Mapping eQTLs in the Norfolk Island Genetic Isolate Identifies Candidate Genes for CVD Risk Traits
Mapping eQTLs in the Norfolk Island Genetic Isolate Identifies Candidate Genes for CVD Risk Traits
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DOI：
10.1016/j.ajhg.2013.11.004
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发表时间：
2013-12-05
影响因子：
9.8
通讯作者：
Griffiths, Lyn R.
中科院分区：
文献类型：
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作者：
Benton, Miles C.;Lea, Rod A.;Griffiths, Lyn R.
心血管疾病(CVD)影响着全世界数以百万计的人，受到包括生活方式和遗传在内的许多因素的影响。表达数量性状基因座(EQTL)影响基因表达，是心血管疾病风险的良好候选基因。创始人效应家系可以为绘制与疾病风险相关的基因图谱提供额外的力量。因此，我们在诺福克岛(NI)的遗传分离株中确定了eQTL，并测试了这些基因与心血管疾病危险因素之间的关联。我们测量了330个个体的血液淋巴细胞全基因组转录水平，并使用基于系谱的遗传力分析来识别可遗传的转录水平。通过对这些转录本进行全基因组关联测试，确定了eQTL。对心血管疾病危险因素(即血脂、血压和体脂指数)和eQTL之间的相关性的测试显示，1712个可遗传转录本(p<；0.05)的遗传度值在0.18到0.84之间。从中，我们确定了200个顺式作用eQTL和70个反式作用eQTL(p<；1.84×10(-7))。以eQTL为中心的心血管风险性状分析揭示了多种关联，其中包括12个先前与心血管相关性状相关的关联。性状与eQTL回归模型确定了四个心血管风险候选基因(NAAA、PAPSS1、NME1和PRDX1)，所有这些基因都在疾病中具有已知的生物学作用。此外，我们还发现了几个以前与心血管疾病风险特征相关的基因，包括MTHFR和FN3KRP。我们已经成功地在NI家系中确定了一组eQTL，并利用这一点将几个基因与心血管疾病的风险联系起来。未来的研究需要进一步评估这些eQTL的功能重要性，以及这里的发现是否也与远交种群有关。
Cardiovascular disease (CVD) affects millions of people worldwide and is influenced by numerous factors, including lifestyle and genetics. Expression quantitative trait loci (eQTLs) influence gene expression and are good candidates for CVD risk. Founder-effect pedigrees can provide additional power to map genes associated with disease risk. Therefore, we identified eQTLs in the genetic isolate of Norfolk Island (NI) and tested for associations between these and CVD risk factors. We measured genome-wide transcript levels of blood lymphocytes in 330 individuals and used pedigree-based heritability analysis to identify heritable transcripts. eQTLs were identified by genome-wide association testing of these transcripts. Testing for association between CVD risk factors (i.e., blood lipids, blood pressure, and body fat indices) and eQTLs revealed 1,712 heritable transcripts (p < 0.05) with heritability values ranging from 0.18 to 0.84. From these, we identified 200 cis-acting and 70 trans-acting eQTLs (p < 1.84 x 10(-7)) An eQTL-centric analysis of CVD risk traits revealed multiple associations, including 12 previously associated with CVD-related traits. Trait versus eQTL regression modeling identified four CVD risk candidates (NAAA, PAPSS1, NME1, and PRDX1), all of which have known biological roles in disease. In addition, we implicated several genes previously associated with CVD risk traits, including MTHFR and FN3KRP. We have successfully identified a panel of eQTLs in the NI pedigree and used this to implicate several genes in CVD risk. Future studies are required for further assessing the functional importance of these eQTLs and whether the findings here also relate to outbred populations.