Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits

Abstract

We developed a likelihood-based approach for analyzing summary-level statistics and external linkage disequilibrium information to estimate effect-size distributions of common variants, characterized by the proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of results available across 32 genome-wide association studies showed that, while all traits are highly polygenic, there is wide diversity in the degree and nature of polygenicity. Psychiatric diseases and traits related to mental health and ability appear to be most polygenic, involving a continuum of small effects. Most other traits, including major chronic diseases, involve clusters of SNPs that have distinct magnitudes of effects. We predict that the sample sizes needed to identify SNPs that explain most heritability found in genome-wide association studies will range from a few hundred thousand to multiple millions, depending on the underlying effect-size distributions of the traits. Accordingly, we project the risk-prediction ability of polygenic risk scores across a wide variety of diseases.

Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

“…Which window the causal SNP is in matters, leading to w + 1 SNP categories, as noted above. Setting w = 1 would result in only a very rough approximation for the model pdf, reducing our multinomial to a binomial involving just two categories of SNPs: null and causal, with all causal SNPs treated the same, regardless of their LD with the tag SNP and their heterozygosity, as is done for the "M2" and "M3" models in [14]. The effect of this is shown in two examples in the Supplementary Material ("Relation to Other Work").…”

“…For real phenotypes, we calculated SNP minor allele frequency (MAF) and LD between SNPs using the 1000 Genomes phase 3 data set for 503 subjects/samples of European ancestry [28,29,30]. In order to carry out realistic simulations (i.e., with realistic heterozygosity and LD structures for SNPs), we used HAPGEN2 [31,32,33] [38]; (7) late onset Alzheimer's disease (LOAD; N cases = 17,008, N controls = 37,154) [39] (in the Supplementary Material we present results for a more recent GWAS with N cases = 71,880 and N controls = 383,378 [40]); (8) amyotrophic lateral sclerosis (ALS) (N cases = 12,577, N controls = 23,475) [41]; (9) number of years of formal education (N = 293,723) [42]; (10) intelligence (N = 262,529) [43,44]; (11) body mass index (N = 233,554) [45]; (12) height (N = 251,747) [46]; (13) putamen volume (normalized by intracranial volume, N = 11,598) [47]; (14) low-(N = 89,873) and (15) high-density lipoprotein (N = 94,295) [48]; and (16) total cholesterol (N = 94,579) [48]. Most participants were of European ancestry.…”

“…The effects of population structure [10], combined with high polygenicity and linkage disequilibrium (LD), leading to spurious degrees of SNP association, or inflation, considerably complicate matters, and are also areas of much focus [11,12,13]. Despite these challenges, there have been recent significant advances in the development of mathematical models of polygenic architecture based on GWAS [14,15]. One of the advantages of these models is that they can be used for power estimation in human phenotypes, enabling prediction of the capabilities of future GWAS.…”

Mapping causal pathways from genetics to neuropsychiatric disorders using genome‐wide imaging genetics: Current status and future directions

“…Effect size distributions are inversely related to polygenicity: the greater the number of susceptibility SNPs, the smaller the effect of each of those SNPs on the trait. Genetic variants impacting psychiatric disorders are amongst the most polygenic studied with an estimated 10 000–50 000 susceptibility SNPs, indicating that many genetic variants, each of exceedingly small effect size, impact risk for these disorders . In comparison, ulcerative colitis and asthma are estimated to have only 1000–2000 susceptibility SNPs .…”

“…94,95 In comparison, ulcerative colitis and asthma are estimated to have only 1000-2000 susceptibility SNPs. 94 Within the realm of imaging genetics, the degree of polygenicity of the putamen is over 30-fold less than that of schizophrenia, again indicating a higher effect size distribution of a brain structure trait as compared to a disorder. 96 Effect size distributions have not been comprehensively evaluated for all associations to neuroimaging traits and then compared with those from neuropsychiatric disorder risk, although this is an interesting research direction.…”