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dc.contributor.authorWojcik GL
dc.contributor.authorFuchsberger C
dc.contributor.authorTaliun D
dc.contributor.authorWelch R
dc.contributor.authorMartin AR
dc.contributor.authorShringarpure S
dc.contributor.authorCarlson CS
dc.contributor.authorAbecasis G
dc.contributor.authorKang HM
dc.contributor.authorBoehnke M
dc.contributor.authorBustamante CD
dc.contributor.authorGignoux CR
dc.contributor.authorKenny EE
dc.date.accessioned2018-11-12T15:22:54Z
dc.date.available2018-11-12T15:22:54Z
dc.date.issued2018
dc.identifier.issn2160-1836
dc.identifier.urihttp://dx.doi.org/10.1534/g3.118.200502
dc.identifier.urihttp://hdl.handle.net/10863/7166
dc.description.abstractThe emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. These strategies have benefited from improvements in imputation methods and association tests, however little attention has been paid to ways in which array design can increase rare variant association power. Therefore, we developed a novel framework to select tag SNPs using the reference panel of 26 populations from Phase 3 of the 1000 Genomes Project. We evaluate tag SNP performance via mean imputed r2 at untyped sites using leave-one-out internal validation and standard imputation methods, rather than pairwise linkage disequilibrium. Moving beyond pairwise metrics allows us to account for haplotype diversity across the genome for improve imputation accuracy and demonstrates population-specific biases from pairwise estimates. We also examine array design strategies that contrast multi-ethnic cohorts vs. single populations, and show a boost in performance for the former can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Using our framework, we demonstrate increased imputation accuracy for rare variants (frequency < 1%) by 0.5-3.1% for an array of one million sites and 0.7-7.1% for an array of 500,000 sites, depending on the population. Finally, we show how recent explosive growth in non-African populations means tag SNPs capture on average 30% fewer other variants than in African populations. The unified framework presented here will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.en_US
dc.language.isoenen_US
dc.rights
dc.subjectGenomicsen_US
dc.subjectImputation
dc.subjectStatistical Genetics
dc.subjectArray design
dc.subjectTag SNPs
dc.titleImputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studiesen_US
dc.typeArticleen_US
dc.date.updated2018-11-12T14:26:59Z
dc.language.isiEN-GB
dc.journal.titleGenes Genomes Genetics
dc.description.fulltextnoneen_US


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