Accurate liability estimation improves power in ascertained case-control studies
Accurate liability estimation improves power in ascertained case-control studies
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New York: Nature Publishing Group US
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English
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New York: Nature Publishing Group US
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LEAP (liability estimator as a phenotype) corrects for confounding factors in case-control GWAS with increased power.
Linear mixed models (LMMs) have emerged as the method of choice for confounded genome-wide association studies. However, the performance of LMMs in nonrandomly ascertained case-control studies deteriorates with increasing sample size. We propose a framework called LEAP (liability estimator as a phenotype;
https://github.com/omerwe/LEAP
) that tests for association with estimated latent values corresponding to severity of phenotype, and we demonstrate that this can lead to a substantial power increase....
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Accurate liability estimation improves power in ascertained case-control studies
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TN_cdi_proquest_miscellaneous_1669449144
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_1669449144
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ISSN
1548-7091
E-ISSN
1548-7105
DOI
10.1038/nmeth.3285
