Rosenthal Elisabeth, Blue Elizabeth, Jarvik Gail P
aDepartment of Medicine (Medical Genetics) bDepartment of Genome Sciences, University of Washington, Seattle, Seattle, Washington, USA.
Curr Opin Lipidol. 2015 Apr;26(2):114-9. doi: 10.1097/MOL.0000000000000156.
Detection of high-impact variants on lipid traits is complicated by complex genetic architecture. Although genome-wide association studies (GWAS) successfully identified many novel genes associated with lipid traits, it was less successful in identifying variants with a large impact on the phenotype. This is not unexpected, as the more common variants detectable by GWAS typically have small effects. The availability of large familial datasets and sequence data has changed the paradigm for successful genomic discovery of the novel genes and pathogenic variants underlying lipid disorders.
Novel loci with large effects have been successfully mapped in families, and next-generation sequencing allowed for the identification of the underlying lipid-associated variants of large effect size. The success of this strategy relies on the simplification of the underlying genetic variation by focusing on large single families segregating extreme lipid phenotypes.
Rare, high-impact variants are expected to have large effects and be more relevant for medical and pharmaceutical applications. Family data have many advantages over population-based data because they allow for the efficient detection of high-impact variants with an exponentially smaller sample size and increased power for follow-up studies.
脂质性状高影响变异的检测因复杂的遗传结构而变得复杂。尽管全基因组关联研究（GWAS）成功鉴定出许多与脂质性状相关的新基因，但在鉴定对表型有重大影响的变异方面却不太成功。这并不意外，因为GWAS可检测到的较常见变异通常影响较小。大型家系数据集和序列数据的可用性改变了成功进行基因组发现以揭示脂质紊乱潜在新基因和致病变异的模式。
在家系中已成功定位了具有重大影响的新基因座，并且新一代测序使得能够鉴定出具有大效应大小的潜在脂质相关变异。该策略的成功依赖于通过聚焦于分离极端脂质表型的大型单一家系来简化潜在的遗传变异。
罕见的高影响变异预计具有较大效应，并且在医学和制药应用中更具相关性。家系数据相对于基于人群的数据具有许多优势，因为它们能够以指数级更小的样本量高效检测高影响变异，并增强后续研究的效力。
