ATP2B4, but are unable to identify a likely causal mechanism at the chromosome 6 locus.  Previously reported HLA associations do not replicate in these samples. This large dataset will provide a foundation for further research on the genetic determinants of malaria resistance in diverse populations.The human genetic factors that affect resistance to infectious disease are poorly understood. Here we report a genome-wide association study in 17,000 severe malaria cases and population controls from 11 countries, informed by sequencing of family trios and by direct typing of candidate loci in an additional 15,000 samples. We identify five replicable associations with genome-wide levels of evidence including a newly implicated variant on chromosome 6. Jointly, these variants account for around one-tenth of the heritability of severe malaria, which we estimate as ~23% using genome-wide genotypes. We interrogate available functional data and discover an erythroid-specific transcription start site underlying the known association in  Four genome-wide associated loci are currently known for malaria susceptibility. Here, the authors expand on earlier work by combining data from 11 malaria-endemic countries and additional population sequencing informing an African-enriched imputation reference panel, with findings including a previously unreported association on chromosome 6. The Malaria Genomic Epidemiology Network  was established in 2005 to overcome these obstacles with standardized protocols, common phenotypic definitions, agreed policies for equitable data sharing and local capacity building for genetic data analysis, enabling large collaborative studies across different countries where malaria is endemic4.Genome-wide association studies (GWASs) have been very successful in identifying common genetic variants underlying chronic non-communicable diseases, but have proved to be more difficult for acute infectious diseases that represent a substantial portion of the global disease burden and are most prevalent in tropical regions. This is partly due to the practical difficulties of establishing large sample collections and reliable phenotypic datasets in resource-constrained settings, but also theoretical and methodological challenges associated with the study of pathogenic diseases in populations with high levels of genetic diversity and population structureATP2B4 locus, via disruption of GATA1-mediated expression of an erythroid-specific transcript. Across all loci, empirical models of allele frequencies suggest there has been a systematic positive selection of malaria-protective alleles and we conclude by noting that coevolutionary selection pressure may also affect Plasmodium falciparum populations, raising the need to incorporate parasite genetic variation into future studies.Here we extend previous work by using data collected from 11 countries to perform a comprehensive GWAS of human resistance to severe malaria (SM). We incorporate DNA from 17,056 SM cases and population controls genotyped and statistically phased at over 1.5 million single-nucleotide polymorphisms (SNPs) across the human genome, and impute from a modified version of the 1000 Genomes genetic variation reference panel enriched with 773 additional African genome sequences, plus locus-specific panels in the glycophorin and human leukocyte antigen (HLA) regions. We analyse association in a Bayesian meta-analysis framework, allowing for differences between populations and subphenotypes, and identify five loci with genome-wide levels of evidence which are further supported in a set of replication samples and jointly explain ~10% of the estimated heritability. Among these, a newly implicated locus on chromosome 6 appears most strongly associated with cerebral malaria (CM), but lies over 700 kb from the nearest protein-coding gene. We analyse available functional data, including RNA-sequencing (RNA-seq) from erythroid lineage cells, but are unable to identify a putative function for this locus. However, this analysis does provide insights into the potential mechanism of protection at the previously reported P. falciparum malaria, and 22,000 population controls5. In brief, individuals were sampled from 12 study sites, including 10 in sub-Saharan Africa and the others in Vietnam and Papua New Guinea  and other malaria-related symptoms (here referred to as other SM). For our GWAS discovery phase, a subset of samples, preferentially chosen for phenotype severity and DNA quality, were genotyped on the Illumina Omni 2.5 M platform. We jointly processed these samples to produce a single set of estimated haplotypes for 17,960 individuals at the set of over 1.5 M SNPs genome-wide which passed our quality control process (Methods). Subsets of these data from The Gambia, Malawi and Kenya7, from Tanzania8 and from selected control samples9 have been reported previously , including 207 family trios . In principle, the additional representation of African DNA in this panel should lead to improvements in imputation accuracy for African study populations and we found that this was indeed the case, with the use of our panel leading to a large increase in accuracy relative to panels used in our previous GWAS12 and a more modest improvement relative to using the 1000 Genomes Phase 3 panel alone  was imputed with r > 0.9 in all African populations, as compared with genotypes obtained through direct typing5. We did note that some potentially relevant loci still appear not to be accessible through these data, including the common deletion of HBA1-HBA2 that causes alpha-thalassaemia . Specifically, association tests were conducted within each population using logistic and multinomial logistic regression , including principal components (PCs) to control for population structure, and we computed a fixed-effect meta-analysis summary of association across populations  methods suggest a relatively small number of our top signals may represent real associations . Compatible with this, direct genotyping of 15,548 cases and controls not included in our GWAS discovery phase revealed evidence of replication  for one newly identified locus  = 0.66–0.95; estimated using only replication samples; Fig. SMA = 0.93, 95% CI = 0.77–1.13; OROTHER = 1.10, 95% CI = 0.96–1.25). rs62418762 lies in a region of chromosome 6 between MAP3K7 and the nearest gene EPHA7, which encodes Ephrin type-A receptor 7, a regulator of neurodevelopment and neural cell adhesion16. Other type-A ephrin receptors have been implicated in liver-stage malaria17. However, rs62418762 lies over 700 kb distant from EPHA7 and it is not immediately clear whether any functional link between them exists. A number of non-protein-coding transcripts lie nearby , as do reported signals of association with neurodegenerative disorders19, and these may provide clues as to an underlying mechanism. However, we caution that no functional effect of rs62418762 is apparent at present and additional replication may therefore be warranted.rs62418762 has the second strongest evidence of all SNPs in our discovery phase and the strongest evidence overall  with rs567544458 in our genome-wide imputation . Our data also demonstrate differences in effect size between malaria subphenotypes, with all of the replicating variants displaying smaller effects in nonspecific cases of SM compared with cerebral or severely anaemic cases  = 0.23, 95% CI = 0.16–0.30; computed across African populations using PCGC22 assuming a 1% population prevalence of SM23; Supplementary Fig. HBB, ABO, glycophorin and ATP2B4 loci appear to contribute around 11% of this total  and we noted a trend for the remaining heritability to concentrate near protein-coding genes and at lower-frequency variants .We used published methods to estimate the heritability of SM captured by genome-wide genotypes 34 and association evidence from other traits38. This analysis revealed a number of potentially functional variants with evidence of association  that is specific to erythroid cells  disrupts a GATA motif32 and the same SNPs have previously been shown to associate with ATP2B4 expression levels in whole blood34, in experimentally differentiated erythrocyte precursors32 and with PMCA4 levels in circulating red blood cells (RBCs)40. We noted that the GATA1 site lies just upstream of an exon that is not listed in the GENCODE41, RefSeq42 or ENSEMBL43 transcript models, but that can be found in FANTOM544  of ATP2B4, but not with the annotated first exon (P = 0.25). These results indicate that the malaria-associated SNPs affect ATP2B4 expression in a erythroid-specific manner, by affecting the promoter of a TSS that is only active in these cells.We uncovered compelling evidence that the association in lls Fig. . Specifi544 Fig. . Inspects33 Fig. . MoreoveATP2B4 is reminiscent of the well-known mutation at the Duffy blood group locus (DARC/ACKR1), which protects against Plasmodium vivax malaria by preventing erythrocytic expression of the Duffy antigen receptor through disruption of a GATA1-binding site47. However, the mechanism by which ATP2B4 affects parasite processes remains unknown. The malaria-protective allele at rs10751451 is known to associate with RBC indices (notably with increased mean corpuscular haemoglobin concentration (MCHC)48. We found some evidence that genetic predisposition to high MCHC levels may itself be associated with decreased malaria susceptibility  framework; Fig. ATP2B4 appears substantially stronger than this trend and suggests a genuinely pleiotropic effect whose mechanism against malaria is yet to be determined.The situation outlined above for VAC14, which has been implicated in Typhoid fever49 and Salmonella typhi invasion35 detailed further in Supplementary Note Our analysis of functional annotations revealed a number of additional variants with some evidence of function. These include variants in the HLA region, discussed further below, and a variant upstream of 50 to impute classical HLA alleles and tested for association with each allele as described above for genome-wide variants . By contrast, an order of magnitude stronger evidence was observed at regional SNPs, including rs2523650 . Most notably, the HLA-B*53 allele, which has previously been reported as associated with strong protection against SM in the Gambia51, showed no evidence for association across populations  or in any individual population . We sought to assess whether variants associated with malaria susceptibility show evidence of selection by comparing with genome-wide allele frequency variation between populations  < 0.5 for 10 of 12 regions with BFavg > 10,000; 45 of 91 regions with BFavg > 1,000; based on reference panel allele counts; Fig. P = 3 × 10−3 against the null model of no differentiation, for the 12 regions with BFavg > 10,000; P = 0.02 across 92 regions with BFavg > 1000, excluding all but one variant within the HLA region; Fig. Malaria is regarded as having played a strong role in shaping the human genome through natural selectionons Fig. –10. We fPXtX = 8.9 × 10−15), variants in CD36 , as well as variants in regions associated with skin pigmentation58  are not associated with susceptibility to P. falciparum malaria .Sample collection and study design was approved by Oxford University Tropical Research Ethics committee (OXTREC), Oxford, UK (OXTREC 020–006). Local approving bodies are detailed in Supplementary Table 76 with base quality score recalibration and local realignment around known indels as implemented in GATK77.Blood samples were collected from a total of 773 individuals from Gambia, Burkina Faso, Cameroon and Tanzania, and sequenced to an average of 10× coverage on the Illumina HiSeq 2000 platform at the Wellcome Sanger Institute, using 100 bp paired-end reads. In addition, one Gambian trio was additionally sequenced using three alternate library preparation methods (low-quantity and whole-genome amplification pipelines) for a total of 782 sequenced samples. Sequence reads were mapped to the GRCh37 human reference genome with additional sequences as modified by the 1000 Genomes Project (hs37d5.fa), using BWAWe used GATK UnifiedGenotyper to identify potential polymorphic sites on autosomal chromosomes, using all 782 samples as well as the 680 samples of African ancestry from the 1000 Genomes Project, and running in 50 kb chunks. GATK VQSR was used to separately filter SNPs and indels. For SNPs, we included three training sets: HapMap SNPs, SNPs on the Omni 2.5 M array and 1000 Genomes Phase 3 sites. For indels we used the ‘Mills’ training set as recommended in the GATK documentation. We based filtering on read depth, mapping quality, quality by depth, the MQRankSum and ReadPosRankSum measures of bias in reference vs. alternate allele-mapping quality or read positions, and tests for strand bias and inbreeding coefficient. We filtered variants applying a sensitivity setting of 99.5% (--ts_filter_level 0.95) for SNPs and 95% for INDELs (--ts_filter_level 0.95). We further restricted to biallelic sites.78 to generate genotype calls from the genotype likelihoods. We ran BEAGLE in windows of 50,000 SNPs, including trio information for Gambian samples. Due to the observed long runtimes when sampling trios, we specified trioscale = 3 when running BEAGLE to reduce computation times. We then concatenated results across chromosomes.We computed genotype likelihoods at all sites passing the filter described above and sites in 1000 Genomes Project Phase 3 reference panel using GATK HaplotypeCaller. We then used BEAGLE v4.079. We then used SHAPEIT2 to phase each chromosome. We included trio information for Gambian samples, specified a window size of 0.5 (--window), an effective sample size of 17,469 (--effective-size), 200 model states (--states), included 1000 Genomes Phase 3 haplotypes in the phasing process (--input-ref) and used the hapmap-combined recombination map in build 37 coordinates (-M).To estimate haplotypes, we first removed variants not present in the 1000 Genomes Phase 3 panel or variants that had alleles differing from those in the 1000 Genomes Phase 3 panel, using the ‘check’ mode of SHAPEIT211.To construct a combined imputation reference panel, we first removed repeated samples and then combined phased haplotypes at the remaining samples with those from the 1000 Genomes Project phase 3 panel. The final panel contains 77,931,101 variants and 3277 samples, of which 773 are from our set of sequence data and 2504 are from the 1000 Genomes panel. In total, the panel includes 1434 individuals with recent African ancestry (including 157 African American samples) and non-African samples as previously described23 for CM (Blantyre coma score < 3 in children or Glasgow coma score  < 11 in adults), SMA (haemoglobin < 5 g/100 mL or haematocrit < 15%) and other malaria-related symptoms (here referred to as ‘other SM’). The distribution of these phenotypes between countries is summarized in Supplementary Fig. 5. For our main discovery and replication analyses, we maximized sample size by including all samples collected as cases and controls. For specific estimation of effect sizes .Cases of SM were recruited on admission to hospital as part of ongoing studies by local investigators  and we formed final genotype calls by taking the consensus of the three algorithms, or of two algorithms where the third algorithm reported a missing call, as used previously7. Genotypes for which a discrepant call were made were treated as missing.Sample genotyping was performed at the Wellcome Sanger Institute using the Illumina Omni 2.5 M platform. Genotype calling was performed using three genotype calling algorithms  to realign study genotypes. The resulting ‘aligned’ datasets are encoded so that the first allele reflects the reference allele, the second allele the non-reference allele and all alleles are expressed relative to the forward strand of the reference sequence, simplifying downstream analysis.As a sanity check, we plotted the estimated frequency of each non-reference allele against the frequency in the closest reference panel group in each populations. The Kenyan dataset (which was typed using the Omni 2.5 M quad ‘D’ version manifest) showed a substantial number of SNPs with frequencies obviously wrongly specified. Comparison of the flanking sequence and alleles reported in the manifest file identified 18,763 SNPs that had alleles coded incorrectly in the manifest file. We repaired fixed these SNPs by recoding these alleles, making them consistent with the flanking sequence, the more recent ‘H’ version of the manifest, and the Omni 2.5 M ‘octo’ chip manifest. We also updated the positions of 495 SNPs annotated as lying on the pseudo-autosomal part of the X chromosome (Chr = ‘XY’ in the Illumina manifest) but with position = 0. We applied these updates, repeated the alignment process for Kenya, and regenerated the frequency plot. A small number of SNPs in each population still show distinct frequencies compared with reference panel data  against average heterozygosity in each population. We excluded samples with >10% missingness or heterozygosity outside the range 0.2–0.4 in African populations, below 0.175 in Vietnam or below 0.13 in PNG outright computed the QC SNP set. We then applied ABERRANT to the remaining non-excluded samples choosing a value of the lambda parameter per population based on inspection. As for intensity outliers, we specifically included some groups of samples visually clustering with the main group of samples but called as outlier by ABERRANT. We plotted heterozygosity against missingness annotating the removed samples. Supplementary Figs. We used QCTOOL to compute the proportion of missing genotype calls and the average proportion of heterozygous calls per sample, and the average X channel and Y channel intensities across autosomal chromosomes for each sample. Values were averaged over a subset of SNPs  SNP set’) chosen to have low missing rates in all populations. We then ran ABERRANTThe above process results in a set of 18,515 samples that appear to be relatively well genotyped across the autosomes. In addition, for some purposes described below, we used a smaller set 17,664 samples (referred to as the ‘conservative’ set of samples) obtained by repeating the above process with more conservative values of the ABERRANT lambda parameter.5. To confirm the assigned sex, we plotted overall intensity from the Illumina chip (X channel plus Y channel intensity) averaged across the X chromosome, against overall intensity averaged across the Y chromosome  where rij reflects the correlation in genotype between sample i and j, normalized by variant frequency. The number rij may be treated as an estimate of relatedness with values near 1 reflecting identity and values near 0 reflecting unrelated samples, relative to the average relatedness in the population84.To identify relationships between samples, we used QCTOOL to compute a matrix of pairwise relatedness coefficients in each population. To do this, we first chose a subset of SNPs using a preliminary version of the SNP QC process described below and used inthinnerator (see Code availability) to thin this set of SNPs to lie no closer than 0.02 cM apart in the Hapmap-combined recombination map. We additionally excluded SNPs from regions of known associations, the region chr6:25,000,000–40,000,000, which contains the HLA, and the region of the common inversion on chromosome 8. We used QCTOOL with the resulting set of 157,085 SNPs to compute the normalized allele sharing (‘relatedness’) matrix For each pair of samples with relatedness > 0.75, we noted the sample with the highest rate of missing genotype calls. A total of 538 samples were identified in this way, the majority of which were from the Malawi and Kenya datasets, and likely reflect duplicate genotyping. We removed these samples from the datasets. For each remaining pair of samples with relatedness > 0.2, we again noted the sample with the highest rate of missing genotype calls; these represent samples that are relatively closely related to other samples in the same study sample. A total of 801 samples were identified in this way. These samples were retained in the dataset for phasing and imputation, but excluded from PC analysis and downstream association testing.R and plotted PCs in each population. Based on visual inspection of these plots, we further identified a total of 40 samples outlying on any of the first 10 PCs. We recomputed R and the PCs after excluding these individuals, to produce a set of PCs for association analysis. PCs were observed to correlate with population structure (as represented by the reported ethnicity) and in some populations with case/control status and/or with technical factors such as missing genotype call rates.We computed PCs by taking the eigendecomposition of In total, following our sample QC process, 17,960 samples passed QC criteria, had non-missing/non-mixed sex assignment and were not identified as duplicates as described above. We took all of these samples through to the phasing and imputation as described below. Of these samples, a total of 17,056 samples were identified as not outlying on PCs, not closely related and having assigned case/control status, and were included in downstream analyses. Supplementary Table We observed study sample sets to vary considerably in quality of typing (as well as in size), with the two smallest African sets  and, to a lesser extent, Malawi having higher missing data rates and Kenya having lower rates 85 computed in control samples (PHWE), a plate test P-value against the null model that genotypes are uncorrelated with the plate on which each sample was genotyped (Pplate) and a recall test P-value, which compares the genotype frequencies with frequencies after a re-calling process based on the estimated cluster positions across populations. The plate test was implemented using a logistic regression model treating the genotype as outcome and an indicator of the 96-well plate on which the sample was supplied for genotyping as a predictor in each population, and including case/control status and an initial set of five PCs as covariates. For each SNP we computed a likelihood ratio test P-value for the inclusion of plate as a predictor. The recall test was motivated by the observation that some otherwise well-typed SNPs were poorly genotyped in individual populations and was implemented as follows. We first used intensities and genotype calls for the ‘conservative’ sample set to re-estimate the positions of genotype clusters at each SNP in each study population using QCTOOL, fitting a multivariate t-distribution with 5 degrees of freedom to each genotype cluster. For each SNP in each target population, we then re-called genotypes using intensities based on a mixture of the cluster positions learned in all other populations. We used Fisher’s exact test to compute a p-value against the null that the frequency in the original and re-called genotypes were the same.We based SNP QC on several metrics computed separately in each population: the estimated minor allele frequency (MAF), the proportion of missing genotype calls (missingness), a P < 1 × 10−20 for HWE, P < 1 × 10−3 for the plate test and P < 1 × 10−6 for the recall test in each population. Finally, we included each of the remaining SNPs that was at MAF > 1% in at least two populations and passed the above thresholds in all nine populations considered. Data from the Mali and Nigeria populations, which had the lowest sample counts  and empirically lower rates of high-quality genotyping  with statistical phasing across populations would likely produce a high-quality set of haplotypes for downstream inference. We therefore aimed to produce a single set of SNPs with high-quality data across populations for input to the phasing process. We chose a set of criteria applied to the nine largest sample sets to compute a list of SNPs to include. We removed SNPs with missingness >2.5% in Kenya and PNG, >5% in Gambia, Burkina Faso, Ghana, Cameroon and Vietnam, and >10% in Malawi and Tanzania. We also excluded SNPs with P < 1 × 10−6). We did not implement the recall test for X chromosome SNPs.SNP QC on the X chromosome was performed as for autosomes with the following adjustments. We excluded the pseudo-autosomal region . We also observed elevated rates of males called as heterozygous within the X transposed region  and excluded this region as well. Approximately 3400 SNPs were removed this way. In computing summary statistics for males, we treated all heterozygous calls as missing and homozygous calls as the corresponding hemizygous calls. We applied the SNP missingness threshold and the plate test separately in males and females; we replaced the HWE test with a test of difference in frequency between males and females  using the same settings throughout.We used IMPUTE2 (v2.3.2)r2’ (which measures correlation between input genotypes and re-imputed genotype dosages), which we refer to here as accuracy. We plotted mean accuracy in 1% MAF bins for each subset of samples imputed  for each sample for each imputation chunk, reflecting the correlation between input genotypes and re-imputed genotype dosages across variants within each chunk. We plotted the distribution of per-sample accuracy, averaged across chunks, for samples in each of the largest ethnic groups in our data  of a study panel haplotype S and a reference panel population P, we computed the proportion of 1 Mb chunks such that the closest reference panel haplotype to S lies in P. Proximity was measured by absolute number of differences with ties broken by randomly choosing one of the closest haplotypes. We averaged the proportions over samples in each study ethnicity; these proportions are plotted in Fig. 50 to impute HLA classical alleles for all study samples. We used the unphased, post-QC set of genotype calls across populations in the region chr6:28Mb-36Mb as input. This version of HLA*IMP outputs imputed diploid allele calls and posterior probabilities for alleles at two-digit and four-digit resolution, and uses an allele naming scheme that is similar to the pre-2010 IPD-IMGT/HLA naming convention. For downstream analysis, we split HLA*IMP output into per-allele posterior probabilities. Specifically, for each allele we computed the posterior probability of zero, one or two copies of the allele, against all other alleles at the locus, from the HLA*IMP output. We encoded this data in the GP field of a VCF file with one row per allele. This functionality may be generally useful and is implemented in QCTOOL. For reference purposes, we assigned each allele to the midpoint of the corresponding gene.We used HLA*IMP:0214. We based our imputation on the phased set of haplotypes, but to avoid potential issues with phasing in this region we treated these genotypes as unphased. We then ran IMPUTE2 in 2 Mb chunks across a 10 Mb region surrounding the glycophorins, specifying 1000 reference panel haplotypes, a 500 kb buffer region, an effective population size of 20,000, and using the HapMap combined recombination map. As previously14, we excluded SNPs in the region of segmental duplication when imputing CNV calls.We used IMPUTE2 to impute genotypes from a previously published reference panel of SNPs, indels and large copy number variants (CNVs) in the glycophorin region on chromosome 4We used SNPTEST (see Code availability) to test each typed or imputed variant for association with SM status in each study population. Specifically, for each population we ran SNPTEST for each of the 2 Mb chunks output by imputation, including either 0, 2, 5 or 10 population-specific PCs as covariates and under additive, dominant, recessive, heterozygote and general models of association. For the main analyses presented here, we used the versions based on including five PCs. A total of 17,056 samples had PCs and assigned case/control status, and were included in association analyses. We tested under additive, dominant, recessive and heterozygote inheritance models.P-value, against the null model where all genetic effect-size parameters are zero. In addition, for each case phenotype we compute a Wald test statistic and P-value against the null model that the genetic effect on that phenotype is zero. Of the 17,056 samples included in association testing, 537 had no subphenotype assignment or were assigned as having both CM and SMA phenotypes. For simplicity we excluded these samples from the genome-wide test against subphenotypes. We tested under additive, dominant, recessive and heterozygote inheritance models.To test association with the main SM subphenotypes in our data—namely CM and SMA, and other SM cases (OTHER)—we extended SNPTEST to perform maximum likelihood inference for multinomial logistic regression. A full description of this method is presented in Supplementary Note We additionally tested variants on the X chromosome using a logistic regression model including sex as a covariate and treating male hemizygote genotypes in the same way as homozygous females.7 (see Code availability). BINGWA is written in C++ and takes a list of SNPTEST files, along with options that control variant filtering and output, and computes meta-analysis results using both frequentist and Bayesian methods. Per-population results for each variant were included in meta-analysis if the minor allele count  was at least 25, the IMPUTE info was at least 0.3 and no issues were reported with model fitting. For the non-additive tests, we defined the minor predictor count as the minimum of the expected number of individuals having the effect genotype  and the number having the baseline genotype .To efficiently meta-analyse the association results, we further developed our software package BINGWAβmeta, its variance–covariance matrix Vmeta and overall meta-analysis P-value as described in Supplementary Note βmeta is three dimensional ; we additionally computed a Wald test P-value for each estimated parameter.For each case/control or subphenotype test, we computed a frequentist fixed-effect meta-analysis estimate 12. In brief, we implemented Bayesian inference using the asymptotic or approximate Bayes factor approach88. This approach treats the observed effect sizes  as arising from a set of true population effect sizes, together with estimation noise that is represented by the SEs and parameter covariances in each population. The ‘true’ population effects are modelled as being drawn from a multivariate normal distribution with mean zero and a prior covariance matrix Σ, which is chosen to represent a desired model of true effects. We write this covariance in the formΡ is a correlation matrix specifying the prior correlation in true effect sizes between populations and/or between subphenotypes, and σ is a scalar , which determines the prior variance of effect sizes, and thus the overall magnitude of modelled true effects.To assess evidence for association under a more flexible set of models of association, we used a Bayesian meta-analysis framework similar to that described previouslyβ and the parameter covariance matrix V, a Bayes factor for the model encoded by Σ can now be computed as a ratio of multivariate normal densitiesGiven the vector of per-population estimates 1, Σ2, … by computing the model-averaged Bayes factori is the BF computed using prior covariance Σi and the prior weights wi are chosen to sum to 1. The specific choices of prior correlation matrix Ρ and weights w used for our primary analysis are described below and in Supplementary Table Ρ, we computed the BF for that model as an equally weighted average over four values of σ—namely 0.2, 0.4, 0.6 and 0.8. We also repeated all analyses under additive, dominant, recessive and heterozygote modes of inheritance with prior weights 0.4, 0.2, 0.2 and 0.2, respectively.Different choices of Σ correspond to different assumptions about the underlying true effect sizes. The true pattern of effects is unknown, so we assess evidence under a collection of plausible assumptions ΣΡ set to 0.99)fixed effects correlated effects independent effects structured effects West and Central West African populations Central West African populations Central and East African populations East African populations All African populations Non-African populations We placed a total of 60% prior weight on fixed and correlated effects and 4% prior weight on each of independent and structured effects. In addition to effects across all populations, we included models of effect restricted to population subgroups. Specifically, we focused on the following groupings:σ to 0.001 for non-associated populations. These subsets are chosen to correspond to geographical groupings, as well as to groups apparent along the first PC of African populations .Effects on all three subphenotypes .Effects on two of three subphenotypes (σ set to 0 for two phenotypes)Effects restricted to one subphenotype , as well as effects that are only present for only two or one of the subphenotypes  and the model with second highest posterior weight.avg, these are computed by model averaging over parameters σ = 0.2, 0.4, 0.6, 0.8. For subphenotype tests we assume independence between effects in different subphenotypes).A per-population BF reflecting the evidence from each individual population . This computation thus reflects our belief about the top signals in our data, namely, that there is overwhelmingly strong evidence for signals at HBB, ABO, ATP2B4 and at the glycophorin region (for which BFavg > 5 × 107), good evidence at ARL14 and rs62418762 (BFavg > 5 × 106) and some evidence at further loci among the top 12 (BFavg > 1 × 105). Except for the association at ARL14, these observations are further reinforced by the replication data .Given the assumptions underlying BFavg gives a direct measure of evidence for association present in the data conditional on the assumptions. However, in the context of GWAS in which many variants are tested, it is also useful to understand the distribution of Bayes factors that would be observed by chance if no nonzero effect were present. A general way to do this is to treat the BFavg as a test statistic and to compute or estimate its null distribution90. To do this for rs62418762, we adopted a simulation-based approach in which we first estimated the frequency of the three genotype classes in each population, across case and control samples. Next, we conducted multiple rounds of simulation. In each simulation we simulated genotype counts for controls and for CM, SMA, OTHER and CM + SMA cases in each population by sampling genotypes from a multinomial distribution asN is the number of samples with the given phenotype in the population and f is the three-vector of estimated genotype frequencies for the three genotype classes in that population. Each simulation is thus performed by generating a 5 × 3 array of genotype counts in each population. We implemented the simulations in R. For each simulation we then used the vcd package91 to compute the log-odds ratios for all cases against controls and for each SM subphenotype against controls, for each mode of inheritance, along with SE and covariances. We passed these estimates into the meta-analysis pipeline described above for discovery data to compute BFavg. Similar to the discovery analysis, to exclude spurious results due to small counts, populations with minor allele count < 10 (for additive model tests) or minor predictor count < 10 (for non-additive model tests) were excluded from meta-analysis.The BFavg under the null, conditional on observed genotype frequencies. In total, we conducted 712,650,010 rounds of simulation, of which 5 had BFavg larger than the observed BFavg for rs62418762. The distribution of resulting Bayes factors is presented in Fig. The procedure described above produces a simulated distribution of BFThe Bayesian framework outlined above naturally extends to a discovery/replication setting; this is described further below and in Supplementary Note To identify a set of variants with high-quality evidence, we first filtered variants based on minor allele count and imputation quality as follows. In each population, we computed an effective minor allele count (EMAC) by the formulaavg and used inthinnerator to define a set of ‘lead’ variants and association regions. Inthinnerator works by iteratively picking the variant with the highest rank as lead variant and excluding all other variants in a recombination interval around the chosen variant. We specified a recombination interval of 0.125 cM plus a margin of 25 kb on either side of each lead variant, with distances determined by interpolating the HapMap combined recombination map.We ranked the filtered list of variants in decreasing order of BFHBB and around the glycophorin gene cluster on chromosome 4 are especially extensive. We treated these regions specially, defining them as the regions chr11:3.5 Mb–6.5 Mb and chr4:143.5 Mb–146 Mb, respectively, based on visual inspection of the association signal and we excluded these regions from the inthinnerator region definition process. Including these regions, a total of 97 association regions contained a variant with BFavg > 1000 and are listed in Supplementary Data Association regions around avg), colouring points by LD with the lead variant computed using the African reference panel haplotypes and annotating variants that were directly typed and included in our phased set.The association signal .To visually inspect regions, we created a set of plots showing the evidence for association. Specifically, we created hit plots showing:27 from Ensembl tools release 75, to predict the functional consequence of each typed and imputed variants within each association region using the –everything flag. We additionally annotated variants with previously reported association for traits from the NHGRI/EBI GWAS catalog38 and a previously published GWAS of haematopoetic traits35, and with measured RNA expression levels (processed as described below), transcription factor-binding sites, chromatin state in erythrocyte precursors and other cells31, topologically associated domains92 and reported eQTLs across cell types34.To annotate variants with functional information, we ran Variant Effect Predictorthe effect-size estimate and confidence interval estimated in each populationthe number of samples, variant frequency and IMPUTE info in each populationthe frequentist meta-analysis resultsa bar plot summarizing the Bayesian meta-analysis resultsFor each lead variant, we also created forest plots showing, for each mode of inheritance,Forest plots for lead variants can be found in Supplementary Figs. 93 from the Gene Expression Omnibus (GEO) using the sra toolkit. In total, data on eight cell types was processed: CD34+, BFU-E, CFU-E, early basophilic, late basophilic, proerythroblast, polychromatic and orthochromatic erythrocyte progenitor cells, with three replicates of each93. We also downloaded data on 12 fetal and 12 adult experimentally differentiated erythroblasts reported by Lessard et al.32. To process these data, we extracted FASTQ files from the SRA archives and mapped reads to the build 37 reference sequence using TopHat v2.0.14, using the Gencode v19 release of the human reference sequence and gene annotations. For the Lessard et al.32 data, we included the options --library-type fr-firststrand and --microexon-search to match the original processing. We also downloaded RNA-seq data from circulating erythrocytes45 from the GEO. These data were aligned to the build 37 reference sequence using bwa-mem76 and we marked duplicate reads using Picard (https://broadinstitute.github.io/picard).We obtained raw RNA-seq data from erythrocyte precursorsATP2B4, we restricted attention to reads with mapping quality at least 50 and used bedtools genomecov94 to compute coverage across the gene and across the genome. We plotted per-base coverage divided by the total coverage across ATP2B4 exons, summed over samples within each dataset and developmental stage  in each exon asTo visualize coverage profiles across 32. For comparison across exons, we additionally standardized this residual FPKM . We computed eQTL results by fitting a linear model of genotype on the standardized, residual FPKM values.We then used a linear model to compute residual FPKM after regressing out the developmental stage 29 at http://egg2.wustl.edu. We used the stranded normalized coverage files and summed across the two strand for visualization purposes  as follows, following a similar protocol used previously5. A subset of 955 individuals were typed from multiple blood samples in our database. For these individuals, we first removed samples with discrepancies between the clinical record of gender and the amelogenin-based sex assignment, and samples with missingness > 10% across all SNPs for which typing had been conducted. We then merged genotypes across the remaining samples for each individual to produce a single set of genotypes per individual, by taking a consensus call across the repeat typing .In total 37,571 individuals from this study had Sequenom genotyping available, including 15,866 SM cases, 19,845 controls and samples collected as parents. For data release purposes, we curated data across these individuals together with the 773 reference panel individuals Fig.  and 797 To provide a list of reliably typed individuals and variants, we excluded each individual with >10% missingness  or with fewer than 50 called genotypes. Using the remaining individuals, we computed per-variant missing data rates and labelled variants with >10% missing data or fewer than 100 called genotypes for exclusion. We then iterated this procedure, updating the list of included samples and variants for two additional iterations, at which point no change in included samples or variants was observed. This process produced a total of 37,732 individuals typed across 582 variants with low rates of missing data, including 15,024 SM cases and 18,556 controls.We separated the curated individuals into discovery and replication cohorts as follows. All individuals included in the GWAS phased genotype dataset were marked as discovery individuals . The remaining 15,548 cases and controls who were not included in discovery data and who had an assigned ethnic group as described in ‘Replication analysis’ below, were used for replication analysis.r2 > 0.75 with the lead imputed SNP across discovery samples and typed at least 1000 replication samples were present  and Burkina Faso . We used BINGWA to compute a fixed-effect meta-analysis across discovery data  and replication data (based on Sequenom typing) at each Sequenom-typed variant.avg as described above), an overall BF across discovery and replication samples , and a replication BF (BFreplication), which is computed under the posterior effect-size distribution learnt from discovery samples. These quantities satisfyWe extended the Bayesian analysis described above to a discovery/replication analysis. A full description of this method can be found in Supplementary Note replication measures the evidence for association under the set of models and effect-size distribution learnt from discovery samples. In practice, requiring replication effect sizes to match the distribution learnt from discovery may be too restrictive—e.g., this could happen if discovery effects sizes at chosen SNPs are affected by Winner’s curse or because the distribution of phenotypes in replication samples differs from that in discovery. We therefore assessed replication data assuming true effect sizes in discovery and replication samples had a correlation of 0.9. The effects of this assumption are discussed in Supplementary Note avg for the lead variant and compute BFreplication using the tag variant  This P-value is larger than the replication P-value for CM alone and is reported in Fig. For rs62418762, discovery data suggests a protective effect of the reference allele on both CM and SMA susceptibility. We therefore also computed a replication P > 0.05 against null model of no between-population heterogeneity), but we note that power to detect heterogeneity is limited due to the relatively small subphenotype sample counts in some populations , and rs8176746, which is indicative of A/B blood type, were included as they are proximal to rs334 and rs8176719, respectively, and have been previously implicated in malaria susceptibilityWe used control samples to compute the frequency of the protective dosage  for each variant across populations. To visualize frequencies, we computed the minimum, maximum and mean frequency for each variant across populations, and plotted frequencies as circles with width proportional to the maximum frequency Fig. .To investigate potential interactions between variants, we computed the combined multilocus genotype for each sample across these variants, omitting rs8176746. Combined genotypes with <25 samples were collapsed into a single, ‘other genotype’ category (not shown). We refit the model for SM across populations using these multilocus genotypes as predictors and plotted the estimated effect sizes and confidence intervals against the expectation given the estimates from individual variants and the assumption that variants contribute independently to the log odds of disease. These estimates are shown in Fig. 96) using summary statistics for 36 RBCs, white blood cells (WBCs) and platelet traits previously published36. We treated each RBC, WBC or platelet trait separately. For each trait T, we assumed a model of an effect of T on SM, such that a unit increase in T increases the log odds of SM by a quantity ρ. If this is the case then for any genetic variant affecting T we should haveβT and βSM denote the effect of the variant on T and on SM, and ζ represents any additional effect of the variant on SM, over and above the effect through T. Under the assumptions traditionally underlying MR analysis96, ζ = 0 and we assumed this throughout.We conducted MR analysis  are assumed distributed around the true effects according to the estimated SEs, as:We used a Gaussian likelihood formulation of MRζ = 0 and the assumption that effects follow a Gaussian distribution.The latter equation reflects Eq.  when ζ =ρ, we focused on the 2706 ‘sentinel’ variants identified as associated with blood traits by Astle et al.36. A subset of 2130 of these variants were included in the meta-analysis in our study after filtering by EMAC. We removed 89 variants lying in any of the 95 regions of association containing a variant with BFavg > 1 × 103 in our discovery analysis (including the ATP2B4 region). We treated the remaining variants as independent and estimated ρ and σ2T by minimizing Eq.  and computed the likelihood ratio P-value.To estimate zing Eq.  using thρ and its 95% confidence interval. To avoid visualization problems stemming from large estimates at variants with high SEs, we used effect-size estimates regularized using a N prior in plots. These estimates are plotted in Fig. We visualized effect size and the model fit by plotting the effect-size estimates on T and on SM across variants, overlaid with a line indicating the estimated value of = MCHC, our estimate of effect variance was σ2T = 0.0132. We plotted the empirical distribution of observed effects overlaid with the Gaussian density and observed some evidence that effects are overdispersed relative to the Gaussian distribution with this variance. To verify that this did not adversely affect results, we refit the model assuming a fixed prior value σ2T = 0.12, with similar results .For T 51). One pair of alleles were particularly discordant . We used the IPD-IMGT/HLA dictionary to confirm that the B70 antigen is expressed by B*15 alleles and we therefore computed the combined B70/B15 frequencies in Fig. We tested for association and conducted meta-analysis for imputed HLA alleles in the same way as described above for other variants. To compare imputed HLA allele calls with previously published antigen frequencies, we first computed a predicted antigen frequency for each allele as the proportion of individuals heterozygote or homozygote for the allele. We plotted these frequencies in control samples from each Gambian ethnic group against previously published HLA Class I antigen frequencies  for HLA typing based on available DNA quantities. These samples were typed at 11 HLA gene loci through exon sequencing. Sequenced regions included exons 2–4 of HLA-A, -B, -C, -DPB1, exons 2–3 of HLA-DPA1, -DQA1, DQB1 and DRB1, and exon 2 of HLA-DRB3, -DRB4 and -DRB5. Typing was performed by the Accredited Tissue Typing Laboratory at Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, using the proprietary uTYPE software version 7 . The list of possible ambiguous calls were minimized by using the ‘allele pair’ export function in this software, which lists all possible and permissible allele pair possibilities for each locus for each individual. Alleles were defined using the IMGT/HLA Release: 3.22.0 2015 October 10. Best-call allele pairs for each locus in each individual were determined based on local guidelines prioritizing alleles that were denoted Common and Well-Documented.We tested for association and conducted meta-analysis for glycophorin region variants in the same way as described above for other variants. Four CNVs were imputed with reasonable frequency and imputation quality . We note that glycophorin region SNPs appear in our meta-analysis results twice—once using the genome-wide combined reference panel and once based on this imputation of glycophorin variants. In our main presentation for consistency, we refer to the genome-wide panel imputation results, except for glycophorin CNVs, which are imputed from the glycophorin panel.We aimed to assess the degree to which additional polygenic effects explain SM susceptibility. The epidemiology of malaria differs between sub-Saharan Africa and elsewhere, and we chose to restrict attention to the African populations in our data for this analysis. We further restricted attention to a subset of 13,088 samples having pairwise relatedness < 0.05 . We used the genotypes at directly typed SNPs in the phased dataset for heritability estimation.98 and PCGC22, in the implementation by Gaurav Bhatia (https://github.com/gauravbhatia1/PCGCRegression)), to estimate the heritability of SM. These methods are both based on the infinitesimal model of genotype-trait association, under which all variants contribute equally, in expectation, to trait variation. However, they differ in how estimation is performed. In particular, PCGC is thought to be more robust for case/control traits and we use it for our main results.We used two previously published methods . For some analyses, we also included the protective dosages of risk alleles at HbS, rs8176719 (ABO), rs4951377 (ATP2B4) and DUP4 as covariates. We also explored including 10 or 50 PCs as covariates, with little difference in results. We observed slightly smaller estimates of heritability using GCTA than PCGC, consistent with the previously reported theory and observations22. Although our populations are highly structured, estimated of per-chromosome heritability when fitting chromosomes independently or jointly were similar  be the matrix of derived allele counts, where i ranges from 0 to the number of haplotypes in G and j ranges from 0 to the number of haplotypes in AFR. For any given derived allele x, we computed upper and lower ranks of the allele in G, conditional on its count in AFR, asa is the number of x alleles in G and b is the number of x alleles in AFR. These quantities can thus be interpreted as the upper and lower rank of the allele x among all derived alleles with the same allele count in AFR, and satisfy rank+G(x) + rank−G(x) = 1. To compute the African allele counts in D, we used only samples from non-admixed African populations .For a malaria-associated variant, the malaria hypothesis suggests that natural selection will maintain the protective allele at higher frequency in populations of high childhood mortality (such as African populations in our study) due to malaria. To assess this while avoiding confounding by selection of variants through our GWAS (conducted in a sample that is largely from sub-Saharan Africa), we aimed to condition on the African allele frequency and ask whether the allele is at lower frequency in non-endemic populations. We first computed the empirical allele count distribution between the African continental group (AFR) and non-African continental groups G (where G = EUR or EAS) in the reference panel, based on all variants which had an ancestral allele assignment and which were not masked by the 1000 Genomes strict mask. Let v associated with malaria, let x be the derived allele of v. We defined rankG(v) as rank−G(x) if x is associated with decreased risk, or rank+G(x) if x is associated with increased risk. Where unclear, we determine the direction of effect of x based on the effect size estimated for CM. Fig. EUR(v) for the 91 autosomal variants with BFavg > 1000, which have an ancestral allele assignment in the 1000 Genomes ancestral allele sequence. We also annotated variants genome-wide with rankEUR and rankEAS.For any variant 99 but with simplifications that we now describe. The Bayenv model assumes that the vector of underlying allele frequencies in sampled populations (denoted F) follows a multivariate normal distribution of the formf0 denotes the conceptual ancestral allele frequency (assumed to reflect a time point T before the populations from which study samples were drawn separated) and the matrix Ω captures the effects on allele frequency of the co-ancestry of populations since T. In particular, diagonal entries of Ω reflect levels of genetic drift (relative to the population at time point T) and off-diagonal entries reflect shared ancestry between populations.To assess differentiation within Africa, we implemented a model similar to Bayenvf0 is not too small and the levels of genetic drift are not too large99. As the co-ancestry matrix Ω is assumed to be the same across all neutrally evolving variants, it can be estimated from a set of neutral SNPs across the genome. Specifically, for each variant, a vector of scaled frequencies can be computed asThe model above is expected to hold approximately provided Ω = cov(F′).Under the model above, Ω can now be estimated as the covariance of scaled allele frequencies, F is accurately estimated by the observed allele frequencies (using only control samples). Second, we assumed that the ancestral allele frequency f0 is well estimated as the mean of the per-population frequency estimates. Third, we restricted attention to directly typed SNPs with mean allele frequency in the range 2–98% and we assumed that the effects of allele frequencies reaching frequency 0 or 1, which is not explicitly modelled in the above framework, would be minor. We sampled a subset of 100,000 such SNPs randomly across the genome with the above properties and computed Ω as the empirical covariance in scaled estimated allele frequencies. The estimated matrix Ω' is visualized in Supplementary Fig. 99, and by modelling the population frequencies as bounded between 0 and 1, but requires a computationally expensive Markov Chain Monte-Carlo (MCMC) process.To implement this in practice, we made the following simplifying assumptions. First, we restricted attention to the 7 African study populations with >200 control samples  and assumed that the population allele frequency vector F' may arise due to the selection of variants used in estimation. In addition to Ω', we therefore additionally estimated the mean scaled frequency, denoted μ.In practice, systematic effects in Ω' provides a model for allele frequency variation of putatively neutral alleles in the populations considered. We visualized this model by plotting the joint distribution of observed and simulated allele frequencies. Specifically, we simulated 1,000,000 variants by sampling f0 from the observed list of mean allele frequencies and then sampling allele frequencies from Eq. , or equivalentlyX = L−1(F′ − μ) and LLt = Ω′ denotes the Cholesky decomposition of the estimated matrix Ω'. In practice, the use of mean allele frequency implies that Ω' has rank one less than the number of populations99 and we therefore remove one population from the vector F' and from Ω' before computing this decomposition. In the results presented here, we chose to remove Cameroon, which is the most central population both geographically and within Ω (Supplementary Fig. X, i.e., XtX, is now distributed as χ2-distribution with 6 degrees of freedom. We denote the corresponding P-value as PXtX.For each typed or imputed variant, we tested for differentiation against this model as follows. We first computed the mean allele frequency and scaled allele frequency vector XtX test statistic against the χ2-distribution for typed and imputed variants at different frequencies. Supplementary Fig. PXtX for all imputed variants with mean allele frequency in the range 2–98%.Supplementary Fig. Further information on research design is available in the Description of Additional Supplementary FilesSupplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters.Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population  for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30%  when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program.The online version of this article (10.1007/s00122-018-3186-3) contains supplementary material, which is available to authorized users. W). The length of the curve (L), or the time required to break it, is the extensibility, and the height of the peak (P) represents the tenacity or maximum dough resistance to rupture . However, the multi-trait model showed good performance to predict low heritability traits with the help of correlated traits with high heritability , Uruguay, were used for this study. We used 820 advanced inbred lines for the phenotypic analysis and 1974 advanced inbred lines for the genotypic analyses. Finally, only 495 advanced inbreed lines, having both genotypic and phenotypic information, were used to adjust the genomic selection models.The advanced inbred lines from INIA’s wheat breeding program were phenotyped for eight baking quality traits. The lines were grown in 82 trials in nine location-year combinations (environment) as part of the wheat breeding program (Table S1). There were trials from different breeding scheme stages in each environment: preliminary and advanced trials, and two maturity groups: short and long maturity. The traits were evaluated in field nurseries located in La Estanzuela , Colonia, Uruguay, over 5 years (2010–2014). Additionally, approximately one-third of the lines were also evaluated in Young  and Ruta2 . There were two to eight lines that linked the trials within and among environments (Table S2).W and L), and mixograph parameters (stability (MH) and time (MT)), were evaluated using the AACC methods 38–12, 54–30 and 54–40 , while the most promissory lines were evaluated in multiple environments (~ 140 lines). Grain protein content (Pt) and test weight (TW) were determined with methods 46–12 and 55–10 of the American Association of Cereal Chemists AACC , respect–40 AACC , respect–40 AACC .i-th genotype in j-th year location for k-th trial, µ is the overall mean, gi is the fixed effect of the i-th genotype, ej is the fixed effect of j-th environment, tk(j) is the random effect of the k-th trial nested within j-th environment, and εijk is the residual error for the i-th genotype in the in j-th environment and k-th trial, where tk(j) and εijk were random variables being tk(j) ~ N and εijk ~ N. The BLUEs were estimated using ‘nlme’ (Pinheiro and Bates Best linear unbiased estimation (BLUE) for bread baking quality traits was estimated from data coming from the different trials and environments using the following model:To evaluate the genotype by environment interaction and the impact of unbalanced designs, Pearson’s correlation between environments was estimated using BLUEs computed by environment . Additionally, genotypic, environmental and genotype by environment interaction variance components were estimated. Finally, Pearson’s correlation and principal component analysis between traits were estimated to evaluate the correlation among traits using the BLUEs estimated from model 1. The final additive variance–covariance matrix between traits was later estimated from the genomic prediction model.σg2 is the genotypic variance and Broad sense heritability for baking quality traits was estimated as follows (Piepho and Möhring K) was estimated as W is the centered genotypic matrix, i-th individual for the m-th marker as {− 1,0,1} and K was estimated using the ‘rrBLUP’ package in R Statistical Software  were predicted as f) as 5 and scale parameters were calculated as S = var(y) × 0.5 following Pérez and de los Campos (Using the single-trait model (ST), the prediction performance values were obtained for the eight baking quality traits using a Bayesian ridge regression (BRR) model by trait.s Campos . We useds Campos . Predicts Campos , explainmentclass2pt{minimR). The multi-trait model is:y is a vector of N × t length , µ is the means vector of length N × t; u is a vector of predicted genetic values of the individuals for all traits with ε is a vector of residuals with K is the realized additive relationship matrix among individuals estimated from the markers, and ∑ and R are the variance–covariance matrices for the genetic and residual effects for each individual in all traits, respectively, estimated using a Gibbs sampler algorithm with 1500 burn-in and 3000 iterations. ∑ was estimated as an unstructured matrix and R as a diagonal matrix. We used a diagonal matrix for R instead of an unstructured matrix because the predictive ability was higher with the diagonal matrix. To estimate ∑ and R, scaled inverse Chi-square prior distributions were assigned to ∑ ~ R ~ t, R  were estimated fitting a Bayesian multivariate Gaussian model estimating an unstructured variance–covariance matrix between traits (∑) and a residual matrix , two traits from the group (3T) or one trait from the group (2T). The 2T models were constructed with SV for both W and MH and with WG for L. The 3T models were constructed with SV and MH for W, SV and TW for MH, and WG and Pt for L. We evaluated the accuracy of the predictions using 100 iterations in all traits and CV2.In order to evaluate the possibility to train the models with fewer phenotyped lines, we trained the MT models using 50 to 396 individuals (10 to 80%) from the training population and 100% of the lines phenotyped for the other correlated traits for W and L using different depth of phenotypic information on correlated traits. The mixograph parameter MT was not evaluated because it is poorly correlated with other traits in its group. First, we evaluated different sizes of this training population for the predicted traits when information on the other three traits was present only in 50% of the lines in a balanced or unbalanced manner. We used the same approach as before with 10–80% of the training population for predicting the traits but using phenotypic information for only 50% of the lines for each correlated trait. To mask 50% of the lines, we followed two strategies: designed as balanced (MT-CV2-50%b) or unbalanced (MT-CV2-50%u). The balanced strategy masks the same 50% of the lines in all correlated traits. In the unbalanced strategy, each trait is also phenotyped in 50% of the individuals, but different individuals are phenotyped for different traits. To mask 50% of the lines, both, the training and testing sets were divided in four equal parts. Then, two different sets from the training and two different sets from the testing were masked in each correlated trait. The lines were randomly assigned to each of the four sets. This procedure was conducted for 100 random iterations . Two groups of correlated traits were defined using principal component analysis Fig. . The firits Fig. . Group 2L  increased more than 50% the predictive ability compared to the single-trait model for both W and L  increased the predictive ability a 14% for W and a 3% for L compared to the model with one correlated trait . In this case, each trait was phenotyped for 50% of the individuals but for different individuals with some overlaps. This strategy yielded higher predictive ability than using even more phenotyping but only in the training population , and in consequence the training population size, by using correlated traits without compromising the predictive ability of the model. It has been widely proven that smaller population sizes reduce prediction accuracy  increase the predictive ability of the models, and this was somewhat already shown. The next question we wanted to address was how much could we reduce the depth of phenotyping of an expensive trait  was lower than in the full phenotyping (MT-CV2) models. However, the predictive ability was still high. In addition, the use of an unbalanced strategy to reduce phenotyping on correlated traits was slightly better than reducing phenotyping using a balanced strategy (MT-CV2-50%u vs MT-CV2-50%b). Therefore, unbalanced phenotyping of correlated traits could be another approach to predict traits that are expensive or labor-intensive.Finally, we evaluated whether the inclusion of more than one trait increases the predictive ability of the model if this trait is highly correlated. We found that models with two highly correlated traits are better than models with one highly correlated trait. However, the increase in predictions is low with the addition of a second correlated trait. Therefore, it will be important to evaluate the cost of prediction using two instead of one correlated trait, balancing gain in accuracy with the costs of using another trait to help predictions. The use of mildly correlated traits such as TW, MT, and Pt was not useful.The use of multi-trait models is useful to improve the predictive ability of partially phenotyped individuals. Expensive or difficult-to-phenotype traits can be phenotyped in smaller population sizes if the predicted individuals are phenotyped fully or partially for less expensive correlated traits. Particularly, we found that the use of only one correlated trait in the model was the most effective way to increase the predictive ability with fewer resources.DV and MQ designed the phenotyping experiments. BL and PS performed genotyping analyses. BL, LG and IA performed statistical analyses. BL, LG, PS and IA wrote the paper. LG designed the study and hypothesis. All authors read and approved the final manuscript.Supplementary material 1 (PDF 232 kb)Below is the link to the electronic supplementary material.


Herbicide tolerance is an important trait that allows effective weed management in wheat crops in dryland farming. Genetic knowledge of metribuzin tolerance in wheat is needed to develop new cultivars for the industry. Here, we investigated gene effects for metribuzin tolerance in nine crosses of wheat by partitioning the means and variances of six basic generations from each cross into their genetic components to assess the gene action governing the inheritance of this trait. Metribuzin tolerance was measured by a visual senescence score 21 days after treatment. The wheat 90 K iSelect SNP genotyping assay was used to identify the distribution of alleles at SNP sites in tolerant and susceptible groups.5–7 generations of Chuan Mai 25 (tolerant) × Ritchie (susceptible) cross indicated a significant contribution of additive genetic effects to metribuzin tolerance in wheat. Several genes related to photosynthesis (e.g. photosynthesis system II assembly factor YCF48), metabolic detoxification of xenobiotics and cell growth and development  were identified on different chromosomes  governing metribuzin tolerance.The scaling and joint-scaling tests indicated that the inheritance of metribuzin tolerance in wheat was adequately described by the additive-dominance model, with additive gene action the most significant factor for tolerance. The potence ratio for all the crosses ranged between − 1 and + 1 for senescence under metribuzin-treated conditions indicating a semi-dominant gene action in the inheritance of metribuzin tolerance in wheat. The number of segregating genes governing metribuzin tolerance was estimated between 3 and 15. The consistent high heritability range (0.82 to 0.92) in FThe simple additive–dominance gene effects for metribuzin tolerance will help breeders to select tolerant lines in early generations and the identified genes may guide the development of functional markers for metribuzin tolerance. Triticum aestivum L.) is a major global cereal crop in terms of production and area coverage (FAO 2018) [Wheat (ge FAO 208 [1]. WhGlycine max L.) [Solanum tuberosum L.) [Lupinus angustifolius L.). The inheritance of tolerance to metribuzin in durum wheat (T. turgidum L.) is a complex character controlled by both nuclear and cytoplasmic genes in wheat [Inheritance of metribuzin tolerance has a different modes of genetic control in crop plants. A monogenic recessive inheritance was reported in soyabean ( max L.) , 4 and posum L.) . Si et aosum L.)  reportedin wheat , 8. ThisVariation in metribuzin tolerance in wheat from six continents, reported in our previous investigation , provideDNA markers have enormous potential for improving the efficiency and precision of conventional plant breeding via marker-assisted selection (MAS). The molecular mechanism of metribuzin tolerance in wheat is poorly understood. Advances in next-generation sequencing have facilitated the discovery of SNPs in the whole genome , 12 to p5–7 RILs of the most diverse cross (Chuan Mai 25 × Ritchie) (3) conduct 90 K iSelect SNP genotyping assay in diverse cultivars to discover allelic variants in SNP markers in tolerant and susceptible groups, and (4) determine the likely chromosomal locations and candidate genes responsible for metribuzin tolerance in wheat.This study aimed to (1) characterize the inheritance of tolerance to metribuzin in nine wheat crosses, (2) investigate heritability in FP < 0.05) higher SS than the tolerant parents. Average SS for F1, F2, BCT, and BCS populations are in Table 2 means had a comparable range to F1 means. The mean SS of the backcrosses varied depending on the crossed parents. The abbreviations representing crosses are in Table 1 lines (BCT) to tolerant parents had lower SS than the mid-parent (mp) value, except for the K × D cross, indicating positive additive gene action and higher expression of metribuzin tolerance. In contrast, BCS had higher SS than the mid-parent value. The crosses of F1 with susceptible Dagger differed the most from the mp value, by 31.3, 38.6 and 29% for the crosses CM × D, F × D and K × D, respectively. The comparisons of reciprocal crosses revealed significant differences (P ≤ 0.05) in average SS except for three reciprocal cross combinations (Table The average senescence (SS) for the tolerant and susceptible parents used in this study are in Table P = 0.05) for all nine crosses, and dominance gene effects were significant (P = 0.05) for four crosses . The additive-dominance model fitted well for all crosses. The model significance was checked using χ2 statistic, which showed insignificant difference between the expected and observed generation mean values, confirming a significant additive–dominance model for metribuzin tolerance in wheat  of nine hybrids Table  were notat Table .Table 41 means (lower phytotoxic effect) and were more similar to the tolerant parents, indicating the presence of partial dominance gene effects. The crosses with a positive potence ratio  had higher F1 means (higher phytotoxic effect), indicating recessive dominance , F6 (0.95) and F7 (0.92) RILs of the cross Chuan Mai 25/Ritchie  with no clustering patterns for all genotypes were removed. A total of 12,294 loci had no call and were removed. The remaining 8,661 loci (12.9%) had ≥2 clusters and were used for principal component analysis (PCA) analysis; the results for allelic variation in seven genotypes are presented in Fig. Triticum aestivum IWGSC_refseqv1.0. The results suggested that metribuzin tolerance was a quantitative trait governed by several loci on different chromosomes  (Table . Multiple SNPs and candidate genes identified on chromosome 7B (photosynthesis system II assembly factor YCF48 and ABC transporter), chromosome 4A (cytochrome P450 family), chromosome 7A (glutathione S-transferase), chromosome 2A and 3B (glycosyltransferase), and chromosome 2D (glutathione peroxidase) represented the possible genes/gene families with significant association with metribuzin tolerance in wheat.Putative genes related to the identified SNPs with differences between tolerant and susceptible groups were investigated by a blastN search of markers on 1 hybrids. Previously, Ratliff et al. [The mode of inheritance and gene action of pre-emergent herbicide tolerance will help breeders to choose appropriate breeding methods to develop more tolerant cultivars and combat early weed competition to enhance wheat yields. The efficiency of selection and plant breeding programs depend on the existence of genetic variability . Geneticf et al.  reportedf et al.  reportedf et al. .Metribuzin tolerance is explained by the simple additive–dominance model, indicating absence of epistasis or non-allelic interaction. The absence of epistasis and significant additive effect efficiently responds to selection . The allgenetics is a relationship between the alleles of one gene, where the effect on phenotype of one allele masks the contribution of a second allele at the same locus. It is a key concept in Mendelian inheritance and classical genetics. Often the dominant allele codes for a functional protein whereas the recessive allele does not [quantitative genetics, phenotypes are measured and treated numerically. In the present investigation, F1 hybrids with lower SS exhibited a partial dominant gene action. Therefore the F1 hybrids with a negative potence ratio had mid- to low- metribuzin phytotoxic effects and expressed a phenotype similar to the tolerant parent  and glutathione S-transferase (GSTs) glycosyltransferase (GT), ATP-binding cassette transporters and glutathione peroxidase (GPX) are essentially xenobiotic detoxifying enzymes involved in vacuolar sequestration of conjugated pesticide metabolites –23. Plan6f complex, and the ATP synthase. These multiportion complexes harness solar energy and, together with ATP synthase, produce reducing power (NADPH) and chemical energy (ATP) for the production of carbohydrates in the Calvin cycle [The identified glycosyltransferase and oxidoreductase mediate different biological processes. They are involved in sucrose metabolism and metabolic detoxification of xenobiotic detoxification. The candidate genes detected from our previous investigation  of QTL min cycle –29.. Thein cycle . In concUnderstanding the genetics of herbicide tolerance in wheat will guide breeders in the development of herbicide-tolerant cultivars with wider safety margins. Metribuzin tolerance in wheat has high heritability and significant additive gene action with no epistasis. Therefore, MAS may be a feasible routine solution for selecting herbicide-tolerant lines in crop improvement programs. Metribuzin tolerance in wheat is most likely a non-target-based mechanism where metribuzin is detoxicated by a series of metabolic enzymes. However, transcriptome-wide gene expression profiling is needed to reveal genes and pathways endowing metabolic herbicide resistance in wheat.The simple additive-dominance mode of gene action suggests that a simple selection procedure could be successfully exploited in an early segregating generation to select lines for metribuzin tolerance breeding in wheat. The present investigation emphasized the degree of gene expression in the PSII assembly factor, antioxidants and detoxifying systems  as the responsible factors for determining metribuzin tolerance in wheat. The identified markers could be used in marker-assisted selection of lines for breeding tolerant cultivars. Alternatively, tolerant genes could be introduced into elite wheat cultivars by natural introgression to enhance metribuzin tolerance.8H14N4OS), a triazinone herbicide was purchased from Syngenta Crop Protection. Metribuzin binds its target site D1 protein in PSII and inhibits electron flow between the primary electron acceptor to plastoquinone, arresting photosynthesis. The metribuzin dose of 400 g a.i. ha− 1 was used to assess tolerance status in parents, F1, F2, BCT and BCS populations and F5–7 RILs of the cross, CM × R  and F1 maternal S and paternal R (F1 SR) hybrids. Reciprocal crosses were used to check maternal effects of herbicide resistance. Subsequently, RS F1s were selfed and backcrossed to their R and S plants to produce F2 and backcross (BCT and BCS) generations, respectively. Additionally, the Chuan Mai 25 (T) × Ritchie (S) cross was selected to develop recombinant inbred lines (RILs) in the growth chamber using rapid generation single seed-descent in-vitro embryo culture technique /visual damage was measured 21 days after treatment (DAT) Fig. . Plants The distribution of alleles at the SNP sites was assessed using the wheat 90 K iSelect SNP genotyping assay, containing 81,587 genome-wide distributed SNPs following the procedure described by Wang et al. . Allele Triticum aestivum IWGSC_refseqv1.0) to find the Traes numbers of genes. BLAST hits were filtered with an e-value threshold of 10− 5 and sequence similarity higher than 95%. The Traes numbers were searched in UniProt in TrEMBL (http://www.uniprot.org) and UniParc (https://www.uniprot.org/uniparc/) to obtain more information including protein domain, family, molecular and biological functions of the potential candidate genes. Further, the key features of the domain and InterPro annotation were searched in pfam and Prosite to check the characteristics of the protein. Only those genes with known function and/or related to photosynthesis and metabolic detoxification were considered as potential candidate genes for metribuzin tolerance in wheat.The candidate genes controlling metribuzin tolerance were identified using BLASTN program, against the Ensembl Plants , F1, F2, BCT and BCS for nine cross combinations, were subjected to a scaling  and joint-scaling test using the weighted least squares method, which testifies the presence or absence of epistasis [2) statistic, and the significance of each parameter was tested using a t-test [The contribution of maternal or cytoplasmic effects on the differences between population means was assessed by comparing the means of reciprocal Fpistasis –37. Whenpistasis . This pra t-test , 36.1 is the first generation mean, P1 is the mean of the lower parent, P2 is the mean of the higher parent, and M.P. is the mid-parent value. Complete dominance was indicated when P was − 1 or + 1, while partial dominance was indicated when ‘P’ was − 1 or + 1, except for zero, which indicates the absence of dominance. Over dominance was indicated when the potence ratio exceeded + 1. The positive and negative signs indicate the direction of the dominance of either parent.The nature of dominance was determined from the potence ratio according to  P \docum5, F6 and F7 RILs from the, Chuan Mai 25 × Ritchie cross using glm function in R and heritability was calculated based on ANOVA using the formula: h2 = The generalized linear model based on Poisson regression was fitted to the SS data of Fformulae , 40.Additional file 1: Table S1. The SNP’s and their alleles in tolerant and susceptible bulk.


SD = 13.64, range 17–74 years). They completed a questionnaire battery containing the TEIQue and measures of the Big Five, depression, and anxiety (both trait and state). Results indicated that the performance of the TEIQue-FF in the Italian context was comparable to the original United Kingdom version as regards its reliability and factor structure. Moreover, the instrument showed incremental validity in the prediction of depression and state-trait anxiety after controlling for the Big Five.Trait Emotional Intelligence  is a constellation of emotional perceptions assessed through questionnaires and rating scales . This pa In the first case, emotional intelligence was conceived as a cognitive-emotional ability assessed via performance-based tests, whereas in the second case emotional intelligence was conceived as a personality trait often referred to as emotional self-efficacy and assessed via self-report instruments  contribute directly to the global trait EI score. In Table Trait Emotional Intelligence (EI) is defined as a constellation of emotional perceptions assessed through questionnaires and rating scales . Based oAs regards the location of trait EI in the factorial space of personality and its discriminant validity, factor analyses have demonstrated that trait EI facets define a distinct oblique factor in Giant Three and Big Five factor space . This paThere is also a growing body of evidence, including from meta-analyses , showingThe TEIQue has been translated in many languages and proved to be reliable and valid in different cultural and linguistic contexts e.g., . The preMore specifically, we firstly aimed to assess the underlying TEIQue factor structure. The TEIQue-FF was not designed to be factor analyzed at the item level. As Specifically, the TEIQue factor structure was examined applying an Explorative Structural Equation Modeling approach ESEM; . ESEM reThe reliability of the 20 TEIQue variables  and gender differences in trait scores were also assessed. Subsequently, we investigated the incremental validity over the Big Five of the Italian adaptation of the TEIQue in relation to the prediction of depression and anxiety. It was expected that trait EI would remain a reliable negative predictor of depression and anxiety in the presence of the Big Five traits.SD = 13.64, range = 17–74 years). As regards educational level, about 7% of participants completed junior high-school, 85% completed high-school, and 8% had a university degree. Concerning occupation, 7.1% of participants were blue-collar workers, 16.4% white-collar workers, 7.1% self-employed individuals, 4.7% housewives, and 53.5% university students (about 11.3% reported other occupations).Participants were 1343 Italian adults balanced for gender . Average age was 29.65 years  was employed to measure trait and state anxiety . This meSD = 13.07, range = 17–66 years), while the gender distribution was 221 females and 188 males.All 1343 participants completed the TEIQue-FF. For the validity investigations, a subsample of 409 participants also completed the measures of the Big Five, depression, and trait and state anxiety. The mean age for the subsample was 28.06 years  = 331.42, p < 0.001, CFI = 0.95, RMSEA = 0.08, SRMR = 0.03, and in line with those reported by other authors who have conducted similar analyses  had reasonably normal distributions. None of the variables had a skew or kurtosis greater than 1 . Eight of the 15 facets had high alphas  at step 1, and the incremental variance accounted by the TEIQue-FF traits at step 2 (R2 change). If the TEIQue-FF traits have incremental validity over the Big Five ones, then they would be expected to explain a significant amount of additional variance at step 2 .To examine the incremental validity of the TEIQue-FF factors beyond the Big Five, we conducted three separate hierarchical regression analysesR2 = 0.30, p < 0.001. When the TEIQue traits were included in the second step of the hierarchical regression, an additional 16% of variance was accounted for, R2 = 0.46, R2change = 0.16, p < 0.001. Regression coefficients for the second step are reported in Table Concerning depression, the Big Five traits collectively explained 30% of the variance, R2 = 0.32, p < 0.001. The TEIQue traits subsequently explained a further 9% of criterion variance, R2 = 0.41, R2change = 0.09, p < 0.01. Regression coefficients for the second step are reported in Table Concerning state anxiety, the Big Five traits collectively explained 32% of the variance, R2 = 0.45, p < 0.001. The TEIQue traits subsequently explained an additional 21% of criterion variance, R2 = 0.66, R2change = 0.21, p < 0.001. Regression coefficients for the second step are reported in Table Concerning trait anxiety, the Big Five traits collectively explained 45% of the variance, As can be noted, the TEIQue-FF showed clear predictive capability in the presence of the Big Five, incrementally explaining significant proportions of variance across all three criteria.The present study scrutinized the psychometric characteristics of the Italian TEIQue-FF by investigating the instrument’s distributional properties, reliability, factor structure, and gender differences. Moreover, criterion and incremental validity were established by demonstrating the ability of the four TEIQue factors to predict mental health  over and above the Big Five. The distributional properties of the Italian version of the TEIQue-FF were satisfactory, with all of its variables showing near-normal distributions with means and standard deviations comparable to those obtained for the original scale .In general, the reliability of TEIQue scales reached satisfactory levels although, for the Italian version, they appear to be a little lower than for other translations e.g., . HoweverDespite the robust psychometric properties of the Italian version of the TEIQue highlighted by this study, some limitations should be listed. Due to its length, the full form of the TEIQue may be not suitable for rapid clinical screenings or for research designs where there is limited availability of time and space for data collection and wherein trait EI is not a central variable. In those cases, the short form could be more appropriate. Conversely, however, the full form of the TEIQue would be more useful for in-depth analyses at the factor and facet levels of the construct. The richness of information derived from the administration of the full form is most valuable, for instance, in clinical examinations, coaching assessments, or career and vocational counseling contexts. In the present study all the variables were operationalized through self-report questionnaires, which may have created common method variance that could potentially inflate construct relationships. Future research could profitably employ a multi-method approach in order to demonstrate the incremental validity of the TEIQue in relation to external criteria measured using, for example, observational methods. Moreover, the present findings are based on correlational and cross-sectional data. Thus, they cannot be used to infer causal relationships between trait EI and the criteria examined.The TEIQue and its underlying theory of trait emotional intelligence, have a wide range of important applications in educational, clinical, and organizational contexts see . High trIn vocational contexts, trait EI, in general, and the TEIQue, in particular, have been linked, phenotypically as well as genetically, to vocational interests . In addiTrait EI is also a solid predictor of important outcomes in business and organizational contexts e.g., , which iLast, trait EI and the TEIQue, specifically, is a very strong negative predictor of psychopathology , a relatIn conclusion, the present study tested and demonstrated, in a large adult sample, that the TEIQue-FF shows robust psychometric properties in the Italian context, just as it has shown in many other countries around the world. It also provides further evidence of the stability of the TEIQue factors across different countries and cultures, thus offering support for the construct’s universality. This validation of the TEIQue builds on previous work in the Italian context that had been conducted on adolescents , young aThis study was carried out in accordance with the recommendations of “APA ethical standards” with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Psychological Science Departmental ethical committee at the D’Annunzio University of Chieti–Pescara.AC designed the study, wrote the introduction, part of results and discussion, run the analyses and revised the manuscript. LP designed the study, collected the data, run the analyses and wrote most of the result section. MM wrote part of the discussion. KP supervised the entire project, wrote several part of the manuscript, and deeply revised all the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor declared a past co-authorship with one of the authors KP.