Literature DB >> 33955455

Genetic meta-analysis of twin birth weight shows high genetic correlation with singleton birth weight.

Jeffrey J Beck1,2, René Pool2, Margot van de Weijer2, Xu Chen3, Eva Krapohl4, Scott D Gordon5, Marianne Nygaard6, Birgit Debrabant6, Teemu Palviainen7, Matthijs D van der Zee2, Bart Baselmans2,8, Casey T Finnicum1, Lu Yi3, Sebastian Lundström9, Toos van Beijsterveldt2, Lene Christiansen6,10, Kauko Heikkilä7, Julie Kittelsrud1, Anu Loukola7, Miina Ollikainen7, Kaare Christensen6, Nicholas G Martin5, Robert Plomin4, Michel Nivard2, Meike Bartels2, Conor Dolan2, Gonneke Willemsen2, Eco de Geus2, Catarina Almqvist3, Patrik K E Magnusson3, Hamdi Mbarek2, Erik A Ehli1, Dorret I Boomsma1,2, Jouke-Jan Hottenga2.   

Abstract

Birth weight (BW) is an important predictor of newborn survival and health and has associations with many adult health outcomes, including cardiometabolic disorders, autoimmune diseases and mental health. On average, twins have a lower BW than singletons as a result of a different pattern of fetal growth and shorter gestational duration. Therefore, investigations into the genetics of BW often exclude data from twins, leading to a reduction in sample size and remaining ambiguities concerning the genetic contribution to BW in twins. In this study, we carried out a genome-wide association meta-analysis of BW in 42 212 twin individuals and found a positive correlation of beta values (Pearson's r = 0.66, 95% confidence interval [CI]: 0.47-0.77) with 150 previously reported genome-wide significant variants for singleton BW. We identified strong positive genetic correlations between BW in twins and numerous anthropometric traits, most notably with BW in singletons (genetic correlation [rg] = 0.92, 95% CI: 0.66-1.18). Genetic correlations of BW in twins with a series of health-related traits closely resembled those previously observed for BW in singletons. Polygenic scores constructed from a genome-wide association study on BW in the UK Biobank demonstrated strong predictive power in a target sample of Dutch twins and singletons. Together, our results indicate that a similar genetic architecture underlies BW in twins and singletons and that future genome-wide studies might benefit from including data from large twin registers.
© The Author(s) 2021. Published by Oxford University Press.

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Year:  2021        PMID: 33955455      PMCID: PMC8444448          DOI: 10.1093/hmg/ddab121

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   6.150


  74 in total

1.  Genetic influence on birthweight and gestational length determined by studies in offspring of twins.

Authors:  B Clausson; P Lichtenstein; S Cnattingius
Journal:  BJOG       Date:  2000-03       Impact factor: 6.531

2.  Genetic study of the height and weight process during infancy.

Authors:  Paula van Dommelen; Mathisca C M de Gunst; Aad W van der Vaart; Dorret I Boomsma
Journal:  Twin Res       Date:  2004-12

Review 3.  Discordant growth in twins.

Authors:  John C P Kingdom; Ori Nevo; Kellie E Murphy
Journal:  Prenat Diagn       Date:  2005-09       Impact factor: 3.050

4.  PLINK: a tool set for whole-genome association and population-based linkage analyses.

Authors:  Shaun Purcell; Benjamin Neale; Kathe Todd-Brown; Lori Thomas; Manuel A R Ferreira; David Bender; Julian Maller; Pamela Sklar; Paul I W de Bakker; Mark J Daly; Pak C Sham
Journal:  Am J Hum Genet       Date:  2007-07-25       Impact factor: 11.025

5.  No evidence of a higher 10 year period prevalence of diabetes among 77,885 twins compared with 215,264 singletons from the Danish birth cohorts 1910-1989.

Authors:  I Petersen; M M F Nielsen; H Beck-Nielsen; K Christensen
Journal:  Diabetologia       Date:  2011-04-13       Impact factor: 10.122

6.  Relation between weight and length at birth and body mass index in young adulthood: cohort study.

Authors:  H T Sørensen; S Sabroe; K J Rothman; M Gillman; P Fischer; T I Sørensen
Journal:  BMJ       Date:  1997-11-01

7.  LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants.

Authors:  Mitchell J Machiela; Stephen J Chanock
Journal:  Bioinformatics       Date:  2015-07-02       Impact factor: 6.937

8.  Avera Twin Register Growing Through Online Consenting and Survey Collection.

Authors:  Julie M Kittelsrud; Erik A Ehli; Vikki Petersen; Tammy Jung; Jeffrey J Beck; Noah Kallsen; Patricia Huizenga; Brittany Holm; Gareth E Davies
Journal:  Twin Res Hum Genet       Date:  2019-10-14       Impact factor: 1.587

9.  Birth weight and cardiovascular risk factors in a cohort followed until 80 years of age: the study of men born in 1913.

Authors:  M Eriksson; M-A Wallander; I Krakau; H Wedel; K Svärdsudd
Journal:  J Intern Med       Date:  2004-02       Impact factor: 8.989

10.  Quality control and conduct of genome-wide association meta-analyses.

Authors:  Thomas W Winkler; Felix R Day; Damien C Croteau-Chonka; Andrew R Wood; Adam E Locke; Reedik Mägi; Teresa Ferreira; Tove Fall; Mariaelisa Graff; Anne E Justice; Jian'an Luan; Stefan Gustafsson; Joshua C Randall; Sailaja Vedantam; Tsegaselassie Workalemahu; Tuomas O Kilpeläinen; André Scherag; Tonu Esko; Zoltán Kutalik; Iris M Heid; Ruth J F Loos
Journal:  Nat Protoc       Date:  2014-04-24       Impact factor: 13.491

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