PSII) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both ΦPSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ GP models tackle this curse of dimensionality (p > n) by regularization  to accommodate genetic architectures having a larger number of high to moderate effect sizes  and projected leaf area (PLA) in Arabidopsis thaliana. Both of these traits are related, in the sense that the ΦPSII directly illustrates the photosynthetic light use efficiency and can capture the most immediate physiological and regulatory response to varying irradiance levels  and clusters of co-expressed genes (COEX). This information was incorporated using the GFBLUP modeling approach, grouping markers in genes according to either their predicted function or co-expression, respectively. As complex traits of study, we focused on photosynthetic light use efficiency of photosystem II  for 344 accessions of the Arabidopsis HapMap population, switching from low light (100 μmol m−2 s−1) to high light (550 μmol m−2 s−1) irradiance at the onset of day 25. In total, they took 6 measurements before and 12 after applying light stress to identify potential QTLs during acclimation to high light. As we intend to use this population to explore the utility of biological knowledge in genomic prediction, we combined projected leaf area (PLA), another indicator of plant growth, with ΦPSII. We first assessed whether GP works with reasonable performance for these complex traits. For this purpose, a classical Genomic Best Linear Unbiased Prediction (GBLUP) model was constructed to assess how well the infinitesimal modeling assumptions fit and to calculate markers-based heritability. In this model (Equation 2), all marker effects are treated as arising from a single normal distribution PSII is lower  than at high light , as shown in PSII ranged from 0.08 to 0.13 under LL and 0.56 to 0.87 under HL, and 0.05 to 0.17 for PLA  during association testing. In summary, these results show that predictive performance for these complex traits is low and there may be room for improvement by incorporating prior biological knowledge, decomposing the total genomic variance into biologically relevant subsets.An extension of GBLUP is MultiBLUP  and constructed a GBLUP model based only on these. The Genomic Relationship Matrix (GRM) was constructed from all markers within the ORFs of PSGENES, leaving ~17% of the total genotyped markers after filtering. Interestingly, the models performed equally well  terms and previously reported clusters of co-expressed genes (COEX) of th percentile of the total number of genes within GO features was lower (496) as compared to COEX . Note that both GO and COEX groups may overlap, i.e., a gene can be in multiple functionally related GO/COEX groups. In the following results, the improvement in genomic prediction has been quantified in terms of percent gain in accuracy compared to the GBLUP benchmark, GFBLUP model's goodness of fit measured using likelihood ratio test (LR), and genomic heritability  provided the highest increase in accuracy (~60%), for ΦPSII measurements under low light on the second day , showing a tendency to improve ΦPSII traits and yielding a significant increase in GFBLUP model accuracy  were potentially informative  horpFDR < 0.05) in predictive ability  resulted in significant improvement . The genomic heritability explained individually  as well as with the likelihood ratio  of both trait models, indicating that incorporating meaningful prior subsets into the GFBLUP model improves goodness of fit.The maximum number of genes annotated with the informative GO terms for Φd growth . Moreove for PLA . InteresPSII at multiple measurement times.From this we infer that GO-based prior knowledge can improve GP performance. The improvement is most prominent for traits with low heritability, where some of the GO terms appeared more frequently for PLA than ΦSimilar to genomic features based on GO, we made subsets of markers based on COEX clusters by selecting the markers within the ORFs of genes which were part of a given COEX cluster. Similar to GO based models, COEX models with zero and with 100% variance explained were discarded . In genePSII we found 172 informative COEX gene groups potentially improving predictive ability, one of which was statistically significant (p < 0.05) after correcting for multiple testing using FDR (PSII (7 to 89%) and was negatively correlated with genomic heritability , like for GO informed prediction. This improvement was attributed to a maximum of only ~5% of the total genomic markers in all groups. Interpretation of COEX gene groups is not as straightforward as of GO terms, which by nature carry an informative name. Interestingly, ~90% of genes were common in the COEX groups for both traits, possibly due to the relatedness of the traits. To attach biological meaning to these groups we performed GO enrichment analysis on all groups together. We found 113 BP, 29 MF, and 24 CC most specific GO terms enriched in these clusters. The top 10 GO terms with highest fold enrichment include photosynthesis machinery, i.e., chloroplast stroma (GO:0009570), chloroplast envelope (GO:0009941) cellular components; ATPase activity coupled with transmembrane ion transport (GO:0015662); and glucose metabolic process  for both traits, but here this is close to what we expect by chance. This indicates that, even though the COEX groups contain only a limited subset of all genes, they are not biased toward photosynthesis genes. The gain in predictive ability and explained genomic heritability (PSII by the top COEX gene group was higher (89% resp. 14%) than those for the top GO feature (60% resp. 13%). Similarly, for PLA the top COEX gene group achieved a higher accuracy gain (242%) than the top GO group (197%), as shown in PSII and PLA resp.). These common genes in COEX based prediction were mainly enriched for many fundamental photosynthesis and growth related GO terms , but on average, COEX groups were larger than the GO groups for both traits. The 95th percentile of genomic heritability explained individually by the COEX groups  and likelihood ratio , indicating that incorporating meaningful prior subsets into the COEX model improved goodness of fit.The largest informative COEX groups for ΦTogether, our results illustrate that both of the meaningfully specific GO terms and more general COEX groups of genes with interrelated functions may improve GP predictive performance.Drosophila melanogaster using Gene Ontology data as biological prior knowledge  , but a number of gene groups were able to improve PLA at multiple measurements . Phenotyping at an extended scale and GP modeling thus provides an opportunity to obtain biological insights. As an alternative to modeling at each timepoint separately, a whole time series or growth curve can be used instead. We did not pursue this here, as time series data is not generally available in most practical scenarios and we were interested to learn whether performance improvement was specific to growth stages and conditions e.g., models for ΦPSII behaved differently under low and high light conditions.With recent technological advances in both field and controlled environment high-throughput phenotyping systems, phenotypes can be measured at unprecedented scales. Phenotypes can vary in space and time due to genetics and environment alone, genotype-by-environment (GxE) interactions as well as stochastic and development effects. Component variances due to these factors can be calculated by precise modeling. If multiple measurements are available, GP models can be developed on individual measurements, treated as individual phenotypes, or on derived parameters, e.g., growth curves. We found that at each measurement timepoint, at least some GO (in particular cellular component terms) or COEX group could help to improve performance, and some were more frequent . For exaHere, we mainly investigated two approaches to incorporate publicly available trait-specific biological information into GP, i.e., pre-selecting a list of genes and selecting sets or groups of genes based on predicted functional  or expression (COEX) information. The approach using predicted functional information proved to be more useful in this context, but more approaches and sources of information can also be incorporated with a focus on prioritizing biologically related genomic regions. Moreover, knowledge from multiple heterogeneous sources can be combined to further pinpoint potential QTLs, termed as poly-omics GP models , employing different prior statistical assumptions on these parameters. This makes the approach fairly simple and interpretable; therefore, biological knowledge can be incorporated straightforwardly by employing these statistical assumptions. But with the increase in the ratio between markers and available phenotypes, serious overfitting problems may be encountered in these models -based genomic prediction, as used in this work, makes use of raw genotypes and parameter regularization to estimate thousands of SNP marker effects using only a few hundred observations (PSII) and PLA. Significant improvement in prediction accuracy over the benchmark GBLUP model was obtained for several GO terms and COEX groups. This improvement is trait-specific and negatively correlates with genomic heritability; whereas, for projected leaf area we found more added value than for ΦPSII. Many known photosynthesis-specific GO terms lead to improvements, providing evidence of the potential usefulness of this approach in future breeding practice. We foresee incorporation of heterogeneous prior biological information into machine learning algorithms as an active area of research in future.The wealth of publicly available transcriptomics and Gene Ontology based prior biological knowledge can be incorporated for genomic prediction of photosynthetic light use efficiency of photosystem II electron transport  < 0.999, one was removed, using PLINKv1.9  dataset was obtained from Van Rooijen et al.  for 2 days and for four continuous days after induction of high light stress at 550 μmol m−2 s−1 to study the photosynthetic acclimatory response. We measured PLA every 3 h starting from the afternoon of day 22 after sowing until early morning of day 29 using the “Phenovator” high-throughput automated phenotyping system . The system design allows spatial uniformity and temporal reproducibility by minimizing the design parameter variances. Therefore, we expected low variances of interactions between genotype and the design parameters; whereas, within image position and sowing position could have larger main effects and thus could be corrected for. Phenotypic values were taken as the average of one to four replicates of Best Linear Unbiased Estimators (BLUE) using the linear mixed model adjusted for experimental design factors (x) and column (y) coordinate, the image position and the sowing block. Thus, the BLUE for phenotypic mean is calculated based on this equation, implemented in R with the lmer function  using the lme4 package  for both traits, except between measurements under low vs. high light conditions of ΦPSII; therefore, these were analyzed separately.Both traits, at all measurement times, showed approximately normal distributions , 14. The for PLA . CorrelaCo-expressed gene groups were obtained from the Arabidopsis expression compendium by Movahedi et al. . GO dataMoreover, a set of 7,242 photosynthesis related genes was manually compiled  using foThe Linear Mixed Model (LMM) with one random genomic component was used as baseline. This model (Equation 2), known as Genomic Best Linear Unbiased Prediction (GBLUP) , providing variance-covariance structure of genotypes calculated from all genomic markers and I is the identity matrix.Here, Accordingly, for each GO and COEX gene groups, another linear mixed model similar to GBLUP but with two random genomic components (Equation 3), known as Genomic Feature Best Linear Unbiased Predictor (GFBLUP)  and by the remaining markers , centered and scaled such that its ith column zi is the ith column vector of Z having minor allele counts  as entries and pi is the MAF of the ith marker. In our case, all genotypic locations were homozygous, so genotypes are coded as 0 or 2. For the GFBLUP model, the genomic relationship matrix Gf for each GO or COEX group was calculated from the markers linked to that group; Gr was constructed from the remaining markers.This model differs from GBLUP in that the total estimated genomic value s et al. . The genp < 10−5) and adjacent regions were merged if pBonferroni < 0.05. Subsequently, separate covariance matrices K1, K2,., KM were constructed for each region  based on its markers and genomic values g1, g2,., gM were estimated. The GRM based on all markers  was used if no region was found significant. The total genomic value is The MultiBLUP model (Equation 4) was constructed according to the Adaptive MultiBLUP strategy proposed by  for MultiBLUP.Variance components in all of these LMMs were estimated using the average information restricted maximum-likelihood (REML) procedure  between observed phenotypic values and predicted genomic values of the test population in each fold. The procedure was repeated 10 times, thus modeled predictive ability distributions consisted of 80 correlations or fewer if variances were over- or underestimated as described earlier by simulation studies  based on total number of GO/COEX groups multiplied by total number of time points  genotypes and using the remaining 43 for testing in each fold. The exact same accessions were used for both GBLUP and GFBLUP during each split to enable a fair comparison. Prediction accuracy of models was defined as Pearson correlation   having predictive abilities greater than the benchmark GBLUP  were filtered for subsequent analysis. Genomic parameters were calculated from variance estimates of both models to analyze only models passing the abovementioned filtering criteria. This includes total genomic heritability explained  we tested the null-hypothesis Revigo  plot in R, where maximum %gain in accuracy by each GO term was used to color the bubbles. GO terms enriched in COEX groups were found using the PANTHER classification system .All data and scripts have been uploaded to the Wageningen University & Research git server (MA and T-PN provided the genotype and phenotype datasets. MF performed the analyses. DR, AD, and HN were involved in designing the analyses and interpreting the results. WK helped with statistical analysis. MF wrote the manuscript with DR, AD, HN, and SM. All authors read the final 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.


We observed a high correlation between the meta-analysis results for baseline and longitudinal average BP (rho = 0.48). BP trajectory results were more correlated with those of average BP (rho = 0.35) than baseline BP(rho = 0.18). Heritability estimates trended higher for longitudinal traits than for cross-sectional traits, providing evidence for different genetic architectures. Furthermore, the longitudinal data identified up to 20% more BP known associations than did cross-sectional data. Our analyses of longitudinal BP data in diverse ethnic groups identified novel BP loci associated with BP trajectory, indicating a need for further longitudinal GWAS on BP and other age-related traits.Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with cross-sectional blood pressure (BP) traits; however, GWAS based on longitudinal BP have been underexplored. We performed ethnic-specific and trans-ethnic GWAS meta-analysis using longitudinal and cross-sectional BP data of 33,720 individuals from five cohorts in the US and one in Brazil. In addition to identifying several known loci, we identified thirteen novel loci with nine based on longitudinal and four on cross-sectional BP traits. Most of the novel loci were ethnic- or study-specific, with the majority identified in African Americans (AA). Four of these discoveries showed additional evidence of association in independent datasets, including an intergenic variant (rs4060030,  BP has high familial clustering3 with heritability estimates ranging from 30 to 60%2, pointing to genetic association studies as valuable tools for discovering genetic determinants of BP. To date, genome-wide association studies (GWAS) have identified more than 2,000 variants significantly associated with BP (p < 5 × 10–8)4; however, these genetic variants explain only approximately 3.5% of BP variance5. This suggests a need to go beyond the common designs for BP GWAS, which use cross-sectional data almost exclusively and are enriched for European-ancestry individuals.High blood pressure (BP) is a major risk factor for cardiovascular diseases (CVD), which are leading causes of premature death worldwide7, and, as expected, individuals who experience higher rates of BP increase are more likely to reach the elevated BP levels associated with increased risk of cardiovascular disease10. Despite this clear correlation between BP trajectory and cardiovascular-related outcomes, GWAS using empirical longitudinal BP data are currently lacking.Longitudinal data recording BP trajectory have shown progressive upward trends from childhood to middle age, with some evidence of plateauing at older ages11 and may outperform the commonly used cross-sectional designs13. Simulations suggest that longitudinal data can more accurately identify diastolic BP loci compared to baseline data, which performs better for systolic blood pressure13. These findings indicate that using longitudinal BP in GWAS may enable the discovery of loci14 that play key roles in BP trajectory and the risk of developing CVD. Furthermore, the vast majority of BP GWAS include only or mostly individuals of European ancestry. The inclusion of multiple ethnic groups in BP GWAS has allowed the discovery of novel loci that were not detected in Europeans2.Longitudinal designs for BP genetic associations can identify variants associated with BP trajectoryTo identify novel BP loci, we performed a series of ethnic-specific and trans-ethnic GWAS meta-analyses of longitudinal and cross-sectional data using 33,720 individuals from five longitudinal studies in the US and one in Brazil.15; (ii) the Bambui-Epigen Cohort Study of Aging (Bambui)16; (iii) the Health, Aging and Body Composition (HABC) Study17 and (iv) the Multi-Ethnic Study of Atherosclerosis (MESA)18. The studied individuals showed ancestry profiles consistent with expectations given their self-reported ethnic backgrounds , and higher by ~ 10 mm Hg compared to the other analyzed groups , the mean BP and prevalence of hypertension (> 65%) were similar even among different ethnicities. Furthermore, the longitudinal data showed that increasing baseline age is correlated with a lower slope of systolic and diastolic blood pressure (SBP and DBP) in European- and African-ancestry populations  or age + age2, as separate covariates, there was no difference between DBP trajectory estimates  from different ethnic backgrounds: 5304 African Americans (AA); 1439 Brazilians; 13,137 European Americans (EA); 703 Chinese Americans; and 1317 Hispanic Americans. These individuals were included from four longitudinal cohorts: (i) Atherosclerosis Risk in Communities (ARIC) Studynds Fig. . Interesons Fig. , consisttes Fig. .Table 1S. mean of longitudinal BP measurements) of (iii) systolic and (iv) diastolic BP. To facilitate comparisons between the longitudinal BP analyses and analyses conducted using a more standard cross-sectional approach, we also performed meta-analysis for cross-sectional SBP and DBP at baseline using the individuals included in the longitudinal analyses. We present associations from the discovery data that reached the standard genome-wide statistical significance (p < 5 × 10–8) and borderline genome-wide statistical significance (p < 9 × 10–8). For the statistically significant associations, we present the statistical significance for the three outcomes analyzed: trajectory, average, and baseline  of (i) systolic and (ii) diastolic BP, and the averages (i.ene Table .Table 2Gp < 0.05) in the cross-sectional analyses, consistent with the fact that the cross-sectional data are included in the calculation of average BP. This trend of overlapping associations was not observed for BP trajectory  associated with BP outcomes Table , of whicy Tables  and S1. y Tables . We idenOut of the 19 variants with a GWAS statistically significant association (or borderline) in the discovery analysis, 10, 11, and eight variants were available (passed frequency and QC criteria) in the EA, AA, and Latinos/Brazilians datasets, respectively Fig. . We idenC8orf37-AS1 was associated  with decreasing DBP trajectory in AA populations . This variant was also associated in AA with decreasing SBP trajectory  and decreasing SBP average , and associated with decreasing SBP average  in Hispanic women from the Women's Health Initiative (WHI). The rs4060030 variant is involved in epigenetic regulation in several tissues, including cardiac, and differentially binds 14 regulatory motifs, including YY1, for which expression is increased in hypertensive elastic arteries23. Furthermore, this variant is a statistically significant eQTL of an uncharacterized gene (LOC101927039) in the heart atrial appendage24.The C allele of an intronic variant (rs4060030) in the RNA gene ons Fig. . This asp = 4.2 × 10–8) in the AA meta-analysis with replication observed in Hispanics from the WHI study  , which regulates the accumulation of adipose tissue29.The SNP rs74838709 was genome-wide associated with baseline SBP  Fig. . rs74838p = 3.9 × 10–8)  and was borderline in the UK BioBank , which has low-density lipoprotein particle receptor activity30.An intronic deletion rs565370904 was associated with SBP trajectory in EA populations  Fig. . The rs5p = 4.3 × 10–8)  associated with DBP trajectory in EA  Fig. . rs19984–8) Fig. . rs19984–6) are shown in Table –8) two common frequency loci, represented by the variants rs6599176 and rs11105368 in the genes ULK4 and ATB2B1, respectively. These two loci have been consistently reported to be associated with BP and hypertension32.In addition to our discovery and replication design, we also conducted meta-analyses of discovery and replication studies together. All statistically significant and suggestive results (p < 1 × 10h2), we used European American (EA) individuals from the ARIC and MESA studies. Regardless of antihypertensive medication use, DBP outcomes tended to have higher h2 than SBP outcomes  was higher than the h2 of baseline DBP . The use of antihypertensive medication appeared to systematically decrease h2. We did not evaluate h2 in AA individuals due to limited sample size.To calculate SNP heritability (es Table . Notably4 (Methods). From those associations with reported risk alleles, 1957 variants  were present in our combined data and 317 associations had the risk allele associated in the same direction reported in the GWAS Catalog , the highest number (132) was identified by the trajectory, followed by average (80) and baseline (64). These results suggest that the BP longitudinal outcomes may have greater power to identify variants associated with BP.The power of longitudinal compared to cross-sectional data to identify known BP loci was evaluated in GWAS Catalog associated BP traitslog Fig.  and S8. log Fig. , for botlog Fig. . The lonp-values  longitudinal data identified novel BP loci not identified using cross-sectional data; (ii) despite the correlation between longitudinal and cross-sectional BP outcomes, there is evidence for different genetic associations underlying these traits; (iii) longitudinal BP outcomes identified more of the previously published BP associations than cross-sectional outcomes identified; (iv) as was found with cross-sectional associations, most of the associations identified using longitudinal outcomes were ethnic-specific; and (v) longitudinal BP traits had higher heritability than baseline BP traits.13. We identified three novel BP trajectory associations with additional evidence of association in replication datasets  using cross-sectional data, they did not reach genome-wide statistical significance . These results indicate that novel variants identified by the longitudinal analysis could not be identified using the standard cross-sectional design alone and that, despite some degree of overlap between longitudinal and cross-sectional data, there is evidence for different genetic associations underlying these traits. Genetic variants associated with BP trajectory but not with cross-sectional measurements may be of particular significance, as understanding the underlying biological mechanisms could give us new insight into causes of steeper declines in BP over time and could potentially lead to new therapies for hypertension prevention. Furthermore, our results warrant new studies to address the question of whether BP trajectory-associated variants, singly or in aggregate, are also associated with the development of cardiovascular diseases such as myocardial infarction and stroke.As expected, we observed correlation between the meta-analyses results using longitudinal and cross-sectional BP data, with the cross-sectional results more correlated with average than trajectory results. Although most of the statistically significant associations using longitudinal data showed some evidence of association  included in our discovery data were not using antihypertensive medications, we recognize their potential effect on modeling BP trajectory. The effects of BP-lowering treatment and regimen adherence and compliance on BP outcomes are problematic, not only for longitudinal studies but also for cross-sectional BP studies37. Furthermore, our longitudinal data showed that BP trajectories show more non-linear (quadratic) behavior on an individual basis  the Bambui-Epigen Cohort Study of Aging (Bambui)16; (iii) the Health, Aging and Body Composition (HABC) Study17 and (iv) the Multi-Ethnic Study of Atherosclerosis (MESA)18.Four studies were included in the discovery analyses: (i) the Atherosclerosis Risk in Communities (ARIC) Study15 is a prospective cohort study of atherosclerosis (dbGaP Study Accession: phs000090.v1.p1) including 15,792 African Americans (AA) and European Americans (EA) aged 45–64 at baseline, residing in four communities in the United States . The ARIC study participants were examined up to four times, with visits occurring in 1987–1989 (baseline), 1990–1992, 1993–1995, and 1996–1998.The ARIC study38 is a population-based cohort study of aging in the city of Bambui in Southeast Brazil. The population eligible for the cohort comprised all residents aged 60 years and over on 1 January 1997  and 92% of the eligible population participated in the study16. BP was measured at baseline (1997) and in three subsequent waves 38. This cohort is composed of admixed individuals with European (78.5%), African (14.7%), and Amerindian (6.7%) ancestries39.The Bambui study17 is a cohort study of aging (dbGaP Study Accession: phs000169.v1.p1) comprising AA and EA  who were recruited in 1997–1998 and had annual clinical follow-up for six years.HABC18 is a prospective study of atherosclerosis (dbGaP Study Accession: phs000209.v13.p3) involving a population-based sample of 6,814 asymptomatic men and women aged 45–84. Participants included African Americans, Hispanic Americans, Chinese Americans, and European Americans from six field centers in the United States . The first examination (baseline) took place from July 2000 to July 2002 and was followed by three examination periods that were 17–20 months in length.MESA40 replication analysis: the Framingham Heart Study (FHS) (dbGaP Study Accession: phs000007.v30.p11) and the Women's Health Initiative (WHI) (dbGaP Study Accession: phs000200.v1.p1). Exact replication is when the replication dataset replicates  the same (“exact”) variant and trait identified during the discovery analysis. Local replication accounts for possible differences in LD patterns between studies and therefore interrogates a set of variants in LD (r2 > 0.3 in the discovery dataset) with the exact variant. The p-values for the associations of these correlated variants are adjusted for the effective number of tests, following the example in40. In addition, we performed in silico replication by looking up the BP results (summary statistics) in public cross-sectional studies: UK BioBank41, The Uganda Genome Resource42 and The Africa America Diabetes Mellitus study43.We used two additional longitudinal datasets to perform exact and local39. For the ARIC and MESA studies, we used the genotype data (Affymetrix Genome-Wide Human SNP Array 6.0) from 12,773 and 6814 participants, respectively. For the HABC study, we used the genotype data (Illumina Human1M-Duo) from 2870 individuals. We split all genotype study datasets by ethnicity  before quality control, phasing, imputation, and downstream analyses. The Brazilian Bambui Study could not be meaningfully subdivided by ethnicity because this population is highly admixed39.We used the genotype data (Illumina Infinium Omni2.5) from 1442 participants from the Bambui Study in the context of the EPIGEN project44 by applying the following filters: minor allele frequency (–maf 0.01), missing rate per variant (–geno 0.05), missing rate per individual (–mind 0.05), and Hardy–Weinberg equilibrium (–hwe 0.000001).For all genotype datasets, we applied standard data quality control and imputation procedures. Quality control was performed using PLINK software2 > 0.2, PLINK filter—indep-pairwise 50 10 0.2). Considering that the datasets included in this study have multiple ethnicities, we performed principal component analysis (PCA) to assess population structure. We merged each study genotype dataset with the 1000 Genomes Project Phase 3 data45 and performed PCA using EIGENSTRAT46. To infer the level of relatedness among individuals, we calculated the pairwise kinship matrix for each dataset using EPACTS47.Before inference of population structure and relatedness, we pruned strand-ambiguous SNPs and SNPs in linkage disequilibrium (rhttps://genome.sph.umich.edu/wiki/CheckVCF.py) and bcftools48 to check and correct for monomorphic sites, consistency of reference alleles with the reference genome, variants with invalid genotypes, and non-SNP sites.To generate valid VCF files before imputation and association tests, we used the tool checkVCF.py (https://imputation.sanger.ac.uk/) for phasing and imputation of the genotype data, using the EAGLE249 and PBWT50 software, respectively. For Chinese, European, and Hispanic Americans, we used the 1000 Genomes Phase 3 reference panel, and for African Americans and Brazilians, we used the African Genome Resources panel51. For the HABC genotype data, we used the Michigan Imputation Server (https://imputationserver.sph.umich.edu/) with EAGLE2 phasing49. The HABC African Americans were imputed with the CAAPA African American reference panel and the European Americans were imputed using the Haplotype Reference Consortium (HRC r1.1 2016) panel.For the ARIC, Bambui, and MESA studies, we used the Sanger server  to obtain the SBP and DBP measurements for each exam. For the exams with no data for the third BP measurement, we included only the second measurement. All the BP outcomes were obtained or calculated by study and ethnic group.We used two different longitudinal blood pressure (BP) outcomes, trajectory and average, obtained for systolic and diastolic BP (SBP and DBP) measurements across consecutive exams of each participant. To reduce the bias known as the white coat syndrome53 to assess cognitive trajectory in Brazilians. We used the linear mixed models function implemented in the R package lme454 to fit and assess the per-individual systolic and diastolic BP trajectories  for individuals with at least two BP measurements during follow-up. Specifically, we fitted the mixed effects model (1) below for individual j at visit i, where Xk  are n covariates with fixed-effects βk .0j is a random variable that allows variation around the intercept β00 for each individual j, u1j allows for variation around the coefficient β1, and eij is the subject-level residual. In line with standard practice, we assumed each of the random effects to be independent and normally distributed with unstructured correlation between u0j and u1j. We then used the predicted random effect u1j from model (1) as the phenotype for GWAS. Models were adjusted for age2, sex, BMI, use of antihypertensive drugs as a binary covariate, and population structure using the two first principal components (PCs). We used visit-specific covariates for those  that may vary over time.To assess the BP trajectory outcome, we followed the methodology used in Gouveia et al.The BP average outcome was obtained by calculating the average of blood pressure measurements (SBP and DBP) over time. Then, we used the BP longitudinal trajectory and average values as outcomes for our analyses. To compare the longitudinal BP outcomes with cross-sectional BP outcomes, we also performed the analyses using the baseline BP measurements as a cross-sectional outcome using the same set of individuals included in the longitudinal analysis Table .47. For cross-sectional and average BP, models were adjusted for baseline age2, sex, BMI (except for average BP), use of antihypertensive drugs, and population structure using the two first principal components (PCs). We used average BMI in the average BP models. To avoid over-adjustment, models previously adjusted during the estimation of BP trajectory (see previous section) were not adjusted in the association analysis. To account for relatedness among samples, kinship matrices were incorporated as random effects in association analyses. Markers with minor allele frequency > 0.01 and imputation info score > 0.3 were retained for the GWAS analyses. We used genotype dosages instead of hard genotype calls to allow for the uncertainty of the imputed calls to be considered within the association model. From each GWAS results, we extracted summary statistics  to perform fixed-effect meta-analysis weighted by sample size of each study using METAL55. We set the mode HETEROGENEITY to determine whether observed effect sizes (or test statistics) were homogeneous across samples. Genome-wide statistical significance for the meta-analysis was set at a p-value of 5 × 10–8 and borderline statistical significance was set at a p-value of < 9 × 10–8. We considered an association from meta-analysis to be novel if no previous BP genome-wide associations with cross-sectional BP were reported in the GWAS Catalog (reference file “All associations v1.0.2”) within ± 250 kb of our detected association.We performed genome-wide association studies (GWAS) by cohort and by ethnic group Table  followed56, Ensembl24, the human genome browser at UCSC57, GTEx Portal release V758, HaploReg v4.159, GWAS catalog4, PANTHER60, STRING61, International Mouse Phenotyping Consortium (IMPC)62 and Rat Genome Database63.For our comprehensive annotation of the statistically significant associations, we utilized a series of sources: dbSNP4 . From this database, we considered only the statistically significant associations (p < 5 × 10–8) for which the risk allele was reported, with the following disease/trait terms: (i) “blood pressure”, (ii) “systolic blood pressure”, (iii) “diastolic blood pressure”, (iv) “systolic blood pressure change trajectories”, or (v) “pulse pressure”. Then, we evaluated 1957 GWAS Catalog associations for association using our meta-analysis data. We considered identification successful for associations with p < 0.05 for the same risk allele and the same direction of effect as reported in the GWAS Catalog.To test if our ethnic-specific and trans-ethnic meta-analysis approaches using longitudinal and cross-sectional data identify previously published associations with BP, we used the GWAS Catalog2 > 0.8, PLINK filter –indep-pairwise 50 10 0.8). We used the GCTA-GREML method64 to estimate the heritability of the longitudinal and cross-sectional BP outcomes adjusted for age2, sex, BMI, medication, and study. Because this method assumes unrelated individuals, we removed one of a pair of individuals with estimated relatedness > 0.2, maximizing the samples remaining for analysis64.For the heritability analysis, we used the European Americans (EA) and African Americans (AA) from the ARIC and MESA studies, since these samples have similar BP characteristics Table . The genAll dbGaP studies (dbGaP Study Accession described in the Methods section) obtained ethical approval from the relevant institutions and written informed consent from each participant prior to participation. We obtained approval for controlled access (protocol number: 12-HG-N185) of each of the dbGaP studies. Brazil’s national research ethics committee approved genotyping as part of the Epigen-Brazil protocol , resolution 15,895). All methods were performed in accordance with the CONEP guidelines and regulations.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


Phaseolus vulgaris L.) is one of the most important crops worldwide and is considered an essential source of proteins, fibers, and minerals in the daily diet of several countries. Nitrogen (N) is considered the most important nutrient for common bean crop. On the other hand, the reduction of chemical fertilizers is a global challenge, and the development of cultivars with more N use efficiency (NUsE) is considered one of the main strategies to reduce the amount of N fertilizers. Genetic progress of NUsE has been reported in several crops; however, there was still no quantity in common bean. In this study, our goal was to analyze the genetic progress of seed yield (SY) and NUsE-related traits of 40 carioca common bean cultivars release from 1970 to 2017 in eight environments under low (zero) or high N (40 kg ha−1) in top-dressing. Genetic progress, principal component analysis, correlations among traits, and cultivar stability were analyzed using Bayesian approaches. The lowest values of the deviance information criterion (DIC) for the full model tested indicated the presence of the genotype × N × environment interaction for all evaluated traits. Nitrogen utilization efficiency (NUtE) and nitrogen uptake efficiency (NUpE) were the traits that most contributed to discriminate cultivars. The genetic progress of SY under high N  was similar to that obtained in low N conditions . These results indicate that modern cultivars do not demand more N fertilizers to be more productive. In addition, we observed a high genetic variability for NUsE-related traits, but there was no genetic progress for these variables. SY showed negative correlation with seed protein content (Prot) in both N conditions, and there was no reduction in Prot in modern cultivars. Both modern and old cultivars showed adaptability and stability under contrasting N conditions. Our study contributed to improve our knowledge about the genetic progress of common bean breeding program in Brazil in the last 47 years, and our data will help researchers to face the challenge of increase NUsE and Prot in the next few years.Common bean ( Phaseolus vulgaris L.) is considered the main legume species for human consumption and represents an essential source of proteins, carbohydrates, fibers, and trace minerals in several countries worldwide (−1 year−1 (carioca (cream seed coat with brown stripes) and black beans, which represent around 70 and 15% of total common bean production, respectively  interacting symbiotically with Rhizobium genus bacteria (Common bean plants can fix atmospheric nitrogen (Nbacteria . This sybacteria . N is thbacteria . Therefobacteria .NUsE can be divided into two components : (i) N uInformation on the quantification of genetic progress for any crop is important for plant breeders, allowing them to design future breeding program strategies . In Braza priori information  were evaluated , Londrina, Brazil, and subsequently multiplied in order to standardize seed germination. Information about the genealogy of the cultivars is available in Forty Brazilian valuated Table 1.−1 (urea form) at V3 stage development, while the experiments under low N did not receive N top-dressing. In all experiments, the crops were fertilized at sowing with 300 kg ha−1 of NPK (04–30–10).Cultivars were evaluated at the Experimental Stations of IDR-Paraná in Londrina and Ponta Grossa during the rainy season of 2017 and in Santa Tereza do Oeste and Ponta Grossa in the dry season of 2018. Soil physical–chemical analyses and other characteristics related to the assessment sites are shown in 9 development stage) from each experimental plot. Seeds and aerial part  were dried in an oven at 70°C for 72 h to determine later the seeds and shoot dry biomass. These samples were also crushed separately using a Willey MA340 type knife mill  to determine seed and shoot N content by the Kjeldahl method  was obtained after manual removal and mechanical threshing of plants from the two central rows of each plot. NUsE components were determined according to Seed yield  Full model: considering triple interaction among genotype (G) × nitrogen (N) × environment (E); (ii) Reduced model 1: considering double interaction between G × N; (iii) Reduced model 2: considering double interaction between G × E; and (iv) Null model: considering interaction absence among factors G, N, and E. The Full model follows the mathematical model below:µ is the overall mean, ig is the random effect of genotype i, j/k/mb is the random effect of block j within environment k and within N fertilization m, ke is the fixed effect of environment mk, n is the fixed effect of N fertilization m, ikge is the random effect of interaction between genotype × environment, imgn is the random effect of interaction between genotype × N fertilization, kmen is the fixed effect of interaction between environment × N fertilization, ikmgen is the random effect of interaction among genotype × environment × N fertilization, and ijknϵ ~ N is the random effect of error associated with each experimental plot.Where: a posteriori distributions were performed considering non-informative a priori distributions for all model parameters using R software (https://www.r-project.org/) through the ‘MCMCglmm’  between DIC values of models a and b are given by D = | aDIC – bDIC |, and thus, if D < 5, there is no significant difference among the compared models; if 5 ≤ D ≤ 10, the difference is significant; and if D > 10, the difference is highly significant.The models tested were compared by deviance information criterion (DIC) as proposed by a posteriori distributions were performed considering non-informative a priori distributions for all model parameters. A total of 100,000 values were generated by MCMC (Monte Carlo Markov Chain) process, assuming a burn-in period and thinning interval of 10,000 and 10 iterations, respectively. The MCMC convergence was verified using the Correlations and principal component analysis (PCA) were performed using ‘BayesianFirstAid’  and ‘bPCGenetic progress was calculated for each trait evaluated in both N conditions using the following simple linear regression model:β0 is the intercept or linear coefficient, β1 is the slope or angular coefficient to adjust the data of variable yi as a function of Xi (year of cultivar release), and iϵ are errors normally distributed with mean zero and variance σ², that is iϵ ~ N. The parameter σ² represents a variability in which the results differ from their predictions based on the model. This model can also be described as yi ~ Normal (β0 + β1Xi + σ²). The difference between genetic progresses (Δβ1) at high and low N fertilization was considered significant when there was no overlap between their HPD intervals.Where: a posteriori distributions were performed considering non-informative a priori distributions for all model parameters using the ‘MCMCglmm’  method was used to study cultivar stability following the model below:n is the vector of the order n × 1, µ is the overall mean, X1 is the matrix of genotypes of order n × g, τ is the effect vector g × 1 for genotypes, X2 and δ are the matrices for environments of the order n × a and the effect vector a × 1 for environment, respectively. kλ is the singular value for the kth principal component, t is the number of multiplicative terms [t ≤ min  − 1), kα and kγ are the singular vectors of k for genotypes and environment, respectively; and ϵ is the vector n of error effect. Vector ϵ has a multivariate normal distribution with zero mean and variance–covariance matrix y also has a multivariate normal distribution.Where: 1y, given that µ, τ, δ, λ, α, γ, and For the BAMMI model, the estimation of the parameters of the above equation model assumes that the conditional distribution of nI is the identity matrix of order n. The a priori distributions used for the parameters were the same as those presented by μ and σ2 denote mean and variance of the a priori distribution of whatever parameter is shown as subscript:Where: kλ > 0 and k–λ1 ≥ kλ;k ~ spherical uniform distribution on the corrected subspace;σk ~ spherical uniform distribution on the corrected subspace; andγN denotes the normal distribution, +N is the positive normal distribution, and Inv − Scaled − χ2 is the inverse χ2 distribution. In this study, the a priori distributions were non-informative. The value zero was used as prior distribution for the mean in all genotypic and environmental effects and high values for the variances, resulting in: µµ = 0, τµ = 1g × 0, δµ = 1a × 0 and λkµ = 0, and for the variances 15.Where: a posteriori distribution was estimated by:The A total of 1,000,000 values were generated by MCMC (Monte Carlo Markov Chain) process, assuming a burn-in period and thinning interval of 100,000 and five iterations, respectively. The MCMC convergence was verified using the DIC results showed a positive evidence of a triple interaction among genotype, environment, and N levels (G × E × N) for all evaluated traits since the full model presented lower DIC values Table 3.The overall mean for all evaluated traits and their respective HPD intervals are shown in r) and their respective HPD intervals among traits evaluated are shown in r = 0.47, 95% HPD = 0.36; 0.58), and NUpE  under low N. NUsE showed a positive correlation with NUpE under low  and high N  conditions. In addition, we observed a positive and negative correlations between NUsE and NUtE under high  and low N  levels, respectively.Correlation coefficients  or 0.53% year−1 . Under low N condition there was a progress of 10.2 kg ha−1  or 0.48% year−1 . There was no difference between the a posteriori slope estimates (Δβ1), indicating that the genetic progress of SY was the same in both N conditions.Genetic progress estimates for the traits evaluated under high and low N are shown in aposteriori means of genotypic (τ) and environmental (δ) effects and their respective HPD intervals are shown in µ) of SY from 40 carioca common bean cultivars evaluated in eight contrasting N conditions was 2,155.71 kg ha−1 . Twenty-one cultivars showed a posteriori positive means with HPD intervals that did not overlap the zero value , IPR Quero-quero , IPR Bem-te-vi , IAC Sintonia , IPR Campos Gerais , and BRS Notável . It is worthwhile to mention that not all environments under high N conditions were considered as favorable environments since they did not show a posteriori positive means  are shown in Comp.1 and Comp.2 coordinates allowed us to infer about the genotype specific adaptation to certain environments Figure 2r = 0.50, 95% HPD = 0.37; 0.62), while weak correlations were observed in STO18 , LD2017 , and PG2017 .The environmental correlations of SY between high and low N conditions are shown in carioca common beans in Brazil over the past 47 years under N contrasting conditions. In Brazil, these studies have mainly focused on the impact of breeding programs on plant architecture and yield components. For example, −1), seeds per plant (4.5% year−1), mass of seeds (2.08% year−1), and tolerance to lodging (2.0% year−1) (Our study was the first to quantify the genetic progress of  year−1) . In comm year−1) .The best adjustment of the full model for all agronomic traits evaluated indicated that cultivars have a differential behavior based on the combination of N fertilization levels and environments in which the cultivars were submitted. The interaction between genotypes and environments is frequently observed in common bean breeding programs in Brazil  and worlcarioca common bean cultivars which observed an increase of approximately 21.71% in plants under high N condition (SY increased under high N top-dressing (17.51%), corroborating a previous study that reported N as the macro-element that most influences SY in common bean crop . Similarondition .carioca common bean cultivars in this study were higher under low N compared to the high N condition. Studies have already been reported that plants improve their NUsE under low N availability, especially because under high N conditions this element can be lost by leaching, denitrification, and volatilization processes  observed 18.33 and 19.36% of Prot in common beans cultivated in low and high N condition, respectively, where 17 genotypes did not show an increase in Prot in the presence of N top-dressing  similar to those obtained in the present study using 140 common bean recombinant inbred lines (RIL) under contrasting N condition  and high N , indicating that modern cultivars do not demand more N fertilization to be more productive than cultivars released earlier. Around ten years are needed to develop a new common bean cultivar . As the carioca and black common beans cultivars have already been reported (−1 (17.3 kg ha−1). In the IAC bean breeding program, genetic progress was 1.07% year–1 (13.17 kg ha–1) between 1989 and 1996. Differences between germplasm, environments, methodologies, and statistical methods are the main reasons for the differences observed among studies (Genetic progress estimates for SY using Brazilian reported , but not studies .We did not detect a significant difference in Prot content among modern and old cultivars. Our hypothesis is that selection on Prot may only result in the elimination of low Prot lines and not in increasing Prot, likewise reported in wheat  and oils2 by symbiosis. However, NUsE genetic progress and their components have been reported in other crops that do not have this N2 fixation ability, such as, wheat (Although there is wide genetic variability observed for NUsE, NUtE, and NUpE in this study, no genetic progress was detected for these traits. Possibly the common bean breeding programs are not focusing in the improvement of NUsE since common beans are able to fix Ns, wheat , maize (s, wheat , and oils, wheat . In addis, wheat  and can s, wheat .In our study, the genetic progress of HI was absent and indicated that superior genotypes selection was not performed focusing on this agronomic trait. However, the genetic progress of HI has been historically reported in several other crops . In a meThe development of genotypes with environment adaptive plasticity, good stability, and high SY mean is one way to mitigate G × E interaction effects . StatistIn the present study, the large number of cultivars considered stable is due to the selection for stability in several environment conditions by their respective breeding programs. In addition, there was no relationship between stability and release year since both modern and old cultivars showed high stability. The modern cultivars IPR Sabiá, IPR Quero-quero, IPR Bem-te-vi, IAC Sintonia, and IPR Campos Gerais were the most adapted and stable in the present study. These results confirm that common bean breeding programs are developing cultivars that combine SY and stability for different environmental conditions.We observed that not all environments under high N condition were considered favorable environments by BAMMI. In addition, the positive correlations between high and low N for all environments explain the similar genetic progress among them for SY. These results indicated that even though common bean breeding programs have made selection under N fertilization, modern cultivars do not require high N levels to achieve their genetic potential. Similar results were already reported in maize  and wheacarioca common bean cultivars launched from 1970 to 2017 under N contrasting conditions. Among the traits evaluated, there was genetic progress only for SY under high and low N in top-dressing. The similar genetic progress in both N conditions rejected our hypothesis that modern cultivars are more N-dependent to reach their productive potential. Our results indicate that the challenges for common bean breeders for the coming years will be to improve the NUsE of new cultivars in order to reduce the dependence on N fertilizers as well as to increase the levels and/or quality of Prot in the seeds.Our study is the first to quantify the genetic progress for SY and NUsE-related traits in Brazilian The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.DZ, IM, VM-C, and LG conceived and designed the experiments. DZ, IM, and JD conducted the experiments and collected the data. DZ and GF performed the statistical analyses. DZ wrote the original draft. S-IS, VM-C, JN, GF, CS, and LG read and edited the manuscript. All authors contributed to the article and approved the submitted version.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.


Genetic studies of the transcriptome help bridge the gap between genetic variation and phenotypes. To maximize the potential of such studies, efficient methods to identify expression quantitative trait loci (eQTLs) and perform fine-mapping and genetic prediction of gene expression traits are needed. Current methods that leverage both total read counts and allele-specific expression to identify eQTLs are generally computationally intractable for large transcriptomic studies. Here, we describe a unified framework that addresses these needs and is scalable to thousands of samples. Using simulations and data from GTEx, we demonstrate its calibration and performance. For example, mixQTL shows a power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. To showcase the potential of mixQTL, we apply it to 49 GTEx tissues and find 20% additional eQTLs  that are significantly more enriched among trait associated variants and candidate cis-regulatory elements comparing to the standard approach. Methods for eQTL mapping from total and allele-specific gene expression typically are too computationally heavy to apply to large scale studies. Here, the authors describe a computationally efficient method to identify eQTLs, fine-map and predict expression from total and allele-specific gene expression Association-based approaches correlate genetic predictors of gene expression with complex traits of interest. Colocalization-based approaches rely on high-quality eQTL mapping and fine-mapping results to identify potentially causal genes.Genome-wide association studies (GWAS) have identified tens of thousands of genomic loci associated with complex traits, but most of these loci lie in noncoding regions of the genome, indicating transcriptome regulation as a potential key driver of disease biology. Multiple methods have been developed to integrate GWAS results with expression quantitative trait loci (eQTLs) and inform mechanisms underlying GWAS loci. Two strategies are commonly employed: (1) association-based approaches including PrediXcan7, WASP8, and RASQUAL9). However, running these methods on sample sizes beyond a few hundred is generally computationally intractable, and as a result they have not been applied to large-scale studies like GTEx, which includes over 15,000 samples across 49 tissues. For fine-mapping, two approaches that combine both allele-specific expression and eQTL mapping via meta-analysis have been recently proposed11. However, to our knowledge, no existing method provides a scalable unified framework combining total and allele-specific counts with explicit multi-SNP modeling for QTL mapping, fine-mapping, and prediction.In addition to gene expression levels measured by total read counts, allele-specific expression (the relative expression difference between the two haplotypes) provides valuable additional information that can be leveraged to improve eQTL mapping and fine-mapping. Several methods have been proposed to combine total and allele-specific read counts for QTL mapping, such as TReCASE12, we derive two approximately independent equations for allelic imbalance (read count ratio between the two haplotypes) and total read count. In this work, we develop a unified framework and computationally efficient algorithms combining total and allele-specific reads for QTL mapping, fine-mapping, and prediction. We demonstrate the resulting gain in performance with simulations under a range of different settings, applications to GTEx v8 data13, and comparisons to a large-scale eQTL meta-analysis from eQTLGen14. We also generated mixQTL results for the full set of GTEx data and make this resource publicly available. The software, simulation, data preprocessing, and analysis pipelines can be found at https://github.com/hakyimlab/mixqtl15, https://github.com/liangyy/mixqtl-pipeline16, and https://github.com/liangyy/mixqtl-gtex17. A computationally efficient GPU-based implementation of mixQTL has been embedded in tensorQTL https://github.com/broadinstitute/tensorqtl18.By assuming a log-linear model for transcript expression levels with independent reads from each haplotype and weak genetic effects, as proposed in ref. 12. For a given gene, we modeled the haplotypic read count h of individual i asLi is the library size for individual i, θi0, is the baseline abundance , To develop a computationally efficient approach that integrates total and allele-specific count data, we assumed multiplicative cis-regulatory effects and noise, similarly to the model proposed in ref. ϵ(1)⊥⊥ϵ(2). Here, we let the ϵ terms have variance inversely proportional to the actual count and by doing so, we ensure that the variance of the count scales approximately linearly to the mean of the count as demonstrated in Supplementary Notes Calculating the total read count as the sum of the two haplotypic counts and assuming weak cis-regulatory effects, we derived an approximately linear model for the logarithm of the haplotypic and total read counts (see details in Methods and Supplementary Notes zi (zi). Then, we merge the total count term zi into one term ϵasc.We further simplified the models by combining the two allele-specific counts and defining the baseline abundance variation as a random effect Xi1, ⋯ , Xip) and genetic effects  instead of the scalar values above. Here, p represents the number of genetic variants in the cis-window of the gene under consideration ,,4), solv19 and elastic net20. We term these methods mixFine and mixPred, for fine-mapping and prediction, respectively.For the fine-mapping and prediction problems, we also leveraged the approximate independence of the allelic imbalance and total read count equations. We used a two-step approach in which we first scale the two equations so that they become independent data points with equal variances. In the second step, we combined these data points into an augmented dataset and applied the existing algorithms SuSiETo assess the benefits of this unified framework over using only total read counts or allele-specific expression, we simulated haplotypic reads according to the framework illustrated in Fig. θi0, in Eq.  and used a series of expression levels : from 5To assess the gain in power of combining total and allele-specific read counts, we simulated 200 replicates with allelic fold change varying among 1, 1.01, 1.05, 1.1, 1.25, 1.5, 2, 3. We compared mixQTL with two methods: using either only allele-specific counts (ascQTL) or total counts (trcQTL). See details in Supplementary Notes All three methods had calibrated type I errors Fig. . mixQTL θ controlling the expression level, ascQTL yielded much higher power for higher expression levels. In contrast, trcQTL was less sensitive to the number of reads observed under the range of read counts in our simulation settings. Such sensitivity differences between ascQTL and trcQTL are consistent with the nature of count data, where the magnitude of the noise is inversely related to the count.The power of ascQTL was sensitive to the number of allele-specific reads, as expected. As shown in Fig. 21 within ±1 MB cis-windows of 100 randomly selected genes. We applied mixFine and trcFine , corresponding to high-quality allele-specific signals. The gain in power decreased with larger sample sizes. The increased power was also reflected in the number and size of 95% CSs containing the true signals. As shown in Fig. t-test p = 5.88 × 10−29) demonstrating that mixFine can pinpoint causal SNPs more accurately.To realistically simulate LD structure, we used the genotypes of European individuals from the 1000 Genomes projects phase 311.Overall, the combined method was more powerful for identifying causal variants, which is consistent with recent reportsUsing the data from the fine-mapping simulation, we tested the performance of mixPred and trcPred . We included variants within a ±1 Mb cis-window around the transcription start site of each gene. Although mixQTL can be applied to all genes regardless of the number of allele-specific counts, we focus on examining the benefit of integrating allele-specific information and therefore limit these comparisons to genes with sufficient allele-specific counts, based on the following criteria: (1) at least 15 samples having at least 50 allele-specific counts for each haplotype; and (2) at least 500 samples having a total read count of at least 100. Five thousand seven hundred and thirty four (28%) genes passed these filters. We then stratified these genes by their median expression level (read counts) into low, medium, and high expression tertiles. For genes with below-threshold allele-specific counts, the calculation can be performed using total read counts only, such that all genes considered using the standard approach are also tested in mixQTL. Performance for these genes was similar to the standard eQTL approach  used by the GTEx consortiumAll three approaches mixQTL, aseQTL, and trcQTL were relatively well-calibrated when permuting data in four randomly selected genes  and precision-recall (PR) curves to compare the performance of mixQTL and standard eQTL approaches using the eQTLGen “ground truth” and “non-significant” eQTLs. We found that mixQTL achieves higher performance in both ROC Fig. d and PR To determine whether the eQTLGen-based analysis above depended on the selected random subset of cis-eQTLs, we repeated the analysis for multiple samplings of eQTLGen results and found no substantive differences in the results.To compare the performance and computational cost of mixQTL and the existing QTL mapping approaches which can leverage both total and allele-specific counts, we ran RASQUAL on two of the GTEx tissues, kidney cortex  and whole blood .n = 706) taking 0.34 seconds per gene on average.mixQTL’s runtime scaled linearly as a function of sample size , and 60 PEER factors. We refer to the latter as the “standard approach” below for simplicity.We applied mixFine to the GTEx v8 whole-blood RNA-seq data, using the same subset of genes with high expression and allelic counts that were used in the comparison of mixQTL vs. standard eQTL approach above. We compared mixFine to the SuSiE fine-mapping approachTo compare the power of causal variant detection, we performed a subsampling analysis on a random subset of 1000 genes. First, we defined “consensus SNPs” as the variants with PIP > 0.5 in both mixFine and the “standard approach” using all samples. Similarly, a variant was defined as “top SNP” if it was the most significant variant within the 95% CS for both mixFine and the “standard approach”. Then, we compared how well the “consensus SNPs” and “top SNPs” were detected by mixFine and the standard fine-mapping approach using only a subset of samples. We subsampled to 90%, 80%, ⋯ , 30% of samples, and repeated each random subsampling step 10 times.22, and the GWAS catalog  folds. We applied this evaluation pipeline to mixPred and the standard approach (elastic net as in ref. 1) on the same 1000 genes as the subsampling analysis with k = 10, 9, …, 2 . At the same sample size, we observed, on average, significantly higher performance in mixPred as compared to the standard approach, and the performance gain was greater for smaller sample sizes  can be scaled to much larger sample sizes (thousands) due to the underlying log-linear approximation. By assuming weak multiplicative genetic effects consistent with observations  can simply be input into existing fine-mapping and prediction algorithms.9, mixQTL has slightly lower power  to represent the genetic effect on cis-expression. We denote θi0, as the baseline abundance of the transcripts originating from each of the gene haplotype without considering genetic effect. Let β be the genetic effect of a variant of interest, which is defined as the log fold change relative to the reference allele. Then, the transcript abundance of each haplotype h after accounting for the genetic effect is eβ if For individual αi denote the fraction of allele-specific reads in individual i, which depends on the number of heterozygous sites within the transcript. Instead of observing haplotypic counts θi0, × αi, so we haveIn an RNA-seq experiment, a fraction of reads contribute to allele-specific read counts. Let zi is the individual-level random effect capturing the between-individual variation of θi,0. Notice that the individual-level random effect cancels out when we take the difference between the two log-scale allele-specific read counts . The scaling of ϵtrc and ϵasc in Eq. (Y) ≈ constant × E(Y), such that over-dispersion is implicitly taken into account.Based on the model described above along with approximations under weak effect assumptions, we propose the following linear mixed effects model . Specifically, we fit βtrc and βasc using total and allele-specific observations as two separate linear regression problems, and meta-analyze the results using inverse-variance weighting  ap ap13) apβk is non-zero. Existing solvers for both prediction and fine-mapping use total read information only and assume that data  follows the model y = Xβ + ϵ, where the noise term ϵ is independent across the rows of the data matrix. We will refer to this model as the ‘canonical’ linear model. We propose a two-step inference procedure that first processes the data such that it approximates y = Xβ + ϵ, and then uses existing solvers for prediction and fine-mapping problems, respectively.The prediction and fine-mapping problems both rely on the linearized model Eqs. , 13), b, b13), bσ2 from  based on Eq. . To account for the intercept term μ0 in Eq.  by Yasc, Xasc) by Y = Xβ + ϵ. The implementation details are described in Supplementary Notes For the first step, we process total and allele-specific reads separately to fit the ‘canonical’ linear model. Specifically, we estimate d on Eq.  by furthd on Eq.  and the mentclasspt{minima12. We regress out the effect of covariates beforehand and use the residual as the response in both QTL mapping and fitting multi-SNP model. Specifically, let c1, ⋯ , cK denote the K covariates to be considered. We first regress Ytrc against c1, ⋯ , cK jointly and select the covariates with nominally significant coefficients (p < 0.05). Then we regress Ytrc against the selected covariates jointly and set the residuals as the adjusted Ytrc for QTL mapping and multi-SNP inference downstream.When analyzing real data, we need to take covariates such as sex, batch effect, population stratification into account. Here, we adapt the procedure which has been proposed previouslyi, the position of heterozygous sites within the gene body. The expected read count from each haplotype transcripts, Li, the baseline abundance of the transcript θi0,, and the genetic effect β. In step 2, given the expected haplotypic count, we draw We simulate RNA-seq reads with total and allele-specific readouts as sketched in three steps in Fig. θi0, to cover the gene with different expression levels, among 5 × 10−5, 2.5 × 10−5, 1 × 10−5, 5 × 10−6, 2.5 × 10−6, 1 × 10−6. The genetic effect, aFC, is set to 1 (null), 1.01, 1.05, 1.1, 1.25, 1.5, 2, 3 in the single-SNP model. For the multi-SNP scenario, we set the number of causal SNPs between 1 and 3 with heritability from 0.2 to 0.55. The number of polymorphic sites within the gene body is centered around 10 with minor allele frequency from 0.05 to 0.3. A detailed description and parameter settings are provided in the Supplementary Notes To survey a wide range of parameters, we simulate data with a grid of parameters. We vary sample size among 100, 200, ..., 500. At library size around 90 million, we vary the level of 13 via dbGaP (accession number phs000424.v8.p2). To obtain gene-level read counts, we summed over allele-specific counts at all the heterozygous sites for each gene haplotype. We also obtained library size, sex, and genotype PCs from GTEx v8. For comparisons with the inverse normalization-based approach, we also downloaded normalized expression matrices.We downloaded the phased genotypes, total read count matrix, and variant-level allele-specific read counts for whole blood from GTEx release 813, we restricted the analysis to the cis-regulatory window defined as 1Mbp up/downstream of the transcription start site of each gene.Similarly to the GTEx v8 analyses28 on a matrix with value i and gene g .To obtain the PEER factors for mixQTL analysis, we ran peertoolK fold to the smallest one, where We considered very large allele-specific counts to be likely alignment artifacts and removed individuals with allele-specific read counts greater than 1000. To further limit the influence of large count outliers on the estimated log fold change, Specific analyses focused on high or low expression were performed with different gene filtering criteria as stated in the Results section.https://github.com/liangyy/mixqtl-gtex/tree/master/mixqtl which relied on the tensorQTL implementation of mixQTL. We included all genes regardless of expression level and analyzed the 22 autosomes for each of the 49 tissues. Specifically, since mixQTL can only work with non-zero total read count, we imputed the samples with missing total read count as 1. And in the mixQTL call, all total read counts were included and all allele-specific counts with more than 15 reads (on both haplotypes) were included.For analyses of the full GTEx v8 dataset, we built a data analysis pipeline at Further information on research design is available in the Supplementary InformationPeer ReviewReporting SummaryDescription of Additional Supplementary FiiesSupplementary Data 1


In tropically-adapted beef heifers, application of genomic prediction for age at puberty has been limited due to low prediction accuracies. Our aim was to investigate novel methods of pre-selecting whole-genome sequence (WGS) variants and alternative analysis methodologies; including genomic best linear unbiased prediction (GBLUP) with multiple genomic relationship matrices (MGRM) and Bayesian (BayesR) analyses, to determine if prediction accuracy for age at puberty can be improved.Genotypes and phenotypes were obtained from two research herds. In total, 868 Brahman and 960 Tropical Composite heifers were recorded in the first population and 3695 Brahman, Santa Gertrudis and Droughtmaster heifers were recorded in the second population. Genotypes were imputed to 23 million whole-genome sequence variants. Eight strategies were used to pre-select variants from genome-wide association study (GWAS) results using conditional or joint (COJO) analyses. Pre-selected variants were included in three models, GBLUP with a single genomic relationship matrix (SGRM), GBLUP MGRM and BayesR. Five-way cross-validation was used to test the effect of marker panel density , analysis model, and inclusion of pre-selected WGS variants on prediction accuracy.In all tested scenarios, prediction accuracies for age at puberty were highest in BayesR analyses. The addition of pre-selected WGS variants had little effect on the accuracy of prediction when BayesR was used. The inclusion of WGS variants that were pre-selected using a meta-analysis with COJO analyses by chromosome, fitted in a MGRM model, had the highest prediction accuracies in the GBLUP analyses, regardless of marker density. When the low-density (6 K) panel was used, the prediction accuracy of GBLUP was equal (0.42) to that with the high-density panel when only six additional sequence variants  were included.While BayesR consistently outperforms other methods in terms of prediction accuracies, reasonable improvements in accuracy can be achieved when using GBLUP and low-density panels with the inclusion of a relatively small number of highly relevant WGS variants. The lifetime reproductive capacity of tropically-adapted cows has a major impact on herd productivity and profitability –4. AppliAge at puberty is defined as the age at which an animal first ovulates , 8. Age 2 = 0.23) and is highly genetically correlated (rg = − 0.83) to AGECL in tropically-adapted heifers [In contrast to AGECL, reproductive maturity score (RMS), a proxy trait for AGECL, is a categorical trait measured on a 0 to 5 scale where 0 = infantile reproductive tract, 1 = small ovarian follicles, 2 = ovarian follicles with a diameter larger than 10 mm, 3 = presence of corpus luteum, 4 = pregnancy to 10 weeks, and 5 = pregnancy longer than 10 weeks , 13. Unl heifers . This reB. indicus and B. indicus × B. taurus crossbred cattle [Genomic selection can be used to select for difficult and expensive to measure traits, such as age at puberty –17. The d cattle , 21. To d cattle . Recent d cattle . Howeverd cattle .Genomic prediction accuracies for single-scan puberty indicators are currently quite low (0.03–0.42) even when high-density SNP panels are used , 20. In In the dairy industry, recent research has investigated the use of novel methods to identify or ‘prune’ WGS variants for inclusion in genomic selection models –28. RaymGiven the promising results observed for dairy cattle, there may be opportunity to adapt similar methodologies for the north Australian beef industry. Currently, the cost of genotyping large numbers of cattle with high-density genotypes can be considerable and, as such, one of the aims of this research was to investigate if pre-selected whole-genome sequence variants can be incorporated into genomic evaluation models to improve the prediction accuracy of the more cost-effective, low-density panels. We hypothesised that pre-selected WGS variants can be used to improve prediction accuracy for RMS in a multi-breed population of tropically-adapted beef heifers. We tested several WGS pruning techniques in SGRM, MGRM and BayesR models to determine if the accuracy of genomic breeding values for RMS could be improved.Fertility records were obtained from two research populations, the Northern Breeding Project research herd from the Beef Cooperative Research Centre for Beef Genetic Technologies (Beef CRC)  and the In total, 3695 reproductive maturity scores (RMS) were obtained from the SMF database on heifers from three breeds, Brahman (n = 979), Santa Gertrudis (n = 1802) and Droughtmaster (n = 914). Full information on herd structure, management and data recording is described in Burns et al.  and EnglBeef CRC heifers were genotyped with the BovineSNP50 BeadChip   and SMF Genomic predictions were estimated using three base SNP densities: 6 K (BovineLD array), 50 K (BovineSNP50 BeadChip) and 800 K (BovineHD array). Since animals were genotyped at different SNP densities, the genotypes for the 6 K and 50 K array datasets were constructed by extracting only the SNPs that were present on the commercial BovineLD or BovineSNP50 BeadChip array from the imputed 800 K data.Identification of variants associated with AGECL in Beef CRC populations  using genome-wide association studies (GWAS) of imputed WGS data in each population separately.Assessment of the accuracy of genomic predictions for RMS in the SMF population when WGS variants pre-selected from the Beef CRC GWAS analyses are added to each analysis.Three datasets were used in these analyses. The Beef CRC data was split into Brahman and Tropical Composite data, which were used for the discovery of variants associated with AGECL, whereas the SMF heifers were treated as a single population that was used for genomic prediction. The analysis proceeded in two steps:B. indicus percentage of each Tropical Composite animal, variant is the association between the WGS variant being tested and phenotype and error is the random residual effects of the model. Eight strategies were then applied to preselect WGS variants associated with AGECL in GWAS for inclusion in genomic prediction models (Table Genome-wide association studies were performed using the GCTA software . The GWAmentclass2pt{minimP ≤ 5.0e−08 (whole-genome significance) was tested and if no WGS variants met this threshold it was sequentially and arbitrarily lowered  were performed in GCTA  to pre-sed Table . For eased Table .Single cohort GWAS COJO was performed on each of the Beef CRC datasets and any significant WGS variant identified in either of the datasets was used in genomic predictions. Meta-analyses on the Beef CRC datasets were performed using the program Metal . The P-vb is a vector of fixed effects including the mean, age at measurement fitted as a covariate, contemporary groups fitted as a fixed effect, defined as herd, year and season, e is a vector of random error terms. The distribution of Genomic relationship matrices were constructed for each of the datasets and each marker panel using GCTA . Pre-selanalysis  and \docThe MGRM model can be written asmentclass2pt{minimW is the standardised genotype matrix . Furthermore, The BayesR model for each analysis is shown below:e et al.  describeThe second parameter, The prior of r et al. . GEBV weFive-fold cross-validation was used to determine prediction accuracy. Validation groups were populated by randomly assigning 20% of the SMF animals to one of five validation groups. Individual animals appeared only in a single validation group and these groups were used across all analyses. This random assignment to validation group was designed to reflect the heterogeneous mix of breeds that will most likely occur in genomic evaluations of the northern Australian beef industry.Correlations between predicted GEBV and RMS phenotypes adjusted for fixed effects were averaged across validation groups for each analysis. Then, average correlations were divided by the square root of the heritability of RMS to calculate the prediction accuracy. The heritability estimates used in the calculation of accuracies were obtained from the 800 K CONTROL analysis Table . ControlModel significance was tested using a linear model function in R  that tesInitial statistical investigations showed that RMS is an approximately normally distributed categorical trait with a mean of 2.27 and a range from 0 to 5, which indicates that variation in this trait exits between the Smart Futures heifers. Variance component estimates were similar between marker panels, therefore for ease of reference, only the results estimated from the 800 K panel are presented in Table P ≤ 5.0e−03). Of the six variants identified in this pre-selection method, two were located on chromosome 5, and one on each chromosome 14, 21, 22 and 25  effect. The effect of the WGS variant pre-selection method was bordering upon statistical significance (P < 0.06) in these analyses. Furthermore, the interactions between analysis and marker panel (P < 0.001) and analysis and type of GWAS (P < 0.01) had significant effects on the prediction accuracy for RMS in these models. Pairwise comparisons of factor levels showed that the prediction accuracy of RMS was significantly different (P < 0.001) between the GBLUP (SGRM and MGRM) analyses and the BayesR analyses, but there was no difference between the SGRM and MGRM GBLUP analyses. Panel density also had a significant effect on prediction accuracy in these models, and the pairwise comparisons showed that there was a significant difference between the 6 and 50 K and the 6 K and 800 K panels (P < 0.001), but no difference between the 50 and 800 K panels. There was also a significant difference in accuracy between the GWAS and META WGS variant pre-selection methods (P < 0.001).When examining the effect of each potential factor in the model on prediction accuracy; validation group, the type of GWAS, analysis and marker panel all had a significant  has been shown to improve the accuracy of GEBV, including in tropical beef breeds , 42. In Bayesian methodologies resulted in the highest prediction accuracies for RMS, regardless of the panel density and WGS variant pre-selection strategy. In our study, Bayesian analyses had significantly higher prediction accuracies across all markers and pre-selection methods than the GBLUP analyses. A number of other studies have shown that genomic selection using Bayesian methods performed better in across-breed predictions than GBLUP analyses , 39, whiThe use of a MGRM model in some of the GBLUP analyses showed potential for improving prediction accuracy for RMS in the SMF heifers. A MGRM model for genomic analyses can improve the accuracy of GBLUP predictions in dairy populations , 27. ThePrediction accuracies obtained with meta-analysis GWAS were slightly higher than with single cohort GWAS analyses, particularly in the GBLUP MGRM models. Meta-analyses are used to account for differences in population size and trait measurements when combining datasets from unrelated populations . LinkageOf the eight WGS variant pre-selection strategies, two of the meta-analysis pre-selection strategies that we used resulted in improved prediction accuracies in the GBLUP analyses, TOP and COJO CHR. Figure ADAMTS17, CNTN6 and ZNF598 genes. An association between ADAMTS17 and height has been reported in horses [ADAMTS17 on AGECL in this study. Furthermore, a recent study has identified a SNP in ADAMTS17 that is significantly associated with age at puberty in a population of tropically-adapted heifers [ZNF598 is associated with low fertility in dairy cattle [CNTN6 is associated with neural development and spatial learning in mice and is differentially expressed between the sexes in the developing brain [PLAG1 gene. Due to its proximity, this variant is very likely tracking a mutation in the PLAG1 gene or its regulators. Recent research has shown that PLAG1 is associated with age at puberty in tropically-adapted cattle [The most significant improvement in prediction accuracy due to the inclusion of WGS variants from the meta-analysis was observed when the lower density panels, particularly the 6 K MGRM models, were used. Of these models, the COJO CHR pre-selection had the highest prediction accuracy in the GBLUP analyses from the inclusion of only six WGS variants. Three of these variants were identified on introns of the n horses , dairy cn horses  and human horses . Increasn horses , which m heifers . Similary cattle . The varng brain , and to d cattle . The incWhile the results from this study suggest that the use of pre-selected WGS variants and novel analysis methodologies can be used to improve the prediction accuracy for RMS in a multi-breed population of tropically-adapted heifers, it is worth noting that these are not true across-breed GEBV. Across-breed GEBV can only be calculated with direct breed comparisons through mixed breed cohorts, which were not available in the SMF dataset. As each of the three breeds of heifers from the Smart Futures dataset were managed separately, in our analyses the definition of the contemporary group accounted for breed effect. The development of an across-breed GEBV for the northern Australian beef industry will require the collection of genotypes and phenotypes on animals that are produced in multi-breed cohorts, in which breed performance can be directly compared. When these datasets become available, further research will be necessary to develop methods to predict across-breed GEBV and methods to account for differences between breeds within these analyses.Both BayesR and some GBLUP MGRM analysis with pre-selected WGS variants resulted in improved prediction accuracies for RMS in this population of heifers. Generally, BayesR analyses had higher prediction accuracies than GBLUP, and the addition of WGS variants had very little effect on BayesR estimates. Pre-selection of WGS variants was most beneficial in the GBLUP analyses, particularly when using variants that were pre-selected by using meta-analysis COJO CHR in multi-GRM analyses. The most pronounced improvements in prediction accuracy were observed when genotypes were based on the 6 K panel for both the BayesR and GBLUP methods. The 6 K panel is the most cost-effective option and, if the prediction accuracy of this panel can be improved, there will be a financial benefit to end-users. More RMS phenotypes will be required to improve accurate detection of WGS variants that can explain variation in RMS across a number of tropically-adapted breeds and the best methods to use this data in genomic prediction analyses.


Drosophila Genetic Reference Panel.Presently, multi-omics data  are available to improve genomic predictors. Omics data not only offers new data layers for genomic prediction but also provides a bridge between organismal phenotypes and genome variation that cannot be readily captured at the genome sequence level. Therefore, using multi-omics data to select feature markers is a feasible strategy to improve the accuracy of genomic prediction. In this study, simultaneously using whole-genome sequencing (WGS) and gene expression level data, four strategies for single-nucleotide polymorphism (SNP) preselection were investigated for genomic predictions in the Using genomic best linear unbiased prediction (GBLUP) with complete WGS data, the prediction accuracies were 0.208 ± 0.020 (0.181 ± 0.022) for the startle response and 0.272 ± 0.017 (0.307 ± 0.015) for starvation resistance in the female  lines. Compared with GBLUP using complete WGS data, both GBLUP and the genomic feature BLUP (GFBLUP) did not improve the prediction accuracy using SNPs preselected from complete WGS data based on the results of genome-wide association studies (GWASs) or transcriptome-wide association studies (TWASs). Furthermore, by using SNPs preselected from the WGS data based on the results of the expression quantitative trait locus (eQTL) mapping of all genes, only the startle response had greater accuracy than GBLUP with the complete WGS data. The best accuracy values in the female and male lines were 0.243 ± 0.020 and 0.220 ± 0.022, respectively. Importantly, by using SNPs preselected based on the results of the eQTL mapping of significant genes from TWAS, both GBLUP and GFBLUP resulted in great accuracy and small bias of genomic prediction. Compared with the GBLUP using complete WGS data, the best accuracy values represented increases of 60.66% and 39.09% for the starvation resistance and 27.40% and 35.36% for startle response in the female and male lines, respectively.Overall, multi-omics data can assist genomic feature preselection and improve the performance of genomic prediction. The new knowledge gained from this study will enrich the use of multi-omics in genomic prediction. Genomic prediction, also known as genomic selection (GS), was initially proposed in 2001 ; S is a vector of the SNP, and the other terms are defined as above. A Wald test was applied to test the alternative hypotheses of each SNP in the univariate models. After eQTL mapping, the significant eQTLs of each gene were divided into different categories based on P values of less than 0.05, 0.001, 0.0001, 0.00001, or 0.000001. Then, the different categories of significant eQTLs of each gene were extracted as feature markers from the WGS data, respectively. Because polymorphic inversions had a direct impact on gene expression, moreover, for avoiding spurious associations due to adjustment on both eQTL mapping and TWAS, we added five major polymorphic inversions as fixed effect only in eQTL mapping.In order to link genome variation with the gene expression level, eQTL mapping was performed for each sex separately for each gene expression level using the WGS data. Univariate tests of association were performed using a mixed model approach implemented in the GEMMA v0.98.1 software . The modAfter the TWAS and eQTL mapping analysis, genes and eQTLs were divided into different categories according to the significance threshold as described above. Using different categories of combination, these significant eQTLs of significant genes were extracted as feature markers from the WGS data, respectively.y is a vector of the phenotypic values; b is Walachia infection status as a fixed effect; and the other parameters are defined as above.The breeding values of the genotyped individuals were estimated via genomic best linear unbiased prediction (GBLUP)  and a gey,b,X, and e are the same as GBLUP, f is the vector of the genomic values captured by the genetic markers linked to the genomic feature of interest, following a normal distribution of r is a vector of genomic values captured by the remaining set of genetic markers, following a normal distribution of Z1 and Z2 are the incidence matrices relating the additive genetic values (g and f) to the phenotypic records.Gf and Gr were constructed according to [The GFBLUP model was an extended BLUP including two random genetic effects:rding to  using thIn this study, the variance components were estimated in the training set using the REML algorithm via the LDAK software . FinallyThe Pearson’s correlation and regression coefficients between the predicted genetic values and the true phenotypic values were used to assess the accuracy and the bias of genomic prediction. The true phenotypic values represent the fixed effects of original phenotypic observations were corrected. Ten replicates of five-fold cross-validation were used to avoid the uncertainty of predictive correlations in this study. Briefly, the genotyped individuals were randomly divided into five subsets. One subset was selected as the validation set, and the remaining four were used as the reference set. Then the cross-validation process was repeated five times to ensure that each subset was validated once. Finally, the average accuracy values and the bias of genomic prediction for the ten replicates of five-fold cross-validation were reported.Before performing genomic prediction, the summary of statistics and genetic parameter estimations for the traits were performed in the male and female lines, and the detailed results were shown in Table P-value cutoffs, the accuracy values of both GBLUP and GFBLUP were shown in Table P-value cutoffs (P <  0.05), the accuracy values of GBLUP were 0.186 ± 0.021 and 0.158 ± 0.022 for the startle response and 0.207 ± 0.020 and 0.268 ± 0.020 for starvation resistance in the female and male lines, respectively  was 0.189 ± 0.022 and 0.118 ± 0.022 for the startle response and 0.128 ± 0.017 and 0.196 ± 0.015 for starvation resistance in the female and male lines, respectively   were used to preselected 177,035 and 227,569 SNPs in female and male lines as the genomic feature, the accuracy values of GBLUP with the genomic feature were 0.437 and 0.414 for starvation resistance in the female and male lines, respectively, which were higher than the accuracy value of GFBLUP   Fig. . HoweverP-value cutoffs less than 0.001 . Table S2. The number of preselected SNPs based on the GWAS results (S_GWAS). Table S3. The bias values of genomic prediction using preselected SNPs based on TWAS results (S_TWAS). Table S4. The number of preselected SNPs based on the TWAS results (S_TWAS). Table S5. The bias values of genomic prediction using preselected SNPs based on the results of eQTL mapping of all genes (S_eQTL_A). S6. The number of the preselected SNPs based on the results of eQTL mapping of all genes (S_eQTL_A). Table S7. The bias values of genomic prediction using preselected SNPs based on the results of eQTL mapping of significant genes (S_eQTL_S). Table S8. The number of preselected SNPs based on the results of eQTL mapping of significant genes (S_eQTL_S). Table S9. The variance component of GBLUP using preselected SNPs based on the GWAS results (S_GWAS). Table S10. The variance component of GFBLUP using preselected SNPs based on the GWAS results. Table S11. The variance component of GBLUP using preselected SNPs based on the TWAS results (S_TWAS). Table S12. The variance component of GFBLUP using preselected SNPs based on the TWAS results (S_TWAS).