[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124605-en":3,"doc-seo-124605-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124605,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Semiparametric efficient estimation of genetic relatedness with machine learning methods","This paper develops semiparametric efficient estimators of genetic relatedness between two traits under a model-free framework. It addresses limitations of existing approaches that rely on parametric model specification, where misspecification can bias inference, and where semiparametric efficient bounds remain unavailable. Using machine learning to estimate conditional genetic values, the work constructs valid confidence intervals for genetic covariance and genetic correlation for both continuous and discrete responses. Efficient influence functions support a consistent covariance estimator when at least one genetic value is consistently estimated, with numerical validation and application to mouse genome-wide association data.","arXiv :2304 .01849v2 [ stat .ME] 2 Jun 2023  \nSemiparametric efficient estimation of genetic relatedness with machine learning methods  \nXu Guo 1 , Yiyuan Qian 1 , Hongwei Shi 1 , Weichao Yang 1 , and Niwen Zhou2 *  \n1 School of Statistics, Beijing Normal University, Beijing, China  \n2 Center for Statistics and Data Science, Beijing Normal University, Zhuhai, China  \nAbstract  \nIn this paper, we propose semiparametric efficient estimators of genetic relatedness between two traits in a model-free framework. Most existing methods require specifying certain parametric models involving the traits and genetic variants. However, the bias due to model misspecification may yield misleading statistical results. Moreover, the semiparametric efficient bounds for estimators of genetic relatedness are still lacking. In this paper, we develop semiparametric efficient estimators with machine learning methods and construct valid confidence intervals for two important measures of genetic relatedness: genetic covariance and genetic correlation, allowing both continuous and discrete responses. Based on the derived efficient influence functions of genetic relatedness, we propose a consistent estimator of the genetic covariance as long as one of genetic values is consistently estimated. The data of two traits may be collected from the same group or different groups of individuals. Various numerical studies are performed to illustrate our introduced procedures. We  \n* Corresponding Author: Niwen Zhou. All authors contributed equally to this work and are listed in the alphabetical order.  \nalso apply proposed procedures to analyze Carworth Farms White mice genome-wide association study data.  \nKeywords: Model misspecification; Genetic covariance; Semiparametric efficient bound; Confidence interval.  \n1 Introduction  \nUnderstanding genetic relatedness between complex traits is an important problem in human genetics research. In practical genetic studies, shared common genetic variants have been found in many complex diseases, such as various autoimmune diseases (Zhernakova et al., 2009) and psychiatric disorders (Craddock and Owen, 2005) . Genetic relatedness analysis has a variety of downstream applications, which can help to find diseaseassociated genetic variation, improve polygenic risk prediction, and may contribute to improving nosology and diagnosis, risk stratification, and lifestyle interventions (Van Rheenen et al., 2019) .  \nGenetic covariance and genetic correlation are two popular measures of genetic relatedness. Consider two responses Y ∈ R and Z ∈ R, such as complex traits, disease outcomes, or gene expressions, and X ∈ Rp being p-dimensional predictors, denoting as p genetic variants. As introduced by Van Rheenen et al. (2019) and Wang et al. (2021), the genetic covariance of Y and Z can be defined as the covariance of their conditional mean functions  \nI = cov{m(X), h(X)},  \nwhere m(X) = E ( Y | X) and h(X) = E (Z | X) are the genetic values of Y and Z, respectively. Subsequently, the genetic correlation can be defined as follows:  \nρ =  \n cov{m(X), h(X)} √var{m(X)}var{h(X)}  \n  .  \nAccordingly, ρ is normalized as −1 ≤ ρ ≤ 1, and thus it can be used to compare the genetic relatedness among multiple pairs.  \nIn the genetic literature, methodological developments for estimating genetic relatedness are mainly based on family studies or genome-wide association studies (GWAS) .  \nCompared with traditional family-based approaches, GWAS-based methods do not require the studied phenotypes to be measured on the same individuals (Zhang et al., 2021) . Thus it is promising in quantifying the overlapping genetic effects between pairs of traits based on GWAS data. Most of the GWAS-based methods are derived based on some specified regression models, such as linear mixed-effect model (Lee et al., 2012 ; Vattikuti et al., 2012 ; Yang et al., 2013), linear fixed-effect model (Guo et al., 2019), and generalized linear model (Ma et al., 2022 ; Wang et ","cbCaihpH9a0XO3S2","https://ap.wps.com/l/cbCaihpH9a0XO3S2","pdf",332861,1,46,"English","en",105,"# Introduction\n## Motivation and problem setup\n## Definitions of genetic covariance and genetic correlation\n## Limitations of existing parametric GWAS methods\n## Goal and contributions","[{\"question\":\"Why do existing genetic relatedness methods risk biased results?\",\"answer\":\"They often depend on specified parametric regression models, and when those model assumptions fail in practice, model misspecification can produce biased estimators and inaccurate inference.\"},{\"question\":\"What does the paper estimate, and for which response types?\",\"answer\":\"It estimates genetic covariance and genetic correlation, constructing valid confidence intervals for both continuous and discrete trait responses.\"},{\"question\":\"How are semiparametric efficient bounds obtained?\",\"answer\":\"The paper derives efficient influence functions for genetic covariance and genetic correlation, which in turn provide semiparametric efficient bounds for the corresponding estimators.\"}]","Semiparametric efficient estimation of genetic relatedness with machine learning methods | 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do existing genetic relatedness methods risk biased results?","Question",{"text":75,"@type":76},"They often depend on specified parametric regression models, and when those model assumptions fail in practice, model misspecification can produce biased estimators and inaccurate inference.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the paper estimate, and for which response types?",{"text":80,"@type":76},"It estimates genetic covariance and genetic correlation, constructing valid confidence intervals for both continuous and discrete trait responses.",{"name":82,"@type":73,"acceptedAnswer":83},"How are semiparametric efficient bounds obtained?",{"text":84,"@type":76},"The paper derives efficient influence functions for genetic covariance and genetic correlation, which in turn provide semiparametric efficient bounds for the corresponding 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