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. 2014 Mar 30;9(3):399. doi: 10.1007/s12263-014-0399-1

High frequency of rare variants with a moderate-to-high predicted biological effect in protocadherin genes of extremely obese

Edwin C M Mariman 1,✉, F G Bouwman 1, Erik E J G Aller 1, Marleen A van Baak 1, Ping Wang 1,2
PMCID: PMC4026441  PMID: 24682882

Abstract

Relatively rare variants with a moderate-to-high biological effect may contribute to the genetic predisposition of common disorders. To investigate this for obesity, we performed exome sequencing for 30 young (mean age: 29.7 years) extremely obese Caucasian subjects (mean body mass index: 51.1 kg/m2; m/f = 11/29). Rare variants with a moderate-to-high predicted biological effect were assembled and subjected to functional clustering analysis. It showed that the 55 clustered protocadherin genes on chromosome 5q31 have a significantly (P = 0.002) higher frequency of rare variants than a set of 325 reference genes. Since the protocadherin genes are expressed in the hypothalamus, we tested another 167 genes related to the function of the hypothalamus, but in those genes, the frequency of rare variants was not different from that of the reference genes. To verify the relation of variation in the protocadherin genes with extreme obesity, we analyzed data from more than 4,000 European Americans present on the Exome Variant Server, representing a sample of the general population. The significant enrichment of rare variants in the protocadherin genes was only observed with the group of extremely obese individuals but not in the “general population”, indicating an association between rare variants in the protocadherin cluster genes and extreme obesity.

Keywords: Extreme obesity, Genetic variation, Functional clustering analysis, Protocadherins, Neuronal plasticity

Introduction

Common traits and diseases in humans often have a considerable heritability indicating that genetic factors account for a major part of their variance. For obesity, the heritability is about 0.4–0.7 (Berndt et al. 2013), whereas also for obesity-related parameters like body mass index (BMI), it has been shown that the variance is to a significant extent genetically determined (Farooqi and O’Rahilly 2007). Two types of studies are intensively performed to find the genetic factors: detailed analysis of candidate genes in relatively severe, early onset cases, and genome-wide association studies (GWAS) with large cohorts of overweight and normal weight subjects. Looking at single nucleotide polymorphisms (SNPs) and copy number variants (CNVs), the GWAS approach has led already to the detection of 56 different loci (Wheeler et al. 2013). According to this study, the risk contribution of each locus may differ between phenotypically different groups, i.e., with more or less severe obesity.

Remarkably, all the variation detected by GWAS together with the highly penetrant single gene mutations so far explains only a small fraction of the genetic background of obesity (Speliotes et al. 2010; Loos 2012). This led scientists to speculate on the nature of the missing variation such as CNVs and epigenetic influences. Based on our previous research on neural tube defects, we, as others, proposed a contribution of relatively rare genetic variants with a moderate-to-high biological effect (Mariman 2009; Manolio et al. 2009). The low population frequency and incomplete penetrance of these variants prevent their identification by association and linkage studies. Persons with the same phenotypic outcome, in this case obesity, may have largely different sets of such rare predisposing variants. Yet part of this variation may occur in genes of the same functional cluster or belong to particular pathways. According to this assumption, we used exome sequencing and functional clustering analysis on a selected cohort of extremely obese subjects and compared the outcome to available population data to try to identify sets of genes enriched for rare variation potentially involved in predisposing to extreme obesity.

Methods and procedures

Subjects

Subjects belonged to a cohort of 561 obese people, who were referred by their family doctor to a private obesity clinic (CO-EUR center for obesity in Europe, http://www.co-eur.eu/) for advice on lifestyle. Thrity extremely obese Caucasian subjects (19 females, 11 males) were selected from this cohort based on a relatively high body mass index (BMI, average 51.1 kg/m2, range 45.3–65.1 kg/m2) and relatively young age (29.7 years, range 19–40.4 years). Eight of them had reported overweight during childhood (average BMI 54.2 kg/m2), but for the others such information was lacking. Informed consent for genetic studies was obtained from all participants, and permission was granted by the ethical committee of Maastricht University Medical Center.

DNA isolation and sequencing

Genomic DNA was isolated from peripheral blood leukocytes using the QIAamp DNA blood kit from Qiagen (Amsterdam, The Netherlands) and was then outsourced for exome sequencing in a CLIA-certified laboratory (EdgeBio, http://www.edgebio.com/). The Nimblegen capture kit was used, followed by sequencing on the Ilumina HiSeq2000. Sequence results were compared with GRCh37 (b37) as the reference genome (http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/human/). For every subject, data were returned as a SNP-file and an indel-file (insertion/deletion). For every variant, information was provided including genetic data (chromosome, cytoband, reference position, gene, zygosity, rs-number, alleles, etc.), quality information on the sequencing (quality score, depth), and information on predicted biological effect by various methods [SNPeffect (Reumers et al. 2005), SIFT score (http://sift.jcvi.org/), Polyphen2 class and score (http://genetics.bwh.harvard.edu/pph2/), etc.].

Selection of rare variants

From the files with SNPs and indels (insertion/deletion), rare genetic variants with a moderate-to-high predicted biological effect were first selected on the absence of an rs-number and a read depth of at least 20. Next, from the remaining list relevant SNPs were selected as follows: either a high biological impact based on SNPeffect or a SIFT score of 0.06 or less in case of a missense variant; relevant indels were selected in the following way: either a high biological impact, or a codon deletion or in-frame insertion that was marked as moderate impact. Absence of rs-number as a marker for low frequency was checked by searching variants in the Exome Variant Server (EVS, http://evs.gs.washington.edu/EVS/). Of 50 randomly chosen variants for which there was no rs-number indicated by EdgeBio, only two were found in the EVS with an rs-number and an allele frequency of 1/8,586 and 1/8,600, respectively, indicating that our assumption was correct.

Functional clustering analysis

We used the online Panther program (www.pantherdb.org) to analyze the functions of the genes carrying the rare variants. In this program, a gene list is mapped to selected categories/pathways showing representation of molecular functions, biological processes, cellular components, protein class, or biological pathways. We mapped our genes to the biological pathways, which were sorted by the number of mapped genes. The top-ranked pathways were checked manually to search for large gene clusters.

Background variant hit score

In order to assess the basal number of rare variants occurring in a gene, the VeryGene database of tissue- and organ-specific genes (www.verygene.com) was used to extract 325 lung/kidney genes that to our knowledge do not have a relation to obesity. Screening of all 30 persons for rare variants in those 325 genes resulted in 24 hits putting the background hit score at 1/406 variants per gene per person.

Statistics

Statistically significant enrichment was calculated by the Chi-square method with Yates correction (http://www.graphpad.com/quickcalcs/contingency1/).

Population data

For investigating population data from the EVS, missense variants in European Americans with a Polyphen2 class other than ‘benign’ were selected. As we noticed that due to the overlap of transcripts some of the variants were called for more than one gene of the protocadherin cluster, identical repeats of variants were deleted from the final variant list.

Results

Number of rare variants

On average 107,279 variants were detected per obese subject, composed of 98,352 SNPs and 8,927 indels. Of those, 2,693 were without an rs-number and had a read-depth of at least 20. After selection on biological impact or SIFT score as described above, on average 109 (0.1 %) SNP and indel variants per person remained with a total of 3,301 hits in the 30 obese subjects.

The total number of 3,301 hits was present in 1,761 different genes. A total of 1,467 genes were hit only once. Fifty-five genes were carrying variation in more than six subjects. Two variants were detected in all 30 persons, which might be accounted for by (local) polymorphisms, locally fixed alleles differing from the reference genome or sequence errors.

Functional clustering analysis

The total number of 1,761 genes with rare variants was analyzed by the Panther program as described in the Methods section. The Wnt signaling pathway scored highest (Fig. 1a; Table 1). Looking in more detail, it appeared that cadherin genes accounted for more than half of the genes carrying the rare variants in the Wnt signaling pathway (Fig. 1b). Focusing on the cadherin genes revealed that 15 variants occurred in 12 of the 55 genes of the human protocadherin cluster (PCDH) on chromosome 5q31 (Table 2) (Wu and Maniatis 2000). Compared with the background hit frequency, the protocadherin cluster displays a significant enrichment (P = 0.0002) of relatively rare genetic variants with a moderate-to-high predicted biological effect in the extremely obese subjects. This cluster was also responsible for the high ranking of the cadherin signaling pathway in the Panther analysis (Table 1). We also found another two clusters in the top ten-ranked pathways, namely the laminin genes as part of the integrin signaling pathway and the myosin genes as part of the nicotinic acetylcholine receptor signaling pathway (Fig. 1c, d). In the 30 subjects, we observed 6 variants in 4 of the 12 human laminin genes (P < 0.0001) and 12 variants in 9 of the 37 human myosin genes (P < 0.0001). The identity of the variants is shown in Table 3. In these three groups of genes, rare variants were detected in both females and males without significant differences between the sexes.

Fig. 1.

Fig. 1

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Functional clustering analysis of genes with selected rare variants. a Pie chart of the top-ranked pathways with numbers referring to the names of the pathways as indicated in Table 1. b Pie chart of pathway 1 in a, the Wnt signaling pathway. c Pie chart of pathway 3 in a, the integrin signaling pathway. d Pie chart of pathway 6 in a, the nicotinic acetylcholine receptor signaling pathway

Table 1.

Pathways with the highest number of genes carrying rare variants with a high-to-moderate predicted biological effect ranked by Panther

Pathway Genes
1 Wnt signaling 32
2 Inflammation mediated by chemokine and cytokine signaling 26
3 Integrin signaling pathway 21
4 Cadherin signaling pathway 19
5 Gonadotropin releasing hormone receptor 17
6 Nicotinic acetylcholine receptor signaling 14
7 PDGF signaling 13
8 Angiogenesis 13
9 Cytoskeletal regulation by Rho GTPase 12
10 Huntington disease 12
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Table 2.

Rare variants in the clustered protocadherin genes

Gene Subject code Variant Protein SIFT/SNPeffect/Indel impact Polyphen2/SNPeffect/Indel impact
PCDHA6 698 GAC/CAC D376H 0.00 D
PCDHA8 869 –/−65 High High
PCDHA12 841 GAA/GAC E253D 0.00 D
PCDHB2 975 AAC/AACATCACC N420NIT Moderate Moderate
PCDHB3 692 –/−577 High High
PCDHB4 1321 GAG/GGG E648G 0.00 P
PCDHB8 816 –/ACAGAGACACC −494TET (?) High High
PCDHB10 698 GAC/GAG D443E 0.00 D
PCDHB14 698 CCC/TCC P449S 0.00 D
PCDHB16 698 GGC/AGC G665S* 0.45 P
PCDHGA8 709 GGT/GTT G70 V 0.01 D
852 TCC/CCC S179P 0.06 D
PCDHGB1 816 CAT/CGT H755R* 0.04 B
1255 CGA/CCA R89P 0.00 D
PCDHGB7 588 CCC/– P679− Moderate Moderate
629 –/−105 High High
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(?) Unable to make the exact prediction for the protein

* Variant scored only by SIFT or by Polyphen2

Table 3.

Rare variants in the laminin and myosin genes

Gene Subject code Variant Protein SIFT/SNP effect/Indel impact
Laminins
LAMA2 633 GGT/GTT G420V 0.04
LAMB3 692 CGC/CAC R1143H 0.05
LAMC1 841 GCT/ACT A610T 0.02
LAMC3 628 TTT/TTG F794L 0.01
1255 CCC/CTC P382L 0.00
1321 CGC/CTC R311L 0.04
Myosins
MYH4 836 GAG/− E1149− Moderate
869 –/A −990(?) High
MYH7B 709 ACA/CCA T1824P 0.06
MYH11 633 AAG/− K1386− Moderate
692 GCA/GTA A1896V 0.01
836 GGC/TGC G1125C 0.00
MYH15 1003 TGG/TGA W503stop High
MYO1F 1003 CGT/TGT R798C 0.03
MYO1H 612 GAG/TAG E455stop High
MYO7A 811 CGC/CAC R900H 0.05
MYO10 975 GTG/ATG V1468 M 0.00
MYO18A 1255 Splice donor High
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(?) Unable to make the exact prediction for the protein

Variants in the genome arise by mutation, and the chance that a mutation occurs is a function of the length of the DNA, i.e., the number of nucleotides. As such, the chance to find a variant depends in general on the length of the DNA that is examined. When comparing genes, a correction for difference in length should be made. Therefore, we corrected our data for the length of the protein coding region of the genes based on UniProt data (www.uniprot.org). The average coding length of the lung/kidney genes was 1,641 nucleotides (547 aminoacids), which is close to the reported average length (509.5 aminoacids) of human proteins (Sakharkar et al. 2006). When taking the coding length into account, the results for both the laminin and myosin genes were no longer significant (P = 0.16, 0.29, respectively). However, the enrichment of the protocadherin cluster remained significant (P = 0.002).

The pathway with the second highest number of genes carrying rare variants in Table 1 is the inflammation mediated by chemokine and cytokine signaling pathway. Obesity is associated with chronic inflammation and is sometimes regarded as an inflammatory disorder (Bluher 2008). However, no substantial functional cluster was indicated by the Panther program for this pathway. Most of the genes that carried rare variants and caused the ranking of this pathway had more general functions overlapping with other pathways like protein kinases (CAMK2D, CAMK2G, PAK6, PTK2B, TYK2) and genes involved in inositol triphosphate signaling (ITPR1, PLCB1). Yet, genes with an obvious relation to inflammation were also present like the cytokine fractalkine (CX3CL1), the chemokine receptors C5AR1 and CCR8, and the nuclear factor NFATC4 that in T-cells induces IL2 and IL4 but also may promote adipogenesis (Yang et al. 2002).

Comparison to other hypothalamus-related genes

The high expression of the clustered protocadherins in the hypothalamus prompted us to analyze other genes expressed in or functionally related to the hypothalamus. A list of 167 hypothalamus genes was composed from VeryGene data, specific literature (St-Amand et al. 2011), and by text mining of titles and abstracts of PubMed articles selected on ‘obesity’ and ‘hypothalamus’. In the extreme obese subjects, 14 rare variants selected by our protocol were scored in those genes, which had an average length of 1,626 nucleotides (542 aminoacids). We found that hypothalamus-related genes other than the clustered protocadherin genes do not display an increased frequency of rare variants (P = 0.63).

Comparison with a general population

To compare the frequency of rare variants with a moderate-to-high predicted biological effect in the protocadherin cluster of extremely obese people with that of a general population, we decided to perform a similar analysis on the European American population using data from the EVS focusing on missense variants in approximately 4,300 subjects.

In EVS, biological effect of variants is based on Polyphen2 analysis instead of SIFT. Although there is a moderate correlation between those methods (Wei et al. 2011), we decided to first redo the analysis in our extreme obese cohort for the protocadherin genes. For this, we defined the rare variants using a Polyphen2 class other than ‘benign’ instead of a SIFT score of 0.06 or less in case of a missense variant. The other criteria for selection were the same as aforementioned. In this way, 15 hits were scored in the protocadherin genes (Table 2) and 28 hits in the reference genes. This showed as before a significant enrichment of rare variants in the protocadherin gene cluster (P = 0.01). Taking only the missense variants in account, the analysis with Polyphen2 still resulted in a significant enrichment (P = 0.05).

In the general population, the rare variants in the missense dataset were selected based on an allele frequency of no more than 2/8,600 and a non-benign Polyphen2 class. The 55 protocadherin genes on chromosome 5q31 scored 1,068 rare non-benign missense variants, whereas in the lung/kidney reference genes, 4,347 rare non-benign missense variants were scored. It shows that in the general population, we found a 1.14-fold lower abundance of rare missense variants in the protocadherin cluster (P < 0.001) in comparison with the reference genes, whereas in extremely obese subjects, there was a 1.9-fold higher (analyzed with Polyphen2 class) or 2.5-fold higher (analyzed with SIFT score) abundance of such variants in the protocadherin genes (P = 0.05, 0.007, respectively).

Discussion

Exome sequencing is a new powerful tool for studying genetic variation in common traits and disorders. Recently, it was shown by this approach that coding variants with a minor allele frequency of >1 % do not have a strong effect on metabolic traits including obesity, BMI, and waist circumference (Albrechtsen et al. 2013). Here, we looked into the relevance of rare variants with an allele frequency of (far) <1 %. Exome sequencing of the DNA of extreme obese subjects followed by functional clustering analysis revealed a significantly higher than expected frequency of relatively rare variants with a moderate-to-high predicted biological effect in the clustered protocadherin genes on 5q31. These protocadherin genes have attracted attention because of their peculiar genomic organization with the genes in the α- and γ-subclusters sharing constant exons coding for the cytoplasmic part of the proteins (Wu and Maniatis 2000). The genes are expressed in the central nervous system, some of which in a monoallelic and combinatorial manner (Esumi et al. 2005), and probably function in (re)forming neuronal circuitries (Lefebvre et al. 2012). Clustered protocadherins have been shown to be necessary for the development of the central nervous system in mice during the late-embryonic stage (Wang et al. 2002).

Although it was found that 11 % of Europeans carry a 16.7-Kb deletion in the alpha-cluster without an apparent phenotype (Noonan et al. 2003), an involvement in obesity was not examined. Our observations do point to a link of protocadherin genes with (extreme) obesity.

Interestingly, a GWAS study (www.ashg.org/2009meeting/abstracts/fulltext/f10729.htm) also showed the strongest association between extreme obesity (BMI > 35 kg/m2) and the protocadherin cluster. Besides genetic association, functional evidence for involvement of the protocadherin gene cluster in obesity also supports our observation. A conditional knockout of the protocadherin-gamma subcluster in the mouse resulting in the absence of gamma-protocadherins in hypothalamic neurons was accompanied by hyperphagia and obesity as the phenotype (Su et al. 2010). Since we did not find that hypothalamus-related genes other than the clustered protocadherin genes displayed an increased frequency of rare variants, we suspect that a role of the protocadherin cluster in extreme obesity might go beyond weight regulation through the hypothalamus.

Interesting information further linking the protocadherin cluster to obesity is provided by the observation that obesity is associated with the restless leg syndrome and both are associated with reduced dopamine activity in the central nervous system (Gao et al. 2009). The restless leg syndrome was diagnosed in about 5 % of adults with a 42 % higher risk for the obese, especially with a high BMI in early adulthood. Linkage analysis with exome sequencing in a German family with the restless leg syndrome revealed a heterozygous Ser584Pro mutation in the PCDHA3 gene co-segregating with the disorder (Lod score 1.8) (Weissbach et al. 2012). Two additional rare heterozygous missense variants in this gene were detected in 64 non-related patients (3.1 %) and only one in 250 healthy controls (0.4 %).

Data are accumulating that the protocadherin cluster genes are subject to epigenetic regulation. The protocadherin cluster is known to change its methylation status in tumors (Dallosso et al. 2009), and recently, it was shown that the methylation status of this cluster in adults is associated with early-life socioeconomic status (Borghol et al. 2012). Notably, several studies have shown that childhood socioeconomic status is inversely related with long-term weight gain and adult BMI, particularly in women (Baltrus et al. 2007; Giskes et al. 2008; Gustafsson et al. 2012). Moreover, it was observed that in the mouse the Smchd1 gene, which is involved in silencing clusters of genes on the inactive X-chromosome, was also important for silencing genes from the alpha- and beta-protocadherin subclusters as well as a cluster of four imprinted genes in the Prader–Willi syndrome locus, a well-known hyperphagia syndrome (Gendrel et al. 2013).

It should be noted that the present study may have some limitations. One limitation might be the relatively small cohort size of 30 extreme obese subjects. On the other hand, those subjects enter 3,300 clustered protocadherin gene-copies into the analysis. Secondly, genetic differences between the Dutch and European American population will occur and also the reference genome may be of influence on the analysis. In order to minimize the effect of population differences, we have compared the hit frequency of the protocadherin cluster genes with that of a set of reference genes within each population. Finally, the way in which we used the Panther program might favor the detection of relatively large functional gene clusters. Therefore, we may have missed other genes of which the genetic variation could be involved in extreme obesity. Nonetheless, our observation is in line with other genetic and functional findings pointing to a role of the protocadherin cluster in the development of (extreme) obesity.

Although the laminin and myosin genes were rendered statistically non-significant after correcting for length of the coding region, they remain interesting candidates for further research into a central role in extreme obesity, since genes predisposing to disease are known to be relatively large on average (Sakharkar et al. 2006). SNPs in the LAMA5 gene have been shown to be associated with adiposity parameters in European and African Americans (De Luca et al. 2008). Intriguingly, four of the six variants that we detected in the laminin genes, are present in the genes coding for LAMC1 and LAMC3, which have been shown to interact with NTN4 (Schneiders et al. 2007), of which the gene also has a rare variant (R225H, SIFT score = 0.00) in another of the investigated subjects. As such, five of the 30 extreme obese subjects have a dramatic variant in a gene for this laminin-netrin complex. Both laminins and netrins play critical roles in guiding the growth of peripheral and central axons [refs in (Yin et al. 2002)]. Also, MYO10 has been shown to mediate laminin-induced growth of neurites after peripheral nerve injury (Plantman et al. 2013), whereas laminin with myosin II is able to mediate axon branching (Liu et al. 2013). Together with what is known about the function of the PCDH-genes, this suggests that rare variants with a moderate-to-high biological effect have an impact on central nervous system development and function. Recently, Wheeler et al. examined 490 genes of which the coding part was affected by an obesity-specific deletion (Wheeler et al. 2013). Functional enrichment analysis of those genes revealed “Nervous System Development and Function” as the most significant geneset with as the most important subsets: neuritogenesis, development of brain, synaptic transmission, and synaptic transmission of neurons. It seems that our findings and those of Wheeler point to the same predisposing mechanism for obesity with a genetic impediment of brain development and function.

Knocking out the protocadherin gamma subcluster leads to a change in eating behavior in the mouse (Su et al. 2010). It suggests that rare variants in the PCDH-genes may also have an impact on food intake in humans. However, so far, no research has been done in this direction. On the other hand, inter-individual differences in food intake are undoubtedly influenced by genetic variation as for instance by polymorphisms of taste receptor genes and mutations in genes regulating feelings of hunger and satiety (Grimm and Steinle 2011; Mariman 2009). The latter refers to homozygous or compound heterozygous cases of early onset obesity, but evidence is accumulating that heterozygous mutations in those genes can influence food intake as well. In a three-generation family, a heterozygous mutation in the pro-opiomelanocortin gene (POMC) was found to segregate and all carriers had hyperphagia and obesity but no red hair or adrenal impairment, which are characteristic for homozygous POMC mutations (Challis et al. 2002). Recently, an intermediate-frequency variant of the leptin receptor gene (LEPR) was found by GWAS among subjects with severe early onset obesity. This variant in monocytes is associated with lower LEPR expression (Wheeler et al. 2013). It shows that merely the fact that all variants reported here are heterozygous, does not rule out an effect on the phenotype, presumably on food intake. Hopefully, our results will initiate profound research into the role of PCDH-genes in food intake. Genetic variation in those genes may then be used to identify persons at risk for overeating, allowing them to receive proper guidance to prevent severe overweight and associated health complications.

Here, we have provided data indicating that in extremely obese subjects relatively rare variants with a moderate-to-high predicted biological effect occur with an increased frequency in the protocadherin gene cluster on chromosome 5q31, which is supported by other genetic and functional observations. In addition, also the lamininC1/3-netrin4 complex is a suspected target for such variation with relevance to obesity. Together with reported studies, our findings suggest a role for early life formation or adaptation of neuronal networks in the development of (extreme) obesity. In this respect, it is tempting to speculate that genetically modified neuronal plasticity relates to childhood tracking of eating behavior and body weight (Craigie et al. 2011).

Acknowledgments

Conflict of interest

All authors state that there is no conflict of interest.

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