Oncorhynchus kisutch) from two geographically distant rivers at different epigenome scales. Our results showed that coupling WGBS with fine‐scale analyses  allows the detection of parallel early‐life hatchery‐induced epimarks that differentiate wild from hatchery‐reared salmon. Four chromosomes and 183 differentially methylated regions (DMRs) displayed a significant signal of methylation differentiation between hatchery and wild‐born Coho salmon. Moreover, those early‐life epimarks persisted in germ line cells despite about 1.5 year spent in the ocean following release from hatchery, opening the possibility for transgenerational inheritance. Our results strengthen the hypothesis that epigenomic modifications environmentally induced during early‐life development persist in germ cells of adults until reproduction, which could potentially impact their fitness.Human activities induce direct or indirect selection pressure on natural population and may ultimately affect population's integrity. While numerous conservation programs aimed to minimize human‐induced genomic variation, human‐induced environmental variation may generate epigenomic variation potentially affecting fitness through phenotypic modifications. Major questions remain pertaining to how much epigenomic variation arises from environmental heterogeneity, whether this variation can persist throughout life, and whether it can be transmitted across generations. We performed whole genome bisulfite sequencing (WGBS) on the sperm of genetically indistinguishable hatchery and wild‐born migrating adults of Coho salmon ( Both hatcheries are part of the Salmon Enhancement Progam (SEP) and are separated by approximately 100 km (located on watersheds on the northeast (Quinsam) and northwest (Conuma) sides of Vancouver Island). The hatcheries operate with a primary production strategy (PPS) [. All symmetric CpG sites with <10× coverage were removed. The relationship between the number of symmetric CpG sites and the linkage group length was assessed by performing linear mixed models in the R package “LME4”  with fastp  between individuals from both rivers and rearing environment (wild vs. hatchery) was estimated with VCFtools , and only 1 kb regions with at least 3 symmetric CpGs were kept  controlled by the other variable. The correlation between the n R Team .2.6p‐value threshold (p.threshold) of 0.01 in order to keep DMR with strong signals  and a threshold of 0.4 for percentage of CG sites with significant p‐value (pct.sig). The same approach was used to call DMRs between rivers (Conuma and Quinsam). To control for false‐positive discovery, we performed 100 random assignations of individuals to the hatchery or wild‐born groups within each river  and then call parallel DMRs between hatchery and wild‐born individuals. Figure p < 0.01 to find 212 DMRs or more in our dataset, which supports the observation that our DMRs are unlikely to be the results of false discovery rate. Finally, with the goal of removing potential false discovery DMRs, we kept only DMRs with a mean methylation differentiation between wild and hatchery higher than 10%. The correlations between the number and length of DMRs and the chromosome size were tested using Spearman test in R  between hatchery‐born and wild individuals was based on the dispersion shrinkage method implemented in the package DSS  with the published transcripts accompanying this genome using GAWN v0.3.3 (https://github.com/enormandeau/gawn) in order to find transcripts that were close to DMRs. GAWN used GMAP version 2018‐07‐04 to align the transcripts to the genome. For DMRs associated with the rearing environments, we identified the overlapping transcripts by using BEDtools intersect v2.26.0 (26). We also performed the analyses again after adding 5 kbp around each DMR to include potential genes associated with, but not directly overlapping, the DMRs. We then used Go_enrichment (https://github.com/enormandeau/go_enrichment) to annotate the same transcriptome by blasting the transcripts on the swissprot database . We proceeded to test for the presence of over‐ and under‐represented GO terms using GOAtools . We filtered the outputs of GOAtools to keep only GO terms at a level of 3 or less and an FDR value ≤0.1.We annotated the Coho salmon genome  performed on 24 adult male Coho salmon sperm samples produced a mean of 189 ± 20 million of reads per individual of which 92 ± 11 million of reads per individual (49%) were successfully mapped to the reference genome masked for cytosine to thymine (C‐T) polymorphism. A mean of 11 ± 2.8 million of symmetric cytosine‐phosphate‐guanine context (CpGs) per individual with at least 10X coverage was retrieved, with a mean of 0.3 ± 0.1 million of symmetric CpG per chromosome. The number of symmetric CpG was strongly correlated with chromosome length (3.1p < 0.001) with an adjusted R2 of 0.025, but not by rearing environment (p = 0.23). Moreover, the extent of genetic differentiation (FST) was ten times greater between Coho from Conuma and Quinsam rivers (FST = 0.013) than between wild and hatchery individuals . FST < 0.001 indicates that hatchery and wild Coho within a same river are genetically indistinguishable, as previously reported  on Euclidean distance to test for the effect of river and rearing environment on genetic variance partitioning. The genetic variation was significantly explained by the river of origin (3.2p < 0.002) with an adj. R2 of 0.02  to evaluate overall pattern of methylation variation among adult males using rearing environment and river of origin as explanatory variables was significant  for 28 chromosomes with an adj. R2 ranging from 0.009 to 0.029 , 0.012 (p = 0.04), 0.012 (p = 0.04), and 0.011 (p = 0.05), respectively , indicating that the extent of persisting epigenetic differentiation between hatchery and wild adult males is not directly affected by the number of CpG sites occurring on a given chromosome.The same analytical approach was applied at the chromosomal scale, whereby db‐RDA was performed for each of the 30 chromosomes independently. This analysis showed that similar epigenetic modifications induced by hatchery‐rearing environment persisted on specific chromosomes and in parallel in the two river systems until adulthood after more than one year of growth spent in the open ocean. Thus, components of epigenetic alteration caused by hatchery rearing that have persisted in parallel among adult males from both rivers of origin were not randomly distributed but significantly concentrated on specific chromosomes. Models were significant (ly Table . Moreove3.3pthreshold = 0.01) were identified between wild and hatchery‐born adult males  but the number of DMRs increased with chromosome size . The mean difference between hatchery and wild‐born methylation ranged from 0.4% to 73% we kept a total of 183 DMRs with a mean methylation difference higher than 10% for subsequent analyses  relative to hypomethylated DMRs (n = 67) in the sperm of hatchery‐born adult males in both rivers . Finally, we performed partial redundancy analyses where genetic variation was estimated from 7151 filtered SNPs to ensure that the rearing environment significantly explains the variation of observed DMRs between hatchery and wild‐born Coho, as opposed to the sole genetic effect. We observed that rearing environment explained 51.4% of DMRs CpGs variation after controlling by genetic variation, while genetic variation explained 44.1% of that variation.A total of 212 DMRs  were identified between Conuma and Quinsam river ranging from 103 to 1562 bp in size and distributed among the 30 chromosomes  relatively to hypomethylated DMRs (n = 22) in the sperm of hatchery fish  showed that DNA methylation was inherited by the progeny from the sperm methylome in spite of a whole DNA reprogramming during embryogenesis  revealed that a small number of methylation marks associated with captive‐rearing in sperm cells remain in the offspring beyond developmental reprogramming. Our study adds to these previous observations in controlled conditions by extending to an analysis of adult fish in the wild returning from their marine migration. Moreover, we also document that similar epigenetic modifications have been retained and transmitted to germ cells independently in different hatcheries. Taken together, if the epigenome of males is actually transmitted to the next generation, this could represent one way by which captive‐rearing may negatively impact the fitness of progeny. This is consequential for salmon conservation and the management of extensive hatchery production and supplementation of salmonids throughout the world as they strengthen the hypothesis that epigenomic modifications caused by environmental conditions during early‐life stage can be transmitted to the next generation and may partly account for the reduced fitness of hatchery‐produced fish in nature, even in absence of genetic differences between wild and hatchery fish  occurs early in larval development, that is several weeks after hatching  on those four chromosomes is almost equal to that explained by the river of origin which is R2 = 1.5% despite the fact that there is a clear genetic differentiation between salmon from both rivers but absence of significant genetic difference between hatchery vs. wild fish within each river. Also, the absence of genotypic differentiation between wild and hatchery‐born Coho and the fact that shared DMRs were observed in genetically distinct populations rule out the possibility that the observed modifications reflect relatedness among individuals. By showing that hatchery salmon are genetically much more similar  to the wild counterpart from the same river than to hatchery salmon from the second river, our results strengthen the likelihood that early‐life hatchery‐rearing conditions may account for observed fitness differentials between wild and hatchery‐born individuals , another member of the salmonid family  hatchery individuals matured precociously compared to wild individuals  was only marginally significant, we were able to detect epigenomic variation caused by rearing environment both at specific chromosomes and independent of chromosome size and at a local scale with 183 significant DMRs observed. As such, this study illustrates the benefits of performing whole genome bisulfite sequencing (WGBS) to dissect patterns of epigenomic variation at various genomic scales. Our results are also important for salmon conservation as they add further support to the view that environmental conditions during early development may account for at least some of the reduced fitness of the hatchery salmon in the wild which may also be transmitted to the next generation. Our study also further illustrates the relevance and importance of considering not only the genetic consequences, but also the epigenetic consequences of captive breeding in evaluating the costs and benefits of large‐scale supplementation programs to enhance wild populations.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Table S1‐S3Click here for additional data file.Table S4Click here for additional data file.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


There is a long-standing interest in exploring the relationship between blood-based biomarkers and psychiatric disorders, despite their causal role being difficult to resolve in observational studies. In this study, we leverage genome-wide association study data for a large panel of heritable serum biochemical traits to refine our understanding of causal effect in biochemical-psychiatric trait pairings. We observed widespread positive and negative genetic correlation between psychiatric disorders and biochemical traits. Causal inference was then implemented to distinguish causation from correlation, with strong evidence that C-reactive protein (CRP) exerts a causal effect on psychiatric disorders. Notably, CRP demonstrated both protective and risk-increasing effects on different disorders. Multivariable models that conditioned CRP effects on interleukin-6 signaling and body mass index supported that the CRP-schizophrenia relationship was not driven by these factors. Collectively, these data suggest that there are shared pathways that influence both biochemical traits and psychiatric illness. There was extensive genetic overlap between blood-based biochemical traits and psychiatric illness. Psychiatric disorders arise from a complex interplay between genetic and environmental risk factors. Twin-based and genome-wide association studies’ (GWAS) heritability estimates have demonstrated the importance of genetic risk to the spectrum of psychiatric illness  biochemical GWAS from a single cohort [UK Biobank (UKBB)] to investigate genetic overlap with different psychiatric disorders, along with putative causal effects. We found that the majority of biochemical traits tested were genetically correlated with at least one psychiatric trait, with evidence of convergent and divergent correlation profiles among the different disorders. We also demonstrated evidence that there may be a causal relationship on psychiatric phenotypes through circulating C-reactive protein (CRP), glucose, and urate, which may have direct implications for clinical practice.GWAS has proven valuable beyond just gene discovery in psychiatry, in which it allows the study of relationships between sets of traits in terms of genetic correlation (N = 30) of the biochemical traits tested were significantly correlated with at least one psychiatric trait after multiple testing correction, with every psychiatric trait exhibiting a significantly nonzero biochemical correlation after correction, except for Tourette’s syndrome (TS) (We tested the genetic correlation between a panel of blood-based biomarkers from the UKBB and 10 psychiatric GWAS using linkage disequilibrium score regression (LDSR). We found that 61% (P = 1.7 × 10−3), while its correlation with post-traumatic stress disorder (PTSD), major depressive disorder (MDD), and attention-deficit/hyperactivity disorder (ADHD) was strongly positive. These data provide some support to recent cohort studies, including lower CRP observed in patients with eating disorder (rg = −0.151), mean corpuscular volume and AN (rg = 0.087), mean sphered cell volume and SZ (rg = 0.06), and creatinine with bipolar disorder (BIP) and SZ .There was clear evidence of biomarkers with divergent genetic correlations between different psychiatric phenotypes, for instance, CRP, alanine aminotransferase (ALT), and sex hormone binding globulin SHBG; . In the z scores for each psychiatric trait. We observed strong positive and negative correlations between the trait-wise LDSR biochemical z score for each trait, which we term the biochemical correlation profile (z scores within each cluster are plotted in z scores across the psychiatric disorders tested. Component four was notable as it was solely composed of reticulocyte (immature erythrocytes)–related traits, which, analogous to component one, was quite discordant in its correlation profiles. The fifth and final component was composed of other erythrocytic-related traits; however, the differences between disorders were less marked than component four. Together, this demonstrates that groups of biomarkers tend to have similar relationships with different psychiatric traits.We further investigated the relationship between the profiles of 49 LDSR biochemical  profile . For ins profile . We obseN = 391). As cognitive performance is highly variable among SZ cases, a multidimensional grade of membership (GoM) clustering of nine cognitive measures was previously applied to derive these two subgroups of cognitive performance in the ASRB  = 0.744 , P = 5.82 × 10−3, q = 0.07. We emphasize that these signals only survive multiple testing correction using a lenient false discovery rate (FDR) threshold of 10%; however, given the small sample size of this cohort, we believe that these findings remain noteworthy. Immature reticulocyte fraction was negatively correlated with cognition, with a trend toward a negative relationship with SZ as well. Hematocrit percentage was also negatively correlated with cognition; however, there was a depletion of hematocrit percentage alleles among CD versus CS SZ cases, suggesting that a more complex relationship may be present. Both the hematocrit and reticulocyte fraction PGS explained around 1% of phenotypic variance in CD on the liability scale, which is similar to a PGS for general cognitive ability (P < 0.05) explained almost 3% in phenotypic variance. There was a nonsignificant trend of enrichment of SZ polygenic risk score (PRS) (P = 0.054) in CD. While these values are modest, it does suggest that the genetic architecture of biochemical traits is correlated with the severity of cognitive impairment in SZ. In addition, these estimates of phenotypic variance explained are likely inflated relative to that of an external sample, and, thus, future investigation of the relationship between these PGS and cognition in SZ is warranted to test the replicability of these findings.We tested the association between 25 genetically proxied biomarkers  that were correlated with either the SZ or the general cognitive ability GWAS with a severe cognitive deficit (CD) subtype of SZ relative to a subset of cases with less-marked impairment [cognitively spared (CS)] from the Australian Schizophrenia Research Bank (ASRB) cohort ( ability . A modelP = 5.11 × 10−6). Given that the genetic correlation is only small, it is less likely that previous MR studies were unduly biased by genetic correlation. In addition, we also observed an unusual phenomenon in the HbA1c and PTSD model, whereby the posterior mean z score was negative (z = −6.51), which signifies the opposite. As described in the Supplementary Materials and fig. S1, we found that these conflicting data were likely attributable to a rare violation of the LCV model assumptions, whereby the mixed fourth moments had opposite signs to each other and the genetic correlation. This could be explained by certain SNPs having highly divergent effects from the rest of the genome-wide signal. Moreover, there were two other trait pairs that trended toward partial genetic causation  model was constructed between each significantly correlated biochemical-psychiatric trait pair  to estimate partial genetic causality (P = 0.01) and AN , which supports the LDSR-inferred direction of the LCV relationship. Using a less-conservative IVW estimator with fixed effects yielded a more precise estimate in both instances, as expected . There was a trend toward an odds-increasing effect of CRP on MDD (P = 0.19), while there was no indication of a reliable effect in the OCD model (P = 0.82). A nonsignificant estimate from MR does not necessarily preclude the existence of a causal relationship supported by an LCV model, although LCV models with corresponding MR support would perhaps be viewed as stronger evidence. We discuss the sensitivity analyses for each of these univariable models in detail in Supplementary Text. Briefly, the five MR tests deployed with different assumptions regarding IV validity  had very similar point estimates (OR range: 0.88 to 0.91) and were all statistically significant for CRP → SZ with the exception of the simple median (P = 0.07). The CRP → AN estimate across the different models were also directionally consistent; however, they were not statistically significant , and, thus, the total effect of CRP on AN by MR has comparatively weaker evidence compared to SZ. The effect of using robust, penalized, or robust penalized weights in the median, IVW, and Egger models was not marked for each of the CRP to psychiatric models (P = 0.07), and, thus, there was no strong statistical evidence of confounding pleiotropy using this metric. However, there was evidence of heterogeneity between the IV ratio estimates for AN and SZ: The MR-PRESSO global test of pleiotropy was significant and Cochran’s Q statistic was significant for AN and SZ, although both causal estimates remained statistically significant in the MR-PRESSO outlier–corrected estimates (table S19). Given the biological complexity of these phenotypes, heterogeneity does not necessarily imply confounding pleiotropy. Moreover, using a leave-one-out analysis, we found evidence of one outlier IV in the SZ model, while there were three outlier SNPs in the AN model (table S20), although the effects of removing these IVs were relatively small. The outlier IV in the SZ model was also proximal to the CRP gene itself (IVW P = 0.07 when removed), meaning that it is likely to influence SZ through CRP rather than being indicative of confounding pleiotropy. Specifically, the IV used was the lead SNP for the locus mapped to the CRP gene in the GWAS we used and is in high LD with putative functional variants within the 3′ untranslated region of CRP. Given that the CRP estimates on AN and SZ remained consistent upon removing outliers by MR-PRESSO or through iterative single IV exclusion, it is less likely that horizontal pleiotropy fully explains these signals. There was also no evidence using a reverse MR model that, with the psychiatric disorder as the exposure, there were bidirectional effects, although these models are inherently underpowered and are best treated as a test of the null hypothesis only (table S22).CRP displayed strong evidence of partial genetic causality on three psychiatric disorders, and, thus, we sought to further analyze these relationships by estimating the total effects and direct impact of CRP using univariable MR and multivariable MR (MVMR), respectively (tables S16 to S22). We included SZ in these analyses, due to evidence from previous SZ GWAS that CRP exerts a protective effect on SZ liability, as discussed in the previous section. MR methods differ from the LCV approach in that they leverage specific independent SNPs strongly associated with trait one (the exposure trait) as IVs to estimate a causal effect size of the exposure trait on the outcome, given that a variety of assumptions are met . MR estimates can be biased by genetic correlation, which is explicitly modeled by the LCV approach, but they have the advantage of facilitating direct causal estimates and the ability to construct multivariable models (P = 0.01). There was no evidence of an effect of BMI or IL-6 signaling on SZ conditioned on CRP. Similarly, the effect size of the CRP → AN MVMR model remained similar to that estimated in the univariable constructs . While evidence for a direct effect of CRP on MDD conditioned on each variable set was weak (P = 1 × 10−3.We sought to investigate the direct effect of circulating CRP on the above psychiatric phenotypes using MVMR conditioning on interleukin-6 (IL-6) signaling and body mass index . These variables were selected as IL-6 signaling is the key upstream factor that stimulates hepatic CRP secretion, and, thus, genetic IVs for CRP could plausibly operate through IL-6 to independently influence the risk of psychiatric illness . For insnstructs ; howeverP < 5 × 10−8) that also obtained genome-wide significance in the UKBB CRP GWAS . The signals were investigated using colocalization as described more extensively in Supplementary Text. For three of these loci, colocalization analyses using the European-only subset of the SZ GWAS and default prior probabilities demonstrated strong evidence for the association of three of these loci with both SZ and CRP; however, there was likely a different underlying causal variant (table S24). In other words, the posterior probability (PP) for the hypothesis termed H3  was greater than 80% (table S24). These estimates remained consistent using larger prior probabilities for the hypothesis of a shared causal variant (H4) as a sensitivity analysis that is visualized in fig. S2. The remaining two loci also did not exhibit strong evidence of colocalization  or any of the other tested hypotheses (PP > 0.8). Local genetic correlation estimates with ρ-Heritability Estimation from Summary Statistics (HESS) demonstrated that 13 of the LD block partitions of the human genome displayed nonzero local covariance between CRP and SZ (table S25 and fig. S3) after Bonferroni correction. Five of these LD blocks with strong evidence of local genetic covariance were positive, and, thus, for these regions of the genome, SZ and CRP were positively correlated in contrast to the genome-wide estimate of nominal negative correlation.There were five SZ GWAS lead SNPs  that encode the RAC-alpha/beta/gamma serine/threonine-protein kinase complex, which was measured as a single entity in the protein study. Collectively, these data provide preliminary evidence that CRP may influence the expression of neuronally relevant proteins, and subsequent studies should seek to investigate these associations and their significance for different psychiatric phenotypes.We also sought to investigate the downstream consequences of raised CRP. We estimated the effect of raised CRP on the expression of 3284 proteins in blood using univariable MR. Using a liberal FDR cutoff of 10%, we found that 95 proteins were putatively causally influenced by elevated CRP, with 45 of these proteins surviving a stricter FDR threshold of 5% (table S26). We emphasize that these analyses are exploratory in nature, and we treat the effect sizes of the CRP effect on each protein largely as a test of the null hypothesis that the two are not associated. The vast majority of proteins that prioritized using the IVW estimator were directionally consistent in the median, mode, and Egger sensitivity analyses (table S27). However, 20 of the FDR < 0.1 proteins were suggested to act in the opposite direction and causally influence CRP, as the estimated variance explained by the IVs was significantly larger in the outcome than the CRP exposure, although this does not rule out bidirectional effects (table S28). We found that these proteins putatively influenced by CRP were enriched in several pathways including glycemic signaling and lymphocyte biology (tables S29 to S31). Notably, there were neuronal pathways overrepresented with CRP-associated proteins, including axon guidance, dopaminergic synapse, neurogenesis, glial cell differentiation, and cholinergic synapse. For example, dopaminergic and cholinergic synapse overrepresentations were driven by the three genes , with impaired signaling by these serine/threonine kinases implicated to dysregulate dopaminergic neurotransmission and down-regulation of genetically predicted neuronal AKT3 expression associated with SZ via a transcriptome-wide association study . The key advantage of these data is its large sample size  and that the biochemical traits analyzed were obtained from a single large cohort. Specifically, we use a panel of 50 biochemical GWAS that had high or medium confidence estimates of SNP heritability that were significantly different from zero as outlined in table S1 and Supplementary Text. These traits included lipids, micronutrients, hormones, metabolites, and enzymes.We obtained GWAS summary statistics for a series of blood-based biochemical traits from the large UKBB sample performed by the Neale group (https://github.com/bulik/ldsc). Briefly, LDSR estimates genetic covariance by regressing SNP-wise χ2, the product of the marginal SNP effects from both traits (Z1Z2), on its LD score, which is an estimate of total LD existing with that SNP. Trait hertiabilities are used to normalize the genetic covariance to obtain genetic correlation (rg). A key advantage of LDSR is that sample overlap only affects the LDSR intercept and not the slope, meaning that we can accurately estimate rg between UKBB biochemical GWAS and psychiatric GWAS with UKBB samples included. We used the Bonferroni method to correct for the 50 traits tested. One biochemical trait was excluded from further analysis, apolipoprotein B, as it exhibited negative heritability within the block jackknifing procedure to estimate the rg SE in some instances. The resulting 49 by 10 matrix of LDSR rg, divided by its SE to obtain z score, was subjected to a latent clustering method, finite GMM, with the mclust R package version 5.4.6 . The full details of this cohort and the generation of PGS are described in Supplementary Text. We tested the association of each biochemical PGS with CD status using binomial logistic regression covaried for sex and the first three SNP-derived principal components . The variance explained (Nagelkerke’s R2) in the full model, with the PGS versus the null (covariates and intercept only) model, was converted to the liability scale, assuming a population prevalence for CD of 0.33%  of marginal effect sizes for each trait  are compared to evaluate the proportionality of effects on either trait. The main output of the LCV model is the posterior mean Z1Z2), such that https://github.com/lukejoconnor/LCV.Genetic correlation may reveal important insights into shared biology between two traits; however, this should not be interpreted as implying a causal relationship in either direction. To evaluate evidence for a causal relationship, we implemented the LCV model to estimate genetic causality between traits, as outlined extensively elsewhere (F statistic for IVs for this CRP GWAS was sufficiently strong (F > 10), as reported previously . We compared direct estimates for each exposure using four different MVMR models—an IVW MVMR estimator, a median-based MVMR estimator, an Egger regression–based MVMR estimator, and a regularization approach whereby least absolute shrinkage and selection operator (LASSO)-type penalization is applied to shrink intercept terms corresponding to IVs predicted as valid (MVMR-LASSO)  (P < 5 × 10−8). For overlapping genome-wide significant signals, we tested whether there was a shared causal variant or different causal variants underlying these loci, assuming a single causal variant, using the coloc package colocalization method (https://github.com/huwenboshi/hess) to estimate local genetic covariance between SZ and CRP, as opposed to a genome-wide estimate by LDSR, with local genetic covariance  calculated for munged HapMap 3 SNPs in around 1600 approximately independent LD blocks outside the MHC  by dividing ρg, local by the product of the square roots of CRP and SZ local heritability per LD block, respectively.We investigated whether any of the lead SNPs (genome-wide significant) reported in the PGC3 SZ GWAS were also associated with CRP (We investigated the downstream effect of CRP on circulating levels of 3284 proteins in blood using MR (All statistical analyses used in this study were fully described in the previous sections, with additional information as necessary in the Supplementary Materials.


Advancements in statistical methods and sequencing technology have led to numerous novel discoveries in human genetics in the past two decades. Among phenotypes of interest, most attention has been given to studying genetic associations with continuous or binary traits. Efficient statistical methods have been proposed and are available for both types of traits under different study designs. However, for multinomial categorical traits in related samples, there is a lack of efficient statistical methods and software.We propose an efficient score test to analyze a multinomial trait in family samples, in the context of genome-wide association/sequencing studies. An alternative Wald statistic is also proposed. We also extend the methodology to be applicable to ordinal traits. We performed extensive simulation studies to evaluate the type-I error of the score test, Wald test compared to the multinomial logistic regression for unrelated samples, under different allele frequency and study designs. We also evaluate the power of these methods. Results show that both the score and Wald tests have a well-controlled type-I error rate, but the multinomial logistic regression has an inflated type-I error rate when applied to family samples. We illustrated the application of the score test with an application to the Framingham Heart Study to uncover genetic variants associated with diabesity, a multi-category phenotype.Both proposed tests have correct type-I error rate and similar power. However, because the Wald statistics rely on computer-intensive estimation, it is less efficient than the score test in terms of applications to large-scale genetic association studies. We provide computer implementation for both multinomial and ordinal traits. The online version contains supplementary material available at 10.1186/s12864-021-08107-x. Genetic association tests for continuous or binary phenotypes have uncovered many susceptibility genes or variants related to diseases. Various methods and efficient software have been developed and used for continuous and binary traits. For family samples, due to the correlation between relatives and violation of the independence assumption of ordinary linear regression, some alternative approaches were proposed. For example, Therneau and colleagues developed an R package (coxme) implementing linear mixed effects model to evaluate the association between a genetic variant and a continuous trait or survival outcome accounting for correlation present in family samples. Similar extensions to account for familial correlation using mixed effects models have been proposed for gene-based association tests . The proThe results of family-based and unrelated samples are summarized in Table , respectively. Because we have concluded that multinomial logistic regression leads to inflated type-I error rates, the power rate of multinomial logistic regression is not evaluated for family-based samples . The variants that have reached a genome-wide significance threshold of 5 × 10−8 or a suggestive threshold of 4 × 10−7  are summarized in Table CYP3A43, AP3B1 and LOC105370246 genes. AP3B1 is known to have variants associated with fasting insulin and HOMA-IR in African Americans without diabetes  ’. The expected value of zij is E[zij] = ′ and the variance of zij can be derived as:We adopt the idea of EGEE previously proposed , 16 to aR = rJ where J is a matrix of ones with a dimension of (K-1) by (K-1), and r is an unknown correlation parameter to be estimated with value between −1 and 1. The implementation of the approach provided in Additional File R with diagonal elements set to r1 and all off-diagonal elements set to r2. The matrix R is used to model the correlation between any two individuals in the same family along with the use of relationship matrix, such that Ri = Φi ⨂ R ( Φi is the relationship matrix of the i-th family defined as twice the kinship matrix), similar to how the familial correlation was handled in previous publications  = σi. Fisher’s scoring algorithm is used to update both θ and r from m-th iteration to (m + 1)-th iteration, written asD stands for the first-order derivative with respect to , until the pre-specified convergence criterion is met. Estimates of multinomial logistic regression and r = 0 or 0.5 usually work well in terms of starting values.The following score equations of EGEE , 16, 19 Note the score equations will be reduced to the following GEE form  when appH0 : β = 0. We first define the score vectors ,,U(3) = Ur. The score statistic is proposed as follows:To determine if a genetic variant is associated with a multi-category phenotype, the following null hypothesis is tested H0 : β = 0.A =  with subscript 2 denoting rows/columns that correspond to β, subscript 1 denoting rows/columns that correspond to γ1, …,γK − 1, and I is an identity matrix of size (K-1).Where Vi  is pre-specified correctly, then var) will equal to U∗ restricted to β, γ1, …,γK − 1, and the score statistic will be simplified to1, …, βK − 1) and .The score statistic follows a  samples , 22. OneThe Wald test is an alternative test with lower computational efficiency when applied to a large-scale genetic study. The Wald test statistic is proposed as follows:H0 ∪ Ha) until the pre-specified convergence criterion is met.This test statistic follows a The full variance matrix of all parameters stimator , with roYij = k) can be derived from P(Yij = k) = P(Yij ≤ k) − P(Yij ≤ k − 1) using proportional cumulative logit models, then the same EGEE equations are used for parameter estimation. However, the dimensions of EGEE equations are reduced and mathematical formulas of the matrix elements are derived differently due to the use of proportional cumulative logit models. A computer implementation for both multinomial and ordinal phenotypes is provided in Additional File Under the same framework, using the statistical theory of ordinal regression, the above score and Wald tests can be easily extended to test the association of a genetic variant with an ordinal trait for a family-based design. More specifically, because P((SNVs) with different minor allele frequencies (MAF) and a categorical trait with four categories , and compare the score test to the Wald test and the multinomial logistic regression which does not account for related samples. We then conduct simulations to assess the power of all three approaches.We conduct type-I error and power simulation studies to evaluate the validity of our score test in assessing the association between single-nucleotide variants H0 : β1 = … = β3 = 0. Eight SNV scenarios with MAF ranging from 0.01 to 0.3 are explored. For each SNV scenario and sample design, 500,000 replicates are simulated and the type-I error rate is defined as the proportion of simulations significant at the threshold of 0.01, 0.001, and 0.0001. For family-based samples, we also have conducted simulations to evaluate the type-I error of robust score and Wald test when applied to ordinal traits, based on 500, 000 replicates for each MAF scenario.We compare the type-I error rate of the robust score test to the Wald test as well as multinomial logistic regression (without accounting for related samples) in both family-based and unrelated designs. We simulate a 4-category trait under the null hypothesis that there is no genetic association with the trait, i.e. Family-based samples: In each replicate, a total of 1000 independent 3-generation families with 2 grandparents who have one son and one daughter  are simulated. The number of grandchildren (3rd-generation) is randomly determined from a discrete uniform distribution ranging from 1 to 4. Within each of the 1000 families, we simulate additively coded genotypes  of the grandparents under Hardy-Weinberg equilibrium, and the 2nd and 3rd generations’ genotypes are then simulated using random allele dropping. Two covariates (age and sex) are simulated. The sex of the 3rd-generation is randomly assigned, and the covariate of age is simulated in the following way [u1 versus 0.8 and 0.01% of the latent variable u2:Φ is the relationship matrix which is a kinship matrix multiplied by 2.ter Fig. . are simwing way : we staru1, u2 to two binary traits using a threshold model with a disease prevalence of 10 and 35%, assuming a disease with a moderate prevalence such as type 2 diabetes (T2D) and a high prevalence such as obesity. The multinomial trait is then defined by these two binary traits as follows: diabetes & obesity, diabetes but no obesity, obesity but no diabetes and no diabetes & no obesity, in adults.We transform Unrelated samples: In each replicate, we simulate a total of 5000 independent subjects with ages ranging from 18 to 90. A total of 5000 independent additively-coded genotypes are simulated. The sex is randomly assigned . We then simulate two continuous traits influenced by age and sex only, based on the following two equations, so that age and sex explain around 3.2 and 0.8% of the total variance of u1 versus 0.94 and 0.01% of u2 respectively:u1, u2 as described in the family design section..We evaluate the type-I error of the proposed score test and Wald test, and then compare them to the multinomial logistic regression assuming independence among observations (using likelihood ratio test (LRT))We compare the power of the score to the Wald test and multinomial logistic regression under the same allele scenarios and with the same family/unrelated structure as described above. In addition to the effects of age and sex, we also include an additively coded genetic variant g which explains approximately 0.5% of the variance of each continuous trait, i.e.With this phenotype generation model, both traits are simulated under the alternative hypothesis that there is an association between the trait and the genetic variant. For each MAF scenario, a total of 5000 replicates are generated. The power rate is then evaluated for 3 different significance thresholds including the commonly used GWAS threshold for each method.The motivation for developing this efficient score test is to make the application to a large-scale genetic study computationally feasible, especially after the cost of whole-genome sequencing has been greatly reduced in recent years.We apply the robust score test to the Framingham Heart Study (FHS) , 23. A tAdditional file 1.Additional file 2.Additional file 3.


Heat stress (HS) affects pig performance, health and welfare, resulting in a financial burden to the pig industry. Pigs have a limited number of functional sweat glands and their thermoregulatory mechanisms used to maintain body temperature, are challenged by HS to maintain body temperature. The genetic selection of genotypes tolerant to HS is a promising long-term (adaptation) option that could be combined with other measures at the production system level. This review summarizes the current knowledge on the genetics of thermoregulation in pigs. It also discusses the different phenotypes that can be used in genetic studies, as well as the variability in thermoregulation between pig breeds and the inheritance of traits related to thermoregulation. This review also considers on-going challenges to face for improving heat tolerance in pigs. Non-ruminants (pigs and poultry) represent the majority of meat consumed in the world . Pork prPigs are homeothermic animals as they can keep deep body temperature relatively constant, within narrow limits, despite a wide variation of the surrounding climatic environment. Thermoregulation is the physiological process allowing the balance between heat production and heat loss mechanisms . We assuThe regulation of body temperature is a complex physiological process involving regulation from the cell to the whole animal, therefore, the phenome of thermoregulation encompasses a large variety of physical and biochemical traits . The staAs reviewed by Renaudeau et al. , severalThe hormonal, cellular and molecular response to HS is therefore complex and is still being unraveled. Moreover, little data is available on the heritability of these biomarkers and their correlation with other HS phenotypes and with production traits, making it difficult to postulate on the most relevant biomarkers to include in genetic studies. Because HS reduces feed intake, it can be difficult to determine whether these responses are the consequences of direct effects of HS, or of indirect effects linked to the reduced feed intake , 48, 49.Finally, the variation in traits of economic interest in response to the heat load, such as reproduction  and production traits  are indicators of resilience to HS , 51. The4 illustrate the variability of thermoregulatory responses to HS between a tropical local Creole breed  and a Large White breed  in lactating sows. Within the same production environment, high average THI during lactation caused greater increase of rectal temperature    and in d = 0.68) , 95]. To = 0.68) . Foury e = 0.68)  found thDPYSL2 and ADRA1A, and biological pathways  to explain the detected associations.Using feeding behavior as a proxy for thermoregulation, a pangenomic study has been performed  to detecAs reviewed by Renaudeau et al. , there iAs pointed out by several authors, the analysis of high-throughput phenotypic and genomic data to address issues related to HS, and more generally to health and animal welfare, requires the development of new methods and technologies capable of integrating diverse, heterogeneous, and large-scale data. In this context, the advent of new technologies, omic tools , 102  the extent of G × E interactions; (ii) the level of antagonisms between genes involved in thermoregulation and production biological pathways, (iii) the definition of a heat tolerance index, and (iv) the ability to collect phenotypic and genomic information on a large scale in the appropriate environmental conditions.There is no doubt that genomic innovations and precision selection  will be mobilized for future genetic studies on the thermoregulation of pigs . These sJ-LG, WR, HG, and NP assisted in the conception of the study and contributed to manuscript revision, read, and approved the submitted version. 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Ericameria nauseosa), a foundational shrub species of western North America. To quantify landscape genomic structure and provide perspective on patterns of local adaptation, we generated reduced representation sequencing data for 17 wild populations  spanning a range of environmental conditions. Population genetic analyses illustrated pronounced landscape genomic structure jointly shaped by geography and environment. Genetic‐environment association (GEA) analyses using both redundancy analysis (RDA) and a machine‐learning approach (Gradient Forest) indicated environmental variables  influenced spatial genomic structure and were correlated with allele frequency shifts indicative of local adaptation at a consistent set of genomic regions. We compared our GEA‐based inference of local adaptation with phenotypic data collected by growing seeds from each population in a greenhouse common garden. Population differentiation in seed weight, emergence, and seedling traits was associated with environmental variables  that were also implicated in GEA analyses, suggesting complementary conclusions about the drivers of local adaptation across different methods and data sources. Our results provide a baseline understanding of spatial genomic structure for E. nauseosa across the western Great Basin and illustrate the utility of GEA analyses for detecting the environmental causes and genetic signatures of local adaptation in a widely distributed plant species of restoration significance.The spatial structure of genomic and phenotypic variation across populations reflects historical and demographic processes as well as evolution via natural selection. Characterizing such variation can provide an important perspective for understanding the evolutionary consequences of changing climate and for guiding ecological restoration. While evidence for local adaptation has been traditionally evaluated using phenotypic data, modern methods for generating and analyzing landscape genomic data can directly quantify local adaptation by associating allelic variation with environmental variation. Here, we analyze both genomic and phenotypic variation of rubber rabbitbrush ( It is broadly distributed across western North America  that additionally encompass 22 named varieties . We bulk‐collected seeds from 17 localities . Extracted DNA was quality and quantity screened using a QIAxpert microfluidics electrophoresis device (Qiagen).We planted seeds from each sample locality in field soil in a greenhouse on the University of Nevada, Reno campus . Filled seeds (endosperm present) were separated from unfilled seeds, as they are visually distinct on a light table. We weighed ten batches of 10 filled seeds per population to calculate an average seed weight per population. We then planted 100 filled seeds from each site following a randomized block design in the fall of 2018. Seeds were monitored daily for emergence, and seedlings were harvested 40 days after emergence, using methods previously developed for describing seedling traits, which are important predictors of success in restoration scenarios , fragments ranging from 350 to 450 base pairs (bp) were size‐selected using a Pippin Prep quantitative electrophoresis unit. Single‐end sequencing (100 bp read lengths) was performed using two lanes on an Illumina HiSeq 2500 platform at the University of Wisconsin‐Madison Biotechnology Center .Reduced representation libraries were prepared using a double‐digest restriction‐site associated DNA sequencing (ddRADseq) method . Full methods for contaminant cleaning, assembly, variant calling, and additional filtering steps can be found in the Appendix After contaminant cleaning and demultiplexing, reads were assembled to a de novo reference with 2.3entropy; Gompert et al., k), and estimate individual ancestry coefficients (q). This model uses an allele frequency prior and incorporates genotype uncertainty arising from sequencing and alignment error as well as stochastic variation among individuals and loci in sequencing depth during parameter estimation  to seed the MCMC  from entropy were estimated based on 100,000 MCMC iterations with a burn‐in of 30,000 and thinning every tenth step. As the k = 2 model fit the data best , genotype probabilities from this model were used for subsequent analyses (see Table k = 2–8). Due to complex fine‐scale population structure, we do not present ancestry coefficient bar plots as they do not usefully illustrate population differentiation across the landscape. Rather, we used this model primarily to (1) obtain genotype probability estimates and thereby allow for the incorporation of genotype uncertainty into subsequent analyses and (2) generate individual ancestry coefficients (q), which were used to mitigate the influence of neutral population genetic structure in GEA analyses (see below).We used a hierarchical Bayesian model that incorporates genotype uncertainty  with geographic and environmental distances . Geographic distances among populations were calculated as haversine distance using the geosphere R package  using ngsF , following standardization proposed by Patterson et al. , to visu, D Nei,  calculat, D Nei,  and isol, D Nei,  were ass, D Nei,  to assocHijmans, . Using mp‐value estimates from these approaches can be confounded by spatial autocorrelation among the matrices even when controlling for neutral processes  and implemented using lme4 and piecewisesem packages in R.In addition to the Mantel tests, IBD and IBE were estimated using a piecewise structural equation model  and the Climatic Water Deficit Toolbox  to control for multicollinearity. These ten variables represented variation in temperature, precipitation, elevation, evapotranspiration, and soil water capacity as well as precipitation seasonality, which is the degree of variability in monthly rainfall throughout the year  we estimated the proportion of genomic variance explained by environmental variation and the individual contributions of each environmental variable and (2) we quantified locus‐specific allele frequency shifts correlated with environmental variation to detect selection on genome variation and quantify the extent different variables underlie these shifts.q; generated by entropy) were partialled out by regressing q from each environmental variable prior to analyses. This is statistically equivalent to conditioning within RDA  to identify the strongest predictor. Gradient Forest also gives a relative importance metric (R2) for each locus  and the associated environmental variable. Results from RDA and GF analyses were compared using the correlation between the first RDA axis loading and GF R2 for environmental variables and outlier loci, and by quantifying the degree to which loci were associated with the same environmental predictor variables. To further show that these associations were not due to chance alone, a permutation test (n = 1000) was implemented in GF to verify there was a greater number of overlapping outlier loci between the two methodologies in the observed data set than in random subsets of loci of equal size. Absolute values of the RDA loadings were used for any comparative analysis because only the relative strength of association was being assessed, not directionality. Most analyses and data manipulation were performed using the base stats library or custom R functions, the vegan library , days to emergence, shoot biomass, average root diameter, and seed weight (average of 10 seeds weighed in 10 batches per population). It is important to note that seed weight variation was sampled and measured from populations in nature and was thus not an outcome of the common garden experiment. Shoot biomass was chosen as the representative trait for seedling size as it is highly correlated (r = 0.62–0.92) with additional traits  that are likely to be important in establishment  and were collapsed to a single composite variable using the first principal component axis, capturing 90.07% of the variance. This single composite variable was used for any multivariate analyses while the mean values for each trait in each population were used for univariate analyses.To complement GEA‐based inference of local adaptation and its environmental drivers, we measured phenotypes from field‐collected seeds of 14 of the above populations in a greenhouse common garden, focusing on seed and seedling traits that have been shown to affect seedling establishment, an important component of restoration, in other taxa  to determine the critical value for c/h2  for each phenotype. More detailed methods and justifications for this analysis are available in the Appendix R2) from RF for the environmental variables on the mean trait values of seedling emergence, seed weight, days to emergence, shoot biomass, and average root diameter were correlated with the same environmental variable loading/importance from the GEA analyses (both RDA and GF).While phenotype‐environment associations can be influenced by genetic drift, assessing how phenotypic variation relates to genetic variance can provide insight into the presence and mode of selection through Brommer, , was estPVE) by the predictors and adjusted r2  was estimated with the varpart function in the vegan package in R. Model significance, when appropriate, was assessed using independent RDA and pRDAs, permutation‐based ANOVAs (n = 999), and significance thresholds of α ≥ 0.05. All measurements were centered and standardized prior to analyses.Finally, the extent to which population‐level phenotypic differences were associated with genetic, environmental, and geographic variation was assessed using a variance partitioning technique to estimate individual and shared contributions of each variable . Initial variant calling generated ~1 million variant sites, which reduced to 38,615 loci after stringent filtering. Data from all 222 individuals were retained as coverage was relatively high , and individuals had relatively low percentages of missing data (mean = 5.9%).FST estimates were in the low range . There was less evidence for an association between environmental and geographic distance  for each response variable with fixed effects only  and with random effects (population) included . Thus, variance explained was relatively low for the marginal estimation with only fixed effects  but was much greater in the conditional estimation on the full model including random effects . This indicated that including a random effect of population identification to eliminate the inherent autocorrelated structure of pairwise comparisons was most appropriate. Additionally, populations displayed moderate levels of genetic diversity .While multilocus 3.2PVE = 21.65%; Figure R2 from GF being strongly correlated for both the environmental predictors and the identified outlier loci  in GF. A permutation test (n = 1000) using 176 randomly sampled loci across all 38,615 loci indicated the RDA outlier loci had more overlapping loci with GF outliers than would be expected by chance . Furthermore, 65 out of the 96 RDA and GF outlier loci were most strongly associated with the same environmental predictor. An additional 27 loci (92 or 95.8% of RDA and GF outlier loci) had the same top two environmental associations, with minimal difference (average <0.095 SD) between the first and second association strength to that locus (r for RDA or R2 for GF). In other words, RDA and GF approaches typically agreed on the top two environmental predictors driving the outlier loci, only flipping between first and second when the difference in association strength was negligible.Genetic‐environment association analyses provided evidence of local adaptation to specific environmental variables, and an overlapping set of loci potentially under selection was detected by the two methods employed. The signature of local adaptation could be attributed to a set of environmental predictors that were largely consistent between the two methods employed Table . The fir3.3p < 0.001; seed weight: p < 0.001; days to emergence: p < 0.001; shoot biomass: p < 0.001; average root diameter: p = 0.014; Figure r = 0.790, seed weight: r = 0.710, shoot biomass: r = 0.635; Figures Populations grown from wild collected seed in the common garden differed in all measured phenotypes  while days to emergence and average diameter were not  indicating the robustness of our comparisons and conclusions. Because days to emergence and average diameter were not predicted by environmental variation and did not deviate from neutral expectations, the composite variable of seedling emergence, seed weight, and shoot biomass was used as representative of phenotypic variation when assessing environmental associations and comparing to the GEA analyses , mostly driven by associations with precipitation seasonality in both analyses.ot Table . Furtheres Table . Even stPVE = 69.84%; Table PVE = 54.50%) and genetics (PVE = 52.66%) explained substantially more variance than geography (PVE = 21.26%). When looking at the partial contributions of each predictor independent of correlative effects of other predictors, environment contributed the most (adj. r2 = 16.12%), followed by genetics (adj. r2 = 12.94%), and geography contributing very little (adj. r2 = 2.57%).Further evidence for the importance of environmental and genetic variation on the phenotypic variation was provided by the variance partitioning approach combined with RDA and pRDAs. Taken together, genetics, environment, and geography explained a large proportion of phenotypic variance . However, GEA analyses can be limited by the spatial resolution of environmental data Daly, , confoun4.1E. nauseosa is wind dispersed and fairly continuously distributed across its range  estimates were close to 0 (mean F = 0.003). Although past studies of Ericameria have suggested high rates of self‐fertilization  for inferring adaptation as they interrogate environmental variation associated with allele frequency shifts while detecting locus‐specific signals of selection , was overwhelmingly the strongest predictor in our analyses . Nonetheless, a variance partitioning approach allowed us to examine the contribution of genetic, environmental, and geographic variation among populations to phenotypic variation measured in the common garden. Genetic and environmental variation were much stronger predictors of phenotypic variation than geography Table . Environ4.4E. nauseosa of the western Great Basin exhibited fairly pronounced spatial genomic structure, driven both by stochastic processes and local adaptation to environmental variation. Because E. nauseosa has not been widely included in large‐scale restoration projects to‐date, it is an ideal system for examining processes underlying natural genomic variation  in plants of the Great Basin as extant population likely exhibit an unmanipulated representation of genetic diversity across its range. Inference of local adaptation from our population genomic and common garden approaches implicated a largely consistent set of environmental variables and strongly pointed to precipitation seasonality as a major driver of local adaptation. Given the apparent importance of this variable in this and other recent studies of local adaptation in arid land plants (Baughman et al., Despite being widespread, disturbance oriented, and wind dispersed, The authors declare no conflict of interest.Appendix S1Click here for additional data file.Table S1Click here for additional data file.Table S3Click here for additional data file.Appendix S2Click here for additional data file.


LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines. LightGBM exhibits superior performance in terms of prediction precision, model stability, and computing efficiency through a series of benchmark tests. We also assess the factors that are essential to ensure the best performance of genomic selection prediction by taking complex scenarios in crop hybrid breeding into account. LightGBM has been implemented as a toolbox, CropGBM, encompassing multiple novel functions and analytical modules to facilitate genomically designed breeding in crops.The online version contains supplementary material available at 10.1186/s13059-021-02492-y. The rapid advancement of genotyping technology has promoted the integration of genomic prediction into modern breeding programs for both animals and crops –5. Predi1) generated from the crossings of inbred lines from different heterotic pools. An incomplete diallel cross of two parental pools each containing 1000 lines can theoretically generate one million F1 combinations, from which only ~ 15% are planted to measure the phenotypes for training model to predict the rest ~ 85% [1s are desired for crop breeding [GS for crops is associated with many more challenges than that for animals . The maist ~ 85% . With thst ~ 85% –5; howevst ~ 85% . Hence, st ~ 85% . Finallyst ~ 85% , 14. Thebreeding .Machine learning (ML) has been widely employed in Big Data analytics. It can automatically learn data patterns and optimize model parameters , 19. TheOne of the GB variants — LightGBM (light gradient boosting machine) — developed by Microsoft has demonstrated superior performance in coping with extremely large, structured datasets with an ultra-high training speed . In this1 hybrid maize with measured phenotypes of days to tasseling (DTT), plant height (PH), and ear weight (EW), generated from the crossings of a maternal pool and a panel of 30 paternal testers following a North Carolina-II design (Methods) [1s for short) exhibiting diverse patterns of heterosis effects of hybrid maize. The data structure of the thirty F1 subpopulations is illustrated for designing different predictive frameworks to objectively evaluate the model precision and stability  . The matMethods) . The pat1s with field-measured phenotypes were mainly used for evaluating the model precision. Cross-validation (CV) was performed using the following framework repeated 30 times to generate a distribution of the precision  for each method: namely 29 subpopulations of F1s as training samples to predict the rest one using the 32,559 SNP as features. As the precisions of GB and rrBLUP were equivalent and both outperformed other four ML methods . rrBLUP exhibited superior prediction precision and computing efficiency, and it was then selected as the representative of statistical models to compare with ML methods  siblings, only maternal (M only) siblings, only paternal (P only) siblings, and neither parental (Neither) siblings in the testing samples Fig. a. As shoree Fig. c.Fig. 21s as the training samples to first evaluate the fitting ability of the five models. As shown in Fig. An initial evaluation of the basal MLs indicated that ensemble learning with the GB paradigm is the optimal method, making it worthy of further evaluation and comparison with the optimized algorithms. Three more GB variants, namely LightGBM, XGBoost (eXtreme Gradient Boosting), and CatBoost , were compared with GB and rrBLUP in parallel. We utilized the phenotyped 8652 F1 populations with field-measured phenotypes of DTT, PH, and EW. Model evaluations were carried out using six predictive frameworks (E1 to E6) to compare LightGBM and rrBLUP  F1s (E1 and E2), rrBLUP showed slightly higher precision than LightGBM. As for the reciprocal evaluations (E3 and E4) between Zheng58 and Mo17 F1s, LightGBM outperformed rrBLUP overall for DTT and PH , the model precision could be improved; LightGBM was slightly better than or equal to rrBLUP. These results indicate that under different genetic backgrounds, the two methods produce relatively equivalent precision results when using different predictive frameworks.To evaluate the model stability of rrBLUP and LightGBM, we utilized a global collection of a core germplasm containing 527 lines of tropical, subtropical, and temperate maize  Additio: Fig. S41s reciprocally with the 1221 Zheng58 (Jing724) F1s as the training samples to predict the 1221 Jing724 (Zheng58) F1s. Because of the significant increase in the genetic complexity of the 7421 training samples, the influence of population stratification must be considered. One possible solution is to include parental phenotypes as additional features. As expected, the inclusion of additional features significantly improves the precision of LightGBM, from 0.538 to 0.686 (DTT), 0.518 to 0.687 (PH), and 0.386 to 0.400 (EW), on average for the two F1 populations . Once the conversion is determined for a fixed coding scheme, LightGBM may be directly applied for GS. On the same dataset shown in Fig. 1 combinations. Breeders usually select 10 to 15% of the theoretical combinations to measure phenotypes as a training set, and predict the rest 85 to 90% via GS [1s, to compare rrBLUP and LightGBM under four scenarios by setting different ratios of training and testing samples. In scenario 1, the size of testing set (621 sample) holds constant and the number of training samples gradually decreases from 5589 to 62, in order to set 9 ratios of training vs. testing samples. As shown in Fig. In a commercial pipeline of maize single-hybrid breeding, hybridization of inbred lines selected from maternal and paternal pools may generate large amount of F% via GS . This maThe above analyses collectively indicate the importance of sampling rate of training data in determining the baseline precision of GS prediction, which is a critical factor needed to consider when designing a GS project. When the size of training set is fixed at 621 samples accounting for 10% of the population, the baseline precisions for DTT, PH, and EW are 0.637, 0.716, and 0.467 averaged from the results of LightGBM prediction in the four scenarios; in contrast, the baseline precisions for the three traits are 0.623 (DTT), 0.706 (PH), and 0.503 (EW) for rrBLUP , moderate (25 to 75% DTT), and late (lowest 25% DTT) flowering times. The two-class classification with fivefold CVs showed excellent discrimination of early and late-flowering F1s, as reflected by a high area under the curve (AUC) value of 0.878 , moderate (25 to 75%), and low (lowest 25%) GCAs of EW computed based on the F1s crossed with Zheng58 and Jing724 (Methods). Subsequently, two-class and three-class classifications were performed to evaluate the model precision. While the model achieved a high precision with AUC = 0.793 for the two-class classification, the precisions for discriminating the samples with high, moderate, and low GCAs were 0.723, 0.639, and 0.728, respectively  of the grain yield computed on the basis of the average performance of their FDuring model training, LightGBM infers the predictive effectiveness for each SNP by computing the information gain (IG) value to represent feature importance. The higher the IG, the more effective the genotype of an SNP in discriminating the phenotypes. To validate the effectiveness of the features recognized by LightGBM, ten sets of features, including the top 12 to 4000 SNPs sorted by IGs generated from model training on 1428 maternal lines, were compiled (Methods). Simultaneously, the same number of SNPs for each feature set were randomly selected from the genome as controls. With the decreased numbers of LightGBM-selected SNP from 32,559 to 96, precisions for the three traits gradually dropped without great difference, compared to the results when using genome-wide SNPs  of chr8.s_123039570 correlates with ZCN8 expression, we examined the expression levels of ZCN8 in 412 CUBIC lines divided into ZCN8-AA (268 lines) and ZCN8-GG (144 lines) groups. As expected, both the DTT phenotypes and ZCN8 expression concordantly exhibited a significant difference between the two groups  and MADS69, respectively, and a moderate SNP (chr1.s_202563972) fell into the BR2 (Brachytic2) QTL . As expected, the overall IG distribution was in accordance with the GWAS signals Fig. b. The toy module , 31. TheZCN8-AA 28 lines aZCN8-AA 28 lines aing time –34. To tZCN8-AA 28 lines aMADS69 was recognized by LightGBM on PH, which were moderately detected by GWAS of DTT but undetected by GWAS of PH. MADS69 promotes flowering by activating the florigen ZCN8; however, its recognition by LightGBM on PH is worthy of further investigation. Based on the genotype of chr3.s_153816034 (CC and TT), the CUBIC lines were divided into MADS69-CC (124 lines) and MADS69-TT (317 lines) groups, in which the expression of MADS69 between the two groups exhibited a significant difference (p = 0.017). Correspondingly, the MADS69-TT group with a higher expression of MADS69 showed phenotypes of significantly shorter PH and earlier DTT than the MADS69-CC group  as training set. Then, we compared the GWAS signals derived from the 7431 samples with observed phenotypes and the 8652 samples containing the 1221 F1s with predicted phenotypes. As for the DTT trait, both MADS69 and ZCN8 peaks were detected in the two populations, and the 8652 samples exhibited enhanced SNP effects, likely due to the augmentation of population size , showing the same GWAS signals in the 775 lines containing predicted samples  measured in a core germplasm collection of 266 lines  were useynthesis   for prediction and consume a considerable amount of computing resource to solve the model [Accelerated crop breeding refers to the integration of multifaceted technologies, including doubled haploid (DH) techniques, speed breeding (SB), genomic selection (GS), and genome editing –5. Owinghe model , 38, 39.Because of the abovementioned merits of LightGBM, we then implement it as a one-stop toolbox CropGBM. In addition to the core GS function, it also integrates multiple novel features and analytical functions, such as genotype and phenotype data preprocessing and recoding, dimensionality reduction and population visualization via PCA and t-SNE algorithms, sample classification via K-means and OPTICS clustering, feature selection via information gain (IG) analysis to assist marker design, GPU-enabled acceleration, and so on  was lower than that of rrBLUP (r = 0.44) in maize. One possible reason is that the 391 inbred lines in the core germplasm represent a wide range of genetic diversity as the panel contains temperate maize, tropical maize, popcorn maize, sweet maize in America, and some exotic maize [Our work followed a standard procedure whereby an ML system is built and included a series of assessments on the factors influencing the precision and stability of a predictive model. These assessments are critically important and highly recommended to perform prior to carry out a GS task in crop breeding, as model performance may vary by different species, populations, and traits . Using aVarious speed breeding techniques have accelerated the production of inbred lines in major crops like maize, rice, wheat, and soybeans, enforcing the employment of genomic prediction techniques, such as the genomic selection (GS) model, to assist selection of superior lines or hybrids. LightGBM is an ensemble learning framework, which adopts the strategy of leaf-wise tree growth to construct decision trees and features ultrafast efficiency in coping with large dataset. We implemented LightGBM as a one-stop toolbox CropGBM to assist the employment of GS in crop breeding. CropGBM exhibits superior performance in coping with large sample sizes, which may help accomplish model training on hundreds of thousands of samples in a matter of minutes. It also includes novel features and analytical modules to cover multiple aspects of genotype and phenotype data analysis, population genetic analysis, feature importance analysis, and GS prediction. These functions may ensure the best performance of GS prediction, considering the complex scenarios in crop hybrid breeding. We also proved that the intrinsic mechanism of ensemble learning in GS prediction is interpretable in biology, although an ML model has been referred to as a black box. This is based on the results showing that the highly effective SNPs selected by LightGBM exhibit significantly correlated divergence in the genotypes, phenotypes, and expressions of trait-associated genes. Thus, these correlations can form a theoretical basis for implementing data-driven genomic design for accelerated breeding in crop species.1 hybrids were inferred by combining the maternal and paternal alleles derived from the 1428 and 30 lines, respectively. The three phenotypic traits, namely the days to tasseling (DTT), plant height (PH), and ear weight (EW), of the 1458 maternal lines and the 8652 F1 hybrids were measured in five locations. Details of collecting phenotypes were described in Xiao et al. [1 hybrid and each parental line was computed for the phenotypic data in the five locations over 2 years, using the mixed linear model in the R package “lme4.” The BLUP values for each phenotype were then used for the subsequent analysis.Liu et al. previously described the development of the maternal CUBIC pool including the 1428 inbred lines crossed from the 24 founder females . A totalo et al. . To reduThe procedure of constructing an ML system for a specific task involves multiple steps to ensure an objective assessment of the system. For model selection, five basal methods, including SVR, ANN, KNN, RF, and GB, were compared with rrBLUP in parallel. For the feature selection, 32,559 SNPs were selected from the original set of 4.5 million SNPs based on professional knowledge in maize population genetics. For the model evaluation, we designed multiple predictive frameworks and different ratios of training and testing samples to test the model precision and model stability. When ensemble learning is determined as the optimal approach, three more variants of the GB method, namely XGBoost, CatBoost, and LightGBM algorithms, were tested in parallel with rrBLUP. To further optimize the LightGBM model, additional functions, including feature format conversion, hyperparameter grid search, classification prediction, and GPU acceleration, were implemented. The feature importance analysis was based on computing a score of the information gain to represent the predictive effectiveness for each SNP. To interpret the underlying mechanism of LightGBM in G2P prediction, the 384 highly effective SNPs recognized by LightGBM were compared with the trait-associated QTLs identified by the GWAS analysis to prove their regulatory association with gene expression and phenotype variations. Finally, a parallel benchmark testing was performed on the same server to test the computing performance of CropGBM and rrBLUP. The server was equipped with a Xeon E5-2665 CPU clocked at 2.40 GHz (8 cores × 2), 128 GB of memory, and an NVIDIA GeForce GTX-P8 GPU (1080 × 4)p value) of statistical test for the comparison of rrBLUP and LightGBM used in the corresponding figures is presented in Additional file 1s  can be generated for each model.To evaluate the five basal ML models using the 6210  F1s, namely fixed size of testing set (621 samples), fixed size of training set (621 samples), fixed ratio of 9:1 (training vs. testing), and fixed ratio of 1:9 (training vs. testing)  Fig. . For the1s were partitioned into 30 subgroups, 25 of which were used as training samples to predict the F1s in the rest of the 5 subgroups. This procedure was repeated 30 times to generate a distribution of the precision.To evaluate the influence of parental compositions on the prediction Fig. b, the 621 populations  value is used to represent the change in the entropy before and after separating the samples, which may reflect the effectiveness of a tree node (SNP) in terms of discriminating two branches of samples that exhibit a strong phenotypic difference. Therefore, the SNPs with high IGs indicate a high power in classifying samples. After completing all the iterations of tree building during model training, the IG values for each SNP are summed to represent its feature importance that may reflect its power on association between the genotype and phenotype. To exclude the influence of SNP effects from testing samples, the IGs were first computed by LightGBM in each time of sampling of 80% lines as training set, and the top 12, 24, 48, 96, 192, 384, 1000, 2000, 3000, and 4000 SNPs were selected, which were based on ranking the summed IGs from high to low, as features to retrain LightGBM. The model was then used to predict phenotypes of the rest 20% lines. The sampling was repeated for 30 times to generate 30 Pearson correlations used to plot precision distribution for each set of SNPs. Meanwhile, the same procedure was applied on the same number of SNPs randomly sampled from the 32,559 SNPs to plot precision distribution as the contrast. At last, rrBLUP was performed on the 1428 maternal lines using 32,559 SNPs to compute baseline precisions for the three traits.For both rrBLUP and machine learning tools, the genotypic characters should be first converted to numeric features based on the allele frequency of each SNP in the training and testing populations. When using the 0, 1, 2 coding scheme, the homozygous genotype (AA) of the two major alleles is coded as 0, heterozygous genotype (AB) of one major and one minor allele is coded as 1, and the homozygous genotype (BB) of the two minor alleles is coded as 2. In addition to the 0, 1, 2 coding scheme, LightGBM may employ an alternative 0–9 coding scheme to represent all the forms of genotypes, such as the converting rule used in CropGBM, as follows: AA (0), AT (1), TA (1), AC (2), CA (2), AG (3), GA (3), TT (4), TC (5), CT (5), TG (6), GT (6), CC (7), CG (8), GC (8), GG (9).The general combining ability (GCA) is the average value of the inbred line based on its behavior in crosses with other lines. It is calculated as follows:GCAi represents the GCA of parent i, i parental hybrid offspring, and where https://scikit-learn.org/stable/);Machine learning tools in Python sklearn package ;Random forests ;Artificial neural network ;K-nearest neighbor ;Gradient boosting regression tree ;PCA analysis (decomposition);t-SNE analysis (manifold);OPTICS analysis (cluster);eXtremeGB ;Categorical GB ;BLUP (R package ‘lme4’);LightGBM ;https://cran.r-project.org/src/contrib/Archive/rrBLUP/rrBLUP_4.6.tar.gz);Genomic selection analysis: rrBLUP ;GWAS analysis: GEMMA Manhattan plot: CMplot (https://corncyc-b73-v4.maizegdb.org)Metabolic pathway: CornCyc (https://www.cog-genomics.org/plink/)PLINK Evolution analysis: MEGA Cladogram: EvolView (https://github.com/YuetongXU/Cropgbm-Paper)Other scripts for CropGBM (Additional file 1. Supplemental Tables S1 to S5 and Figures S1 to S9.Additional file 2. Review history.


P < 5 × 10−8) were followed up with Bayesian fine-mapping to localize potential causal variants. The predictive power of a genetic risk score (GRS) constructed from previously reported trans-ancestry eGFR lead single nucleotide polymorphism (SNPs) was evaluated in the Uganda GPC. We identified and validated two eGFR loci. At the glycine amidinotransferase (GATM) locus, the association signal  in the Uganda GPC GWAS was distinct from previously reported signals at this locus. At the haemoglobin beta (HBB) locus, the association signal  has been previously reported. The lead SNP at the HBB locus accounted for 88% of the posterior probability of causality after fine-mapping, but did not colocalise with kidney expression quantitative trait loci. The trans-ancestry GRS of eGFR was not significantly predictive into the Ugandan population. In the first GWAS of eGFR in continental Africa, we validated two previously reported loci at GATM and HBB. At the GATM locus, the association signal was distinct from that previously reported. These results demonstrate the value of performing GWAS in continental Africans, providing a rich genomic resource to larger consortia for further discovery and fine-mapping. The study emphasizes that additional large-scale efforts in Africa are warranted to gain further insight into the genetic architecture of CKD.Genome-wide association studies (GWAS) of kidney function have uncovered hundreds of loci, primarily in populations of European ancestry. We have undertaken the first continental African GWAS of estimated glomerular filtration rate (eGFR), a measure of kidney function used to define chronic kidney disease (CKD). We conducted GWAS of eGFR in 3288 East Africans from the Uganda General Population Cohort (GPC) and replicated in 8224 African Americans from the Women’s Health Initiative. Loci attaining genome-wide significant evidence for association ( APOL1 locus with CKD, which has risen to high frequencies within West Africa due to selection pressures related to protection against Lassa fever, highlights the potential for novel discovery in African populations (Chronic kidney disease (CKD) is a global public health problem, with adverse outcomes of kidney failure, cardiovascular disease and premature death. CKD is at least three times more frequent in Africa, which has limited resources, than in developed countries . With raulations . Given tulations ,5,6. Theulations ,10. Howeulations ,10. Whilulations . The AfrHere, we conducted the first continental African GWAS of estimated glomerular filtration rate (eGFR), a measure of kidney function used to define CKD, including 3288 individuals from the Uganda General Population Cohort (GPC). Subsequently, associations were validated through GWAS of eGFR in a large sample of 8224 African Americans from the Women’s Health Initiative (WHI). Together, these GWAS comprised a total of 11 512 African ancestry individuals. We used the Uganda GPC to: (i) identify loci associated with eGFR; (ii) fine-map loci by taking advantage of the finer-grained LD structure in African ancestry populations and (iii) evaluate the predictive power of an eGFR genetic risk score (GRS) into the Ugandan population that was derived from lead single nucleotide polymorphism (SNPs) at previously reported loci from trans-ancestry GWAS meta-analysis ,10.λ) of 1.01. We identified two loci attaining genome-wide significance (P < 5 × 10−8) in GPC (GATM)  (HBB)   threshold of at least 0.5% in a merged panel of imputed GWAS and whole-genome sequences. We tested for association in a linear mixed model implemented in genome-wide efficient mixed-model association (GEMMA), which accounted well for population structure and relatedness. Our association analysis in the Uganda GPC showed no evidence of residual population structure with a genomic inflation factor  in GPC , Fig. 1  × 10−8)  and haem × 10−8) . Both lo × 10−8) .GATM, rs2433603, was distinct from previously reported associations  at this locus . This genetic variant is monomorphic in European ancestry populations and rare in East Asian ancestry populations in the 1000 Genomes Project Phase 3  at this locus, the association with the lead SNP (rs141845179) was no longer significant . This is because the lead SNP (rs141845179) is in strong LD (r2 = 0.95 in Uganda GPC) with the sickle cell SNP (rs334), and therefore, they reflect the same signal. Of note, the lead SNP has MAF of 8% in Uganda but is much less frequent in admixed Africans in the 1000 Genomes Project Phase 3 (1% in African Americans from Southwest USA and 2% in Afro-Carribeans from Barbados). The variant (rs334) in HBB has also been previously associated with other kidney traits, including urinary albumin to creatinine ratio and CKD in both African Americans and US Hispanics/Latinos  in GPC and with the same direction of effect as in the previously reported eGFR meta-analysis . We also replicated eGFR associations of important African population specific variants near APOL1/MYH9   . This laP < 5 × 10−5) in Uganda were considered for replication and meta-analysis in WHI . The lead SNP at the HBB locus was not available in WHI, and it is rare or monomorphic in other populations. However, as described above, rs334 is a close proxy for the lead SNP at the HBB locus and represents the same eGFR signal. This SNP was available in WHI, where the association was replicated . No other proxies for the lead SNP at the HBB locus (r2 > 0.9 in the Uganda GPC) were associated with eGFR in WHI   . At the bability .HBB and GATM loci are mediated, we first considered kidney eQTL in Genotype-Tissue Expression (GTEx)  have support for regulatory impact from eQTL data and transcription factor binding. These SNPs showed the strongest associations with expression of SPATA5L1 in cultured fibroblasts and whole blood in GTEx but were not in strong LD with the lead eQTL SNP  and thus did not support colocalization with the eGFR signal.To gain insight into the causal genes through which eGFR association signals at the n (GTEx) , NephQTLn (GTEx) , the Humn (GTEx)  and Regun (GTEx) . The lea in GTEx . HowevergulomeDB  and S10.P = 0.076) and accounted for only 0.04% of the trait variance after accounting for age, sex and principal components to adjust for population structure. We also leverage the largest trans-ancestry meta-analysis of eGFR (P = 0.524) and accounted for only 0.01% of the trait variance after accounting for age, sex and principal components to adjust for population structure  and distinct from those previously reported SNP in European ancestry and trans-ancestry GWAS , but genetic studies in populations are not suited to address this. Many serum creatinine-based eGFR loci have not been associated with CKD at genome-wide significance. Whilst this may reflect lower power to detect association with the disease outcome, a subset of these loci may represent true genetic influences on physiologic variation of eGFR but not CKD risk. The GRS derived from previously reported trans-ancestry lead SNPs , which was originally defined on the basis of LD structure in European ancestry populations. A more stringent threshold of P < 5 × 10−9 has been proposed in African ancestry populations, to account for shorter range LD than in those of European ancestry  UK in partnership with the Uganda Virus Research Institute (UVRI) to primarily investigate the trends in incidence and prevalence of HIV infection in Uganda. Samples were collected from research participants during a survey from the research study area. The study area is clustered into villages defined by governmental borders ranging in size from 300 to 1500 dwellers and includes numerous families who are resident within households . The GPCThe data collection of GPC Round 22 study consisted of five main stages, which took place in 2011 over the course of the year: mobilization (recruitment and consenting), mapping, census, survey and feedback of results and clinical follow-up. The census consisted of a family questionnaire and questionnaire for the individual recruited from within the family. The family questionnaire was completed by the head of family or another responsible adult or emancipated minor member of the household. The household census questionnaire focused on sociodemographic information about the household, such as the quality of the house, property ownership and employment of workers. The individual survey questionnaire captured information on members of a household including position within household, marital status, resident status, childbirth and fertility, tribe and religion. Information on lifestyle and health was obtained using a standard questionnaire. This included biophysical measurements and blood samples . We genon = 5000) were genotyped on the Illumina HumanOmni2.5-8 array, and 4872 were retained following a pre-quality control stage. GWAS genotype data were subjected to stringent quality control filtering. Of a total of 2 314 174 autosomal variants genotyped, 39 368 were excluded because they did not pass SNP quality thresholds for call rate  and deviation from Hardy–Weinberg equilibrium (HWE)  as reported in  underwent low coverage whole-genome sequencing on the Illumina HiSeq 2000 with 75 bp paired end reads, at low coverage, with an average coverage of 4× for each sample. In total, 1978 of them passed quality control. The workflow for data processing and description of UG2G has been previously described in more detail . BrieflyHaplotype phasing of GWAS data was carried out using SHAPEIT2  with staImputation of the pre-phased genotype data was carried out with IMPUTE2  using a The final dataset used for this analysis included merged genotype data on 4772 and sequence data on 1978 individuals. We note that there are 343 individuals who have been genotyped and sequenced; for these individuals, we only included the sequence data and not the genotype data. The final dataset, therefore, included 6407 individuals (4429 with genotype and imputed data and 1978 with sequence data).n = 20 594 556) that met an MAF threshold of at least 0.5%.Following merging, we assessed and removed any systematic differences between imputed genotype data and sequence data. We did this by carrying out principal component analysis using merged data for the 343 individuals who had been genotyped and sequenced in duplicate to examine whether there was separation by data mode (imputed genotype data and sequenced data). Full details were reported in . For GWA2 and sex.Creatinine was measured using the enzymatic method traceable to an isotope dilution mass spectrometry method (IDSM) . CollectGWAS was performed using the standard mixed-model approach implemented in GEMMA version 24  for analFor all loci attaining genome-wide significance that have been previously reported in GWAS of eGFR, we performed conditional analyses in GEMMA to determine whether the association signals were distinct. Specifically, we included genotypes under an additive model at previously reported lead SNPs as a fixed effect in the mixed model. We also searched for evidence of multiple distinct signals of association in GPC by including genotypes at the lead SNP as a fixed effect in the mixed model.The WHI is a study of postmenopausal women and health outcomes, funded by the National Heart Lung and Blood Institute. A total of 161 808 women aged 50–79 years old were recruited from 40 clinical centres in the United States between 1993 and 1998. Study protocols and consent forms were approved by the institutional review boards at all participating institutions. The WHI SHARe minority cohort includes 8515 self-identified African American women, who provided written informed consent for study participation and DNA analysis.African American women who consented to genome-wide scanning underwent genotyping with the Affymetrix Genome-Wide HumanSNP Array 6.0 containing 906 000 SNPs. The samples underwent initial quality control including removal of samples with poor DNA quality, abnormal sex chromosomes, relatedness and low call rates as previously reported  using the Modification of Diet in Renal Disease equation.After quality control, GWAS scaffolds were pre-phased and imputed using MaCH/minimac P < 5 × 10−5) in the Uganda GPC were considered for replication in WHI. In view of the different scales of the effect sizes, association summary statistics of Uganda GPC and WHI were aggregated using the fixed-effects meta-analysis on the basis of the sample size weighting of Z-scores (Stouffer’s method) in METAL (All lead SNPs (separated by 500 kb) apart showing strong evidence of association . This was done by recoding the genotypes of each SNP for carriage of the homozygous risk genotype. We tested for association with eGFR under this recessive model via linear regression whilst correcting for age, sex and principal components as covariates using only unrelated individuals from the Uganda GPC.GATM and HBB loci, we first conducted meta-analysis (as described above) of association summary statistics from Uganda GPC and WHI for all SNPs mapping within 500 kb of the lead SNP at each locus. We used a Bayesian approach  and (n = 246 matching SNPs from (R2 (variance explained) when it was added to the linear model of eGFR adjusted for age, sex and principal components.After removing 817 first-degree relatives from the Uganda GPC cohort derived from PIHAT values > 0.5, we calculated principal components using –pca in PLINK . Lead SNNPs from ) were seNPs from . The preSupplementaryTables1to11a_ddab088Click here for additional data file.