EGG Birthweight Paper 2016

By Mark McCarthy & Rachel Freathy.

With colleagues in the EGG (Early Growth Genetics) consortium, we recently published a paper in Nature describing genetic analyses of birth weight in over 150,000 samples. In this blog, we detail the rationale behind the study, summarise the main findings, and place the research in a wider context.

The rising prevalence of diabetes, obesity and related conditions represents a major challenge for human health. For any given individual, the risk of developing these conditions is partially dependent on the profile of the genetic variants they inherited from their parents. It is also heavily influenced by a wide range of environmental factors, most obviously those related to dietary intake and physical activity during adulthood.

The impact of the environment on chronic disease risk may however extend to exposures far earlier in life. Around 25 years ago, it was noted that, in some historical cohorts, those who had been recorded as having low birth weight were at substantially increased risk of diabetes and coronary disease many decades later. This observation has been widely reproduced in a variety of data sets around the world. More recently, it has been shown that, in populations with particularly high prevalence of type 2 diabetes, the same appears to be true of individuals with birth weights at the higher end of the scale. In other words, the shape of the distribution between birth weight and later diabetes risk follows a “U” or “reverse J” shape: from the perspective of future disease risk, it is best to be near the average.

The dominant explanation for these observations has been provided by the Developmental Origins of Health and Disease (DOHaD) hypothesis which suggests that these relationships between early growth (for which birth weight is the most readily available measure) and later disease result from the long-term effects of exposing an individual to too little or too much nutrition during early life. For babies exposed to poor intrauterine nutrition, for example, as a result of maternal deprivation or placental insufficiency, the consequences extend beyond poor fetal growth and low birth weight, to include a series of hardwired changes in the “set up” of the metabolic profile of the body that increase disease risk in adulthood. Studies in rodents provide confirmation of many aspects of this hypothesis, as have studies of the offspring of women exposed to extreme deprivation during pregnancy (typically as a result of conflict). Teleologically, this metabolic programming can be seen as an adaptation on behalf of the child for the expectation of an equally harsh postnatal environment, one which has adverse consequences when that postnatal environment turns out to be more luxurious than anticipated.

However, several of us felt that this might not be the only explanation. One other way to connect early growth and later disease could be via DNA variants that influence BOTH birth weight and later disease risk. Perhaps some individuals inherit DNA variant profiles that simultaneously put them at greater risk of reduced growth in early life and of diabetes and other cardiometabolic diseases in adulthood. This is also biologically plausible: the hormone insulin plays a critical role in both early growth and normal adult metabolism, and genetic differences that result in a reduction in insulin secretion would tend to push individuals towards both low birth weight and later diabetes. Thus, two temporally distinct phenotypes could be connected by the same genotype. There are examples of rare genetic conditions (most notably in the glucokinase gene) where such mechanisms are clearly in play.

In the present study, we set out to ask whether or not there was evidence that common, shared genetic differences that influence birth weight showed overlap with the genetic differences already known to be involved in influencing individual risk of diabetes, and other cardiovascular and metabolic conditions.

This study involved 164 scientists from 117 institutions, based in 17 countries on four continents, who have been working as part of the Early Growth Genetics (EGG) consortium. Together, we assembled data from over 150,000 individuals who had the combination of a recorded birth weight and detailed information on genetic variation across their genomes. Nearly half of those samples came from the UK Biobank, a massive study of middle-aged participants from the UK for which data has recently become available (see our other blogs).

We found around 60 regions of the genome that harboured genetic differences that were significantly associated with differences in birth weight. Together, variation at these 60 sites accounts for about 2% of variation in birth weight, similar to the impact of maternal smoking or obesity. In fact, these 60 regions are just the most visible “tip of the iceberg” in terms of the overall contribution of genetic variation to birth weight. Analysis of the full data set allows us to estimate that, in total, around one sixth of the variation in individual birth weight is attributable to genetics (approximately the same impact as an additional week of gestation at term).

Armed with these data, we next compared the genome-wide patterns of birth weight association, with those for late onset diseases such as diabetes, high blood pressure and coronary heart disease. We found substantial overlap between them ─ evidence that genetic variation does indeed contribute to the relationship between early growth and later disease risk. Indeed, in some more detailed (but still somewhat preliminary) analyses, we found that most of the relationship between abnormal early growth and later disease could be explained by shared genetic factors. We were able to show that these genetic factors were acting through a series of shared processes to influence early growth and later disease, including those connected to metabolism, growth and development.

Crucially, we were able to start to look beyond the simple relationships between the child’s genetic profile, their early growth potential, and their risk of later disease. When considering events occurring before birth, one also needs to consider the essential contribution made by the mother to the growth of the fetus. Not only is the baby’s growth dependent on the baby’s own genetic profile, it is also heavily influenced by the gestational environment that the mother provides: that maternal environment will in turn be influenced by the mother’s genetic profile. And, since each baby inherits half of its genes from its mother, the maternal and offspring genetic profiles will partly overlap with one another.

This sets up a complex web of interacting influences, which we need to disentangle (see our other blogs). Could the overlap we detected between the baby’s genetic profile and their future risk of diabetes and heart disease be driven, not by the impact of those genetic variants within the child, but instead through their shared presence in the mother and mediated through their influence on the maternal environment? Rather than a direct effect of those genetic differences on both early growth and later disease, could those genetic differences have their dominant effect at the level of the mother, with only indirect effects on fetal growth and disease risk?

Those are relatively simple questions to pose, but difficult ones to answer. To do so requires large collections of samples, most obviously from offspring and their mothers (and ideally their fathers), that are only now starting to emerge. On the basis of the analyses that we have been able to do so far, the headline answer we can offer is that both direct (fetal) and indirect (maternal) mechanisms are involved, but that the former seems to be more important than the latter. In other words, the more important mechanism linking fetal genotype with adult disease risk involves the direct effects of the child’s genotype on both early growth and later disease.

But this is most definitely not the entire story. When we looked at the links between early growth and diabetes, a complex picture emerged. Some of the genetic differences that we know are responsible for an increased risk of diabetes in adulthood were associated with higher birth weight in earlier life. However, others were clearly associated with lower birth weight.

We believe (and have evidence!) that this reflects competition between two processes. In the baby, diabetes risk alleles have a direct effect that reduces growth. This is because those variants reduce insulin levels (or the sensitivity of tissues to the actions of insulin), which in turn reduce insulin-mediated early growth: many decades later, those same alleles increasing the risk of later diabetes. At the same time, exactly the same alleles, when present in the mother, put that mother at increased risk of gestational diabetes, leading to increased transfer of sugars across the placenta, stimulating the fetus to produce more insulin, and promoting fetal growth. These two oppositional processes play out in different ways for different genetic variants, depending on the variants’ relative impact in early and later life.

Summary of key relationships: Black arrows reflect purely genetic mechanisms, connecting parental to offspring genes, with shared impacts on fetal growth and later disease risk. Red arrows reflect the DoHAD programming model whereby intrauterine environment influences early growth and leads to long term programming effects on future health. In the joint model that emerges from our data, we add (green arrows) the impact of maternal genotype on intrauterine environment, and emphasise (blue arrow) that the baby’s birthweight is a readout of growth, but not of itself on the causal pathway to future disease risk.

Summary of key relationships: Black arrows reflect purely genetic mechanisms, connecting parental to offspring genes, with shared impacts on fetal growth and later disease risk. Red arrows reflect the DoHAD programming model whereby intrauterine environment influences early growth and leads to long term programming effects on future health. In the joint model that emerges from our data, we add (green arrows) the impact of maternal genotype on intrauterine environment, and emphasise (blue arrow) that the baby’s birthweight is a readout of growth, but not of itself on the causal pathway to future disease risk.

Much work remains to be done to further tease apart these processes, but our data provide the strongest evidence yet that direct genetic effects and indirect environmental influences (some of them also secondary to maternal genetic effects) interplay to regulate early growth and influence risk of disease in later life. As a result of this study, we have a clearer idea, a “road map”, for how we should frame and focus future studies to tackle this complexity. In particular, we are planning more genetic analyses of family data (children and their mothers and fathers).

How does this research impact on clinical care? At the heart of this research is a desire to better understand the mechanisms underlying the development of diabetes, obesity and related conditions. This is basic research, but fundamental to developing improved strategies for the prevention and treatment of these conditions.

More immediately, this research matters because, according to the DOHaD hypothesis, improvements in antenatal care and the alleviation of intrauterine deprivation will eventually help to forestall the rising prevalence of diabetes and related conditions. Ongoing research that allows us to quantify the contributions of the various mechanisms that influence the relationship between early growth and subsequent diabetes should provide a better sense as to how far we should expect advances in pregnancy care to achieve those goals.

Sir Henry Dale Fellow and Senior Research Fellow, University of Exeter.

Robert Turner Professor of Diabetic Medicine, Group Head, Wellcome Trust Centre for Human Genetics, Group Head / PI, Grant Holding Senior Scientist, Consultant Physician.

By Mark McCarthy & Rachel Freathy.

Today, we published a paper in Nature in which we explore the genetic contribution to variation in birth weight. We describe how this information can help us to tease apart the contributions made by nature (i.e. inherited genetic variation) and nurture (i.e. the sum of environmental exposures) to the observed relationships between growth in early life and the predisposition to diseases such as diabetes and hypertension many decades later. A couple of blogs describing our main findings are available here and here.

At the heart of the study was a genome wide association study (GWAS) of birth weight involving over 150,000 people for whom we had information on birth weight and on genetic variations throughout the genome (genome-wide genotypes). This wasn’t the first such GWAS effort for birth weight, but it was by far the largest. The EGG consortium which has been leading these analyses for the past six years or so, had published previous GWAS in 2010 and 2013. These studies had involved ~11,000 and ~27,000 individuals in their GWAS discovery stages, and identified two, and seven, birth weight association signals, respectively.

Since the 2013 paper, the EGG consortium effort had been steadily growing as more and more data sets with birth weight phenotypes were genotyped, and since the researchers responsible for those data sets had generously agreed to share those data. The motivation here, of course, is that larger sample sizes typically bring greater power to detect additional signals of lesser effect.

By the time we were ready to kick off the meta-analysis that, at the end of the day, provided around half the data contributing to the current (2016) paper, we had gathered GWAS and birth weight data on over 70,000 individuals. For the most part, these were birth cohorts, bringing the expectation that the birth weight phenotypes to which we had access would be of “high” quality. The birth measurements had been recorded contemporaneously by medical personnel, and additional data were available that allowed us to exclude potential outliers (eg twin and/or preterm births) and to adjust for important factors that influence birth weight (most obviously, gestational age). The increase in GWAS sample size (from ~27,000 to 70,000) had the desired effect, growing the number of genome-wide significant loci for birth weight from the 7 reported in 2013 to around 20.

It was while we were compiling this “traditional” GWAS meta-analysis that the first tranche of GWAS data from UK Biobank was scheduled for release. We knew that around 50% of UK Biobank participants had provided self-reported birth weight data as part of the medical survey conducted at recruitment. Several of the investigators in EGG already had approvals in place to examine birth weight (and related metabolic phenotypes). It seemed natural to consider whether we could include these data in the meta-analysis. With that first tranche of GWAS data including nearly 150,000 individuals (half of them with birth weight), might we be able to double the size of our meta-analysis in one fell stroke?

UK Biobank, for those who aren’t familiar, enrolled, between 2006 and 2010, around 500,000 subjects from the UK, aged 40 to 69 years, to participate in a study of the contributions of genes and environment to human disease. All participants took part in a detailed clinical examination, answered a series of computer-based surveys about diverse aspects of lifestyle and health, and donated biosamples (blood, urine). All agreed to have their biobank data linked to evolving health information collected from hospital episode statistics, registry information and other sources. Selected groups of participants were targeted for repeat visits, and other subgroups for more intensive phenotypic analysis including measurements of physical activity and imaging (MRI). All 500,000 have genome-wide genotype data available. A bespoke genotyping array was used for the genotyping. It was designed to optimise genome-wide coverage of variation in UK populations (through imputation), whilst also being enriched for putatively functional genetic variants such as those in the coding regions of the DNA. (These data are being released in two tranches: the 150,000 we used here, and another 350,000 due to be released in the next few months). The scale, scope and diversity of the data within UK Biobank are remarkable in terms of the types of questions that can be addressed, many of them for the first time. The accessibility of the resource to the global research community has resulted in a data set that has rapidly become transformative for many research groups.

Inevitably, however, not all information is recorded with equal precision in UK Biobank, and birth weight might have been a case in point. The birth weight measures were based entirely on self-report (based on information presumably recalled after an interval of several decades) raising concerns about precision. Crucially, gestational age was not available for adjustment. Worryingly, the distribution of raw BW results was decidedly non-uniform (see figure), presumably reflecting digit preference in recollected birth weight. In other words, many people would have recalled their birth weight as an imperial unit integer (e.g. “around 7 lbs”). Information on birth weight was only present in around half of participants (and only 8% of those eligible had agreed to participate in UK Biobank in the first place), raising questions about representation. The contrast with the meticulously collected birth weight phenotype data available from the other EGG cohorts was marked.

Distribution of Birthweight in 277,070 individuals from UK Biobank

Distribution of Birthweight in 277,070 individuals from UK Biobank

Given these concerns, we were uncertain how to proceed. Would the increased sample size afforded by bringing UK Biobank and EGG together really improve our prospects for detecting real genetic associations? Or would combining the two types of data be detrimental to our power, with the less precise UK Biobank data diluting out true signals emerging from EGG?

There were some reasons for believing that we could rely on the UK Biobank data. Using the full UK Biobank dataset, one of us (RF) working with colleagues in Exeter, had shown that the UK Biobank birth weight data had “face validity” based on the observed relationships to exposures known, from other epidemiological studies, to influence birth weight. So, UK Biobank birth weight measures showed expected variation with regard to the gender and ethnicity of the baby. Babies born to mothers who smoked during pregnancy were smaller than those born to mothers who did not. Of particular interest to us, UK Biobank participants who reported a paternal history of diabetes had birth weights below the average for the entire data set; whilst those with a maternal history, tended to be on the large size. All of these observations encouraged us to have confidence in the robustness of the UK Biobank data.

To decide the best approach for the integration of the EGG and UK Biobank results, we set ourselves a test (and, crucially, agreed to be bound by the outcome). We considered the seven regions of the genome we had first reported to be associated with birth weight in the 2013 paper and compared the association effect sizes observed in the UK Biobank data set with those reported in the earlier paper. Bearing in mind the latter would have benefited from some degree of winners’ curse over-inflation, we agreed that, provided the effect sizes in UK Biobank exceeded (on average) 70% of those seen in the better-curated EGG birth cohorts, we would be safe to proceed to a full meta-analysis. (The alternative would have been to do some variation on the mutual “top-hit look-up” strategy). We were reassured to see directionally consistent replication of all seven signals in UK Biobank, with effect size estimates around 75% of those seen in the birth cohorts.

On the basis of these findings, we proceeded to a full meta-analysis of the 150,000 samples. We quickly felt vindicated. The number of genome-wide significant loci jumped from 20 to 60. We found no evidence of heterogeneity of effect size for those loci between the European EGG cohorts and UK Biobank.

Manhattan Plot for Birthweight GWAS in 150k individuals

Manhattan Plot for Birthweight GWAS in 150k individuals

So how is it that so much information could be extracted from what appeared such a messy and imprecise phenotype? There are some lessons here that speak to the intrinsic value of UK Biobank as well as to some of the potential limitations of GWA meta-analysis approaches that build signal from multiple smaller cohorts. These lessons extend well beyond measures of birth weight.

Essentially, UK Biobank offers a “strength in numbers” that can compensate for what may appear quite marked phenotypic imprecision. Those generic benefits include:

  • A (largely) representative population sample size of 500,000;
  • Unified phenotype (participant characteristic) collection with harmonized protocols and careful study-wide QC;
  • Unified genotyping with a single efficient bespoke GWAS array, followed by a single imputation run with the same reference samples, and the capacity for a single, centralised analysis;
  • A wealth of phenotypes that can used for adjustment, inference and exploration.

These contrast with the heterogeneity of phenotypes, covariate adjustments, arrays, populations, imputation reference panels and analysis methods (and analysts!) that are a feature of many GWAS meta-analysis. As those GWAS meta-analyses get larger and more cumbersome (some involve over 100 participating studies) it is inevitable that, despite the best efforts of everyone involved, heterogeneity and errors creep in that can lead to some attenuation of the true association signal.

That is not to argue against the value of the GWAS meta-analysis approach. Rather it is to make the rather obvious point that, within reason, the more data the better. Our experience with birth weight demonstrates that the judicious combination of both kinds of data can prove hugely rewarding in terms of our ability both to discover genetic loci, and to characterise their impact.

There’s little wonder that UK Biobank has established itself so rapidly as a foundational resource for medical research, and that it has spurred the development of analogous data sets in many other countries. With GWAS data on the remaining 350,000 participants due in the coming months, along with a swath of biochemical measurements, and with the prospect of ever deeper genetic and genomic characterisation, the impact of this study is set to grow.

Sir Henry Dale Fellow and Senior Research Fellow, University of Exeter.

Robert Turner Professor of Diabetic Medicine, Group Head, Wellcome Trust Centre for Human Genetics, Group Head / PI, Grant Holding Senior Scientist, Consultant Physician.

By Rachel Freathy & Mark McCarthy.

With colleagues in the EGG (Early Growth Genetics) Consortium, we have published a paper in Nature describing genetic analyses of birth weight. We have written about our main findings in a separate post. Here, we consider in more detail the genetic influences of mother and child, and how we can better understand their respective roles in early growth and later life disease.

Birth weight is influenced not only by the baby’s genes (inherited from mother and father), but also by the mother’s genes (that the baby may or may not not inherit), because those genes influence the womb environment.

In our study, we analysed information on birth weight in relation to genetic differences between more than 150,000 study participants. We showed that many of the same genetic differences that influence a person’s birth weight also influence their susceptibility to later life disease. Our initial calculations suggested that genetics makes a large contribution to the link between birth weight and adult diseases such as type 2 diabetes and high blood pressure. On the surface, that is a straightforward conclusion: a primary role for genetics might lead to a downplaying of the role of the maternal environment in programming the developing baby’s later risk of disease (see our other post. But the reality is more complex: to build a full picture of what is going on, we need also to consider the essential contribution made by the mother to the growth of the baby.

A baby’s growth is influenced by its own genetic profile, but crucially, it is heavily influenced by the gestational environment that the mother provides, and that maternal environment is in turn influenced by the mother’s genetic profile. Since each baby inherits half of its genes from its mother, the mother and baby’s genetic profiles will partly overlap with one another (see Figure 1 for summary of relationships). So, in our study, when we analysed the genetics of the study participants, we were also capturing some information about their mothers’ genetics. The key question for us was: what proportion of the genetic variation influencing birth weight (and later life disease) is acting directly, having been inherited by the child, and what proportion is acting indirectly, through the gestational environment, under the influence of the mother’s genes?

Figure 1: Summary of key relationships Black arrows reflect purely genetic mechanisms, connecting parental to offspring genes, with shared impacts on fetal growth and later disease risk. Red arrows reflect the DoHAD programming model whereby intrauterine environment influences early growth and leads to long term programming effects on future health. The green arrows represent the impact of maternal genotype on intrauterine environment, and the blue arrow indicates that the baby’s birthweight is a readout of growth, but not of itself on the causal pathway to future disease risk. Possible scenarios leading to an observed association between high/low birth weight and risk of later disease (these are not mutually exclusive): Developmental programming (red arrows) Direct genetic influences on birth weight are the same as those influencing later disease (black arrows) Maternal genetic effects influence baby’s growth via the womb environment, and those same genes, if inherited by the fetus, influence disease risk directly (thicker green, red and black arrows) Figure 1: Summary of key relationships
Black arrows reflect purely genetic mechanisms, connecting parental to offspring genes, with shared impacts on fetal growth and later disease risk. Red arrows reflect the DoHAD programming model whereby intrauterine environment influences early growth and leads to long term programming effects on future health. The green arrows represent the impact of maternal genotype on intrauterine environment, and the blue arrow indicates that the baby’s birthweight is a readout of growth, but not of itself on the causal pathway to future disease risk.

Possible scenarios leading to an observed association between high/low birth weight and risk of later disease (these are not mutually exclusive):

    1. Developmental programming (red arrows)

 

    1. Direct genetic influences on birth weight are the same as those influencing later disease (black arrows)

 

    1. Maternal genetic effects influence baby’s growth via the womb environment, and those same genes, if inherited by the fetus, influence disease risk directly (thicker green, red and black arrows)

 

 

To unravel the genetic effects of child and mother on birth weight, we included mothers in our analyses and found evidence for a greater genetic contribution from the child.

In our study, we would ideally have had access to genetic information from the mothers of all study participants. However, studies on such a scale are not yet possible. We began to tackle the question using available resources.

First, we analysed genetic variations throughout the genomes of 4382 mother-child pairs from the UK-based Avon Longitudinal Study of Parents and Children (ALSPAC) using a “maternal-genome-wide complex trait analysis” (m-GCTA). This analysis enabled us to estimate the overall contribution to birth weight variation made by the mother’s vs. the baby’s genetics. We estimated that the contribution of direct genetic effects from the baby was larger (24% of overall variation in birth weight) than either the contribution of the mother’s genetic effects (4% of overall variation), or the joint contribution of mother and baby’s genetics (4% of overall variation). This is a useful first estimate, but it should be noted that the 4382 mother-child pairs are a relatively small sample in this context. So these results are preliminary estimates, which require confirmation in larger samples.

In addition to taking a global view, we were interested in the relative genetic contribution from mother and child to birth weight at the 60 specific regions of the genome identified in our study. For each region, we wanted to know whether the effect on birth weight was coming directly from the baby’s genetics, or whether it was in fact coming from the influence of the mother’s genetics on the gestational environment. Due to the overlap between the mother and baby’s genetic profiles, it was possible that our study of the baby’s genetics was in fact picking up a primary effect from the mother. We compared the size of the genetic effect on birth weight of the baby (from our study) with that of the mother (in a separate analysis of more than 68,000 women). We found that the baby’s genetic variation had a greater impact than the mother’s at the vast majority (93%) of those genetic regions.

Depending on the adult trait, we see different patterns of relative genetic contribution from mother and child.

The above analyses gave us some insight into the relative contributions made by the genetics of mother and baby to birth weight itself. But what about their relative contributions to the link between birth weight and adult diseases? It is possible that the pattern is different, depending on the adult disease in question. In Figure 1, we set out three scenarios (not mutually exclusive) by which an observed relationship between birth weight and later disease may arise. Each of these involves a different pattern of genetic contribution from mother and baby:
(i) if developmental programming is the only explanation, the mother’s genes would influence birth weight and later disease indirectly through the womb environment, with no direct effects of the baby’s genetics;
(ii) if birth weight and later disease are two “readouts” of the same genetic effects in the baby, there should be no indirect contribution of maternal genetics;
(iii) an association between birth weight and later disease could also arise if maternal genetic variations influence baby’s growth via the womb environment, and those same genetic variations, when inherited by the fetus, influence the disease risk directly.

We explored these scenarios further, selecting genetic variations involved in each of three adult characteristics/diseases: height, type 2 diabetes and blood pressure.

Our (and others’) analyses of height genetics show that only the baby’s inherited genes influence birth weight.

Taller parents tend to have longer babies, and we showed in our study that there is a strong overlap between the genetics of a baby’s size at birth and the genetics of their adult height. Using mother-child pairs from the ALSPAC study again, we selected 422 genetic variations known to influence adult height and compared the collective effects on birth weight of those genetic variations inherited by the child with those in the mother that were not inherited. We concluded, as had others before that only the inherited genetic variations influenced birth weight, and not the non-inherited ones. These results suggest it is unlikely that a mother’s adult height genetics have any influence on the weight of her baby other than through being inherited by the baby. (In theory, a taller mother could influence the size of her baby non-genetically by providing a larger space for growth, but we have no evidence to support this.) The link between birth weight and adult height is consistent with scenario (ii) above.

Analyses of type 2 diabetes genetics show that the baby’s inherited genes tend to reduce birth weight, while the non-inherited genes in the mother tend to increase birth weight.

Type 2 diabetes shows a U-shaped association with birth weight, in that both small and large babies are at greater risk of developing the disease in later life than those with birth weights close to the average. For some time, we and others have been trying to understand how genetics might contribute to these associations.

As mentioned in our other post, the idea that genetic differences could explain both lower birth weight and type 2 diabetes was first put forward in 1999, under the fetal insulin hypothesis. Since that time, our ability to perform larger and larger genetic association studies has strengthened evidence in support of that hypothesis, with genetic variations in particular regions of the baby’s DNA (for example, near the CKDAL1, HHEX-IDE and ADCY5 genes explaining some of the link between lower birth weight and their later risk of type 2 diabetes, most likely through their effects on insulin: lower insulin production by a growing fetus results in reduced growth, while lower insulin production as an adult can predispose to diabetes. In our latest study, analyses in the ALSPAC mother-child pairs of 84 genetic variations that predispose to type 2 diabetes showed a collective effect on reduced birth weight of variations inherited by the child. Moreover, our analyses of the global genetic contribution to the link between lower birth weight and type 2 diabetes lent support for genetics contributing to a large proportion of this association (consistent with scenario (ii), above). However, our global analyses were preliminary and we were unable to model the U-shaped relationship or account for maternal genetics. Further, more detailed studies are needed to confirm our initial estimates.

On the other hand, evidence has been accumulating that genetic variations in mothers, at genomic regions known to predispose to diabetes (near the GCK and TCF7L2 genes are associated with higher birth weight of their babies. This is likely to be because they put that mother at increased risk of elevated glucose levels during pregnancy, so that more sugar is transferred across the placenta, stimulating the fetus to make more insulin, and grow bigger as a result. The baby may be at higher risk of diabetes in later life due to inheriting the genetic risk from the mother (i.e. consistent with scenario (iii) above), or alternatively as a result of exposure to high glucose levels in the womb, as has been shown in other studies (consistent with scenario (i) above).

Our analyses of blood pressure genetics suggest that both the baby’s inherited genes and the non-inherited genes in the mother tend to reduce birth weight

Women with higher blood pressure in pregnancy tend to have lower birth weight babies. Our analyses showed strong overlap between the genetics of blood pressure and birth weight, with those variations that predispose to high blood pressure being associated with lower birth weight. As with type 2 diabetes, our preliminary analyses suggested a large proportion (an estimated 85%) of the observed association between birth weight and blood pressure being attributable to genetics. When we attempted to separate the contribution of genetics of mother and baby using the ALSPAC mother-child pairs, we found evidence to support a contribution of both. Recent work in larger numbers of women also supports a strong contribution of the mother’s genetics. So, for blood pressure, current data are consistent with all three scenarios in Figure 1 being in play: (i) higher maternal blood pressure results in reduced growth and corresponding developmental changes that programme higher blood pressure in adulthood; (ii) genetic variation inherited by the baby predisposes both to reduced growth and to higher blood pressure in later life; (iii) genetic variation raising blood pressure in the mother causes reduced growth, possibly due to reduced placental function, while the same variations inherited by the child raise blood pressure in adulthood.

To conclude: it’s complex!

The results of our study suggest that genetics make a large contribution to the link between birth weight and adult diseases such as type 2 diabetes and high blood pressure. However, we hope to have illustrated in this blog post that this is just the beginning of the story, and we are certainly not saying “it’s all genetics”. The genetic contributions of mother and baby must be further unraveled, and the particular relationships between birth weight and individual adult diseases and traits should be considered separately, before we can build a clear picture. Ultimately, we aim to understand just how much of adult disease risk is under the influence of factors in the gestational environment that are potentially modifiable, because that will inform us how far it will be possible can prevent these diseases through improvements in antenatal care. It is worth clarifying that we are not saying that the value of antenatal care is in dispute. The case for good antenatal care is clear in terms of the impact on the immediate health of mother and baby. What is uncertain is the extent to which improvements in antenatal care that leave more babies appropriately nourished during pregnancy will feed forward into reduced rates of diabetes in the decades ahead, and it is crucial that we understand this.

These are the open questions we would like to tackle next

  • How much of the link between birth weight and later disease is due to genetic vs non-genetic factors? It looks, from our initial analyses, as if quite a lot is genetic, but we need larger samples and the ability to model non-linear (e.g. U-shaped) associations.
  • How much of birth weight variation per se, and how much of the link between birth weight and later disease, is due to fetal vs. maternal genetic factors? We need larger studies of mothers and babies, along with information on adult disease outcomes in the offspring, to resolve this.
  • Are the mother’s and father’s genomes equivalent in terms of their impact on the baby’s genetics in relation to birth weight? We know that at some genes (“imprinted genes”), there is a greater genetic contribution from one parent or the other, but to date, we do not know the extent of such imbalance.
  • Are the observed relationships between aspects of the gestational environment (e.g. mother’s BMI) and birth weight causal? We have begun to use genetics to investigate this.

Excitingly, we have the prospect of more and larger resources that are becoming available to answer these questions. The next tranche of genotype data from the UK Biobank (see our other post) is coming soon, and within our EGG Consortium collaboration, there are more and more studies with genetic data available on both mother and child. We are therefore confident that we will have answers to all the above questions in the months and years ahead.

Sir Henry Dale Fellow and Senior Research Fellow, University of Exeter.

Robert Turner Professor of Diabetic Medicine, Group Head, Wellcome Trust Centre for Human Genetics, Group Head / PI, Grant Holding Senior Scientist, Consultant Physician.