Chapter 11: Genome-Wide Association Studies

Sequence Kernel Association Tests for the Combined Effect of Rare and Common Variants

Diamonds in the Rough: Rare Variants Scratch the Surface

The completion of the Human Genome Project has unleashed a wealth of human genomics information, but it remains unclear how best to implement this information for the benefit of patients. The standard approach of biomedical research, with researchers pursuing advances in knowledge in the laboratory and, separately, clinicians translating research findings into the clinic as much as decades later, will need to give way to new interdisciplinary models for research in genomic medicine. These models should include scientists and clinicians actively working as teams to study patients and populations recruited in clinical settings and communities to make genomics discoveries-through the combined efforts of data scientists, clinical researchers, epidemiologists, and basic scientists-and to rapidly apply these discoveries in the clinic for the prediction, prevention, diagnosis, prognosis, and treatment of cardiovascular diseases and stroke. The highly publicized US Precision Medicine Initiative, also known as All of Us, is a large-scale program funded by the US National Institutes of Health that will energize these efforts, but several ongoing studies such as the UK Biobank Initiative; the Million Veteran Program; the Electronic Medical Records and Genomics Network; the Kaiser Permanente Research Program on Genes, Environment and Health; and the DiscovEHR collaboration are already providing exemplary models of this kind of interdisciplinary work. In this statement, we outline the opportunities and challenges in broadly implementing new interdisciplinary models in academic medical centers and community settings and bringing the promise of genomics to fruition.

Genetics and biology of asthma 2010: La' ci darem la mano…

Extending Rare-Variant Testing Strategies: Analysis of Noncoding Sequence and Imputed Genotypes

this issue of the Journal, Goodship and colleagues report results from an association study for common genetic variants underlying tetralogy of Fallot (TOF). 1 By studying a limited number of single-nucleotide polymorphisms (SNPs) tagging haplotypes at 22 candidate genes, they associated a single SNP at a chromosome 12 locus harboring PTPN11, which encodes the protein tyrosine phosphatase SHP2, and other genes with TOF.This SNP was calculated to have a per-allele risk of 1.34 and a population-attributable risk of roughly 5%.If this study, undertaken with British white subjects, can be replicated in additional populations, it will represent a significant advance in our understanding of the genetics of congenital heart defects (CHD).

IL28B single nucleotide polymorphisms in the treatment of hepatitis C

Genome-Wide Association Studies Go Green: Novel and Cost-Effective Opportunities for Identifying Genetic Associations

SY34-01: Genome-wide association studies in cancer: A step in the right direction

Type 2 diabetes mellitus (T2D) and obesity already represent 2 of the most prominent risk factors for cardiovascular disease, and are destined to increase in importance given the global changes in lifestyle. Ten years have passed since the first round of genome-wide association studies for T2D and obesity. During this decade, we have witnessed remarkable developments in human genetics. We have graduated from the despair of candidate gene-based studies that generated few consistently replicated genotype-phenotype associations, to the excitement of an exponential harvest of loci robustly associated with medical outcomes through ever larger genome-wide association study meta-analyses. As well as discovering hundreds of loci, genome-wide association studies have provided transformative insights into the genetic architecture of T2D and other complex traits, highlighting the extent of polygenicity and the tiny effect sizes of many common risk alleles. Genome-wide association studies have also provided a critical starting point for discovering new biology relevant to these traits. Expectations are high that these discoveries will foster development of more effective strategies for intervention, through optimization of precision medicine approaches. In this article, we review current knowledge and provide suggestions for the next steps in genetic research for T2D and obesity. We focus on four areas relevant to precision medicine: genetic architecture, pharmacogenetics and other gene-environment interactions, mechanistic inference, and drug development. As we describe, the genetic architecture of complex traits has major implications for the prospects of precision medicine, rendering some anticipated approaches decidedly unrealistic. We highlight obstacles to the translation of human genetic findings into mechanism inference but are optimistic that, as these are overcome, there is untapped potential for novel drugs and more effective strategies for treating and preventing T2D and obesity.

Response to Mitchell and Porteus.

Recent breakthroughs in next-generation sequencing technologies allow cost-effective methods for measuring a growing list of cellular properties, including DNA sequence and structural variation. Next-generation sequencing has the potential to revolutionize complex trait genetics by directly measuring common and rare genetic variants within a genome-wide context. Because for a given gene both rare and common causal variants can coexist and have independent effects on a trait, strategies that model the effects of both common and rare variants could enhance the power of identifying disease-associated genes. To date, little work has been done on integrating signals from common and rare variants into powerful statistics for finding disease genes in genome-wide association studies. In this analysis of the Genetic Analysis Workshop 17 data, we evaluate various strategies for association of rare, common, or a combination of both rare and common variants on quantitative phenotypes in unrelated individuals. We show that the analysis of common variants only using classical approaches can achieve higher power to detect causal genes than recently proposed rare variant methods and that strategies that combine association signals derived independently in rare and common variants can slightly increase the power compared to strategies that focus on the effect of either the rare variants or the common variants.

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Common Genetic Variants and Nonalcoholic Fatty Liver Disease

Statins and type 2 diabetes: genetic studies on target