The Nature of Genetic Variation for Complex Traits Revealed by GWAS and Regional Heritability Mapping Analyses

Abstract

We use computer simulations to investigate the amount of genetic variation for complex traits that can be revealed by single-SNP genome-wide association studies (GWAS) or regional heritability mapping (RHM) analyses based on full genome sequence data or SNP chips. We model a large population subject to mutation, recombination, selection, and drift, assuming a pleiotropic model of mutations sampled from a bivariate distribution of effects of mutations on a quantitative trait and fitness. The pleiotropic model investigated, in contrast to previous models, implies that common mutations of large effect are responsible for most of the genetic variation for quantitative traits, except when the trait is fitness itself. We show that GWAS applied to the full sequence increases the number of QTL detected by as much as 50% compared to the number found with SNP chips but only modestly increases the amount of additive genetic variance explained. Even with full sequence data, the total amount of additive variance explained is generally below 50%. Using RHM on the full sequence data, a slightly larger number of QTL are detected than by GWAS if the same probability threshold is assumed, but these QTL explain a slightly smaller amount of genetic variance. Our results also suggest that most of the missing heritability is due to the inability to detect variants of moderate effect (0.03-0.3 phenotypic SDs) segregating at substantial frequencies. Very rare variants, which are more difficult to detect by GWAS, are expected to contribute little genetic variation, so their eventual detection is less relevant for resolving the missing heritability problem.KEYWORDS quantitative trait variation; complex traits; missing heritability; fitness; additive genetic variance S TUDY of the nature of variation for quantitative traits, also known as complex traits, is one of the most active areas of research in genetics. This is particularly the case in human genetics because many common genetic diseases, including cancers, obesity, heart disease, and stroke, are complex traits controlled by many loci and influenced by multiple environmental factors. Large numbers of genetic loci influencing complex traits have been discovered using genome-wide association studies (GWAS) by associating putatively neutral SNPs with variation for the trait. For example, analysis of 160 complex disease phenotypes and quantitative traits has revealed associations with more than 2000 SNPs in humans (Visscher et al. 2012). A key general finding from GWAS is that the significantly associated SNPs account for only a small proportion of the trait's genetic variation, the so-called missing heritability problem (Manolio et al. 2009). For example, based on the resemblance between relatives, narrow-sense heritability for human height has been estimated to be as high as h 2 = 0.7-0.8 (Visscher 2008;Zaitlen et al. 2013), but the 679 variants identified in the latest GWAS meta-analysis of 79 studies analyzing more than 250,000 individuals accounted for only 16% of that her...

A model of compound heterozygous, loss-of-function alleles is broadly consistent with observations from complex-disease GWAS datasets

“…Stratifying markers by minor allele frequency (MAF) prior to performing SNP-based heritability estimation allows the partitioning of genetic variation across the allele frequency spectrum to be estimated [16], which is an important summary of the genetic architecture of a complex trait [16][17][18][19][20][21][22][23]. This approach has inferred a contribution of rare alleles to genetic variance in both human height and body mass index (BMI) [16], consistent with theoretical work showing that rare alleles will have large effect sizes if fitness effects and trait effects are correlated [18,[20][21][22][23][24][25]. Yet, simulations of causal loci harboring multiple rare variants with large additive effects predict an excess of low-frequency significant markers relative to empirical findings [4,26].…”

Evaluating Sequence-Based Genomic Prediction with an Efficient New Simulator

“…For the distribution of allelic effects, numerous studies (Eyre-Walker and Keightley 2007) confirm that it is highly leptokurtic, with many near-zero effects and a few large effects. This distribution can be approximated by a gamma, and here we used a G(shape = 0.2 and scale = 5) based on the simulation study by Caballero et al (2015); the shape of this distribution is not too different from the review in Hayes and Goddard (2001) and employed previously by us (Pérez-Enciso et al 2015). Heritability of the simulated phenotype was 0.5.…”

“…Among those SNPs, we selected as causal QTN those that had a high or moderate effect as inferred from a variant effect predictor tool (McLaren et al 2010) plus a fraction of SNPs in UTR regions. Again, additive effects were sampled from a G (0.2 and 5), but in this case a negative correlation between absolute additive effect and frequency was induced r = 20.60, as found empirically (Caballero et al 2015;Yang et al 2015).…”

“…In addition, a critical parameter determining the distribution of loci variances is the correlation between fitness and the trait studied. Caballero et al (2015) found by simulation that common (q . 0.05) variants of at least modest effects will be responsible for most of the genetic variance in traits loosely correlated to fitness or neutral traits, whereas rare variants of large effect are relevant for highly selected traits or fitness itself.…”

“…This reinforces the hypothesis of abundant small genetic effects. Third, the distribution of QTN effects is highly leptokurtic and can be reasonably well approximated by a gamma density Eyre-Walker and Keightley 2007;Caballero et al 2015). In addition, a critical parameter determining the distribution of loci variances is the correlation between fitness and the trait studied.…”