{"title":"A Family-Based Rare Haplotype Association Method for Quantitative Traits.","authors":"Ananda S Datta, Shili Lin, Swati Biswas","doi":"10.1159/000493543","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The variants identified in genome-wide association studies account for only a small fraction of disease heritability. A key to this \"missing heritability\" is believed to be rare variants. Specifically, we focus on rare haplotype variant (rHTV). The existing methods for detecting rHTV are mostly population-based, and as such, are susceptible to population stratification and admixture, leading to an inflated false-positive rate. Family-based methods are more robust in this respect.</p><p><strong>Methods: </strong>We propose a method for detecting rHTVs associated with quantitative traits called family-based quantitative Bayesian LASSO (famQBL). FamQBL can analyze any type of pedigree and is based on a mixed model framework. We regularize the haplotype effects using Bayesian LASSO and estimate the posterior distributions using Markov chain Monte Carlo methods.</p><p><strong>Results: </strong>We conduct simulation studies, including analyses of Genetic Analysis Workshop 18 simulated data, to study the properties of famQBL and compare with a standard family-based haplotype association test implemented in FBAT (family-based association test) software. We find famQBL to be more powerful than FBAT with well-controlled false-positive rates. We also apply famQBL to the Framingham Heart Study data and detect an rHTV associated with diastolic blood pressure.</p><p><strong>Conclusion: </strong>FamQBL can help uncover rHTVs associated with quantitative traits.</p>","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"83 4","pages":"175-195"},"PeriodicalIF":1.1000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000493543","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Heredity","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1159/000493543","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2019/2/21 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 4
Abstract
Background: The variants identified in genome-wide association studies account for only a small fraction of disease heritability. A key to this "missing heritability" is believed to be rare variants. Specifically, we focus on rare haplotype variant (rHTV). The existing methods for detecting rHTV are mostly population-based, and as such, are susceptible to population stratification and admixture, leading to an inflated false-positive rate. Family-based methods are more robust in this respect.
Methods: We propose a method for detecting rHTVs associated with quantitative traits called family-based quantitative Bayesian LASSO (famQBL). FamQBL can analyze any type of pedigree and is based on a mixed model framework. We regularize the haplotype effects using Bayesian LASSO and estimate the posterior distributions using Markov chain Monte Carlo methods.
Results: We conduct simulation studies, including analyses of Genetic Analysis Workshop 18 simulated data, to study the properties of famQBL and compare with a standard family-based haplotype association test implemented in FBAT (family-based association test) software. We find famQBL to be more powerful than FBAT with well-controlled false-positive rates. We also apply famQBL to the Framingham Heart Study data and detect an rHTV associated with diastolic blood pressure.
Conclusion: FamQBL can help uncover rHTVs associated with quantitative traits.
背景:在全基因组关联研究中发现的变异仅占疾病遗传性的一小部分。这种“缺失的遗传性”的关键被认为是罕见的变异。具体来说，我们关注的是罕见单倍型变异(rHTV)。现有的rHTV检测方法大多是基于人群的，因此容易受到人群分层和混杂的影响，导致假阳性率过高。基于家庭的方法在这方面更加健壮。方法:提出了一种基于家族的定量贝叶斯LASSO (famQBL)方法来检测与数量性状相关的rhtv。FamQBL可以分析任何类型的谱系，并且基于混合模型框架。我们使用贝叶斯LASSO对单倍型效应进行正则化，并使用马尔可夫链蒙特卡罗方法估计后验分布。结果:我们进行了模拟研究，包括分析遗传分析研讨会18的模拟数据，以研究famQBL的特性，并与FBAT (family-based association test)软件中实现的标准基于家族的单倍型关联测试进行比较。我们发现famQBL比FBAT更强大，假阳性率控制良好。我们还将famQBL应用于Framingham心脏研究数据，并检测与舒张压相关的rHTV。结论:FamQBL有助于揭示与数量性状相关的rhtv。
期刊介绍：
Gathering original research reports and short communications from all over the world, ''Human Heredity'' is devoted to methodological and applied research on the genetics of human populations, association and linkage analysis, genetic mechanisms of disease, and new methods for statistical genetics, for example, analysis of rare variants and results from next generation sequencing. The value of this information to many branches of medicine is shown by the number of citations the journal receives in fields ranging from immunology and hematology to epidemiology and public health planning, and the fact that at least 50% of all ''Human Heredity'' papers are still cited more than 8 years after publication (according to ISI Journal Citation Reports). Special issues on methodological topics (such as ‘Consanguinity and Genomics’ in 2014; ‘Analyzing Rare Variants in Complex Diseases’ in 2012) or reviews of advances in particular fields (‘Genetic Diversity in European Populations: Evolutionary Evidence and Medical Implications’ in 2014; ‘Genes and the Environment in Obesity’ in 2013) are published every year. Renowned experts in the field are invited to contribute to these special issues.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
