降低全基因组关联研究的复杂性。
Reducing GWAS Complexity.
作者信息
Hazelett Dennis J, Conti David V, Han Ying, Al Olama Ali Amin, Easton Doug, Eeles Rosalind A, Kote-Jarai Zsofia, Haiman Christopher A, Coetzee Gerhard A
机构信息
a Bioinformatics and Computational Biology Research Center, Biomedical Sciences, Cedars-Sinai Medical Center , Los Angeles , CA , USA.
b Departments of Preventive Medicine and Urology , USC/Norris Cancer Center , USA.
出版信息
Cell Cycle. 2016;15(1):22-4. doi: 10.1080/15384101.2015.1120928.
Genome-wide association studies (GWAS) have revealed numerous genomic 'hits' associated with complex phenotypes. In most cases these hits, along with surrogate genetic variation as measure by numerous single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium, are not in coding genes making assignment of functionality or causality intractable. Here we propose that fine-mapping along with the matching of risk SNPs at chromatin biofeatures lessen this complexity by reducing the number of candidate functional/causal SNPs. For example, we show here that only on average 2 SNPs per prostate cancer risk locus are likely candidates for functionality/causality; we further propose that this manageable number should be taken forward in mechanistic studies. The candidate SNPs can be looked up for each prostate cancer risk region in 2 recent publications in 2015 (1,2) from our groups.
全基因组关联研究（GWAS）已经揭示了许多与复杂表型相关的基因组“命中位点”。在大多数情况下，这些命中位点，以及通过处于连锁不平衡状态的大量单核苷酸多态性（SNP）所测量的替代遗传变异，并不存在于编码基因中，这使得功能或因果关系的确定变得棘手。在此，我们提出通过在染色质生物特征上对风险SNP进行精细定位和匹配，可减少候选功能/因果SNP的数量，从而降低这种复杂性。例如，我们在此表明，每个前列腺癌风险位点平均仅有2个SNP可能是功能/因果关系的候选者；我们进一步提出，在机制研究中应考虑这个可控的数量。可以在我们团队2015年的两篇近期出版物（1,2）中查找每个前列腺癌风险区域的候选SNP。
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