Introducing M-GCTA a Software Package to Estimate Maternal (or Paternal) Genetic Effects on Offspring Phenotypes

被引:0
作者
Zhen Qiao
Jie Zheng
Øyvind Helgeland
Marc Vaudel
Stefan Johansson
Pål R. Njølstad
George Davey Smith
Nicole M. Warrington
David M. Evans
机构
[1] University of Queensland,University of Queensland Diamantina Institute
[2] University of Bristol,Medical Research Council Integrative Epidemiology Unit
[3] Population Health Sciences,Bristol Medical School
[4] University of Bristol,KG Jebsen Center for Diabetes Research, Department of Clinical Science
[5] University of Bergen,Department of Genetics and Bioinformatics
[6] Health Data and Digitalization,Department of Medical Genetics
[7] Norwegian Institute of Public Health,Department of Pediatrics and Adolescents
[8] Haukeland University Hospital,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU
[9] Haukeland University Hospital,undefined
[10] Norwegian University of Science and Technology,undefined
来源
Behavior Genetics | 2020年 / 50卷
关键词
M-GCTA; Maternal effects; Paternal effects; G-REML; Heritability; SNP heritability;
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学科分类号
摘要
There is increasing interest within the genetics community in estimating the relative contribution of parental genetic effects on offspring phenotypes. Here we describe the user-friendly M-GCTA software package used to estimate the proportion of phenotypic variance explained by maternal (or alternatively paternal) and offspring genotypes on offspring phenotypes. The tool requires large studies where genome-wide genotype data are available on mother- (or alternatively father-) offspring pairs. The software includes several options for data cleaning and quality control, including the ability to detect and automatically remove cryptically related pairs of individuals. It also allows users to construct genetic relationship matrices indexing genetic similarity across the genome between parents and offspring, enabling the estimation of variance explained by maternal (or alternatively paternal) and offspring genetic effects. We evaluated the performance of the software using a range of data simulations and estimated the computing time and memory requirements. We demonstrate the use of M-GCTA on previously analyzed birth weight data from two large population based birth cohorts, the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Norwegian Mother and Child Cohort Study (MoBa). We show how genetic variation in birth weight is predominantly explained by fetal genetic rather than maternal genetic sources of variation.
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页码:51 / 66
页数:15
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