A central goal in the biomedical and biological sciences is to link variation in quantitative traits to locations along the genome (single nucleotide polymorphisms). Sequencing technology has rapidly advanced in recent decades, along with the statistical methodology to analyze genetic data. Two classes of association mapping methods exist: those that account for the evolutionary relatedness among individuals, and those that ignore the evolutionary relationships among individuals. While the former methods more fully use implicit information in the data, the latter methods are more flexible in the types of data they can handle. This study presents a comparison of the 2 types of association mapping methods when they are applied to simulated data.
|State||Published - 2016|
Bibliographical noteFunding Information:
This work was partially supported by National Institutes of Health (NIH) grant K25AG043546 (DWF). The Genetic Analysis Workshop (GAW) is supported by NIH grant R01 GM031575. The GAW19 whole genome sequence data were provided by the Type 2 Diabetes Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545. The other genetic and phenotypic data for GAW18 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder Study, which are supported by NIH grants P01 HL045222, R01 DK047482, and R01 DK053889. The GAW is supported by NIH grant R01 GM031575. This article has been published as part of BMC Proceedings Volume 10 Supplement 7, 2016: Genetic Analysis Workshop 19: Sequence, Blood Pressure and Expression Data. Summary articles. The full contents of the supplement are available online at http://bmcproc.biomedcentral.com/ articles/supplements/volume-10-supplement-7. Publication of the proceedings of Genetic Analysis Workshop 19 was supported by National Institutes of Health grant R01 GM031575.
© 2016 The Author(s).
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology (all)