Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.
Investigating the Genetic Architecture Underlying the Developmental Origins of Health and Disease
The prevalence of cardiovascular disease, diabetes, osteoporosis and infertility are increasing and the financial burden on society is substantial. Research has shown a link between birth weight and increased risk of these diseases and other cardiovascular/metabolic disorders. Genetics may be involved as not all individuals born of suboptimal weight go on to develop disease. The aims of this research are to: (1) identify novel genes that explain the adverse relationship between birth weight and cardiometabolic disorders, osteoporosis or infertility, (2) determine biological intermediates that partially mediate the relationship between birth weight and these disorders, using genetic profile scores. Currently, most interventions target individuals after the onset of clinical symptoms, by which time treatment is expensive and of limited effectiveness. The Developmental Origins of Health and Disease (DOHaD) paradigm suggests that there may be critical windows earlier in life that offer opportunities for disease treatment and prevention. The overall aim of this project is to use novel statistical genetics methods to identify genes that explain part of the observational association between birth weight and risk of cardiometabolic disease, osteoporosis or infertility, providing a better understanding of this relationship and its implications for future development of disease. We will conduct a genome-wide association study to identify individual genetic variants and regions of the genome that are 1) jointly associated with birth weight and cardiometabolic disorders or osteoporosis, 2) associated with fracture risk or bone related phenotypes and 3) associated with fertility defects. In addition, we will use genetic risk scores for biological characteristics to investigate whether those characteristics partially mediate the observed relationship between birth weight and subsequent risk of disease in later life. Whole cohort with genetic data that have reported their birth weight, diagnosis of cardiometabolic diseases, osteoporosis or fertility defects.
|Lead investigator:||Dr Nicole Warrington|
|Lead institution:||University of Queensland|
3 related Returns
|Return ID||App ID||Description||Archive Date|
|2735||12703||Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study||2 Nov 2020|
|3141||12703||Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis||26 Feb 2021|
|2002||12703||Using structural equation modelling to jointly estimate maternal and fetal effects on birthweight in the UK Biobank||12 Feb 2020|
|2904||Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors||Warrington et al||2019||Nature Genetics (2019)|