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Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration

机译:基于基于基因的结合性与使用延长的百分点具有普通功能线性混合模型的二分性状:应用于年龄相关性黄斑变性的应用

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摘要

Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes:CFH and ARMS2. Using rare variants, we find suggestive signals in four genes:ASAH1,CLEC6A,TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, andASAH1deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases.for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
机译:遗传学在年龄相关的黄斑变性(AMD)中起着作用,是老年人失明的常​​见原因。需要强大的方法,用于在家庭数据的AMD和遗传变体等二分性质之间进行基于区域的关联试验。在这里,我们应用开发的新的广义功能线性混合模型(GFLMM)以测试基于基于基于基于AMD家族的关联。使用常见和罕见的变体,我们观察到两种已知的AMD基因的重要关联:CFH和Arms2。使用稀有变体,我们在四个基因中发现了暗示信号:ASAH1,CLEC6A,TMEM63C和SGSM1。有趣的是,ASAH1在AMD幽默中下调,AndasaH1D缺陷导致视网膜炎症和对氧化应激的脆弱性增加。通过我们的GFLMM使这些发现是可能的,该结果模拟了主要基因作为固定平均值的效果,作为随机变异的多基因贡献以及血缘关系系数的谱系成员的相关性。模拟表明GFLMM似然比测试(LRT)精确控制I型错误速率。 LRT具有比现有的追溯内核和负担统计数据相似或更高的功率。我们基于GFLMM的统计数据提供了一种用于进行复杂疾病的基于家庭的基于遗传学研究的新工具。对于本文,包括可用于再现工作的材料的标准化描述,可作为在线补充。

著录项

  • 来源
    《Journal of the American statistical association》 |2021年第534期|531-545|共15页
  • 作者单位

    Univ Pittsburgh Grad Sch Publ Hlth Dept Biostat Pittsburgh PA 15261 USA|IBM US 222 S Riverside Plaza Suites 1700 & 1800 Chicago IL USA;

    Univ Tennessee Dept Prevent Med Div Biostat Hlth Sci Ctr Memphis TN USA|NHGRI Computat & Stat Genom Branch NIH Baltimore MD USA|Eunice Kennedy Shriver Natl Inst Child Hlth & Hum Biostat & Bioinformat Branch Div Intramural Populat Hlth Res NIH Bethesda MD 20892 USA;

    Univ Pittsburgh Childrens Hosp Pittsburgh Div Pulm Med Allergy & Immunol Pittsburgh PA 15261 USA|Columbia Univ Dept Obstet & Gynecol Irving Med Ctr New York NY USA;

    Univ Pittsburgh Childrens Hosp Pittsburgh Div Pulm Med Allergy & Immunol Pittsburgh PA 15261 USA;

    Univ Calif Los Angeles David Geffen Sch Med Dept Ophthalmol Stein Eye Inst Los Angeles CA 90095 USA;

    Univ Pittsburgh Dept Hlth Promot & Dev Pittsburgh PA 15261 USA|Univ Pittsburgh Grad Sch Publ Hlth Dept Human Genet Pittsburgh PA 15261 USA;

    Univ Laval Dept Math & Stat Quebec City PQ Canada;

    Dept Stat & Actuarial Sci Waterloo ON Canada;

    Baylor Coll Med Dept Med Houston TX 77030 USA;

    NHGRI Computat & Stat Genom Branch NIH Baltimore MD USA;

    NHGRI Computat & Stat Genom Branch NIH Baltimore MD USA;

    NIMH Human Genet Branch NIH Bethesda MD 20892 USA|NIMH Genet Basis Mood & Anxiety Disorders Sect NIH Bethesda MD 20892 USA;

    Michigan State Univ Dept Epidemiol & Biostat E Lansing MI USA;

    Georgetown Univ Med Ctr Dept Biostat Bioinformat & Biomath Washington DC 20057 USA;

    Georgetown Univ Med Ctr Dept Biostat Bioinformat & Biomath Washington DC 20057 USA;

    Univ Texas Houston Human Genet Ctr Houston TX USA;

    Univ Pittsburgh Grad Sch Publ Hlth Dept Biostat Pittsburgh PA 15261 USA|Univ Pittsburgh Grad Sch Publ Hlth Dept Human Genet Pittsburgh PA 15261 USA;

    NHGRI Computat & Stat Genom Branch NIH Baltimore MD USA|Georgetown Univ Med Ctr Dept Biostat Bioinformat & Biomath Washington DC 20057 USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Age-related macular degeneration; Association study; Complex diseases; Extended pedigree; Generalized functional linear mixed models; Rare variants;

    机译:年龄相关的黄斑变性;结合研究;复杂疾病;扩展血统;广义功能线性混合模型;罕见的变种;

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