Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people

This is a sel -a chi ed e sion o an o iginal a icle. This e sion may di e om he o iginal in pagina ion and ypog aphic de ails. Au ho (s): Ti le: Yea : Ve sion: Copy igh : Righ s: Righ s u l: Please ci e he o iginal e sion: CC BY 4.0 h ps://c ea i ecommons.o g/licenses/by/4.0/ Genome-wide analyses o indi idual di e ences in quan i a i ely assessed eading- and language- ela ed skills in up o 34,000 people © 2022 he Au ho (s). Published by PNAS Published e sion Eising, Else; Mi za-Sch eibe , Nazanin; de Zeeuw, E eline L.; Wang, Ca ol A.; T uong, Dongnhu T.; Alleg ini, And ea G.; Shapland, Chin Yang; Zhu, Gu; Wigg, Ka en G.; Ge i se, Ma go L.; Molz, Ba ba a; Alagöz, Gökbe k; Gialluisi, Alessand o; Abbondanza, Filippo; Rim eld, Kaili; an Donkelaa , Ma jolein; Liao, Zhijie; Jansen, Philip R.; Andlaue , Till F. M.; Ba es, Timo hy C.; Be na d, Manon; Blokland, Ki s en; Bon e, Milene; Bø glum, Ande s D.; Bou ge on, Thomas; B andeis, Daniel; Ce oni, Fabiola; Csépe, Valé ia; Dale, Philip S.; de Jong, Pe e F.; DeF ies, John C.; Démone , Jean-F ançois; Demon is, Di e; Feng, Yu; Go don, Sco D.; Guge , Sha on L.; Hayiou-Thomas, Ma ianna E.; He nández-Cab e a, Juan A.; Ho enga, Jouke-Jan; Hulme, Cha les; Ke e, Juha; Ke , Elizabe h N.; Kooma , Tanne ; Lande l, Ka in; Leona d, Gab iel T.; Lo e , Mau een W.; Lyy inen, Heikki; Ma in, Nicholas G.; Ma inelli, Angela; Mau e , U s; Michaelson, Jacob J.; Moll, K is ina; Monaco, An hony P.; Mo gan, Angela T.; Nö hen, Ma kus M.; Pauso a, Zdenka; Pennell, C aig E.; Penning on, B uce F.; P ice, Kai lyn M.; Rajagopal, Vee a M.; Ramus, F anck; Riche , Louis; Simpson, Nuala H.; Smi h, Shelley D.; Snowling, Ma ga e J.; S ein, John; S ug, Lisa J.; Talco , Joel B.; Tiemeie , Henning; an de Sch oe , Ma c P.; Ve hoe , Ellen; Wa kins, Ka e E.; Wilkinson, Ma ga e ; W igh , Ma ga e J.; Ba , Ca hy L.; Boomsma, Do e I.; Ca ei as, Manuel; F anken, Ma ie-Ch is ine J.; G uen, Je ey R.; Luciano, Michelle; Mülle -Myhsok, Be am; Newbu y, Dianne F.; Olson, Richa d K.; Pa acchini, Sil ia; Paus, Tomáš; Plomin, Robe ; Reilly, Sheena; Schul e-Kö ne, Ge d; Tomblin, J. B uce; an Be gen, Elsje; Whi ehouse, And ew J. O.; Willcu , E ik G.; S Pou cain, Bea e; F ancks, Clyde; Fishe , Simon E. Eising, E., Mi za-Sch eibe , N., de Zeeuw, E. L., Wang, C. A., T uong, D. T., Alleg ini, A. G., Shapland, C. Y., Zhu, G., Wigg, K. G., Ge i se, M. L., Molz, B., Alagöz, G., Gialluisi, A., Abbondanza, F., Rim eld, K., an Donkelaa , M., Liao, Z., Jansen, P. R., Andlaue , T. F. M., . . . Fishe , S. E. (2022). Genome-wide analyses o indi idual di e ences in quan i a i ely assessed eading- and language- ela ed skills in up o 34,000 people. P oceedings o he Na ional Academy o Sciences o he Uni ed S a es o Ame ica, 119(35), A icle e2202764119. h ps://doi.o g/10.1073/pnas.2202764119 2022 Genome-wide analyses o indi idual di e ences in quan i a i ely assessed eading- and language- ela ed skills in up o 34,000 people Else Eising a , Nazanin Mi za-Sch eibe b , E eline L. de Zeeuw c , Ca ol A. Wang d,e , Dongnhu T. T uong , And ea G. Alleg ini g , Chin Yang Shapland h,i , Gu Zhu j , Ka en G. Wigg k , Ma go L. Ge i se a , Ba ba a Molz a ,G € okbe k Alag€ oz a , Alessand o Gialluisi l,m,n , Filippo Abbondanza o , Kaili Rim eld g,p , Ma jolein an Donkelaa a , Zhijie Liao (廖志洁) q , Philip R. Jansen ,s, , Till F. M. Andlaue l,u , Timo hy C. Ba es , Manon Be na d w , Ki s en Blokland x , Milene Bon e y , Ande s D. Bø glum z,aa,bb , Thomas Bou ge on cc , Daniel B andeis dd,ee, ,gg , Fabiola Ce oni hh,ii ,Val  e ia Cs epe jj,kk , Philip S. Dale ll , Pe e F. de Jong mm , John C. DeF ies nn,oo , Jean-F anc¸ois D emone pp , Di e Demon is z,aa , Yu Feng k , Sco D. Go don j , Sha on L. Guge qq , Ma ianna E. Hayiou-Thomas , Juan A. He n andez-Cab e a ss , Jouke-Jan Ho enga c , Cha les Hulme , Juha Ke e uu, , Elizabe h N. Ke qq,ww,xx , Tanne Kooma yy , Ka in Lande l zz,aaa , Gab iel T. Leona d bbb , Mau een W. Lo e x,xx , Heikki Lyy inen ccc , Nicholas G. Ma in j , Angela Ma inelli o , U s Mau e ddd , Jacob J. Michaelson yy , K is ina Moll eee , An hony P. Monaco , Angela T. Mo gan ggg,hhh,iii , Ma kus M. N€ o hen jjj , Zdenka Pauso a w,kkk , C aig E. Pennell d,e,lll , B uce F. Penning on mmm , Kai lyn M. P ice k,x,nnn , Vee a M. Rajagopal z,aa , F anck Ramus ooo , Louis Riche ppp , Nuala H. Simpson qqq , Shelley D. Smi h , Ma ga e J. Snowling qqq,sss , John S ein , Lisa J. S ug uuu, , Joel B. Talco www , Henning Tiemeie ,xxx , Ma c P. an de Sch oe yyy,zzz , Ellen Ve hoe a , Ka e E. Wa kins qqq , Ma ga e Wilkinson x , Ma ga e J. W igh aaaa , Ca hy L. Ba k,x,nnn , Do e I. Boomsma c,bbbb,cccc , Manuel Ca ei as dddd,eeee, , Ma ie-Ch is ine J. F anken yyy , Je ey R. G uen , Michelle Luciano , Be am M€ ulle -Myhsok l,gggg , Dianne F. Newbu y ii , Richa d K. Olson nn , Sil ia Pa acchini o ,Tom  a s Paus hhhh , Robe Plomin g , Sheena Reilly ggg,iiii , Ge d Schul e-K€ o ne eee , J. B uce Tomblin jjjj ,Elsje anBe gen c,bbbb,kkkk , And ew J. O. Whi ehouse llll , E ik G. Willcu oo ,Bea eS Pou cain a,h,mmmm,1 , Clyde F ancks a,mmmm,nnnn,1 , and Simon E. Fishe a,mmmm,2 Edi ed by U a F i h, Uni e si y College London, London, Uni ed Kingdom; ecei ed Feb ua y 18, 2022; accep ed May 31, 2022 The use o spoken and w i en language is a undamen al human capaci y. Indi idual di - e ences in eading- and language- ela ed skills a e influenced by gene ic a ia ion, wi h win-based he i abili y es ima es o 30 o 80% depending on he ai . The gene ic a chi- ec u e is complex, he e ogeneous, and mul i ac o ial, bu in es iga ions o con ibu ions o single-nucleo ide polymo phisms (SNPs) we e hus a unde powe ed. We p esen a mul icoho genome-wide associa ion s udy (GWAS) o fi e ai s assessed indi idually using psychome ic measu es (wo d eading, nonwo d eading, spelling, phoneme awa e- ness, and nonwo d epe i ion) in samples o 13,633 o 33,959 pa icipan s aged 5 o 26 y. We iden ified genome-wide significan associa ion wi h wo d eading ( s11208009, P=1.098 ×10 28 ) a a locus ha has no been associa ed wi h in elligence o educa ional a ainmen . All fi e eading-/language- ela ed ai s showed obus SNP he i abili y, accoun ing o 13 o 26% o ai a iabili y. Genomic s uc u al equa ion modeling e ealed a sha ed gene ic ac o explaining mos o he a ia ion in wo d/nonwo d eading, spelling, and phoneme awa eness, which only pa ially o e lapped wi h gene ic a ia ion con ibu ing o nonwo d epe i ion, in elligence, and educa ional a ainmen . A mul i a i- a e GWAS o wo d/nonwo d eading, spelling, and phoneme awa eness maximized powe o ollow-up in es iga ion. Gene ic co ela ion analysis wi h neu oimaging ai s iden i- fied an associa ion wi h he su ace a ea o he banks o he le supe io empo al sulcus, a b ain egion linked o he p ocessing o spoken and w i en language. He i abili y was en iched o genomic elemen s egula ing gene exp ession in he e al b ain and in ch o- mosomal egions ha a e deple ed o Neande hal a ian s. Toge he , hese esul s p o ide a enues o deciphe ing he biological unde pinnings o uniquely human ai s. eading jlanguage jgenome-wide associa ion s udy jme a-analysis The p ocessing and p oduc ion o complex spoken and w i en language a e capaci ies ha appea o be dis inc o ou species (1). Such skills ha e become undamen al o day- o-day li e in mode n socie y. Decades o amily and win s udies ha e e ealed subs an ial gene ic componen s con ibu ing o indi idual a ia ion in eading- and language- ela ed ai s as well as o suscep ibili y o associa ed diso de s (2). A ecen me a-analysis in eg a ed a ailable da a on hese skills om 49 win s udies, wi h a o al sample size o 38,000 child en and adolescen s aged 4 o 18 y. The me a-analysis yielded he i abili y es ima es o 66% o wo d eading (me a-analysis o 48 s udies), 80% o spelling (15 s udies), and 52% o phoneme awa eness ( he abili y o iden i y and manipula e indi idual sounds o spoken wo ds; 13 s udies) and sugges ed g ea e gene ic influences on eading- ela ed abili ies han language- ela ed measu es ( win he - i abili y o 34%; me a-analysis o 10 s udies wi h measu es on ecep i e and exp essi e ocabula y, o al language, and naming abili ies) (3). Significance Ou unique capaci ies o spoken and w i en language a e undamen al ea u es o wha makes us human, ye he biological bases emain la gely mys e ious. We p esen a la ge- scale well-powe ed genome-wide associa ion s udy me a-analysis o indi idual di e ences in eading- and language- ela ed skills (wo d eading, nonwo d eading, spelling, phoneme awa eness, and nonwo d epe i ion) in ens o housands o pa icipan s. The findings p omp a majo ee alua ion o p io li e a u e claiming candida e gene associa ions in much smalle samples. Mo eo e , we use he no el gene ic da a as windows in o mul iple aspec s o he biology o hese impo an abili ies, e ealing molecula links o indi idual di e ences in neu oana omy o language- ela ed b ain a eas and en iched he i abili y in a chaic dese s o he human genome as well as in e al b ain enhance egions. This a icle is a PNAS Di ec Submission. Copy igh © 2022 he Au ho (s). Published by PNAS. This open access a icle is dis ibu ed unde C ea i e Commons A ibu ion License 4.0 (CC BY). Published Augus 23, 2022. PNAS 2022 Vol. 119 No. 35 e2202764119 h ps://doi.o g/10.1073/pnas.2202764119 1o 12 RESEARCH ARTICLE | PSYCHOLOGICAL AND COGNITIVE SCIENCES GENETICS OPEN ACCESS Downloaded om h ps://www.pnas.o g by BIN 8102 KIRJASTO KAUSI JUL on Augus 25, 2022 om IP add ess 130.234.230.116. Linkage mapping and a ge ed candida e gene s udies ha e epo ed associa ions o SNPs and/o gene ic loci wi h eading- and language- ela ed ai s as well as wi h diso de s such as dyslexia and de elopmen al language diso de (DLD), which encompasses he olde defini ion o specific language impai men (SLI) (4). Howe e , eplica ion e o s ha e been me wi h limi ed success (4). Mo eo e , o he language sciences, in con as o o he a eas o human gene ics, he e ha e so a been ew genome-wide associ- a ion s udies (GWASs), in which SNPs a millions o poin s ac oss he genome a e sys ema ically sc eened o associa ion wi h he ai o in e es in la ge da ase s comp ising housands o indi iduals (2). GWAS e o s a e beginning o iden i y SNPs ha show genome-wide significan associa ions wi h eading- and language- ela ed ai s: s7642482 nea ROBO2 associa ed wi h exp essi e ocabula y in in ancy (5); s17663182 wi hin MIR924HG wi h apid au oma ized naming o le e s (6); and s1555839 nea RPL7P34 wi h apid au oma ized naming and apid al e na ing s imulus, defici s o which a e o en implica ed in dyslexia (7). None heless, insigh s in o he genomic unde pinnings o hese ypes o skills om GWAS app oaches ha e hus a been limi ed, which may eflec low powe due o he ela i ely small sample sizes o he coho s, such ha he majo i y o gene ic a iance emains unexplained. Sample sizes ha e emained limi ed because o he labo -in ensi e assessmen me hods equi ed o pheno ypic cha ac e iza ion o eading- and language- ela ed ai s, which a e di ficul o e en impossible o eplace wi h simple ques ionnai es. Ye , well-powe ed GWAS e o s ha cha ac e ize he molecula gene ic a ia ion in ol ed in eading- and language- ela ed ai s ha e he po en ial o p o ide no el pe spec i es on he biological bases and o igins o human cogni i e specializa ions (8). He e, we p esen la ge-scale GWAS me a-analyses o a se o eading- and language- ela ed ai s, measu ed wi h psychome ic ools. We cap u ed a ia ion ac oss he pheno ypic spec um, ex ending beyond diso de . Ou s udy ocused on ai s assessed using con inuous measu es in mul iple coho s om he in e na- ional GenLang ne wo k (h ps://www.genlang.o g/) oge he wi h se e al public da ase s ha ha e da a a ailable o he ele an pheno ypes ma ched o genome-wide geno ype in o ma ion. Fi e quan i a i e ai s we e iden ified o which pheno ype da a could be aligned ac oss di e en coho s o yield su ficien ly la ge sample sizes o GWAS: wo d eading, nonwo d eading, spelling, pho- neme awa eness, and nonwo d epe i ion. Uni a ia e GWAS me a-analyses we e pe o med o each o he pheno ypes o iden- i y gene ic a ia ion influencing hese ai s and o model gene ic o e laps be ween hem. Fo compa a i e pu poses, a GWAS me a- analysis o pe o mance in elligence quo ien (IQ) was also pe - o med in he same da ase . Toge he wi h publicly a ailable GWAS summa y s a is ics om p io s udies o cogni i e pe o - mance and educa ional a ainmen , hese da a we e used o s udy gene ic ela ionships be ween eading- and language- ela ed ai s, IQ, and educa ional a ainmen . A mul i a ia e app oach allowed us o op imize he powe o GWAS me a-analysis o unc ional ollow-ups, gi ing insigh s in o he issues, cell ypes, b ain egions, and e olu iona y signa u es in ol ed. Resul s Me a-Analyses o Quan i a i e Reading- and Language-Rela ed T ai s in 22 Coho s. Ou s udy ocused on fi e quan i a i e ead- ing- and language- ela ed ai s: wo d eading accu acy, nonwo d eading accu acy, spelling accu acy, phoneme awa eness, and nonwo d epe i ion accu acy (Table 1). These ai s a e hough o ap in o a numbe o unde lying p ocesses in ol ed in w i en and spoken language. Fo example, nonwo d eading elies hea ily on basic decoding skills [ ansla ing g aphemes one by one in o phonemes (9)], while spelling u ilizes lexical and o ho- g aphic knowledge [unde s anding o pe missible le e pa e ns and how hey a e a anged in specific wo ds (10)]. Phoneme awa eness measu es he abili y o dis inguish and manipula e he sepa a e phonemes in spoken wo ds (11). Nonwo d epe i ion asks ap in o speech pe cep ion, phonological sho - e m mem- o y, and a icula ion (12). A o al o 22 coho s agg ega ed by he GenLang Conso ium combined wi h se e al publicly a ailable da ase s p o ided da a o one o se e al o hese ai s (Da ase s S1–S3 and SI Appendix,Figs.S1andS2). The coho s connec ed in he GenLang ne wo k ei he we e o iginally asce ained h ough a p oband wi h a language/ eading diso de (DLD/SLI o dyslexia) o we e sampled om he gene al popula ion; all coho s include quan i a i e pheno ypic da a ga he ed ia alida ed psychome ic es s as well as genome-wide geno ype da a om he es ed indi id- uals. Some o he samples a e bi h coho s, and some in ol e am- ily o win designs. The pheno ype da a we e collec ed ac oss an a ay o di e en ages, es ins umen s, and languages (p ima ily English bu also, Du ch, Spanish, Ge man, F ench, Finnish, and Hunga ian). We educed he e ogenei y o assessmen age by excluding indi iduals o e 18 y o age (excep o h ee coho s) (SI Appendix,Ex ended Me hods) and whe e pheno ype da a we e a ailable om he same pa icipan a mul iple ages, by choosing Table 1. Pheno ypes and sample sizes o he GWAS me a-analyses T ai Pheno ype desc ip ion Me a-analysis o al sample Me a-analysis Eu opean ances y only No. o coho s No. o indi iduals No. o coho s No. o indi iduals Wo d eading Numbe o co ec wo ds ead aloud om a lis in a ime- es ic ed o un es ic ed ashion 19 33,959 18 27,180 Nonwo d* eading Numbe o nonwo ds ead aloud co ec ly om a lis in a ime- es ic ed o un es ic ed ashion 13 17,984 12 16,746 Spelling Numbe o wo ds co ec ly spelled o ally o in w i ing a e being dic a ed as single wo ds o in a sen ence 15 18,514 14 17,278 Phoneme awa eness Numbe o wo ds co ec ly al e ed in phoneme dele ion/elision and spoone ism asks 12 13,633 11 12,411 Nonwo d* epe i ion Numbe o nonwo ds o phonemes epea ed aloud co ec ly 10 14,046 10 12,828 *A nonwo d is a g oup o phonemes ha looks o sounds like a wo d, obeys he phono ac ic ules o he language, bu has no meaning. 2o 12 h ps://doi.o g/10.1073/pnas.2202764119 pnas.o g Downloaded om h ps://www.pnas.o g by BIN 8102 KIRJASTO KAUSI JUL on Augus 25, 2022 om IP add ess 130.234.230.116. heage ha ma chedbes wi h heassessmen ageso hela ges coho (s). We limi ed he he e ogenei y in oduced by di e en es ins umen s by only including hose ha measu ed he pheno- ypes desc ibed in ou analysis plan (SI Appendix,Supplemen al No es) and in cases whe e da a om mo e han one es ins u- men we e a ailable, by selec ing he es ins umen ha was used by he la ges numbe o coho s. We e alua ed whe he , despi e e o s o minimize his, he - e ogenei y ela ed o age and/o use o di e en es ins umen s emained in ou da a. To do so, we gene a ed GWAS me a- analysis esul s o wo d and nonwo d eading s a ified by age o es ins umen (Da ase S4). We es ed hose da a o gene ic co - ela ions using linkage disequilib ium sco e eg ession (LDSC) (13), de e mining o wha ex en he same common gene ic a ia- ion is accoun ing o pheno ypic a iabili y in he di e en s a i- fied GWAS da ase s (de ailed in SI Appendix, Supplemen al No es). The e was limi ed he e ogenei y o GWAS me a-analysis esul s, as also e iden in he Coch an Q s a is ics (SI Appendix,Fig.S3)and LDSC a ios (Da ase S4) o all ai s excep nonwo d epe i ion. We also assessed e ec s o sex, obse ing high gene ic co ela ions o emale- and male-only subse s (SI Appendix Supplemen al No es, Da ase S4). Gi en he lack o gene ic he e ogenei y, he emainde o he s udy in ol ed analyses o he ull non-s a ified da ase , o ensu e la ges a ailable sample size and maximal powe . A Genome-Wide Signi ican Locus Associa ed wi h Wo d Reading. We pe o med uni a ia e GWAS me a-analyses o he eading-/ language- ela ed ai s in he ull GenLang da ase as ollows. Fo each pheno ype, associa ions be ween SNPs and he quan i a i e ai we e calcula ed in e e y coho sepa a ely, hen combined in o a me a-analysis o ha pheno ype. This yielded fi e sepa a e uni a ia e GWAS da ase s, one o each ai . Fo e alua ing s a- is ical significance o SNP associa ions, we de e mined an app o- p ia e h eshold ha was adjus ed no only o genome-wide sc eening (P=5×10 8 ), bu also o mul iple es ing based on he co ela ion s uc u e o ou fi e eading-/language- ela ed ai s, as es ima ed using pheno ypic Spec al Decom- posi ion (phenoSpD; Ma e ials and Me hods). The significance h eshold o assessing he GWAS me a-analysis esul s was hus se o P=5×10 8 /2.15 independen ai s =2.33 × 10 8 . We iden ified a genome-wide significan locus associa ed wi h wo d eading ( s11208009 C/T on ch omosome 1, P= 1.10 ×10 8 , be a =0.048, SE =0.008) (SI Appendix,Fig. S5). No ably, s11208009 has no shown associa ion wi h gene al cogni i e pe o mance o educa ional a ainmen , while o he SNPs in linkage disequilib ium (LD) wi h s11208009 ( 2 >0.6) ha e been associa ed wi h iglyce ide and o al choles e ol le els in blood in p e ious GWAS (Da ase S5). Th ee genes a e loca ed in he icini y o s11208009 and SNPs in LD ( 2 > 0.6): DOCK7,encodingaguaninenucleo ideexchange ac o impo an o neu ogenesis (14); ANGPTL3,whichencodesa g ow h ac o specific o he ascula endo helium ha is exp essed specifically in he li e (15); and USP1,encodingadeu- biqui ina ing enzyme specific o he Fanconi anemia pa hway (16). The associa ed locus ha bo s an exp ession quan i a i e ai locus egula ing DOCK7 and ATG4C [ano he nea by gene ha encodes an au ophagy egula o (17)] in he ce ebellum and DOCK7,ATG4C,andUSP1 in blood (Da ase S6). Genome- wide significan loci we e no iden ified o he o he ai s. Da ase S7 lis s all esul s wi h P<1×10 6 . T ai s Rela ed o W i en and Spoken Language A e Highly Co ela ed a he Gene ic Le el. The indi idual e ec size o ou genome-wide significan hi is small, as is ypical o gene ically complex ai s. We wen on o make use o he com- ple e GWAS signal conside ed in agg ega e ac oss he genome o gain insigh s in o he gene ic a chi ec u e o he eading-/ language- ela ed ai s as well as ela ionships wi h o he aspec s o human biology. Fi s , o each pheno ype, we es ima ed SNP-based he i abili y: he p opo ion o ai a iabili y explained by he SNPs included in he GWAS. All fi e ai s showed significan SNP-based he i abili y, wi h LDSC-based es ima es anging om 0.13 o nonwo d epe i ion o 0.26 o nonwo d eading (Da ase S4), indica ing ha he cap u ed common gene ic a ia ion accoun s o a subs an i e p opo ion o he pheno ypic a iance. These obse a ions allowed o ollow-up analyses ha a e dependen on significan SNP he i a- bili y, including es ima es o gene ic co ela ions: a measu e ha quan ifies he o e all gene ic simila i y be ween wo complex ai s. Pai wise gene ic co ela ion analyses showed significan o e lap among he eading- and language- ela ed ai s (Fig. 1A and Da ase S4). Gene ic co ela ion es ima es we e especially high o wo d eading, nonwo d eading, spelling, and phoneme awa eness, anging om 0.96 (SE =0.07) o 1.06 (SE =0.07). P io li e a u e has shown pheno ypic co ela ions o eading- and language- ela ed ai s wi h gene al cogni i e pe o mance and educa ional a ainmen . Mos cogni i e assessmen s depend on a combina ion o e bal and non e bal es s. To enable he in es iga ion o gene ic o e laps be ween non e bal cogni i e pe o mance and eading- and language- ela ed ai s while closely ma ching he sample cha ac e is ics o ou s udy, we ca ied ou a GWAS me a-analysis o pe o mance IQ in he GenLang ne wo k (n=18,722) (SI Appendix, Figs. S1–S3). Only non e bal sub es s o gene al in elligence es s we e used in his analysis (Da ase S1). Summa y s a is ics we e also ob ained om ano he h ee sou ces: 1) genome-wide s udies o ull-scale IQ (based on bo h e bal and non e bal asks; n= 257,828) and educa ional a ainmen (n=766,345) by he Social Science Gene ic Associa ion Conso ium (18); 2) a GWAS by sub ac ion s udy ha in es iga ed he noncogni i e abili ies in ol ed in educa ional a ainmen (n=510,795) (19); and 3) a ecen GWAS analysis o school g ades in he Danish In eg a i e Psychia ic Resea ch (iPSYCH) coho (n=30,982) ha used a decomposi ion analysis o iden i y gene ic associa ions wi h dis- inc domains o pe o mance (20). The fi e GenLang eading-/language- ela ed ai s showed mode a e o s ong posi i e gene ic co ela ions wi h ull-scale IQ ( ange =0.52 o 0.77), educa ional a ainmen ( ange = 0.54 o 0.68), and school pe o mance ( ange =0.54 o 0.81) (Fig. 1Aand Da ase S8). In e es ingly, gene ic co ela ions wi h ull-scale IQ we e subs an ially highe o wo d eading (95% CI = 0.70 o 0.85) han nonwo d eading (95% CI =0.50 o 0.68), likely eflec ing he impo ance o eading skills o e bal es s o cogni ion. Gene ic co ela ions o eading-/language- ela ed ai s wi h pe o mance IQ ( ange =0.20 o 0.35) we e much lowe han hose o ull-scale IQ, and he 95% CIs did no o e lap. Indeed, only wo d eading showed a significan gene ic co ela ion wi h pe o mance IQ. These esul s indica e ha eading-/language- ela ed ai s and IQ a e a leas pa ly based on dis inc gene ic ac o s. Significan ai -specific gene ic co e- la ions we e obse ed o componen s 2 o 4 o he Danish school g ade decomposi ion analysis (20). Componen 2, eflec - ing ela i ely be e school g ade pe o mance in language han ma hema ics (as compa ed wi h pee s), was posi i ely co ela ed wi h bo h GenLang eading ai s. Componen 3, eflec ing ela i ely be e school g ade pe o mance in o al han in w i en examina ions, showed significan nega i e co ela ions wi h phoneme awa eness and spelling. Las ly, componen 4, PNAS 2022 Vol. 119 No. 35 e2202764119 h ps://doi.o g/10.1073/pnas.2202764119 3o 12 Downloaded om h ps://www.pnas.o g by BIN 8102 KIRJASTO KAUSI JUL on Augus 25, 2022 om IP add ess 130.234.230.116. eflec ing ela i ely be e school g ade pe o mance in Danish han in English, showed significan nega i e co ela ion wi h nonwo d epe i ion. The noncogni i e abili ies in ol ed in edu- ca ional a ainmen iden ified in he GWAS by sub ac ion s udy (19) showed small bu significan posi i e gene ic co ela ions wi h wo d eading, nonwo d eading, and nonwo d epe i ion. Genomic s uc u al equa ion modeling (GenomicSEM) (21) is a me hod ha can use GWAS esul s o fi and compa e models desc ibing gene ic o e laps be ween mul iple pheno- ypes o u he unde s and how ai s a e ela ed. We used his me hod o model he sha ed gene ic a chi ec u e o he fi e eading- and language- ela ed ai s oge he wi h pe o mance IQ, ull-scale IQ, and educa ional a ainmen (Da ase S9). Explo a o y ac o models wi h one o ou ac o s we e fi ed o he da a, o which he h ee- ac o model explained he majo i y o he a iance. The h ee- ac o model was ollowed up using confi ma o y ac o analysis in GenomicSEM. In he final model (Fig. 1B), he fi s ac o explains a ia ion in non- wo d eading, spelling, phoneme awa eness, wo d eading, and ull-scale IQ. Fo he fi s h ee ai s, he e is no e idence o addi ional gene ic influences, sugges ing high gene ic simila i y. The second ac o explains addi ional a ia ion in ull-scale IQ and is also ela ed o pe o mance IQ and educa ional a ain- men . The hi d ac o explains a ia ion in nonwo d epe i- ion, wo d eading, and educa ional a ainmen . Fac o s 1 and 3 a e highly co ela ed, indica ing ha he gene ic a chi ec u e unde lying wo d eading does no di e much om nonwo d eading, spelling, and phoneme awa eness. Nonwo d epe i ion, on he o he hand, is gene ically mo e dis inc , as indica ed by e idence o specific gene ic influences no cap u ed by he model. Specific gene ic influences we e also e iden o ull-scale IQ and educa ional a ainmen . Thus, al hough ead- ing- and language- ela ed ai s show gene ic o e laps wi h ull- scale IQ and educa ional a ainmen , he model indica es ha hese ai s also ha e unique unsha ed componen s, in line wi h gene ic co ela ion es ima es ha a e lowe han one. Limi ed E idence o Genes P e iously Repo ed in Reading-/ Language-Rela ed T ai s and Diso de s. The numbe o p e i- ous GWAS on eading-/language- ela ed ai s and diso de s is small (Da ase S10), and hese ha e iden ified e y ew associa- ions exceeding genome-wide significance. In hose p io s udies, a o al o 48 independen SNPs me a less s ingen h eshold o P<1×10 6 in he espec i e GWAS. We an lookups o each o hose SNPs in ou GenLang GWAS me a-analysis esul s (Da ase S10). Whe e SNP associa ions passed a h eshold adjus ed o mul iple es ing o 48 SNPs and 2.15 independen GenLang ai s (P<4.84 ×10 4 ), we hen e an he associa ion analyses a e exclusion o he o iginal coho (s) in which he associa ion was fi s iden ified o e alua e independen e ec s beyond hose o he espec i e ea lie s udy. Acco ding o hese c i e ia, only one SNP, s1555839, p e iously associa ed wi h apid au oma ized naming in he Genes, Reading, and Dyslexia (GRaD) coho (7) yielded a sig- nifican signal in he emainde o he GenLang coho s, showing associa ion wi h spelling (P=3.33 ×10 4 ). This SNP is one o fi e SNPs ha eached he h eshold o genome-wide significance in he o iginal GWAS o he GRaD coho . Some 20 genes ha e been desc ibed in he li e a u e as candi- da e genes o eading-/language- ela ed ai s and diso de s based on a ange o mapping app oaches and ha e been he ocus o much o he p io published esea ch in his a ea (4). Da ase S11 gi es gene-based P alues om ou GenLang −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 ** * ** ** ** ** *** ** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** Pe o mance IQ Educa ional a ainmen Full-scale IQ GWAS by subs a ion EA minus ull-scale IQ Componen 1 O e all school pe o mance Componen 2 Language be e han ma hs Componen 3 O al exam be e han w i en Componen 4 Danish be e han English Wo d eading Nonwo d eading Spelling Phoneme awa eness Nonwo d epe i ion * ** ***** *** *** *** *** *** *** *** *** *** ** *** *** *** *** ** Wo d eading Nonwo d eading Spelling Phoneme awa eness Nonwo d epe i ion A B 1 .12 (.07) .06 (.12) 1 .001 (.08) 1 .001 (.06) 1 .21 (.04) 1 .94 (.05) .97 (.06) 1.01 (.05) .71 (.13) 1 uWR Phoneme awa enessg uPA Fac o 1g 1 Fac o 2g Full-scale IQg uIQ Nonwo d eadingg uNREAD uSP Spellingg .35 (.12) .28 (.10) .19 (.20) 1 .31 (.06) 1 Pe o mance IQg uPIQ Educa ional a ainmen g uEA .52 (.27) 1 Nonwo d epe i iong uNREP 1 Fac o 3g .60 (.05) .69 (.08) .58 (.13) .51 (.08) .76 (.08) .60 (.05) .35 (.14) .90 (.08) Wo d eadingg Fig. 1. Reading- and language- ela ed ai s ha e a sha ed gene ic a chi ec u e ha is la gely independen o pe o mance IQ. (A) Gene ic co ela ions ( g) among he eading- and language- ela ed ai s es ima ed wi h LDSC. Es ima es a e capped a one. Full LDSC esul s a e epo ed in Da ase S4. In addi ion, gene ic co ela ions a e gi en be ween he GenLang ai s and 1) pe o mance IQ (using GenLang coho s only); 2) educa ional a ain- men (EA; n=766,345) and ull-scale IQ (n= 257,828) (18); 3) noncogni i e abili ies in ol ed in EA, esul ing om a ecen GWAS by sub- ac ion s udy (n=510,795) (19); and 4) com- ponen s associa ed wi h dis inc pe o mance domains iden ified used a decomposi ion analysis o Danish school g ades (n=30,982) (20). Full esul s can be ound in Da ase S8. *Significan gene ic co ela ion a e co ec- ion o 18.28 independen compa isons (P< 2.74 ×10 3 ); **P<2.74 ×10 4 ; ***P<2.74 × 10 5 .(B) Th ee- ac o model fi ed o he Gen- Lang summa y s a is ics o wo d eading, nonwo d eading, spelling, phoneme awa e- ness, nonwo d epe i ion, and pe o mance IQ and o published GWAS summa y s a is ics o ull-scale IQ and EA (18) using Genomic- SEM (21). Black and g ay pa hs ep esen ac o loadings wi h P<0.05 and P>0.05, espec- i ely. S anda dized ac o loadings a e shown, wi h SE in pa en heses. The subsc ip g ep e- sen s he gene ic a iables; he u a iables ep- esen he esidual gene ic a iance no explained by he models. Uns anda dized esul s and model fi indices a e epo ed in Da ase S9. 4o 12 h ps://doi.o g/10.1073/pnas.2202764119 pnas.o g Downloaded om h ps://www.pnas.o g by BIN 8102 KIRJASTO KAUSI JUL on Augus 25, 2022 om IP add ess 130.234.230.116. GWAS me a-analysis, calcula ed by Mul i-ma ke Analysis o GenoMic Anno a ion (MAGMA) (22), o each o hese genes. Va ia ion in one, namely DCDC2, showed associa ion wi h nonwo d eading ha passed he significance h eshold o mul- iple es ing o 20 genes and 2.15 independen GenLang ai s (P<0.0012). DCDC2 was o iginally iden ified in a linkage egion o dyslexia suscep ibili y, and SNPs in and nea his gene we e subsequen ly associa ed wi h dyslexia in candida e gene s udies (23), al hough some in es iga ions, including a me a-analysis o se en s udies, ailed o suppo his (24). No single candida e SNP highligh ed in p io s udies o DCDC2, no inanyo he candida egene,wassignifican ly associa ed wi h any ai s in he GWAS me a-analysis esul s a e co ec ion o es ing o 54 SNPs and 2.15 independen GenLang ai s (P<4. 31 ×10 4 )(Da ase S11). These null findings highligh he impo ance o la ge-scale s udies o obus iden ifica ion o com- mon DNA a ia ion associa ed wi h eading-/language- ela ed ai s and diso de s and wa an a ee alua ion o he con ibu- ions o candida e genes ha a e p ominen om p io li e a u e. A Mul i a ia e GWAS Analysis o GenLang T ai s Maximizes SNP He i abili y. To imp o e he powe o ou GWAS me a- analysis o ollow-up analyses, we ook ad an age o he high gene ic co ela ions be ween he ai s by pe o ming a mul i a i- a e GWAS analysis o wo d eading, nonwo d eading, spelling, and phoneme awa eness wi h he Mul i T ai Analysis o GWAS (MTAG) me hod (25). This app oach imp o es he e ec es i- ma es o uni a ia e GWAS esul s pe SNP by inco po a ing in o ma ion om he o he gene ically co ela ed ai s. Al hough MTAG gene a es ou pu o each p ima y inpu ai , hese we e ex emely simila as a consequence o he pa icula ly high gene ic co ela ions be ween he ai s. Hence, he mul i a ia e esul s o wo d eading we e used o all ollow-up analyses because he uni a ia e wo d eading GWAS me a-analysis has he la ges sam- plesize(27,180 o heEu opeanances yanalysiscompa ed wi h 12,411 o 17,278 o he o he h ee ai s). Al hough no indi idual SNP eached genome-wide significance in his mul i- a ia e analysis (SI Appendix,Fig.S4), he app oach imp o ed powe o ollow-up analyses in h ee ways: 1) by inco po a ing all a ailable da a om he di e en ai s wi hou inc easing mul- iple es ing bu den; 2) by educing e o a iance and he eby, inc easing he p opo ion o pheno ypic a iabili y cap u ed by gene ic a ia ions, wi h SNP-based he i abili y ha was highe han any o he uni a ia e es ima es wi h a smalle SE (0.29, SE =0.02);and3)becausejoin analysiso hedi e en ai smaxi- mized he e ec i e sample size o he da ase . Indeed, MTAG es ima ed he GWAS equi alen sample size o he mul i a ia e esul s as 41,783 compa ed wi h 27,180 o he co esponding uni a ia e esul s. These mul i a ia e GenLang GWAS esul s we e used o ollow-up analyses u ilizing no only gene ic co e- la ion app oaches, bu also o he genome-based me hods, such as gene p ope y analysis and he i abili y pa i ioning, u he explained below. Assessing Links o Gene ics o Neu oana omical Va ia ion in Reading-/Language-Rela ed Ci cui y. The neu obiological ci - cui y in ol ed in spoken and w i en language has been ex en- si ely in es iga ed in p io li e a u e, om pionee ing pos mo em s udies o b ain lesions h ough o he nonin asi e s uc u al and unc ional neu oimaging esea ch ha is now s anda d o he field (26, 27). No ably, mul iple s udies ha e iden ified ela ion- ships be ween measu es o neu oana omical ea u es and pe o - mance on eading-/language- ela ed asks h ough analyses o de elopmen al changes, o indi idual di e ences, and o ele an diso de s (28, 29). A he same ime, a g owing numbe o la ge- scale GWAS e o s ha e epo ed obus ela ionships be ween common DNA a ia ion and indi idual di e ences in mul iple aspec s o human neu oana omy, including b ain olumes, su - ace a ea and hickness o di e en co ical egions, and whi e ma e mic os uc u e, among o he s (30, 31). The a ailabili y o GWAS da a om beha io al/cogni i e pheno ypes and om magne ic esonance imaging (MRI)-based measu es o neu o- ana omy makes i possible o de e mine gene ic o e laps be ween b ains and beha io , e en when he s udy coho s a e inde- penden (31, 32). He e, we applied his s a egy o assess gene ic ela ionships be ween eading-/language- ela ed measu es and neu- oana omical a ia ion, as ollows. Fi s , we pe o med a li e a u e e iew o selec s uc u al neu oimaging ai s ha 1) encompass b ain egions and whi e ma e ac s wi h known links o aspec s o eading and language and 2) ha e been in es iga ed wi h GWASin hela geUKBiobank esou ce(Ma e ials and Me hods). This yielded 58 s uc u al neu oimaging ai s, includ- ing su ace-based mo phome y (su ace a ea and hickness) (SI Appendix,Fig.S6) pheno ypes and di usion enso imaging (mean and weigh ed mean ac ional aniso opy) (SI Appendix, Fig. S7) esul s. Second, as many o hese b ain-based pheno ypes we e significan ly co ela ed wi h each o he , gene ic co ela ions among hei summa y s a is ics we e used o calcula e he numbe o independen ai s o mul iple es ing co ec ion. Thi d, we pe o med gene ic co ela ion analyses be ween he mul i a ia e GenLang GWAS esul s and he 58 neu oimaging ai s o iden- i y gene ic o e laps. One neu oimaging ai showed significan gene ic co ela ion wi h he mul i a ia e GenLang esul s (P< 0.05/24.85 independen ai s =2.01 ×10 3 ): he su ace a ea o he banks o he supe io empo al sulcus (STS) o he le hemi- sphe e ( g =0.21, SE =0.06) (Fig. 2 and Da ase S12). This finding sugges s he exis ence o sha ed gene ic ac o s ha con- ibu e bo h o le STS su ace a ea and o eading-/language- ela ed skills, albei wi hou iden i ying which SNPs unde lie he ela ionship. Func ional MRI s udies ha e linked his egion o di e en aspec s o w i en and spoken language p ocessing (33–36). To in es iga e u he whe he he fi e di e en eading- and language- ela ed ai s show simila o di e se gene ic co ela- ions o banks o he le STS, we wen on o specifically assess he esul s om heo iginaluni a ia eGenLangGWAS me a-analyses, finding consis en gene ic co ela ions ( ange = 0.18 o 0.23) (Da ase S12). Gene ic Co ela ion wi h T ai s om he UK Biobank and B ain-Rela ed T ai s om he LD Hub. Nex , we assessed gene ic co ela ions o ou mul i a ia e GWAS esul s wi h 20 cogni i e, educa ion, neu ological, psychia ic, and sleeping- ela ed ai s and 515 addi ional UK Biobank ai s using LD Hub. To u he in es iga e o e laps wi h and di e ences om IQ, gene ic co ela ions be ween hese 535 ai s and he pub- lished GWAS summa y s a is ics o ull-scale IQ (18) (n= 257,828) we e ob ained as well. A o al o 143 ai s showed significan gene ic co ela ions wi h he mul i a ia e GenLang GWAS esul s a e co ec ion o mul iple es ing [P<0.05/ (535 ×2) =4.67 ×10 5 ](Da ase S13), while 245 ai s we e gene ically co ela ed wi h ull-scale IQ; 135 ai s showed sig- nifican co ela ions wi h bo h ou mul i a ia e GWAS and ull-scale IQ. T ai s wi h s ong gene ic co ela ions we e ela ed o educa ion, eyesigh , ch ono ype, well-being, li es yle, physical heal h and exe cise, and socioeconomic s a us. Rep esen a i e ai s a e plo ed in Fig. 3. Cogni i e and educa ion- ela ed ai s showed highe gene ic co ela ions wi h ull-scale IQ han wi h he mul i a ia e GenLang GWAS da a. Se e al psychia ic and PNAS 2022 Vol. 119 No. 35 e2202764119 h ps://doi.o g/10.1073/pnas.2202764119 5o 12 Downloaded om h ps://www.pnas.o g by BIN 8102 KIRJASTO KAUSI JUL on Augus 25, 2022 om IP add ess 130.234.230.116. well-being ai s showed significan (nega i e) gene ic co ela- ions wi h ull-scale IQ bu no wi h ou mul i a ia e GWAS: o example, dep essi e symp oms; c oss-diso de suscep ibili y ( om he Psychia ic Genomics Conso ium GWAS); and ense, hu , and ne ous eelings. In con as , se e al ai s ela ed o physical heal h and li es yle showed la ge gene ic co ela ions wi h he mul i a ia e GenLang GWAS esul s han wi h ull- scale IQ, including body mass index, educed alcohol in ake as a heal h p ecau ion, and usual walking pace. Analysis o Genomic Anno a ions Rela ed o Human E olu ion. W i ing and eading a e ela i ely ecen cul u al inno a ions, bu mul iple lines o e idence indica e ha he ele an skills, especially hose in ol ed in decoding as assessed he e, a e based on ou capaci ies o spoken language, which eme ged h ough biological e olu ion along he lineage ha led o humans (37, 38). As no ed abo e, ou mul i a ia e GWAS cap u ed common DNA a ia ion accoun ing o a subs an ial p opo ion o in e indi idual a iabili y in eading-/language- ela ed ai s wi hin ou s udy popula ions. We wen on o es in agg ega e whe he he e we e o e laps be ween he genomic egions d i - ing his associa ion signal and egions in ol ed in aspec s o human e olu ion o e a ange o imescales. To do so, we used LDSC he i abili y pa i ioning (39), a me hod ha uses GWAS esul s o in es iga e whe he common DNA a ian s in a ce ain se o genomic egions, named an anno a ion, explain a la ge p o- po iono heSNP-basedhe i abili yo he ai hanisexpec ed based on he size o ha anno a ion. Building on p io wo k on e olu ion o human b ain s uc u e (40), we s udied fi e anno a- ions eflec ing di e en aspec s o human e olu ion spanning pe iods om 30 Mya o 50,000 y ago (SI Appendix,Fig.S8). The es ed anno a ions included human gained enhance s ac i e in e al and adul b ain issue, ancien selec i e sweep egions, Nean- de hal in og essed a ian s, and a chaic in og ession dese s (de ails a e in SI Appendix,Ex ended Me hods). The la e wo anno a ions ela e o admix u e e en s be ween Homo sapiens and Neande hal popula ions ha ook place when he di e en homi- nins encoun e ed each o he ou side A ica some 50,000 o 60,000 y ago, wi h he consequen gene flow lea ing emnan s (in og essed agmen s) ha can be de ec ed in he genomes o li ing humans. A chaic dese s a e long s e ches in he human genome ha , despi e hese admix u e e en s, a e significan ly deple ed o Neande hal alleles in li ing humans, possibly due o c i ical unc ions o he gene ic loci in H. sapiens and in ole ance o gene flow (41). We obse ed significan ly en iched he i abili y o a chaic dese s, which was obus o mul iple es ing co ec- ion o analysis o fi e anno a ions (P<0.01) (Da ase S14). Thus, ou esul s sugges ha common DNA a ia ion in a chaic dese s makes a la ge con ibu ion o indi idual di e - ences in eading-/language- ela ed ai s wi hin p esen -day pop- ula ions han expec ed by chance. Func ional En ichmen Using He i abili y Pa i ioning and MAGMA Gene P ope y Analysis. We nex in es iga ed whe he egions o he genome wi h issue-specific unc ions a e in ol ed in eading-/language- ela ed ai s, again using LDSC he i abili y pa i ioning. We used anno a ions eflec ing ch oma in signa u es om a b oad ange o issues, as mos a ian s iden ified in GWAS a e loca ed ou side coding egions and a e o en ound en iched in issue-specific unc ional egions o he genome, such as p omo e s, enhance s, and egions wi h open ch oma in. A e co ec ion o es ing o 489 anno a ions (P<1.02 ×10 4 ), h ee anno a ions showed significan he i abili y en ichmen : his one-3 lysine-4 monome hyla ion (H3K4me1) in wo e al b ain samples and he adul b ain ge minal ma ix (SI Appendix, Fig. S9 and Da ase S14). H3K4me1 is conside ed a ma ke o enhance egions. These esul s indica e ha SNPs associa ed wi h eading-/ language- ela ed ai s a e o e ep esen ed in e al b ain enhance s. Nex , we used MAGMA gene p ope y analysis (22) o s udy whe he he mul i a ia e GenLang GWAS esul s we e en iched in a specific issue o b ain cell ype using issue-specificandcell ype–specific gene exp ession da a in Func ional Mapping and Anno a ion (FUMA) (42, 43). As MAGMA co ec s o a e age exp ession, each compa ison can only answe he ques ion o whe he he issue o cell ype is mo e ela ed o he mul i a ia e GWAS esul s han he a e age o he issues o cell ypes in he da ase . A e co ec ion o 83 issues (P<6.02 ×10 4 ), no ela- ion was ound wi h issue-specific gene exp ession pa e ns o adul issues om he Geno ype-Tissue Exp ession (GTEx) p ojec and b ain issues o a specific(de elopmen al) ime omB ain- span (Da ase S15 and SI Appendix,Fig.S10). In he cell ype–specific gene exp ession analysis, h ee single-cell RNA- sequencing da ase s o emb yonic, e al, and adul b ain issue we e used. A e co ec ion o 142 cell ypes (P<3.52 ×10 4 ), he mul i a ia e GWAS esul s we e significan ly associa ed wi h one o he ma u e neu ons om he e al da ase : ed nucleus neu- ons (be a =0.24, SE =0.07) (Da ase S15 and SI Appendix,Fig. S11). This obse a ion could eflec an associa ion wi h his spe- cific nucleus o wi h he highe ma u i y o he neu ons compa ed wi h he o he cell ypes in he e al da ase . The ed nucleus is a la ge s uc u e in he en al midb ain ha is pa o he oli oce e- bella and ce ebello– halamo–co ical sys ems. I plays oles in locomo ion and nonmo o beha io in a ious animals, and in humans, i migh also play a ole in highe co ical unc ions (44). Discussion We pe o med GWAS me a-analysis o fi e quan i a i e eading- and language- ela ed ai s (wo d eading, nonwo d eading, spelling, phoneme awa eness, and nonwo d epe i ion) in sample sizes (up o ∼34,000 pa icipan s) ha a e subs an- ially la ge han p e ious gene ic analyses o eading and/o lan- guage skills assessed wi h neu opsychological ools (p io GWAS e o s wi h maximal sizes o n=1,331 o 10,819) (5–7, 45–47). We iden ified genome-wide significan associa ion o wo d eading ( s11208009 a ch omosome 1, P=1.10 ×10 8 ), highligh ing DOCK7,ATG4C,ANGPTL3,andUSP1 as po en ial candi- da es o in ol emen in his ai . O he SNPs om he locus ha e been associa ed wi h iglyce ide and choles e ol le els (48) bu may ep esen an independen associa ion signal. Robus SNP-based he i abili ies we e obse ed, anging om 0.13 o * 0.25 0.2 0.15 0.1 0.05 0 -0.05 -0.1 -0.15 -0.20 -0.25 g Fig. 2. The mul i a ia e GenLang GWAS esul s show significan gene ic co ela ion wi h he co ical su ace a ea a ound he le STS. Gene ic co e- la ions ( g) we e es ima ed wi h LDSC. Included ai s a e 58 s uc u al b ain imaging ai s om he UK Biobank selec ed based on known links o egions and ci cui s wi h language p ocessing. The esul s o he 22 co ical su ace a eas a e shown; g ay a eas we e no included in he analysis. Full esul s can be ound in Da ase S12 and SI Appendix, Figs. S6 and S7. *Significan gene ic co ela ion a e co ec ing o 24.85 independen b ain imaging ai s (P<2.01 ×10 3 ). 6o 12 h ps://doi.o g/10.1073/pnas.2202764119 pnas.o g Downloaded om h ps://www.pnas.o g by BIN 8102 KIRJASTO KAUSI JUL on Augus 25, 2022 om IP add ess 130.234.230.116. nonwo d epe i ion o 0.26 o nonwo d eading. These SNP- based he i abili ies a e simila in magni ude o hose o he ela ed ai dyslexia (es ima es ange om 0.15 o 0.25 on a liabili y scale) (49, 50); psychia ic ai s, such as A en ion Defi- ci Hype ac i i y Diso de symp oms, in adul s (0.22) (51); and b ain imaging ai s, such as co ical su ace a ea ( ange om 0.12 o 0.33 o di e en egions) (52) and co ical hickness ( ange om 0.08 o 0.26) (52). They a e la ge han ha o psychia ic ai s, such as majo dep ession (0.08) (53) and alcohol depen- dence (0.09) (54). So, despi e highligh ing he need o la ge sample sizes o iden i y loci ha indi idually exceed genome-wide significance, ou GenLang GWAS al eady allowed o mul iple in o ma i e ollow-up analyses based on he ull da ase o com- mon a ian s ac oss he genome, yielding findings ha connec o neu oana omical a ia ion, human e olu iona y his o y, and o he aspec s o he biology o spoken and w i en language. Ou wo k shows o e lapping con ibu ions o common gene ic a ian s o indi idual di e ences in eading-/language- ela ed and cogni i e ai s. Such o e laps a e e iden bo h om gene ic co ela ion analyses and om he h ee- ac o s uc u al equa ion model, in which one ac o explains mos o he a ia ion in wo d eading, nonwo d eading, spelling, and phoneme awa eness. This is in line wi h he widesp ead pleio - opy ound be ween many aspec s o cogni i e unc ioning, including language, eading, ma hema ics, and gene al cogni- i e abili y (6, 55). We no e ha summa y s a is ics om he cu en in es iga ion ha e also been used o in es iga e gene ic o e laps wi h sel - epo o dyslexia diagnosis in an independen GWAS by 23andMe (∼52,000 cases), yielding subs an ial nega- i e gene ic co ela ions be ween GenLang quan i a i e ai s and dyslexia s a us (e.g., 0.71 o wo d eading, 0.75 o spelling) as epo ed by Dous e al. (50). Ye , nonwo d epe i- ion, IQ, and educa ional a ainmen ha e, a leas in pa , di - e en gene ic ounda ions, as eflec ed in he esidual gene ic a ia ion con ibu ing o hese ai s ha is no cap u ed by he model. These findings a e consis en wi h mul iple beha io al s udies showing a dis inc ion be ween nonwo d epe i ion and o he eading- and language- ela ed ai s (56). They a e also in line wi h ecen s uc u al equa ion modeling o gene ic ai in e ela edness o di e en eading- and language- ela ed Cogni i e Educa ion Eyesigh Ch ono ype Li es yle (UKBB) Wellbeing (UKBB) Psychia ic Pain (UKBB) Physical heal h and exe cise (UKBB) SES (UKBB) Noisy wo kplace Job in ol es walking o s anding Job in ol es hea y physical wo k Job in ol es shi wo k Financial si ua ion dissa is ac ion Townsend dep i a ion index a ec ui men Age a las li e bi h Age a i s li e bi h Paid empoymen o sel −employed Unpaid o olun a y job Usual walking pace F equency mode a e physical ac i i y F equency walking a leas 10 minu es F equency igo ous physical ac i i y O e all heal h c i ique Den u es Dias olic blood p essu e Sys olic blood p essu e Body a pe cen age BMI Hip ci cum e ence Fo ced expi a o y olume Fo ced i al capaci y Peak expi a o y low S anding heigh Pa ace amol use Pain all o e he body Knee pain Hip pain Neck o shoulde pain Back pain No pain PGC c oss-diso de (me a-analysis 2013) Dep essi e symp oms (me a-analysis 2016) A u ism spec um diso de (me a-analysis PGC) Ne ous eelings Sensi i i y / hu eelings Tense / highly s ung Loneliness o isola ion Fed−up eelings Mood swings F equency o unen husiasm F equency o enseness / es lessness F equency o dep essed mood F iendships dissa is ac ion Time spen wa ching TV Time spen d i ing Time spen using compu e D i e oo as S opped smoking S opped smoking as heal h p ecau ion Cu en smoking Ne e d ank alcohol Reduced alcohol in ake as heal h p ecau ion Alcohol in ake Bee and cide in ake Red wine in ake Whi e wine in ake Mo ning/e ening pe son (UKBB) Glasses o con ac lenses (UKBB) Glasses o sho −sigh edness (UKBB) Glasses o as igma ism (UKBB) No quali ica ions (UKBB) CSE quali ica ion (UKBB) NVQ o HND o HNC quali ica ion (UKBB) Nu sing o eaching quali ica ion (UKBB) O le els/GCSEs quali ica ion (UKBB) A le el quali ica ion (UKBB) College comple ion (me a-analysis 2013) College o Uni e si y deg ee (UKBB) Age comple ed ull ime educa ion (UKBB) Yea s o schooling (me a-analysis 2016) Numbe o inco ec ma ches (UKBB) P ospec i e memo y es di icul y (UKBB) Childhood IQ (me a-analysis 2014) Fluid in elligence (UKBB) −1.0 −0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 1.0 Gene ic co ela ion ( g) Mul i a ia e GenLang Full-scale IQ Fig. 3. Gene ic co ela ion esul s o he mul- i a ia e GenLang GWAS analysis wi h com- pa isons wi h hose o he la ges published GWAS o ull-scale IQ in LD Hub. Summa y s a- is ics o ull-scale IQ (n=257,828) we e ob ained om he Social Science Gene ic Associa ion Conso ium (18). Gene ic co ela- ions be ween he mul i a ia e GenLang esul s (blue–g een), ull-scale IQ (pu ple), and ai s in LD Hub e eal an o e lap wi h cogni- i e ai s, educa ion, eyesigh , ch ono ype, li es yle, well-being, psychia ic diso de s, pain, physical heal h and exe cise, and socio- economic s a us. A subse o ep esen a i e ai s is shown; 143 ai s showed significan associa ions wi h he mul i a ia e GenLang esul s, and 245 ai s showed significan co - ela ions wi h ull-scale IQ, o which 135 ai s o e lap a e co ec ion o mul iple es ing o 535 ×2 ai s (P<4.67 ×10 5 ). Significan co ela ions a e shown in da k colo s; nonsig- nifican co ela ions a e in ligh colo s. Full esul s can be ound in Da ase S13. UKBB: UK Biobank, GCSE: Gene al Ce ifica e o Second- a y Educa ion, NVQ: Na ional Voca ional Quali- fica ion, HND: Highe Na ional Diploma, HNC: Highe Na ional Ce ifica e, CSE: Ce ifica e o Seconda y Educa ion, PGC: Psychia ic Geno- mics Conso ium, BMI: body mass index, SES: socioeconomic s a us. Gene ic co ela ion ( g) is p esen ed as a do , and e o ba s indica e he SE. PNAS 2022 Vol. 119 No. 35 e2202764119 h ps://doi.o g/10.1073/pnas.2202764119 7o 12 Downloaded om h ps://www.pnas.o g by BIN 8102 KIRJASTO KAUSI JUL on Augus 25, 2022 om IP add ess 130.234.230.116. measu es in he A on Longi udinal S udy o Pa en s and Chil- d en (ALSPAC) coho , which demons a ed a sha ed gene ic ac o accoun ing almos ully o he gene ic a iance in li e acy- ela ed pheno ypes bu o only 53% o ha in non- wo d epe i ion (57). The 23andMe GWAS on sel - epo ed dyslexia u he shows he exis ence o e ec s on eading abili y ha a e independen o IQ; o 42 genome-wide significan loci in ha s udy, less han hal had p e iously been associa ed wi h cogni i e abili y o educa ional a ainmen in p io high- powe ed in es iga ions (50). O e all, ou wo k hus enhances unde s anding o no only he o e laps bu also, he dis inc ions be ween di e en eading-/language- ela ed ai s and mo e gen- e al cogni i e abili ies. Fu he e idence o di e ences in ai e iology be ween he fi e eading-/language- ela ed ai s was obse ed in ou gene ic co ela ion analysis wi h GWAS esul s o componen s (iden ified ia decomposi ion analysis) om school g ades in he Danish iPSYCH coho (20) and wi h esul s o a GWAS by sub ac ion analysis o educa ional a ainmen and cogni i e pe o mance (19). Highe sco es on phoneme awa eness and spelling appea o be gene ically co ela ed wi h be e pe o mance in w i en han in o al examina ions. This may eflec he g ea e impo ance o phoneme awa eness and spelling o p oficien w i ing han o o al language. In e es ingly, wo d and nonwo d eading skills we e associa ed wi h be e pe o mance in language han ma hema ics bu no wi h be e pe o mance in w i en han o al language. Such findings may o e u he p oo ha key eading skills, especially hose in ol ed in decoding as assessed in ou GWAS me a-analyses, o igina e om o al language skills (58). “Componen 4,”co esponding o ela i ely be e pe o mance in Danish ( he na i e language o he pa icipan s in ha s udy) as compa ed wi h pe o mance in English, showed nega i e gene ic co ela ions wi h ou GenLang nonwo d epe i ion measu e, possibly eflec ing he pa icula impo ance o e bal sho - e m memo y in second-language lea ning (59, 60). The esul s o he GWAS by sub ac ion, p e iously p oposed o ep esen so-called “noncogni i e”abili ies ela ed o educa ional a ainmen , such as mo i a ion, cu iosi y, and pe sis ence, we e gene ically co ela ed wi h wo d and nonwo d eading as well as nonwo d epe i ion in GenLang. O no e, gene ic co ela ion analyses may be influenced by gene ic nu u e, he p ocess whe eby DNA a ian s o he pa en s a ec pheno ypic ou comes in hei child en (61). Gene ic a ian s ela ing o he socioeconomic s a us o he amily may, o example, be in ol ed, as was ecen ly ound o cogni i e ai s (62). Fu u e in es iga ions ha include in o ma ion abou non ansmi ed alleles (63) and/o da a om siblings (62) may help o disen angle pleio opy om gene ic nu u e. Humanabili ies op ocessspokenandw i enlanguagedepend on an a ay o dis ibu ed b ain ci cui s (28, 29, 64–66). We pe - o med gene ic co ela ion analyses o ou mul i a ia e GenLang GWAS wi h summa y s a is ics om 58 MRI-based neu oana om- ical pheno ypes chosen because hey conce ned b ain a eas and/o ac s wi h known links o language p ocessing (28, 29, 65, 66). We iden ified a significan gene ic co ela ion wi h co ical su ace a ea o he banks o he STS on he le hemisphe e. The STS is a egion whe e he p ocessing o spoken and w i en language con- e ges, in be ween modali y-specific p ep ocessing and language comp ehension (33–36). A b oad ange o language- ela ed unc- ions has been p e iously linked wi h he le STS h ough (me a- analyses o ) unc ional MRI and posi on emission omog aphy s udies, including hose essen ial o he eading- and language- ela ed ai s included in he GWAS me a-analyses: sublexical p oc- essing o speech (67, 68) and ep esen a ion o phonological wo d o ms (69). The impo ance o his b ain a ea o eading- ela ed ai s is also e iden om a me a-analysis o s uc u al MRI s udies ha ound lowe g ay ma e olume in he le STS ela ed o eading disabili y and poo eading comp ehension (70). Thus, findings om he gene ic co ela ion analysis a e consis en wi h he ole o he STS as a hub whe e he p ocessing o di e en lan- guage modali ies ge s in eg a ed as well as he la e aliza ion o such unc ions. No e, howe e , ha while ou wo k suppo s he exis ence o sha ed gene ic ac o s influencing bo h le STS su - ace a ea and psychome ic measu es o eading-/language- ela ed skills, i does no in o m abou po en ial causal ela ionships o di ec ion o e ec s, no does i iden i y which pa icula molecula mechanisms may be in ol ed. Capaci ies o acqui ing spoken and w i en language appea unique o ou species, building on unde lying skills ha eme ged on he lineage leading o mode n humans, bu e olu iona y accoun s emain subjec o conside able deba e (37, 38). To explo e whe he GWAS da a could gi e insigh s in his a ea, we used he i abili y pa - i ioning o analyze fi e anno a ions ep esen ing di e en ime ames and aspec s o human e olu ion. A chaic in og ession dese s, defined as genomic egions ha a e significan ly deple ed o Neande hal ances y, we e en iched o gene ic a ian s showing associa ions in ou mul i a ia e GenLang GWAS. Such egions a e hough o co espond o genomic loci ha we e in ole an o he gene flow om Neande hal popula ions in o H. sapiens,which ook place h ough admix u e e en s a ound 50,000 o 60,000 y ago (41). These loci a e en iched o conse ed and unc ional geno- mic elemen s: p omo e s and egions conse ed in p ima es (71) andenhance sac i einmany issuesaswellas hosespecific o exp ession in e al b ain and muscle (72). O all egula o y egions in a chaic dese s, b ain enhance s show signs o he mos s ingen pu i ying selec ion agains in og essed Neande hal a ia ion. This e idence o conse a ion and selec ion indica es ha a chaic dese s ma k pa s o he genome whe e a ia ion has a high p obabili y o dele e ious consequence (72). En iched he i abili y o eading-/lan- guage- ela ed ai s in a chaic dese s seems b oadly consis en wi h di e ences be ween Neande hals and H. sapiens in e olu iona y a- jec o ies o language eme gence. Howe e , analyses o his kind a e only indi ec ly in o ma i e and canno pinpoin any specific ime ame o human e olu ion du ing which gene ic a ian s associa ed wi h eading- and language- ela ed ai s we e in oduced. In es iga- ions ha u he in eg a e da a on eading-/language- ela ed gene ic signals and e olu iona y anno a ions o hominin genomes would be wa an ed o shed u he ligh on hese complex ques ions. Rega ding unc ional implica ions o ou findings, he i abili y pa i ioning o he mul i a ia e GenLang GWAS esul s iden ified en ichmen in enhance egions p esen in e al b ain issue and he adul ge minal ma ix. The enhance egions o he ge minal ma ix a e highly simila o hose o e al b ain issues and no he o he adul b ain issues we s udied, likely eflec ing he neu al s em cell popula ion p esen in ha issue (73). In hese analyses, he e was no specific associa ion wi h one pa icula b ain cell ype. Howe e , ollow-up wo k wi h MAGMA using single-cell RNA-sequencing da a om e al, emb yonic, and adul b ain unco e ed a significan associa ion wi h e al neu ons om he ed nucleus, which may ela e o he mo e adul s a e o hese neu ons compa ed wi h he o he cell ypes in he e al da ase (74). The MAGMA analysis could no be used o es o eplica ion o he associa ion wi h ( e al) b ain issue o he LDSC he i abili y pa i- ioning, as esul s in his case a e co ec ed o he associa ion wi h he a e age exp ession o he da ase (22, 42), and he da ase s wi h e al da a only included b ain samples. Ou findings p omp a majo ee alua ion o p io li e a u e on gene ic associa ions wi h eading and language ai s, especially 8o 12 h ps://doi.o g/10.1073/pnas.2202764119 pnas.o g Downloaded om h ps://www.pnas.o g by BIN 8102 KIRJASTO KAUSI JUL on Augus 25, 2022 om IP add ess 130.234.230.116.