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Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects.

ABSTRACT: Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

SUBMITTER: Howe LJ

PROVIDER: S-EPMC9110300 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects.

Howe Laurence J LJ Nivard Michel G MG Morris Tim T TT Hansen Ailin F AF Rasheed Humaira H Cho Yoonsu Y Chittoor Geetha G Ahlskog Rafael R Lind Penelope A PA Palviainen Teemu T van der Zee Matthijs D MD Cheesman Rosa R Mangino Massimo M Wang Yunzhang Y Li Shuai S Klaric Lucija L Ratliff Scott M SM Bielak Lawrence F LF Nygaard Marianne M Giannelis Alexandros A Willoughby Emily A EA Reynolds Chandra A CA Balbona Jared V JV Andreassen Ole A OA Ask Helga H Baras Aris A Bauer Christopher R CR Boomsma Dorret I DI Campbell Archie A Campbell Harry H Chen Zhengming Z Christofidou Paraskevi P Corfield Elizabeth E Dahm Christina C CC Dokuru Deepika R DR Evans Luke M LM de Geus Eco J C EJC Giddaluru Sudheer S Gordon Scott D SD Harden K Paige KP Hill W David WD Hughes Amanda A Kerr Shona M SM Kim Yongkang Y Kweon Hyeokmoon H Latvala Antti A Lawlor Deborah A DA Li Liming L Lin Kuang K Magnus Per P Magnusson Patrik K E PKE Mallard Travis T TT Martikainen Pekka P Mills Melinda C MC Njølstad Pål Rasmus PR Overton John D JD Pedersen Nancy L NL Porteous David J DJ Reid Jeffrey J Silventoinen Karri K Southey Melissa C MC Stoltenberg Camilla C Tucker-Drob Elliot M EM Wright Margaret J MJ Hewitt John K JK Keller Matthew C MC Stallings Michael C MC Lee James J JJ Christensen Kaare K Kardia Sharon L R SLR Peyser Patricia A PA Smith Jennifer A JA Wilson James F JF Hopper John L JL Hägg Sara S Spector Tim D TD Pingault Jean-Baptiste JB Plomin Robert R Havdahl Alexandra A Bartels Meike M Martin Nicholas G NG Oskarsson Sven S Justice Anne E AE Millwood Iona Y IY Hveem Kristian K Naess Øyvind Ø Willer Cristen J CJ Åsvold Bjørn Olav BO Koellinger Philipp D PD Kaprio Jaakko J Medland Sarah E SE Walters Robin G RG Benjamin Daniel J DJ Turley Patrick P Evans David M DM Davey Smith George G Hayward Caroline C Brumpton Ben B Hemani Gibran G Davies Neil M NM

Nature genetics 20220509 5

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family ...[more]

PMID: 35534559

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Project description:To explore whether systematic reviewers selectively include trial effect estimates in meta-analyses when multiple are available, and what impact this may have on meta-analytic effects.Cross-sectional study.We randomly selected systematic reviews of interventions from 2 clinical specialties published between January 2010 and 2012. The first presented meta-analysis of a continuous outcome in each review was selected (index meta-analysis), and all trial effect estimates that were eligible for inclusion in the meta-analysis (eg, from multiple scales or time points) were extracted from trial reports.We calculated a statistic (the Potential Bias Index (PBI)) to quantify and test for evidence of selective inclusion. The PBI ranges from 0 to 1; values above or below 0.5 are suggestive of selective inclusion of effect estimates more or less favourable to the intervention, respectively. The impact of any potential selective inclusion was investigated by comparing the index meta-analytic standardised mean difference (SMD) to the median of a randomly constructed distribution of meta-analytic SMDs (representing the meta-analytic SMD expected when there is no selective inclusion).31 reviews (250 trials) were included. The estimated PBI was 0.57 (95% CI 0.50 to 0.63), suggesting that trial effect estimates that were more favourable to the intervention were included in meta-analyses slightly more often than expected under a process consistent with random selection; however, the 95% CI included the null hypothesis of no selective inclusion. Any potential selective inclusion did not have an important impact on the meta-analytic effects.There was no clear evidence that selective inclusion of trial effect estimates occurred in this sample of meta-analyses. Further research on selective inclusion in other clinical specialties is needed. To enable readers to assess the risk of selective inclusion bias, we recommend that systematic reviewers report the methods used to select effect estimates to include in meta-analyses.

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Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects.

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Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects.

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