Next-generation gene discovery for variants of large impact on lipid traits.
Next-generation gene discovery for variants of large impact on lipid traits.
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DOI：
10.1097/mol.0000000000000156
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发表时间：
2015-04
影响因子：
4.4
通讯作者：
Jarvik GP
中科院分区：
文献类型：
--
作者：
Rosenthal E;Blue E;Jarvik GP
对脂质性状的高影响变体的检测由于复杂的遗传结构而变得复杂。虽然全基因组关联研究（GWAS）成功地鉴定了许多与脂质性状相关的新基因，但在鉴定对表型有较大影响的变异方面不太成功。这并不意外，因为GWAS可检测到的更常见的变体通常影响很小。大家族数据集和序列数据的可用性已经改变了成功基因组发现新基因和脂质紊乱相关致病性变体的范式。具有大效应的新基因座已成功地在家族中作图，并且下一代测序允许鉴定大效应大小的潜在脂质相关变体。这一策略的成功依赖于简化潜在的遗传变异，重点是大的单一家庭分离极端脂质表型。罕见的高影响力变体预计具有较大的影响，并且与医疗和制药应用更相关。与基于人群的数据相比，家族数据具有许多优势，因为它们允许以指数级较小的样本量有效检测高影响力变异，并增加后续研究的功效。
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.