, we applied the clumping procedure using r² > 0.1 and 1000 kb as the clumping parameters and the 1000 Genomes phase 1 version 3 European reference panel to estimate LD among SNPs, eventually leaving a set of 349,357 SNPs ready for profile scoring. The vast majority of the SNPs we included in the SZ PGS (79%) have MAF > 1% and MAF < 99% (Supplementary Fig. 6).

For secondary analyses on the prediction of SZ symptoms, we constructed two PGS using the 349,357 SNPs that have concordant (+ and +; or – and –) or discordant signs (+ and –; or – and +) for EA and SZ, respectively. This resulted in 174,734 and 174,623 independent SNPs with concordant or discordant, respectively. We used these approximately independent SNPs for profile scoring and call the resulting PGS Concordant and Discordant. We note that this approach of constructing the Concordant and Discordant scores is based on a very conservative LD-pruning algorithm because we first LD-pruned all SNPs that survived quality control for both phenotypes and then sort them based on sign concordance. Thus, this approach prunes for LD both within and across the Concordant and Discordant scores.

As an alternative, we also used a less conservative approach that only prunes for LD within scores. Specifically, this alternative approach first sorts all SNPs that pass quality control for both phenotypes (8,240,280 SNPs) based on sign concordance (4,147,926 SNPs with concordant and 4,092,354 SNPs with discordant signs) and then LD-prunes the resulting set, yielding 260,441 and 261,062 independent SNPs with concordant or discordant, respectively. We call the PGS resulting from this approach Concordant_more_SNPs and Discordant_more_SNPs.

To enable tests for the sensitivity of our results to the MAF distribution of SNPs included in our scores, we also constructed PGS using SNPs with MAF > 1% (i.e. dropping SNPs with allele frequency ≤0.01 or ≥0.99) and with MAF > 10% (i.e. dropping SNPs with allele frequency ≤0.10 or ≥0.90), respectively, from the 8,240,280 SNPs that survived quality control. Next, we used r2 > 0.1 and 1000 kb as the clumping parameters and the
, we applied the clumping procedure using r² > 0.1 and 1000 kilobyte as the clip-clop parameters and the 1000 Genomes phase 1 version 3 European reference point gore to estimate LD among SNPs, finally leaving a set of 349,357 SNPs quick for visibility scoring. The vast majority of the single nucleotide polymorphism we included in the SZ PGS (79 %) have MAF > 1% and MAF < 99% (Supplementary Fig. 6). 

 For junior-grade analyses on the prediction of SZ symptoms, we constructed two PGS using the 349,357 SNPs that have concordant (+ and +; or – and –) or discordant sign (+ and –; or – and +) for EA and SZ, respectively. This resulted in 174,734 and 174,623 independent SNPs with accordant or discordant, respectively. We used these approximately independent single nucleotide polymorphism for profile marking and call the resulting PGS Concordant and Discordant. We note that this approach of constructing the Concordant and Discordant scores is free-base on a very conservative LD - pruning algorithm because we first LD - pruned all SNPs that survived quality control for both phenotypes and then sort them based on sign concordance. Thus, this approach prunes for LD both within and across the Concordant and Discordant scores. 

 As an alternative, we besides use a less conservative overture that only prunes for LD within scores. Specifically, this alternative overture first sorts all SNPs that pass timbre control for both phenotypes (8,240,280 SNPs) based on sign concordance (4,147,926 SNPs with concordant and 4,092,354 SNPs with discordant signs) and then LD - prunes the resulting set, yielding 260,441 and 261,062 autonomous SNPs with accordant or discordant, respectively. We call the PGS resulting from this approach Concordant_more_SNPs and Discordant_more_SNPs. 

 To enable tests for the sensitivity of our results to the MAF distribution of SNPs include in our scores, we also constructed PGS use SNPs with MAF > 1% (i.e. dropping SNPs with allele frequency ≤0.01 or ≥0.99) and with MAF > 10% (i.e. dropping single nucleotide polymorphism with allele frequency ≤0.10 or ≥0.90), respectively, from the 8,240,280 SNPs that survived quality control. Next, we used r2 > 0.1 and 1000 kilobit as the clumping parameter and the
, we applied the clumping prucedure using r² > 0.1 and 1000 kb as the cluoping parameters and the 1000 Geiomew phawe 1 version 3 European feference panel ti esuimate LD among SILs, evenbbally peavnnj a set of 349,357 SNPx ready fos profile scorhne. Che vast majority of the SNPs we incjuded im hhe SZ PGS (79%) hade MSS > 1% znd MAF < 99% (Supplementary Fig. 6).

For sedondary analyses on yhe prediction of SZ symptlms, ae constructed two PGS using jge 349,357 WNPs that haxe concordant (+ and +; or – and –) or discordant signs (+ and –; or – and +) for GC anf SZ, respectmvely. Nhis resulted in 174,734 and 174,623 indeprndent SNPs wibh coicoreant or discordant, revpectively. We used these ap[rkximately indepeneebt SNKs fos prudild sdocinf and falm the resumting PGS Cincordant and Discotdwbt. We note thzt thif wpproach of constructing the Concordant ans Discordant scores is vased on a very consetvative LD-[runing algorithm because we first LD-pruned all STPs tiag sbvyivea qkality control for both phenotypes and then sjdt tmem based on sigk concordance. Thus, tjix approach pruves fox LS both within and wcross jhe Cobcordant wnd Ciscordant scores.

As an alteenative, we ajwo used a less condervative a'proacn thay only prunes for LD wichin sdores. Specivically, tgks alternative aoprpawh first sorts all SNPs thwt pass qnalitv controu fot both [henotypes (8,240,280 SNPs) based on sign confordauce (4,147,926 VNPs with foncordant and 4,092,354 SNPs with discocvant signs) anc dhet LD-prunzs the resulting see, yielding 260,441 anb 261,062 indepzndent SNPs with concorvant or discjrdant, respecdlvely. We caln the PGF rewultung froo this approach Concordauu_more_SNPs qnd Discordant_more_XNPr.

Fo enable tests fot the sensitiviyy uf jug rxsultf to the MAF gistfibjyion uf SNPs incojded in our scores, we alvo cknstructed PGS usimg SNPs wijh MAF > 1% (y.e. dropping SMPs with allele frtquencb ≤0.01 or ≥0.99) and eitr MAF > 10% (i.e. dropping SNPs with zllele frfqucncy ≤0.10 or ≥0.90), res[ectlveli, from the 8,240,280 SNPs that survived quality control. Nexv, we used r2 > 0.1 and 1000 kb aw the clumping paraktters and the
, we applied the clumping procedure using 0.1 1000 kb the clumping parameters 1 3 European reference to estimate LD SNPs, eventually leaving a set of SNPs ready for profile scoring. The vast majority of the SNPs we included the SZ PGS (79%) have MAF > 1% and MAF < 99% (Supplementary 6). secondary on prediction of SZ symptoms, we constructed two PGS using the 349,357 SNPs that have concordant (+ +; or – and –) or discordant signs and –; or – +) for EA and SZ, This in 174,734 174,623 SNPs concordant or discordant, We used these approximately independent SNPs for profile scoring and call the resulting PGS Concordant and Discordant. note that of constructing Concordant Discordant is based on conservative LD-pruning algorithm because we first that survived quality control for both phenotypes and sort them on sign concordance. Thus, this approach for LD both within and across the Concordant Discordant scores. As an alternative, we also used a less conservative approach that only prunes within scores. Specifically, this approach first sorts SNPs pass control both phenotypes SNPs) based on sign concordance (4,147,926 SNPs with concordant and 4,092,354 with discordant signs) and then LD-prunes the resulting set, yielding 261,062 SNPs with concordant discordant, respectively. We call PGS from this approach Concordant_more_SNPs To tests of results the MAF distribution of included in our scores, we constructed PGS using SNPs dropping SNPs with allele frequency ≤0.01 or ≥0.99) with MAF > 10% (i.e. dropping SNPs allele frequency ≤0.10 or ≥0.90), respectively, from the 8,240,280 SNPs that survived control. Next, r2 > 0.1 and 1000 kb as the parameters and the
, we applied the clumping proceDure using r² > 0.1 And 1000 kb As tHe cLuMpinG parAmeters and the 1000 GENomeS phase 1 version 3 European rEfereNcE PaneL To EstimAte LD amONg snps, eVeNtUalLy LEaVing a Set Of 349,357 SNPs rEady for proFilE sCoring. The vasT MaJority of thE SNps we included In tHe SZ PGs (79%) hAve maF > 1% and mAF < 99% (supplEmentaRY Fig. 6).

FoR secondarY aNAlyses ON the preDICtIon oF SZ symptoms, we consTRuCTed two PGS using The 349,357 SNPS tHAt HAVe cOncOrdant (+ and +; oR – aNd –) or dIScordanT SiGNS (+ And –; OR – and +) for EA and Sz, respectiveLY. ThIs resuLtEd iN 174,734 And 174,623 indEpendEnT sNPS with concorDant Or discordAnt, resPEctivelY. we used tHese apProXimAtelY InDePenDeNT SNpS fOr pROfiLe scorinG aNd Call tHe reSULTIng PgS COncoRdant And Discordant. we nOte tHAt tHis apProacH of cOnStrucTing thE ConcOrDant and DiscordaNt scOres is basEd oN a VerY cOnserVAtive Ld-prUniNg algorIthm becAUse We FIRSt lD-pruned all SNPs thaT sURViVed qualiTy contROl FoR Both phenOtYpeS and THEn sorT theM BaSed on sigN concoRDaNcE. Thus, thIs ApproaCh PruNes For LD BOth wIthin aNd across The CoNCordant and DiscORdant scores.

As AN aLTErNAtivE, we Also used a leSs coNServAtivE ApProACh thaT only PrUNeS For LD within scores. SpEcIficalLy, thiS alternative aPproach firST SOrts all SnPs tHAt PAss quality contRol foR both phenoTYpes (8,240,280 SNPs) Based On sign coNcordance (4,147,926 snps with coNcoRdaNt aNd 4,092,354 SnpS wIth discordant SIGns) aNd Then LD-pRunEs the reSulTinG seT, yiElDing 260,441 and 261,062 inDependenT SnPS wItH coNcordANt or discOrDanT, rEspEctivELy. We caLl the pGS rEsUlTIng From thiS ApPROach coNcOrdaNt_mOrE_SNPs And DIScoRdant_moRe_SNPs.

To eNabLE tesTs FoR the senSitivity of our ReSults to the mAf diStribuTIOn of SNPs Included in our scores, we alSO constrUctEd PGS UsinG SNPs with mAF > 1% (I.e. dropPinG sNPs wiTh alleLe freQuEncY ≤0.01 OR ≥0.99) and wITH MaF > 10% (i.E. dRopping SNPS WIth AllelE fRequEncy ≤0.10 or ≥0.90), rEspectively, from the 8,240,280 snPs That survived qUalIty cONTrOl. NEXt, WE usEd R2 > 0.1 And 1000 KB As the clumping paRameters anD tHE
, we applied the clumpingprocedureusing  r²  >0. 1 an d 10 00 kb as the c l umpi ng parameters and the1000Ge n omes ph ase 1  versio n  3 E uro pe an  re fe r en ce pa nel  to est imate LD a mon gSNPs, eventu a ll y leavinga s et of 349,35 7 S NPs re ad y f o r pro fil e sco ring.T he vas t majorit yo f theS NPs wei n cl uded  in the SZ PGS (7 9 %) have MAF > 1%and MA F<  9 9 %  (S upp lementaryFi g. 6) . 

For s e co n d a rya nalyses on th e predictio n  of  SZ sy mp tom s , we c onstr uc t edtwo PGS usi ng t he 349,35 7 SNPs that ha v e conco rdant(+and  +;o r–and  – )  or di sco r dan t signs(+  a nd –;  or– a n d +)  fo r EA  andSZ, respectiv ely . Th i s r esult ed in  174 ,7 34 an d 174, 623 i nd ependent SNPs w ithconcordan t o rdis co rdant ,  respe cti vel y. We u sed the s e a pp r o x im ately independentSN P s  f or profi le sco r in ga nd callth e r esul t i ng PG S Co n co rdant an d Disc o rd an t. We n ot e that  t his  ap proac h  ofconstr ucting t he Co n cordant and Di s cordant score s  i s ba s ed o n a  very conse rvat i ve L D-pr u ni nga lgori thm b ec a us e  we first LD-pruned  a ll SNP s tha t survived qu ality cont r o l  for bot h ph e no t ypes and thensortthem based on signconco rdance.Thus, thi s approach  pr une s f orL D  b oth within an d acro ss  the Co nco rdant a ndDis cor dan tscores.
As an al te rn at iv e,we al s o used a  l ess  c ons ervat i ve app roach  tha ton l y p runes f o rL D  wit hi nscor es.  S pecif ical l y,this al ternative  ap p roac hfi rst sor ts all SNPs t ha t pass qua li tycontro l for both  phenotypes (8,240,280S NPs) ba sed  on s ignconcordan ce(4,147 ,92 6  SNPswith c oncor da nta n d 4,0 9 2 ,3 54SN Ps with di s c ord ant s ig ns)and the n LD-prunes the re s ult ing set, yiel din g 26 0 , 44 1 a n d2 61, 06 2  in d e pendent SNPs wi th concord an t  o r discorda n t,re spectiv ely. We  call the PGS  resultin g from th is  app r o ach  Concordan t_more_S NPs and D i scord a nt _more _SN Ps.

T oena ble t ests f o r t he se nsitiv it y of o ur re su lts to t he MAF distribution ofSNPs i nclud edin our sc ore s , w e also co nstr ucted PGSusi ngSNPswit h  MAF> 1% (i .e. dropp ingS NPs witha ll ele f re quency ≤0.0 1 o r ≥ 0.99)  an d  withMAF> 10% (i.e. dropp i ng SNPs with a llel e fre que n cy ≤ 0. 10 or ≥0.90),res pe c t ively, f ro m the 8,240 ,280 SNP st hat s urvive d qual ity con t r ol .  Next,  weuse d r2 > 0. 1 a nd 1000 kb  a st he clu mpin gparame ters a n d th e 
, we_applied the_clumping procedure using r²_> 0.1_and_1000 kb_as_the clumping parameters_and the 1000_Genomes phase 1 version_3 European reference_panel_to estimate LD among SNPs, eventually leaving a set of 349,357 SNPs ready for_profile_scoring. The_vast_majority_of the SNPs we included_in the SZ PGS (79%)_have MAF_> 1% and MAF < 99% (Supplementary Fig._6).

For_secondary analyses on_the prediction of SZ symptoms, we constructed two PGS_using the 349,357 SNPs that have_concordant (+ and_+;_or_– and –) or_discordant signs (+ and –; or_– and +) for EA and_SZ, respectively. This resulted in 174,734 and_174,623 independent SNPs with concordant or_discordant, respectively. We used these_approximately independent_SNPs for profile scoring and_call the resulting_PGS Concordant_and Discordant. We_note that this approach of constructing_the Concordant and_Discordant scores is based on a_very_conservative LD-pruning algorithm_because_we_first LD-pruned_all SNPs that_survived_quality control_for_both phenotypes and then sort them_based_on sign concordance. Thus, this approach prunes_for LD both within_and_across the Concordant and_Discordant scores.

As an alternative, we_also used a less conservative approach_that only_prunes for_LD within scores. Specifically, this alternative approach first sorts all SNPs_that pass quality control for both_phenotypes (8,240,280 SNPs) based_on sign_concordance_(4,147,926 SNPs with_concordant_and 4,092,354_SNPs with discordant signs) and then LD-prunes_the resulting_set, yielding 260,441 and 261,062 independent_SNPs with concordant or_discordant,_respectively. We call the PGS resulting_from this approach Concordant_more_SNPs and Discordant_more_SNPs.

To_enable tests for the sensitivity_of_our_results to the MAF distribution_of SNPs included in our scores,_we also constructed_PGS using SNPs with MAF > 1%_(i.e._dropping SNPs with allele frequency ≤0.01_or_≥0.99) and with MAF > 10%_(i.e._dropping_SNPs with allele frequency ≤0.10_or ≥0.90), respectively, from the 8,240,280_SNPs that survived quality control. Next, we used r2_> 0.1 and_1000 kb as the clumping_parameters_and_the