Discovery of shared genomic loci using the conditional false discovery rate approach

Abstract

In recent years, genome-wide association study (GWAS) sample sizes have become larger, the statistical power has improved and thousands of trait-associated variants have been uncovered, offering new insights into the genetic etiology of complex human traits and disorders. However, a large fraction of the polygenic architecture underlying most complex phenotypes still remain undetected. We here review the conditional false discovery rate (condFDR) method, a modelfree strategy for analysis of GWAS summary data, which has improved yield of existing GWAS and provided novel findings of genetic overlap between a wide range of complex human phenotypes, including psychiatric, cardiovascular, and neurological disorders, as well as psychological and cognitive traits. The condFDR method was inspired by Empirical Bayes approaches and leverages auxiliary genetic information to improve statistical power for discovery of single-nucleotide polymorphisms (SNPs). The cross-trait condFDR strategy analyses separate GWAS data, and leverages overlapping SNP associations, i.e. cross-trait enrichment, to increase discovery of trait-associated SNPs. The extension of the condFDR approach to conjunctional FDR (conjFDR) identifies shared genomic loci between two phenotypes. The conjFDR approach allows for detection of shared genomic associations irrespective of the genetic correlation between the phenotypes, often revealing a mixture of antagonistic and agonistic directional effects among the shared loci. This review provides a methodological comparison between condFDR and other relevant cross-trait analytical tools and demonstrates how condFDR analysis may provide novel insights into the genetic relationship between complex phenotypes.

The genetic architecture of human brainstem structures and their involvement in common brain disorders

“…We then employed conditional Q-Q plots and conditional FDR and conjunctional FDR statistics to assess polygenic overlap between brainstem volumes and the eight brain disorders using MATLAB 2017a and Python 3.7.14 33,34 . A mathematical description and review of the applications of these methods in neurological and psychiatric disorders can be found in Smeland et al 35 .…”

“…The conditional FDR builds on an empirical Bayesian framework and uses auxiliary genetic information to re-adjust GWAS test statistics of a primary phenotype 35 . Conditional FDR includes separate GWAS data and leverages overlapping genetic associations to increase discovery of phenotype-associated SNPs.…”

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies new candidate susceptibility genes for breast and ovarian cancer

“…We then imputed models across cancer types (i.e., from breast tissue-derived models into ovarian cancer GWAS and vice versa). Finally, we implemented a powerful conjunction false discovery rate approach (13,14) that has been applied previously to GWAS (15)(16)(17)(18), but not to TWAS, to leverage the combined GWAS sample of over 145,000 breast and ovarian cancer cases. We identify new candidate breast and ovarian cancer susceptibility genes in regions not previously implicated by GWAS or TWAS analyses of these cancers.…”

Quantifying the polygenic architecture of the human cerebral cortex: Extensive genetic overlap between cortical thickness and surface area

“…This claim is, however, based on estimates of genetic correlation, a genome-wide measure of the correlation of genetic effects on the two traits. Complex traits such as brain measures may have a substantial number of shared genetic loci 8 , even in the absence of genetic correlation, due to mixed directions of effects 9 . Identification of the fraction of shared causal variants between two brain phenotypes 9 , beyond genetic correlation, is valuable for an understanding of their biological relation.…”