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Machine learning approaches to explore digenic inheritance.
Okazaki, Atsuko; Ott, Jurg.
Afiliação
  • Okazaki A; Department of Diagnostics and Therapeutics of Intractable Diseases, Juntendo University, Bunkyo-ku, Tokyo 113-8421, Japan; Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10065, USA.
  • Ott J; Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10065, USA. Electronic address: ott@rockefeller.edu.
Trends Genet ; 38(10): 1013-1018, 2022 10.
Article em En | MEDLINE | ID: mdl-35581032
ABSTRACT
Some rare genetic disorders, such as retinitis pigmentosa or Alport syndrome, are caused by the co-inheritance of DNA variants at two different genetic loci (digenic inheritance). To capture the effects of these disease-causing variants and their possible interactive effects, various statistical methods have been developed in human genetics. Analogous developments have taken place in the field of machine learning, particularly for the field that is now called Big Data. In the past, these two areas have grown independently and have started to converge only in recent years. We discuss an overview of each of the two fields, paying special attention to machine learning methods for uncovering the combined effects of pairs of variants on human disease.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Herança Multifatorial / Padrões de Herança Limite: Humans Idioma: En Revista: Trends Genet Assunto da revista: GENETICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Herança Multifatorial / Padrões de Herança Limite: Humans Idioma: En Revista: Trends Genet Assunto da revista: GENETICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos