Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data.

As custom arrays are cheaper than generic GWAS arrays, larger sample size is achievable for gene discovery. Custom arrays can tag more variants through denser genotyping of SNPs at associated loci, but at the cost of losing genome-wide coverage. Balancing this trade-off is important for maximizing experimental designs. We quantified both the gain in captured SNP-heritability at known candidate regions and the loss due to imperfect genome-wide coverage for inflammatory bowel disease using immunochip (iChip) and imputed GWAS data on 61,251 and 38.550 samples, respectively. For Crohn's disease (CD), the iChip and GWAS data explained 19 and 26% of variation in liability, respectively, and SNPs in the densely genotyped iChip regions explained 13% of the SNP-heritability for both the iChip and GWAS data. For ulcerative colitis (UC), the iChip and GWAS data explained 15 and 19% of variation in liability, respectively, and the dense iChip regions explained 10 and 9% of the SNP-heritability in the iChip and the GWAS data. From bivariate analyses, estimates of the genetic correlation in risk between CD and UC were 0.75 (SE 0.017) and 0.62 (SE 0.042) for the iChip and GWAS data, respectively. We also quantified the SNP-heritability of genomic regions that did or did not contain the previous 163 GWAS hits for CD and UC, and SNP-heritability of the overlapping loci between the densely genotyped iChip regions and the 163 GWAS hits. For both diseases, over different genomic partitioning, the densely genotyped regions on the iChip tagged at least as much variation in liability as in the corresponding regions in the GWAS data, however a certain amount of tagged SNP-heritability in the GWAS data was lost using the iChip due to the low coverage at unselected regions. These results imply that custom arrays with a GWAS backbone will facilitate more gene discovery, both at associated and novel loci.

Mining the human phenome using allelic scores that index biological intermediates.

It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.

Genetic variation in the 15q25 nicotinic acetylcholine receptor gene cluster (CHRNA5-CHRNA3-CHRNB4) interacts with maternal self-reported smoking status during pregnancy to influence birth weight.

Maternal smoking during pregnancy is associated with low birth weight. Common variation at rs1051730 is robustly associated with smoking quantity and was recently shown to influence smoking cessation during pregnancy, but its influence on birth weight is not clear. We aimed to investigate the association between this variant and birth weight of term, singleton offspring in a well-powered meta-analysis. We stratified 26 241 European origin study participants by smoking status (women who smoked during pregnancy versus women who did not smoke during pregnancy) and, in each stratum, analysed the association between maternal rs1051730 genotype and offspring birth weight. There was evidence of interaction between genotype and smoking (P = 0.007). In women who smoked during pregnancy, each additional smoking-related T-allele was associated with a 20 g [95% confidence interval (95% CI): 4-36 g] lower birth weight (P = 0.014). However, in women who did not smoke during pregnancy, the effect size estimate was 5 g per T-allele (95% CI: -4 to 14 g; P = 0.268). To conclude, smoking status during pregnancy modifies the association between maternal rs1051730 genotype and offspring birth weight. This strengthens the evidence that smoking during pregnancy is causally related to lower offspring birth weight and suggests that population interventions that effectively reduce smoking in pregnant women would result in a reduced prevalence of low birth weight.

Calculating statistical power in Mendelian randomization studies.

In Mendelian randomization (MR) studies, where genetic variants are used as proxy measures for an exposure trait of interest, obtaining adequate statistical power is frequently a concern due to the small amount of variation in a phenotypic trait that is typically explained by genetic variants. A range of power estimates based on simulations and specific parameters for two-stage least squares (2SLS) MR analyses based on continuous variables has previously been published. However there are presently no specific equations or software tools one can implement for calculating power of a given MR study. Using asymptotic theory, we show that in the case of continuous variables and a single instrument, for example a single-nucleotide polymorphism (SNP) or multiple SNP predictor, statistical power for a fixed sample size is a function of two parameters: the proportion of variation in the exposure variable explained by the genetic predictor and the true causal association between the exposure and outcome variable. We demonstrate that power for 2SLS MR can be derived using the non-centrality parameter (NCP) of the statistical test that is employed to test whether the 2SLS regression coefficient is zero. We show that the previously published power estimates from simulations can be represented theoretically using this NCP-based approach, with similar estimates observed when the simulation-based estimates are compared with our NCP-based approach. General equations for calculating statistical power for 2SLS MR using the NCP are provided in this note, and we implement the calculations in a web-based application.

What are the causal effects of breastfeeding on IQ, obesity and blood pressure? Evidence from comparing high-income with middle-income cohorts.

BACKGROUND: A novel approach is explored for improving causal inference in observational studies by comparing cohorts from high-income with low- or middle-income countries (LMIC), where confounding structures differ. This is applied to assessing causal effects of breastfeeding on child blood pressure (BP), body mass index (BMI) and intelligence quotient (IQ). METHODS: Standardized approaches for assessing the confounding structure of breastfeeding by socio-economic position were applied to the British Avon Longitudinal Study of Parents and Children (ALSPAC) (N ≃ 5000) and Brazilian Pelotas 1993 cohorts (N ≃ 1000). This was used to improve causal inference regarding associations of breastfeeding with child BP, BMI and IQ. Analyses were extended to include results from a meta-analysis of five LMICs (N ≃ 10 000) and compared with a randomized trial of breastfeeding promotion. Findings Although higher socio-economic position was strongly associated with breastfeeding in ALSPAC, there was little such patterning in Pelotas. In ALSPAC, breastfeeding was associated with lower BP, lower BMI and higher IQ, adjusted for confounders, but in the directions expected if due to socioeconomic patterning. In contrast, in Pelotas, breastfeeding was not strongly associated with BP or BMI but was associated with higher IQ. Differences in associations observed between ALSPAC and the LMIC meta-analysis were in line with those observed between ALSPAC and Pelotas, but with robust evidence of heterogeneity detected between ALSPAC and the LMIC meta-analysis associations. Trial data supported the conclusions inferred by the cross-cohort comparisons, which provided evidence for causal effects on IQ but not for BP or BMI. CONCLUSION: While reported associations of breastfeeding with child BP and BMI are likely to reflect residual confounding, breastfeeding may have causal effects on IQ. Comparing associations between populations with differing confounding structures can be used to improve causal inference in observational studies.

Maternal macronutrient and energy intakes in pregnancy and offspring intake at 10 y: exploring parental comparisons and prenatal effects.

BACKGROUND: High maternal dietary intakes in pregnancy may lead to increased fetal growth and program neuroendocrine pathways that result in greater appetite, energy intake, and adiposity in offspring later in life. Few prospective dietary studies have explored this relation. OBJECTIVE: The objective was to assess associations of maternal dietary intake in pregnancy and maternal and paternal dietary intake postnatally with child dietary intake and adiposity. DESIGN: Dietary intakes of energy, protein, total fat, and carbohydrate were assessed prospectively in mothers during pregnancy, in mothers and their partners at 47 mo postnatally, and in children at 10 y (n = 5717 mother-child pairs prenatally, 5593 mother-child pairs postnatally, and 3009 father-child pairs). Child body composition was assessed at 9 and 11 y (n = 5725). RESULTS: Maternal dietary intakes of protein, fat (when adjusted for energy intake), and carbohydrate in pregnancy were positively associated with child dietary intakes of the same nutrients, and these associations were greater than those observed for paternal dietary intake, which was not strongly associated with offspring diet. Associations of maternal prenatal-offspring intakes were stronger than those of maternal postnatal-offspring intakes for protein and fat. Greater child energy and macronutrient intakes were only associated with greater adiposity in children when adjusted for potential energy underreporting. Maternal diet during pregnancy was not associated with offspring adiposity or lean mass. CONCLUSION: The stronger prenatal maternal associations with child dietary intake, particularly protein and fat, compared with both paternal intake associations and maternal postnatal intake associations provide some evidence for in utero programming of offspring appetite by maternal intake during pregnancy.

Modifiable maternal exposures and offspring blood pressure: a review of epidemiological studies of maternal age, diet, and smoking.

Prenatal programming of adult disease is well established in animals. In humans the impact of common in utero exposures on long-term offspring health is less clear. We reviewed epidemiology studies of modifiable maternal exposures and offspring blood pressure (BP). Three maternal exposures were identified for review and meta-analyzed where possible: smoking during pregnancy, diet, and age at childbirth. Meta-analysis suggested there was a modest association between higher offspring BP and prenatal exposure to smoke (confounder-adjusted beta = 0.62 mm Hg, 95% confidence interval: 0.19-1.05, I = 16.4%). However, the level of confounder adjustment varied between studies, which in some studies attenuated the association to the null. There was no strong evidence that any component of maternal diet during pregnancy (maternal protein, energy, calcium, and various other nutrients) influences offspring BP. The results of studies of maternal age varied and there was strong evidence of heterogeneity in the pooled analysis. The association with maternal age, if present, was modest (confounder-adjusted beta = 0.09 mm Hg/y, 95% confidence interval: -0.03 to 0.21, I = 89.8%). In sum, there is little empirical evidence that the maternal exposures reviewed program offspring BP. Other components of offspring health may be more susceptible to effects of programming in utero.

Maternal anemia, iron intake in pregnancy, and offspring blood pressure in the Avon Longitudinal Study of Parents and Children.

BACKGROUND: In animals, maternal iron deficiency during pregnancy results in elevated offspring blood pressure (BP). Studies in pregnant women are limited in number, have had inconsistent results, and have not accounted for maternal iron supplementation. OBJECTIVE: The objective was to assess the association between maternal iron status during pregnancy and offspring BP. DESIGN: Maternal hemoglobin (n = 1255), iron supplementation (n = 7484), food-based iron intake (n = 7130), and offspring BP were assessed in a prospective cohort at 7 y of age. RESULTS: Maternal anemia during pregnancy was associated with lower systolic BP in the offspring at 7 y of age (third trimester, age- and sex-adjusted: beta = -1.09; 95% CI: -2.21, -0.05 mm Hg; P = 0.04). Adjustment for confounders attenuated this association (beta = -0.49; 95% CI: -1.71, 0.72 mm Hg; P = 0.4). In women who did not take iron supplements during pregnancy, the observed association with maternal anemia was even stronger: minimally adjusted models (beta = -2.11; 95% CI: -3.61, -0.61 mm Hg; P = 0.006) and fully adjusted models (beta = -1.48; 95% CI: -3.21, 0.25 mm Hg; P = 0.09). Iron supplementation was not associated with offspring BP after confounding by multivitamin intake was accounted for, and no association with iron intake from food was observed. CONCLUSION: In contrast with animal studies, maternal iron intake during pregnancy is not associated with offspring BP, and some evidence indicates that maternal anemia in contemporary pregnant women is associated with lower offspring BP. It is possible that, in well-nourished populations, low hemoglobin is more likely to reflect greater plasma volume expansion (and thus better maternal and offspring health) than iron deficiency.

Similar associations of parental prenatal smoking suggest child blood pressure is not influenced by intrauterine effects.

Maternal smoking in pregnancy may be associated with higher offspring blood pressure; however, results of previous studies have been inconsistent and included varying confounder adjustments. We studied the association between maternal smoking in pregnancy and offspring blood pressure at 7 years in the Avon Longitudinal Study of Parents and Children, accounting for important social and environmental confounders and using partner smoking to investigate intrauterine effects. Analysis was carried out in 6509 children with maternal smoking data and 7149 children with partner smoking data. In models adjusting for child age and sex, modest differences in systolic blood pressure were observed between children of mothers who did and did not smoke during pregnancy (beta=0.64 mm Hg; 95% CI: 0.09 to 1.20; P=0.02). Adjusting for all of the confounders attenuated this difference toward the null (beta=0.05 mm Hg; 95% CI: -0.59 to 0.68; P=0.9), mostly because of adjustment for breastfeeding, maternal education, and family social class. Associations were similar between maternal and partner smoking with offspring systolic blood pressure (for partner smoking: beta=0.62 mm Hg; 95% CI: 0.17 to 1.07; P=0.07 minimally adjusted and beta=0.26 mm Hg; 95% CI: -0.36 to 0.87; P=0.4 fully adjusted), providing further evidence that differences in child blood pressure observed in minimally adjusted models are not because of a biological influence of maternal smoking on the intrauterine environment.