Accounting for age of onset and family history improves power in genome-wide association studies.
Accounting for age of onset and family history improves power in genome-wide association studies.
复制标题
DOI：
10.1016/j.ajhg.2022.01.009
复制
发表时间：
2022-03-03
影响因子：
9.8
通讯作者：
Vilhjálmsson BJ
中科院分区：
文献类型：
--
作者：
Pedersen EM;Agerbo E;Plana-Ripoll O;Grove J;Dreier JW;Musliner KL;Bækvad-Hansen M;Athanasiadis G;Schork A;Bybjerg-Grauholm J;Hougaard DM;Werge T;Nordentoft M;Mors O;Dalsgaard S;Christensen J;Børglum AD;Mortensen PB;McGrath JJ;Privé F;Vilhjálmsson BJ
全基因组关联研究（GWAS）彻底改变了人类遗传学，使研究人员能够识别数千个疾病相关基因和可能的药物靶点。然而，病例对照状态并不能说明并非所有对照都经历了感兴趣疾病的风险期。这可以通过检查发病年龄分布和对照组的年龄或病例的发病年龄来量化。发病年龄分布也可能取决于性别和出生年份等信息。此外，家族史通常不包括在控制状态的评估中。在这里，我们提出了LT-FH++，一个扩展的责任阈值模型的条件家族史（LT-FH），共同占发病年龄和性别以及家族史。使用模拟，我们表明，当家族史和发病年龄分布可用时，所提出的方法产生统计上显着的功率增益超过LT-FH和大功率增益超过全基因组关联代理研究（GWAX）。我们将我们的方法应用于iPSYCH数据中的四种精神疾病和英国生物库中的死亡率，发现20个全基因组与LT-FH++显著相关，而LT-FH为10个，标准病例对照GWAS为8个。随着越来越多的遗传数据与链接的电子健康记录变得可供研究人员使用，我们希望解释额外健康信息的方法，如LT-FH++，变得更加有益。
Genome-wide association studies (GWASs) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age of onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here, we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), which jointly accounts for age of onset and sex as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields statistically significant power gains over LT-FH and large power gains over genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data and to mortality in the UK Biobank and found 20 genome-wide significant associations with LT-FH++, compared to ten for LT-FH and eight for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.
登录
查看更多内容
影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
影响因子：
30.8
作者：
Dahl A;Iotchkova V;Baud A;Johansson Å;Gyllensten U;Soranzo N;Mott R;Kranis A;Marchini J
通讯作者：
Marchini J
影响因子：
3.7
作者：
Fernandez-Pujals, Ana Maria;Adams, Mark James;McIntosh, Andrew M.
通讯作者：
McIntosh, Andrew M.
影响因子：
4
作者：
Hansen, Stefan N.;Overgaard, Morten;Parner, Erik T.
通讯作者：
Parner, Erik T.
影响因子：
25.8
作者：
Agerbo, Esben;Trabjerg, Betina B.;Musliner, Katherine L.
通讯作者：
Musliner, Katherine L.