Implementation of genome-wide complex trait analysis to quantify the heritability in multiple myeloma
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 ge...
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| Format: | Online |
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Nature Publishing Group
2015
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4513545/ |
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pubmed-45135452015-07-29 Implementation of genome-wide complex trait analysis to quantify the heritability in multiple myeloma Mitchell, Jonathan S. Johnson, David C. Litchfield, Kevin Broderick, Peter Weinhold, Niels Davies, Faith E. Gregory, Walter A. Jackson, Graham H. Kaiser, Martin Morgan, Gareth J. Houlston, Richard S. Article 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. Nature Publishing Group 2015-07-24 /pmc/articles/PMC4513545/ /pubmed/26208354 http://dx.doi.org/10.1038/srep12473 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Mitchell, Jonathan S. Johnson, David C. Litchfield, Kevin Broderick, Peter Weinhold, Niels Davies, Faith E. Gregory, Walter A. Jackson, Graham H. Kaiser, Martin Morgan, Gareth J. Houlston, Richard S. |
| spellingShingle |
Mitchell, Jonathan S. Johnson, David C. Litchfield, Kevin Broderick, Peter Weinhold, Niels Davies, Faith E. Gregory, Walter A. Jackson, Graham H. Kaiser, Martin Morgan, Gareth J. Houlston, Richard S. Implementation of genome-wide complex trait analysis to quantify the heritability in multiple myeloma |
| author_facet |
Mitchell, Jonathan S. Johnson, David C. Litchfield, Kevin Broderick, Peter Weinhold, Niels Davies, Faith E. Gregory, Walter A. Jackson, Graham H. Kaiser, Martin Morgan, Gareth J. Houlston, Richard S. |
| author_sort |
Mitchell, Jonathan S. |
| title |
Implementation of genome-wide complex trait analysis to quantify the heritability in multiple myeloma |
| title_short |
Implementation of genome-wide complex trait analysis to quantify the heritability in multiple myeloma |
| title_full |
Implementation of genome-wide complex trait analysis to quantify the heritability in multiple myeloma |
| title_fullStr |
Implementation of genome-wide complex trait analysis to quantify the heritability in multiple myeloma |
| title_full_unstemmed |
Implementation of genome-wide complex trait analysis to quantify the heritability in multiple myeloma |
| title_sort |
implementation of genome-wide complex trait analysis to quantify the heritability in multiple myeloma |
| description |
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. |
| publisher |
Nature Publishing Group |
| publishDate |
2015 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4513545/ |
| _version_ |
1613251679579275264 |