stratification by cohort age-group (child/adolescent versus adult) resulted in a significantly different association between rs6232 and obesity in children/adolescents (OR = 1.53, 95% CI 1.22–1.93; P_G = 3.68 × 10⁻⁵; P_C = 2.84 × 10⁻⁴) and in adults (OR = 1.10, 95% CI 1.03–1.17; P_G = 0.001; P_C = 0.006; P_difference = 3.00 × 10⁻⁴). Between-study heterogeneity still remained significant for the association between rs6232 and obesity, after stratifying by ethnicity, cohort size or study ascertainment (Table 1). However, no more between-study heterogeneity was observed when children/adolescent and adult subgroups were analyzed apart (P = 0.250 and 0.182, respectively).

Table 1.

Subgroup analysis for the association of rs6232 and rs6234/rs6235 with obesity

rs6232	Random-effects OR (95% CI)	P-value		Heterogeneity		χ²-test for difference	No. of studies/cohorts	Sample size
rs6232	Random-effects OR (95% CI)	Classical	Global	I² (95% CI)	P-value	χ²-test for difference	No. of studies/cohorts	Sample size
Ethnicity
White Caucasian	1.14 (1.06–1.24)	0.001	1.08 × 10⁻⁵	44 (7–66)	0.015	0.485	22	125 579
East Asian							0	0
Hispanic	1.52 (0.65–3.55)	0.335	0.182	67 (0–90)	0.049		3	3679
African	0.83 (0.32–2.13)	0.694	0.810	^a	0.597		2	2026
Cohort age-group
Child/adolescent	1.53 (1.22–1.93)	2.84 × 10⁻⁴	3.68 × 10⁻⁵	25 (0–68)	0.250	3.00 × 10⁻⁴	6	7077
Adult	1.10 (1.03–1.17)	0.006	0.001	22 (0–54)	0.182	3.00 × 10⁻⁴	21	124 207
Cohort size
≤1000	1.14 (0.92–1.40)	0.223	0.351	0 (0–71)	0.536	0.756	7	5983
>1000	1.16 (1.06–1.26)	0.003	1.17 × 10⁻⁵	53 (21–72)	0.003	0.756	20	125 301
Population-based recruitment
No	1.19 (1.02–1.38)	0.030	7.15 × 10⁻⁴	53 (13–75)	0.012	0.222	13	29 799
Yes	1.12 (1.03–1.22)	0.010	0.004	28 (0–62)	0.157	0.222	14	101 485
rs6234/rs6235
Ethnicity
White Caucasian	1.09 (1.04–1.14)	2.04 × 10⁻⁴	4.28 × 10⁻⁹	64 (44–77)	1.25 × 10⁻⁵	5.13 × 10⁻⁷	23	163 385
East Asian	1.00 (0.98–1.03)	0.961	1.00	22 (0–67)	0.274		5	66 721
Hispanic	1.13 (0.99–1.27)	0.050	0.103	0 (0–85)	0.579		4	4711
African	1.16 (0.92–1.46)	0.222	0.115	59 (0–86)	0.064		4	4764
Cohort age-group
Child/adolescent	1.13 (1.00–1.29)	0.053	5.61 × 10⁻⁴	68 (29–86)	0.004	0.005	7	10 296
Adult	1.06 (1.02–1.09)	0.001	6.35 × 10⁻⁵	66 (49–77)	4.19 × 10⁻⁷	0.005	29	229 285
Cohort size
≤1000	1.02 (0.91–1.15)	0.69	0.576	44 (0–77)	0.096	0.439	7	6363
>1000	1.07 (1.04–1.11)	2.56 × 10⁻⁵	3.48 × 10⁻⁷	71 (58–80)	1.71 × 10⁻⁹	0.439	29	23 3218
Population-based recruitment
No	1.11 (1.05–1.17)	1.60 × 10⁻⁴	3.65 × 10⁻⁶	76 (61–85)	1.35 × 10⁻⁷	0.001	16	92 067
Yes	1.05 (1.00–1.09)	0.040	0.005	46 (8–68)	0.014	0.001	20	14 7514

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OR, odds ratio; CI, confidence interval.

^aAnalysis not possible if <2 degrees of freedom.

Stratification by cohort size also did not have a significant impact on the association of rs6234/rs6235 with obesity. However, stratification by ethnicity, cohort age-group (child/adolescent versus adult) or study ascertainment resulted in a significantly different association between rs6234/rs6235 and obesity (Table 1). The OR estimate of rs6234/rs6235 for obesity was comparable in white Caucasian (OR = 1.09, 95% CI 1.04–1.14; P_G = 4.28 × 10⁻⁹; P_C = 2.04 × 10⁻⁴), Hispanic (OR = 1.13, 95% CI 0.99–1.27; P_G = 0.103; P_C = 0.050) and African (OR = 1.16, 95% CI 0.92–1.46; P_G = 0.115; P_C = 0.222) ethnic groups, but the rs6234/rs6235 variant conferred no evidence for an increase in the odds for obesity in East Asian populations (OR = 1.00, 95% CI 0.98–1.03; P_G = 1.00; P_C = 0.961; P_difference = 5.13 × 10⁻⁷), which included Chinese, Japanese, Korean and Malay individuals. A significantly different association between rs6234/rs6235 and obesity was observed in children/adolescents (OR = 1.13, 95% CI 1.00–1.29; P_G = 5.61 × 10⁻⁴; P_C = 0.053) and in adults (OR = 1.06, 95% CI 1.02–1.09; P_G = 6.35 × 10⁻⁵; P_C = 0.001; P_difference = 0.005). The association of rs6234/rs6235 with obesity was significantly different depending on the type of recruitment [population-based or other recruitment (e.g. hospital): OR = 1.05, 95% CI 1.00–1.09; P_G = 0.005; P_C = 0.040 and OR = 1.11, 95% CI 1.05–1.17; P_G = 3.65 × 10⁻⁶; P_C = 1.60 × 10⁻⁴, respectively; P_difference = 0.001]. Between-study heterogeneity still remained significant for the association between rs6234/rs6235 and obesity, after stratifying by ethnicity, cohort age-group, cohort size or study ascertainment (Table 1).

There was no evidence of small study effects in the analysis of rs6232 for obesity (Supplementary Material, Figure S1). However, a funnel plot for rs6234/rs6235 demonstrated asymmetry for the obesity analysis (Supplementary Material, Figure S2), which was supported by the Egger's (P < 0.05), but not the Begg's (P > 0.05) tests. Additionally, the association of rs6234/rs6235 with obesity status did not significantly differ in studies with greater than 1000 individuals compared with those with 1000 participants or less (P = 0.439).

Finally, published data from the Genetic Investigation of Anthropometric Traits (GIANT) consortium for the association of rs6232 and rs6235 with different classes of obesity are presented in Table 2. In all analyses, the control group consisted of normal weight individuals (BMI < 25 kg/m²). For the rs6232 variant, there was an OR of 1.04 (95% CI 1.00–1.08), 1.06 (95% CI 1.00–1.13), 1.08 (95% CI 0.98–1.20) and 1.13 (95% CI 0.95–1.34), across the overweight and obese classes I, II and III groups, respectively (0.057 ≤ P_C ≤ 0.18). Similarly, the rs6235 analysis showed a similar increasing risk pattern with an OR of 1.03 (95% CI 1.01–1.05), 1.04 (95% CI 1.01–1.07), 1.07 (95% CI 1.02–1.11) and 1.11 (95% CI 1.02–1.20), across the overweight and obese classes I, II and III groups, respectively (0.003 ≤ P_C ≤ 0.011). However, the ORs of rs6232 and rs6235 SNPs for different classes of obesity were not significantly different (P > 0.05).

Table 2.

Association of rs6232 and rs6235 with obesity class in GIANT

	OR (95% CI)	P-value	Cases	Controls
rs6232
Overweight	1.04 (1.00–1.08)	0.078	78 671	60 578
Obesity class I	1.06 (1.00–1.13)	0.057	22 947	47 263
Obesity class II	1.08 (0.98–1.20)	0.120	5983	35 721
Obesity class III	1.13 (0.95–1.34)	0.180	1534	23 221
rs6235
Overweight	1.03 (1.01–1.05)	0.005	92 808	65 660
Obesity class I	1.04 (1.01–1.07)	0.010	32 746	64 864
Obesity class II	1.07 (1.02–1.11)	0.003	9723	61 085
Obesity class III	1.11 (1.02–1.20)	0.011	2550	35 900

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OR, odds ratio; CI, confidence interval.

Discussion

In this study, we provide evidence that common variants in PCSK1 contribute to BMI variation and common obesity. The proprotein convertase 1 encoded by the PCSK1 gene belongs to the subtilisin-like proprotein convertase family. Proprotein convertase 1 is known to cleave key peptides in the regulation of energy balance such as proinsulin or proopiomelanocortin (49). Rare loss-of-function coding mutations in PCSK1 have been associated with a Mendelian form of hyperphagic obesity in mice and humans (30,32,50). Benzinou and colleagues provided evidence of association between the frequent coding variants N221D (rs6232) and Q665E/S690T (rs6234/rs6235) and childhood and adult severe obesity in European populations (27). However, conflicting results have been reported regarding the association of the rs6232 and rs6234/rs6235 polymorphisms with common obesity (27,37–45) or BMI variation (12,35–40,43,44,46,47) in diverse ethnic backgrounds. This prompted us to assess the contribution of rs6232 and rs6234/rs6235 with common obesity and BMI variation using a meta-analytic approach. We enhanced the power of our analyses, and therefore the ability to detect associations, using both a classic meta-analytic approach in addition to data extraction from pre-existing GWAS or custom-arrays from consortia and single studies as well as internal data and collaboration. In total, we collected phenotypic and genetic data in up to 331 175 individuals from diverse ethnic groups, which represents to our knowledge the largest meta-analysis published to date in the field of genetic epidemiology.

Overall, our data demonstrate that the common functional variants rs6232 and rs6234/rs6235 in PCSK1 not only predispose to severe obesity but also modestly increase the risk of common obesity and increased BMI within populations. Our results are in line with the conclusions of a large-scale GWAS meta-analysis for BMI and different clinical classes of obesity performed in up to 263 407 subjects (20). In this study, Berndt et al. found a large overlap of SNPs contributing to both quantitative BMI and different thresholds of obesity and concluded that there was little etiological heterogeneity between these traits at least at the level of common SNP variation (20). Our data are only partially concordant with a recent meta-analysis published by Stijnen and colleagues (51). However, the 65% lower sample size in this study in comparison with ours (N = 200 000 versus 331 175), added to methodological considerations lead us to interpret the conclusions of this study with caution (52).

Children with congenital proprotein convertase one-third deficiency display early growth abnormalities and reduced height as a consequence of growth hormone deficiency (53). Therefore, we cannot totally exclude that the association of rs6232 and rs6234/rs6235 SNPs with BMI may be in part confounded by an additional genetic effect on height variation. We consulted the publically available data released in 2014 by the GIANT consortium (54) and did not find an association between the rs6232 SNP and height (B = −0.0086 ± 0.0068, P = 0.21, N = 233 697). On the contrary, and in line with the observations made in monogenic patients, the rs6235 SNP BMI-increasing allele evidenced a strong association with decreased height (B = −0.0224 ± 0.0033, P = 5.4 × 10⁻¹¹, N = 251 342).

If the genetic architecture of BMI variation in general populations and the risk of common obesity includes many overlapping genetic variants, these variants may be more prevalent in severe and/or familial forms of childhood and adult obesity. Benzinou et al. indeed reported odds ratios (OR) for obesity of 1.34 (95% CI 1.20–1.49; P = 7.27 × 10⁻⁸) and 1.22 (95% CI 1.15–1.29; P = 2.31 × 10⁻¹²) for rs6232 and rs6234/rs6235 in a European sample enriched in familial forms of childhood and adult extreme obesity in European populations (27). Lower OR for common obesity were observed for rs6232 (OR = 1.15) and rs6234/rs6235 (OR = 1.07) in our meta-analysis. Importantly, the larger ORs for obesity observed in the Benzinou et al. original study are less likely to result from initial overestimation of the true effect (i.e. winner's curse), as they were estimated from a meta-analysis of seven independent cohorts (27). Additionally, exclusion of the initial PCSK1 discovery cohort in sensitivity analyses did not impact the significance of our results. A more plausible explanation is that extreme familial forms of early-onset and adult obesity are enriched for susceptibility variants. Consistent with this hypothesis, a non-significant progressive enrichment of rs6232 and rs6234/rs6235 effect variants was observed for increasing degrees of obesity in the GIANT sample (Supplementary Material, Table S3). Our results indicate that an enrichment sampling strategy (selection of obese individuals having a strong family background of the disease, an early age of onset and/or a more severe phenotype) is a cost-effective and efficient approach to identify loci that also contribute to BMI variation and risk for common obesity in general populations (20,55).

Our data show a substantial degree of between-study heterogeneity in the association of PCSK1 SNPs rs6232 and rs6234/rs6235 with obesity. Benzinou et al. similarly reported significant between-study heterogeneity while analyzing the association of the SNP rs6235 with severe obesity in seven independent cohorts (27). We investigated the potential causes of this heterogeneity and made several observations. Ethnicity significantly modulated the association between rs6234/rs6235 and obesity. Whereas similar effect sizes for the association of rs6234/rs6235 with obesity were found in white Caucasian, African and Hispanic ethnic groups, no evidence for association was found in East Asian populations. As the rs6234/rs6235 coding non-synonymous SNPs exhibit functional effects on the proprotein convertase 1 activity (34), and therefore do not represent proxy SNPs, the absence of evidence for an association restricted to only East Asian populations is very unlikely to be explained by differential linkage disequilibrium structure at the PCSK1 locus in certain ethnic groups. Notably, another SNP in PCSK1, independent of the rs6234/rs6235 signal, has been recently identified as an important contributor to BMI variation in East Asian populations (15). Therefore, the lack of association of rs6234/rs6235 with obesity in East Asians in the current study is unlikely to be explained by an interaction between the PCSK1 locus and ethnic-specific lifestyle factors that may inhibit the genetic effect on obesity at this specific locus. Ethnic-specific epistasis effects may account for this intriguing pattern of association. In contrast to rs6234/rs6235, ethnicity did not modulate the association between rs6232 and obesity. However, this may have been secondary to decreased power and no available data examining the association of rs6232 and obesity or BMI in East Asian populations.

Study ascertainment in the overall meta-analysis significantly modulated the association of rs6234/rs6235 with obesity. Not surprisingly, obese cases recruited in hospitals, which tended to be more obese, or obtained from pedigrees enriched in obese cases displayed 2-fold higher ORs for obesity in comparison with cases issued from the general population. Additionally, there is a suggestion of possible small study effects for the rs6234/rs6235, due to a lower than predicted representation of smaller negative studies in our analysis, which may also explain a degree of the observed heterogeneity.

Cohort age-group significantly modulated the association between rs6232, rs6234/rs6235 and obesity with the effect sizes for both SNPs being stronger in children/adolescents than in adults (Table 1). Similar trends have been observed for the association of rs6232 with BMI and obesity in younger versus older adult Europeans in two independent reports (38,44). As prohormone convertase 1 cleaves proopiomelanocortin, a key peptide in the regulation of energy balance and appetite, it is tempting to speculate that the genetic effect of common functional variants in PCSK1 may be more pronounced in the context of the more recent ‘obesogenic’ environment with unlimited access to high-caloric food (56).

Additional environmental or biological factors (e.g. physical activity) are likely to modulate the association between rs6234/rs6235 and obesity in the current study. Between-study heterogeneity indeed remained significant after stratifying the genetic association test by ethnicity, cohort age-group or population recruitment. Our data, in line with previous reports, confirm that heterogeneity may be a common feature of genetic association studies involving obesity predisposing variants (57–59). In that context, the global meta-analytic random-effects method recently developed by Lebrec et al. is especially relevant as it is designed to detect genetic associations among multi-cohort studies that convey a high level of between-study heterogeneity (48). Neupane and colleagues recently demonstrated that the global method achieves higher power and lower rates of false positives compared with classic methods in the presence of high between-study heterogeneity, using both simulated and real datasets (60). We observed the same trends in our study with the global method providing greater precision of estimates than the classic random-effects inverse variance weighted method in the setting of high between-study heterogeneity. We therefore support the wider use of global meta-analytic random-effects model for the genetic dissection of complex traits.

The strengths of this meta-analysis include a comprehensive data collection strategy, an exceptionally large sample size representative of high ethnic diversity, the use of the most up-to-date meta-analytic methods and the selection of functionally relevant coding polymorphisms. Limitations of this study include the modest statistical power of subgroup analyses and the limited access to a broad range of environmental exposure information that may modulate the association of PCSK1 SNPs with obesity.

In summary, we provide evidence that SNPs rs6232 and rs6234/rs6235 in PCSK1 contribute to BMI variation as well as increased susceptibility to common obesity. Our data confirm the power of gene identification strategies based on extreme forms of obesity to identify loci that also contribute to BMI variation and risk for common obesity in general populations.

Methods

The PRISMA statement guidelines were utilized for this systematic review and meta-analyses (61).

Eligibility criteria

Individual studies, meta-analyses and GWAS consortia examining obesity status or BMI variation with respect to exposure to the previously defined effect alleles of rs6232 (G), rs6234 (G) or rs6235 (C) were eligible for inclusion (27,41). As rs6234 and rs6235 are in perfect linkage disequilibrium among diverse ethnic backgrounds (r² = 1.0), they are considered as identical and are analyzed together (62,63). Additionally, studies in East Asian, South Asian and white Caucasian populations reporting on the SNP rs7713317 (effect allele: G) were eligible, as this variant is in perfect linkage disequilibrium (r² = 1.0) with rs6234 and rs6235 among these groups in both the HapMap (62) and 1000 Genomes projects (63). Only analyses of variants under the additive model were eligible for inclusion as this has been demonstrated to be the most likely inheritance model (27). In addition, studies that recorded BMI or obesity status and used genotyping platforms known to contain these variants were also eligible and corresponding authors were contacted to share the unpublished data in a collaborative manner. Studies examining obesity status were eligible if they compared an obese group with a non-obese group as defined according to the study population. Our exclusion criteria were clustered datasets such as family-based studies (unless clustering was accounted for in the analysis), data from the analysis of variants not shown to be in Hardy–Weinberg equilibrium of P ≥ 0.05 and data with an imputation quality of <0.9.

Information sources

The search strategy was designed to identify all sources of published and unpublished data both in the literature and in available databases. Electronic searches without language or date restriction were carried out in PubMed (1966–present), Web of Science (1899–present), Embase (1974–present) and the NIH GWAS catalog (64). Search terms used in all databases to identify relevant studies, along with a representative search strategy used for the PubMed query, can be found in Supplementary Material, Table S4. All articles identified through the search were evaluated based on the title and abstract. Clearly, irrelevant studies were excluded from further consideration. The remaining articles received a full text review. The last search was undertaken on 9 June 2013.

Studies recording data on study participant BMI and/or obesity status and utilizing genotyping arrays known to include rs6232, rs6234, rs6235 or rs7713317 were searched for in dbGaP (65). Relevant genotyping arrays and proxy SNPs were identified using the SNP Annotation and Proxy Search (66). Additional data were obtained through collaboration with other investigators and through consortia (Supplementary Material, Table S1).

Descriptive information was extracted from each study including: (i) SNPs available, (ii) study design, (iii) participant selection, (iv) eligible phenotype (obesity or BMI), (v) genotyping method, (vi) sample size, (vii) number of obese and non-obese individuals (for obesity analyses), (viii) obese and non-obese definitions, (ix) ethnicity, (x) included age groups and (xi) model adjustments. As data were gathered from individual studies, meta-analyses and GWAS consortia, particular care was taken to avoid the inclusion of duplicate data. Detailed cohort information was requested from participating consortia and key study characteristics were compared across all eligible studies to determine if multiple publications with data from the same study were present. If study duplication existed, data from the publication with the most complete analysis of the duplicated study was used. Where possible, corresponding authors were contacted to provide study data that did not overlap with other data sources. The literature search and article review were carried out independently by two reviewers (K.N. and D.M.) with consensus reached by discussion. Details of study selection can be found in Figure 1.

Unpublished data from the EpiDREAM (Epidemiological arm of the Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication) (67), SHARE (Study of Health Assessment and Risk in Ethnic groups) (68) and SHARE-AP (Study of Health Assessment and Risk Evaluation in Aboriginal Peoples) (69) cohorts were also included. Briefly, the EpiDREAM multi-ethnic longitudinal cohort is the epidemiological arm of the DREAM study comprised of individuals with an increased risk for T2D who were screened for trial eligibility. The SHARE and SHARE-AP studies are cross-sectional cohorts investigating atherosclerosis and cardiovascular diseases among different ethnic groups living in Canada. Genetic and clinical data were available from 1172 participants from the SHARE and SHARE-AP population. In the EpiDREAM study, 17 453 subjects from six ethnic groups (South Asian, East Asian, European, African, Latin American and Native North American) and having both genetic and baseline clinical information have been included here. Self-reported ethnicity has been validated in the SHARE, SHARE-AP and EpiDREAM studies using the eigensoft software (http://genepath.med.harvard.edu/~reich/Software.htm). The SHARE, SHARE-AP and EpiDREAM cohorts were genotyped using the cardiovascular gene-centric 50 K SNP ITMAT-Broad-CARe (IBC) array (70). Subjects in the SHARE and SHARE-AP cohorts were genotyped using the Illumina HumanCVD BeadChip. SNPs rs6232, rs6234 and rs6235 in PCSK1 were part of the 50K array SNP list and were extracted for further study.

Statistical analysis

The primary analyses examined the effect of exposure to the effect alleles of rs6232 (G) or rs6234/rs6235/ rs7713317 (G/C/C) on obesity status and BMI variation. The OR and 95% CI were collected to examine the effects of the variants on obesity status. The beta estimates and SE were collected to examine the effects of the variants on BMI. If eligible studies did not report data necessary for inclusion in the meta-analysis, the corresponding authors were contacted directly.

We implemented a global random-effects meta-analytic method, designed to detect genetic associations among multi-cohort studies with high heterogeneity (48), to calculate a summary OR and 95% CI or summary beta estimate and SE for the obesity status and BMI variation analyses, respectively. Compared with classical random-effects meta-analytic techniques, which consider each study to be a random sample of the true effect distribution and calculates the combined effect estimate as the mean of this distribution, the global random-effects method tests if the overall association or between-cohort variance of associations is non-zero. This approach was implemented because it has demonstrated improved power and lower rates of false positives compared with classical methods in the setting of heterogeneity (60). The meta-analyses were additionally performed using classical random-effects inverse variance weighted methods.

Between-study heterogeneity was evaluated using Cochran's Q-statistic and the proportion of heterogeneity due to study variation was quantified using the I² statistic (71). If substantial heterogeneity was detected (P < 0.1), sources of this heterogeneity were explored. The presence of small study effects was evaluated using funnel plots, by calculating Begg and Egger statistics and by comparing subgroup analysis of small studies (n ≤ 1000) versus large studies (n > 1000) as previously described (72). Subgroup analysis was further examined according to pre-specified categories including ethnicity (white Caucasian, East Asian and Hispanic, African), studies using population-based recruitment compared with studies using other recruitment methods (e.g. hospital based) and by age category. Within subgroup heterogeneity was examined using a chi-squared test for difference.

Additionally, using summary statistics from the GIANT consortium (20), we examined the risk of obesity conferred by the PCSK1 variants with increasing classes of obesity including overweight (BMI ≥ 25 kg/m²) and obesity classes I (BMI ≥ 30 kg/m²), II (BMI ≥ 35 kg/m²) and III (BMI ≥ 40 kg/m²) compared with a lean control group (BMI < 25 kg/m²).

All analyses were carried out using Stata version 12 (StataCorp, College Station, TX, USA) or the R software.

Supplementary Material

Supplementary Material is available at HMG online.

Funding

K.T.N. was supported by the Gates Cambridge Trust. The EpiDREAM study was funded by a grant from the Canadian Institutes of Health Research University Industry competition with partner funding from Glaxo-SmithKline and Sanofi Aventis Global, Sanofi Aventis Canada, the McGill University and Genome Quebec Innovation Centre, Heart and Stroke Foundation of Canada. The SHARE/SHARE-AP study was funded by the Canadian Institutes of Health Research, the Heart and Stroke Foundation of Ontario and Genome Canada through the Ontario Genomics Institute. D.M. holds a Canada Research Chair in Genetic Epidemiology at McMaster University. S.S.A. holds a Canada Research Chair in Ethnic Diversity and Cardiovascular Disease, and is the Michael G. DeGroote and Heart and Stroke Foundation of Ontario Chair in Population Health, McMaster University. H.G. holds the Population Health Research Institute Chair in Diabetes Research sponsored by Aventis. S.Y. holds a Heart and Stroke Foundation Chair in Cardiovascular Research, McMaster University. R.A. Hegele is a Career Investigator of the Heart and Stroke Foundation of Ontario and holds the Edith Schulich Vinet Canada Research Chair (Tier I) in Human Genetics, the Martha G. Blackburn Chair in Cardiovascular Research, and the Jacob J. Wolfe Distinguished Medical Research Chair at the University of Western Ontario. G.S.G. and C.D.S. were supported by National Institute of Health grants DK072488 and DK088231. The CAMP Genetics Ancillary Study is supported by contracts U01 HL076419, U01 HL65899, P01 HL083069 and T32 HL07427 from the National Heart, Lung, and Blood Institute, National Institutes of Health. The GLACIER Study was funded primarily by grants from the Swedish Research Council, The Swedish Heart-Lung Foundation, The Swedish Diabetes Association, and Novo Nordisk (all to P.W.F.). A.S. is supported by the Federal Ministry of Education and Research (BMBF), Germany (FKZ: 01EO1002). T.O.K. was supported by grant no. DFF-1333-00124 from The Danish Council for Independent Research.

Supplementary Material

Supplementary Data

supp_24_12_3582__index.html^{(1.4KB, html)}

Acknowledgements

We thank all staff of the Laboratory for Statistical Analysis, Medical Informatics and Genotyping Development at the Center for Genomic Medicine, RIKEN and the BioBank Japan Project for the support of this the study. BioBank Japan Project was supported by Ministry of Education, Culture, Sports, Science and Technology, Japan.

Conflict of Interest statement. None declared.

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supp_ddv097_ddv097supp_table1.xlsx^{(24KB, xlsx)}

supp_ddv097_ddv097supp_table2.docx^{(18.9KB, docx)}

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PERMALINK

Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals

Kevin T Nead

Aihua Li

Mackenzie R Wehner

Binod Neupane

Stefan Gustafsson

Adam Butterworth

James C Engert

A Darlene Davis

Robert A Hegele

Ruby Miller

Marcel den Hoed

Kay-Tee Khaw

Tuomas O Kilpeläinen

Nick Wareham

Todd L Edwards

Göran Hallmans

Tibor V Varga

Sharon L R Kardia

Jennifer A Smith

Wei Zhao

Jessica D Faul

David Weir

Jie Mi

Bo Xi

Samuel Canizales Quinteros

Cyrus Cooper

Avan Aihie Sayer

Karen Jameson

Anders Grøntved

Myriam Fornage

Stephen Sidney

Craig L Hanis

Heather M Highland

Hans-Ulrich Häring

Martin Heni

Jessica Lasky-Su

Scott T Weiss

Glenn S Gerhard

Christopher Still

Melkaey M Melka

Zdenka Pausova

Tomáš Paus

Struan F A Grant

Hakon Hakonarson

R Arlen Price

Kai Wang

Andre Scherag

Johannes Hebebrand

Anke Hinney

Paul W Franks

Timothy M Frayling

Mark I McCarthy

Joel N Hirschhorn

Ruth J Loos

Erik Ingelsson

Hertzel C Gerstein

Salim Yusuf

Joseph Beyene

Sonia S Anand

David Meyre

Abstract

Introduction

Results

Study selection

Figure 1.

Study quality

Binary obesity analysis

Figure 2.

Continuous BMI variation analysis

Figure 3.

Heterogeneity and subgroup analysis

Table 1.

Table 2.

Discussion

Methods

Eligibility criteria

Information sources

Statistical analysis