Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data

Abstract

Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1 , but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2-5 . It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be fully recovered from whole-genome sequence (WGS) data on 21,620 unrelated individuals of European ancestry. We assigned 47.1 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned variation accordingly. The estimated heritability was 0.79 (SE 0.09) for height and 0.40 (SE 0.09) for BMI, consistent with pedigree estimates. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein altering variants, consistent with negative selection thereon. Cumulatively variants in the MAF range of 0.0001 to 0.1 explained 0.54 (SE 0.05) and 0.51 (SE 0.11) of heritability for height and BMI, respectively. Our results imply that the still missing heritability of complex traits and disease is accounted for by rare variants, in particular those in regions of low LD.

Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative

“…Other methods may be needed in future analysis to soften this assumption, though some of these cannot be easily meta-analyzed across multiple cohorts directly from summary statistics (e.g., SKAT-O 41 ). Similarly, methods that combine both rare and common variants might also provide additional insights into disease outcomes 30,42 . Second, our results highlight the importance at looking at different categories of variants through different masks to increase sensitivity and specificity of our burden tests.…”

Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics

“…In this work, we showcased its effectiveness in cross-population risk prediction using an annotation derived from local genetic correlations. But we note that it is a general framework that can incorporate arbitrary sets of annotation data, such as the epigenetic annotations used in the PRS literature or LD and allele frequencies which have been shown to improve heritability estimation 20,22,32,53 ( Supplementary Note ). It may also be applied to improve PRS portability across other non-ancestry-related demographic groups 54 .…”

Estimating the number of diseases – the concept of rare, ultra-rare, and hyper-rare

“…The most common polymorphisms in the genome are single nucleotide variations (SNVs) (polymorphism and variation are here used as synonyms), which are found throughout the genome, including within coding regions ( Klein et al., 2022 ; Wainschtein et al., 2022 ). The total number of genes has been estimated to be at least 24,000 ( International Human Genome Sequencing Consortium, 2004 ; Salzberg, 2018 ), each of which could harbor such variations.…”