Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

<p class="first" id="P2">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 novel). We used structural equation modelling to decompose the contributions of direct fetal and indirect maternal genetic effects, and 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 those 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. </p>

Related collections

Most cited references 26

Record: found
Abstract: found
Article: not found

Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

Stephen Burgess, Robert A H Scott, Nicholas J Timpson … (2015)

Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval −0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect. Electronic supplementary material The online version of this article (doi:10.1007/s10654-015-0011-z) contains supplementary material, which is available to authorized users.

0 comments Cited 592 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

R: A Language for Data Analysis and Graphics

Ross Ihaka, Robert C Gentleman (1996)

0 comments Cited 456 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Type 2 (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia (syndrome X): relation to reduced fetal growth

D Barker, C N Hales, C H D Fall … (1993)

Two follow-up studies were carried out to determine whether lower birthweight is related to the occurrence of syndrome X-Type 2 (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia. The first study included 407 men born in Hertfordshire, England between 1920 and 1930 whose weights at birth and at 1 year of age had been recorded by health visitors. The second study included 266 men and women born in Preston, UK, between 1935 and 1943 whose size at birth had been measured in detail. The prevalence of syndrome X fell progressively in both men and women, from those who had the lowest to those who had the highest birthweights. Of 64-year-old men whose birthweights were 2.95 kg (6.5 pounds) or less, 22% had syndrome X. Their risk of developing syndrome X was more than 10 times greater than that of men whose birthweights were more than 4.31 kg (9.5 pounds). The association between syndrome X and low birthweight was independent of duration of gestation and of possible confounding variables including cigarette smoking, alcohol consumption and social class currently or at birth. In addition to low birthweight, subjects with syndrome X had small head circumference and low ponderal index at birth, and low weight and below-average dental eruption at 1 year of age. It is concluded that Type 2 diabetes and hypertension have a common origin in sub-optimal development in utero, and that syndrome X should perhaps be re-named "the small-baby syndrome".