The Genomic Landscape of the Old Order Amish.

The Old Order Amish (OOA) of Lancaster County Pennsylvania are a population isolate with a census size of ~35,000 individuals who descended from ~200 immigrants from Western Europe in the early 1700s. They have a long history of participation in genetic studies, for which their genealogical records and simple lifestyle offer substantial research advantages. However, their demographic history has altered their genomic landscape relative to their European counterparts. Knowledge of this landscape is critical to the design, execution, and interpretation of genetic studies in the OOA. In this dissertation, I evaluate the consequences of population bottleneck and genetic drift on the empirical and/or expected distribution of 1) linkage disequilibrium (LD) for common variants, 2) rare variation (with a focus on the implications for imputation accuracy using an external population) and 3) genomic estimates of inbreeding in the OOA. Using a high-density Single Nucleotide Polymorphism (SNP) map, I compare LD between OOA individuals and a reference population of European ancestry (HapMap CEU). For common SNPs (Minor Allele Frequency (MAF) ≥ 0.05), allele frequencies and LD profiles were similar between the OOA and CEU. Thus, public resources constructed from CEU data are appropriate for analyses of common genetic variation in the OOA. To assess the portability of deep sequencing resources, e.g., 1000 Genomes Project, for rare SNPs (MAF<0.05), I evaluate (via simulation and small-scale empirical study) the impact of using CEU versus OOA haplotype reference panels on imputation accuracy in the OOA. My results establish likely lower and upper bounds (0.50 and 0.75, respectively) of imputation accuracy for rare SNPs using 1000 Genomes Project-like resources in the OOA. Finally, using a subset of SNPs from the high-density map above, I estimate genomic inbreeding coefficients and compare them inbreeding conditional on the OOA pedigree, and describe the distribution of autozygous segments in the study participants. I observed strong agreement between genomic- and pedigree-based estimates, with a mean inbreeding coefficient of ~0.035, approximately the offspring of half 1st cousins. Furthermore, I establish that approximately 92% of the inbreeding in the OOA pedigree is due to inbreeding loops more distant than offspring of 2nd cousins.Ph.D.Human GeneticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86255/1/crisvh_1.pd

Extent and distribution of linkage disequilibrium in the Old Order Amish

Knowledge of the extent and distribution of linkage disequilibrium (LD) is critical to the design and interpretation of gene mapping studies. Because the demographic history of each population varies and is often not accurately known, it is necessary to empirically evaluate LD on a population-specific basis. Here we present the first genome-wide survey of LD in the Old Order Amish (OOA) of Lancaster County Pennsylvania, a closed population derived from a modest number of founders. Specifically, we present a comparison of LD between OOA individuals and US Utah participants in the International HapMap project (abbreviated CEU) using a high-density single nucleotide polymorphism (SNP) map. Overall, the allele (and haplotype) frequency distributions and LD profiles were remarkably similar between these two populations. For example, the median absolute allele frequency difference for autosomal SNPs was 0.05, with an inter-quartile range of 0.02–0.09, and for autosomal SNPs 10–20 kb apart with common alleles (minor allele frequency≥0.05), the LD measure r 2 was at least 0.8 for 15 and 14% of SNP pairs in the OOA and CEU, respectively. Moreover, tag SNPs selected from the HapMap CEU sample captured a substantial portion of the common variation in the OOA (∼88%) at r 2 ≥0.8. These results suggest that the OOA and CEU may share similar LD profiles for other common but untyped SNPs. Thus, in the context of the common variant-common disease hypothesis, genetic variants discovered in gene mapping studies in the OOA may generalize to other populations. Genet. Epidemiol . 34: 146–150, 2010. © 2009 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64895/1/20444_ftp.pd

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Abstract: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Abstract: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Clinical case study meets population cohort: Identification of a BRCA1 pathogenic founder variant in Orcadians

Acknowledgements The study team wish to thank staff from the NHS Grampian genetics team and the ORCADES Study for their contribution to these datasets, in particular, Barbara Gibbons for genetic counselling of family members, the NHS Grampian genomics laboratory team for finding and testing for the variant in the clinically ascertained cases, and Laura Taylor of NHS Grampian and the Public Health Scotland genealogy team for assembling the clinical pedigree. ORCADES DNA extractions were performed at the Edinburgh Clinical Research Facility, University of Edinburgh. ORCADES Sanger sequencing was performed by Camilla Drake and the technical services team at the MRC HGU. Emily Weiss and Reka Nagy assembled the ORCADES pedigree using records at the General Register Office and study information, building on earlier pedigree work by Ruth McQuillan and Jim Wilson (45). Regeneron Genetics Center performed the exome sequencing. We thank Thibaud Boutin for phasing the GSA chip data and Kiera Johnston for help with analysis of other cancer susceptibility genes. The data in the EHR was provided by patients and collected by the NHS as part of their care and support. The authors acknowledge the support of the eDRIS Team (Public Health Scotland) for their involvement in obtaining approvals, provisioning and linking this data. We would also like to acknowledge the invaluable contributions of the research nurses in Orkney and the administrative team in Edinburgh. Finally and most importantly, we thank the people of Orkney for their involvement in and ongoing support for our research. Funding: This work was funded by the MRC University Unit award to the MRC Human Genetics Unit, University of Edinburgh, MC_UU_00007/10. LK was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). ORCADES was supported by the Chief Scientist Office of the Scottish Government (CZB/4/276 and CZB/4/710), a Royal Society URF to JFW and Arthritis Research UK.Peer reviewedPublisher PD

2116-P: A Pipeline to Explore Rare Variation Which Can Contribute to Extreme Obesity in American Indians

Identification of high-impact loss-of-function variants for common disease is challenging because these variants are typically rare and thus statistical power is low when sample sizes are limited. In this study, we utilized whole-exome variant detection, variant prediction programs, tissue expression data, and case-control analysis to identify potentially highly penetrant obesity-susceptibility variants for prioritization for functional testing. Whole-exome sequencing was performed on DNA samples collected from a longitudinal, population-based study of American Indians (N=5432; maximum BMI recorded at ≥18 years). Among the 1 million biallelic SNVs detected in this cohort, those that were exonic or splicing, have minor allele frequency (MAF) ≤0.05, predicted to be damaging (CADD score ≥15), and located in genes expressed in hypothalamus were selected for analysis. These selection criteria resulted in 94,444 SNVs, and odds ratios were calculated for them by comparing the group of individuals with BMI in the top 5th percentile for this population (N=271, BMI ≥53.13 kg/m2) vs. those below the median (N=2,717, BMI &lt;35.92 kg/m2). The resulting 19,382 variants with positive OR values were then divided into 4 bins based on MAFs, and those with a OR in the top 1% of the distribution of each bin were prioritized. In the resulting list of 291variants in 266 different genes, a missense variant p.I330V (rs201597085; OR=2.66; CADD=20.3; MAF=0.02) in GNAS was the best candidate, since GNAS was the only gene linked to obesity in humans. GNAS is a highly complex imprinted locus, and the main transcript derived from this locus (Gs-alpha) encodes the alpha subunit of the stimulatory guanine nucleotide-binding protein. The XLas isoform, a large variant of Gs-alpha, is paternally expressed. An association analysis with accountance for parental origin of the alleles is ongoing. Future functional studies would entail examining the effects of this missense variant on G protein-coupled receptor signaling

Exome sequencing identifies a nonsense variant in DAO associated with reduced energy expenditure in American Indians

Background: Obesity and energy expenditure (EE) are heritable and genetic variants influencing EE may contribute to the development of obesity. We sought to identify genetic variants that affect EE in American Indians, an ethnic group with high prevalence of obesity. Methods: Whole-exome sequencing was performed in 373 healthy Pima Indians informative for 24-h EE during energy balance. Genetic association analyses of all high-quality exonic variants (≥5 carriers) was performed, and those predicted to be damaging were prioritized. Results: Rs752074397 introduces a premature stop codon (Cys264Ter) in DAO and demonstrated the strongest association for 24-h EE, where the Ter allele associated with substantially lower 24-h EE (mean lower by 268 kcal/day) and sleeping EE (by 135 kcal/day). The Ter allele has a frequency=0.5% in Pima Indians, while is extremely rare in most other ethnic groups (frequency&amp;0.01%). In vitro functional analysis showed reduced protein levels for the truncated form of DAO consistent with increased protein degradation. DAO encodes D-amino acid oxidase, which is involved in dopamine synthesis which might explain its role in modulating EE. Conclusion: Our results indicate that a nonsense mutation in DAO may influence EE in American Indians. Identification of variants that influence energy metabolism may lead to new pathways to treat human obesity