Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value.
Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value.
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DOI：
10.1038/s42003-023-05413-w
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发表时间：
2023-11-24
影响因子：
5.9
通讯作者：
Johnson, Eric O.
中科院分区：
文献类型：
--
作者：
Moore, Amy;Marks, Jesse A.;Quach, Bryan C.;Guo, Yuelong;Bierut, Laura J.;Gaddis, Nathan C.;Hancock, Dana B.;Page, Grier P.;Johnson, Eric O.
在目前没有足够大的全基因组关联研究（GWAS）样本的情况下，利用变异生物学功能知识的方法可能会在不增加样本量的情况下阐明发现。我们综合评价了17种识别新关联的功能加权方法。我们使用已发表的五个复杂特征的多个GWAS波的结果来评估这些方法的性能。虽然没有一种方法能够对任何性状同时获得高灵敏度和阳性预测值（PPV），但利用多效性和表达数量性状位点的方法子集对于多个性状具有高PPV（>75%）。应用功能加权方法来提高基因座发现的GWAS功率不太可能避免在真正功率不足的GWAS中需要更大的样本量，但这些结果表明，对GWAS应用功能加权可以准确地从可用样本中指定额外的新基因座用于后续研究。对17种已发表的用于提高GWAS统计能力的功能加权方法的评估表明，在高灵敏度和高阳性预测值之间进行了权衡。
Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies. Evaluation of 17 published functional weighting methods for improving GWAS statistical power demonstrates a tradeoff between high sensitivity and high positive predictive value.
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影响因子：
4
作者：
Bonevski B;Randell M;Paul C;Chapman K;Twyman L;Bryant J;Brozek I;Hughes C
通讯作者：
Hughes C
影响因子：
6.2
作者：
Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium
通讯作者：
Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium
遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
30.8
作者：
Atkinson EG;Maihofer AX;Kanai M;Martin AR;Karczewski KJ;Santoro ML;Ulirsch JC;Kamatani Y;Okada Y;Finucane HK;Koenen KC;Nievergelt CM;Daly MJ;Neale BM
通讯作者：
Neale BM
影响因子：
14.9
作者：
Frankish A;Diekhans M;Ferreira AM;Johnson R;Jungreis I;Loveland J;Mudge JM;Sisu C;Wright J;Armstrong J;Barnes I;Berry A;Bignell A;Carbonell Sala S;Chrast J;Cunningham F;Di Domenico T;Donaldson S;Fiddes IT;García Girón C;Gonzalez JM;Grego T;Hardy M;Hourlier T;Hunt T;Izuogu OG;Lagarde J;Martin FJ;Martínez L;Mohanan S;Muir P;Navarro FCP;Parker A;Pei B;Pozo F;Ruffier M;Schmitt BM;Stapleton E;Suner MM;Sycheva I;Uszczynska-Ratajczak B;Xu J;Yates A;Zerbino D;Zhang Y;Aken B;Choudhary JS;Gerstein M;Guigó R;Hubbard TJP;Kellis M;Paten B;Reymond A;Tress ML;Flicek P
通讯作者：
Flicek P