A multi-ancestry genetic reference for the Quebec population

Domain: neuroscience
Published: 2026-02-03
Editorial outcome: minor_revision

Abstract
While internationaleffortshavecharacterizedgeneticvariationinmillionsof
individuals,theinterplayofenvironmental,social,culturalandgeneticfactors
ispoorlyunderstoodformostworldwidepopulations.TheprovinceofQue-
becinCanadahasbeenthesiteofnumerousgeneticstudies,oftenfocusingon
Mendeliandiseasesinfoundersub-populations.Here,weanalyzegenome-
widegenotypedvariationin29,337Quebecresidentsfromthemulti-ancestry
population-basedcohortCARTaGENE(CaG)whoprovidedDNAsamples.We
alsosequencethewhole-genomeof2,173CaGparticipantswithfourgrand-
parentsborninCanada(n=1879),Haiti(n=163)andMorocco(n=131).Weuse
thisgeneticinformationtogaininsightintoQuebec’sdemographyandtohelp
interpretthepotentialsignificanceofvariantsidentifiedinclinicallyimportant
genes(e.g.,SPG7implicatedinhereditaryspasticparaplegia).Wevalidatean
imputationpanelconstructedbyphasingtheCaGwhole-genomesequence
dataandfind,usinggenome-wideassociationstudies(GWAS)of42clinically
relevanttraits,thatitincreasesthenumberofassociatedlociby~7%when
comparedtoresultsobtainedafterimputationwiththelargerTOPMed
imputationpanel.WeprovideallelefrequencyinformationandGWASresults
throughdedicatedandpubliclyavailablewebsites.Thegeneticdata,paired
withphenotypicandenvironmentalinformation,isavailableforglobal
researchuse.

Introduction
The field of human genetics has benefited immensely from large The province of Quebec in Canada has a population of 8.8M
nationalpopulation-basedcohortssuchasdeCODE,theUKBiobank, individuals.MostQuebecresidentsderiveancestryfrom~8500indi-
Biobank Japan, and FinnGen. While these cohorts offer unprece- viduals, primarily settlers from France, who moved to New France
dentedopportunitiestoimprovestatisticalmethodsanddiscover duringtheFrenchcolonialerainthe17thand18thcenturies(Supple-
newgenotype-phenotypeassociations,theyonlycaptureasubsetof mentaryFig.1)1.ManygeneticstudieshavestudiedQuebecresidents
thegeneticandenvironmentalvariationthatissusceptibletoaffect withFrench-Canadianancestry(QFC)toidentifypathogenicvariants
humanpopulationsinhealthanddisease.Cohortsthataredesigned thatarerelativelyfrequentincertainregionsofQuebecbutveryrare
to represent populations with unique histories, environments, or elsewhere2–4. Collectively, Quebec residents have ancestry from the
geographic contexts can provide novel insights into the genetic Americas (especially among Indigenous individuals), Europe (e.g.,
architecture of human diseases, revealing population-specific var- earlyFrenchandBritishcolonists,ormorerecentimmigrants),aswell
iants, gene-environment interactions, and mechanisms of disease. as regions around the world primarilyvia more recent immigration
They also can help tailor public health and precision medicine waves(e.g.,fromSouthAsia,NorthernAfricaortheCaribbean)(Sup-
strategies (e.g., genetic screening programs or polygenic risk plementary Fig. 1)5. Genetic ancestry is not distributed uniformly
scores)usingdatathatcapturetheuniquecharacteristicsofthese acrossthepopulationbutcorrelateswithgeographic,ethnicandlin-
populations.

Toprovideacomparativeframeworkforanalyzinggeneticpatterns, weusepopulationdescriptorsthroughoutthisstudythataimtocap-
ture geographical differences among ancestors of participants (see glossary in Supplementary Note 1), although we acknowledge that these labels do not account for the complexity of human ancestral relationships.

CARTaGENE (CaG) is the largest population-based cohort in Quebec.Itwasinitiatedin2007tofacilitategeneticandepidemiolo-
gicalresearchinthecosmopolitanpopulationoftheprovince9.This cohortincludes43,032participants(55%women)thatwererecruited fromsixdifferentmetropolitanareasofQuebec,includingMontreal (70%ofthecohort),QuebecCity(15.3%),Sherbrooke(4.5%),Saguenay (4.2%), Gatineau (3.7%) and Trois-Rivières (2.3%). These six regions accounted for ~72% of the population of Quebec as of 2024. Approximately 16% of participants self-identified as having non- Europeanethnicity.Participantswere40-69yearsoldatthebaseline visitandprovidedextensivehealthandsocio-demographicinforma-
tion.Theyarebeingfollowedregularlythroughrecontactphasesand ancillarystudies,anditispossibletolinkeachparticipantwiththeir correspondinghealthrecordthroughtheprovincialhealthcaresystem and theirenvironmentaldata through the Canadian UrbanEnviron-
mental Health Research Consortium (CANUE). The genealogies of 7894CaGparticipantswerereconstructedbytheBALSACProject,a vastreconstructionofQuebecgenealogythatisespeciallycomplete amongQFCindividuals10.CaGalsohasabiobankwithbiospecimens (e.g.,DNA,blood,plasma)availablefor29,337participants.

Herewepresentanalysesofgenome-widegenotypingarraydata for29,337CaGparticipants,showinghowhistoricalanddemographic eventssuchaswavesofimmigrationhaveshapedthegeneticstructure of modern Quebec. We generated whole-genome sequence (WGS) datafrom2173CaGparticipantsandshowedhowthisresourcefacil-
itates the study of rare genetic diseases as well as complex traits throughimprovedgenotypeimputationofsingle-nucleotidevariants (SNVs), insertions-deletions (indels), structural variants (SVs) and humanleukocyteantigen(HLA)alleles.Weconductedgenome-wide associationstudies(GWAS)for42quantitativephenotypes,identifying >350independentloci(inparticipantsofEuropeanancestry,>450loci in cross-ancestry GWAS) with variants that reach genome-wide sig-
nificance(P-value<5×10−8).WereleasedCaGallelefrequencyinfor-
mation,andGWASandexpressionquantitativetraitloci(eQTL)results throughpubliclyavailableCaGbrowsers.Individual-leveldatafromthe CaG dataset is available to both academic and industry-sponsored researchprojects.

Results
GeneticstructureinCaG
Genome-widegenotypingarraydataontheIlluminaGSAplatformis available for 29,337 participants who consented to DNA research (Supplementary Tables 1, 2 and Supplementary Fig. 2). Principal component (PC) analysiscaptures main axes ofgenetic variation in CaG.The first and second PCs capturecontinentalgenetic ancestry patterns(Fig.1A),andthethirdandfourthPCsdistinguishabranch correspondingtoawell-knownfoundereventintheregionsofChar-
levoixandSaguenay-Lac-St-Jean(SLSJ)inQuebec,aclinewithindivi-
dualsofMediterraneanancestries(NorthAfricaandMiddleEastern), andabranchwithancestryfromLatinAmerica(SupplementaryFig.3).
Uniformmanifoldapproximationandprojection(UMAP)11ofthefirst tenPCsischaracterizedbyclustersoftencorrelatingwithcountryand regionofbirthoftheparticipants(Fig.1B,SupplementaryFig.3and Supplementary Table 1). Results from identity-by-descent(IBD) seg-
mentanalyses(Methods)wereinlinewiththePC-basedgeneticsimi-
larity patterns but helped reveal finer clusters of participants (SupplementaryNote2,SupplementaryFigs.4,5).AmongQFC,clus-
tersprimarilyreflectgeographicalvariation,includingthepreviously described founder effect from the SLSJ region (Supplementary Fig.3)6,12.BothIBDandUMAPalsorevealedagroupofparticipants with ancestors from the Gaspé Peninsula or the Canadian Atlantic provinces,presumablyrelatedtotheAcadianpopulation12,13.

MigrationsthatshapedQuebec
AncestralcomponentanalysisinCaGreflectedarangeofdemographic effects related to human migrations. Admixture analysis of partici-
pantsborninNorthAfricashowedalongitudinalgradientofancestry acrossNorthAfrica,consistentwithback-to-Africamigrations(Fig.1C and Supplementary Fig. 6A)14. The analysis also reflected the more recentwavesofmigrationtoQuebec.Forexample,the201individuals withfourMorocco-borngrandparents(QMO)formtwoclustersonan admixtureplot(Fig.1C),whichcorrelatewithself-reportedethnicity variables.Admixturecluster1(AC1,primarilyblue)has“Arab”asmodal self-reportedethnicity,whereas admixture cluster 2 (AC2, primarily purple)has“Jewish”asmodalself-reportedethnicity.MostQMOpar-
ticipantsinCaGwerethemselvesborninMorocco(97%)andreported either the year of immigration to Quebec or the age at which they immigrated. QMO individuals in AC2 are born earlier on average (mean=1951,standarddeviation[SD]=7.6)comparedtoindividualsin AC1(mean=1962,SD=6.5).Theaverageyearofimmigrationdiffered between clusters (AC1, mean=1998 and SD=9; AC2, mean=1977, SD=11) and the reported ethnicity in each cluster reflects the two historicallydocumentedwavesofMoroccanimmigrationtoQuebec (Supplementary Fig. 6B)15,16. In contrast to older individuals, QMO individualsbornafter1965inAC2aremorelikelytobechildrenof migrants,asthemajoritywerebornoutsideofMorocco(5outof7).
Therefore,anyfocusedanalysisoftheQMOwillrequirecarefulcon-
siderationofconfoundingbetweenage,geneticancestry,generation status,andethnicity.

HaitiisanotherimportantsourceofimmigrationinQuebec.The distribution of immigration year (mode=1974, mean=1986, SD=13) among 267 individuals with four Haiti-born grandparents (QHA) reflectstheincreaseofimmigrationfromHaititoQuebecintheearly 1970s, referred to as the “second wave”, which formed the largest immigrationgroupinQuebecbetween1973and1974(Supplementary Fig. 7)17,18. At the continental level, we find appreciable variation in estimated European and African ancestry proportions, and limited ancestryfromtheAmericas(fromK=4,meanAMR=0.5%,EUR=13.3% andAFR=86.2%,SDforAFR=13.2%)(Fig.1D).TheproportionofEur-
opeanancestryinQHAindividualsiscomparableifmarginallylower thanthevalueof16%reportedpreviouslyinasmallsampleofHaiti-
born individuals in South Florida19. At K=5, the African ancestry componentseparatesintwo,withapurplecomponentpredominantin CaG individuals borninCameroon and Democratic Republic ofthe Congo, and a green component predominant in Yoruba individuals fromIbadan,Nigeria(YRI)andCaGindividualsfromIvoryCoast.Inthis model,QHAindividualsarerepresentedashavingcomparablelevels ofbothancestries,incontrastwithAfro-Caribbeanindividualsfrom Barbados (ACB) from the 1000 Genomes project, which have more Yoruba-like(green)ancestry.

AncestryfromtheAmericas
The originalinhabitants of Quebec werethe First Nations and Inuit Peoples.IndividualslivinginQuebectodayhavevariableamountsof ancestrythatcanbetracedtoancestorslivingintheAmericaspriorto Europeancolonization3,20.Outof500randomlyselectedCaGpartici-
pantswithfourgrandparentsborninCanada,theproportionofesti-
mated ancestry from the Americas was 0.4%, from K=4, with no individual carrying more than 5% ancestry from the Americas (Sup-
plementaryFig.8).Thus,CaGonlycaptureslimitedamountsofgenetic variationpresentinNorthAmericapriortoEuropeancolonization.The CaG cohort was recruited in urban areas of Quebec and excluded individualslivingonreservations,bothofwhichmayresultinunder-
samplingindividualswithappreciableancestryfromtheAmericas.

VariantdiscoveryinCaGusinghigh-depthwhole-genomeDNA sequencing
Tocomplementthegenotypingarraydataandidentifyrarevariants foundintheQuebecpopulation,weperformedhigh-coverageWGS of2184CaGparticipants,including1765QFC,163QHAparticipants, and 132 QMO participants (the remaining 124 participants were non-QFC European-ancestry individuals) (Supplementary Fig. 9 and SupplementaryData1).Afterquality-control,weobtainedWGSdata for2173participantsatameancoverageof33.4X(1756QFC,163QHA, 131QMO).Intotal,weidentified80,407,530variants(16.8%notpre-
sent in dbSNP v154), including >3.1M singletons (Supplementary Data2).WhiletheQHAandQMOsamplesaccountedfor14%ofour WGSeffort,theyyielded22%ofthenewlydiscoveredvariants(Sup-
plementaryFig.10).Toenablefastvariantallelefrequencyqueriesand support clinical genetic applications, we have released allele fre-
quencies for each of the three main groups (QFC, QHA, QMO) sequenced in CaG through a publiclyavailableCaGvariantbrowser (https://cartagene-bravo.cerc-genomic-medicine.ca/).Weexpectthat the data in this browser will facilitate the interpretation of variants discovered in patients of French-Canadian, Haitian and Moroccan geneticancestriesinQuebecandelsewhere.

Fromthe WGS data, we identified high-impact coding variants, definedaspotentiallyfunctional(annotatedasnonsense,frameshiftor essentialsplicesite)and/orpathogenic(basedonClinVar),andcom-
paredtheirfrequencywiththefrequencyinthebestmatchedpopu-
lation from gnomAD v4 (Supplementary Fig. 11 and Supplementary Data2)21.Asexpected,giventhefoundereventsintheQFCpopulation history,theallelefrequenciesofmanyclinicallyimportantrarevariants arehigherinCaGparticipantsthaningnomADnon-FinnishEuropeans (NFE)(SupplementaryTable3).Althoughthesamplesizeislimited,we alsorecoveredClinVar-annotatedpathogenicvariantsintheQHAand QMOWGSsubsetsthataremorefrequentthaninthematchedgno-
mADpopulations,suchasavariantinAGPAT2(causinglipodystrophy) previouslyfoundinfamiliesofAfricandescent,andvariantsinthe FAM161A (causing retinitis pigmentosa) and DSG4 (causing hypo-
trichosis) previouslyidentified in MoroccanJewishfamilies (Supple-
mentaryTable3).

Structuralvariants
FromtheWGSdata,weidentifiedandgenotyped25,820SVs,including 24,022deletionsand1,798duplications(SupplementaryFig.12A).On average,anindividualCaGgenomeincludes4,118SVs(Supplementary Fig. 12B). When wecompared with the gnomAD-SV database(allele frequenciesfromallgnomADpopulations)22,wefoundthat74%ofthe CaGSVswereinthedatabasewithhighlyconcordantallelefrequencies (Pearson’sr=0.81fordeletionsandr=0.54forduplications,Supple-
mentaryFig.12C).MostSVsarerare(80%haveaminorallelefrequency [MAF] <5%) and encompass on average 10.5kb of genomic DNA sequence (median length 1.3kb, Supplementary Fig. 12D, E). The majority (~70%) of the SVs were specific to an ancestral group, including14.0%and4.2%thatwereonlyfoundinQHAandQMOpar-
ticipants,respectively(SupplementaryFig.12F,G).UsingAnnotSV version3.2.3,weannotatedtheCaGSVs:46.5%overlapagene,and107 arepredictedtobepathogenic(class5,AnnotSVrankingscore≥0.99, basedonthejointconsensusrecommendationofAmericanCollegeof MedicalGenetics[ACMG]andClinGen)(SupplementaryFig.12Hand SupplementaryData3).Forinstance,wefoundonecarrierofa7.9kb deletionencompassingthepromoterandfirstexonoftheHEXAgene: this deletion has been reported in QFC patients with Tay-Sachs disease23.Wealsoidentifiedsixcarriersofthe15+kbLDLRdeletion, whichisknowntocause60%offamilialhypercholesterolemiacasesin QFC24.IntheCaGWGSdataset,the15+kbLDLRdeletionisassociated withanincreaseinplasmaLDL-cholesterollevels(+1.45mmol/dele-
tion,P-value=4.8×10−5).

HLAgenotyping
UsingtheWGSdata,wealsoperformedHLAallelegenotypingfor11 classical class I and class II HLA genes (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1,-DQB1, -DRB1, -E, -F, and -G) to G-group resolution using the softwareHLA*LA25.WenotethatourHLAanalysesaresubjecttothe inherentlimitationsofcallingHLAallelesfromshort-readsequencing technology,whichleadstolowercallingaccuracycomparedtoSNVs. Aftervariant-andsample-levelqualityfiltering,wegenotyped43,189 HLA alleles, on average 0.9 per gene and per haploid genome. We observed307distinctHLAalleles,distributedasfollows:55HLA-A,73 -B,38-C,9-DPA1,26-DPB1,11-DQA1,17-DQB1,55-DRB1,5-E,2-F,and 16-G(SupplementaryFig.13A).HLA-E,-F,and-Gallelespassedquality thresholdsbutwerenotusedinsubsequentanalysesduetolackof comparativeallelefrequencydata.For57one-fieldHLAallelespresent inCaGandareferencedatasetforQuebec26,allelefrequenciescorre-
lated well (Pearson’s r=0.97) (Supplementary Fig. 13B and Supple-
mentaryData4).RegionalallelefrequenciesacrossQuebecwerealso well correlated, with Pearson’s r ranging from 0.86–0.96, demon-
stratingrepresentativenessandconfidenceofHLAallelecallinginCaG (Supplementary Data 4). Cohort-wide and population-specific HLA allele frequencies were compared to the largest publicly available multi-ancestryHLAreferencepanel(N=21,546)27.18HLAallelesfrom theCaGdatasetwerenotpresentinthisglobalreferencepanel,andan additional89alleleswereatsignificantlydifferentfrequencies(Fisher’s exacttestP-value<2.3×10−4).Frequenciesremainedsignificantlydif-
ferentfor51alleleswhentestedintheQFCCaGsubsetascomparedto Europeanfrequenciesinthereferencepanel(SupplementaryFig.13C andSupplementaryData5).Ouranalysisidentifiedonealleleofpos-
sibleinterest,theHLA-A*24:02:02allele(allelefrequency[AF]=0.0023 in CaG, AF=2.3×10−5 in global reference panel, P-value=4.2×10−8), whichisassociatedwithincreasedriskofadversedrugreactions28,29 and was one of the most significantly enriched alleles in CaG. Other examples include the enriched HLA allele DRB1*11:14:01 (CaG AF=0.0012, global reference AF=7.0×10−5, P-value=9.7×10−5), whichhasbeenassociated withjuvenileidiopathicarthritis(JIA)30,31, and HLA-B*51:01 (CaG AF=0.07, global reference AF=0.04, P-value=4.0×10−13), which is also associated with adverse drug reaction32andBehçet’sdisease33.Whiletheenrichmentinthefirsttwo casescouldbeexplainedbydriftassociatedwithasingleFrenchsettler carrying the allele, an increase of 3 percentage points in allele fre-
quencyoverthesameperiodisnotconsistentwithfoundereventsin Quebec.Rather,itpointstoamismatchbetweentheFrenchsource populationfrequencyandgnomADpopulationsofEuropeanancestry (withahigherproportionofUKandNorthernEuropeancestrycon-
tributions).Indeed,HLA-B*51:01hasafrequencyof7%inFrance34,35, and can reach up to 25% in Silk Road nations35,36. Thus, variant enrichmentinQuebecrelativetotherestofNorthAmericamaynot alwaysreflectrecentfounderevents,butratherdifferencesinances-
tralcomposition.

VariantsofclinicalinterestinSPG7
Variants of clinical interest (VCI) involved in disease are typically classified as pathogenic or likely pathogenic based on ACMG guidelines37.KnowledgeofVCIinagenecontributestotheabilityto confirmdiagnosesinaffectedindividuals,assesstheprevalenceofthe associated condition, and estimate the population carrier rates for autosomal recessive conditions. We used CaG WGS to compare different approaches to study VCI in the gene SPG7 (MIM 602783). PathogenicvariantsinSPG7areassociatedwithanautosomalrecessive form of hereditary spastic paraplegia (HSP, MIM 607259) that is characterized by adult-onset progressive bilateral leg weakness and spasticity, sensory neuropathy, and in some cases ataxia and dysarthria38.Diagnosisisbasedontypicalclinicalfindingsandidenti-
ficationofbiallelicpathogenicvariantsinSPG7.HSPduetoSPG7var-
iants is a frequent cause of spastic ataxia in French Canadians in Quebec(QFC)39.

First, we retrieved two well-documented pathogenic SPG7 var-
iants—c.1529C>T(p.Ala510Val)andc.988-1G>A—intheCaGWGS data(Fig.2andSupplementaryTable4)39.Thec.1529C>Tvariantwas foundatasimilarfrequencyinCaGWGS(0.0051)relativetognomAD NFE(0.0058),whereasc.988-1G>Awassignificantlyenriched(three carriers;oddsratio(OR)=36.6;P-value=8.3×10−4).Second,weiden-
tifiedallSPG7variantsintheCaGWGSdatawithClinVarpathogenicity interpretation and found six with previous evidence, of which five passedmanualcurationforpathogenicityevidencebasedonACMG criteria(Fig.2andSupplementaryTable4)37,includingthetwovariants describedabove.Third,startingwith2,054potentialpathogenicvar-
iantsinSPG7(includingputativeloss-of-functionandsplicesitevar-
iants),weprioritizedninevariantsincludingthesixvariantsidentified in the first and second approaches. Amongst the additional three variants, the missense c.2293G>A (p.Asp765Asn) variant was sig-
nificantlyenrichedintheCaGcohort(OR=21.9;P-value=0.0019).A newsplicevariantwasalsosignificantlyenriched:c.376+1G>T(OR= 11.2, P-value=0.032). This shows that putative founder variants identifiedinstudiesofaffectedindividualsmayinfactbeasfrequentin regional or worldwide populations (e.g., p.Ala510Val), whereas pre-
viously unreported variants are likely to be founder variants (e.g., p.Asp765Asnandc.376+1G>T).

Ancestralrecombinationgraph(ARG)reconstruction40ofeightof thenineSPG7variantsdescribedabovesuggestedcommonancestry withinthelast15generations,consistentwiththeintroductionofeach oftheseallelesbyasingleindividualinearlyNewFrance.Theexcep-
tionwasc.1529C>T(p.Ala510Val),withatimetomostrecentcommon ancestor (TMRCA) of 96 generations (Fig. 2 and Supplementary Table5),suggestingmultipleintroductions,consistentwiththehigh gnomAD NFE frequency mentioned above. We used ARG-based imputationtoidentifylikelycarriersofeachvariantamongthegeno-
typedCaGindividuals,andselectedthreeSPG7variants(c.988-1G>A, c.1045G>A, and c.2293G>A) with over 40 carriers linked to the BALSAC genealogical record to perform genealogical analysis using ISgen41.Weidentifiedthesamefounderfamilyasthemostlikelyto haveintroducedbothc.988-1G>Aandc.1045G>A.Thisfamilycon-
tainstwocoupleswhoweremarriedinPerche,Franceinthe1600s.We estimatedtheallelefrequencyforthethreeSPG7variantsfor23his-
toricregionsofQuebec(Fig.2).Despiteacommoninferredfounder family,resultsshowunevendistributionacrossregions,likelyaresult of regional founder events: for c.988-1G>A, we identified Côte-de-
Beaupré(carrierfrequency1/210)andPortneuf(1/288)astheregions withhighestestimatedcarrierfrequency.Forvariantc.1045G>A,we estimatedthehighestfrequenciesinCharlevoix(1/188)andSaguenay-
Lac-St-Jean(1/197).Finally,variantc.2293G>Ashowedhighercarrier frequencyinBas-Saint-Laurent(1/167)andCôte-du-Sud(1/225).Thus, founder variants in QFC are strongly affected by regional founder events.

Insummary,theanalysisofpopulation-basedsequencedatafrom individual genes, together with unique historical migration records andgenealogiesinQFC,canhelpsystematicallyclarifytheprevalence ofknownandputativenoveldisease-causingvariantsandtheirfine-
scaledistributioninthepopulation.

Haplotypereferencepanelandgenotypeimputation
Genotype imputation in populations with unique histories benefits from using an ancestry-matched reference haplotype panel42. We assembledaCaGreferencepanelusinghaplotypesfrom2173WGSCaG participants,whichincluded15,483SVs,307HLAalleles,and~44mil-
lionSNVs/indels.Weassessedthequalityofthereferencepanelusing independentsamplesof10familytriosand141unrelatedindividuals fromQuebec.Weobservedthebestaccuracyofstatisticalphasingin the CaG panel inQFC individuals (average switch errorrate [SER]= 0.152%andstandarderror[SE]=0.005)(SupplementaryFigs.14–16 andSupplementaryData6–8).Addingpriorphasinginformationfrom the~2timeslarger1000GenomeProjectandHumanGenomeDiver-
sity Project’s combined panel (N=3941)43 to the statistical phasing modelprovidedonlyminimalgaintophasingaccuracyforcommon variants in QFC individuals (reference panel alternate allele count >200,theaveragepairwiseSERwasreducedby0.003-0.010%),sug-
gesting that the CaG panel captures the majority of QFC-specific haplotypes.

Validationwasperformedbydown-samplingWGSdatatogeno-
typingarraypositionfromindividualsnotusedinthereferencepanel (MethodsandSupplementaryFig.17).Inregionsthatarenotchallen-
gingtosequence44,theCaGreferencepanelenabledtheimputationof 179,840(SE=352)SNVs/indelsperindividualthatweremissedbythe multi-ancestryTOPMedpanel(N=133,597)whilebeingpresentinthe original WGS data (Supplementary Fig. 17A, B). These had average concordanceof99.33%(SE=0.04%).Asexpected,variantsenrichedin CaGweremorelikelytobewell-imputedwiththeCaGbutnotwiththe TOPMed panel: variants with 8-fold enrichment in CaG relative to gnomADNFE(andwithAF<5%ingnomADNFE),were3.51-foldmore likelytobepresentintheoriginalWGS,imputedbyCaG,butmissingin TOPMed-imputed data relative to all variants with frequency below 0.05 in gnomAD NFE (Supplementary Fig. 17C). On average, 31,410 (SE=1,635) SNVs/indels per individual were missed by both panels (SupplementaryFig.17A).Thesevariantswerealsomorelikelytobe enrichedinCaGrelativetognomADNFE(SupplementaryFig.17C,up to1.66-fold),andtobeatlowgnomADallelefrequency(Supplemen-
taryFig.17D),suggestingthatallelecountsformanyvariantsinCaG werenotquitehighenoughtoimputecorrectly,andthatevengreater number of QFC reference haplotypes is needed to capture all rare allelesthataremorefrequentinQFC.Wealsoconfirmedtherobust-
ness of the CaG panel in imputing SVs and HLA alleles. The con-
cordancebetweenSVsidentifiedusingWGSdataandimputedvalues wasbetween96.3%and98.8%,dependingontheSVtypeandimpu-
tationqualitythresholds.Similarly,96.2-99.1%ofimputedHLAalleles wereconcordantwithtypedHLAallelesdependingontheirresolution and imputation quality thresholds (Methods and Supplemen-
taryNotes).

Genome-wideassociationstudies
WeperformedGWASon42continuoustraits(SupplementaryData9) usinggenotypedosagesfromimputationwiththeCaGpanel,withthe TOPMedpanel,andwiththemeta-imputationofthesetworeference panels(Methods).WeusedaP-value<5×10−8todefineprimaryGWAS loci;correctingfor42phenotypesdidnotimpactourconclusionsbut reducedthenumberofdiscoveries(SupplementaryFig.18andSup-
plementaryData10,11).Despitecontaining~75timesfewerreference haplotypes than the multi-ancestry TOPMed panel, the CaG panel sampledfromthestudypopulationallowedfortheidentificationof moreindependentgenome-widesignificantlociwhenanalyzingCaG participantsofEuropeangeneticancestry(378lociwithCaGvs.356 withTOPMed,N=27,037)orallparticipants(482lociwithCaGvs.452 withTOPMed,N=29,334).Themeta-imputationapproachshowedthe largestnumberofgenome-widestatisticallysignificantloci,especially in the multi-ancestry case (379 in European-ancestry and 515 in all ancestries).

TheoverlapinidentifiedGWASlocibetweendifferentimputation approaches was high but not complete (Fig. 3 and Supplementary Fig.19).Among58additionallociidentifiedusingonlytheCaGpanel, 23(~40%)hadaleadvariantabsentintheTOPMedpanel(Fig.3).In contrast, among 33 additional loci identified when using only the TOPMedpanel,onlysix(~18%)leadvariantswereabsentfromtheCaG panel(Fig.3).Thus,theincreasedvariantcoveragewhenusinglocal referencepanelsseemscriticaltouncoveradditionalassociationsig-
nals,whilelargerreferencepanelsmayhelpimprovetheimputation accuracyofmorecommonvariantspresentinbothdatasets.Ingen-
eral,theTOPMedpanelresultedinbetterimputationqualityscores thanCaGforvariantswithAF<0.01,whileCaGshowedslightlybetter scoresforvariantswithAF≥0.01(SupplementaryFig.20).Theimpu-
tationqualitywasbetterforleadvariantsatsignificantGWASlociin theCaG-imputedthanintheTOPMed-imputeddata,exceptforloci sharedbetweenTOPMedandmeta-imputationresultsonly(subsetD, Fig. 3B). The largest improvement in imputation quality (median pairwisedifferenceof0.034)wasfoundatlocisharedonlybetween CaGandmeta-imputationresults(subsetB).Thisimprovementwas higherthanexpectedforthesamenumberofvariantswithsimilarAF acrosstheentiregenome(two-tailedP-value=0.009after1,000,000 permutations). Thus, local reference panels may provide additional GWAS signals through improved imputation of variants that are alreadyavailableinglobalpanels(Fig.3andSupplementaryFig.19).

TofacilitateaccesstotheCaGGWASresultsandenablereplica-
tionstudies,wereleasedthesingle-variantassociationresultsintoa publicly available PheWeb browser (https://cartagene-pheweb.cerc-
genomic-medicine.ca/).WealsosummarizetheGWASresultsforthe imputed SVs and HLA alleles in the Supplementary Notes, Supple-
mentary Fig. 21 and Supplementary Data 12, 13. In this section, we emphasize two noteworthy examples that demonstrate the advan-
tages of using the CaG imputation panel to perform GWAS in the Quebec population. First, a one base-pair deletion (rs796144540) insideanintronoftheinsulinreceptorgeneINSRwasassociatedwith increased thyroid-stimulating hormone (TSH) levels (MAF=0.42, P-
value=3.3×10−8) (Supplementary Fig. 22). This variant was absent from the TOPMed panel, presumably because it was missed during sequencingorremovedatQC,butwell-imputedwiththeCaGpanel (INFO=0.96).Conditionalanalysiscontrollingforrs796144540iden-
tifiedasecondindependentrareINSRintronicvariantassociatedwith TSHlevels(rs8107575,INFO=0.85,MAF=0.001,P-value=8.5×10−6). Thyroid abnormalities were previously reported in patients with mutations in INSR45. Second, we identified an association between basophil counts and a rare intergenic variant (rs867418280, INFO= 0.77, MAF=0.0032, P-value=3.7×10−8) that was well-imputed with theCaGhaplotypesbutabsentfromtheTOPMedimputationpanel (SupplementaryFig.23).

We found thatseveralsentinel variants that werewell-imputed withtheCaGpanelorthemeta-imputationweremorefrequentinQFC comparedtognomADNFE(SetsA,BandCinFig.3).Thisobservation motivatedasecondaryanalysisinwhichwefocusedonassociations identifiedbetweenthesame42quantitativetraitsandwell-imputed, high-impactvariantsthatareenrichedatleastfour-foldinQFCrelative tognomADNFE(SupplementaryFig.24).Fortheseanalyses,wecon-
sideredbothenrichedhigh-impactcoding(seeabove)andnon-coding variants(annotatedasconservedorepigeneticallyactivebyFAVOR, Methods).Weselectedvariant-phenotypepairswithaP-value<1×10−5 inCaGandattemptedtheirreplicationintheCanadianLongitudinal StudyonAging(CLSA,N=23,752),whichincludes4,602individuals recruitedinQuebec(Methods).Wewerelimitedtovariantsandtraits alsoassessedintheCLSA;intotal,wereplicated6/15(40%)variant-
phenotypepairsatanominalP-valueof<0.05withthesamedirection ofeffect(Table1;anadditionalpairwasreplicatedintheUKBiobank, see below). The frequency of the tested variants and the limited numberofQuebecresidentsinCLSAtomatchtheCaGanalysesmay explainthislowreplicationrate.

Thelistofreplicatedvariant-phenotypepairsincludesthewell-
knownLPLfounderp.Pro207Leuvariant,associatedwithincreased HDL-cholesterol(HDL-C)andtriglyceridelevels,whichaccountsfor ~72% of all pathogenic LPL alleles found in the QFC population (Table1)46.TheLeualleleatthisvariantis48timesmorefrequentin QFC than ingnomADNFE. Wealso found a rare nonsensevariant (p.Arg8ter,MAF=0.1%)inthePDZK1genethatassociateswithlower uric acid levels;thevariantis10timesmorefrequentinQFCthanin gnomAD NFE (Table 1). Common variants near PDZK1, which encodesauratetransportaccessorymolecule,havebeenassociated with uric acid levels and gout risk47,48. The variant is missing in the CLSA, but in the UK Biobank, the coding PDZK1 p.Arg8ter variant (MAF=0.02%) is associated with lower uric acid levels (P-value=2.3×10−13) and protection against gout (OR=0.32, P-value=0.024)49. We identified not previously reported associa-
tionsbetweenHDL-Candtworarenon-codingvariants(Table1).We alsofoundtwonon-codingvariantsassociatedwithplateletcount, although these two variants likely represent a single association signalastheyareinstrongLD(r2=0.91;D’=0.97)(Table1).Finally, wefoundanassociationbetweenararecodingvariant(p.Pro162Thr) intheTSHreceptorgene(TSHR)andTSHlevels(Table1).ThisTSHR variantis5-timesmorefrequentinQFCthaningnomADNFEparti-
cipantsandhaspreviouslybeenfoundinpatientswithcongenital hypothyroidismandnonautoimmunehyperthyrotropinemia50,51.

Expressionquantitativetraitlocianalyses
To generate mechanistic hypotheses that may explain our GWAS results,weperformedeQTLanalyseswith504individualsforwhom WGSandwhole-bloodRNA-sequencing(RNAseq)datawasavailable52. Focusing on variants with a MAF>1%, we identified 2,223,590 sig-
nificant associations (eSNPs) implicating 12,774 genes (eGenes) at a false discovery rate (FDR)<5% (Methods). We observed a strong concordancebetweenourCaGresultsandthesignificantwhole-blood eQTLresultsfromGTExv8(N =1,523,724,Spearman’sρ=0.82,P-
eQTL
value<2.2×10−16) (SupplementaryFig. 25A). Many eQTL association results(42.5%)weremoresignificantinCaGthaninGTExdespitea similar sample size, potentially due to differences in the RNAseq experimentalprotocolordesign,thegeneticancestryofthepopula-
tions analyzed or their environmental exposure (Supplementary Fig. 25B)52. All CaG eQTL results are available through a publicly availablebrowser(https://eqtlbrowser-cag.mhi-omics.org/).

Methods
Ethicscommitteeapproval
TheCARTaGENEcohort(CaG)isapprovedbytheEthicsCommitteeof theCHUSte-Justine.AlocalInstitutionalReviewBoard(IRB)foreach investigatorinvolvedinthisstudyhasalsoapprovedgeneticanalyses oftheCaGdata.

CARTaGENE
The CaG Cohort recruited 43,032 participants (40-69 years old at recruitment)acrossfivedifferentregionsintheprovinceofQuébec, Canada. Participants have each provided informed consent. The cohort has been described elsewhere9 and Supplementary Table 1 providesasummaryofitsdemographicsandgeneticancestry.29,337 participants provided biospecimens, including whole blood from whichweextractedDNA.

Genome-wideDNAgenotyping
WegenotypedtheCaGparticipantsseparatelyinfivedifferentprojects between2017and2021,eachtimeusingaslightlydifferentversionof Illumina’sGSAarrays(SupplementaryFig.2).Oneofthesubsets(2017) wasenrichedforrestlesslegsyndromecases.Theremainingsetswere notascertainedforparticulardiseasesorancestralgroups.Weused PLINK 1.954 to perform genotype data quality-control in each batch separately(SupplementaryFig.2).Weremovedsampleswith:(1)low genotypingsuccessrate(<95%),(2)sexmismatchwiththedatabase, (3) duplicates based on IBD analyses, and (4) samples that share a fractionoftheirgenomewithmany(>50%)oftheparticipants(i.e. contaminants).Weexcludedvariantswith:(1)lowgenotypingsuccess rate(<95%),(2)monomorphicorduplicates,(3)Hardy-WeinbergP-
value<1×10−6,(4)indelswithunspecifiedalleles(e.g.,D,I),(5)Y-linked variants,and(6)variantswithalargeallelefrequencyvariancebetween the five batches (measured using the perpendicular distanceto the unityline).Wealsoalignedallallelesonthepositivestrand(https:// www.well.ox.ac.uk/~wrayner/strand).Wemergedthedatasets,keeping only variants available to all five batches. We obtained a cleaned datasetof29,337participantsand472,467variants.

Whole-genomeDNAsequencing(WGS)
Participantselection.Weselected2,184unrelatedparticipants(based onidentity-by-descentsharing,<3rddegreefamilialrelationship)for theWGSphaseofthisproject,ofwhich2173passedqualitycontrol. Theseincluded1,878European-ancestryindividualswithfourgrand-
parentsborninCanadafallingwithinthemajorityclusterinPCAspace (including 1756 QFC), as well 163 and 131 participants with four grandparentsborninHaiti(QHA)andMorocco(QMO),respectively. Otherselectioncriteriaincluded:(1)availabilityofgenome-widegen-
otyping array data, (2) possibility to re-contact via email for future studies,(3)availabilityofwhole-bloodRNA-sequencingdata(N=504), and(4)linkagewiththeBALSACgenealogydatabase(N=1,074). Finally, we preferentially selected participants of Haitian and Mor-
occandescentintheWGSstudy,astheyhelpcapturevariationrela-
tivelycommoninQuebecbutpoorlycharacterizedinternationally.

DNAsequencingandvariantcalling.DNAsamplesweresequenced attheGenomeQuebecCentred’ExpertiseetdeServices(CES)onIllu-
minaNovaSeqsequencersusingPCR-freelibrariesandapaired-end (2×150bp) protocol. The mean coverage was 33.4X (range 16.3 to 72.0). We used the Illumina DRAGEN pipeline (v3.9.5) to call single nucleotide variants (SNVs) and short insertion-deletions (indels, <50bp)atthesamplelevel,andacrossthecompleteWGSdatasetto create a multi-sample VCF, using default parameters.Before quality control,thedatasetincluded64,503,029SNVsand7,025,566indels: 27,915,550 singletons, 8,693,233 doubletons, and 3,638,863 multi-
allelicvariants.

WGS data quality-control. We used PLINK 1.9 to perform quality controlontheCaGWGSdataset.Weexcludedvariantswith>5%data missing, aswell as samples with >5%data missing or with extreme heterozygosity(|Fstat|>0.4).WecomputedHWEstatisticsforeach variantandreportedthoseinasupplementaryfileavailablefromthe CaG data access portal. We used bcftools v1.16 (options “norm -m -both”) to convert 3,148,393 multi-allelic variants into multiple bi-
allelicvariants.OuranalysiswiththeseGMMsoftwaretoanalyzethe karyotypeofsexchromosomesdidnotdetectsexmismatches55.The finaldatasetincluded2,173individualsand80,407,530bi-allelicvar-
iants, of which 62,457,187 are single-nucleotide variants (SNVs), 15,645,143 are insertion-deletions (indels), and 852,021 are substitu-
tions (Supplementary Data 1). The concordancebetween genotypes fromtheWGSandgenotyping(IlluminaGSAarrays)variantcallsets for these individuals and calculated from >645,000 variants is>99.87%.

Variantannotation.Wecalculatedtheallelefrequencyofeachvariant intheentireWGSdatasetaswellaswithineachofthethreeancestral groups and provide thisinformation in supplementaryfiles (on the dataaccessportal)aswellasonourallelefrequencybrowser(https:// cartagene-bravo.cerc-genomic-medicine.ca/). We annotated variants using the ENSEMBL Variant Effect Predictor (VEP) module56 and FAVOR57;theseannotationsarealsoavailableassupplementaryfiles throughtheCaGdataaccessportal.VEPv108wasrunlocallyusingthe following parameters: “--symbol --pick --check_existing --af_gnomadg --siftb--polyphenb”.High-impactvariants(ashighlightedbyVEPwith ‘IMPACT=HIGH’),belongtothesecategories:frameshift,stopgained andloss,splicedonorandacceptor,startloss,andtranscriptablation, resulting in 26,874 variants, of which 23% are not found in dbSNP version 154. To this list, we added 980 variants defined as(strictly) pathogenicintheClinVardatabase.Thus,thelistofhigh-impactcod-
ing variants considered for downstream analyses included 27,361 variants.Toprioritizenon-codingvariants,weusedcompositescores (aPC≥20)generatedbyFAVORthatconsidermultiplevariantanno-
tations based on conservation (aPCcons) or epigenomic (aPCepi) scores57,58. From the list of high-impact coding and non-coding var-
iants,wethendefinedalistofenrichedhigh-impactvariants(hereafter referredtoasenrichedvariants)usingthesetwocriteria:(1)aminor allelefrequency(MAF)inCaG≥0.1%and(2)aratioofallelefrequency (CaG)/allelefrequency(gnomAD)≥4.FromgnomAD,weusedallele frequenciesfromNon-FinnishEuropean-,African-,andMiddleEastern-
ancestrypopulationsasreferencefortheQFC,QHAandQMOparti-
cipants,respectively.Intotal,wefound1,375,2,639,and2,970enri-
chedcodingvariantsinQFC,QHA,andQMOparticipants,respectively. Innon-codingregions,wefound7981variantswithhighaPCconsand 20,896variantswithhighaPCepithatwereenrichedinQFC.

Populationgeneticanalyses
Principalcomponent(PC)analysisanduniformmanifoldapprox-
imation projection (UMAP). We generated principal components (PCs)usingPLINK.WeprunedforlinkagedisequilibriumusingPLINK2 (parameter --indep-pairwise 1000 50 0.1). We generated PCs using PLINK1.9,maskingchromosome6from25000000to33500000to filter out HLAdue to the long-range LD patterns in thisregionand usingparameters:--maf0.5--mind0.1--geno0.1--hwe1e-6.Tocalcu-
late the percentage of variance explained, we used the R imple-
mentationofflashpca59.

WegeneratedUMAPcoordinatesusingtheumap-learnpackage (v0.5.1)inPython60.Forthevisualization,weranUMAPonthetop10 PCsusingthe15nearestneighborsandaminimumdistanceof0.5to focusonvisualclarity11,60.ColorsforthePCanalysisandUMAPvisua-
lizations were based on clusters defined via density clustering as describedinDiaz-Papkovichetal.11.WeusedHDBSCAN(v0.8.27)with parameters^ε=0:3andaminimumclustersizeof25pointsona3D UMAPofthetop25PCsparameterizedforthe10nearestneighbors and a minimum distance of 0.001. Word clouds, created with the Python wordcloud package (https://github.com/amueller/word_
cloud),weregeneratedusingthecountryofbirth,provinceofbirth (forthoseborninCanada),selectedethnicity,andcollectionregion variables.

Admixture analysis. We used genotype data to estimate ancestry proportionsforthreepopulationswithWGSdataavailable:European-
ancestrywithfourgrandparentsborninCanada(includingQFCand non-QFC participants), QMO and QHA. To highlight population structurewithineachcountryoforigin,weselectedindividualsthat reportedtheirfourgrandparentstobeborninthesamecountry.For eachofthepopulationsofinterest,weusedKINGwithoption--kinship toidentifykinshiprelationshipsbetweenindividuals.Wefilteredout related individuals to avoid relations closer to 3rd-degree (Kinship coefficient>0.0442)usingPLINK1.9flag–king-cutoff.Wefoundfour pairsofrelatedindividualsintheQHAdataset:3pairsat3rd-degree and 1 pair at 1st-degree. One individual per pair was removed. No individualswereremovedfromtheQMOdataset.Thefinalpopulation setswerecomposedof267QHA,201QMO,and500randomlysam-
pled individuals with four grandparents born in Canada (which includes,butisnotlimitedto,QFC).

Tostudythepopulationstructureofeachgroupofinterest,we built three datasets with populations based on similargeographical and historical background. For CaG-based ancestry references, we selectedparticipantswithfourgrandparentsborninthesamecountry.The reference dataset for QMO included CaG participants with ancestry from North and Sub-Saharan Africa, the Middle East, and WesternEurope.ThereferencedatasetforQHAincludedCaGancestry fromSub-SaharanAfricaandWesternEurope,alongwithfourpopu-
lationsfromthe1000GenomesProjectPhase3(YRI,ACB,CEU,and CHB). For the continental ancestry estimation in individuals with grandparents in Canada, our dataset included French, British, Latin AmericanandSub-SaharanAfricanancestriesfromCaG.

We used the ADMIXTURE algorithm in unsupervised mode to estimateancestryproportionsfromthemergedgenotypedata61.We filteredgenotypesforLDusingPLINK1.9withparameter--indep-pair-
wise50100.5.Thisleft421,356,403,640,and204,003variantsafter pruning forthe Canadian(with four grandparents bornin Canada), Moroccan(QMO),andHaitian(QHA)datasets,respectively.Eachof the three pruned datasets were run independently for admixture estimation. We performed three independent runs of admixture at eachvalueofKforK=3–7(QHAandQMO),andK=2–5(CA).Optimal Kwasdeterminedbycomputingthecross-validationerrorineachrun, option –cv in ADMIXTURE. Visualization of ancestry components acrossmultiplerunsandKswasdonewithPONG62.

IBDdetectionandclustering.Sharedgeneticvariantsfromthefive genotyping arrays in CaG were used to detect identity-by-descent (IBD)segments.VariantswerephasedusingBeagle5.4(beagle.22Ju-
l22.46e.jar)andtheplink.chrCHR.GRCh38.mapgeneticmapfromthe HapMap project. (http://faculty.washington.edu/browning/beagle/ beagle.html). We then filtered the merged file using plink --mac 1 --hwe0.000001--max-missing0.95,resultinginadatasetof29,330 individualsand356,441variants.IBDdetectionwasperformedusing theHapIBDtool63withdefaultparameters.IBDsegmentsoverlapping genomegapregions(shortarm,heterochromatin,telomere,contig, scaffold, and centromere) were removed. We utilized Louvain clus-
teringmethodology,implementedthroughtheigraphRpackage,to partitionindividualsintodistinctgroupsbasedonthetotalIBDsharing betweenpairsofsamples.Eachindividualwasrepresentedasanodein thenetwork,withedgesdenotingtheextentofIBDsharing.Following three iterations of Louvain clustering, a total of 56 clusters were identified.Tomitigatebiasesintroducedbysmallpopulationsizesor excessivevariationinpopulationcomposition,wecomputedF values st
using PLINK2 between clusters containing more than 10 samples. Notably,clusters48and54wereexcludedfromsubsequentanalyses astheyhadlessthan10individuals,resultinginafinaldatasetcom-
prising29,209samples.Subsequently,wemergedclustersexhibiting F valuesbelow0.0005,yieldingarefinedsetof21clustersofvarying st
sizes,asdetailedintheSupplementaryNotes.

IdentificationofvariantsofclinicalinterestinSPG7
UsingCaGWGSsequencingdata,weextractedallvariantsfoundinany transcripts of the SPG7 gene. We selected variants classified as Pathogenic(P),LikelyPathogenic(LP),orwithconflictinginterpreta-
tions of pathogenicity according to ClinVar (accession date: March 2024),andhigh-confidenceputativeloss-of-function(pLoF)variants. Variants were filtered for MAF≤1% in gnomAD (version v3.1.2), CADD≥1564 and for putative splice variants with a Splice AI score≥0.865. Then, we performed subsequent manual curation of pathogenicityevidenceindatabasessuchasClinVarandLOVDaswell as literature review. Variants were classified using the ACMG 2015 revisedguidelinesusingfivedistinctcategories(benign,likelybenign, uncertainsignificance,LPandP)37.Variantswithevidencesupporting LPorPwereconsideredforfurtheranalysis.Tocalculateenrichment, we estimated the allele frequency for each variant as the count of alternateallelesdividedbythetotalnumberofallelesaspreviously described66.Theallelefrequenciesobservedwerecomparedtothose observedingnomAD,versionv3.1.2.

ARG-basedimputationandregionalfrequencyestimation
Weusedphasedgenotypedata(659,029variants)toreconstructthe ancestralrecombinationgraph(ARG)foralltheindividualsintheCaG cohortusingARGneedle40.VCFfileswerepre-processedtokeeponly biallelicSNPsusingPLINK2--max-alleles2and--snps-onlyflags,fora totalof566,073variants.Moreover,geneticmapfilesfortheGRCh38
referencegenomewereobtained inplink format, and interpolation wasperformedtoincludeallSNPsfromtheVCFfilesinthemapusing anin-houseRscript(https://github.com/almejiaga/ARG_needle).Var-
iantswithadistanceincMlowerthan0.00001wereexcludedfromthe analysisduetoARG-needlesoftwarerequirements.Finally,VCFfiles wereconvertedtotheOxfordHAPformatusingPLINK2.Attheend,a totalof548,663variantswereretainedforsubsequentanalyses.The ARG wasreconstructed using the ARG-needle softwareingenotype modewithdefaultparametersforchromosomes1–22.After,ARGfiles were converted to tree sequences in the tskit format using the arg2tskit ARG-needle function. We analyzed nine pathogenic muta-
tionsin SPG7,and wechecked intheARGifcarriersshare arecent commonancestoramongthem.Forthatpurpose,weusedTSKITto visualizetheARGsandtoverifyifthepatternsofcoalescencebetween thecarriersareconsistentwithafoundereffect.Wecomputedtimeto themostrecentcommonancestorofcarriersofamutationusingthe tmrcafunctioninTSKIT.Forimputation,weassumedthatallindivi-
dualsbelowthisMRCAcarrythemutation.Formutationswithmore than 40 carriers, we used the BALSAC genealogy to perform allele frequencyestimationwithinthe24historicregionsusingtheISGen software41.Forthisanalysis,100,000climbingsimulationsfromcar-
riersto founderswereperformed and 100,000treeswereused for allelefrequencyestimation;confidenceintervalswerecalculatedusing bootstrappingwith100,000iterations.WecreatedthemapsinFig.2 using the geopandas package in Python 3.12 (https://zenodo.org/ records/3946761).

Structuralvariants(SVs)
SV calling and genotyping. We detected SVs using Manta (imple-
mentedinDRAGEN)67andDELLY1.1.368ineachparticipantwithWGS dataavailable.WefilteredthetwoSVsetswithbcftools1.11tokeep onlyvariantsthathadsufficientreadsupport(tag‘PASS’inthe‘FILTER’ columnofthevcffiles)69.WeusedSURVIVOR1.0.7tomergetheManta and DELLY filtered SV sets (minimum variant length 50-bp and breakpoint variant overlap 100-bp)70. We then used muCNV 1.0.0 (fromtheworkingbranchoftheGitHubrepository)togenotypeSVs and generate a harmonized multi-samples vcf file: (1) we created a binary intervalfile of candidate SVs,(2) wegenerated pileups from each individual’sCRAMfileusingourcandidatelistofSVs,and(3)we genotyped each chromosomeseparately(withoutmergingpileups). WeperformedanadditionalfilteringsteptoidentifySVpairsthatwe consideredtobethesameSVbecauseoftheproximityoftheirstart andendcoordinates.TwoSVswereconsideredapairifboththeirstart andendpositionswerewithinawindowof≤100-bp,evenafterthe intersection and merging steps done with SURVIVOR. We identified 465SVpairswithahome-madepythonscriptandmadesurethatboth SVs in a pair were of the same type. Next, we compared the allele countsofthetwoSVsinapair.Forthepairswiththeexactsameallele counts,we filtered out the SV with the smallest length. For the remaining pairs, we selected those with a percentage of difference betweentheallelecountsandapercentageofdifferencebetweenthe allelefrequencieslowerthan1%andfilteredouttheSVwiththelowest allelecount,assumingthattheywerethesameSVs.Wefilteredout301 SVsintotal.

ComparisonofSVallelefrequencieswithgnomAD-SV.Weusedthe gnomADSVbedfilesandvcffilesversion2.1forGRCh3722.Welifted overthebedfilefromGRCh37toGRCh38usingCrossMap71.Wesplit thelifted-overgnomADbedfileintodeletionsandduplications,and thefinalSVsetwiththebcftoolsfiltercommand.Weusedbedtools version2.30.0intersect(minimumof50%reciprocaloverlaprequired) tokeeptheSVsthatwerecommonbetweenthegnomADbedfileand the CaG final SV set vcf file, processing deletions and duplications separately.

HLAgenotyping
WeextractedtheextendedHLAregion(chr6:25Mb–35Mb)andper-
formedHLAtypingfor11classicalclassIandclassIIHLAgenes(HLA-A, HLA-B,-C,-DPA1,-DPB1,-DQA1,-DQB1,-DRB1,-E,-F,and-G)toG-group resolution using HLA*LA25. After filtering on allele inference quality (Q =1.0)andproportionofbaseswith>10xdepthateachHLA HLA*LA
genelocus,andremovingsampleswith>3HLAallelesmissing,43,150 HLAalleleswereretrievedfrom2,088samples.Althoughtheypassed allqualityfilters,wealsoremovedHLA-E,-F,and-Gallelesfromthe panel for downstream analyses due to a lack of comparable allele frequenciesandinteroperabilitywithcomputationaltoolsused.Thus, the final HLA WGS panel included 31,199 alleles in 2,088 samples. However,itisimportanttonotethatourHLAanalysesaresubjectto inherent limitations from short-read sequencing technology, which leads to lower calling accuracy compared to SNVs. Furthermore, referencegraph-basedHLAcallingtoolsmaymisscertainHLAalleles thatareabsentfromthereference.HLAallelefrequencieswerecal-
culated in the entire WGS cohort and within each ancestral group. DifferencesinHLAallelefrequencieswerecalculatedviaFisher’sexact test,withP-valuethresholdadjustedformultipletestingviaBonferroni correction,accordingtothenumberofallelescomparedpergroup. Globalandpopulation-specificHLAallelefrequencieswerecompared to the largest publicly available multi-ancestry HLA reference panel (N=21,546)27andtolow-resolutionHLAtypespreviouslyreportedin the Héma-Québec donor biobank26 for the overall WGS cohort and stratifiedbyQuebecregionoforigin.

Imputationreferencepanelandgenotypeimputation
Preparing independent samples for statistical phasing bench-
marking.Weused40samplesfrom10families(mother,father,twins) collectedfromtheQuebecStudyofNewbornTwins72.Thegenomesof samples were sequenced with the Illumina HiSeq 2000 platform at >30Xaveragedepthofcoverage.Weremappedthepaired-endreads fromtheoriginalmappingontheGRCh37(hg19)toGRCh38(hg38) human genome assembly using bwa-mem (v0.7.17-r1188)73. We per-
formedjoint-variantcallingusingGATK(v4.3)andfollowingGATK’s BestPractices74.Usingthepedigreeinformation,weperformedgen-
otype refinement (GATK’s CalculateGenotypePosteriors, VariantFil-
tration, and VariantAnnotator tools) to mark de novo variants and Mendellawviolations.WeusedonlygenotypesthatpassedallGATK’s qualityfiltersinthedownstreamanalyses.Weperformedapedigree-
based phasing ofthe genotypes inchildren using the makeScaffold utilityinSHAPEIT475.UsingourIBDanalyses,weconfirmedthatthese samples cluster together with QFC from CaG. We also randomly selected50trios(father,mother,child)fromthe1000GenomesPro-
ject and Human GenomeDiversity Project(1GP+HGDP) (N=4,904) dataset. The 1GP+HGDP dataset was phased using the pedigree information as part of the gnomAD v3 project43. The selected trios includedanequalnumber(N=10)ofAfrican(AFR),AdmixedAmer-
ican(AMR),EastAsian(EAS),CentralSouthAsian(CSA),andEuropean (EUR)geneticancestries.

Preparing independent samples for genotype imputation bench-
marking.Weused141unrelatedsamplescollectedinthelaboratoryof Dr.GuyRouleauinQuebec.Theseweremostlyunaffectedmembersof familialdiseasecohortsstudiedindifferentprojectsoverthecourseof adecade76,77.ThegenomesofsamplesweresequencedwiththeIllu-
minaplatformat30Xaveragedepthofcoverage.Thepaired-endreads weremappedtoGRCh38(hg38)humangenomeassembly,andSNVs andindelswerecalledusingtheIlluminaDRAGENpipeline(v3.5.3and higher).WeusedonlygenotypesthatpassallDRAGEN’squalityfilters inthedownstreamanalyses.UsingourIBDanalyses,weconfirmedthat these samples cluster together with QFC from CaG. To synthesize genotyping array data for the SNVs, indels, SVs, and HLA alleles imputationbenchmarking,wesubsetWGS-basedSNVandindelcalls to genomic positions of variants on the Illumina GSA v2 array. We typedHLAallelesfromWGSdatausingHLA*LAinthesamewaywedid withCaGWGSdata.WecalledSVsfromWGSdatathesamewaywedid with CaG WGS data, the only difference being that we used Manta version1.6.0withdefaultparametersinsteadoftheDRAGENimple-
mentation (see the corresponding Methods section). We used the “diploid”VCFoutputsincetheSVswerescoredunderadiploidmodel. BecauseweincludedonlySVswithnomorethan10%genotypemiss-
ingnessintheCaGreferencepanel,weappliedthesamefilteronthe calledSVsetforconsistency.

PreparingSNVs,indelsandSVsinWGSdataforstatisticalphasing.
WeutilizedWGSdatafrom2,173individuals,including1,756QFC,131 QMO,163QHA,and123additionalparticipantsofmostlyEuropean genetic ancestry to construct the genotype imputation reference panel. We replaced individual genotypes that did not pass variant callingqualityfiltersfromtheIlluminaDRAGENpipeline(v3.9.5)with missingvalues(i.e.,genotypes).Thesemissinggenotypeswereimpu-
ted during the subsequent statistical phasing. We left-aligned the variantsusingthevtalgorithm78andremovedanyresultingduplicated records.Werecalculatedtheallelefrequenciesandremovedmono-
morphic(alternateallelecountis0)variants.Weaddedpolymorphic high-qualitySVstothedataset(seethecorrespondingMethodssec-
tions on SVs). After thesepre-processing steps, we had 70,047,094 variants (59,136,049 SNVs, 10,890,760 indels and 20,285 SVs) for subsequentstatisticalphasing.

StatisticalphasingofSNVs,indelsandSVsinWGSdata.Weused
SHAPEIT5(v5.1.1)statisticalphasingsoftwaretobuildaCaGreference panel for imputing SNVs, indels and SVs79. To improve statistical phasing accuracy, we performed reference-based phasing with the harmonized high-quality reference haplotypes from the 1000 Gen-
omes Project and Human Genome Diversity Project (1GP+HGDP) (N=4,904)43.First,westatisticallyphasedgeneticvariantsoverlapping CaGand1GP+HDGPwithminorallelefrequency(MAF)>0.1%inCaG tocreateapreliminaryscaffoldofCaGhaplotypesofknowncommon variants.Second,westatisticallyphasedtheremainingcommonCaG geneticvariants(MAF>0.1%)notfoundin1GP+HDGPusingthepre-
liminaryscaffoldfromthefirststeptocreateasecondscaffoldofCaG haplotypesofallcommonvariants.Lastly,westatisticallyphasedthe low-frequencyCaGvariants(MAF≤0.1%)usingthescaffoldfromthe second step to create a final set of CaGhaplotypes. We performed statisticalphasinginthepseudo-autosomal(PAR)regionsinchromo-
some X inthe same way asin all autosomal chromosomes.For the statisticalphasinginnon-PARregionsinchromosomeX,weusedthe --haploids option in SHAPEIT5 to provide male haploid sample identifiers.

Statistical phasing of HLA alleles in WGS data. We constructed a high-resolutionHLAimputationreferencepanelcombiningtheSNVs, indels,andHLAallelesfromWGSdatafor2,088individualsusingthe HLA-TAPAS27pipeline,which uses BEAGLEv4.1tostatisticallyphase together HLA alleles and single nucleotide polymorphisms (SNPs) restrictingitselftotheHLAregiononchromosome6.WeusedtheHLA alleles called from WGS data which passed quality checks (31,199 allelesfrom2,088samples;see“HLAgenotyping”section).FortheSNV and indel data, we extracted the extended HLA region (chr6:25Mb–35Mb) from the WGS data after quality control (see “PreparingSNVs,indelsandSVsinWGSdataforstatisticalphasing”)for thoseindividualsforwhomHLAcallingwasavailable,andweexcluded variantswhichweremonomorphic(alternateAC<1)orwithmissing-
nessgreaterthan0.1inthoseindividuals(228,651SNVsand59,903 indels).

Preparinggenotypingarraydataforstatisticalphasingandimpu-
tation.WeusedtheUCSCLiftOvertool80toliftthedataproducedby eachofthefivegenotypingarraysfromtheGRCh37(hg19)toGRCh38 (hg38)humangenomeassembly.Weremovedliftedvariantrecords that were not single nucleotide polymorphisms (SNPs), were palin-
dromic,hadnoneofthetwoallelespresentinhumangenomerefer-
ence, and were duplicated (keeping variant record with the lowest genotypemissingness).Weflippedthereportedstrandifnecessaryto align with the human genome reference. We set all heterozygous genotypesinthenon-PARregioninchromosomeXinmalesamplesto missingvalues.Then,wemergedthelifteddatafromthefivegeno-
typingarraysandremovedgeneticvariantsiftheyweremissinginat leastoneofthem.Ifthesamesamplewasgenotypedusingmultiple arrays,wekeptonlytheversionwiththemostgenotypedpositions. Aftermerging,weremovedSNPsthatdisplayedstatisticallysignificant differences in alternate allele frequencies (AF) across genotyping arrays.Weperformedalikelihoodratiotest(LRT)comparingtwolin-
earregression modelsG~m+PC1+…+PC4+A2+…+A5and G~
m+PC1+…+PC4,whereG-individualgenotype(0,1,or2alternate allele copies), m - intercept corresponding to the average AF, PCi - principal component i, Aj - indicator variable for array j. Assuming randomsamplingandnobatcheffects,thearrayvariablesAjshould notimpactthepredictionofindividual-specificAF.Wecomputedthe LRT P-value from a Chi-squared distribution with four degrees of freedomandsetthestatisticalsignificancethresholdof0.05divided bythenumberoftestedSNPs.WeobtainedPCsusingTRACE(v1.03)81 byprojectinggenotypearraydataintothePCspaceofthe1GP+HGDP referencepanel.WhenperformingLRT,weignoredallrelatedindivi-
dualswith≤3rddegreeofrelatedness(PLINK’s--king-cutoffoptionset at0.0442).WeperformedLRTforbothsexesseparatelyforvariantsin thenon-PARregionintheXchromosome.

Statisticalphasingofgenotypingarraydatabeforeimputation.We statisticallyphasedthegenotypingarraydataon29,333samples(four sampleswereexcludedbecauseofwithdrawnconsent)inCaGusing theTOPMedr3referencepanelthroughTOPMedImputationServer73, whichusesEagle2(v2.4.1)forthereference-basedstatisticalphasing82. DuetothemaximalsamplesizerestrictionsintheTOPMedImputation Server, we randomly allocated samples into two non-overlapping batches with N=14,666 (472,467genotyped non-monomorphicvar-
iants)andN=14,667(472,396genotypednon-monomorphicvariants) sampleseachforstatisticalphasing.TheTOPMedImputationServer’s quality checks removed 231 variants from each batch due to allele mismatchwithTOPMedr3;28,530and28,460variantsthatwerenot presentinTOPMedr3,respectively;3variantsfromeachbatchwith genotypecallrate<90%;4and3multi-allelicvariants,respectively. Thestatisticalphasinginnon-PARregionsofchromosomeXwasdone separatelyfromtheautosomalchromosomesandchromosomeXPAR regions.WecodedgenotypesofmalesamplesinchromosomeXnon-
PARregionsashaploids,i.e.,0sand1sinsteadof0/0and1/1.Afterthe statisticalphasingineachbatch,wemergedallsamplesbacktogether usingthebcftoolsmergecommand69.

Genotypeimputationofthepre-phasedgenotypingarraydata.We separatelyimputedgenotypearraydataon29,333samplesinCaG, whichwaspre-phasedusingtheTOPMedr3referencepanel,intothe CaG reference panel and TOPMed r3 reference panel. Then, we mergedtheseimputationresultsusingameta-imputationapproach. WeusedMinimac4(v4.1.6)83tolocallyimputethepre-phasedarray data into the CaG reference panel with default parameters and without explicitly chunking the chromosomes. The genotype imputation in non-PAR regions of chromosome X was done sepa-
rately fromtheautosomal chromosomes andchromosomeXPAR regions.Wecodedstatisticallyphasedgenotypesofmalesamplesin chromosomeXnon-PARregionsashaploids,i.e.,0|0and1|1.WeutilizedtheTOPMedImputationServer(v2.0.0-
beta2),whichalsousedMinimac4(v4.1.6),toimputethepre-phased arraydataintotheTOPMedr3referencepanel.Duetotheserver’s maximal sample size restriction, we performed imputation in two non-overlapping batches (see statistical phasing in Methods). We concatenatedtheimputeddosagesfromtwobatchesandrecalcu-
lated Rsq imputation qualities using the post-processing hds-util tool for Minimac4, which was developed by Michigan Imputation Server83. The chromosome X processing and male sample coding wereidenticaltothelocalimputationintotheCaGreferencepanel. We combined the imputed genotype dosages from the two refer-
ence panels using a meta-imputation approach implemented in MetaMinimac2(v1.0.0)84.

Genome-wideassociationstudy(GWAS)usingimputed genotypesinCaG
Relatedness and population structure modelling. Our association models used genotyping array data to account for the relatedness between individuals and for population stratification. Using PLINK2 (v2.00a6LM)85, we selected only bi-allelic single nucleotide poly-
morphisms (SNPs) with minor allele frequency (MAF)>1%, Hardy-
Weinberg test P-value<10−15, per-variant and per-sample missing genotype frequencies <10%. We further pruned SNPs based on the linkage-disequilibrium(LD)usingthePLINK2--indep-pairwiseoption withthewindowsizeof1000kbp,stepsizeof100SNPs,andr2LD thresholdof0.9(i.e.,twoSNPswithr2>0.9wereconsideredinLD).We also removed SNPs in known long-range LD regions86 and low-
complexity regions defined in UCSC Genome Browser (RepeatMas-
ker and WM+SDust tracks)87. We performed principal component analyses (PCA) on pruned genotyping array data using the PLINK2 --pcaoptionandextractedthetop10principalcomponents(PCs).We performed this variant pruning procedure and PCA on genotyping array data for all 29,333 individuals and 27,036 individuals of European-geneticancestryseparately.

Single variant association testing. For single variant genome-wide associationtestingbetweentheimputedgenotypesandcontinuous traits inCaG,we applied a linearregression model implemented in regenie (v3.6)88. We applied rank-inverse normal transformation (RINT)foralltraitsusingthebuilt-inregenieoptionduringtheesti-
mation of polygenic effects on each trait (regenie Step 1) and the associationtesting(regenieStep2).Toestimatepolygeniceffects,we usedprunedgenotypingarraydata.Weincludedsex,age,age2,gen-
otypingarray,andtop10PCsascovariatestoourregressionmodels. Imputed variants with minor allele count (MAC)<5 were not con-
sidered. Subsequent filters on genotype imputation quality (INFO score reported by regenie) and minor allele frequency/count were appliedanddescribedindownstreamanalyseswhenrequired.

Genome-widestatisticallysignificantloci,leadandindexvariants.
WeusedacommonlyacceptedBonferroni-correctedP-valuethresh-
oldof5×10−8forthegenome-widestatisticalsignificance.Wedeter-
mined independent genome-wide significant loci, lead and index variantsforeachanalyzedcontinuoustraitseparately.Weusedonly geneticvariantswiththeminorallelecount(MAC)>20andgenotype imputation quality INFO score >0.3. First, we defined a window spanning500Kbpupstreamand500Kbpdownstreamaroundeach genome-widesignificantassociationforatrait.Then,wemergedall overlappingorimmediatelyadjacentwindowsintoasingleindepen-
dentgenome-widesignificantlocus,whichdoesnotoverlapwithany otherlocusforthattrait.Withineachindependentgenome-widesig-
nificantlocus,wedefinedasingleleadvariantasthevariantwiththe lowestP-valuefromtheassociationtestandoneormoreindexvar-
iantsasthosethatshowedstatisticallysignificantassociationsusing stepwiseconditionalanalysiswithineachindependentlocus.Wedid stepwise conditional analyses with regenie (v3.6) using the same polygeniceffectsandcovariatesasintheprimarygenome-widescan and defined the locus-wide statistical significance P-value threshold as10−5.

ReplicationofCaGvariant-traitassociations
We aimed to replicate the variant-trait pairs of interest in an inde-
pendent dataset, the comprehensive cohort of the Canadian Long-
itudinal Study on Aging (CLSA) and the UK Biobank. To replicate associationswithcodingvariantsintheUKBiobank,wequeriedthe Genebassserver49.TheCLSAisalongitudinalcohortof26,622females andmaleswithgeneticdataandvariousphenotypes(questionnaires andlabmeasurements)aged45–85whenrecruitedbetween2010and 2015fromacrossCanada89,90.Tomaximizesamplesize,weconsidered phenotypic information from the baseline visit. For sample quality control, we excluded participants identified as having batch or sex discordance betweenself-reported orchromosomal sex(ormissing values for self-reported or chromosomal sex) or outlying hetero-
zygosityaspreviouslydescribedinref.89.Intotal66participantswere excluded. We confirmed that the variants of interestforreplication were in Hardy-Weinberg Equilibrium (P-value>1×10-6) in the CLSA dataset.

In the replication analysis, we included CLSA participants who passedqualitycontrolandwereclassifiedashavingEuropeangenetic ancestry (N=24,655 samples) as previously described in ref. 89. In brief,ancestryestimationwascarriedoutbyapplyingK-meansclus-
teringonprincipalcomponentsusingPhase3ofthe1000Genomes Projectsamplesasareference.GiventhatasubsetofCLSAparticipants were also recruited from the province of Quebec, we assessed kin-
shipcoefficients between the CLSA Quebec subset and CaG using KING to ensure independence of samples between the cohorts. We consequently removed 240 CLSA participants with a kinship coefficient with a CaG participant denoting a monozygotic twin or duplicate(>0.354).

Forthequantitativetraitsofinterest,usingRv4.3.1,weidentified andthensubsequentlyremovedindividualswithoutlyingvalueforthe traitofinterest,specificallythosewithvalueslessthanorgreaterthan5 standarddeviationsfromthemeanvalueforthetrait.Wecarriedout genome-wideassociationanalysesusingRegeniev3.2.188usingthefol-
lowingcovariates:genotypingbatch,age,agesquared,geneticsex,and thefirst10principalcomponentsofEuropeanancestry.Rankinverse normal transformation was applied to the quantitative phenotypes. Samplesthathadmissinginformationregardingphenotypeinformation orcovariateswereremoved.Weconsequentlyhadupto23,752indivi-
dualsofEuropeangeneticancestry(ofwhichupto4,602individuals wererecruitedfromtheprovinceofQuebec).Thealternativeallelewas testedastheeffectalleleaswhatwasdoneintheCaGanalyses.Variants withanominalassociationP-value<0.05inourCLSAreplicationana-
lysesandexhibitingthesamedirectionofeffectasintheCaGassociation analyseswereconsideredassuccessfullyreplicating.

eQTLanalyses
RNAseqdatafromwholebloodwaspreviouslygeneratedfor911CaG participants52. We mapped reads onto the GRCh38 human tran-
scriptome,derivedfromEnsemblv98genedefinitions,usingkallisto v0.46.1 with the rf-stranded mode to get pseudoaligned reads to isoforms91.Weobtainedgenecountsusingthetx2genefunctionfrom thetximportRpackage92.TheRNAseqlibrarieswereprocessedwith the edgeR and sva R packages93,94. The batch effect correction was assessedwithComBat,fromthesvapackage,andgeneshavingless thananaverageLCPMvalueof0wereremoved,resultinginasetof 13,641genes.The60PEERfactorswereobtainedfromthenormalized LCPMgenecounts,accordingtotherecommendationsoftheGTEx consortium95.Afterthisstep,twooutliers’sampleswereidentified,and PEERwasrunasecondtime.

Afterquality-controlsteps,weretrieved504CaGparticipantswith RNAseqandWGSdata,fromwhich496areQFC,5QHAand3QMO. UsingPLINKv1.9,weremovedvariantswithmissingrate>1%,MAF<1% andaHardy-WeinbergP<1×10−6.Thelowcomplexityregionswere alsoremoved,resultinginatotalnumberof11,164,382variants.Ten-
sorQTLv1.0.896wasrunonageneexpressiondatasetthatwasusedfor thePEERcorrectionandforwhichgenesweresubsequentlyinverse normal transformed using the cis-nominal mode. The linear regres-
sionswerecorrectedforage,age-squared,geneticsex,the10firstPCs ofthepopulationstructure,theWGSsequencingbatchand60PEER factors.Thegeneexpressiondatawerefurtherannotatedwithaddi-
tionalinformation,includingthetranscriptionstartsitesofthegenes, based on the reference genome used to derive the gene counts (Ensemblrel.98,GRCh38).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study, authors presents the Quebec Reference Genome Panel (QGP), a newly developed whole-genome sequencing\n(WGS) reference panel based on a multi-ancestry cohort of ~30k individuals from the CARTaGENE biobank (30k for GWAS\ngenotyping and 3k for WGS). The QGP reflects the genetic diversity within Quebec, a Canadian province with complex\ndemographic history shaped by French colonial founders, British settlers, and recent immigrants. The panel improves\ngenetic imputation accuracy across ancestries, especially for low-frequency and population-specific variants, HLA variants,\nand includes annotated data on rare coding variants and their geographic distributions. GWAS results for the set of the\nphenotypes were presented. This resource serves as a critical tool for precision health research in Quebec and globally,\nwhile acknowledging the importance of inclusivity in genetic references. This is a well-presented manuscript for the\npopulation- and region-specific cohorts.\n1. Population demography of the cohort looks interesting. This reviewer is interested in more locus-specific story on the\ntarget population, derived from the genome-wide natural selection screening.\n2. HLA genotype calling based on WGS is still methodologically under development and lacks accuracy. Validation of the\ncalled HLA variants using other typing methods is recommended. Calling of non-classical HLA genes such as HLA-E/F/G is\ngenerally still challenging, and as far as this reviewer knows, WGS-based calling is not accurate to date.\n3. Overview of the GWAS design and results (No. phenotypes for each category, No. identified loci) can better to be\npresented in the main Figure.\n4. As always, there exist discussions on multiple comparisons of GWAS significance based on the No. phenotypes. This\nreviewer does not request to strictly apply the threshold considering the multiple comparison burden, but it would be better to\nindicate how the number of the significant loci would change after application.\n5. The value of the manuscript depends on data availability. Current statement ‘The CaG genetic and phenotypid data is\navailable from … after scientific and ethical review’ may not satisfy the Nature journal data deposit request. Direct deposit of\nthe data to public resources such as dbGAP or EGA would be necessary.", "reviewer_2": "The authors have performed a very detailed and interesting study on explorative genetic insights of the Quebec region\npopulation in Canada and identification of novel genetic variants (basis sequencing associated imputation) with multiple\nhealth outcomes.\nI have some comments on the manuscript draft as follows:\n1. Abstract: The authors mention about including 2173 CaG participants for sequencing followed by special mention of two\nnon-European populations. It would be good if the authors can mention the overall ethnic groups covered in their study\n(genotyping and sequencing) eg. (Europeans %, Non-Europeans % (African/Morroco/Haiti, Asians etc.)).\n2. Abstract: The authors have highlighted that they analyzed genes for rich phenotype and environmental data. Following\nthis they mention that they interpret variants of clinical importance followed by a mention how this is methodologically\nadvanced. It seems like a new database has been created through this study for future research use (eg. Quebec Genomics\nHealth db).\nHowever, no key results have been mentioned in the abstract. A mention of the key findings: number and type of traits\nanalyzed (any top findings: monogenic or complex traits), accuracy and/or coverage of sequencing based imputation\ncompared to existing reference panels/previous studies on this population and/or how many new variants were identified\nand if the focus was ona any specific population, would send out a clear message of the study strengths/novelty.\nIn the last sentence it is unclear if all this data is available for global or local research use? Is it the existing CaG study\ndatabase available for access or an extension to the current dataset (https://cartagene.qc.ca/en/researchers/genetic-\ndata.html)?\n3. Methods/Results: Supplementary table 1: Include details of all participants studied (n=29337). Currently the ancistry-wise\n% does not match the total number studied (19,342 (QFC + QHA + QMO)). Mention acronyms QHA and QMO in the Supp\ntable 1 as in main text. Similarly for the Whole genome sequenced CaG subset column. In case the authors studied only the\nsubset (QFC, QHA, and QMO) it needs to be speciied under methods section.\n4. Methods:\na. Genotyping methods should mention some details of the quality control on GSA array data.\nb. The authors mention about genotyping performed from 5 different projects: Do they mean different projects or batches?\nAlso if these were in regard to differing ethnic groups? Provide more details and if possible a flow chart showing steps\nthrough the genotyping.\nc. Also clearly define the MAF thresholds and variants filtered after genotyping (relevant for the five different projects/batches\nand/or for the overall merged dataset).\n5. Methods: WGS Data QC and Supp Table 2: The authors have mentioned biallelic SNVs and indels and corresponding\nnumbers in the main text that do not match the numbers in Supp table 2. Kindly check again, correct and use same\nlabel/name for these in both places for ease of the reader (eg. SNV or \"biallelic SNV\" in both places).\n6. Genetic structure in CaG (Supp Figure 1A and B): The authors have nicely conducted a PC analyses but a clear\ncomposition of subcluseters (1-14) needs to be mentioned (possibly in tabular form) clearly highlighting the differences\nbetween clusters.\n7. Results (Ancestry form Americas): IS the CartaGene a representative population for the entire Quebec as it mostly covers\nregions from the southern Quebec region. This needs to be mentioned. Compared to the original Quebec population\ncomprising\nFrench, Canadian (overlapping with French), British, Indigenous, Other European and Non-European population needs to\nbe discussed in regard to the current study population. Also a chart as per timeline when these populations originated in\nQuebec could be a good overall demonstration of the Quebec population with a highlight of what is covered by the current\nstudy (sample collection centers). This will provide a clear image of the studied population at present time, which might be\npredominant at some time point in history.\n8. The study highlights enriched genetic variants in this population relating metabolic outcomes. The authors should\nhighlight this in the abstract. In case there is information on other health outcomes, such as diabetes, cardiovascular\ndisease, obesity etc. is available in CaG, their prevalence could be mentioned in a supplementary table making this dataset\ncomparable to other similar large datasets mentioned by the authors."}, "author_response": "We thank both Reviewers for their insightful comments. We provide below a point-by-point\nresponse to the questions that were raised. When appropriate, we also revised the\nmanuscript, with edits marked with blue font and new display items.\nReviewer #1:\n1.1) Population demography of the cohort looks interesting. This reviewer is\ninterested in more locus-specific story on the target population, derived from the\ngenome-wide natural selection screening.\nResponse: We agree with the Reviewer that this would be interesting, but probably\nbetter left for a future study. Identifying outlier variants would be doable, but we\nbelieve that the interpretation of the results would be best accomplished in a\nseparate publication that would focus on natural selection in the Quebec population.\n1.2) HLA genotype calling based on WGS is still methodologically under development\nand lacks accuracy. Validation of the called HLA variants using other typing\nmethods is recommended. Calling of non-classical HLA genes such as HLA-\nE/F/G is generally still challenging, and as far as this reviewer knows, WGS-based\ncalling is not accurate to date.\nResponse: We thank the reviewer for highlighting this limitation in our analyses. We\nagree that short-read sequencing technology, even at >30X whole genome coverage\nas used in our study, has limitations in accurately resolving HLA haplotypes, and it is\nnot suitable for clinical applications or for detecting ultra-rare genetic variants.\nHowever, using high-depth WGS data for HLA calling and imputation in population-\nbased biobanks is widely accepted, as demonstrated in studies such as PMID:\n34611364 (HLA calling from WGS), PMID: 37923823 (HLA calling from WES), and\nPMID: 35221124 (HLA imputation). In our analyses, we used HLA-LA (PMID:\n30942877) for calling, which is well-established and was thoroughly evaluated in its\n its\noriginal manuscript and independent works (e.g., PMID: 36426357, PMID: 37161318)\nand is considered one of the most accurate HLA callers available; the authors report\n99.4% accuracy from whole-genome sequencing data. To minimize false positives as\nmuch as possible, we performed the following: (1) we applied the most strict\nthresholds on HLA-LA’s documented quality metrics; (2) we compared the WGS-\nbased HLA alleles frequencies against their frequencies in Hema-Quebec donor\ndatabase to eliminate any outlier HLA allele with discordant frequency; (3) as the\nreviewer noted, the calling accuracy varies by HLA gene, and thus, we excluded from\nour analyses alleles in genes HLA-DRB3 and -DRB4 that are known to be highly\nproblematic to call. Although non-classical genes (HLA-E, -F, -G) were not excluded\n1\nby these technical filters, they were not included in downstream analyses due to the\ncurrent lack of comparable frequency data and interoperability with the tools used.\nWe also acknowledge potential false negatives due to the HLA-LA software relying on\na pre-built population reference graph, which may lack alleles from our study\npopulation.\nWe have added the following sentences to the Methods section to be explicit about\nspecific numbers of alleles used in analyses, and limitations of the HLA calling:\n“We also removed HLA-E, -F, and -G alleles from the panel for downstream analyses\ndue to a lack of comparable allele frequencies and interoperability with\ncomputational tools used. Thus, the final HLA WGS panel included 31,199 alleles in\n2,088 samples. However, it is important to note that our HLA analyses are subject to\ninherent limitations from short-read sequencing technology, which leads to lower\ncalling accuracy compared to SNVs. Furthermore, reference graph-based HLA calling\ntools may miss certain HLA alleles that are absent from the reference.”\nWe also added the following sentence to the “HLA Genotyping” section in Results:\n“We note that our HLA analyses are subject to the inherent limitations of calling HLA\nalleles from short-read sequencing technology, which leads to lower calling accuracy\ncompared to SNVs.\"\n“HLA-E, -F, and -G alleles passed quality thresholds but were not used in subsequent\nanalyses due to lack of comparative allele frequency data. ”\n1.3) Overview of the GWAS design and results (No. phenotypes for each category, No.\nidentified loci) can better to be presented in the main Figure.\nResponse: We have created a new Supplementary Figure 18 to answer the\nReviewer’s question.\n1.4) As always, there exist discussions on multiple comparisons of GWAS\nsignificance based on the No. phenotypes. This reviewer does not request to\nstrictly apply the threshold considering the multiple comparison burden, but it\nwould be better to indicate how the number of the significant loci would change\nafter application.\nResponse: We used a standard P-value <5x10-8 threshold for our analysis, considering\nthat a single phenotype represents a separate “family” of tests, which aligns with the\ncommon situation where researchers focus on just one phenotype. As the reviewer\nsuggested, we also adjusted our Bonferroni threshold to account for 42 different\nphenotypes (i.e., 0.05 / (1,000,000 x 42)), reflecting a more exploratory approach to\nidentify associations across all traits. Although the number of loci decreased, we still\nobserved that the CaG reference panel and meta-imputation enhance the number of\n2\ngenome-wide significant loci compared to imputation based only on TOPMed r3. We\nadded these numbers in Supplementary Table 15.\nImputation Number of loci in Number of loci in\nreference panel All-ancestry European-\nGWAS ancestry GWAS\nCaG 250 227\nTOPMed r3 235 221\nMeta-imputation 245 232\n1.5) The value of the manuscript depends on data availability. Current statement ‘The\nCaG genetic and phenotypid data is available from … after scientific and ethical\nreview’ may not satisfy the Nature journal data deposit request. Direct deposit of\nthe data to public resources such as dbGAP or EGA would be necessary.\nResponse: We apologize for the confusion. The CaG data is available for global use.\nCaG has a very similar data access procedure as the UK Biobank. Investigators who\nwish to access the data need to have local ethical approval and submit a project\napplication to the CaG data access committee. Projects are scientifically reviewed to\nensure that they align with CaG’s broad mission to advance biomedical research. The\ndata access procedure is detailed under the URL included in our DATA AVAILABILITY\nSTATEMENT: https://cartagene.qc.ca/en/researchers/access-request.html. For these\nreasons, the CaG data cannot be submitted to dbGaP/EGA.\nReviewer #2:\n2.1) Abstract: The authors mention about including 2173 CaG participants for\nsequencing followed by special mention of two non-European populations. It\nwould be good if the authors can mention the overall ethnic groups covered in\ntheir study (genotyping and sequencing) eg. (Europeans %, Non-Europeans %\n(African/Morroco/Haiti, Asians etc.))\nResponse: We clarified in the abstract that CaG is a multi-ancestry cohort. The\ngenotyping component included all individuals who provided consent for DNA\nanalysis in the biobank, and as such have a wide diversity of ancestral profiles. This\nhas been clarified in the abstract, and details have been added in Figure 1 (we added\nsample size for each cluster) and Supplementary Table 1. By contrast, sequencing\ndata has focused on individuals with four grandparents from one of three countries\n(Canada, Morocco and Haiti).\n2.2) Abstract: The authors have highlighted that they analyzed genes for rich\nphenotype and environmental data. Following this they mention that they\ninterpret variants of clinical importance followed by a mention how this is\nmethodologically advanced. It seems like a new database has been created\nthrough this study for future research use (eg. Quebec Genomics Health db).\nHowever, no key results have been mentioned in the abstract. A mention of the\nkey findings: number and type of traits analyzed (any top findings: monogenic or\ncomplex traits), accuracy and/or coverage of sequencing based imputation\ncompared to existing reference panels/previous studies on this population\nand/or how many new variants were identified and if the focus was ona any\nspecific population, would send out a clear message of the study\nstrengths/novelty.\nResponse. The Reviewer is correct that this manuscript describes a new dataset\nlinked to data and a biobank, and this manuscript serves as an overall presentation\nof the data and findings from a range of basic population and statistical genetic\nanalyses. Given the diversity of analyses presented, it is challenging to summarize all\nthe results within 200-words (word limit for the abstract). We did add the following\ninformation in the revised abstract: (1) clarification that CaG is a multi-ancestry\ncohort, (2) number of sequenced participants by ancestry, (3) mention that we used\nSPG7 to illustrate the value of the CaG dataset to expand the study of rare diseases in\nQuebec, (4) number of quantitative traits analyzed by GWAS (n=42 traits), and (5)\nmention that imputation with the CaG panel increases the GWAS yield by ~7% when\ncompared to results generated with data imputed using the much larger TOPMed\nimputation panel.\n2.3) In the last sentence it is unclear if all this data is available for global or local\nresearch use? Is it the existing CaG study database available for access or an\nextension to the current dataset\n(https://cartagene.qc.ca/en/researchers/genetic-data.html)?\nResponse: Data is available for research use globally. It is indeed an extension to the\ncurrent dataset. See also our response to Reviewer #1, Question 5 above. We have\nedited the abstract accordingly.\n2.4) Methods/Results: Supplementary table 1: Include details of all participants\nstudied (n=29337). Currently the ancestry-wise % does not match the total\nnumber studied (19,342 (QFC + QHA + QMO)). Mention acronyms QHA and QMO\nin the Supp table 1 as in main text. Similarly for the Whole genome sequenced\nCaG subset column. In case the authors studied only the subset (QFC, QHA, and\nQMO) it needs to be specified under methods section.\nResponse: This has been clarified in the abstract (see reply to Question 2.1 above).\nThe three specified groups (QFC, QHA and QMO) form the bulk of the WGS efforts.\nData for 123 additional individuals of European ancestry with grandparents born in\nCanada is also available in the dataset, but were not analyzed here due to our focus\non the three specified groups. All 29,337 individuals in the cohort who gave consent\n4\nto DNA analysis were genotyped. This is a diverse cohort that was not recruited\naccording to ancestry, and as such a simple breakdown in terms of ancestry groups\nis not straightforward. Information about the number of participants in each ancestry\ncluster is now provided in Figure 1 in the main text, and the information is further\ndetailed in Supplementary Table 1.\n2.5) Methods:\na. Genotyping methods should mention some details of the quality control on\nGSA array data.\nResponse: We have added the following paragraph to the Methods section: “We\ngenotyped the CaG participants separately in five different projects between 2017\nand 2021, each time using a slightly different version of Illumina’s GSA arrays\n(Supplementary Fig. 2). One of the subsets (2017) was enriched for restless leg\nsyndrome cases. The remaining sets were not ascertained for particular diseases\nor ancestral groups. We used PLINK 1.9 to perform genotype data quality-control\nin each batch separately (Supplementary Fig. 2). We removed samples with: (1)\nlow genotyping success rate (<95%), (2) sex mismatch with the database, (3)\nduplicates based on IBD analyses, and (4) samples that share a fraction of their\ngenome with many (>50%) of the participants (i.e. contaminants). We excluded\nvariants with: (1) low genotyping success rate (<95%), (2) monomorphic or\nduplicates, (3) Hardy-Weinberg P-value <1x10-6, (4) indels with unspecified alleles\n(e.g. “D”, “I”), (5) Y-linked variants, and (6) variants with a large allele frequency\nvariance between the five batches (measured using the perpendicular distance to\nthe unity line). We also aligned all alleles on the positive strand\n(https://www.well.ox.ac.uk/~wrayner/strand). We merged the datasets, keeping\nonly variants available to all five batches. We obtained a cleaned dataset of\n29,337 participants and 472,467 variants.”\nb. The authors mention about genotyping performed from 5 different projects:\nDo they mean different projects or batches? Also if these were in regard to\ndiffering ethnic groups? Provide more details and if possible a flow chart\nshowing steps through the genotyping.\nResponse: We have added this information in the form of a flow chart in the new\nSupplementary Fig. 2. Except for the first batch genotyped in 2017, which was\nenriched for restless leg syndrome cases, the genotyping project were not\nascertained for phenotypes nor ancestral groups.\nc. Also clearly define the MAF thresholds and variants filtered after genotyping\n(relevant for the five different projects/batches and/or for the overall merged\ndataset).\notherwise did not explicitly filter out variants based on MAF. Because we only kept\nvariants common to all five genotyping batches when merging the datasets, many\nrare variants were filtered out at this step as well.\n2.6) Methods: WGS Data QC and Supp Table 2: The authors have mentioned biallelic\nSNVs and indels and corresponding numbers in the main text that do not match\nthe numbers in Supp table 2. Kindly check again, correct and use same\nlabel/name for these in both places for ease of the reader (eg. SNV or \"biallelic\nSNV\" in both places).\nResponse: We apologize for the confusion, which arose when we converted multi-\nallelic to bi-allelic variants (an essential step to annotate variants). We have corrected\nthe Methods paragraph and Supplementary Table 3 (formerly Supplementary Table\n2): “The final dataset included 2,173 individuals and 80,407,530 bi-allelic variants, of\nwhich 62,457,187 are single nucleotide variants (SNVs), 15,645,143 are insertion-\ndeletions (indels), and 852,021 are substitutions (SupplementaryTable 3)”.\n2.7) Genetic structure in CaG (Supp Figure 1A and B): The authors have nicely\nconducted a PC analyses but a clear composition of subcluseters (1-14) needs to\nbe mentioned (possibly in tabular form) clearly highlighting the differences\nbetween clusters.\nResponse: We have expanded on this description: We have added counts per\nclusters in Figure 1 as well as in Supplementary Table 1. Each cluster is ventilated\nby leading declared countries of birth.\n2.8) Results (Ancestry form Americas): Is the CartaGene a representative population\nfor the entire Quebec as it mostly covers regions from the southern Quebec\nregion. This needs to be mentioned.\nResponse: The cohort was recruited by randomly contacting people from provincial\nhealthcare lists in predetemined regions in the South of Quebec. This led to regional\nbiases, as well as a bias against recruitment of participants from Canadian\nIndigenous backgrounds, since they are covered by a different healthcare system. In\naddition, we expect different forms of response biases (e.g., participation rates were\nmuch higher in individuals with higher education levels). As a result, the cohort\ncannot be taken as statistically representative. With these limitations in mind, its\nparticipants are diverse and represent a range of ancestries, regional origins,\nsocioeconomic statuses, and environmental exposures.\nThis is outlined in the discussion:\nparticipants with a range of ancestral backgrounds, CaG is not completely representative\nof the province’s population. On average, CaG participants have higher education\nattainment and income than the overall population. Furthermore, because recruitment took\nplace in six cities, several geographical regions of Quebec such as the Northwestern\nAbitibi-Temiscamingue region are not well captured in the CaG dataset. Indigenous\nresidents are also not well represented in CaG.\nWe also have added context in the section on ancestry from the Americas, as this\ncontext is important to interpret these results.\n“The CaG cohort was recruited in the south of Quebec from individuals enrolled in the\nprovincial healthcare plan, both factors that likely led to reduced participation by\nindividuals with elevated ancestry from the Americas: many indigenous communities live\noutside our sampling areas, and Indigenous people are more likely to be enrolled in a\nfederal rather than provincial healthcare plan.”\n2.9) Compared to the original Quebec population comprising French, Canadian\n(overlapping with French), British, Indigenous, Other European and Non-\nEuropean population needs to be discussed in regard to the current study\npopulation. Also a chart as per timeline when these populations originated in\nQuebec could be a good overall demonstration of the Quebec population with a\nhighlight of what is covered by the current study (sample collection centers). This\nwill provide a clear image of the studied population at present time, which might be\npredominant at some time point in history.\nResponse: The CARTaGENE cohort is sampled from the present-day population of\nQuebec which, as the Reviewer describes, is the result of multiple waves of migration.\nThe regions sampled include the largest metropolitan areas in the province, and as\nsuch, are expected to represent most major ancestral groups present in the province,\nsubject to the biases highlighted in the text. A thorough description of the migration\nhistory of Quebec is out of scope for this work. We do agree with the reviewer that\nmore context would help the reader unfamiliar with the history of the province to\nsituate the cohort in a broader context. We have therefore added a few lines in the\nintroduction pointing the reader to relevant resources describing this history and\nadded a timeline in Supplementary Figure 1 which summarizes the main\nestablishments and migration events in Quebec.\n2.10) The study highlights enriched genetic variants in this population relating\nmetabolic outcomes. The authors should highlight this in the abstract. In case\nthere is information on other health outcomes, such as diabetes, cardiovascular\ndisease, obesity etc. is available in CaG, their prevalence could be mentioned in\na supplementary table making this dataset comparable to other similar large\ndatasets mentioned by the authors.\nResponse: We have now included a Supplementary Table 2 summarizing\nprevalences of various health outcomes in CaG alongside prevalences in the large UK\nBiobank for comparison.\nSupplementary Table 2. Chronic disease prevalence* in CARTaGENE participants with\ngenotyping data available. Prevalence is calculated as: N cases/(N cases + N controls) among\nindividuals with genotyping data.\nCARTaGENE UK Biobank White British (PheCode-\nbased definitions)\nPrevalence N cases N controls Prevalence N cases N controls\nBladder cancer 0.0022 55 24,879 0.0059 2409 403,027\nLupus 0.0029 73 24,847 0.0010 409 399,660\nAtrial 0.0095 84 8788 0.0375 14,756 379,217\nfibrillation\nLeukemia 0.0017 42 24,892 0.0041 1656 402,713\nType 1 diabetes 0.0057 143 24,735 0.0071 2762 387,081\nUlcerative 0.0205 227 10,848 0.0095 3183 333,377\ncolitis\nProstate cancer 0.0096 239 24,695 0.0342 5993 168,999\nBreast cancer 0.0187 467 24,467 0.0320 12,790 387,413\nRheumatoid 0.0288 631 21,570 0.0119 4380 363,562\narthritis\nMyocardial 0.0260 648 24,271 0.0311 12,038 374,657\ninfarction\nPsoriasis 0.1321 1608 10,561 0.0055 2197 396,508\nType 2 diabetes 0.0666 1657 23,221 0.0446 18,105 387,087\nAnemia 0.1981 1752 7091 0.0192 7627 388,399\nAsthma 0.1241 3088 21,801 0.0654 26,167 373,887\nReviewer #2 (Remarks on code availability):\nThe code could be added to a Github repository with comments (optional).\nResponse: We are committed to open science and reproducibility. As such, we have ensured\nthat the main source code used to process the data and generate data for tables and figures\nis available through figshare at https://doi.org/10.6084/m9.figshare.28891670, as well as\nbeing available in a Github repository. The figshare download includes the following sections:\n ARG-based imputation and regional frequency - contains source code used to\nidentify likely carriers in genotyping array data in the \"Variants of clinical interest (VCI):\nusing CaG to go beyond founder variants\" section of the manuscript.\n8\n GWAS - contains source code used for genome-wide association scans, fine-\nmapping, and generating tables and figures in the \"Genome-wide association studies\n(GWAS)\" section of the manuscript.\n Genotype_processing - contains source code used for the genotyping array data pre-\nprocessing before genotype imputation and GWASs.\n HLA - contains source code used for the HLA data generation, analysis and generating\ntables and figures in the \"HLA genotyping\" section of the manuscript.\n IBD - contains source code used for the identity-by-descent (IBD) analyses and\ngenerating tables and figures in the \"Genetic structure in CaG\" section of the\nmanuscript.\n Imputation - contains source code used for preparing and benchmarking the\ngenotype imputation reference panel in the \"Haplotype reference panel and genotype\nimputation \" section of the manuscript.\n PCA_UMAP_clustering - contains source code used for the Principal Component\nAnalyses (PCA) and Uniform Manifold Approximation and Projection (UMAP) in the\n\"Genetic structure in CaG\" section of the manuscript.\n Phasing - contains source code used for statistical phasing and benchmarking\nphasing quality in the \"Haplotype reference panel and genotype imputation\" section\nof the manuscript.\nThese files are also available in the following GitHub repositories:\n https://github.com/CERC-Genomic-Medicine/CARTaGENE_flagship_paper - for\nmanuscript tables and figures; data processing steps.\n https://github.com/CERC-Genomic-Medicine/WEGS_pipelines - for pipelines used\nfor the variant calling in benchmark WGS datasets.\n https://github.com/CERC-Genomic-Medicine/Regenie_nextflow - for pipelines used\nfor the genome-wide association scans\nTo make it easier to navigate the source code used in analyses and figures, we have included\nSupplementary Table 19."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Authors well responded the reviewer's comments, thanks.", "reviewer_2": "Thanks to the authors for making the necessary changes in the revised version. I congratulate the authors and have no\nfurther comments."}, "author_response": ""}]

Decision letter