Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA.
Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA.
J Invest Dermatol. 2018 Mar;138(3):e23-e29. doi: 10.1016/j.jid.2018.01.004.
Complex cutaneous disorders result from the combined effect of many different genes and environmental factors, with individual genetic variants often having only a modest effect on disease risk. The ability to examine large numbers of samples is required for correlating genetic variants with diseases/traits. Technological advances in high-throughput genotyping, along with mapping of the human genome and its associated inter-individual variation, have allowed genetic variants to be analyzed at high density in large case-control cohorts for many diseases, including several major skin diseases. These genome-wide association studies focus on showing differences in the frequencies of variants between case and control groups, rather than co-transmission of a variant and disease through a family, as is done in linkage studies. In this review, we provide overall guidance for genome-wide association study analysis and interpreting the results. Additionally, we discuss challenges and future directions for genome-wide association studies, focusing on translation of findings to provide biological and clinical implications for dermatology.
复杂的皮肤疾病是由许多不同的基因和环境因素共同作用的结果,个体遗传变异通常对疾病风险的影响只有中等程度。为了将遗传变异与疾病/特征相关联,需要能够检查大量的样本。高通量基因分型技术的进步,以及人类基因组及其相关个体间变异的图谱绘制,使得在许多疾病(包括几种主要的皮肤疾病)的大病例对照队列中,可以在高密度水平上分析遗传变异。这些全基因组关联研究侧重于显示病例组和对照组之间变异频率的差异,而不是像连锁研究那样通过家族共传递变异和疾病。在这篇综述中,我们提供了全基因组关联研究分析和解释结果的总体指导。此外,我们还讨论了全基因组关联研究的挑战和未来方向,重点是将研究结果转化为皮肤病学的生物学和临床意义。