Brief. Bioinformatics - Probabilistic graphical models for genetic association studies.

Tópicos

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{ model(3404) distribut(989) bayesian(671) }
{ patient(2315) diseas(1263) diabet(1191) }
{ general(901) number(790) one(736) }
{ result(1111) use(1088) new(759) }
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{ concept(1167) ontolog(924) domain(897) }
{ howev(809) still(633) remain(590) }
{ state(1844) use(1261) util(961) }
{ imag(1947) propos(1133) code(1026) }
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{ algorithm(1844) comput(1787) effici(935) }
{ featur(1941) imag(1645) propos(1176) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genetics, many successful methods have recently emerged to dissect the genetic architecture of complex diseases. In this review article, we cover the applications of these models to the population association studies' context, such as linkage disequilibrium modeling, fine mapping and candidate gene studies, and genome-scale association studies. Significant breakthroughs of the corresponding methods are highlighted, but emphasis is also given to their current limitations, in particular, to the issue of scalability. Finally, we give promising directions for future research in this field.

Resumo Limpo

probabilist graphic model wide recogn power formal bioinformat field especi gene express studi linkag analysi although less well known associ genet mani success method recent emerg dissect genet architectur complex diseas review articl cover applic model popul associ studi context linkag disequilibrium model fine map candid gene studi genomescal associ studi signific breakthrough correspond method highlight emphasi also given current limit particular issu scalabl final give promis direct futur research field

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