Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother child pairsto quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P<5x10^8. In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.
Understanding how maternal and fetal genetic and environmental factors influence offspring birth weight
We aim to identify genetic and environmental factors that are causally associated with birth weight. Both lower and higher birth weights in the normal range are observationally associated with a higher risk of type 2 diabetes in later life, but the causes of these associations are poorly understood.
We propose to investigate three related research questions:
(1) Which common fetal genetic variants are robustly associated with offspring birth weight?
(2) Which common maternal genetic variants are robustly associated with offspring birth weight?
(3) Which maternal intra-uterine environmental exposures are causally associated with offspring birth weight? Improving the prevention, diagnosis and treatment of diabetes:
(1) Fetal genetic variants associated with an individual?s own birth weight will highlight biological pathways relevant to fetal growth and may indicate links with pathways relevant to diabetes, enabling a better understanding of what causes the disease.
(2) Maternal genetic variants known to influence diabetes- or obesity-related traits may be used to test the hypothesis that those traits are causally associated with the birth weight of her offspring. This will improve understanding of the factors responsible for increased fetal growth and associated risks in a diabetic or obese pregnancy. We will perform
(a) [hypothesis-free] genome-wide association studies examining
(i) associations between a participant's genotype (fetal) and their own birth weight,
(ii) associations between a female participant's genotype (maternal) and the birth weight of their first child, and
(b) [hypothesis-driven] analyses of associations between maternal genetic variants known to influence traits relevant to the maternal environment (fasting glucose, blood pressure etc) and offspring birth weight. Since genetic variants are unlikely to be confounded, the latter will be a Mendelian randomization analysis to investigate causality in associations between maternal environmental factors and offspring birth weight. We propose to use the full UK Biobank cohort for the fetal genome-wide association study, while for the maternal genome-wide association study and Mendelian randomization analyses, we plan to use the subset of women with data on birth weight of first child (n=221,522).
|Dr Rachel Freathy
|University of Exeter
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