Hearing problems (HP) in adults are common and are associated with several comorbid conditions. Its prevalence increases with age, reflecting the cumulative effect of environmental factors and genetic predisposition. Although several risk loci have been already identified, HP biology and epidemiology are still insufficiently investigated by large-scale genetic studies.N = 501,825; replication N = 226,043; cross-ancestry replication N = 20,800). We leveraged the GWAS findings to characterize HP polygenic architecture, exploring sex differences, polygenic risk across ancestries, tissue-specific transcriptomic regulation, cause-effect relationships with genetically correlated traits, and gene interactions with HP environmental risk factors.Leveraging the UK Biobank, the Nurses’ Health Studies (I and II), the Health Professionals Follow-up Study, and the Million Veteran Program, we conducted a comprehensive genome-wide investigation of HP in 748,668 adult participants (discovery We identified 54 risk loci and demonstrated that HP polygenic risk is shared across ancestry groups. Our transcriptomic regulation analysis highlighted the potential role of the central nervous system in HP pathogenesis. The sex-stratified analyses showed several additional associations related to peripheral hormonally regulated tissues reflecting a potential role of estrogen in hearing function. This evidence was supported by the multivariate interaction analysis that showed how genes involved in brain development interact with sex, noise pollution, and tobacco smoking in relation to their HP associations. Additionally, the genetically informed causal inference analysis showed that HP is linked to many physical and mental health outcomes.The results provide many novel insights into the biology and epidemiology of HP in adults. Our sex-specific analyses and transcriptomic associations highlighted molecular pathways that may be targeted for drug development or repurposing. Additionally, the potential causal relationships identified may support novel preventive screening programs to identify individuals at risk.The online version contains supplementary material available at 10.1186/s13073-023-01186-3. Acquired hearing problems (HP) are the third most common chronic health condition  and the Heritability estimates from family studies of HP range from 30 up to 70% , highligIn the present study, we conducted an extensive genome-wide investigation across the UK Biobank  , the NurN = 501,825; replication N = 226,043; cross-ancestry replication N = 20,800).Leveraging genome-wide information from UKB, NHS I, NHS II, HPFS, and MVP cohorts, we investigated the polygenic architecture of HP in 748,668 adult participants (discovery N = 144) were excluded from the analysis to reduce the likelihood of including congenital forms of HP [UKB is a large population-based research resource, containing in-depth genetic and health information from over 500,000 UK participants 40–69 years at enrollment . UKB HP-ms of HP . After eAdditional genome-wide information was derived from NHS I, NHS II, and HPFS cohorts. The NHS I began in 1976 when 121,700 female registered nurses, aged 30–55 years, were enrolled by completing a baseline questionnaire about their health and lifestyle . In 1989The MVP is a biobank funded by the US Department of Veterans Affairs that to date enrolled more than 800,000 participants among active users of the Veterans Health Administration healthcare system . In our In the present study, we analyzed multiple samples that were genotyped with different arrays. Quality control (QC) of the genetic data was conducted in accordance with cohort-specific criteria to ensure that high-quality data were used in the discovery meta-analysis of the UKB, NHS, and HPFS samples and in the subsequent replication conducted in the MVP sample. Accordingly, some of the QC cutoffs applied varied across the cohorts investigated.p-values < 10−6, and missingness > 0.1. After quality control, we analyzed up to 9,547,865 variants. For ancestry, sex, and relatedness assignments, we used definitions from the Pan-UKB analysis [Our primary analysis was conducted in UKB EUR participants. Considering this ancestry group, we used UKB data imputed using the Haplotype Reference Consortium , filterianalysis . Brieflyanalysis  recommenanalysis . To accoP < 10−5), with high duplicate discordance rates, or that are monomorphic were excluded. Genotyped data from each of the NHS and HPFS studies were imputed using the 1000 Genomes Project Phase 3 reference panel. After imputation, SNPs with imputation INFO score < 0.8 or MAF < 0.05 were excluded. We analyzed a total of 5,550,954 and 5,549,467 imputed variants in HPFS and NHS samples, respectively.In the NHS and HPFS cohorts, quality checks included the exclusion of those participants with a poor genotype call rate (< 95%) and a check for relatedness. A detailed description of quality control criteria was previously reported . BrieflyDetails regarding genotyping and imputation of the MVP samples were described elsewhere . Relatedp < 5 × 10−8). The number of variants shared across the three cohorts was 5,331,308.GWAS were carried out in the UKB, NHS, and HPFS by logistic regression (case–control HP phenotype coded as dependent variable), using PLINK 2.0  and inclp < 5 × 10−8). Statistical differences between sex association statistics were calculated using z-test: Z scores were then converted to two-tail p-values, hereafter referred as difference-p.We defined sex-specific genetic associations comparing sex-specific effect sizes among those surviving genome-wide multiple testing correction in one of the two sexes  is calculated as: In MVP, the replication of the loci identified in the UKB-NHS-HPFS GWAS meta-analysis was performed using logistic regression of the HP phenotype (case–control variable coded as the dependent term) on imputed SNP dosages including as covariates: the top-10 within-ancestry PCs, age, and sex, using PLINK 1.9 software . In addi2) and genetic correlation (rg) for all hearing traits were estimated using the linkage disequilibrium score regression (LDSC) method [2 and genetic correlations were evaluated among HP traits assessed in the UKB, NHS, HPFS, and their meta-analyses. Since functional categories of the genome contribute disproportionately to the heritability of complex diseases, SNP-h2 partitioning was conducted with LDSC using 95 baseline genomic annotations such as allele frequency distributions, conserved genomic regions, regulatory elements, and annotations for genic, loss-of-function (LoF) intolerant, and positively and negatively selected regions [p < 5.26 × 10−4) was applied to define significant SNP-h2 enrichments.SNP-based heritability (SNP-h) method . The ana regions . Bonferrp < 6.99 × 10−6; female-specific p < 1.52 × 10−5; male-specific p < 1.59 × 10−5). To maximize the statistical power of this analysis, we used European-ancestry genome-wide association statistics for the sex-combined analysis available in the Pan-UKB data release [We also used the LDSC method  to condu release . We used release .p < 0.05) with HP, we tested 879 traits in the sex-combined analysis, 323 traits in the female-specific analysis, and 332 traits in the male-specific analysis. For each comparison, the genetic causality proportion (gcp) can range from zero  to one . Positive and negative gcp values reflect the direction of the putative causal effect . Information regarding the sign of the LCV effect is provided by the LCV rho statistics: rho > 0 corresponds to a positive effect while rho < 0 corresponds to a negative effect. To define statistically significant gcp estimates, we applied a multiple testing correction accounting for the number of phenotypes tested .To evaluate whether the genetic correlations of HP are due to cause-effect relationships, we used the latent causal variable (LCV) method to conduct a genetically informed causal inference analysis . As reco−8; gwasP = 0.05; R2 = 0.1; refpanel = 1000 Genomes Project Phase3 EUR reference populations; MAF = 0.01; refSNPs = 1; mergeDist = 250. Positional mapping was performed considering MapWindowSize = 10 and the minimum Combined Annotation Dependent Depletion (CADD) [To identify the causal loci underlying the statistical association observed, we functionally annotated GWAS findings and prioritized the most likely causal SNPs and genes using pre-calculated LD structure based on 1000 Genomes Project EUR reference populations. The risk loci identified in the sex-combined and sex-specific meta-analyses were classified considering the following parameters derived from Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) : leadP =n (CADD)  score fon (CADD)  integratn (CADD) .2 = 0.1; window = 250 bp). Each region for the respective association was fine-mapped to determine 95% credible set  using susieR [We fine-mapped the association statistics for a 3-Mb region around the lead SNP of the genomic risk locus identified by FUMA .To further explore transcriptomic regulation in the context of HP, we performed a multi-tissue TWAS (transcriptome-wide association study), using the S-MultiXcan approach to combine information across 49 GTEx tissues adjusting for tissue–tissue correlation . SignifiN = 6636, 11% cases), admixed American , Central/South Asian , East Asian , and Middle Eastern  ancestries (target datasets). A total of 9,781,251 variants were analyzed in the Pan-UKB analysis. In addition to performing a single-variant replication, we also conducted a cross-ancestry PRS analysis. This was performed by analyzing genome-wide association statistics  using the gtx R package incorporated in PRSice v1.25 [2 statistics) was calculated from a multivariate regression model [P-value–informed clumping with an LD cutoff of R2 = 0.1 within a 250-kb window. Because the training dataset  was generated from EUR individuals, we used EUR samples from the 1000 Genomes Project as the LD reference panel. False discovery rate (FDR) accounting for the number of p-value thresholds tested was considered to define PRS associations surviving multiple testing correction.We used Pan-UKB data  to conduce v1.25 . Althougon model . The PRS2, and top-10 within-ancestry PCs were included as covariates. Since the environmental factors entered are converted to a covariance matrix by the StructLMM approach [q < 0.05) were retained as significant.To explore further the interplay of HP genetics with non-genetic factors beyond sex differences (investigated in our sex-specific GWAS), we conducted a multivariate gene-by-environment genome-wide interaction study (GEWIS) using StructLMM . This isapproach , no issuapproach . A Gene approach . GO term2 and genetic correlation among HP traits available from UKB, NHS, and HPFS. In UKB, multiple HP traits were available in the UKB  were less accurate than those from UKB (Table 2 estimates. The UKB genome-wide statistics of HP traits were characterized by inflation due to polygenicity (genomic-control lambda > 1.09) and not because of possible confounders , while the lowest rg between sexes was observed for the HP-ORD trait (rg = 0.782 ± 0.050). Considering the HPFS HP outcome and male-specific UKB analyses, the highest genetic correlation was observed for “Hearing difficulty” (rg = 0.633 ± 0.136). The same trait was the one with the highest genetic correlation with the NHS HP outcome among the UKB-female-specific analyses (rg = 0.737 ± 0.115). Based on the genetic correlations among UKB, HPFS, and NHS, the UKB “Hearing difficulties” was defined as the primary UKB phenotype. To maximize the discovery of our analyses, we meta-analyzed UKB, NHS, and HPFS cohorts .We also conducted a sex-stratified analysis, testing the genetic correlation among HP traits assessed in UKB and with HP phenotypes assessed in NHS  and HPFS . With respect to genetic correlation, “N = 501,825, 29% cases) identified 4382 genome-wide significant (GWS) variants related to 54 LD-independent loci   for four female-specific GWS variants , rs1808828 , rs11738813 , and rs35624969 .The HP GWAS meta-analysis of UKB, NHS, and HPFS (p < 0.05). Considering concordance between discovery and replication cohorts, 49 loci showed consistent effect direction. The probability of observing concordant directions of 49 or more loci out of the 54 loci tested by chance is 5.9 × 10−6. Leveraging our discovery GWAS meta-analysis as a training dataset, we conducted a PRS analysis in the MVP cohort that showed a small but highly statistically significant association , we tested whether the effects detected were replicated in an independent sample, 226,043 EUR participants from the MVP cohort. We observed that 34 loci were at least nominally replicated in the MVP cohort . Considering FDR multiple testing correction (FDR q < 0.1), we replicated the associations of two variants: rs6662164 in CSA  and rs34993346 in EAS . Nominally significant replication was observed for other twelve variants  and the non-EUR replication cohorts . However, we observed a significant cross-ancestry transferability in the HP PRS: AFR R2 = 4.73%, p = 4.65 × 10−68  into information regarding potential causal genes associated with HP, we integrated different approaches ranging from positional mapping to imputation of genetically regulated transcriptomic variation.ARID5B, CTBP2, and FTO) were previously identified as causal loci of Mendelian forms of deafness [First, we performed a fine-mapping analysis for each GWS locus. Based on the PIP of the 4382 SNP associations reaching GWS in the sex-combined GWAS meta-analysis, we identified 218 variants that are most likely to be causal , we conducted a multi-tissue TWAS using the S-MultiXcan approach . In the CRIP3 p = 1.19 × 10−23), spinal cord c-1 (PIK3R3 p = 1.32 × 10−19), pituitary gland (TMEM69 p = 2.14 × 10−18), caudate basal ganglia (IPP p = 3.04 × 10−18), cerebellar hemisphere (SLC22A7 p = 3.56 × 10−18), and cerebellum (MAST2 p = 3.17 × 10−17). The strongest non-brain-related TWS associations included tibial artery (ARHGEF28 p = 3.29 × 10−17), cultured fibroblasts (ACAN p = 5.17 × 10−17), left ventricle (DLK2 p = 1.03 × 10−16), and tibial nerve (NASP p = 1.42 × 10−16). In the sex-stratified meta-analysis, we identified 55 and 26 multi-tissue TWS associations in females and males, respectively . Additionally, among female-specific associations, we identified associations related to female organs: mammary gland (ABCC10 p = 6.16 × 10−15) and vagina (BAIAP2L2 p = 7.45 × 10−13).Considering the top tissue-specific effect underlying the cross-tissue associations, we observed that five of the top-10 strongest associations were brain-related: putamen basal ganglia  with respect to regulatory elements to uncover enrichment of relevant biological annotations and processes. We observed that several annotations related to the regulatory function of the human genome are more likely to be involved in HP predisposition than that expected by chance  and “elements involved in epigenetic and transcriptomic regulation . In a complementary approach, we performed gene-set enrichment analysis of HP-associated genes . Two biological pathways were observed to be statistically overrepresented: “response to trabectedin” (p = 1.58 × 10−8) and “sensory perception of mechanical stimulus” (p = 1.69 × 10−7). With respect to both analyses, we did not identify statistical differences in the enrichments calculated from the sex-specific genetic associations.To dissect further HP polygenic architecture, we partitioned its SNP-hN = 5337, p < 6.99 × 10−6; female N = 2,353, p < 1.52 × 10−5; male N = 2249, p < 1.59 × 10−5), we identified 309, 79, and 109 significant genetic correlations with HP, respectively . Other strong positive genetic correlations included “Long-standing illness, disability or infirmity” ; and “Frequency of tiredness / lethargy in last 2 weeks” . Among negative HP genetic correlations, we observed “Leisure/social activities”  and “Belief that own life is meaningful” . Although many of the sex-specific genetic correlations overlapped with those shared with sex-combined analysis, we also identified five traits with sex differences statistically significant after Bonferroni correction accounting for the number of phenotypes available in both female and male analyses . Four of them were related to educational attainment with the strongest one observed for “Qualification: College or University degree” . The fifth genetic correlation was related to “Time spend outdoors in summer” .We analyzed the genetic correlation of HP with human traits and diseases, testing our HP discovery GWAS  with respect to EUR genome-wide association statistics available from Pan-UKB analysis. Considering a Bonferroni correction accounting for the number of phenotypes tested in sex-combined, female, and male analyses , neurological disease , gastrointestinal outcomes , urogenital outcomes , medication use , cardiovascular diseases , immunological conditions , and workplace environment . We also identified genetic evidence that supports a putative causal role on HP for three outcomes: “Tinnitus” ; “Manifestations of mania or irritability” , and “Felt distant from other people in past month” . No gcp estimate showed a statistically significant difference between sexes after multiple testing correction.To distinguish genetic correlations due to shared genetic mechanisms from those due to possible cause-effect relationships, we conducted an LCV analysis, identifying 22 significant putative causal effects after Bonferroni multiple testing correction  with respect to 14 environmental factors simultaneously  and tobacco smoking habits (N = 8). The GEWIS was conducted using the structLMM approach, which is not affected by the degree of correlation among the environmental factors investigated [q < 0.05; See Additional file p = 2.0 × 10−6; “Average evening sound level of noise pollution” p = 1.3 × 10−6; “Maternal smoking around birth” p = 3.6 × 10−7).To expand our understanding of possible interactive effects beyond those already observed in the sex-stratified GWAS, we conducted a multivariate GEWIS exploring variables related to noise pollution and tobacco smoking, well-known HP risk factors –15. Becastigated . Althoug2 of different HP definitions available from UKB. These included three questionnaire-derived self-reported hearing traits and one trait derived from audiometric measures. Consistent with the missing heritability observed for complex traits [2 that was informative to investigate their polygenic architecture. This is in line with previous studies that showed self-reported HP traits appear to be reliable instruments to investigate “real-world” hearing impairment [2 for the trait based on the audiometric measures. Further studies are needed to understand the potential issues of assessments based on audiometric tests.We conducted a large-scale investigation integrating information from genome-wide associations with tissue-specific transcriptomic variation and a genetically informed causal inference analysis to translate genetic findings into insights regarding HP biology and epidemiology. Initially, we compared the SNP-hx traits , we obsex traits , 47) wasx traits . Neverthpairment –53, althpairment . Converspairment , 47, we We observed a high genetic correlation (rg > 0.7) between self-reported HP outcomes with the only exception being “Hearing difficulty with background noise” and “Hearing aid use” where the genetic correlation was much lower (rg < 0.4). This may be because the proportion of individuals who seek hearing aids for their HP is very low, even in the UK where hearing aids are covered by the National Health Service. Considering the HP outcome available in NHS and HPFS cohorts, we observed the highest correlation with the UKB outcome “Hearing difficulties”. Accordingly, these traits were used for the GWAS meta-analysis and the subsequent in silico investigations.In the sex-combined GWAS meta-analysis including 501,825 individuals, we identified 54 LD-independent GWS associations, 12 of which are novel with respect to those identified by previous HP GWAS , 54, 55.UKB-NHS-HPFS → AFR-UKB R2 = 4.73%) is larger than the one observed in the same-ancestry PRS association . This can be explained by the fact that the cross-ancestry results are based on the UKB cohort where the participants share the same HP assessment and sample characteristics. However, we cannot exclude that the different LD structures of the ancestries investigated may have partially contributed to inflate the results observed.Although this primary analysis was conducted in EUR individuals, we also investigated 20,800 UKB participants of AFR, AMR, CSA, EAS, and MID descent. Due to the limited non-EUR sample, we replicated only few single-variant associations and none of them were present across multiple ancestries. While the low statistical power likely played a key role in the lack of multiple-ancestry single-variant replications, differences in allele frequency and LD structure may also have contributed . ConversThe fine-mapping of the risk loci led to identifying putatively pathogenic variants (CADD score > 10) mapped to genes previously demonstrated to be involved in HP pathogenesis  and vagina (BAIAP2L2). These associations in hormonally regulated tissues may reflect a potential role of estrogen in hearing function [SPTBN1, ABLIM3, and ARHGEF28). With respect to the loci identified by the female-specific TWAS, BAIAP2L2 is particularly interesting. In mice, mutations of the homologous gene Baiap2l2 were associated with alterations in hair cell transduction and deafness while the human locus BAIAP2L2 is associated with suppression of the estrogen-mediated S–phase entry pathway in cell cycle [BAIAP2L2 transcriptomic regulation and estrogen levels may play a role in HP in women. With respect to HP pleiotropy, our genetic correlation analysis also identified five traits that showed statistically significant sex differences in their correlation with HP. Four of them were related to educational attainment where the genetic correlation with HP was positive in females and negative in males. A previous study demonstrated that the characteristics and the recruitment strategy of the UKB cohort  influenced some of the sex differences observable in this study population [With respect to sex differences, our UKB-NHS-HPFS GWAS meta-analysis identified three variants with statistical differences between sexes in their effect size. Specifically, rs13399656 in egulator . Similar pathway , 62. Thefunction . Indeed,ll cycle . As HP ipulation .Although our findings advance our understanding of HP and its consequences in adults, we also acknowledge several limitations. Similar to previous HP GWAS , 54, 55,We conducted a comprehensive investigation of the polygenic architecture of HP in adults that (i) identified novel risk loci, (ii) provided evidence of the shared HP pathogenesis across human populations, (iii) integrated genetic and transcriptomic data to dissect HP biology, (iv) leveraged genome-wide information to explore the mechanisms underlying HP comorbidities, and (v) uncovered possible biological processes that could underlie inter-individual differences in susceptibility to the effects of HP environmental risk factors. In particular, our sex-specific analyses and transcriptomic associations highlighted molecular pathways that may be targeted for drug development or repurposing. Additionally, the potential causal relationships identified may support novel preventive screening programs to identify individuals at risk.Additional file 1: Table S1. Definition of the combined four-category ordinal phenotype based on the traits defined in the UKB. Binary definitionswere combined to define a severity/probability scalAQe of hearing problems. Table S2. Environments investigated in the GEWIS analysis. Table S3. Inflation statistics of hearing problem genome-wide association studies. Table S4. Genetic correlations among the HP traits assessed in UKB considering sex-combined and and sex-stratified analyses. rg: genetic correlation; se: standard error. Table S5. Effect size comparison between sex-specific findings in the meta-analyses. Results surviving multiple testing correction are highlighted in red. GWS:genome-wide significance in; REF: reference allele; ALT: alternative allele; A1:Effect allele; SE:standard error. Table S6. Association of the hearing-problem polygenic risk score derived from the UKB-NHS-HPFS sex-combined meta-analysis in the MVP cohort. Table S7. Association of the hearing-problem polygenic risk score derived from the UKB-only sex-combined GWAS in the NHS-HPFS combined sample. Table S8. Association of the hearing-problem polygenic risk score derived from the UKB-only female-specific GWAS in the NHS sample. Table S9. Association of the hearing-problem polygenic risk score derived from the UKB-only male-specific GWAS in the HPFS sample. Table S10. Single-variant replication in other ancestry groups available in UKB. REF: reference allele; ALT: alternative allele; se:standard error; pval:P; af:allele frequency. FDR significantand nominally significant replications are highlighted in yellow and red, respectively. Table S11. Association of the hearing-problem polygenic risk score derived from the UKB-NHS-HPFS sex-combined meta-analysis in the UKB AFR sample. Table S12. Association of the hearing-problem polygenic risk score derived from the UKB-NHS-HPFS sex-combined meta-analysis in the UKB AMR sample. Table S13. Association of the hearing-problem polygenic risk score derived from the UKB-NHS-HPFS sex-combined meta-analysis in the UKB CSA sample. Table S14. Association of the hearing-problem polygenic risk score derived from the UKB-NHS-HPFS sex-combined meta-analysis in the UKB EAS sample. Table S15. Prioritized genes considering a CADD score threshold of 10. MAF:Minor allele frequency; se: standard error. Table S16. HP-associated genesin the Multi-tissue TWAS using S-MultiXcan. Table S17. Multi-tissue TWS associations after Bonferroni multiple testing correction identified in the sex-stratified analyses. Table S18. Enrichment for specific genomic features with respect to sex-combined and sex-stratified analyses. Results surviving Bonferroni multiple testing correction are highlighted in yellow. Table S19. Phenome-wide genetic correlation with HP considering sex-combined GWAS. Results surviving multiple testing correction are highlgihted in red rg: genetic correlation; se:standard error. Table S20. LCV analysis for HP. Results surviving Bonferroni multiple testing correction are indicated in red. gcp p: p-value for the genetic causality proportion; gcp: posterior genetic causality proportion; gcp se: standard error for the genetic causality proportion estimate; rho.est: genetic correlation estimate from LCV; rho.err: standard error for genetic correlation estimate from LCV. Table S21. Environment-specific Bayes factors of the LD-independent variants with nominally significant multi-environment interactions.Additional file 2. Contains Supplementary Results and Supplementary Discussion.Additional file 3: Figure S1. Manhattan plot of the sex-combined GWAS meta-analysis of UKB, NHS, and HPFS cohorts. The red line refers to the genome-wide significance threshold. Figure S2. Visual representation of the fine-mapping analysis for sex-combined meta-analysis. Each panel refers to a GWS risk locus. The variants were identified according to their LD with respect to the lead SNP and inclusion in the credible set with at most ten causal variants. Figure S3. Visual representation of the fine-mapping analysis for female meta-analysis. Each panel refers to a GWS risk locus. The variants were identified according to their LD with respect to the lead SNP and inclusion in the credible set with at most ten causal variants. Figure S4. Visual representation of the fine-mapping analysis for male meta-analysis. Each panel refers to a GWS risk locus. The variants were identified according to their LD with respect to the lead SNP and inclusion in the credible set with at most ten causal variants.


Approximately 1500 offspring were produced in each of three sib families and clonally replicated across three geographically diverse sites. Genotypes from 1525 offspring from all three families were obtained by RADseq, followed by imputation using 1630 loci segregating in all families and mapped using the newly developed linkage map of Sitka spruce. The analyses employed a new approach for estimating fa and fd, which combined all available genotypic and phenotypic data with spatial modelling for each trait and site. The consensus estimate for fa increased with age for height from 0.58 at 2 years to 0.75 at 11 years, with only small overlap in 95% support intervals (I95). The estimated fa for bud burst was 0.83  and 0.84  for pilodyn depth. Overall, there was no evidence of family heterogeneity for height or bud burst, or site heterogeneity for pilodyn depth, and no evidence of inbreeding depression associated with genomic homozygosity, expected if dominance variance was the major component of non-additive variance. The results offer no support for the development of sublines for crossing within the species. The models give new opportunities to assess more accurately the scale of non-additive variation.Many quantitative genetic models assume that all genetic variation is additive because of a lack of data with sufficient structure and quality to determine the relative contribution of additive and non-additive variation. Here the fractions of additive (The online version contains supplementary material available at 10.1007/s11295-023-01627-5. Picea sitchensis [Bong.] Carr.) in the UK is an example of a well-planned and executed breeding programme which is faced with this challenge of long intervals. Sitka is a conifer species originating from the Pacific North West extending from south-eastern Alaska to northern California. It was first brought to the UK in the 1830s  to obtain faster rates of improvement , while the genotypic data can be used to estimate genomic relationships, and hence estimate the additive genetic variance and narrow sense heritability (h2). Consequently, the extent and potential impact of the non-additive genetic variance can be estimated.Most attention in tree breeding (as in other fields) has focused upon developing genomic predictions of breeding value, i.e. additive genetic merit, and it is the variance of the breeding values that defines the additive genetic variance. However, the total genetic variance includes contributions from non-additive genetic variation, and predicting non-additive effects can be used to improve the merit of those deployed in the wider forest population for timber. The molecular data make it possible to access the non-additive genetic effects more directly and to predict non-additive components of the total genetic merit  belongs to a group of reduced representation sequencing methods that have been particularly popular in non-model species. The benefits of RADseq are its flexibility and relatively low cost compared to whole genome resequencing . The experiment was designed as three full-sib families  each with 1500 offspring (where offspring represents a unique genotype), clonally replicated across three contrasting sites: Huntly, Llandovery and Torridge , with four ramets of each genotype on each site and, within sites, one ramet of each genotype in each of four replicate blocks. In addition to the intended 1500 offspring trees, each block contained 46 control trees raised from open pollinated seed collected from Haida Gwaii (formerly Queen Charlotte Islands), British Columbia. Control trees were not cloned. The trees of SF1 at Torridge formed the data for a previous publication  as an indicator trait for wood density, but due to resource constraints only at the Torridge site. Trees from family SF1 were also scored for the timing of bud-burst at the start of the fifth year in 2009 following the scale of Krutzsch  at all shttps://cordis.europa.eu/project/id/211868), and those for SF2 and SF3 were produced for Procogen (https://cordis.europa.eu/project/id/289841). DNA was extracted from the needles of the 6 known parents and from randomly selected subsets of the offspring in each family. The protocols for DNA extraction and RADseq digestion were fully described in Fuentes-Utrilla et al.  set to 2, distance between stacks (M) set to 3 and distance between catalogued loci (n) set to 3. The resulting genotypes were exported to a ‘vcf’ format using the ‘populations’ package of Stacks v.2, parameterised so that a locus was processed if it was detected in at least 3 populations (p=3), and in at least 3% of all individuals across all populations (R=3). The parameter settings were chosen to minimise the number of Mendelian inconsistencies and missing values across the resulting genotypes.SNP markers were identified and genotyped using the Stacks v.2 pipeline: ‘ustacks’ to build loci within a sample; ‘cstacks’ to construct a catalogue of loci from parental samples; ‘sstacks’ to match loci from all samples to the catalogue; ‘tsv2bam’ to transpose data to become locus oriented; ‘gstacks’ to call variants sites and genotyping individuals. The parameters used for the genotyping were optimised as recommended in Paris et al. . The outhttps://github.com/joannailska/Mendelian_inconsistencies). Firstly, the observed genotype frequencies were compared to the expected frequencies conditional on the parental genotypes. SNPs were removed if the corresponding χ2-test exceeded the P<0.05 threshold having applied a Bonferroni correction for the number of SNPs. Secondly, the stochastic nature of sequence reads results in a background number of opposing homozygotes in offspring and parent, and offspring genotypes were removed if incompatible, given the manifold greater coverage of the parents. The summary of the latter step identified anomalies for 194 of the 478 offspring in SF3. This led to the discovery of the family structure of SF3 being a mix of two full-sib families, denoted SF3A and SF3B, with a common maternal parent, and SF3A comprising a larger fraction  of the offspring . For a given value of p, the SNP call rate over offspring and offspring call rates over SNPs were calculated. An additional assessment described in Supplementary Information Among common SNPs, 1630 (78%) had been reliably assigned to the 12 linkage groups of the linkage map compiled by Tumas et al.  and thisA site and family combination is hereafter referred to as a trial . Each trial was designed as a randomised complete block design with four replicate blocks. Each block was nominally allocated 1500 offspring trees and the controls. Given the objectives, the controls were treated as filler plots with missing phenotypes in the analysis. Due to topographic constraints, some blocks were spatially separated (non-contiguous) which required ‘master blocks’ to be constructed. For trials with non-contiguous blocks, two master blocks were created and filler plots with missing phenotypes were added to ensure a continuous spatial structure within each master block. Trials with contiguous blocks were treated as having a single master block. The number of master blocks in each trial is shown in Table All models were fitted separately for each trial and included: (i) a preliminary spatial model without genomic data and (ii) an extended spatial model with genomic data. The novel feature of (ii) is that phenotypic data was included on all offspring trees (regardless of whether they have been genotyped) while genomic data was also included on the subset of genotyped trees in each family. For SF3, all data was included for both SF3A and SF3B offspring, genotyped or otherwise, as described in the following sections. This preserved all data to estimate the genetic and non-genetic variance parameters, and enabled estimates of additive and non-additive genetic variance parameters to be obtained. A similar approach was proposed by Tolhurst et al. , but noty, the vector of available phenotypic data on the 1500 offspring trees in each trial, can be written as:b is a vector of fixed effects, here only the mean, with X being a vector 1’s, u is a vector of random genetic effects for all offspring trees with design matrix Zu, v is a vector of random non-genetic effects, here only blocks, with design matrix Zv, and e is the vector of residuals. The genetic effects are assumed to be distributed as u ~ MVN, where R is the residual variance matrix which includes a model for natural and extraneous variation, i.e. variation due to random error and spatial trend  constructs a separable model between the columns and rows in each master block. Note that the model for each master block is parameterised by different auto-correlation matrices, i.e. Σc(k) and Σr(k), but the same auto-correlation parameters, ρc and ρr. This constructs a common spatial model for all master blocks within a trial. The significance of the spatial models was informally assessed by log-likelihood ratio tests and the Akaike Information Criterion, and showed considerable improvements compared to models with independent residuals, i.e. e ~ MVN. This model included phenotypic data on all offspring trees, while genomic data were included on the subsets of genotyped trees in SF1, SF2 and SF3 respectively. Let the vector of genetic effects be partitioned as u = T where u1 and u2 are the vectors for the ungenotyped and genotyped trees, respectively. Since there was clonal replication, the genetic effects for the genotyped trees can be further partitioned into additive and non-additive effects, where u2 = ua)2( + ud)222. The allele frequencies (pi for locus i) used for centring the dosages and calculating the scalar (∑i2pi(1 − pi)) were calculated from the full-sib parents for each family for SF1 and SF2 and were either 0.25, 0.50 or 0.75 as each known parent had been genotyped to high coverage and imputed. For SF3, the values of pi were as observed.The genomic relationship matrix, ASReml-R, which obtains REML estimates of the variance parameters and empirical BLUPs of the random effects. Following Tolhurst et al. (vcc argument in ASReml-R. Model 2 was fitted with the constraint in Eq. (ua)2=Models 1 and 2 were fitted separately for each trait and trial in t et al. , the spat in Eq.  using thSample variograms showing the residual semi-variance between plots were constructed after fitting all models to informally assess the spatial models and detect any additional extraneous variation in the column or row directions. An example is presented and summarised in Supplementary Information fa and fd), as P2. The confidence intervals for 21. Estimates of fa were pooled across families and sites by summing these profiles. For the estimates of spatial parameters and heritabilities, pooling was done by weighting estimates by the reciprocal of their sampling variance.Model 2 provided estimates of the fractions of additive and non-additive genetic variance within each family. For SF1 and SF2, these directly estimated the fractions within a full-sib family (p) of the posterior probability required to call a genotype following imputation by AlphaPeel. All such functions will tend to 1 as the call rate tends to 1, and if all genotypes were known with certainty, the function would be a step function, where f(x)=0 for x<1 and f(x)=1 for x=1. The distribution functions will asymptote towards this step function as the number of genotypes called increases, and the sensitivity of the distribution functions to the value of p decreases as confidence in the imputation increases. When the threshold was set to p=0.9, 96% of SNPs had call rates exceeding 95% over all offspring . These differences were largely due to the differing environmental variances. Considerable additive genetic variance, h2 that reflected the differences in H2. This correspondence was due to a relative constancy in the fraction of additive genetic variance, fa was 0.84 with 95% confidence interval of ; this estimate was within the 95% confidence intervals for each family, and the hypothesis of a common value across families was not rejected .Pilodyn depth was measured at 10 years in all three families at Torridge only, with the results shown in Table entclass1pt{minimaH2 differed between sites  and these differences were, again, reflected in the estimates of h2 for the sites. However, fa was 0.83 with 95% confidence interval of . There was no evidence to reject the hypothesis of a common value across sites . Similar results were obtained for measurements 5B and 5C, which had consensus estimates of 0.91 (s.e. 0.03) and 0.89 (s.e. 0.04) respectively.Bud burst at 5 years was only measured in SF1 and Table H2, and phenotypic variances, H2 with age. The estimates of H2 for Llandovery were generally smaller than for Huntly and Torridge, which were associated with generally larger H2 or Table fa pooled across families are given in Table fa after pooling across sites with fa at 11 years does not include 1, i.e. not all genetic variance is additive. However, some individual families at some individual sites do include 1 in their confidence intervals, which are wider, and the best point estimate for SF2 at 11 years at Llandovery was 1. There was no overall evidence of differences between families across sites from the goodness of fit tests , although Torridge at 4 and Huntly at 11 years of age gave isolated significance .The consensus estimates of G, is a ridge regression model on marker allele counts and therefore only describes what is observed as an additive sum of effects over loci, whereas the total genetic variance obtained from the clonal replication includes dominance and epistasis. The experimental design had several aspects that made the study feasible, or more powerful, beyond the clonal replication of the offspring. Firstly, the experiment’s large sib families made it possible to consolidate genotypes obtained from RADseq by imputation, using the recent availability of a molecular map for Sitka spruce  is defined within full-sib families, even though this required an adjustment in SF3. Therefore, the estimates presented here are partitions of the Mendelian sampling, and not the full genetic variance for a random-mating population, which may be obtained from other approaches. The expectations for the additive and non-additive components can be scaled up to the corresponding variance for a full random-mating population and, based on these expectations, the fraction fa would increase. Although half the additive variation lies within families, a greater portion (3/4) of the dominance and the additive × additive epistatic variation is within families, and more than 3/4 for higher order epistatic terms  in a random mating population, e.g. fa = 0.6 and 0.8 corresponds to 0.69 and 0.86. While only three sib families were sampled, the consensus estimates for fa for the traits measured on all families is important in providing guidance on the wider population.An important theoretical perspective to consider when comparing the current results with those from other published studies is that here the parameter reported  by Joshi et al.


Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants.To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans.CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.The online version contains supplementary material available at 10.1186/s12859-023-05511-w. The basic principle underlying Genome Wide Association Studies (GWAS) is a test for association between genotyped variants for each individual and the trait of interest. GWAS have been extensively used to estimate the signed effects of trait-associated alleles and also map genes to disorders. Over the past decade, about 10,000 strong associations between genetic variants and one (or more) complex traits have been reported . One unalinkage disequilibrium (LD) is captured in two ways: the average LD in sub-populations owing to migrations and the covariance between concerned genetic loci capturing epistatic interactions [Genetic Relationship Matrix (GRM) [Linear Mixed Model (LMM) with an estimated kinship or GRM from the individual’s genotypes [One of the key challenges in GWAS are confounding factors, such as population stratification, which can lead to spurious genotype-trait associations , 5. In sractions . Naturalractions . Admixturactions  and hencractions . Two popix (GRM) , 10 invoix (GRM) , 11, andRecently, three independent studies –14 faileAgglomerative hierarchical clustering (AHC) using a regularized Mahalanobis distance-based GRM, which captures the population-level covariance (LD) matrix for the available genotype data. We test CluStrat on large-scale simulated data of discrete and admixed, complex-structured populations with over one million genetics markers (Single Nucleotide Polymorphisms or SNPs for short). We observe that our approach identifies more less frequent variants at causal loci while maintaining low spurious associations when compared to standard stratification correction strategies across varying thresholds of significance. Computing the GRM by low-rank Mahalanobis distance, we apply CluStrat to large cohorts such as Wellcome Trust Case Control Consortium 2 (WTCCC2) and UK Biobank (UKBB) to find biologically significant associations in two complex diseases, namely Schizophrenia (SCZ) and Acute Myocardial Infarction (AMI) with potential variants implicated in the disease of interest which are often overlooked by GWAS. CluStrat also corrects for the uncorrected population structure in polygenic adaptation of height in Europeans, as highlighted in previous studies [Our work proposes a simple clustering-based approach to correct for stratification better than existing methods. As discussed above, it is important to consider the covariance matrix of genetic variants while constructing the GRM to account for the LD between genetic variants and synthetic LD due to population structure as potential confounders while performing association studies. This method takes into account the linkage disequilibrium while computing the distance between the individuals in a sample. Our approach, called CluStrat, performs  studies , 13. Of p-value threshold set to We applied CluStrat to 100 simulation scenarios, modelling proportions of true genetic effect and admixture using three well-known models to generate simulated data: Balding-Nichols (BN) ; PritchaTA tools  to simulThe BN and PSD model simulate scenarios with unrelated isolated populations. The PCA plot of the samples clearly show three isolated clusters with no connections between them in the BN model. In the PSD model, we see admixed populations between the clusters . The linear regression fit for CluStrat is almost identical to the null-expectation. We also found that the height-increasing tSDS and the p-values from CluStrat have a negligible Spearman’s correlation coefficient  genotyped on 44,818 SNPs, related to the largest effect sizes in relation to height, from summary statistics data generated by . We founentclass1pt{minimaWe applied CluStrat on data from two complex diseases: SCZ data from WTCCC2’s and AMI data from the UK biobank. In both cases, CluStrat identified biologically relevant associations.CluStrat corrected SCZ SNPs We applied CluStrat using two clusters on SCZ data and identified 5 variants with a p-value threshold of neurofibroma in the DOSE database; immunoglobulin isotypes (IgG) in GO  and neurofibroma were identified as significantly enriched enriched in the CluStrat-corrected SNPs. SCZ is characterized by an interrelated activation of the immune-inflammatory response system and there is established evidence of immunoglobulin’s role in the immune response [Neurofibromatosis (NF) is often associated with neurodevelopmental disorders, which are more frequent in NF than in general population [aortic diseases, aortic aneurysms, kinase activity, and cellular senescence were shown to be significantly enriched in the CluStrat-corrected SNPs. Aortic aneurysms occur when the aorta weakens and bulges. Ruptures of this vessel can cause life-threatening bleeding. These types of aneurysms can also force blood away from organs and tissues, leading to AMI. Protein kinases are intimately involved in different signal pathways for the regulation of cardiac function to maintain healthy cardiac function, but also participate in the development of cardiac dysfunction in AMI and heart failure  [Cellular senescence has received recent attention as a potential target preventing cardiovascular diseases [Applying CluStrat to complex diseases, such as SCZ and AMI, we found novel variants and replicated previously associated SNPs/genes with these diseases. In SCZ, pathways such as response . Neurofipulation . In AMI,failure  . Celluladiseases . The amoThe power of CluStrat is further revealed when we pruned for LD in the genotype data after QC with differing In summary, CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance. We evaluated CluStrat on multiple simulated data for arbitrarily structured populations with and without admixture. We concluded that CluStrat outperforms PCA or LMM based population stratification correction techniques in a variety of simulated datasets. CluStrat accurately corrected for population stratification in height GWAS in UKBB and identified numerous previously annotated genes and pathways for SCZ and AMI, as well as novel candidate loci. Thus, structure informed clustering of genetic data can remove cryptic population stratification in association studies and can be used to mitigate confounding in polygenic risk scores and precision medicine initiatives.m samples genotyped on n SNPs). The matrix is appropriately normalized as is common in population genetics analyses to have zero mean and variance one (columnwise). The vector i-th entry is set to one for cases and to zero for controls (for binary traits). We let i-th row of the matrix i-th column of the matrix k left singular vectors of the matrix i-th row of Let CluStrat provides an LD based clustering framework to capture the population structure and the tests for association within each cluster, as described in Algorithm 1.k, selected using five-fold cross validation. We perform the association test in CluStrat by using linear models (logistic or linear regression based on the input) on each cluster. Then, we take the results for each cluster and perform meta-analysis, using METAL [The algorithm computes the distance matrix ng METAL , improviWe now briefly discuss the use of the Mahalanobis distance at the first step of the proposed algorithm. In an arbitrarily structured breeding population, correlation between loci due to LD often results in block-diagonal structures in the covariance matrix of genetic variants. Thus, it is important to account for this LD structure in the computation of the distance matrix . One waymentclass2pt{minimmentclass2pt{minimi and j:k leverage scores of the genotype matrix k left singular vectors i-th row of the matrix k statistical leverage scores of the rows of k -th cross-leverage score, i-th and j-th rows of k leverage and cross-leverage scores  Balding-Nichols (BN) model [(ii) different levels of admixture by varying the parameter (iii) structure estimated from 1000 Genomes Project (TGP) [For the genotype data, we simulated allele frequencies using N) model  based onel (PSD) ; (iii) sct (TGP)  (see Addmentclass2pt{minimct (TGP) , 36. Forct (TGP)  that empReal data To capture real world population structure, we applied CluStrat on two complex diseases: SCZ and AMI. SCZ data was available from the Wellcome Trust Case Control Consortium (WTCCC2) study containing 5893 individuals (5416 SCZ controls and 477 cases) with 18,683 markers after performing quality control (QC) using PLINK v2 [PLINK v2 . We alsoOn the genotypes passing QC, we applied CluStrat before and after pruning for LD to showcase the utility of considering the genotype covariance matrix while correcting for LD due to population structure and epistatic effects. We used multiple correlation  . We usedAdditional file 1: Methods related to CluStrat including theoretical background and proof and additional results of different simulation scenarios and real data from WTCCC2 and UKBB.


Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates. Robotized indoor phenotyping allows measuring of plant physiological traits at high throughput, but is often considered as non-relevant for field conditions. Here, the authors show that maize traits measured indoor translate to the field and can be inferred via genomic prediction. Most current breeding schemes are based on yield measurement of thousands of genotypes grown under diverse environmental scenarios, assisted by genomic selection that allows yield prediction for many thousands of untested genotypes based on their genomic information4. In this approach, the measurement of other traits is most often limited to crop cycle duration, which defines the growing areas in which resulting genotypes can be grown, and to traits that may jeopardize the commercialization of selected candidates, such as resistance to diseases or quality performance 5. However, in the context of climate change, other traits that affect light interception, plant development, transpiration and growth are important for predicting, via statistical or crop models, the suitability of genotypes to future environmental conditions8. Furthermore, a recent analysis of maize genetic progress suggests that physiological traits involved in plant response to heat and drought, such as leaf growth rate or stomatal conductance, have not been improved over the last 60 years of maize selection9. Yield was improved via other traits such as the fine-tuning of phenology and the constitutive increase of grain number, but physiological adaptive traits are still a potential reservoir of interesting alleles for climate change9.Breeding for the improvement of crop resilience is increasingly necessary for the sustainability of cropping systems and for food security in the context of climate change and growing population13. They also allow estimation of traits controlling transpiration, e.g. stomatal conductance14 and those controlling plant architecture, e.g. the vertical distribution of leaf area and the azimuthal distribution of leaves along the stem, with good heritability15. Then, light interception, transpiration, and radiation use efficiency can be simulated in virtual field canopies, which reproduce 3D plants characterized in the indoor platform17. Field phenotyping also allows measuring leaf area at several dates, for hundreds of genotypes in different fields characterized by measured environmental conditions20. This can result in the estimation of intercepted light in the same fields and, via model inversion, of leaf area and plant architecture23.The progress of high-throughput phenotyping now allows one to measure physiological traits for hundreds of genotypes. Robotized indoor phenotyping platforms allow estimation, with typical time definitions of some minutes to one day, of traits that underlie the genetic variability of leaf area and their responses to environmental conditions, e.g. leaf expansion rate, leaf width, phyllochron and leaf number27. This difficulty is not limited to the extrapolation of trait values from indoor to field conditions: most of these traits also largely vary between fields depending on environmental conditions, making difficult the prediction of traits in one field from those measured in another field29. The relationship between these traits and yield is also highly depending on environmental scenarios31. Consequently, physiological adaptive traits have not been considered per se in breeding programs32.However, the use in breeding of these physiological and growth-related traits faces the difficulty of their high sensitivity to environmental conditions, resulting in large genotype x environment interactions34. Yield and agronomic traits like disease resistance are predicted based on genomic information at each generation, while a full phenotyping of the most promising genotypes is carried out in the field after some generations35. However, this approach also potentially includes, in breeding schemes, other traits measured indoor for training a prediction model used in genomic selection, and phenotyped for selected candidates after a few generations. For instance, in wheat, Watson et al.36 performed speed breeding involving the length of flag leaves and ear length, in addition to yield. Conditions for the use of speed breeding in our case are that physiological adaptive traits translate from indoor conditions to the field, and are accurate enough to make it feasible to implement rapid cycling based on indoor phenotyping and genomic prediction. Three panels of maize hybrids were used to test these conditions . In both cases . The latter are considered here as a benchmark for evaluating the quality of translation from indoor to field experiments. Importantly, the ranking of hybrids and their distribution in highest and lowest quartiles were essentially conserved between indoor and field conditions, a necessary condition for breeding (Supplementary Table Leaf appearance rate (LAR) represents the simplest case, as it is measured with the same protocol indoor and in the field. Its genomic heritability was 0.63 in the diversity and genetic progress panels Table . In the PAD) was derived from 3D reconstructions of individual plants, via the difference in altitude between the top of the plant and the point where half of leaf area is reached, normalized by plant height15. This trait is closely related to light interception by a canopy15 and had high heritability  can only be measured indoor, with good heritability Table 43. Corre2). Although heritable within a given field, it largely differs between fields in relation to environmental conditions and plant density45. It can be measured indoor, but a direct comparison with the field would make no sense because the density and spatial arrangement of plants in indoor experiments make the considered canopy irrelevant to the field46. Indeed, LAI measured in the field was not correlated to the LAI calculated by considering plant leaf area measured indoor at flowering time, multiplied by the plant density in the corresponding field . This was because environmental conditions and management practices were too different between the greenhouse and the field. Instead, we calculated LAI based on the genotypic values of upstream traits measured indoor (Table 42 with (ii) the LAI simulated by a crop model11. Model inputs were the genotypic values of four traits measured in indoor platform , responses of leaf growth rate to VPD and soil water potential, and final leaf number), plus plant density and the environmental conditions recorded every hour in the considered field. The correspondence between measured and estimated LAI, tested on the ‘genetic progress’ panel suggested that this approach is promising in well-watered (WW) condition , a key feature for light interception and transpiration, is defined for a fraction of field canopy .Finally, stomatal conductance is a difficult case in which traits cannot be directly measured at high throughput, either in the field or in indoor platforms. Its measurement for one leaf requires between 3 and 15 min, depending on the considered device, making high-throughput measurements impossible. However, it can be indirectly estimated at plant level in platform experiments by inversion of the Penman-Monteith equation, based on measurements of individual plant transpiration, leaf area, light, and VPDOverall, the ranking of genotypes for leaf appearance rate, plant architecture, and LAI were consistent between field and indoor conditions, thereby opening the way for a prediction of values in the field based on platform information Table . This co47. Meta-analyses showed that phenotypic values differ between controlled conditions and field24, but they also largely vary from one field to another one24. Hence, we tested if the difficulty for translating values between two experiments may not be specific to field – platform comparisons, but applies to comparisons between any environment and another one, depending on environmental conditions in each experiment.Beyond the correlations between genotypic values of traits measured indoor and in the field, it is the absolute values of traits, measured in each experiment, that eventually drive the adaptation of studied genotypes to drought and high temperature. For example, a correct estimation of genotype ranking for LAI has a very small impact on light interception if all genotypes have a LAI higher than 4, whereas the same genotype ranking in a range of LAI from 2 to 4 has a large impact44. A superficial analysis would suggest that the mean dimensions of leaf 8 largely differed between indoor and field conditions, with a mean leaf length of 115 vs 76 cm, respectively, and a mean leaf width of 6.8 cm vs 7.5 cm, respectively (note the inversion of ranking between the two traits). However, leaf dimensions also largely varied among field experiments, from 6.8 to 10 cm for leaf width and from 68 to 102 cm for leaf length , and the same relationship accounted for the difference between experiments in fields and platform , consistent with studies showing a linear effect of VPD on leaf elongation rate43. Hence, leaf width and length did not differ intrinsically between indoor and field conditions: differences were accounted for by the same environmental conditions than those that accounted for differences between one field and another one, and could be calculated via a crop model11.This hypothesis was first tested by examining the mean absolute values of maize leaf length and width between field and indoor platforms for the diversity panel in Lacube et al.gth Fig. . We showorm Fig. . In the 49. Expressed in this way, measured values were similar between field experiments for the duration of the vegetative phase and for LAR  of the duration of the vegetative phase, the leaf appearance rate, maximum leaf expansion rate , the architectural trait rhPAD and stomatal conductance. Predictions were performed using the genomic information at 440 000 polymorphic SNPs. Prediction accuracies and RMSEs were assessed either with a 5-fold cross-validation (CV1) scheme28  whereas many thousands of genotypes are required for breeding53  between observed BLUEs and predicted G-BLUPs values, or by the prediction accuracy of genomic selection (Acc), calculated by dividing the correlation coefficient (r) by the square root of trait genomic heritability53 Table . This waPAD and stomatal conductance, respectively, Table The range of G-BLUP predicted values was expectedly smaller, for all tested traits, than that of observed values. This bias is linked to the fact that the narrow-sense heritability estimated using genomic additive and dominance relationships of studied traits was lower than 1 , so indoor breeding is relevant to field conditions. Secondly, the absolute value of indoor traits should translate to that in fields with diverse climate scenarios, either directly or via models. Finally, indoor traits need to be predicted with sufficient accuracy from the genomic information of non-phenotyped genotypes.24 for a set of growth-related traits meta-analysis (median r = 0.51). Two reasons may explain this disparity. (i) Traits considered in Poorter’s meta-analysis, namely yield, leaf nitrogen concentration and specific leaf area are more integrated than those studied here (except LAI) and, therefore more prone to high genotype x environment interactions and changes in the ranking of genotypes. (ii) The traits studied here had moderate to high heritabilities over experiments, thereby showing a low residual variance resulting from experimental errors. Furthermore, measuring yield, yield components, or leaf area index in phenotyping platforms is probably not relevant because these traits result from cumulative processes over a long period, during which conditions indoor are very different from those in the field. The methods presented here for comparing indoor and field trait values considerably reduced the genotype x environment interaction (GEI) for such integrated traits. For example, a direct comparison of LAI indoor and in the field resulted in a high GEI, without correlation between them. Conversely, the GEI was largely reduced when upstream traits measured indoor (with a low GEI) were combined, via a crop model, with the management practices and environmental conditions in the considered field37.The traits presented here satisfied the first condition. Close correlations were observed between the genotypic values of traits measured indoor and in multi-site field experiments. This was the case when the considered trait was measured with similar protocols indoor or in the field, for instance leaf appearance rate or the duration of the vegetative phase. It was also the case when the trait was measured with different methods as in the case of plant architecture. Finally, the integrated trait LAI, which is highly dependent on the plant density and environmental conditions in the considered canopy, required a method involving crop modeling. The correlations observed in these three cases between indoor platform and fields are therefore higher . Its effect can be predicted from the cell scaleThe second condition, namely that trait values can translate from indoor conditions to a diversity of fields, was fulfilled for the traits reported here if the differences in environmental conditions were taken into account, thereby dealing with the GEI via a previously reported model24. However, taking into account specific environmental conditions allowed this translation for the traits presented here, which depend on one or two environmental conditions. Again, more integrated traits such as leaf area index, grain number or grain yield measured in a platform cannot be directly extended to field via simple relationships as presented in former paragraphs. These traits can be predicted in a range of field conditions based on genotype-specific parameters and environmental conditions measured in the considered fields. This was the case here for leaf area index, but was also the case for grain number and grain yield in a multi-site field experiments, based on a mixed model involving genetic parameters and environmental conditions31.Overall, we confirmed that raw phenotypic traits cannot translate directly from indoor platforms to fields, as reviewed in Poorter et al.56, Yuan et al.55, or Toda et al.51), with r ranging from 0.56 to 0.84 for the studied traits  and acceptable CV of errors (from 2.5% to 15.6%) , Lancaster (45 hybrids), Stiff-Stalk (55 hybrids), and diverse-dent hybrids (107) consisting in an admixture of the former three groups67. Second, a ‘genetic progress’ panel included 56 highly successful commercial hybrids released on the European market from 1950 to 20159. This panel showed a limited range of maturity classes, from mid-early (FAO 280) to mid-late (FAO 480), covering the largest growing area in Europe. Finally, a ‘recent hybrids’ panel included 86 commercial hybrids released from 2008 to 2020, belonging to mid-early to mid-late maturity classes (Supplementary Data www.varmais.fr).Three panels of maize hybrids were used in this study (Supplementary Table  hybrids 07 consishttps://www6.montpellier.inrae.fr/lepse/Plateformes-de-phenotypage-M3P/Montpellier-Plant-Phenotyping-Platforms-M3P/PhenoArch)16 hosted at Montpellier Plant Phenotyping Platforms (M3P). The diversity panel was evaluated in four experiments  as described in Prado et al.14. Three or two plants per hybrid were grown depending on the experiment  and water deficit . Soil water content in pots was maintained at target values by compensating transpired water three times per day via individual measurements of each plant16. Soil water potential was estimated from soil water content based on a water release curve14. Air temperature and humidity were measured at six positions in the platform every 15 min. Daily incident photosynthetic photon flux density (PPFD) over each plant within the platform was estimated by combining a 2D map of light transmission, and the outside PPFD measured every 15 min with a sensor placed on the greenhouse roof16. The greenhouse temperature was maintained at 25 ± 4 °C during the day and 17 ± 2 °C during the night. Supplemental light was provided either during daytime when external solar radiation was below 300 W m−2 or to extend the photoperiod by using 400 W HPS Plantastar lamps.Platform experiments were performed in PhenoArch, an indoor robotized and image-based phenotyping platform that allows precise measurement of plant architecture, plant phenology and growth, transpiration, stomatal conductance and water use efficiency  where half of the cumulative leaf area is reached. Whole‐plant stomatal conductance was calculated over 4 time‐periods per day for 20 days for each hybrid plant in PhenoArch platform, via inversion of the Penman–Monteith equation based on transpiration, plant growth, net radiation and VPD collected in the experiment14. Its value under saturating light was estimated for each hybrid by combining coupled values of stomatal conductance and incident light observed in all experiments. The maximum leaf expansion rate (LER) was extracted from time courses of leaf area in the platform and corresponded to the maximum first-order derivative of P-spline fitted growth curves from 24 to 45 days at 20 °C after emergence69. Because this method provided somewhat unstable results, we also calculated maximum LER as the slope of the linear regression between leaf area and thermal time during the whole period from 24 to 45 days at 20 °C.In each experiment, the number of visible leaves of every plant was manually scored weekly during the vegetative phase. Leaf appearance rate  was calculated as the slope of the linear relationship between the number of visible leaves and thermal time, during the period from plant emergence to 12-leaf stage. Red‐Green‐Blue (2056 × 2454) images taken from 13 views  were captured daily for each plant during the night. Plant pixels from each image were segmented from those of the background and used for estimating the whole plant leaf area and fresh biomass70,), with a fixed term for genotype and random effects for rows and columns as well as a smooth surface defined on row and column coordinates. Broad-sense heritabilities were calculated daily with the same R package, using the same model but with the genotype effect included as a random term. Regarding longitudinal traits, genotypic values at individual time points, t, were obtained from their smoothed time series using a generalized additive model fitted to the spatially adjusted daily measurements, i is a genotype-specific intercept, fi (t) is a genotype-specific thin plate regression spline function on time, and ϵi,k (t) is a random error term .Genotypic values (BLUEs) for each trait were estimated by correcting raw traits values for spatial effects, by fitting a mixed model (R package SpATSg2) was estimated for each trait, panel and experiment with a model considering genomic-based additive and dominance relationship matrices72, using the R package “BGLR”73.Genomic heritability , either rainfed or irrigated, in ten sites in 2012 or 201348. Leaf appearance rate was estimated as in platform experiments.The duration of the vegetative phase was defined as the period from plant emergence to anthesis, expressed in thermal time −1 for about 20 min per flight, with images captured at a one-second interval. Flights were performed during clear and cloudless days between 8:00 and 10:00 solar time. An automatic image-processing pipeline was applied by Hiphen, Avignon, France (http://www.hiphen-plant.com), following methods presented in Blancon et al.20. Environmental variables were recorded every hour in all experiments, including light, air temperature, relative humidity (RH), rainfall and wind speed. Soil water potential was measured every day with tensiometers at 30 and 60 cm depths with three or two replicates, located in plots sown with a common reference hybrid.UAV flights were performed three times in one experiment of the ‘genetic progress’ panel during the period from plant emergence to flowering, and seven times in two experiments for the ‘recent hybrids’ panel during the same period  and Leaf Area Index (LAI) were calculated by inversion of the PROSAIL model11) parameterized with the genotypic values (BLUEs) of four traits measured in PhenoArch platform , plus the environmental and growing conditions recorded in the considered field.Leaf area index was also calculated by using a crop model  between experiments were also assessed, using a multivariate Bayesian Gaussian mixed model, fitted for each couple of experiments  and Spearman (rho) correlation coefficients were calculated to evaluate to which extent the genotypic values (BLUEs) of traits match between experiments, either in one field and another one, or in one field and the indoor platform. Both types of correlations was performed on the hybrids that were common to considered experiments  with GA as the genomic-based additive relationship matrix described below and Va as the additive effects variance–covariance matrix, i ∼ MVN  with GD as the genomic-based dominance relationship matrix described below and Vd as the dominance effects variance–covariance matrix, Variance components were calculated assuming: 77. Additionally, the theoretical accuracy of indirect selection (iAcc), i.e. in case of an indirect phenotypic selection based on observed values in a given experiment (indoor or in a field), was calculated as the genetic correlation between the considered couple of experiments, multiplied by the square root of trait genomic heritability in the reference experiment for selection52 (Supplementary Table 52.Standard errors (SE) of all correlation coefficients were estimated using Bonett and Wright approximationsRMSE) or bias (CVBias), which is the RMSE or bias expressed as a percentage of the mean value. Finally, to appreciate the consistency between experiments of the highest and lowest genotypic values, we evaluated the frequency of similar assignment to the highest or the lowest quartile between experiments for each trait. This consisted of estimating how many hybrids of the highest quartile of one experiment were also present in the highest quartile of the other experiment (Supplementary Table Root mean squared error of estimations (RMSE) and bias showing the discrepancy between experiments genotypic values (BLUEs) were calculated too. We present them as a coefficient of variation of the error  or resulted from the direct genotyping of the hybrids (genetic progress and recent hybrids panels). After quality control, 440,000 polymorphic SNPs were retained for diversity analyses and genomic prediction . Missing values were otherwise imputed using BEAGLE v379.All panels were genotyped using the 600 K Affymetrix® Axiom® array80 was used to estimate observed heterozygosity asj is the number of heterozygous hybrids at the jth biallelic locus, L is the total number of loci, and pj is the frequency of the reference  at locus j  j was coded as 0 , 1 (the heterozygote) or 2 (the other homozygote). “snpReady” R packageAσa2) with GA as the genomic-based additive relationship matrix described below and σa2 as the additive genetic variance; d is the vector of random dominance effects which follows a normal distribution ∼ N  with GD as the genomic-based dominance relationship matrix described below and σd2 as the dominance genetic variance; ε ∼ N  is the vector of random residual effects, where I denotes the identity matrix and σε2 is the residual variance. Za and Zd are the incidence matrices for Genomic predictions of each trait was performed with a genomic best linear unbiased prediction model (GBLUP-AD), including random additive and dominance effects:72 and González-Diéguez et al.81. The genomic-based additive relationship matrix (GA), called realized relationship matrix was estimated asa is a rescaled genotype matrix Ha = M–P, where M is the genotype matrix coded as 0, 1, and 2 for genotypes BB, Bb and bb respectively and with dimensions number of hybrids (N) by number of loci (L); P is the matrix of locus scores 2pj, with pj being the reference allele frequency of the jth SNP biallelic locus ; tr is the trace. The genomic-based dominance relationship matrix (GD) was estimated asd is the matrix containing elements hd for each individual and locus equal to:BB, pBb, and pbb are the genotypic frequencies for the genotypes BB, Bb, and bb respectively at the locus.The genomic-based relationship matrices were built as defined in Vitezica et al.max), in addition to random additive and dominance effects estimated from genomic-based relationship matrices, the genotypes of markers associated to quantitative trait loci (QTLs), previously identified in a genome-wide association study (GWAS) of the diversity panel14, were added as fixed effects in prediction models:X is an n × l marker genotype matrix for n hybrids and I markers associated to trait QTLs and β is the markers fixed effects vector.For moderate heritability physiological traits , were used as predictors. Other genomic prediction models  were also tested but showed no significantly better results than those presented in this paper.73, based on 60,000 iterations, after discarding 10,000 cycles for burn-in period and using a thinning rate of 5.All prediction models were fitted using the Bayesian Generalized Linear Regression (BGLR) R package28. For each iteration, the two panels genotypes were split into 5 subsets, then each subset (one fifth) was predicted using the remaining four fifths as training set. This generated a total of 5 × 10 testing sets. Each training set was sampled randomly but proportionally to ‘diversity panel’ genetic groups and across years of release of ‘genetic progress’ hybrids  repeated 10 times, applied to diversity and genetic progress panels datasets. In CV1, we aimed to measure the ability of the models to predict the performance of hybrids that would not have been evaluated in any of the observed environments53), the root mean squared error of predictions (RMSE) showing the discrepancy between predicted and observed values and the coefficient of variation of the error (CVRMSE), which is the RMSE expressed as a percentage of mean observed value. For cross-validation scheme, the statistics estimations were performed within each fold and then averaged across folds and iterations. Standard errors (SE) of correlation coefficients were calculated using Bonett and Wright approximations77.Five statistics were calculated to assess the performance of prediction models for each trait: the Pearson (r) and Spearman (rho) correlation coefficients between observed genotypic means (BLUEs over experiments) and predicted values (G-BLUPs), the prediction accuracy of genomic selection  divided by the square root of trait genomic heritabilityFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary informationSupplementary Data 1Reporting SummarySource Data


A precise description of traits is essential in genetics and genomics studies to facilitate comparative genetics and meta-analyses. It is an ongoing challenge in research and production environments to unambiguously and consistently compare traits of interest from data collected under various conditions. Despite previous efforts to standardize trait nomenclature, it remains a challenge to fully and accurately capture trait nomenclature granularity in a way that ensures long-term data sustainability in terms of the data curation processes, data management logistics and the ability to make meaningful comparisons across studies. In the Animal Quantitative Trait Loci Database and the Animal Trait Correlation Database, we have recently introduced a new method to extend livestock trait ontologies by using trait modifiers and qualifiers to define traits that differ slightly in how they are measured, examined or combined with other traits or factors. Here, we describe the implementation of a system in which the extended trait data, with modifiers, are managed at the experiment level as ‘trait variants’. This has helped us to streamline the management and curation of such trait information in our database environment.Database URL https://www.animalgenome.org/PGNET/ The two databases were originally developed separately, and joint development efforts were undertaken for parts they share in common, such as livestock trait management. The QTLdb houses QTL, single nucleotide polymorphism and phenotype association data, and the CorrDB houses trait correlation and heritability data, published in the past 20+ years for multiple livestock animal species . The development and expansion of these ontologies have been undertaken in collaboration with the Medical College of Wisconsin and Mouse Genome Informatics  the development of a curation route and database structure for the new ‘trait variant’ management and (ii) the migration of existing data from the previous ‘sibling traits’ to the new ‘trait variant’ data curation and management structure.A trait term may be ‘modified’ by a property, or ‘descriptor’, such as an experimental or environmental factor, to produce an extended version of the trait term as a new term. Examples of such extensions are intramuscular fat content (IMF) measured in different muscles (e.g. gluteus medius and longissimus) or using different methods (e.g. ultrasound and petroleum ether extraction); or milk yield (MY) measured daily or over the entire lactation period or compared between the first and second lactations. For these examples, IMF and MY are the base traits, the additional properties are modifiers and the extended names of the base traits plus modifiers are trait variants. In order to compartmentalize the modifier terms for a controlled list, we introduced qualifiers to further define the use of a modifier. This effectively partitioned the ‘modifiers’ used previously in ‘sibling traits’ into two parts: modifiers and qualifiers.https://www.animalgenome.org/QTLdb/doc/meta/tvarinfo) provides a reference for curators as well as a review of curated data by (base) trait.By partitioning modifiers from qualifiers, we were able to create a modifier list with a smaller number of categories  for a drop-down menu in the curator panel . Each ofWith this new management scheme at the experiment level, we are able to maintain minimum numbers of controlled vocabulary terms for modifiers and qualifiers in order to facilitate the consistent use of terms over time. Currently, the system can accommodate up to three  modifierAs part of the database transition to using the new curation scheme described earlier, we have begun a migration of all ‘sibling trait’ data curated in previous years to the ‘trait variant’ scheme under the new structure in both the QTLdb and CorrDB. Throughout this transition, a total of 1256 new trait variants have been created for 278 base traits. The new trait variants include 418 for QTL/associations, 425 for correlations and 413 for heritability. This process has affected 22 205 curated data, including 16 227 QTL/associations, 5573 correlations and 415 heritability data . As a reThe successful migration of ‘sibling traits’ to ‘trait variants’ in a relatively short period of time demonstrates that the new data management implementation works well as designed. Furthermore, this implementation has also significantly reduced many of the frustrations of our data curators, as well as database maintainers, regarding the day-to-day work dealing with emerging cases when curating ‘sibling traits’. Allowing trait variants to be curated at the experiment level gives curators the flexibility to address them on a case-by-case basis and helps reduce clutter in the database trait hierarchy while maintaining data stringency at the database level.From a database management perspective, this work added ‘trait variants’ as an extension to trait ontology terms (‘base traits’), which separates the management of trait variants from the handling of the trait ontology hierarchy . The addAppended to base traits, trait variant information is valuable to facilitate data comparisons for end users evaluating data across time and experiments. At the time of this report, we are in the process of making the newly produced trait variant information available in data downloads and web tools. These data will be visible to the public by the April 2023 database release.Not only does the sheer volume of newly published data create challenges for Animal QTLdb and CorrDB curation, but also curation/database processes must be adapted to accommodate different data formats, new analysis methods and varying levels of trait data granularity. In contrast to our earlier ‘sibling traits’ system, which attempted to add trait variations into a trait ontology and presented extra challenges for ontology development, our method of developing ‘trait variants’ as extensions of ontology terms (‘base traits’) helps isolate complex trait handling outside of trait ontology development. While the concept partitioning method is effective in simplifying the management of complex trait information, we wish to point out that the level of granularity captured needs careful consideration in order to maximize the overall benefits. For example, the need to consider how traits are defined in multiple animal species further increases the level of complexity.et al.  While the modifier factor partitioning approach provides possibilities for a more scalable system, it also opens additional opportunities for complex trait curation and management. For example, while we have implemented mechanisms to handle ‘chained’ or ‘parallel’ modifiers, more complex modifier relationships  may exist which require solutions in the near future. This is one area in which the current system is still subject to further development to refine the details.Note that on the trait variant curation form, there is a free-text field  to colleTrait ontology development is an ongoing process, and it is expected that the trait variant system will also need to be expanded or updated in the future. It is important to carefully consider the details regarding the implementation of the trait variant system to ensure its ongoing stability and viability. For instance, it is necessary to appropriately distinguish ‘base traits’ and ‘trait variants’. As an example, since 305-day MY is such a widely used measurement standard for bovine dairy production, people may consider it to be synonymous with MY, but there are several other potential modifiers that may apply to the base ‘MY’ trait. In cases like these, there are multiple factors to consider before determining the most appropriate base trait.https://www.animalgenome.org/QTLdb/doc/meta/tvarinfo). It could be a steep learning curve for new curators, however, necessitating further improvements to the trait curation environment. One possibility is the implementation of an artificial intelligence helper to suggest trait variants and make them easier to introduce. Overall, these changes have not only provided a workable solution for curating complex traits but also given opportunities for further improvements with better-structured data that are more accessible using programs.Since trait variants are now created and managed at the experiment level, each trait variant must be re-created for every experiment in which it is used. This will be simplified once the patterns of common complex traits partitions/compositions are established. However, it requires curators to be familiar with the commonly reused complex traits or to refer to the established trait variant list for references (