Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci

Peer reviewe

Bias, Precision and Power of Some Techniques in Genome-Wide Association Analysis

Genome-wide association studies (GWAS) have successfully identified thousands of genetic loci associated with a wide variety of human phenotypic traits. In this thesis, we evaluate the bias, precision and power of three statistical techniques employed in GWAS. In Chapter 2, we assess bias and power for adjusted-trait regression (ATR). ATR is a modification to the traditional ordinary least-squares estimation and F-test hypothesis testing techniques for quantitative trait multiple linear regression models. ATR involves performing bivariate correlation analysis between a genetic variant (or set of genetic variants) and a covariate-adjusted trait, obtained by regressing the trait on covariates. We show that ATR effect size estimates for single variant analysis are biased towards the null by a factor equal to coefficient of determination obtained from the regression of genetic variant onto covariates. We derive the exact distributions of ATR test statistics and show that ATR is less powerful than traditional methods when the genetic variant are correlated with covariates. The loss of power increases as stringency of Type 1 error control increases. The maximum possible power loss for the ATR multi-variant test is completely characterized by the canonical correlation between genetic variants and covariates. We show that, for typical covariates like genetic principal components, the loss of power will likely be low in practice. In Chapter 3, we assess three genetic imputation quality scores (allelic-RSQ, MACH-RSQ and INFO) as predictors for realized imputation quality (squared correlation between true genotypes and imputed dosages) for low-frequency and rare variants. We assess the impact of using different imputation algorithms (Beagle 4.2, minimac3 and IMPUTE 2) and reference panels (1000 Genomes [1KG] and Haplotype Reference Consortium [HRC]) on the relationship between imputation quality scores and realized quality. We imputed genotypes into 8,378 participants using each imputation algorithm with the 1KG panel and minimac3 with the HRC panel. We show that MACH-RSQ and INFO are identical when calculated on the same data. We observe that allelic-RSQ predicts realized quality less well than MACH-RSQ/INFO for low-frequency and rare variants. Realized quality decreases as minor allele frequency (MAF) decreases. The mean absolute difference (MAD) between quality scores and realized quality increases as MAF decreases. Imputation with HRC resulted in better realized quality for low-frequency and rare variants compared to imputation with 1KG. However, the MAD between quality scores and realized quality for low-frequency and rare variants was similar for both panels. In chapter 4, we assess the efficiency gained or lost by adding an external sample with missing case-control status to an (internal) case-control study sample. We propose a method for estimation and testing that accounts for the known (or presumed) proportion of cases in the external sample. Misspecification of the external sample case proportion leads to biased estimation; in particular, treating the external sample as a control sample leads to underestimation of the effect size. However, the proposed test controls Type 1 error regardless of the particular value chosen for the presumptive external sample case proportion. When treating the external participants as controls, addition of external participants improves power if the proportion of cases in the internal sample is at least twice that in the external sample.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163049/1/pyajnik_1.pd

Twins in Guinea-Bissau have a 'thin-fat' body composition compared to singletons

The 'thrifty phenotype' hypothesis proposed that fetal undernutrition increases risk of diabetes in later life. Undernourished low birthweight Indian babies are paradoxically more adipose compared to well-nourished European babies, and are at higher risk of diabetes in later life. Twin pregnancies are an example of in utero growth restrictive environment due to shared maternal nutrition. There are few studies of body composition in twins. We performed secondary analysis of anthropometric body composition of twins and singletons in Guinea-Bissau, an economically deprived African country. Anthropometric data were available on 7-34 year-old twins (n = 209, 97 males) and singletons (n = 182, 86 males) in the Guinea-Bissau Twin Registry at the Bandim Health Project. Twins had lower birthweight (2420 vs 3100 g, p < 0.001); and at follow-up, lower height (HAZ mean Z-score difference, -0.21, p = 0.055), weight (WAZ -0.73, p = 0.024) and BMI (BAZ -0.22, p = 0.079) compared to singletons but higher adiposity (skinfolds: +0.33 SD, p = 0.001). Twins also had higher fasting (+0.38 SD, p < 0.001) and 2-hour OGTT glucose concentrations (+0.29 SD, p < 0.05). Linear mixed-effect model accounting for intrapair correlations and interactions confirmed that twins were thinner but fatter across the age range. Data on maternal morbidity and prematurity were not available in this cohort. African populations are known to have a muscular (less adipose) body composition. Demonstration of a thin-fat phenotype in twins in a low socio-economic African country supports the thesis that it could be a manifestation of early life undernutrition and not exclusive to Indians. This phenotype could increase risk of diabetes and related conditions

Multigenerational Undernutrition Increases Susceptibility to Obesity and Diabetes that Is Not Reversed after Dietary Recuperation

SummaryPeople in developing countries have faced multigenerational undernutrition and are currently undergoing major lifestyle changes, contributing to an epidemic of metabolic diseases, though the underlying mechanisms remain unclear. Using a Wistar rat model of undernutrition over 50 generations, we show that Undernourished rats exhibit low birth-weight, high visceral adiposity (DXA/MRI), and insulin resistance (hyperinsulinemic-euglycemic clamps), compared to age-/gender-matched control rats. Undernourished rats also have higher circulating insulin, homocysteine, endotoxin and leptin levels, lower adiponectin, vitamin B12 and folate levels, and an 8-fold increased susceptibility to Streptozotocin-induced diabetes compared to control rats. Importantly, these metabolic abnormalities are not reversed after two generations of unrestricted access to commercial chow (nutrient recuperation). Altered epigenetic signatures in insulin-2 gene promoter region of Undernourished rats are not reversed by nutrient recuperation, and may contribute to the persistent detrimental metabolic profiles in similar multigenerational undernourished human populations

Molecular characterization and meta-analysis of gut microbial communities illustrate enrichment of prevotella and megasphaera in Indian subjects

The gut microbiome has varied impact on the wellbeing of humans. It is influenced by different factors such as age, dietary habits, socio-economic status, geographic location, and genetic makeup of individuals. For devising microbiome-based therapies, it is crucial to identify population specific features of the gut microbiome. Indian population is one of the most ethnically, culturally, and geographically diverse, but the gut microbiome features remain largely unknown. The present study describes gut microbial communities of healthy Indian subjects and compares it with the microbiota from other populations. Based on large differences in alpha diversity indices, abundance of 11 bacterial phyla and individual specific OTUs, we report inter-individual variations in gut microbial communities of these subjects. While the gut microbiome of Indians is different from that of Americans, it shared high similarity to individuals from the Indian subcontinent i.e., Bangladeshi. Distinctive feature of Indian gut microbiota is the predominance of genus Prevotella and Megasphaera. Further, when compared with other non-human primates, it appears that Indians share more OTUs with omnivorous mammals. Our metagenomic imputation indicates higher potential for glycan biosynthesis and xenobiotic metabolism in these subjects. Our study indicates urgent need of identification of population specific microbiome biomarkers of Indian subpopulations to have more holistic view of the Indian gut microbiome and its health implications