Genome-wide association studies have been very successful in identifying hundreds of variants associated to complex diseases and phenotypes. In contrast, due to high levels of linkage disequilibrium at any given locus, only a handful of causal variants have been identified so far. In an attempt to bridge this gap, several fine- mapping studies involving dense genotyping or sequencing are currently being performed in multiple populations such as Europeans, Asians, African Americans or Latinos. Fine mapping studies over multiple populations can leverage different genetic variation across populations to increase the accuracy for localizing the causal variant in a joint analysis of multiple populations as compared to studies in which only one population is analyzed at a time. Surprisingly, despite the large potential of multi ethnic fine mapping studies, current multi population fine mapping studies employ standard statistical techniques within locus specific ad- hoc frameworks. In this application we will introduce novel metrics and automated frameworks for quantifying the performance of fine mapping methods as well as novel statistical methods that leverage multi ethnic genetic variation to increase the localization accuracy for fine mapping.
Resistance to a wide range of cancers, including breast cancer and various other diseases, is known to include a substantial genetically heritable component. Genome wide association studies have been very successful in identifying loci associated to various diseases including breast cancer. In contrast, the underlying genetic causal variants have yet to be identified for large number of phenotypes including most cancers. In this application, we will develop novel methods and metrics for multi-ethnic fine mapping studies and apply them to real fine mapping breast cancer data sets.
|Brown, Robert; Lee, Hane; Eskin, Ascia et al. (2016) Leveraging ancestry to improve causal variant identification in exome sequencing for monogenic disorders. Eur J Hum Genet 24:113-9|
|Yang, Wen-Yun; Hormozdiari, Farhad; Eskin, Eleazar et al. (2015) A spatial haplotype copying model with applications to genotype imputation. J Comput Biol 22:451-62|
|Yang, Wen-Yun; Platt, Alexander; Chiang, Charleston Wen-Kai et al. (2014) Spatial localization of recent ancestors for admixed individuals. G3 (Bethesda) 4:2505-18|
|Hormozdiari, Farhad; Kostem, Emrah; Kang, Eun Yong et al. (2014) Identifying causal variants at loci with multiple signals of association. Genetics 198:497-508|
|Brown, Robert; Pasaniuc, Bogdan (2014) Enhanced methods for local ancestry assignment in sequenced admixed individuals. PLoS Comput Biol 10:e1003555|
|Kaser, Arthur; Pasaniuc, Bogdan (2014) IBD genetics: focus on (dys) regulation in immune cells and the epithelium. Gastroenterology 146:896-9|
|Kichaev, Gleb; Yang, Wen-Yun; Lindstrom, Sara et al. (2014) Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet 10:e1004722|
|Pasaniuc, Bogdan; Zaitlen, Noah; Shi, Huwenbo et al. (2014) Fast and accurate imputation of summary statistics enhances evidence of functional enrichment. Bioinformatics 30:2906-14|
|Pasaniuc, Bogdan; Sankararaman, Sriram; Torgerson, Dara G et al. (2013) Analysis of Latino populations from GALA and MEC studies reveals genomic loci with biased local ancestry estimation. Bioinformatics 29:1407-15|
|Baran, Yael; Quintela, Ines; Carracedo, Angel et al. (2013) Enhanced Localization of Genetic Samples through Linkage-Disequilibrium Correction. Am J Hum Genet 92:882-94|
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