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Practical issues in imputation-based association mapping.

ABSTRACT: Imputation-based association methods provide a powerful framework for testing untyped variants for association with phenotypes and for combining results from multiple studies that use different genotyping platforms. Here, we consider several issues that arise when applying these methods in practice, including: (i) factors affecting imputation accuracy, including choice of reference panel; (ii) the effects of imputation accuracy on power to detect associations; (iii) the relative merits of Bayesian and frequentist approaches to testing imputed genotypes for association with phenotype; and (iv) how to quickly and accurately compute Bayes factors for testing imputed SNPs. We find that imputation-based methods can be robust to imputation accuracy and can improve power to detect associations, even when average imputation accuracy is poor. We explain how ranking SNPs for association by a standard likelihood ratio test gives the same results as a Bayesian procedure that uses an unnatural prior assumption--specifically, that difficult-to-impute SNPs tend to have larger effects--and assess the power gained from using a Bayesian approach that does not make this assumption. Within the Bayesian framework, we find that good approximations to a full analysis can be achieved by simply replacing unknown genotypes with a point estimate--their posterior mean. This approximation considerably reduces computational expense compared with published sampling-based approaches, and the methods we present are practical on a genome-wide scale with very modest computational resources (e.g., a single desktop computer). The approximation also facilitates combining information across studies, using only summary data for each SNP. Methods discussed here are implemented in the software package BIMBAM, which is available from http://stephenslab.uchicago.edu/software.html.

SUBMITTER: Guan Y

PROVIDER: S-EPMC2585794 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Practical issues in imputation-based association mapping.

Guan Yongtao Y Stephens Matthew M

PLoS genetics 20081205 12

Imputation-based association methods provide a powerful framework for testing untyped variants for association with phenotypes and for combining results from multiple studies that use different genotyping platforms. Here, we consider several issues that arise when applying these methods in practice, including: (i) factors affecting imputation accuracy, including choice of reference panel; (ii) the effects of imputation accuracy on power to detect associations; (iii) the relative merits of Bayesi ...[more]

PMID: 19057666

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Project description:BACKGROUND:People who are at elevated risk of suicide stand to benefit from internet-based interventions; however, research in this area is likely impacted by a range of ethical and practical challenges. The aim of this study was to examine the ethical issues and practical barriers associated with clinical studies of internet-based interventions for suicide prevention. METHOD:This was a mixed-methods study involving two phases. First, a systematic search was conducted to identify studies evaluating internet-based interventions for people at risk of suicide, and information pertaining to safety protocols and exclusion criteria was extracted. Second, investigators on the included studies were invited to complete an online survey comprising open-ended and forced-choice responses. Quantitative and qualitative methods were used to analyse the data. RESULTS:The literature search identified 18 eligible studies, of which three excluded participants based on severity of suicide risk. Half of the 15 suicide researchers who participated in the survey had experienced problems obtaining ethics approval, and none had encountered adverse events attributed to their intervention. Survey respondents noted the difficulty of managing risk in online environments and the limitations associated with implementing safety protocols, although some also reported increased confidence resulting from the ethical review process. Respondents recommended researchers pursue a collaborative relationship with their research ethics committees. CONCLUSION:There is a balance to be achieved between the need to minimise the risk of adverse events whilst also ensuring interventions are being validated on populations who may be most likely to use and benefit from them (i.e., those who prefer anonymity). Further research is required to obtain the views of research ethics committees and research participants on these issues. Dialogue between researchers and ethics committees is necessary to address the need to ensure safety while also advancing the timely development of effective interventions in this critical area.

Dataset Information

Practical issues in imputation-based association mapping.

Publications

Practical issues in imputation-based association mapping.

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