Progress and promise in understanding the genetic basis of common diseases

被引:106
作者
Price, Alkes L. [1 ,2 ]
Spencer, Chris C. A. [3 ]
Donnelly, Peter [3 ,4 ]
机构
[1] Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA
[2] Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA
[3] Univ Oxford, Wellcome Trust Ctr Human Genet, Roosevelt Dr, Oxford OX3 7BN, England
[4] Univ Oxford, Dept Stat, Oxford OX1 3TG, England
基金
英国惠康基金;
关键词
common diseases; genome-wide association studies; human genetics; GENOME-WIDE ASSOCIATION; LARGE-SCALE; MISSING HERITABILITY; GENOTYPE IMPUTATION; FETAL-HEMOGLOBIN; PROSTATE-CANCER; VARIANTS; RISK; SUSCEPTIBILITY; ARCHITECTURE;
D O I
10.1098/rspb.2015.1684
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that-even at large sample sizes-these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.
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页数:10
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