Genetic Association-Guided Analysis of Gene Networks for the Study of Complex Traits

Exploring the underlying biology of intrinsic cardiorespiratory fitness through integrative analysis of genomic variants and muscle gene expression profiling

Intrinsic cardiorespiratory fitness (CRF) is defined as the level of CRF in the sedentary state. There are large individual differences in intrinsic CRF among sedentary adults. The physiology of variability in CRF has received much attention, but little is known about the genetic and molecular mechanisms that impact intrinsic CRF. These issues were explored in the present study by interrogating intrinsic CRF-associated DNA sequence variation and skeletal muscle gene expression data from the HERITAGE Family Study through an integrative bioinformatics guided approach. A combined analytic strategy involving genetic association, pathway enrichment, tissue-specific network structure, cis-regulatory genome effects, and expression quantitative trait loci was used to select and rank genes through a variation-adjusted weighted ranking scheme. Prioritized genes were further interrogated for corroborative evidence from knockout mouse phenotypes and relevant physiological traits from the HERITAGE cohort. The mean intrinsic V̇o2max was 33.1 ml O2·kg-1·min-1 (SD = 8.8) for the sample of 493 sedentary adults. Suggestive evidence was found for gene loci related to cardiovascular physiology (ATE1, CASQ2, NOTO, and SGCG), hematopoiesis (PICALM, SSB, CA9, and CASQ2), skeletal muscle phenotypes (SGCG, DMRT2, ADARB1, and CASQ2), and metabolism (ATE1, PICALM, RAB11FIP5, GBA2, SGCG, PRADC1, ARL6IP5, and CASQ2). Supportive evidence for a role of several of these loci was uncovered via association between DNA variants and muscle gene expression levels with exercise cardiovascular and muscle physiological traits. This initial effort to define the underlying molecular substrates of intrinsic CRF warrants further studies based on appropriate cohorts and study designs, complemented by functional investigations. NEW & NOTEWORTHY Intrinsic cardiorespiratory fitness (CRF) is measured in the sedentary state and is highly variable among sedentary adults. The physiology of variability in intrinsic cardiorespiratory fitness has received much attention, but little is known about the genetic and molecular mechanisms that impact intrinsic CRF. These issues were explored computationally in the present study, with further corroborative evidence obtained from analysis of phenotype data from knockout mouse models and human cardiovascular and skeletal muscle measurements.

Convergence between biological, behavioural and genetic determinants of obesity

Multiple biological, behavioural and genetic determinants or correlates of obesity have been identified to date. Genome-wide association studies (GWAS) have contributed to the identification of more than 100 obesity-associated genetic variants, but their roles in causal processes leading to obesity remain largely unknown. Most variants are likely to have tissue-specific regulatory roles through joint contributions to biological pathways and networks, through changes in gene expression that influence quantitative traits, or through the regulation of the epigenome. The recent availability of large-scale functional genomics resources provides an opportunity to re-examine obesity GWAS data to begin elucidating the function of genetic variants. Interrogation of knockout mouse phenotype resources provides a further avenue to test for evidence of convergence between genetic variation and biological or behavioural determinants of obesity.

Implicating candidate genes at GWAS signals by leveraging topologically associating domains

Genome-wide association studies (GWAS) have contributed significantly to the understanding of complex disease genetics. However, GWAS only report association signals and do not necessarily identify culprit genes. As most signals occur in non-coding regions of the genome, it is often challenging to assign genomic variants to the underlying causal mechanism(s). Topologically associating domains (TADs) are primarily cell-type-independent genomic regions that define interactome boundaries and can aid in the designation of limits within which an association most likely impacts gene function. We describe and validate a computational method that uses the genic content of TADs to prioritize candidate genes. Our method, called 'TAD_Pathways', performs a Gene Ontology (GO) analysis over genes that reside within TAD boundaries corresponding to GWAS signals for a given trait or disease. Applying our pipeline to the bone mineral density (BMD) GWAS catalog, we identify 'Skeletal System Development' (Benjamini-Hochberg adjusted P=1.02x10^-5) as the top-ranked pathway. In many cases, our method implicated a gene other than the nearest gene. Our molecular experiments describe a novel example: ACP2, implicated near the canonical 'ARHGAP1' locus. We found ACP2 to be an important regulator of osteoblast metabolism, whereas ARHGAP1 was not supported. Our results via BMD, for example, demonstrate how basic principles of three-dimensional genome organization can define biologically informed association windows.

Pathway and network-based strategies to translate genetic discoveries into effective therapies

One way to design a drug is to attempt to phenocopy a genetic variant that is known to have the desired effect. In general, drugs that are supported by genetic associations progress further in the development pipeline. However, the number of associations that are candidates for development into drugs is limited because many associations are in non-coding regions or difficult to target genes. Approaches that overlay information from pathway databases or biological networks can expand the potential target list. In cases where the initial variant is not targetable or there is no variant with the desired effect, this may reveal new means to target a disease. In this review, we discuss recent examples in the domain of pathway and network-based drug repositioning from genetic associations. We highlight important caveats and challenges for the field, and we discuss opportunities for further development.