%0 Journal Article %1 carter2012pleiotropy %A Carter, Gregory W. %A Hays, Michelle %A Sherman, Amir %A Galitski, Timothy %D 2012 %I Public Library of Science %J PLoS Genet %K pleiotropy population_genetics reading_group regulatory_networks %N 10 %P e1003010 %R 10.1371/journal.pgen.1003010 %T Use of Pleiotropy to Model Genetic Interactions in a Population %U http://dx.doi.org/10.1371%2Fjournal.pgen.1003010 %V 8 %X Parallel advances in genotype and phenotype measurement technologies are yielding large-scale, multidimensional datasets that can potentially decipher the genetic etiology of complex traits. Understanding these data will require methods that combine the experimental power of molecular biology and the quantitative power of statistical genetics. In this work, we describe a novel approach that uses the complementary information encoded by multiple phenotypes in conjunction with genetic data to map genetic interaction networks in terms of quantitative variant-to-variant and variant-to-phenotype influences. We tested this method using a population of yeast strains with random combinations of five genetic mutations and derived an interaction network using molecular and colony-level assays of mating phenotypes. Distinct biological processes that underlie the two phenotypes were identified with gene expression analysis, validating the method's ability to exploit complementary biological information in multiple phenotypes. Our method generates data-driven models and testable hypotheses of how the genetic variation in a population combines to affect complex traits. It is designed to be flexible and scalable for application to populations with extensive genetic diversity.

@article{carter2012pleiotropy, abstract = {Parallel advances in genotype and phenotype measurement technologies are yielding large-scale, multidimensional datasets that can potentially decipher the genetic etiology of complex traits. Understanding these data will require methods that combine the experimental power of molecular biology and the quantitative power of statistical genetics. In this work, we describe a novel approach that uses the complementary information encoded by multiple phenotypes in conjunction with genetic data to map genetic interaction networks in terms of quantitative variant-to-variant and variant-to-phenotype influences. We tested this method using a population of yeast strains with random combinations of five genetic mutations and derived an interaction network using molecular and colony-level assays of mating phenotypes. Distinct biological processes that underlie the two phenotypes were identified with gene expression analysis, validating the method's ability to exploit complementary biological information in multiple phenotypes. Our method generates data-driven models and testable hypotheses of how the genetic variation in a population combines to affect complex traits. It is designed to be flexible and scalable for application to populations with extensive genetic diversity.}, added-at = {2013-06-12T00:18:43.000+0200}, author = {Carter, Gregory W. and Hays, Michelle and Sherman, Amir and Galitski, Timothy}, biburl = {https://www.bibsonomy.org/bibtex/2eb99528d7ba89ab10aac050a0758ec15/peter.ralph}, description = {PLOS Genetics: Use of Pleiotropy to Model Genetic Interactions in a Population}, doi = {10.1371/journal.pgen.1003010}, interhash = {f09a33f3f91f6d9e3e8e8ed911a34733}, intrahash = {eb99528d7ba89ab10aac050a0758ec15}, journal = {PLoS Genet}, keywords = {pleiotropy population_genetics reading_group regulatory_networks}, month = {10}, number = 10, pages = {e1003010}, publisher = {Public Library of Science}, timestamp = {2013-06-12T00:18:43.000+0200}, title = {Use of Pleiotropy to Model Genetic Interactions in a Population}, url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1003010}, volume = 8, year = 2012 }