Evaluation of methods incorporating biological function and GWAS summary statistics to accelerate discovery

Abstract

Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 18 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies.

Multi-omics analyses cannot identify true-positive novel associations from underpowered genome-wide association studies of four brain-related traits

“…While machine learning out-performed individual methods and even linear combinations of methods, it only correctly identified an additional 1-8 genes, and only for highly heritable traits with a well-powered smaller GWAS (SCZ and ICV). This observation converges with related work evaluating methods for SNP and locus annotation (13,14,48), which has concluded that such methods can only marginally increase the number of true-positive observations. Similarly, prior work examining positional gene-based methods in a simulated trait with relatively few causal SNPs (n=602) observed a tradeoff between sensitivity and specificity (49), which is also seen here.…”

“…The gene-level focus reflects an additional weakness of the omics-integration approach, in that it could lead to improved knowledge of biology without narrowing down the identity of causal loci in human populations. Thus, while recent related studies using locus-level analyses yielded similar findings (14), we cannot rule out that alternative methods could have led to stronger results. We note some additional limitations of the present study.…”

“…Analyses focused on trait associations at the gene level, rather than individual SNPs (14). This enabled the additional integration of multi-omics data that did not use GWAS information, including results from post mortem studies of gene expression in patients and rodent models, and a gene-set for AUD/PAU that integrates information from a variety of rodent data sources (12).…”

“…As greater evidence arises for genome-wide significant signals to be enriched in regulatory regions, these multi-omics data have proved to be valuable in gene prioritization. There has also been speculation that leveraging these everincreasing omics sources might "recover" true signal from smaller GWAS, i.e., signals that may not meet criteria for genome-wide significance but are supported by multi-omics data, and eventually are identified as GWAS become larger, thus serving as a substitute for additional sample size (13)(14)(15).…”

Multi‐omics cannot replace sample size in genome‐wide association studies

“…15,16 Some have speculated that multi-omics could serve as a substitute for additional sample size, and "recover" true signal from smaller GWAS by increasing support for signals that do not meet criteria for genome-wide significance. [17][18][19] This is an appealing proposition, because it would reduce the need (and expense) for collection and phenotyping of larger samples. Some early work using expressionbased methods (e.g., transcriptome-wise association analyses -TWAS) found that they can identify genes that would eventually achieve genome-wide significance in subsequent, larger GWAS, 20,21 although these studies did not evaluate what proportion of all novel genes were first identified by TWAS.…”