Title |
Assessing Disease Risk in Genome-wide Association Studies Using Family History
|
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Published in |
Epidemiology, July 2012
|
DOI | 10.1097/ede.0b013e31825583a0 |
Pubmed ID | |
Authors |
Arpita Ghosh, Patricia Hartge, Mark P. Purdue, Stephen J. Chanock, Laufey Amundadottir, Zhaoming Wang, Nicolas Wentzensen, Nilanjan Chatterjee, Sholom Wacholder |
Abstract |
We show how to use reports of cancer in family members to discover additional genetic associations or confirm previous findings in genome-wide association (GWA) studies conducted in case-control, cohort, or cross-sectional studies. Our novel family history-based approach allows economical association studies for multiple cancers, without genotyping of relatives (as required in family studies), follow-up of participants (as required in cohort studies), or oversampling of specific cancer cases (as required in case-control studies). We empirically evaluate the performance of the proposed family history-based approach in studying associations with prostate and ovarian cancers, using data from GWA studies previously conducted within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. The family history-based method may be particularly useful for investigating genetic susceptibility to rare diseases for which accruing cases may be very difficult, by using disease information from nongenotyped relatives of participants in multiple case-control and cohort studies designed primarily for other purposes. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Practitioners (doctors, other healthcare professionals) | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Germany | 1 | 5% |
Unknown | 20 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 5 | 24% |
Researcher | 4 | 19% |
Lecturer | 1 | 5% |
Student > Bachelor | 1 | 5% |
Student > Master | 1 | 5% |
Other | 3 | 14% |
Unknown | 6 | 29% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 6 | 29% |
Biochemistry, Genetics and Molecular Biology | 4 | 19% |
Agricultural and Biological Sciences | 1 | 5% |
Social Sciences | 1 | 5% |
Computer Science | 1 | 5% |
Other | 2 | 10% |
Unknown | 6 | 29% |