J. Comput. Biol. - Efficiently identifying significant associations in genome-wide association studies.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ analysi(2126) use(1163) compon(1037) }
{ case(1353) use(1143) diagnosi(1136) }
{ algorithm(1844) comput(1787) effici(935) }
{ spatial(1525) area(1432) region(1030) }
{ method(1219) similar(1157) match(930) }
{ age(1611) year(1155) adult(843) }
{ framework(1458) process(801) describ(734) }
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{ method(1969) cluster(1462) data(1082) }
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{ take(945) account(800) differ(722) }
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{ howev(809) still(633) remain(590) }
{ model(3480) simul(1196) paramet(876) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ can(981) present(881) function(850) }
{ use(976) code(926) identifi(902) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ measur(2081) correl(1212) valu(896) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ data(1714) softwar(1251) tool(1186) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ featur(1941) imag(1645) propos(1176) }
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{ record(1888) medic(1808) patient(1693) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
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{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ sampl(1606) size(1419) use(1276) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ use(2086) technolog(871) perceiv(783) }
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{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Over the past several years, genome-wide association studies (GWAS) have implicated hundreds of genes in common disease. More recently, the GWAS approach has been utilized to identify regions of the genome that harbor variation affecting gene expression or expression quantitative trait loci (eQTLs). Unlike GWAS applied to clinical traits, where only a handful of phenotypes are analyzed per study, in eQTL studies, tens of thousands of gene expression levels are measured, and the GWAS approach is applied to each gene expression level. This leads to computing billions of statistical tests and requires substantial computational resources, particularly when applying novel statistical methods such as mixed models. We introduce a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the single nucleotide polymorphisms (SNPs). In the first stage, a small number of informative SNPs, or proxies, across the genome are tested. Based on their observed associations, our approach locates the regions that may contain significant SNPs and only tests additional SNPs from those regions. We show through simulations and analysis of real GWAS datasets that the proposed two-stage procedure increases the computational speed by a factor of 10. Additionally, efficient implementation of our software increases the computational speed relative to the state-of-the-art testing approaches by a factor of 75.

Resumo Limpo

past sever year genomewid associ studi gwas implic hundr gene common diseas recent gwas approach util identifi region genom harbor variat affect gene express express quantit trait loci eqtl unlik gwas appli clinic trait hand phenotyp analyz per studi eqtl studi ten thousand gene express level measur gwas approach appli gene express level lead comput billion statist test requir substanti comput resourc particular appli novel statist method mix model introduc novel twostag test procedur identifi signific associ effici test singl nucleotid polymorph snps first stage small number inform snps proxi across genom test base observ associ approach locat region may contain signific snps test addit snps region show simul analysi real gwas dataset propos twostag procedur increas comput speed factor addit effici implement softwar increas comput speed relat stateoftheart test approach factor

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