Merck Sharp + Dohme Corp.

Microsoft Technology Licensing, LLC

Merck + Co., Inc.

Rosetta Inpharmatics, Inc.

Eric E Schadt

Stephanie A Monks

Serguei B Stepaniants

Pek Yee Lum

John Lamb

Roy Kuraisa

Jun Zhu

A method for confirming the association of a query QTL or a query gene in the genome of a second species with a clinical trait T exhibited by the second species. A first QTL or a first gene in a first species that is linked to a trait T' is found. The trait T&#x2032; is indicative of trait T. A region of the genome of the first species that comprises the first QTL or the first gene is mapped to a particular region of the genome of the second species. A query QTL or a query gene in the second species that is potentially associated with the trait T is found. The potential association of the query QTL or the query gene with the clinical trait T is confirmed when the query QTL or the query gene is in the particular region of the genome of the second species.

Computer systems and methods for associating genes with traits using cross species data

Systems and methods for determining a functional relationship between pairs of cellular constituents are provided. A plurality of datasets is received. Each dataset represents an experimental condition and comprises measurement data for a plurality of cellular constituents from each of a plurality of organisms. Each respective dataset is represented by correlation coefficients. Each correlation coefficient for a respective dataset in the plurality of datasets represents a correlation between abundance measurement data for a pair of cellular constituents across the dataset. The plurality of correlation coefficients that represents a first dataset in the plurality of datasets is clustered, thereby determining their order. This order is applied to each remaining dataset thereby forming a plurality of correlation matrices. When a conserved area in the plurality of matrices is identified, the functional relationship between the first cellular constituent and the second constituent is determined to be present.

Computer systems and methods for identifying conserved cellular constituent clusters across datasets

The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human disease, but living systems more generally. The present invention provides novel gene network reconstruction algorithms that utilize naturally occurring genetic variations as a source of perturbations to elucidate the networks. The algorithms incorporate relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of these novel algorithms can be demonstrated via application to gene expression data from a segregating mouse population. The network derived from such data using the novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.

Systems and methods for reconstructing gene networks in segregating populations

Methods, computer program products and systems for identifying cellular constituents in a secondary tissue that serve as surrogate markers for a target gene expressed in a primary tissue of a species are provided. A classifier is constructed using cellular constituent abundances of cellular constituents in a first plurality of cellular constituents measured in the secondary tissue in a population. This population comprises a first and second subgroup. The classifier is based on a second plurality of cellular constituents that comprises all or a portion of the first plurality of cellular constituents. Abundance levels of each cellular constituent in the second plurality of cellular constituents varies between the first and second subgroup. All or portion of the population is classified into a plurality of subtypes using the classifier. Then, one or more cellular constituents that can discriminate members of the population between a first subtype and a second subtype in the plurality of subtypes are identified.

Computer systems and methods for identifying surrogate markers

A method for identifying a quantitative trait loci for a complex trait that is exhibited by a plurality of organisms in a population. The population is divided into a plurality of sub-populations using a classification scheme. Depending on what is known about the population, either a supervised or unsupervised classification is used. The classification scheme is derived from a plurality of cellular constituent measurements obtained from each organism in the population. For each sub-population in the plurality of sub-populations, a quantitative genetic analysis is performed on the sub-population in order to identify one or more quantitative trait loci for the complex trait.

Computer systems and methods for subdividing a complex disease into component diseases

A method for associating a gene with a trait exhibited by one or more organisms in a plurality of organisms from a species. A genetic marker map is constructed from a set of genetic markers associated with the plurality of organisms. For each gene in a plurality of genes, a quantitative trait locus analysis is performed using the genetic marker map and a quantitative trait. The quantitative trait locus analysis produces quantitative trait locus data. A quantitative trait comprises an expression statistic for a gene. The expression statistic for a gene is derived from a cellular constituent level that corresponds to the gene in each organism in the plurality of organisms. The quantitative trait locus data are clustered from each quantitative trait locus analysis to form a quantitative trait locus interaction map. Clusters of genes in the map are identified as a candidate pathway group. An expression cluster map is used to refine the candidate pathway group. Multivariate analysis is used to validate the candidate pathway group as a set of genes that are genetically interacting.

Growing community of inventors

Kirkland, WA, United States of America

Eric E Schadt