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Selecting cases and controls for DNA sequencing studies using family histories of disease
Author(s) -
Kim Wonji,
Qiao Dandi,
Cho Michael H.,
Kwak Soo Heon,
Park Kyong Soo,
Silverman Edwin K.,
Sham Pak,
Won Sungho
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7248
Subject(s) - missing heritability problem , liability , heritability , selection (genetic algorithm) , disease , dna sequencing , computer science , statistical power , genetic association , statistics , biology , computational biology , econometrics , genetics , medicine , machine learning , mathematics , single nucleotide polymorphism , gene , genotype , finance , economics
Recent improvements in sequencing technology have enabled the investigation of so‐called missing heritability, and a large number of affected subjects have been sequenced in order to detect significant associations between human diseases and rare variants. However, the cost of genome sequencing is still high, and a statistically powerful strategy for selecting informative subjects would be useful. Therefore, in this report, we propose a new statistical method for selecting cases and controls for sequencing studies based on family history. We assume that disease status is determined by unobserved liability scores. Our method consists of two steps: first, the conditional means of liability are estimated with the liability threshold model given the individual's disease status and those of their relatives. Second, the informative subjects are selected with the estimated conditional means. Our simulation studies showed that statistical power is substantially affected by the subject selection strategy chosen, and power is maximized when affected (unaffected) subjects with high (low) risks are selected as cases (controls). The proposed method was successfully applied to genome‐wide association studies for type 2 diabetes, and our analysis results reveal the practical value of the proposed methods. Copyright © 2017 John Wiley & Sons, Ltd.