Leveraging family history in genetic association analyses of binary traits.
Leveraging family history in genetic association analyses of binary traits.
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DOI：
10.1186/s12864-022-08897-8
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发表时间：
2022-10-01
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影响因子：
4.4
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中科院分区：
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在病例对照全基因组关联研究（CC-GWAS）的逻辑回归中考虑亲属健康史可能提供新的信息，提高检测疾病相关遗传变异的准确性和能力。我们对来自Framingham心脏研究（FHS）的2型糖尿病（T2D）数据进行了模拟和分析，以比较两种方法：以病例对照状态和家族史为条件的责任阈值模型（LT-FH）和将家族史纳入CC-GWAS的Fam-meta。在我们的模拟场景中，具有中等T2D遗传力（h2 = 0.28）的性状，变异次要等位基因频率范围为1%至50%，遗传变异解释的表型方差为1%，Fam-meta具有最高的总功率，而两种纳入家族史的方法都比CC-GWAS更强大。三种方法均能控制I型错误率，其中LT-FH最为保守，错误率低于预期。此外，与CC-GWAS相比，我们观察到当表型的患病率随着年龄的增长而增加时，两种家族史方法的功效显著增加。此外，我们发现，当只有较远亲的表型可用时，Fam-meta仍然比CC-GWAS更强大，证实利用近亲和远亲的病史可以增加关联分析的能力。利用FHS数据，我们证实了TCF7L2区域与T2D在全基因组阈值< 5 × 10-8下的众所周知的关联，并且与CC-GWAS相比，两种家族史方法都增加了该区域的重要性。我们在5q35 （ADAMTS2）和5q23 （PRR16）上发现了两个位点，以前没有使用CC-GWAS和Fam-meta报道过T2D；这两种基因都在心血管疾病中发挥作用。此外，CC-GWAS在13q31 （GPC6）上检测到一个与t2d相关性状相关的重要位点。总体而言，LT-FH和Fam-meta在模拟中比CC-GWAS具有更高的功效，特别是使用在老年人群中更普遍的表型，并且两种方法在实际数据应用中都检测到具有较低p值的已知遗传变异，这突出了在遗传关联研究中包含家族史的好处。在线版本包含补充材料，可在10.1186/s12864-022-08897-8获得。
Considering relatives’ health history in logistic regression for case–control genome-wide association studies (CC-GWAS) may provide new information that increases accuracy and power to detect disease associated genetic variants. We conducted simulations and analyzed type 2 diabetes (T2D) data from the Framingham Heart Study (FHS) to compare two methods, liability threshold model conditional on both case–control status and family history (LT-FH) and Fam-meta, which incorporate family history into CC-GWAS. In our simulation scenario of trait with modest T2D heritability (h2 = 0.28), variant minor allele frequency ranging from 1% to 50%, and 1% of phenotype variance explained by the genetic variants, Fam-meta had the highest overall power, while both methods incorporating family history were more powerful than CC-GWAS. All three methods had controlled type I error rates, while LT-FH was the most conservative with a lower-than-expected error rate. In addition, we observed a substantial increase in power of the two familial history methods compared to CC-GWAS when the prevalence of the phenotype increased with age. Furthermore, we showed that, when only the phenotypes of more distant relatives were available, Fam-meta still remained more powerful than CC-GWAS, confirming that leveraging disease history of both close and distant relatives can increase power of association analyses. Using FHS data, we confirmed the well-known association of TCF7L2 region with T2D at the genome-wide threshold of P-value < 5 × 10–8, and both familial history methods increased the significance of the region compared to CC-GWAS. We identified two loci at 5q35 (ADAMTS2) and 5q23 (PRR16), not previously reported for T2D using CC-GWAS and Fam-meta; both genes play a role in cardiovascular diseases. Additionally, CC-GWAS detected one more significant locus at 13q31 (GPC6) reported associated with T2D-related traits. Overall, LT-FH and Fam-meta had higher power than CC-GWAS in simulations, especially using phenotypes that were more prevalent in older age groups, and both methods detected known genetic variants with lower P-values in real data application, highlighting the benefits of including family history in genetic association studies. The online version contains supplementary material available at 10.1186/s12864-022-08897-8.
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