| Title |
Identity-by-descent-based heritability analysis in the Northern Finland Birth Cohort
|
|---|---|
| Published in |
Human Genetics, September 2012
|
| DOI | 10.1007/s00439-012-1230-y |
| Pubmed ID | |
| Authors |
Sharon R. Browning, Brian L. Browning |
| Abstract |
For most complex traits, only a small proportion of heritability is explained by statistically significant associations from genome-wide association studies (GWAS). In order to determine how much heritability can potentially be explained through larger GWAS, several different approaches for estimating total narrow-sense heritability from GWAS data have recently been proposed. These methods include variance components with relatedness estimates from allele-sharing, variance components with relatedness estimates from identity-by-descent (IBD) segments, and regression of phenotypic correlation on relatedness estimates from IBD segments. These methods have not previously been compared on real or simulated data. We analyze the narrow-sense heritability of nine metabolic traits in the Northern Finland Birth Cohort (NFBC) using these methods. We find substantial estimated heritability for several traits, including LDL cholesterol (54 % heritability), HDL cholesterol (46 % heritability), and fasting glucose levels (39 % heritability). Estimates of heritability from the regression-based approach are much lower than variance component estimates in these data, which may be due to the presence of strong population structure. We also investigate the accuracy of the competing approaches using simulated phenotypes based on genotype data from the NFBC. The simulation results substantiate the downward bias of the regression-based approach in the presence of population structure. |
Mendeley readers
The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| United Kingdom | 2 | 3% |
| Spain | 1 | 1% |
| New Zealand | 1 | 1% |
| Unknown | 64 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 25 | 35% |
| Student > Ph. D. Student | 20 | 28% |
| Professor | 5 | 7% |
| Student > Master | 5 | 7% |
| Professor > Associate Professor | 3 | 4% |
| Other | 4 | 6% |
| Unknown | 9 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 34 | 48% |
| Biochemistry, Genetics and Molecular Biology | 13 | 18% |
| Computer Science | 8 | 11% |
| Medicine and Dentistry | 4 | 6% |
| Mathematics | 2 | 3% |
| Other | 1 | 1% |
| Unknown | 9 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 07 November 2013.
All research outputs
#5,507,301
of 22,747,498 outputs
Outputs from Human Genetics
#699
of 2,950 outputs
Outputs of similar age
#39,628
of 172,118 outputs
Outputs of similar age from Human Genetics
#5
of 15 outputs
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