Analysis of case-control association studies with known risk variants
(2012) In Bioinformatics 28(13). p.1729-1737- Abstract
- Motivation: The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique... (More)
- Motivation: The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature. Results: We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3001427
- author
- organization
- publishing date
- 2012
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Bioinformatics
- volume
- 28
- issue
- 13
- pages
- 1729 - 1737
- publisher
- Oxford University Press
- external identifiers
-
- wos:000305825600009
- scopus:84864026028
- pmid:22556366
- ISSN
- 1367-4803
- DOI
- 10.1093/bioinformatics/bts259
- language
- English
- LU publication?
- yes
- id
- 4c8c59b9-3301-47ac-8605-38393af7ff9f (old id 3001427)
- date added to LUP
- 2016-04-01 11:14:34
- date last changed
- 2024-03-25 03:50:43
@article{4c8c59b9-3301-47ac-8605-38393af7ff9f,
abstract = {{Motivation: The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature. Results: We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants.}},
author = {{Zaitlen, Noah and Pasaniuc, Bogdan and Patterson, Nick and Pollack, Samuela and Voight, Benjamin and Groop, Leif and Altshuler, David and Henderson, Brian E. and Kolonel, Laurence N. and Le Marchand, Loic and Waters, Kevin and Haiman, Christopher A. and Stranger, Barbara E. and Dermitzakis, Emmanouil T. and Kraft, Peter and Price, Alkes L.}},
issn = {{1367-4803}},
language = {{eng}},
number = {{13}},
pages = {{1729--1737}},
publisher = {{Oxford University Press}},
series = {{Bioinformatics}},
title = {{Analysis of case-control association studies with known risk variants}},
url = {{http://dx.doi.org/10.1093/bioinformatics/bts259}},
doi = {{10.1093/bioinformatics/bts259}},
volume = {{28}},
year = {{2012}},
}