Statistical Resources for the Interpretation and Integration of Human Genetic Association Studies

Dear Colleagues,

Genome-wide association studies (GWAS) have advanced our understanding of the genetic architecture of human complex traits and have identified scores of novel susceptibility loci. While the ultimate goal of GWAS is to facilitate the translation of genetic research into clinical application, the path from sequence to biology is often complicated. Unlike Mendelian traits, the highly polygenic nature of most complex traits has rendered it challenging to pin down specific “core” genes and pathways, owing to reasons such as linkage disequilibrium (LD) of correlated SNPs, small effect sizes, and identified variants falling in regulatory regions of the genome.

Many statistical or bioinformatics methods have been developed to functionally characterize the associated loci following GWAS discovery, often in conjunction with the maturation of genome annotation projects (e.g., ENCODE, GTEx). These include integrating GWAS data with molecular phenotypes (e.g., gene expression, DNA methylation) or other -omics data to infer targets for functional follow-up; partitioning SNP-heritability by functional categories in disease-relevant tissues or cell types to discover heritability-enriched regions; examining cross-trait associations to detect pleiotropic SNPs implicating a common pathogenesis; and many more. Other efforts include the increasing use of polygenic risk scores to aid in phenotype prediction and stratification and Mendelian randomization to determine causal relationships between pairs of traits. Recently, machine learning approaches and network-based methods have also been proposed to construct gene regulatory networks. However, despite some promising results, most underlying biological consequences remains to be unraveled.

It has now been ten years since the first applications of GWAS, new discoveries, as well as new methodological challenges, will continue to accumulate as sample sizes increase and the sequencing technology advances. In the next decade, the focus will inevitably shift from variant discovery to functional genomics. In this special issue, we would like to feature a series of statistical and computational methods or tools, based on SNP data or summary statistics, that facilitate the interpretation and/or integration of GWAS findings towards a better understanding of biological mechanisms underpinning complex traits and disease development (e.g., TWAS, MR/causal inference, methods for multiple phenotypes, etc.). We welcome any original articles relating to, but not limited to, the topics described herein.

Dr. Liming Liang
Dr. Yen-Chen Anne Feng
Guest Editor

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• Genome-wide association studies (GWAS)
• Human complex traits
• Single nucleotide polymorphism (SNP)
• Linkage disequilibrium (LD)
• Polygenicity
• Pleiotropy
• Omics data integration
• Post-GWAS functional characterization