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Hum Genet. 2019 Feb;138(2):199-210. doi: 10.1007/s00439-019-01975-0. Epub 2019 Jan 22.

Leveraging linkage evidence to identify low-frequency and rare variants on 16p13 associated with blood pressure using TOPMed whole genome sequencing data.

Author information

1
Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA.
2
Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA.
3
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, 02115, USA.
4
Division of Sleep Medicine, Harvard Medical School, Boston, MA, 02115, USA.
5
Department of Medicine, Stanford University, Palo Alto, CA, 94305, USA.
6
GeneSTAR Research Program, Divisions of Cardiology and General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
7
Program for Personalized and Genomic Medicine, Division of Endocrinology Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
8
Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT, 84132, USA.
9
Departments of Pediatrics and Medicine, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA.
10
Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
11
Division of Cardiovascular Diseases, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA.
12
Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC, 27599, USA.
13
Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, 39216, USA.
14
Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, 39216, USA.
15
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.
16
Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, 01702, USA.
17
Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AB, 35294, USA.
18
GeneSTAR Research Program, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
19
Division of Cardiology, Department of Medicine, George Washington University, Washington, DC, 20052, USA.
20
Jackson Heart Study, Jackson State University, Jackson, MS, 39217, USA.
21
Division of General Medicine, Columbia University Medical Center, New York, NY, 10032, USA.
22
Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA.
23
Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.
24
Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA.
25
GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
26
Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA.
27
GeneSTAR Research Program, Divisions of Allergy and Clinical Immunology and General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
28
Geriatrics Research and Education Clinical Center, Veterans Affairs Medical Center, Baltimore, MD, 21201, USA.
29
Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, 98195, USA.
30
Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA.
31
Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, 63110, USA.
32
Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA.
33
Department of Medicine, Center for Human Genetics and Genomics, New York University Langone Health, New York, NY, 10016, USA.
34
Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
35
University of Kentucky College of Public Health, Lexington, KY, 40508, USA.
36
Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
37
Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA. xxz10@case.edu.

Abstract

In this study, we investigated low-frequency and rare variants associated with blood pressure (BP) by focusing on a linkage region on chromosome 16p13. We used whole genome sequencing (WGS) data obtained through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program on 395 Cleveland Family Study (CFS) European Americans (CFS-EA). By analyzing functional coding variants and non-coding rare variants with CADD score > 10 residing within the chromosomal region in families with linkage evidence, we observed 25 genes with nominal statistical evidence (burden or SKAT p < 0.05). One of the genes is RBFOX1, an evolutionarily conserved RNA-binding protein that regulates tissue-specific alternative splicing that we previously reported to be associated with BP using exome array data in CFS. After follow-up analysis of the 25 genes in ten independent TOPMed studies with individuals of European, African, and East Asian ancestry, and Hispanics (N = 29,988), we identified variants in SLX4 (p = 2.19 × 10-4) to be significantly associated with BP traits when accounting for multiple testing. We also replicated the associations previously reported for RBFOX1 (p = 0.007). Follow-up analysis with GTEx eQTL data shows SLX4 variants are associated with gene expression in coronary artery, multiple brain tissues, and right atrial appendage of the heart. Our study demonstrates that linkage analysis of family data can provide an efficient approach for detecting rare variants associated with complex traits in WGS data.

PMID:
30671673
DOI:
10.1007/s00439-019-01975-0

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