Title |
Exome Chip Analysis Identifies Low-Frequency and Rare Variants in MRPL38 for White Matter Hyperintensities on Brain Magnetic Resonance Imaging
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Published in |
Stroke, August 2018
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DOI | 10.1161/strokeaha.118.020689 |
Pubmed ID | |
Authors |
Xueqiu Jian, Claudia L. Satizabal, Albert V. Smith, Katharina Wittfeld, Joshua C. Bis, Jennifer A. Smith, Fang-Chi Hsu, Kwangsik Nho, Edith Hofer, Saskia P. Hagenaars, Paul A. Nyquist, Aniket Mishra, Hieab H.H. Adams, Shuo Li, Alexander Teumer, Wei Zhao, Barry I. Freedman, Yasaman Saba, Lisa R. Yanek, Ganesh Chauhan, Mark A. van Buchem, Mary Cushman, Natalie A. Royle, R. Nick Bryan, Wiro J. Niessen, Beverly G. Windham, Anita L. DeStefano, Mohamad Habes, Susan R. Heckbert, Nicholette D. Palmer, Cora E. Lewis, Gudny Eiriksdottir, Pauline Maillard, Rasika A. Mathias, Georg Homuth, Maria del C. Valdés-Hernández, Jasmin Divers, Alexa S. Beiser, Sönke Langner, Kenneth M. Rice, Mark E. Bastin, Qiong Yang, Joseph A. Maldjian, John M. Starr, Stephen Sidney, Shannon L. Risacher, André G. Uitterlinden, Vilmundur G. Gudnason, Matthias Nauck, Jerome I. Rotter, Pamela J. Schreiner, Eric Boerwinkle, Cornelia M. van Duijn, Bernard Mazoyer, Bettina von Sarnowski, Rebecca F. Gottesman, Daniel Levy, Sigurdur Sigurdsson, Meike W. Vernooij, Stephen T. Turner, Reinhold Schmidt, Joanna M. Wardlaw, Bruce M. Psaty, Thomas H. Mosley, Charles S. DeCarli, Andrew J. Saykin, Donald W. Bowden, Diane M. Becker, Ian J. Deary, Helena Schmidt, Sharon L.R. Kardia, M. Arfan Ikram, Stéphanie Debette, Hans J. Grabe, W.T. Longstreth, Sudha Seshadri, Lenore J. Launer, Myriam Fornage |
Abstract |
White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. At 17q25, we confirmed the association of multiple common variants in TRIM65, FBF1, and ACOX1 (P<6×10-7). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; PEA=4.5×10-8) partially independent of known common signal (PEA(conditional)=1.4×10-3). We further identified a locus at 2q33 containing common variants in NBEAL1, CARF, and WDR12 (lead, rs2351524; Pall=1.9×10-10). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants (Prs34136221=2.8×10-8). Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 50% |
United States | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 50% |
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 58 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 13 | 22% |
Student > Ph. D. Student | 8 | 14% |
Student > Master | 5 | 9% |
Student > Bachelor | 4 | 7% |
Student > Doctoral Student | 3 | 5% |
Other | 9 | 16% |
Unknown | 16 | 28% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 9 | 16% |
Medicine and Dentistry | 9 | 16% |
Neuroscience | 5 | 9% |
Agricultural and Biological Sciences | 3 | 5% |
Psychology | 3 | 5% |
Other | 6 | 10% |
Unknown | 23 | 40% |