Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics

被引:8
作者
Williams, Camille M. [1 ,2 ]
Poore, Holly [3 ]
Tanksley, Peter T. [2 ]
Kweon, Hyeokmoon [4 ]
Courchesne-Krak, Natasia S. [5 ]
Londono-Correa, Diego [2 ]
Mallard, Travis T. [6 ,7 ,8 ]
Barr, Peter [9 ]
Koellinger, Philipp D. [4 ]
Waldman, Irwin D. [10 ]
Sanchez-Roige, Sandra [5 ,11 ]
Harden, K. Paige [1 ,2 ]
Palmer, Abraham A. [5 ,12 ]
Dick, Danielle M. [3 ,13 ]
Linner, Richard Karlsson [14 ]
机构
[1] Univ Texas Austin, Dept Psychol, Austin, TX 78712 USA
[2] Univ Texas Austin, Populat Res Ctr, Austin, TX 78712 USA
[3] Rutgers State Univ, Robert Wood Johnson Med Sch, Dept Psychiat, Piscataway, NJ 08854 USA
[4] Vrije Univ Amsterdam, Sch Business & Econ, Dept Econ, Amsterdam, Netherlands
[5] Univ Calif San Diego, Dept Psychiat, La Jolla, CA USA
[6] Massachusetts Gen Hosp, Psychiat & Neurodev Genet Unit, Ctr Genom Med, Boston, MA USA
[7] Harvard Med Sch, Dept Psychiat, Boston, MA USA
[8] Broad Inst MIT & Harvard, Stanley Ctr Psychiat Res, Boston, MA USA
[9] SUNY Downstate Hlth Sci Univ, Dept Psychiat & Behav Sci, Brooklyn, NY USA
[10] Emory Univ, Dept Psychol, Atlanta, GA USA
[11] Vanderbilt Univ, Div Med Genet, Dept Med, Nashville, TN USA
[12] Univ Calif San Diego, Inst Genom Med, La Jolla, CA USA
[13] Rutgers State Univ, Brain Hlth Inst, Rutgers Addict Res Ctr, Piscataway, NJ 08854 USA
[14] Leiden Univ, Dept Econ, Leiden, Netherlands
关键词
Genomic SEM; Summary statistics; Data removal; Down-sample; Leave-one-out; Meta-analysis; Genomics; Genome-wide association study; ADOLESCENT; GENETICS;
D O I
10.1007/s10519-023-10152-z
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
Proprietary genetic datasets are valuable for boosting the statistical power of genome-wide association studies (GWASs), but their use can restrict investigators from publicly sharing the resulting summary statistics. Although researchers can resort to sharing down-sampled versions that exclude restricted data, down-sampling reduces power and might change the genetic etiology of the phenotype being studied. These problems are further complicated when using multivariate GWAS methods, such as genomic structural equation modeling (Genomic SEM), that model genetic correlations across multiple traits. Here, we propose a systematic approach to assess the comparability of GWAS summary statistics that include versus exclude restricted data. Illustrating this approach with a multivariate GWAS of an externalizing factor, we assessed the impact of down-sampling on (1) the strength of the genetic signal in univariate GWASs, (2) the factor loadings and model fit in multivariate Genomic SEM, (3) the strength of the genetic signal at the factor level, (4) insights from gene-property analyses, (5) the pattern of genetic correlations with other traits, and (6) polygenic score analyses in independent samples. For the externalizing GWAS, although down-sampling resulted in a loss of genetic signal and fewer genome-wide significant loci; the factor loadings and model fit, gene-property analyses, genetic correlations, and polygenic score analyses were found robust. Given the importance of data sharing for the advancement of open science, we recommend that investigators who generate and share down-sampled summary statistics report these analyses as accompanying documentation to support other researchers' use of the summary statistics.
引用
收藏
页码:404 / 415
页数:12
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