Rare Genomic Structural Variants in Complex Disease: Lessons from the Replication of Associations with Obesity

Abstract

The limited ability of common variants to account for the genetic contribution to complex disease has prompted searches for rare variants of large effect, to partly explain the ‘missing heritability’. Analyses of genome-wide genotyping data have identified genomic structural variants (GSVs) as a source of such rare causal variants. Recent studies have reported multiple GSV loci associated with risk of obesity. We attempted to replicate these associations by similar analysis of two familial-obesity case-control cohorts and a population cohort, and detected GSVs at 11 out of 18 loci, at frequencies similar to those previously reported. Based on their reported frequencies and effect sizes (OR≥25), we had sufficient statistical power to detect the large majority (80%) of genuine associations at these loci. However, only one obesity association was replicated. Deletion of a 220 kb region on chromosome 16p11.2 has a carrier population frequency of 2×10−4 (95% confidence interval [9.6×10−5–3.1×10−4]); accounts overall for 0.5% [0.19%–0.82%] of severe childhood obesity cases (P = 3.8×10−10; odds ratio = 25.0 [9.9–60.6]); and results in a mean body mass index (BMI) increase of 5.8 kg.m−2 [1.8–10.3] in adults from the general population. We also attempted replication using BMI as a quantitative trait in our population cohort; associations with BMI at or near nominal significance were detected at two further loci near KIF2B and within FOXP2, but these did not survive correction for multiple testing. These findings emphasise several issues of importance when conducting rare GSV association, including the need for careful cohort selection and replication strategy, accurate GSV identification, and appropriate correction for multiple testing and/or control of false discovery rate. Moreover, they highlight the potential difficulty in replicating rare CNV associations across different populations. Nevertheless, we show that such studies are potentially valuable for the identification of variants making an appreciable contribution to complex disease.

Eating in the absence of hunger but not loss of control behaviors are associated with 16p11.2 deletions

“…The chromosomal deletion between breakpoints 4 and 5 at the 16p11.2 locus (chr16:29,652,999 to 30,199,351; hg19) has been associated with obesity (1)(2)(3)(4)(5)(6)(7)(8)(9)(10). This copy number variation (CNV) spans 600 kb and 29 genes and has a population prevalence of 1/2000.…”

Future management of human obesity: understanding the meaning of genetic susceptibility

“…7. 16p11.2: independent associations with obesity of large deletions at two locations in 16p11.2 have been detected following a strategy of resequencing at loci known to be associated with rare forms of extreme obesity, 100,101 and one of the locations was replicated using continuous BMI in a population sample 101 represented in Figure 2. The BMI effect size in Figure 2 is based on a very small sample (4) of heterozygous carriers in that one population, but the combined effect on BMI in all populations sampled was +1.1 SD units (n=8), similar to our effect size on Adiposity (+0.93 SD).…”

“…The search for rare variants with large effects on obesity susceptibility is in progress and has resulted in some success, 101 as have related approaches in other complex diseases. 103,104 However, the contributions of the newly identified variants to population susceptibilities are very small, and if, as we conclude, most of the obesity susceptibility is due to rare variants, there are many more to find.…”

“…There are formidable problems in design and statistical analysis to be solved before identification of rare variants becomes a routine procedure, and we may currently be able to find only the "low-hanging fruit" that are amenable to current techniques. 101 The variants' effects must be large, and it might therefore be hoped that simple phenotypes such as BMI would be adequate for reliable discovery, more so than in GWAS where the small effect sizes must be interpreted with caution due to biases in BMI. However, even large effects can be generated or obscured by BMI bias; measured biases in different ethnic groups range from −6 kg/m 2 to +4.5 kg/m 2 at equivalent body fat percentage 105,106 and in our ethnically unselected sample from −3.6 kg/m 2 to +6.7 kg/m 2 (95% CI −3.1, 4.3) at equivalent Adiposity ( Figure 1C).…”

“…Power and accuracy would undoubtedly be improved by the use of better phenotypes than BMI, but it is unlikely that the improvements would be enough to capture many rare variants. The increased costs associated with better phenotyping would be better used in smaller scale studies following the types of approaches recommended by Walters et al 101 Many variants may produce similar effects on obesity-related traits Under a heterogenetic model, the separation between modes of Adiposity in Figure 1A represents the average effect size of the causative variants. Our analysis treats it as a fixed (ie, constant) effect, and while we were unable to model the range of dispersion of effects, nor any heterogeneity of background variability, the data are consistent with a very narrow range of effect sizes superimposed on a constant background variability.…”