 given the power of current GWASs (Lee et al., 2011, 2013; Ridge et al., 2013, 2016).

With the current sample sizes, however, the power of GWASs can be boosted by leveraging polygenic overlap between complex traits (Andreassen et al., 2013a,b, 2015). Shared genetic influences are common among complex traits (Visscher et al., 2017). Statistical methods based on the conditional FDR framework can detect polygenic overlap between complex traits and utilize this polygenic overlap to increase the power to identify common genetic variants associated with each trait and jointly with two or more traits (Andreassen et al., 2013a,b, 2015). We aimed to use these methods to identify the shared genetic basis between AD and BIP.

Materials and Methods

Data Sources

We obtained summary statistics (i.e., effect sizes and corresponding p-values for all SNPs) from the IGAP (Lambert et al., 2013) and the PGC2-BIP (Stahl et al., 2019).

International Genomics of Alzheimer’s Project

The IGAP is a two-stage study. We used data from stage 1 of the study, which is based upon four previously published GWASs [The European Alzheimer’s Disease Initiative (Dreses-Werringloer et al., 2008; Heath et al., 2008), the Alzheimer Disease Genetics Consortium (Jun et al., 2010), The Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (Psaty et al., 2009), The Genetic and Environmental Risk in AD consortium (Harold et al., 2009)] on 17,008 AD cases and 37,154 controls of European ancestry. The IMPUTE2 (Howie et al., 2009) or MaCH/Minimac (Li et al., 2010) software were used to impute SNPs from the European ancestry haplotypes in the 1000 Genome Project (Altshuler et al., 2010). In stage 2 of the study, SNPs with p-values < 1 × 10-3 from stage 1 were selected for genotyping in independent samples. We did not use data from stage 2 of the study since the conditional FDR method require genome-wide summary statistics which are not inflated.

Diagnoses of AD in the sub-studies of IGAP were in most cases made clinically according to the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s disease and Related Disorders Association criteria (Mc
given the power of current GWASs (Lee et al. , 2011, 2013; Ridge et al. , 2013, 2016). 

 With the current sample sizes, however, the exponent of GWASs can be promote by leveraging polygenic overlap between complex traits (Andreassen et al. , 2013a, b, 2015). share genetic influences are coarse among complex trait (Visscher et al. , 2017). Statistical methods based on the conditional FDR framework can detect polygenic overlap between complex traits and use this polygenic overlap to increase the power to identify common familial variants associated with each trait and jointly with two or more traits (Andreassen et al. , 2013a, b, 2015). We aimed to use these method to identify the shared genetic footing between AD and BIP. 

 Materials and Methods 

 Data Sources 

 We obtained summary statistics (i.e., consequence sizes and match p - values for all SNPs) from the IGAP (Lambert et al. , 2013) and the PGC2 - BIP (Stahl et al. , 2019). 

 International Genomics of Alzheimer ’s Project 

 The IGAP is a two - stage study. We use data from stage 1 of the study, which is based upon four previously published GWASs [ The European Alzheimer ’s Disease Initiative (Dreses - Werringloer et al. , 2008; Heath et al. , 2008), the Alzheimer Disease Genetics Consortium (Jun et al. , 2010), The Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (Psaty et al. , 2009), The Genetic and Environmental hazard in AD consortium (Harold et al. , 2009) ] on 17,008 AD case and 37,154 controls of European ancestry. The IMPUTE2 (Howie et al. , 2009) or MaCH / Minimac (Li et al. , 2010) software were used to impute SNPs from the European ancestry haplotypes in the 1000 Genome Project (Altshuler et al. , 2010). In stage 2 of the study, SNPs with phosphorus - value < 1 × 10 - 3 from stage 1 were choose for genotyping in autonomous samples. We did not use data from stage 2 of the report since the conditional FDR method acting require genome - wide summary statistics which are not balloon. 

 Diagnoses of AD in the sub - studies of IGAP were in most cases make clinically according to the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer ’s disease and Related Disorders Association criteria (Mc
 gigen the power of current GWASs (Lee et al., 2011, 2013; Ridje et am., 2013, 2016).

With ghe current sample sizes, howxver, the kjwer of GWASs can ce boostef by levwragmng polygenic ovxdlap bebceen dlmplzx traits (Andreaxsen et al., 2013a,b, 2015). Shared getegie influences are common among complev traitx (Gisscher et al., 2017). Stseistjbao methods based on the conditjonal FVR framework cam detect polygenic overlap betaeen complex traitd and utilies trus polygenic overlap to increase tge power to identify common gendtic rariants aswoxiahgd with each train and jointly with twm or mote traits (Andrcassei et al., 2013a,b, 2015). We aimed to uve these methods tj identiff che shared genetic bawiw betfeen AD xbd CIP.

Javerjals ajd Jethods

Datz Sources

We obtained summary suatywtics (i.e., effedt sizqs and corresponding p-values for all SNPs) frkm the IGAP (Lambert et ql., 2013) and the PGC2-BIP (Stwhl et al., 2019).

International Genomics of Alzheimer’s Project

The HGAP ms a uwi-stagd shudy. We used data from stage 1 of the study, whydh ix based upon fjur previouxlj loblished GWASs [The Ebdolean Alzheimer’s Didease Igitiarive (Drests-Wertingloer et al., 2008; Heath et al., 2008), the Alzheiiwr Disease Geneticd Consortiuo (Jum et sl., 2010), The Cohorts for Heaxt and Aging Resewrch in Gsvomic Epidemioloey bonsmrtium (Psaty et al., 2009), The Gegetic and Envnronmentxl Rosk in AD consorhium (Mdrold et al., 2009)] on 17,008 AF casgs and 37,154 controls of European ancestry. The IMPUTX2 (Howie et al., 2009) mr KaCH/Miniiac (Ll et al., 2010) softwawe were used tp imputz SNPs from the Vuropean encestry hapjotypes in tha 1000 Genome Prooect (Altsrulee et al., 2010). In rtage 2 of the xtudy, SNPs with p-vaoues < 1 × 10-3 from stagc 1 wete selected for gzuouyping in indepemdevt falpkef. We did not gse aatx from stage 2 of bhe stucy since the conditimnal FDR method requirr nenome-widg summary statistics wnich are not inflaued.

Diajnoses of AC ig the sub-studies of IGAP were jn most cwsef made clinisalln acsording to the National Institute of Neurological and Communicativg Disorders and Strokg akd the Alzhemmer’s qisease atd Related Disorders Association crittria (Mc
given the power of current GWASs (Lee 2011, Ridge et 2013, 2016). With the of GWASs can boosted by leveraging overlap between complex traits (Andreassen et 2013a,b, 2015). Shared genetic influences are common among complex traits (Visscher et al., Statistical methods based on the conditional FDR framework can detect polygenic overlap between traits utilize polygenic to increase the power to identify common genetic variants associated with each trait and jointly with or more traits (Andreassen et al., 2013a,b, 2015). aimed to use these to identify the shared genetic between and BIP. and Data We obtained summary (i.e., effect sizes and corresponding p-values for all SNPs) from the IGAP (Lambert et al., 2013) and PGC2-BIP (Stahl 2019). International of Project IGAP is a We used data from stage 1 which is based upon four previously published GWASs European Alzheimer’s Initiative (Dreses-Werringloer et al., 2008; Heath al., 2008), the Alzheimer Disease Genetics Consortium (Jun al., 2010), The Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (Psaty et The Genetic and Environmental in AD consortium et 2009)] 17,008 cases and controls of European ancestry. The IMPUTE2 (Howie et al., 2009) or (Li et al., 2010) software were used to impute SNPs European haplotypes in the Genome Project (Altshuler et 2010). stage 2 of the with < from 1 selected for genotyping in samples. We did not use from stage 2 of FDR method require genome-wide summary statistics which are inflated. Diagnoses of AD in the sub-studies IGAP were in most cases made clinically according to the National Institute Neurological and and Stroke and the Alzheimer’s disease and Related Association criteria (Mc
 given the power of current GWAss (Lee et al., 2011, 2013; RIdge eT al., 2013, 2016).

witH tHe cuRrenT sample sizes, hoWEver, The power of GWASs can be boOsted By LEverAGiNg polYgenic oVErLAP beTwEeN coMpLEx TraitS (AnDreasseN et al., 2013a,b, 2015). ShaRed GeNetic influenCEs Are common aMonG complex traiTs (VIsscheR eT al., 2017). sTatisTicAl metHods baSEd on thE conditioNaL fDR fraMEwork caN DEtEct pOlygenic overlap beTWeEN complex traits And utiLiZE tHIS poLygEnic overlaP tO incrEAse the pOWeR TO IdeNTify common genEtic variantS AssOciateD wIth EAch traIt and JoINtlY with two or mOre tRaits (AndrEassen ET al., 2013a,b, 2015). We AImed to uSe thesE meThoDs to IDeNtIfy ThE ShaREd GenETic Basis betWeEn aD and bIP.

MATERIals And methOds

DaTa Sources

We obTaiNed sUMmaRy staTistiCs (i.e., EfFect sIzes anD corrEsPonding p-values fOr alL SNPs) from The iGaP (LAmBert eT Al., 2013) and tHe PgC2-BiP (Stahl Et al., 2019).

IntERnaTiONAL GEnomics of Alzheimer’S PROJeCt

The IGAp is a twO-StAgE Study. We uSeD daTa frOM Stage 1 Of thE StUdy, which Is baseD UpOn Four preViOusly pUbLisHed gWASs [tHe EuRopean alzheimeR’s DisEAse Initiative (DREses-WerringloER eT AL., 2008; HEAth eT al., 2008), The AlzheimeR DisEAse GEnetICs conSOrtiuM (Jun eT aL., 2010), thE cohorts for Heart and AGiNg ReseArch iN Genomic EpideMiology conSORTium (PsatY et aL., 2009), thE genetic and EnviRonmeNtal Risk in ad consortIum (HaRold et al., 2009)] On 17,008 AD cases AND 37,154 controlS of eurOpeAn aNCEsTry. The IMPUTE2 (HOWIe et Al., 2009) Or MaCH/MIniMac (Li et Al., 2010) sOftWarE weRe Used to impUte SNPs fRoM tHe euRopEan anCEstry hapLoTypEs In tHe 1000 GenOMe ProjEct (AlTshuLeR eT Al., 2010). IN stage 2 oF ThE STudy, sNps With P-vaLuEs < 1 × 10-3 froM staGE 1 weRe selecTed for genOtyPIng iN iNdEpendenT samples. We did NoT use data frOm StaGe 2 of thE STudy sincE the conditional FDR methoD Require GenOme-wiDe suMmary statIstIcs whiCh aRE not inFlated.

diagnOsEs oF ad in thE SUb-StuDiEs of IGAP weRE In mOst caSeS madE clinicAlly according to the nAtiOnal Institute Of NEuroLOGiCal ANd cOmmUnICatIVE Disorders and StRoke and the alZHeImer’s diseaSE anD RElated DIsorderS AssoCIation cRiteria (Mc
 given the power of curren t GWASs (L ee et  al .,20 11,2013 ; Ridge et al. ,  201 3, 2016).

With the cu rrent  s a mple si zes,however ,  t h e  po we rofGW A Ss  canbeboosted  by levera gin gpolygenic ov e rl ap between  co mplex traits  (A ndreas se n e t  al.,  20 13a,b , 2015 ) . Shar ed geneti ci nfluen c es arec o mm on a mong complex trai t s( Visscher et al ., 201 7) .  S t a tis tic al methods  b asedo n the c o nd i t i ona l  FDR framewor k can detec t  po lygeni cove r lap be tween  c o mpl ex traits a nd u tilize th is pol y genic o v erlap t o incr eas e t he p o we rtoid e nti f ycom m ongeneticva ri antsasso c i a t ed w ith  eac h tra it and jointl y w itht woor mo re tr aits  ( Andre assenet al .,  2013a,b, 2015) . We  aimed to  us ethe se  meth o ds toide nti fy thesharedg ene ti c b as is between AD andBI P . 
Material s andM et ho d s

DataSo urc es
W e  obta ined su mmary st atisti c s(i .e., ef fe ct siz es  an d c orres p ondi ng p-v alues fo r all SNPs) from the IGAP (Lambert et a l. ,  201 3)and the PGC 2-BI P  (St ahle tal. ,  2019 ).

I nt e rn a tional Genomics ofAl zheime r’s P roject

The I GAP is a t w o - stage st udy. We used data from  stag e 1 of the study, w hichis based  upon fou r previous lypub lis hed G WA Ss [The Europ e a n Al zh eimer’s  Di sease I nit iat ive  (D re ses-Werri ngloer e tal .,  2 008 ; Hea t h et al. ,200 8) , t he Al z heimer  Dise aseGe ne t ics  Consor t iu m (Jun  e tal.,  20 10 ), Th e Co h ort s for H eart andAgi n g Re se ar ch in G enomic Epidem io logy conso rt ium  (Psat y et al.,2009), The Genetic andE nvironm ent al Ri sk i n AD cons ort ium (H aro l d et a l., 20 09)]on  17 , 0 08 AD c as esan d 37,154 c o n tro ls of  E urop ean anc estry. The IMPUTE2 (Ho wie et al., 2 009 ) or M aC H/M i ni m ac(L i  et a l., 2010) softw are were u se d  t o impute S N Psfr om theEuropea n anc e stry ha plotypesin the 10 00  Gen o m e P roject (Al tshuleret al., 2 0 10).I nstage  2of the  s tud y, SN Ps wit h  p- value s < 1×10-3 f rom s ta ge 1 wer e selected for genotypi ng inindep end ent sampl es. Wedid not u se d ata from s tag e 2  of t hes tudysinc e  t hec ondit iona l  FDR meth o dreq u i re  genome-wid e s umm ary s tat i sticswhic h are not inflate d .

Diagnoses o f AD i n t hes ub-s tu dies of IGAP w ere  i n most cas es  made clini cally ac co r dingto the  Natio nal Ins t i tu t e of N euro log ical andCom mu n icative  D is o rdersandSt roke a nd the Alzh e i mer’s disease an d Rel a t ed Di s ord ers A ss ociatio n  cri teria (Mc
 given_the power_of current GWASs (Lee_et al.,_2011,_2013; Ridge_et_al., 2013, 2016).

With_the current sample_sizes, however, the power_of GWASs can_be_boosted by leveraging polygenic overlap between complex traits (Andreassen et al., 2013a,b, 2015). Shared_genetic_influences are_common_among_complex traits (Visscher et al.,_2017). Statistical methods based on_the conditional_FDR framework can detect polygenic overlap between complex_traits_and utilize this_polygenic overlap to increase the power to identify common_genetic variants associated with each trait_and jointly with_two_or_more traits (Andreassen et_al., 2013a,b, 2015). We aimed to_use these methods to identify the_shared genetic basis between AD and BIP.

Materials_and Methods

Data Sources

We obtained summary statistics_(i.e., effect sizes and corresponding_p-values for_all SNPs) from the IGAP_(Lambert et al.,_2013) and_the PGC2-BIP (Stahl_et al., 2019).

International Genomics of Alzheimer’s_Project

The IGAP is_a two-stage study. We used data_from_stage 1 of_the_study,_which is_based upon four_previously_published GWASs_[The_European Alzheimer’s Disease Initiative (Dreses-Werringloer et_al.,_2008; Heath et al., 2008), the Alzheimer_Disease Genetics Consortium (Jun_et_al., 2010), The Cohorts_for Heart and Aging Research_in Genomic Epidemiology consortium (Psaty et_al., 2009),_The Genetic_and Environmental Risk in AD consortium (Harold et al., 2009)] on_17,008 AD cases and 37,154 controls_of European ancestry. The_IMPUTE2 (Howie_et_al., 2009) or_MaCH/Minimac_(Li et_al., 2010) software were used to impute_SNPs from_the European ancestry haplotypes in the_1000 Genome Project (Altshuler_et_al., 2010). In stage 2 of_the study, SNPs with p-values <_1 × 10-3 from stage_1_were_selected for genotyping in independent_samples. We did not use data_from stage 2_of the study since the conditional FDR_method_require genome-wide summary statistics which are_not_inflated.

Diagnoses of AD in the sub-studies_of_IGAP_were in most cases made_clinically according to the National Institute_of Neurological and Communicative Disorders and Stroke and the_Alzheimer’s disease and_Related Disorders Association criteria (Mc