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
来源
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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