Genetic association studies illuminate the role of low frequency and rare variation in explaining the variation of blood pressure traits. A. Manning1, X. Sim2, H. M. Highland3, M. A. Rivas4, H. K. Im5, A. Mahajan4, A. E. Locke2, N. Grarup6, P. Fontanillas1, A. P. Morris4, T. M. Teslovich2, J. Flannick1, C. Fuchsberger2, K. Gaulton4, H. M. Kang2, J. B. Meigs7, C. M. Lindgren1,4 for T2D-GENES and GoT2D 1) Medical and Population Genetics Program, Broad Institute, Cambridge, MA; 2) Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA; 3) Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX; 4) Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; 5) Department of Health Studies, University of Chicago, Chicago, USA; 6) Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, DK; 7) Massachusetts General Hospital, Boston, Massachusetts, USA.

   Introduction: A goal of the T2D-GENES and GoT2D consortia is to illuminate the role that low frequency (LF) and rare genetic variants play in diabetes-related metabolic traits. To that end we performed genetic association studies of systolic and diastolic blood pressure (SBP and DBP, respectively) in ethnically diverse T2D case-control samples sequenced and genotyped on a variety of platforms. Methods: Analyses were performed on (1) whole genome sequencing in European samples (N=2300), (2) whole exome sequencing in 10K individuals from 5 ethnicities (African American, East Asian, European, Hispanic and South Asian), and (3) exome chip genotyping in European samples (N=39K). SBP and DBP values were adjusted and inverse normalized by cohort and separately in T2D cases and controls. The statistical analyses were single variant and gene-based tests accounting for relatedness and population structure. Three gene-based tests (SKAT, burden tests and a Bayesian analysis) were applied to sets of rare variants: non-synonymous, loss of function (LOF) and pathogenic mutations. Statistical significance was defined as P<0.05/number of tests performed. Results: For the single variant analysis, no LF or rare variants reached statistical significance, although common variants at several known loci were observed with genome-wide significance. Among the gene based results for SBP are genes (listed below) that are expressed in blood plasma, platelets and liver, making them plausible biological candidates. In the European sample with N=2300, two genes show suggestive results: MFGE8 (SKAT P=0.0002), LTK (SKAT P=0.004). One gene shows suggestive results in the Bayesian analysis: CD300LB (log10BF=2.2 with empirical P=0.0003). In the 10K sample, no genes were statistically significant with SBP using SKAT or burden tests, but suggestive results were found with the gene SNUPN in the Bayesian LOF test (log10BF=3.1). In the exome chip analysis, a statistically significant association with SBP was observed with LF variants in PIK3R3 (P=1.510-6, N=36668), which according to Gene Ontology is related to biological processes such as blood coagulation, platelet activity and the insulin receptor signaling pathway. Conclusion: Our results suggest that LF and rare coding variants contribute to variability in blood pressure and that larger sample sizes may be needed to detect genome-wide significant LF and rare coding variants that contribute to the genetic architecture of blood pressure.