American Society of Human Genetics 66th Annual MeetingOctober 1822, 2016 VANCOUVER, CANADA

POSTER ABSTRACTS

Copyright 2016 The American Society of Human Genetics. All rights reserved.

Posters should remain on the board for all three days (Wednesday through Friday)

The program and abstract/poster board number next to each listing is followed by an W (Wednesday), T (Thursday), or F (Friday) to indicate the day on which authors must be present

at their poster boards.

Session Topic/Title Abstract/Poster Board Numbers

Start # End #Statistical Genetics and Genetic Epidemiology 328W 591F Cardiovascular Phenotypes 592W 723F Molecular and Cytogenetic Diagnostics 724W 1032F Evolution and Population Genetics 1033W 1178T Complex Traits and Polygenic Disorders 1179F 1649T Bioinformatics and Computational Approaches 1650F 1956F Epigenetics and Gene Regulation 1957W 2157F Developmental Genetics and Gene Function 2158W 2254W Mendelian Phenotypes 2255T 2661F Cancer Genetics 2662W 2989W Genetic Counseling, ELSI, Education, and Health Services Research 2990T 3133W Omics Technologies 3134T 3227T Prenatal, Perinatal, and Reproductive Genetics 3228F 3303F Genome Structure and Function 3304W 3397W

Statistical Genetics and Genetic Epidemiology 1

Copyright 2016 The American Society of Human Genetics. All rights reserved

329T The Type 2 Diabetes Knowledge Portal: A paradigm for the democratiza-tion of human genetic information. M.C. Costanzo, Accelerating Medicines Partnership for Type 2 Diabetes Knowledge Portal. Medical and Population Genetics, Broad Institute, Cambridge, MA. Discovery of the association of human genetic variants with diseases and traits can spur novel insights in health and medicine. But most association data are warehoused in disparate silos and are diffi cult to access. Even in a specifi c disease area, results from diff erent consortia are often not integrat-ed with each other. Thus it can be prohibitively diffi cult for researchers even to access the data, let alone use them to generate hypotheses that could catalyze the clinical translation of genetic discoveries. The Accelerating Med-icines Partnership in Type 2 Diabetes (AMP T2D) addresses this problem by forging collaboration between the National Institutes of Health and academic, industrial, and nonprofi t stakeholders in order to produce and aggregate data, develop analysis methods and tools, and build infrastructure for data storage and presentation. The results of this collaboration are available via the Type 2 Diabetes Knowledge Portal (http://www.type2diabetesgenetics.org), an open-access resource that aggregates genetic and phenotypic data in a framework that facilitates dynamic analysis across disparate data sets while protecting patient privacy. The Portal knowledgebase contains T2D associa-tion studies from all the large-scale T2D consortia and projects, covering more than 260,000 individuals, along with harmonized genome-wide association study (GWAS) meta-analysis data from over 600,000 samples for an addi-tional 25 relevant traits. We have recently integrated variant data from 1000 Genomes, 2,500 whole genomes from GoT2D, and 60,000 exome sequences from the Exome Aggregation Consortium. By the end of 2016 we will release results for an additional 9,000 exomes and GWAS data for over 30,000 T2D samples: 4,000 multiethnic samples from a clinical pre-diabetic study (CAMP); 14,000 African-American samples from BioMe; and 11,800 Finnish samples from METSIM and FUSION. Portal data are currently stored in a Data Coor-dinating Center (DCC) at the Broad Institute. Soon, they will also be stored at connected federated nodes, including the European Bioinformatics Institute, enabling each node to protect patient data in accordance with local regulations while facilitating global access to analyses. To spark insights from these data, we have developed tools for custom analysis of variant associations. Software and methods developed within the AMP T2D project will be publicly available and are generally applicable to variant associations with any disease or trait.

328W Genetic determinants of glycated hemoglobin levels in the Greenlandic Inuit population. E.V.R. Appel 1 , I. Moltke 2 , M.E. Jrgensen 3 , P. Bjerregaard 4,5 , A. Linneberg 6,7,8 , O. Pedersen 1 , A. Albrechtsen2 2 , T. Hansen 1 , N. Grarup 1 . 1) NNF-CBMR: Section for Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Copenhagen , Denmark; 2) The Bioinformatics Centre, Department of Biology, University of Copenha-gen, 2200 Copenhagen, Denmark; 3) Steno Diabetes Center, 2820 Gentofte, Denmark; 4) National Institute of Public Health, University of Southern Den-mark, 1353 Copenhagen, Denmark; 5) Greenland Centre for Health Research, University of Greenland; 6) Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark; 7) Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark; 8) Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. We recently showed that a common genetic variant leads to a remarkably increased risk of type 2 diabetes (T2D) in the small and historically isolated Greenlandic population. Motivated by this, we aimed at discovering novel genetic determinants for glycated hemoglobin (HbA 1C ) and at estimating the eff ect of known HbA 1C associated loci in the Greenlandic population. We an-alyzed genotype data from 4,049 Greenlanders, which have previously been generated using the Illumina Cardio-Metabochip. We performed the discovery association analysis by applying an additive linear mixed model. To estimate the eff ect of known HbA 1C associated loci we modeled the eff ect in the Europe-an and Inuit ancestry proportions of the Greenlandic genome (EPG and IPG). We found no novel signifi cant associations after correcting for multiple testing. When we investigated loci known to associate with HbA 1C levels, we found that the lead variant in the GCK locus associated signifi cantly with HbA 1C levels in the IPG (P IPG =4.810 -6 , IPG =0.13 SD). Furthermore, for 10 of 15 known HbA 1C loci the eff ect was similar to previously reported eff ects. Interestingly, the ANK1 locus showed a signifi cant ancestral population specifi c eff ect, with op-posing directions of eff ect in each ancestral population. We found no evidence of additional independent association signals in the known HbA 1C associated loci. In conclusion, we found only 1 of the 15 known HbA 1C loci to signifi cant associate within the IPG and that 10 of 15 loci showed similar eff ects as found in European and East Asian populations. Our results shed light on the genetic eff ects across ethnicities.

Statistical Genetics and Genetic Epidemiology2

Copyright 2016 The American Society of Human Genetics. All rights reserved

331W Type 2 diabetes and risk of stroke subtypes: A Mendelian randomization analysis based on individual participant data of 105,000 Chinese adults from the China Kadoorie Biobank. W. Gan 1,2 , M. Holmes 3 , R. Walters 3 , I. Millwood 3 , F. Bragg 3 , Y. Guo 4 , J. Vaucher 3 , Z. Bian 4 , A. Mahaja 1,2 , R. Clarke 3 , L. Lee 4,5 , M. McCarthy 1,2,6 , Z. Chen 3 . 1) Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom; 2) Oxford Centre for Diabetes, Endocrinology, and Metabolism (OCDEM), University of Oxford, Oxford, United Kingdom; 3) Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU),Nuffi eld Department of Population Health, University of Oxford, Oxford, United Kingdom; 4) Chinese Academy of Medical Sciences, Dong Cheng District, Beijing, China; 5) School of Public Health, Peking Uni-versity Health Sciences Center, Beijing, China; 6) National Institute of Health Research, Oxford Biomedical Research Centre, Oxford, United Kingdom. Background: Observational studies report that type 2 diabetes (T2D) is associated with increased risk of both ischaemic and haemorrhagic stroke, but the underlying causal relationships are unclear. Given the increasing preva-lence of T2D, clarifying the nature of these relationships could have important repercussions for stroke incidence and highlight potential opportunities for prevention. Method: We developed genetic risk scores (GRSs) based on 49 established T2D risk variants, weighting individual genetic variants using the most recent eff ect size estimates (Gan et al). We used data on 105,696 partici-pants from the population-based China Kadoorie Biobank (CKB) prospective study, 93,119 randomly-selected subjects supplemented with 12,580 from a nested stroke case-control study (n= 8,284 combined prevalent/incident T2D cases and 12,122 stroke cases). We conducted Mendelian randomization (MR) analyses of individual participant data, using the GRS as an instrumen-tal variable to investigate the causal role of T2D for overall stroke and stroke subtypes. Both observational and MR analyses were adjusted for age, sex, geographic region, BMI, smoking, alcohol intake, educational level and house-hold income. Results: In observational analyses of the CKB study (512,891 participants), individuals with T2D had higher risks of total stroke (OR [95% CI] = 1.78 [1.72 - 1.84]), ischaemic stroke 1.88 (1.81 - 1.95) and haemorrhagic stroke 1.21 (1.10 - 1.32). In MR analysis, genetically-instrumented T2D was causally associated with an increased risk of stroke (OR = 1.81 [1.25 - 2.46], P = 3.31 10 -3 ), mainly with ischaemic stroke (n=6,859 cases; OR = 2.20 [1.50-3.04], P = 3.56 10 -4 ) but not haemorrhagic stroke (n=4,733 cases; OR = 1.00 [0.24 - 2.0], P = 0.40). For both stroke subtypes, the observational and causal estimates were similar ( P heterogeneity > 0.46). No diff erences in