| Title |
Genome-wide searching of rare genetic variants in WTCCC data
|
|---|---|
| Published in |
Human Genetics, June 2010
|
| DOI | 10.1007/s00439-010-0849-9 |
| Pubmed ID | |
| Authors |
Tao Feng, Xiaofeng Zhu |
| Abstract |
Although they have demonstrated success in searching for common variants for complex diseases, genome-wide association (GWA) studies are less successful in detecting rare genetic variants because of the poor statistical power of most of current methods. We developed a two-stage method that can apply to GWA studies for detecting rare variants. Here we report the results of applying this two-stage method to the Wellcome Trust Case Control Consortium (WTCCC) dataset that include seven complex diseases: bipolar disorder, cardiovascular disease, hypertension (HT), rheumatoid arthritis, Crohn's disease, type 1 diabetes and type 2 diabetes (T2D). We identified 24 genes or regions that reach genome wide significance. Eight of them are novel and were not reported in the WTCCC study. The cumulative risk (or protective) haplotype frequency for each of the 8 genes or regions is small, being at most 11%. For each of the novel genes, the risk (or protective) haplotype set cannot be tagged by the common SNPs available in chips (r (2) < 0.32). The gene identified in HT was further replicated in the Framingham Heart Study, and is also significantly associated with T2D. Our analysis suggests that searching for rare genetic variants is feasible in current GWA studies and candidate gene studies, and the results can severe as guides to future resequencing studies to identify the underlying rare functional variants. |
Mendeley readers
The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| France | 2 | 2% |
| Colombia | 1 | 1% |
| Unknown | 84 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 26 | 29% |
| Student > Ph. D. Student | 12 | 13% |
| Student > Bachelor | 8 | 9% |
| Student > Postgraduate | 7 | 8% |
| Professor > Associate Professor | 7 | 8% |
| Other | 19 | 21% |
| Unknown | 10 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 27 | 30% |
| Agricultural and Biological Sciences | 21 | 24% |
| Biochemistry, Genetics and Molecular Biology | 6 | 7% |
| Mathematics | 5 | 6% |
| Computer Science | 4 | 4% |
| Other | 13 | 15% |
| Unknown | 13 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 14 July 2018.
All research outputs
#7,454,951
of 22,790,780 outputs
Outputs from Human Genetics
#933
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Outputs of similar age
#34,063
of 95,874 outputs
Outputs of similar age from Human Genetics
#9
of 13 outputs
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