Skip to main content
An official website of the United States government
Grant Details

Grant Number: 1R03CA162200-01A1 Interpret this number
Primary Investigator: Pasaniuc, Bogdan
Organization: University Of California Los Angeles
Project Title: Metrics and Methods for Cross-Population Fine Mapping
Fiscal Year: 2012


Abstract

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.



Publications

Leveraging Ancestry To Improve Causal Variant Identification In Exome Sequencing For Monogenic Disorders
Authors: Brown R. , Lee H. , Eskin A. , Kichaev G. , Lohmueller K.E. , Reversade B. , Nelson S.F. , Pasaniuc B. .
Source: European Journal Of Human Genetics : Ejhg, 2016 Jan; 24(1), p. 113-9.
PMID: 25898925
Related Citations

A spatial haplotype copying model with applications to genotype imputation.
Authors: Yang W.Y. , Hormozdiari F. , Eskin E. , Pasaniuc B. .
Source: Journal Of Computational Biology : A Journal Of Computational Molecular Cell Biology, 2015 May; 22(5), p. 451-62.
PMID: 25526526
Related Citations

Spatial Localization Of Recent Ancestors For Admixed Individuals
Authors: Yang W.Y. , Platt A. , Chiang C.W. , Eskin E. , Novembre J. , Pasaniuc B. .
Source: G3 (bethesda, Md.), 2014 Dec; 4(12), p. 2505-18.
PMID: 25371484
Related Citations

Fast And Accurate Imputation Of Summary Statistics Enhances Evidence Of Functional Enrichment
Authors: Pasaniuc B. , Zaitlen N. , Shi H. , Bhatia G. , Gusev A. , Pickrell J. , Hirschhorn J. , Strachan D.P. , Patterson N. , Price A.L. .
Source: Bioinformatics (oxford, England), 2014-10-15 00:00:00.0; 30(20), p. 2906-14.
PMID: 24990607
Related Citations

Identifying causal variants at loci with multiple signals of association.
Authors: Hormozdiari F. , Kostem E. , Kang E.Y. , Pasaniuc B. , Eskin E. .
Source: Genetics, 2014 Oct; 198(2), p. 497-508.
PMID: 25104515
Related Citations

Integrating functional data to prioritize causal variants in statistical fine-mapping studies.
Authors: Kichaev G. , Yang W.Y. , Lindstrom S. , Hormozdiari F. , Eskin E. , Price A.L. , Kraft P. , Pasaniuc B. .
Source: Plos Genetics, 2014 Oct; 10(10), p. e1004722.
PMID: 25357204
Related Citations

Ibd Genetics: Focus On (dys) Regulation In Immune Cells And The Epithelium
Authors: Kaser A. , Pasaniuc B. .
Source: Gastroenterology, 2014 Apr; 146(4), p. 896-9.
PMID: 24566108
Related Citations

Enhanced Methods For Local Ancestry Assignment In Sequenced Admixed Individuals
Authors: Brown R. , Pasaniuc B. .
Source: Plos Computational Biology, 2014 Apr; 10(4), p. e1003555.
PMID: 24743331
Related Citations

Leveraging reads that span multiple single nucleotide polymorphisms for haplotype inference from sequencing data.
Authors: Yang W.Y. , Hormozdiari F. , Wang Z. , He D. , Pasaniuc B. , Eskin E. .
Source: Bioinformatics (oxford, England), 2013-09-15 00:00:00.0; 29(18), p. 2245-52.
EPub date: 2013-09-15 00:00:00.0.
PMID: 23825370
Related Citations

Enhanced Localization Of Genetic Samples Through Linkage-disequilibrium Correction
Authors: Baran Y. , Quintela I. , Carracedo A. , Pasaniuc B. , Halperin E. .
Source: American Journal Of Human Genetics, 2013-06-06 00:00:00.0; 92(6), p. 882-94.
PMID: 23726367
Related Citations

Analysis Of Latino Populations From Gala And Mec Studies Reveals Genomic Loci With Biased Local Ancestry Estimation
Authors: Pasaniuc B. , Sankararaman S. , Torgerson D.G. , Gignoux C. , Zaitlen N. , Eng C. , Rodriguez-Cintron W. , Chapela R. , Ford J.G. , Avila P.C. , et al. .
Source: Bioinformatics (oxford, England), 2013-06-01 00:00:00.0; 29(11), p. 1407-15.
PMID: 23572411
Related Citations



Back to Top