Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Etienne J. Orliac"'
Autor:
Marion Patxot, Daniel Trejo Banos, Athanasios Kousathanas, Etienne J. Orliac, Sven E. Ojavee, Gerhard Moser, Alexander Holloway, Julia Sidorenko, Zoltan Kutalik, Reedik Mägi, Peter M. Visscher, Lars Rönnegård, Matthew R. Robinson
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-16 (2021)
Improving inference in large-scale genetic data linked to electronic medical record data requires the development of novel computationally efficient regression methods. Here, the authors develop a Bayesian approach for association analyses to improve
Externí odkaz:
https://doaj.org/article/d0005592df464e3ea0bdfbf2ebf8a4d7
Autor:
Sven E. Ojavee, Athanasios Kousathanas, Daniel Trejo Banos, Etienne J. Orliac, Marion Patxot, Kristi Läll, Reedik Mägi, Krista Fischer, Zoltan Kutalik, Matthew R. Robinson
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-17 (2021)
Few genome-wide association studies have explored the genetic architecture of age-of-onset for traits and diseases. Here, the authors develop a Bayesian approach to improve prediction in timing-related phenotypes and perform age-of-onset analyses acr
Externí odkaz:
https://doaj.org/article/98babe5c070d44148cee67c1123085fa
Autor:
Kristi Läll, Etienne J. Orliac, Marion Patxot, Zoltán Kutalik, Athanasios Kousathanas, Reedik Mägi, Sven Erik Ojavee, Daniel Trejo Banos, Matthew R. Robinson, Krista Fischer
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-17 (2021)
Nature communications, vol. 12, no. 1, pp. 2337
Nature Communications
Nature communications, vol. 12, no. 1, pp. 2337
Nature Communications
While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides prob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a1db646a40ae5f974debd9ff3bb6990
https://doaj.org/article/98babe5c070d44148cee67c1123085fa
https://doaj.org/article/98babe5c070d44148cee67c1123085fa
Autor:
Kristi Läll, Etienne J. Orliac, Sven Erik Ojavee, Peter M. Visscher, Reedik Mägi, Daniel Trejo Banos, Matthew R. Robinson
Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6045777ceff598a5f939c264fcfc2ce1
https://doi.org/10.1101/2021.08.12.456099
https://doi.org/10.1101/2021.08.12.456099
Autor:
Lars Rönnegård, Athanasios Kousathanas, Gerhard Moser, Etienne J. Orliac, Alexander Holloway, Julia Sidorenko, Marion Patxot, Daniel Trejo Banos, Matthew R. Robinson, Peter M. Visscher, Sven Erik Ojavee, Reedik Mägi, Zoltán Kutalik
Publikováno v:
Nature Communications
Nature Communications, Vol 12, Iss 1, Pp 1-16 (2021)
Nature Communications, Vol 12, Iss 1, Pp 1-16 (2021)
We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritabilit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80a6c14b16a6e0c7d3fb308f3634b6c5
http://urn.kb.se/resolve?urn=urn:nbn:se:du-39016
http://urn.kb.se/resolve?urn=urn:nbn:se:du-39016