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biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements.
Pirinen, Matti; Benner, Christian; Marttinen, Pekka; Järvelin, Marjo-Riitta; Rivas, Manuel A; Ripatti, Samuli.
Afiliación
  • Pirinen M; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Benner C; Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
  • Marttinen P; Department of Public Health, University of Helsinki, Helsinki, Finland.
  • Järvelin MR; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Rivas MA; Department of Public Health, University of Helsinki, Helsinki, Finland.
  • Ripatti S; Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
Bioinformatics ; 33(15): 2405-2407, 2017 Aug 01.
Article en En | MEDLINE | ID: mdl-28369165
ABSTRACT

SUMMARY:

Genetic research utilizes a decomposition of trait variances and covariances into genetic and environmental parts. Our software package biMM is a computationally efficient implementation of a bivariate linear mixed model for settings where hundreds of traits have been measured on partially overlapping sets of individuals. AVAILABILITY AND IMPLEMENTATION Implementation in R freely available at www.iki.fi/mpirinen . CONTACT matti.pirinen@helsinki.fi. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Variación Genética / Programas Informáticos / Modelos Estadísticos / Genética de Población Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2017 Tipo del documento: Article País de afiliación: Finlandia
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Variación Genética / Programas Informáticos / Modelos Estadísticos / Genética de Población Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2017 Tipo del documento: Article País de afiliación: Finlandia