nrg2579

Th sarch fr gtic factrs that ifc cmm cmpx traits ad th charactrizati f th ffcts f ths factrs is bth a ga ad a chag fr mdr gticists. I rct yars, th fid has b rvti- izd by th sccss f gm-wid assciati (GWA) stdis 1–5 . Mst f ths stdis hav sd a sig-cs aaysis stratgy , i which ach variat is tstd idivid- ay fr assciati with a spcific phtyp. Hwvr, a ras that is ft citd fr th ack f sccss i gtic stdis f cmpx disas 6,7 is th xistc f itractis btw ci. If a gtic factr f ctis primariy thrgh a cmpx mchaism that ivvs mtip thr gs ad, pssiby, virmta factrs, th ffct might b missd if th g is xamid i isa- ti witht awig fr its pttia itractis with ths thr kw factrs. Fr this ras, svra mthds ad sftwar packags 8–15 hav b dvpd that csidr th statistica itractis btw ci wh aaysig th data frm gtic assciati stdis. Athgh i sm cass th mtivati fr sch aayss is t icras th pwr t dtct ffcts 16 , i thr cass th mtivati has b t dtct statistica itractis btw ci that ar ifrmativ abt th bigica ad bichmica pathways that drpi th disas 7 . W rtr t this cmpx iss f bigica itrprtati f statistica itracti atr i th artic. Th prps f this Rviw is t prvid a srvy f th mthds ad ratd sftwar packags that ar cr- rty big sd t dtct th itractis btw th gtic ci that ctribt t hma gtic disas. Athgh th fcs is hma gtics, may f th ccpts ad apprachs ar strgy ratd t mthds sd i aima ad pat gtics. I bgi by dscribig what is mat by statistica itracti ad by sttig p th dfiitis ad tati fr th fwig sctis. I th xpai hw might tst fr itracti btw tw r mr kw gtic factrs ad hw might addrss th sighty diffrt qsti f tst- ig fr assciati with a sig factr whi awig fr itracti with thr factrs. I practic, rary wishs t tst fr itractis that ccr y btw kw factrs, ss prhaps t rpicat a prvis fidig r t tst a spcific bigica hypthsis. It is mr cmm t sarch fr itractis r fr ci that might itract, giv gtyp data at pttiay may sits (fr xamp, frm a GWA aaysis r f rm a mr fcsd cadidat g stdy). I cti th artic by tiig diffrt mthds ad sftwar packags that sarch fr sch itractis, ragig frm simp xhastiv sarchs t data-mining ad machin-laning apprachs t Bayian modl l ction apprachs. Thrght ths sctis I s th aaysis f a pb- icy avaiab gm-wid data st Crh’s disas frm th Wcm Trst Cas Ctr Csrtim (WTCCC) as a rcrrig xamp 1 . I ccd th artic with a scti discssig th bigica itrprtati f rsts fd frm sch statistica itracti aayss. Thr is a g histry f th ivstigati f itractis i gtics, ragig frm cassica qatitativ gtic stdis f ibrd p at ad aima ppatis 17–19 t vtiary gtic stdis 20 ad, fiay, t ikag ad assciati stdis i tbrd hma ppatis. I this artic, I fcs primariy hma gtic assciati stdis; radrs ar rfrrd t refs 20–25 fr a Institute of Human Genetics , Newcastle University, International Centre for Life , Central Parkway, Newcastle upon Tyne NE1 3BZ, UK. e‑mail: [email protected] doi:10.1038/ng2579 Publihd onlin 12 May 2009 Data mining Th poc o xtacting hiddn pattn and potntially uul inomation om lag amount o data. Machine learning Th ability o a pogam to lan om xpinc, that i, to modiy it xcution on th bai o nwly acquid inomation. A majo ocu o machin-laning ach i to automatically poduc modl (ul and pattn) om data. Bayesian model selection A tatitical appoach o lcting modl by incopoating both pio ditibution o paamt o th modl and th obvd xpimntal data. Detecting gene–gene interactions that underlie human diseases Heather J. Cordell Abstract | Following the identification of several disease-associated polymorphisms by genome-wide association (GWA) analysis, interest is now focusing on the detection of effects that, owing to their interaction with other genetic or environmental factors, might not be identified by using standard single-locus tests. In addition to increasing the power to detect associations, it is hoped that detecting interactions between loci will allow us to elucidate the biological and biochemical pathways that underpin disease. Here I provide a critical survey of the methods and related software packages currently used to detect the interactions between genetic loci that contribute to human genetic disease. I also discuss the difficulties in determining the biological relevance of statistical interactions. Genome-wide association studies REVIEWS 392 | june 2009 | VoluMe 10 www.atur.m/rw/gt © 2009 Macmillan Publishers Limited. All rights reserved

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