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Omicia Inc. and University of Utah | Date: 2015-10-07

The present disclosure provides methods and systems for prioritizing phenotype-causing genomic variants. The methods include using variant prioritization analyses and in combination with biomedical ontologies using a sophisticated re-ranking methodology to re-rank these variants based on phenotype information. The methods can be useful in any genomics study and diagnostics; for example, rare and common disease gene discovery, tumor growth mutation detection, drug responder studies, metabolic studies, personalized medicine, agricultural analysis, and centennial analysis.

Yandell M.,University of Utah | Huff C.,University of Utah | Hu H.,University of Utah | Singleton M.,University of Utah | And 4 more authors.
Genome Research | Year: 2011

VAAST (the Variant Annotation, Analysis & Search Tool) is a probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences. VAAST builds on existing amino acid substitution (AAS) and aggregative approaches to variant prioritization, combining elements of both into a single unified likelihood framework that allows users to identify damaged genes and deleterious variants with greater accuracy, and in an easy-touse fashion. VAAST can score both coding and noncoding variants, evaluating the cumulative impact of both types of variants simultaneously. VAAST can identify rare variants causing rare genetic diseases, and it can also use both rare and common variants to identify genes responsible for common diseases. VAAST thus has a much greater scope of use than any existing methodology. Here we demonstrate its ability to identify damaged genes using small cohorts (n = 3) of unrelated individuals, wherein no two share the same deleterious variants, and for common, multigenic diseases using as few as 150 cases. © 2011 by Cold Spring Harbor Laboratory Press. Source

Hu H.,University of Texas M. D. Anderson Cancer Center | Huff C.D.,University of Texas M. D. Anderson Cancer Center | Moore B.,University of Utah | Flygare S.,University of Utah | And 2 more authors.
Genetic Epidemiology | Year: 2013

The need for improved algorithmic support for variant prioritization and disease-gene identification in personal genomes data is widely acknowledged. We previously presented the Variant Annotation, Analysis, and Search Tool (VAAST), which employs an aggregative variant association test that combines both amino acid substitution (AAS) and allele frequencies. Here we describe and benchmark VAAST 2.0, which uses a novel conservation-controlled AAS matrix (CASM), to incorporate information about phylogenetic conservation. We show that the CASM approach improves VAAST's variant prioritization accuracy compared to its previous implementation, and compared to SIFT, PolyPhen-2, and MutationTaster. We also show that VAAST 2.0 outperforms KBAC, WSS, SKAT, and variable threshold (VT) using published case-control datasets for Crohn disease (NOD2), hypertriglyceridemia (LPL), and breast cancer (CHEK2). VAAST 2.0 also improves search accuracy on simulated datasets across a wide range of allele frequencies, population-attributable disease risks, and allelic heterogeneity, factors that compromise the accuracies of other aggregative variant association tests. We also demonstrate that, although most aggregative variant association tests are designed for common genetic diseases, these tests can be easily adopted as rare Mendelian disease-gene finders with a simple ranking-by-statistical-significance protocol, and the performance compares very favorably to state-of-art filtering approaches. The latter, despite their popularity, have suboptimal performance especially with the increasing case sample size. © 2013 WILEY PERIODICALS, INC. Source

Disclosed is a method for determining whether an individual has an enhanced, diminished, or average probability of exhibiting one or more phenotypic attributes and related methods of selecting a set of genetic markers; for providing relevant genetic information to an individual; of evaluating the probability that progeny of two individuals of the opposite sex will exhibit one or more phenotypic attributes; and for determining the genomic ethnicity of an individual.

Omicia Inc. | Date: 2011-09-09

Disclosed are methods for detecting and/or prioritizing phenotype-causing genomic variants and related software tools. The methods include genomic feature based analysis and can combine variant frequency information with sequence characteristics such as amino acid substation. The methods disclosed are useful in any genomics study; for example, rare and common disease gene discovery, tumor growth mutation detection, personalized medicine, agricultural analysis, and centennial analysis.

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