Meta-analysis of genetic association with diagnosed Alzheimer’s disease identifies novel risk loci and implicates Abeta, Tau, immunity and lipid processing
B K KunkleBenjamin Grenier‐BoleyRebecca SimsJC BisA.C. NajAnne BolandMaria VronskayaSJ van der LeeAlexandre Amlie‐WolfCéline BellenguezAura FrizattiVincent ChourakiE. P. SELIGMAN MARTINKristel SleegersNandini BadarinarayanJóhanna JakobsdóttirK.L. Hamilton‐NelsonR AlosoRachel RaybouldYong ChenAB KuzmaMikko HiltunenTaniesha MorganShahzad AhmadBN VardarajanJacques EpelbaumPer HoffmannMerçé BoadaG W BeechamJ-G GarnierDenise HaroldA.L. FitzpatrickOtto ValladaresML MoutetAmy GerrishA.V. SmithLiming QuDelphine BacqNicola DenningXueqiu JianYi ZhaoMD ZompoNC FoxML GroveSung‐Hyuk ChoiIgnacio MateoJT HughesHH AdamsJohn MalamonFS GarciaYogen PatelJ. A. BrodyBeth A. DombroskiM.C.D. NaranjoMakrina DaniilidouGudny EiriksdottirShubhabrata MukherjeeDavid WallonJames UphillThor AspelundL.B. CantwellFabienne GarziaDaniela GalimbertiEdith HoferM ButkiewicsBertrand FinElio ScarpiniChloé SarnowskiWilliam S. BushStéphane MeslageJohannes KornhuberC. Jackson WhiteYeunjoo E. SongRC BarberSebastiaan EngelborghsSabrina PichlerDina VoijnovicPM AdamsRik VandenbergheManuel MayhausL. Adrienne CupplesAlbert MsPP De DeynWei GuJJ HimaliDuane BeeklyAlessio SquassinaAM HartmannAdela OrellanaDeborah BlackerEloy Rodríguez‐RodríguezSimon LovestoneM. E. GarciaR.S. DoodyC.M. FernadezRebecca SussamsHonghuang LinTJ FairchildY.A. BenitoClive HolmesHata ComicM P FroschHåkan ThonbergW. MaierG RoschupkinBernardino GhettiVilmantas GiedraitisAmit KawaliaS LiRM HuebingerLena KilanderSusanne MoebusIsabel HernándezM. Ilyas KambohRoseMarie BrundinJames TurtonQiong YangMJ KatzLetizia ConcariJenny LordAS BeiserC. Dirk KeeneSeppo HelisalmiIwona KłoszewskaWA KukullAM KoivistoAoibhinn LynchLluís TárragaLarson EbAnnakaisa HaapasaloBrian LawlorTH MosleyRB LiptonVincenzo SolfrizziMichael GillW. T. LongstrethTJ MontineVincenza FrisardiSara Ortega‐CuberoFernando RivadeneiraRC PetersenVincent DeramecourtAntonio CiaramellaEric BoerwinkleReiman EmNathalie FiévetCarlo CaltagironeJI RotterJS ReischOlivier HanonChiara CupidiAndré G. UitterlindenDR RoyallCarole DufouilRaffaele MalettaSonia Moreno–GrauMary SanoAlexis BriceRoberta CecchettiPeter St George‐HyslopKaren RitchieMagda TsolakiDW TsuangBruno DuboisDavid CraigCK WuHilkka SoininenDespoina AvramidouRoger L. AlbinLaura FratiglioniAntonia GermanouL.G. ApostolovaLina KellerMaria KoutroumaniSE ArnoldFrancesco PanzaOlymbia GkatzimaS. AsthanaDidier HannequinPatrice L. WhiteheadCS AtwoodPaolo CaffarraHarald HampelCT BaldwinLars LannfeltD.C. RubinszteinLL BarnesFlorence PasquierLutz FrölichSandra BarralBernadette McGuinnessTG BeachJ.O. JohnstonJT BeckerPeter PassmoreEH BigioJonathan M. SchottTD BirdJason D. WarrenBF BoeveMK LuptonJD BowenPetroula ProitsiAdam BoxerJF PowellJ R BurkeJK KauweJM BurnsMichelangelo MancusoJoseph D. BuxbaumUbaldo BonuccelliNJ CairnsAndrew McQuillinChuanhai CaoGill LivingstonCS CarlsonNicholas BassCM CarlssonJohn HardyRM CarneyJosé BrásMM CarrasquilloRita GuerreiroMariet AllenHC ChuiElizabeth FisherDH CribbsCarlo MasulloE.A. CroccoCharles DeCarliGina BisceglioMalcolm DickLi MaRanjan DuaraNR Graff-RadfordDA EvansAngela HodgesKM FaberMartin SchererK.B. FallonMarkus J. RiemenschneiderDavid W. FardoReinhard HeunMR FarlowSteven H. FerrisMarkus LeberTM ForoudIsabella HeuserGalasko DrIna GieglingMarla GearingM. HüllGeschwind DhJ.R. GilbertJ.C. MorrisRC GreenKevin H. MayoJH GrowdonThomas FeulnerHamilton RlLE HarrellDmitriy DrichelLS HonigTD CushionMJ HuentelmanPaul HollingworthHulette CmBT HymanIain MarshallGP JarvikAlun MeggyErin L. AbnerGeorgina MenziesLW JinGanna LeonenkoGyungah R JunDetelina GrozevaAnna KarydasGiancarlo RussoJA KayeYoung‐Jung KimFrank JessenKowall NwBruno VellasJH KramerEmma VardyFM LaFerlaKH JöckelLah JjMartin DichgansJB LeverenzDavid MannAI LeveyStuart Pickering‐BrownA.P. LiebermanN. KloppKathryn L. LunettaH-E WichmannLyketsos CgKevin MorganDC MarsonKristelle BrownFrank MartiniukChristopher MedwayMash DcMarkus M. NöthenEliezer MasliahNigel M. HooperWC McCormickAntonio DanieleSM McCurryAntony BayerAN McDavidJohn GallacherAC McKeeHendrik van den BusscheM.‐Marsel MesulamCarol BrayneBruce L. MillerSteffi G. Riedel‐HellerC A MillerJ.H. MillerAmmar Al‐ChalabiJ.C. MorrisChristopher E. ShawA MyersJens WiltfangSid E. O’BryantEliécer CotoJ.M. OlichneyVictoria ÁlvarezJ. E. ParisiAB SingletonHL PaulsonJohn CollingeWilliam PerrySimon MeadElaine R. PeskindMartin RosserAimee PierceNatalie S. RyanWW PoonBenedetta NacmiasHuntington PotterSandro SorbiQuinn JfEleonora SacchinelliAshok RajGianfranco SpallettaMurray A. RaskindPaola BossùBarry ReisbergRobert ClarkeChristiane ReitzA. David SmithJ.M. RingmanDonald WardenED RobersonGordon WilcockEkaterina RogaevaAc BruniRosen HjMaura GalloRosenberg RnYoav Ben‐ShlomoSager MaPatrizia MecocciAJ SaykinPau PástorML CuccaroJM VanceJA SchneiderLS SchneiderWW SeeleyAG SmithJA SonnenSalvatore SpinaRA SternRH SwerdlowTanzi ReTrojanowski JqJ. C. TroncosoVM Van DeerlinLJ Van EldikH.V. VintersJ-P VonsattelSandra WeıntraubKA Welsh-BohmerK. C. WilhelmsenJennifer WilliamsonThomas S. WingoRL WoltjerC.B. WrightCE YuLei YuPK CraneBennett DaVirginia BoccardiPL De JagerN WarnerOL LopezStefan McDonoughMartin IngelssonPanos DeloukasCarlos CruchagaCaroline GraffRhian GwilliamMyriam FornageAlison GoatePascual Sánchez‐JuanPG KehoeNajaf AminNilüfer Ertekin‐TanerClaudine BerrStéphanie DebetteShelly‐Ann LoveLJ LaunerYounkin SgDartigues JfChris CorcoranMA IkramDW DicksonDominique CampionJoAnn T. TschanzHelena SchmidtHákon HákonarsonRon MungerRyder M. SchmidtLindsay A. FarrerChristine Van BroeckhovenMC O’DonovanAL DeStefanoLesley JonesHaines JlJ.-F. DeleuzeM J OwenVilmundur GuðnasonRichard MayeuxValentina Escott‐PriceBM PsatyAgustı́n RuizAlfredo Ramı́rezLS WangCM van DuijnPA HolmansSudha SeshadriJulie WilliamsPhilippe AmouyelSchellenberg GdJean‐Charles LambertMA Pericak-Vance
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Introduction Late-onset Alzheimer’s disease (LOAD, onset age > 60 years) is the most prevalent dementia in the elderly 1 , and risk is partially driven by genetics 2 . Many of the loci responsible for this genetic risk were identified by genome-wide association studies (GWAS) 3–8 . To identify additional LOAD risk loci, the we performed the largest GWAS to date (89,769 individuals), analyzing both common and rare variants. We confirm 20 previous LOAD risk loci and identify four new genome-wide loci ( IQCK , ACE , ADAM10 , and ADAMTS1 ). Pathway analysis of these data implicates the immune system and lipid metabolism, and for the first time tau binding proteins and APP metabolism. These findings show that genetic variants affecting APP and Aβ processing are not only associated with early-onset autosomal dominant AD but also with LOAD. Analysis of AD risk genes and pathways show enrichment for rare variants ( P = 1.32 × 10 −7 ) indicating that additional rare variants remain to be identified.Keywords:
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Genome-wide association studies (GWAS) and sequencing studies are routinely conducted for the identification of genetic variants that are associated with complex traits. Many genetic studies for association mapping include related individuals. When relatives are included in an association analysis, familial correlations must be appropriately taken into account to ensure correct type I error and to increase power. This unit provides an overview of statistical methods that are available for GWAS and sequencing association studies of complex traits in samples with related individuals.
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Genome-wide association studies (GWAS) have become a widely used approach for genetic association studies of various human traits. A few GWAS have been conducted with the goal of identifying novel loci for pigmentation traits, melanoma, and non-melanoma skin cancer. Nevertheless, the phenotype variation explained by the genetic markers identified so far is limited. In this review, we discuss the GWAS study design and its application in pigmentation and skin cancer research. Furthermore, we summarize recent developments in post-GWAS activities such as meta-analysis, pathway analysis, and risk prediction.
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Abstract Genome-wide association studies (GWAS) can serve as strong evidence in correlating biological pathways with human diseases. Although ischemic stroke has been found to be associated with many biological pathways, the genetic mechanism of ischemic stroke is still unclear. Here, we performed GWAS for a major subtype of stroke—small-vessel occlusion (SVO)—to identify potential genetic factors contributing to ischemic stroke. GWAS were conducted on 342 individuals with SVO stroke and 1,731 controls from a Han Chinese population residing in Taiwan. The study was replicated in an independent Han Chinese population comprising an additional 188 SVO stroke cases and 1,265 controls. Three SNPs (rs2594966, rs2594973, rs4684776) clustered at 3p25.3 in ATG7 (encoding Autophagy Related 7), with P values between 2.52 × 10 −6 and 3.59 × 10 −6 , were identified. Imputation analysis also supported the association between ATG7 and SVO stroke. To our knowledge, this is the first GWAS to link stroke and autophagy. ATG7 , which has been implicated in autophagy, could provide novel insights into the genetic basis of ischemic stroke.
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Recent genome-wide association studies (GWAS) have identified a number of novel genetic associations with complex human diseases. In spite of these successes, results from GWAS generally explain only a small proportion of disease heritability, an observation termed the 'missing heritability problem'. Several sources for the missing heritability have been proposed, including the contribution of many common variants with small individual effect sizes, which cannot be reliably found using the standard GWAS approach. The goal of our study was to explore a complimentary approach, which combines GWAS results with functional data in order to identify novel genetic associations with small effect sizes. To do so, we conducted a GWAS for lymphocyte count, a physiologic quantitative trait associated with asthma, in 462 Hutterites. In parallel, we performed a genome-wide gene expression study in lymphoblastoid cell lines from 96 Hutterites. We found significant support for genetic associations using the GWAS data when we considered variants near the 193 genes whose expression levels across individuals were most correlated with lymphocyte counts. Interestingly, these variants are also enriched with signatures of an association with asthma susceptibility, an observation we were able to replicate. The associated loci include genes previously implicated in asthma susceptibility as well as novel candidate genes enriched for functions related to T cell receptor signaling and adenosine triphosphate synthesis. Our results, therefore, establish a new set of asthma susceptibility candidate genes. More generally, our observations support the notion that many loci of small effects influence variation in lymphocyte count and asthma susceptibility.
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Genome-wide association study (GWAS)-based pathway association analysis is a powerful approach for the genetic studies of human complex diseases. However, the genetic confounding effects of environment exposure-related genes can decrease the accuracy of GWAS-based pathway association analysis of target diseases. In this study, we developed a pathway association analysis approach, named Mendelian randomization-based pathway enrichment analysis (MRPEA), which was capable of correcting the genetic confounding effects of environmental exposures, using the GWAS summary data of environmental exposures. After analyzing the real GWAS summary data of cardiovascular disease and cigarette smoking, we observed significantly improved performance of MRPEA compared with traditional pathway association analysis (TPAA) without adjusting for environmental exposures. Further, simulation studies found that MRPEA generally outperformed TPAA under various scenarios. We hope that MRPEA could help to fill the gap of TPAA and identify novel causal pathways for complex diseases.
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The application of high-throughput genotyping in humans has yielded numerous insights into the genetic basis of human phenotypes and an unprecedented amount of genetic data. Genome-wide association studies (GWAS) have increased in number in recent years, but the variants that have been found have generally explained only a tiny proportion of the estimated genetic contribution to phenotypic variation. This article summarizes the progress made in the development of gene set analysis (GSA) and network analysis for GWAS was a way to identify the underlying molecular processes of human phenotypes. It also highlights some promising findings and indicates future directions that may greatly enhance the analysis and interpretation of GWAS.
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