| Title |
Implementation of genome-wide complex trait analysis to quantify the heritability in multiple myeloma
|
|---|---|
| Published in |
Scientific Reports, July 2015
|
| DOI | 10.1038/srep12473 |
| Pubmed ID | |
| Authors |
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 |
| Abstract |
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. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 29% |
| United Kingdom | 2 | 29% |
| Denmark | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 71% |
| Scientists | 2 | 29% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 34 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 32% |
| Student > Ph. D. Student | 5 | 15% |
| Student > Master | 3 | 9% |
| Student > Bachelor | 2 | 6% |
| Other | 2 | 6% |
| Other | 5 | 15% |
| Unknown | 6 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 8 | 24% |
| Agricultural and Biological Sciences | 8 | 24% |
| Medicine and Dentistry | 8 | 24% |
| Computer Science | 1 | 3% |
| Nursing and Health Professions | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 6 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 May 2016.
All research outputs
#6,737,973
of 22,818,766 outputs
Outputs from Scientific Reports
#45,065
of 123,162 outputs
Outputs of similar age
#77,818
of 263,414 outputs
Outputs of similar age from Scientific Reports
#651
of 1,902 outputs
Altmetric has tracked 22,818,766 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
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We're also able to compare this research output to 1,902 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.