AIMS:

Aim 1: Genome-wide bivariate association analysis of birth weight and key quantitative phenotypes from the 20 year follow-up studies within Raine and ALSPAC.

To date, two genome-wide association studies (GWAS) have been conducted for birth weight (involving the investigators of this proposal), which identified seven single nucleotide polymorphisms (SNPs) that effect birth weight (14, 15). Two of these SNPs are in genes previously associated with type 2 diabetes, ADCY5 and CDKAL1, highlighting the genetic links between fetal growth and postnatal metabolism. At both of these loci, the birth weight lowering allele was associated with increased risk of type 2 diabetes. In addition, the authors investigated a further 47 type 2 diabetes associated variants and found the type 2 diabetes risk alleles at GCK and MTNR2B were associated with higher birth weight. These results indicate that there may be multiple genetic pathways which link birth weight to adult disease. A further link with the DOHAD hypothesis was through the ADRB1 gene, a locus which was associated with birth weight and adult blood pressure. Of the final four SNPs, two were in height associated genes, indicating a continued link with growth over the postnatal period. Although these SNPs found to date for effecting birth weight have pleiotropic effects with risk of disease in adults, the percentage of variance explained by these confirmed loci (0.76%) is lower than many other complex phenotypes (for example, 1.45% for BMI (16), 5.8% for BMD at the femoral neck (17)). Given twin and family studies estimated heritability of birth weight from the fetal genome to be between 10 and 30% (18, 19), there is still a substantial genetic component to be described.

Bivariate genetic association analysis has been shown to have increased statistical power over univariate analysis to detect genetic variants that contribute pleiotropically to the two phenotypes in the opposite direction to the prevailing phenotypic correlation (20-22). This approach is ideal for detecting variants that underlie DOHAD. We will conduct a bivariate genome-wide analysis of birth weight and each of our key phenotypes, with the hypothesis of increasing the statistical power to identify novel genetic variants that have pleiotropic effects.

Aim 2: Gene-based bivariate analysis of birth weight and key quantitative phenotypes from the 20 year follow-up studies within Raine and ALSPAC.

There has recently been a lot of debate regarding the 'missing heritability', or the amount of heritability of common traits that is not accounted for by the known genetic variants (23). As with many common traits, this is seen with birth weight (as outlined above) and each of our key phenotypes. Statistical methods have been developed to estimate the proportion of variability explained by common genetic variants on genome-wide SNP chips (24). By partitioning the genome into smaller chunks of SNPs, these methods can be applied to estimate and test the heritability of a particular genomic region (25, 26). This novel statistical approach efficiently and effectively combines genetic variants across a region to assess whether regions of the genome influence trait values. Utilizing this novel approach and the bivariate structure of the data, we will conduct a bivariate analysis of birth weight and the key quantitative phenotypes by partitioning the genome-wide chip data into groups of SNPs that belong to genes.