Implementation of genome-wide complex trait analysis to quantify the heritability in multiple myeloma

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作者
Jonathan S. Mitchell
David C. Johnson
Kevin Litchfield
Peter Broderick
Niels Weinhold
Faith E. Davies
Walter A. Gregory
Graham H. Jackson
Martin Kaiser
Gareth J. Morgan
Richard S. Houlston
机构
[1] Molecular and Population Genetics,Division of Genetics and Epidemiology
[2] The Institute of Cancer Research,Department of Haemato
[3] The Institute of Cancer Research,Oncology, Division of Pathology
[4] Myeloma Institute for Research and Therapy,Division of Molecular Pathology
[5] University of Arkansas for Medical Sciences,undefined
[6] Leeds Institute of Molecular Medicine,undefined
[7] Section of Clinical Trials Research,undefined
[8] University of Leeds,undefined
[9] Royal Victoria Infirmary,undefined
[10] Newcastle upon Tyne,undefined
[11] Centre for Myeloma Research,undefined
[12] The Institute of Cancer Research,undefined
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Scientific Reports | / 5卷
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摘要
A sizeable fraction of multiple myeloma (MM) is expected to be explained by heritable factors. Genome-wide association studies (GWAS) have successfully identified a number of common single-nucleotide polymorphisms (SNPs) influencing MM risk. While these SNPs only explain a small proportion of the genetic risk it is unclear how much is left to be detected by other, yet to be identified, common SNPs. Therefore, we applied Genome-Wide Complex Trait Analysis (GCTA) to 2,282 cases and 5,197 controls individuals to estimate the heritability of MM. We estimated that the heritability explained by known common MM risk SNPs identified in GWAS was 2.9% (±2.4%), whereas the heritability explained by all common SNPs was 15.2% (±2.8%). Comparing the heritability explained by the common variants with that from family studies, a fraction of the heritability may be explained by other genetic variants, such as rare variants. In summary, our results suggest that known MM SNPs only explain a small proportion of the heritability and more common SNPs remain to be identified.
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[1]  
Kyle RA(2004)Multiple myeloma N Engl J Med 351 1860-73
[2]  
Rajkumar SV(2002)A long-term study of prognosis in monoclonal gammopathy of undetermined significance N Engl J Med 346 564-9
[3]  
Kyle RA(2006)Familial risks and temporal incidence trends of multiple myeloma Eur J Cancer 42 1661-70
[4]  
Altieri A(2014)Inherited genetic susceptibility to multiple myeloma Leukemia 28 518-24
[5]  
Chen B(2012)Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk Nat Genet 44 58-61
[6]  
Bermejo JL(2013)Common variation at 3q26.2, 6p21.33, 17p11.2 and 22q13.1 influences multiple myeloma risk Nat Genet 45 1221-5
[7]  
Castro F(2013)The CCND1 c.870G>A polymorphism is a risk factor for t(11;14)(q13;q32) multiple myeloma Nat Genet 45 522-5
[8]  
Hemminki K(2011)GCTA: a tool for genome-wide complex trait analysis Am J Hum Genet 88 76-82
[9]  
Morgan GJ(2013)Genome-wide complex trait analysis (GCTA): methods, data analyses and interpretations Methods Mol Biol 1019 215-36
[10]  
Broderick P(2011)Genome partitioning of genetic variation for complex traits using common SNPs Nat Genet 43 519-25