Chronic kidney disease (CKD) affects ~10% of the global population, with considerable ethnic differences in prevalence and aetiology. We assemble genome-wide association studies of estimated glomerular filtration rate (eGFR), a measure of kidney function that defines CKD, in 312,468 individuals of diverse ancestry. We identify 127 distinct association signals with homogeneous effects on eGFR across ancestries and enrichment in genomic annotations including kidney-specific histone modifications. Fine-mapping reveals 40 high-confidence variants driving eGFR associations and highlights putative causal genes with cell-type specific expression in glomerulus, and in proximal and distal nephron. Mendelian randomisation supports causal effects of eGFR on overall and cause-specific CKD, kidney stone formation, diastolic blood pressure and hypertension. These results define novel molecular mechanisms and putative causal genes for eGFR, offering insight into clinical outcomes and routes to CKD treatment development. Estimated glomerular filtration rate (eGFR) is a measure of kidney function used to define chronic kidney disease. Here, Morris et al. perform trans-ethnic genome-wide meta-analyses for eGFR in 312,468 individuals and identify novel loci and downstream putative causal genes. CKD is associated with premature cardiovascular disease and mortality, and has enormous healthcare costs for treatment, prescriptions and hospitalizations6. The underlying mechanisms for CKD predisposition and development are unknown, limiting progress in the identification of prognostic biomarkers or the advancement of treatment interventions.Chronic kidney disease (CKD) affects ~10% of the global population, with considerable racial/ethnic differences in prevalence and risk factors9 and East Asian10 ancestry. Despite the success of these GWAS in identifying loci contributing to kidney function and risk of CKD, the common single nucleotide variants (SNVs) driving the association signals explain no more than ~4% of the observed-scale heritability of eGFR, and efforts to replicate these findings in other ancestry groups have been limited11. Furthermore, efforts to localise the variants driving eGFR association signals at these loci, and the putative causal genes through which their effects are mediated, have been hampered by the extensive linkage disequilibrium (LD) across common variation in European and East Asian ancestry populations.Large-scale genome-wide association studies (GWAS) of estimated glomerular filtration rate (eGFR), a measure of kidney function used to define CKD, have mostly been undertaken in populations of European12. These investigations provided no evidence of heterogeneity in allelic effects on eGFR association signals between ancestry groups, emphasizing the power of trans-ethnic GWAS meta-analysis for locus discovery that will be relevant to diverse populations.To enhance understanding of the genetic contribution to kidney function and CKD across diverse populations, and to inform global public health and personalised medicine, we recently established the Continental Origins and Genetic Epidemiology Network Kidney (COGENT-Kidney) Consortium. We undertook trans-ethnic meta-analysis of eGFR GWAS in 71,638 individuals ascertained from populations of African, East Asian, European and Hispanic/Latino ancestryTo further extend characterization of the genetic contribution to eGFR, and determine the molecular mechanisms and putative causal genes through which association signals impact on kidney function, we expand the COGENT-Kidney Consortium in this investigation by assembling GWAS in up to 312,468 individuals of diverse ancestry. With these data, we identify novel loci and distinct associations for kidney function, assess the evidence for heterogeneity in their allelic effects on eGFR, and determine genomic annotations in which these signals are enriched. We identify high-confidence variants driving eGFR association signals through annotation-informed trans-ethnic fine-mapping, and highlight putative causal genes through which their effects are mediated via integration with expression in kidney tissue. Finally, we evaluate the causal effects of eGFR on clinically-relevant renal and cardiovascular outcomes through Mendelian randomisation (MR) with our expanded catalogue of kidney function loci.12 to include additional individuals of Hispanic/Latino descent; (ii) a published meta-analysis of 33 studies of European ancestry from the CKDGen Consortium9; and (iii) a published study of East Asian ancestry from the Biobank Japan Project10. Each GWAS was imputed up to the Phase 1 integrated 1000 Genomes Project reference panel13, and SNVs passing quality control were tested for association with eGFR, calculated from serum creatinine, accounting for age, sex and ethnicity, as appropriate (Methods).We assembled GWAS in up to 312,468 individuals from three sources (Methods): (i) 19 studies of diverse ancestry from the COGENT-Kidney Consortium, expanding the previously published trans-ethnic meta-analysis10. Assuming homogeneous allelic effects on eGFR across populations, we had more than 80% power to detect an association (p < 5 × 10−8) with SNVs explaining at least 0.0127% of the trait variance under an additive genetic model. This corresponded to common/low-frequency SNVs with minor allele frequency (MAF) ≥5%/≥0.5% that decrease eGFR by ≥0.0366/≥0.113 standard deviations.The current study represented a 2.2-fold increase in sample size over the largest published GWAS of kidney function14, because allelic effect sizes were reported on different scales in each of the three sources contributing to the meta-analysis. We identified 93 loci attaining genome-wide significant evidence of association with eGFR (p < 5 × 10−8), including 20 mapping outside regions previously implicated in kidney function , SHH , XYLB  and ORC4 .To discover novel loci contributing to kidney function in diverse populations, we first aggregated eGFR association summary statistics across studies through trans-ethnic meta-analysis (Methods). We employed Stouffer’s method, implemented in METALp < 10−5) through approximate conditional analyses implemented in GCTA15 (Methods), each arising from different underlying causal variants and/or haplotype effects .Across the 93 loci, we then delineated 127 distinct association signals  of heterogeneity in allelic effects on eGFR that was correlated with ancestry  of eGFR associations in protein-coding exons (p = 0.0049), but not in 3’ or 5’ UTRs. We then interrogated chromatin immuno-precipitation sequence (ChIP-seq) binding sites for 161 transcription factors from the ENCODE Project19, which revealed significant joint enrichment of eGFR associations for HDAC2 (p = 0.0088) and EZH2 (p = 0.030). Class I histone deacetylases (including HDAC2) are required for embryonic kidney gene expression, growth and differentiation20, whilst EZH2 participates in histone methylation and transcriptional repression21. Finally, we considered ten groups of cell-type-specific regulatory annotations for histone modifications 23. Significant enrichment of eGFR associations was observed only for kidney-specific annotations (p = 7.4 × 10−14). In a joint model of these four enriched annotations, the odds of eGFR association for SNVs mapping to protein-coding exons, binding sites for HDAC2 and EZH2, and kidney-specific histone modifications were increased by 3.06-, 2.13-, 1.76- and 4.29-fold, respectively . We began by considering genic regions, as defined by the GENCODE Project p = 0.009, but no24. To further enhance fine-mapping resolution, we incorporated an annotation-informed prior model for causality, upweighting SNVs mapping to the globally enriched genomic signatures of eGFR associations (Methods). Under this prior, we derived credible sets of variants for each distinct signal, which together account for 99% of the posterior probability (π) of driving the eGFR association (Supplementary Table 25 (Supplementary Table p < 0.05) with eGFR calculated from cystatin C, blood urea nitrogen and urine albumin creatinine ratio, with the expected direction of effect of the eGFR decreasing allele.We performed trans-ethnic fine-mapping to localise putative causal variants for distinct eGFR association signals that were shared across global populations by taking advantage of differences in the structure of LD between ancestry groupsWe sought to identify the most likely target gene(s) through which the effects of each of the 40 high-confidence SNVs on eGFR were mediated via functional annotation and colocalisation with expression quantitative trait loci (eQTLs) in kidney tissue.CACNA1S p.Arg1539Cys , CPS1 p.Thr1406Asn , GCKR p.Leu446Pro  and CERS2 p.Glu115Ala . Functional annotation of these high-confidence missense variants highlighted predicted deleterious impact of CPS1 p.Thr1406Asn and CERS2 p.Glu115Ala (Methods). CACNA1S  encodes a subunit of L-type calcium channel located within the glomerular afferent arteriole, is the target of anti-hypertensive dihydropyridine calcium channel blockers (such as amlodipine and nifedipine), and regulates arteriolar tone and intra-glomerular pressure26. CACNA1S missense mutations cause hypokalemic periodic paralysis28, malignant hyperthermia29 and congenital myopathy30. CACNA1S is highly expressed in skeletal muscle tissue, raising the possibility that the high-confidence missense variant may influence eGFR through creatinine production. CPS1 (Carbamoyl-Phosphate Synthase 1) is involved in the urea cycle, where the enzyme plays an important role in removing excess ammonia from cells31. GCKR (Glucokinase Regulator) produces a regulatory protein that inhibits glucokinase, and the p.Leu446Pro substitution is a highly pleiotropic variant with reported effects on a wide range of phenotypes, including metabolic traits and type 2 diabetes32.Only four of the SNVs were missense variants Table , encodinCERS2 (Ceramide Synthase 2) variants have previously been associated with albuminuria in individuals with diabetes33, and interrogation of the Human Protein Atlas34 revealed that the CERS2 protein is abundantly expressed in the glomerulus and tubules of the kidney. Cers2-deficient mice exhibit changes in the structure of the kidney35. We verified that Cers2 mRNA is expressed in primary podocytes isolated from the mouse using a previously published method36 . We compared the podocyte migration rate among treated and untreated cells using the scratch wound-healing assay 41; and (ii) kidney biopsies obtained from 134 healthy donors from the TransplantLines Study42 (Methods). We observed that high-confidence eGFR SNVs colocalised with lead renal eQTL variants in the TRANSLATE Study and TGCA , TBX2 , and both UMOD and GP2 for the same signal at the UMOD-PDILT locus . Of these three high-confidence SNVs, rs8872528 was a significant eQTL  for TBX2 across multiple tissues in the GTEx Project43, whilst the associations of rs12509595 and rs77924615 with an expression of FGF5 and UMOD/GP2, respectively, were specific to kidney. FGF5 (Fibroblast Growth Factor 5) is expressed during kidney development, but knockout models have not shown a kidney phenotype44. FGF5 has been implicated in GWAS of blood pressure and hypertension45, and other fibroblast growth factors are increasingly recognised as contributors to blood pressure regulation through renal mechanisms40. TBX2 (T-Box 2) plays a role in defining the pronephric nephron in experimental models46. UMOD encodes uromodulin , the most abundant urinary protein. The eGFR lowering allele at the high-confidence SNV is associated with increased UMOD expression  non-cancer affected healthy kidney tissue obtained from 260 individuals from the TRANScriptome of renaL humAn TissuE (TRANSLATE) StudyCA Table  for FGF549. To provide insight into cellular specificity of the signals at the UMOD-PDILT, FGF5 and TBX2 loci, we mapped the four genes identified through eQTL analyses to cell types from single nucleus RNA-sequencing (snRNA-seq) data obtained from a healthy human kidney donor 49. UMOD and GP2 demonstrated expression specific to epithelial cells of the ascending loop of Henle ; SPTBN1 ; PIP5K1B ; and LARP4B, BCAS3, and MPPED2 . Of these, DPEP1, which encodes the protein dipeptidase 1, is implicated in the renal metabolism of glutathione and its conjugates, and regulates leukotriene activity. This localisation fits with the previously suggested connection between glutathione metabolism and defence against chemical injury in proximal tubule cells52. Taken together, these findings suggest a potential role of these genes in influencing kidney structure and function through regulation of: (i) glomerular capillary pressure, determining intra-glomerular pressure and glomerular filtration; (ii) proximal tubular reabsorption, affecting tubuloglomerular feedback; or (iii) distal nephron handling of sodium or acid load, influencing kidney disease progression. Additional laboratory-based functional studies will be required to delineate the mechanistic pathways that determine kidney function in healthy and disease states, and potential routes to therapeutic targets for pharmacologic development.By localising high-confidence SNVs to introns and UTRs (Methods), we identified eight additional genes with differential expression across nephron single cell-types Fig. , Table 253  causal effect of lower eGFR on higher risk of all-cause CKD, glomerular diseases and CKD stage 5, based on reported association summary statistics from the CKDGen Consortium8 and the UK Biobank  has been previously associated with kidney stone formation55 and is consistent with the role of uromodulin in the inhibition of urine calcium crystallisation56. However, this SNV was excluded from the MR analysis due to heterogeneity in effect size and was therefore not driving the causal eGFR association with risk of calculus of the kidney and ureter  on higher diastolic blood pressure (DBP) and higher risk of essential (primary) hypertension, but not on systolic blood pressure, based on reported association summary statistics from automated readings and ICD10 codes from primary care data available in the UK Biobank 59 . Experiments were carried out in accordance with local and NIH guidelines, and the animal protocol was approved by the UVA Institutional Animal Care and Use Committee.12 but were expanded with the addition of further studies of Hispanic/Latino ancestry to increase the diversity of represented population groups. Samples were assayed with a range of GWAS genotyping products, and quality control was undertaken within each study (Supplementary Table 63 and imputed up to the Phase 1 integrated (version 3) multi-ethnic reference panel from the 1000 Genomes Project13 using IMPUTEv264 or minimac65  are presented in Supplementary Table 68. We tested the association of eGFR with each SNV in a linear regression framework, under an additive dosage model, and with adjustment for study-specific covariates to account for confounding due to population structure , accounting for age, sex and ethnicity, using the four-variable MDRD equation9. Briefly, individuals were assayed with a range of GWAS genotyping products. After quality control, GWAS scaffolds were pre-phased63 and imputed65 up to the Phase 1 integrated (version 1 or version 3) multi-ethnic or European-specific reference panels from the 1000 Genomes Project13. Imputed variants were retained for downstream association analyses if they attained IMPUTEv2 info≥0.4 or MaCH/minimac r2≥0.4. Within each study, eGFR was calculated from serum creatinine (mg/dL), accounting for age and sex, using the four-variable Modification of Diet in Renal Disease (MDRD) equation68. Residuals obtained after regressing ln(eGFR) on age and sex, and study-specific covariates to account for population structure where appropriate, were tested for association with each SNV in a linear regression framework, under an additive dosage model. Association summary statistics within each GWAS were corrected for residual population structure via genomic control69 where necessary and were subsequently aggregated across studies, under a fixed-effects model, with inverse-variance weighting of allelic effect sizes, as implemented in METAL14.Full details of the CKDGen Consortium meta-analysis, which incorporated GWAS in 110,517 individuals of European ancestry, have been previously publishedhttp://ckdgen.imbi.uni-freiburg.de/), we derived the association Z-score from the ratio of the allelic effect estimate and corresponding standard error. No further correction for population structure was required by genomic control69: λGC = 0.977.From the available meta-analysis summary statistics for each SNV  East Asian-specific reference panel from the 1000 Genomes Project13 with minimac65. Imputed variants were retained for downstream association analyses if they attained minimac r2 ≥ 0.7. For each individual, eGFR was derived from serum creatinine (mg/dL) using the Japanese coefficient-modified CKD Epidemiology Collaboration (CKD-EPI) equation73, and adjusted for age, sex, ten principal components of genetic ancestry, and affection status for 47 diseases. The resulting residuals were inverse-rank normalised and tested for association with each SNV in a linear regression framework, under an additive dosage model.Full details of the Biobank Japan Project GWAS, which incorporated 143,658 individuals of East Asian ancestry, have been previously publishedhttp://jenger.riken.jp/en/result), we derived the association Z-score from the ratio of the allelic effect estimate and corresponding standard error, and subsequently corrected for residual population structure by genomic control69: λGC = 1.252.From the available GWAS summary statistics for each SNV (downloaded from Z-scores (Stouffer’s method), as implemented in METAL14, because allelic effect estimates were on different scales in the contributing components. The COGENT-Kidney Consortium included a GWAS of a subset of 23,536 individuals from those contributing to the Biobank Japan Project, which was therefore excluded from the trans-ethnic meta-analysis. Consequently, a combined sample size of 312,468 individuals contributed to the trans-ethnic meta-analysis. SNVs reported in at least 50% of the combined sample size were retained for downstream interrogation. Meta-analysis association summary statistics were corrected for residual population structure via genomic control69: λGC = 1.113.We aggregated eGFR association summary statistics across the three components: COGENT-Kidney Consortium GWAS, the Biobank Japan Project GWAS and the CKDGen Consortium meta-analysis. We performed fixed-effects meta-analysis, with sample size weighting of p < 5 × 10−8) with eGFR in the trans-ethnic meta-analysis that were separated by at least 500kb. Loci were defined by the flanking genomic interval mapping 500kb up- and down-stream of lead SNVs. Where loci overlapped, they were combined as a single locus, and the lead SNV with minimal p-value from the meta-analysis was retained.We first selected lead SNVs attaining genome-wide significant evidence of association  under a fixed-effects model, as implemented in METAL14. The conditional meta-analysis summary statistics were corrected for residual population structure using the same genomic control adjustment69 as in the unconditional analysis (λGC = 1.113). We defined locus-wide significance by p < 10−5, which is a Bonferroni correction for the approximate number of (independent) SNVs at each locus. If no SNVs attained locus-wide significant evidence of residual association with eGFR, the iterative approximate conditional analysis for the locus was stopped. Otherwise, the SNV with the strongest residual association signal was added to the conditional set. This iterative process continued, at each stage adding the SNV with the strongest residual association from the meta-analysis to the conditional set, until no remaining SNVs attained locus-wide significance. Note, that at each iteration, studies with missing association summary statistics for any SNV in the conditional set were excluded from the meta-analysis.For each locus, we first applied GCTA to the study-level association summary statistics and matched LD reference for each GWAS . We adjusted for the conditional set of variants, which in the first iteration included only the lead SNV at the locus, and aggregated 69 as in the unconditional analysis (λGC = 1.113). The SNV with the strongest residual association was defined as the index for the signal.For each locus including more than one SNV in the conditional set, we then dissected each distinct association signal. We again applied GCTA to the study-level association summary statistics and matched LD reference for each GWAS , but this time by removing each SNV, in turn, from the conditional set of variants, and adjusting for the remainder. The conditional meta-analysis summary statistics were corrected for residual population structure using the same genomic control adjustment16 to assess the contribution of variation to the observed scale heritability of eGFR. LD Score regression accounts for LD between SNVs on the basis of European ancestry individuals from the 1000 Genomes Project74. We therefore performed fixed-effects meta-analysis, with sample size weighting of Z-scores (Stouffer’s method), as implemented in METAL14, across European ancestry studies from the COGENT-Kidney Consortium and CKDGen Consortium , and used these association summary statistics in LD Score regression. We first calculated the contribution of genome-wide variation to the observed scale heritability of eGFR. We then partitioned the genome into previously reported and novel loci attaining genome-wide significance in the trans-ethnic meta-analysis  binding sites for 161 transcription factors from the ENCODE Project19. Third, we considered ten groups of cell-type-specific regulatory annotations for histone modifications  obtained from a variety of resources23, which were previously derived for partitioning heritability by annotation by LD Score regression76.We considered three categories of functional and regulatory annotations. First, we considered genic regions, as defined by the GENCODE Projectp < 0.05). We then included all selected annotations across categories in a final model to obtain joint estimates of the fold-enrichment in eGFR association signals for each.Within each category, we first used forward selection to identify annotations that were jointly enriched at nominal significance  ranking all SNVs according to their posterior probability We derived a 99% credible set9; (ii) blood urea nitrogen, obtained from up to 139,818 individuals of East Asian ancestry from the Biobank Japan Project10; and (iii) urine albumin to creatinine ratio, obtained from up to 46,061 non-diabetic individuals of European ancestry from the CKDGen Consortium25. Effects on these traits were aligned to the eGFR decreasing allele.We evaluated the evidence for association of high-confidence variants with measures of kidney function and damage from published GWAS: (i) eGFR calculated from cystatin C, obtained from up to 24,061 individuals of European ancestry from the CKDGen Consortium78 and metaSVM (scores for non-synonymous variants based on SVM model)79.We assessed the predicted functional impact of high-confidence missense variants across a range of databases including FATHMM 36. Briefly, under general anaesthesia with isoflurane, mice were perfused through the heart with 6 × 108 magnetic beads/ml  diluted in 10 ml of phosphate-buffered saline (PBS). The kidneys were extracted, decapsulated and cut into small pieces, then digested in collagenase A (1mg/ml) at 37 oC for 25 min The digest was pressed with a syringe pestle through a 100 uM cell strainer, collected, and washed with 5 mL of PBS, repeated once, and then passed through a 40 uM cell strainer, and washed again with 5 mL of PBS. The isolated glomeruli were then washed off the cell strainer with pre-warmed cell culture media , then further isolated by magnetic particle concentrator (Invitrogen) and washed with a pre-warmed culture media 2–3 times. Glomeruli were then re-suspended in culture media and placed in the 6-well plate coated with rat tail type I collagen and incubated at 37 oC. Light microscopy was used to confirm the characteristic migration of podocytes from glomeruli after 3–5 days.The protocol for isolation and culture of primary podocytes from 129S6 mice has been previously published38. The inhibitor was added to the well containing 1 mL of culture media, at a concentration of 1 mM in DMSO, immediately after wound creation at a volume of 3 µL (3 µM). For the control condition, 3 µL of DMSO alone was added. At least 3 independent experiments were performed with 3–5 wells per condition. The images were analysed using ImageJ software , by measuring pixels of the outlined scratched area immediately after wound creation and after 18 h. The results are expressed as % of area that was not covered by migrating podocytes after 18 h of incubation time, compared to the scratched area created immediately after wound creation.Approximately after 7–9 days, the primary podocytes reached a confluent monolayer, and a rectangular wound was created by scratching the monolayer from the top to the bottom of the well using the base of a sterile 200 µl pipette tip. Images of the scratched area were immediately taken after wound creation and after an additional 18 h of incubation, using EVOS XL Core Cell Imaging System (×10 magnification). We utilised ST-1074, a potent inhibitor that can be used in lower concentrations to avoid cell toxicity, for which the clinical potential of the class of compounds influencing CerS subtypes has been previously published34, CERS4 is only expressed in the tubules and not in podocytes. Although ST-1074 is a CerS2 inhibitor, off-target effects may also influence podocyte function.We note that in vitro (HCT116), ST-1074 significantly inhibits both CerS2 and CerS4 (screening conditions 10 µM). ST-1074 disclosed an IC50 value for C18:0 of 21.4 µM (CerS4 inhibition), and an IC50 value for C24:1 of 28.7 µM (CerS2 inhibition), which do not show significant difference. In HeLa cells, ST-1074 inhibited at 5 µM CerS2 and CerS4 significantly . However, in the Human Protein Atlas40 and TGCA41. In brief, as a source of kidney tissue, both studies used apparently normal samples from European ancestry individuals undergoing nephrectomy due to kidney cancer (the specimens were collected from the cancer-unaffected pole of the organ). The data from both studies were processed in the same manner using procedures described below.We performed eQTL analysis using data from the TRANSLATE Study80. The quality control included: removing outlier samples82, checking consistency between declared and biological sex (using XIST and Y-chromosome genes); removing genes on non-autosomal chromosomes; and removing genes with either interquartile range of zero or those not meeting the minimum expression criterion (TPM > 0.1 and read counts ≥6 in at least 30% of samples within each study/sequencing batch). Before cis-eQTL analysis, the log2-transformed TPM data were normalised using robust quantile normalisation in the R package aroma and then standardised using rank-based inverse normal transformation in GenABEL. To account for technical variation, we used probabilistic estimation of expression residuals (PEER)83: 30 latent factors for the TRANSLATE Study and 15 for TCGA as recommended for different sample sizes in the GTEx Project85.Gene expression was quantified in transcripts per million (TPM) using Kallistop < 0.001); or had MAF <5%. Quality control also removed individuals with: genotyping call-rate <95%; heterozygosity above/below 3 standard deviations from the mean; cryptic relatedness to other individuals; non-European genetic ancestry; and discordant sex information (inconsistency between declared and genotyped sex). For both studies, the resulting scaffold was imputed up to the Phase 3 multi-ethnic reference panel from the 1000 Genomes Project74 using the Michigan Imputation Server86. After imputation, we retained only SNVs, removing those with low imputation coefficient (R2 < 0.4), MAF <5%, or violating HWE (p < 10−6).Kidney DNA samples from individuals from the TRANSLATE Study were genotyped using the Infinium HumanCoreExome-24 BeadChip array, and genotype calls were made using Genome Studio. Individuals from TCGA were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0, and genotype calls were made using the Birdseed algorithm. Quality control removed variants that: had low genotyping rate (<95%); mapped to Y chromosome/mitochondrial DNA or had an ambiguous chromosomal location; violated Hardy–Weinberg equilibrium  were included in the analysis. A total of 2000 permutations were used to derive the empirical distribution of the smallest p-value for each gene, which then was used to adjust the observed smallest p-value for the gene. The correction for testing multiple genes was based on false discovery rate (FDR) applied to permutation-adjusted p-values  with a cut-off of 5%. Furthermore, the thresholds for nominal p-values were derived using a global permutation-adjusted p-value closest to FDR of 5% and the empirical distributions determined using permutations.A total of 260 individuals (160 from the TRANSLATE Study and 100 from TCGA) were included in the analysis, involving 15,711 genes and 5,498,156 SNVs common to both studies. Normalised gene expression was modelled as a function of alternate allele dosage via linear regression, including sex, three axes of genetic variation (to account for population structure) and PEER latent factors as additional covariates. The regression coefficients of the alternate allele from the two studies were then combined in a fixed-effects meta-analysis under an inverse-variance weighting scheme. For each gene, only those SNVs in r2>0.8) at a 5% FDR, and reported the corresponding eGene.We identified high-confidence SNVs from the trans-ethnic fine-mapping that were colocalised with lead eQTL variants  multi-ethnic reference panel from the 1000 Genomes Project13 using IMPUTEv264. Expression and genotype data were available for 236 kidney biopsies obtained from 134 donors, and analyses have been described previously59. Briefly, residuals of gene expression for each probe were obtained after adjusting for the first 50 expression principal components to filter out environmental variation87. A linear mixed model was used to test the association of residual expression of each probe with the allele dosage of each SNV mapping within 1Mb of the transcription start/stop sites using the R package lme3. Sex, age, donor type, time of biopsy and three axes of genetic variation (to account for population structure) were included in the model as fixed effects. Random effects were then included for donor to account for multiple samples obtained from the same individual.We performed eQTL analysis using data from the TransplantLines Studyr2>0.8) at a 5% FDR, and reported the corresponding eGene.We identified high-confidence SNVs from the trans-ethnic fine-mapping that were colocalised with lead eQTL variants ; 86.7% tubular cells ; the remaining 5.5% cells are mostly macrophages and novel cells that do not map to known cell types. An average of 1803 genes were detected per cell. We generated a differential expression gene (DEG) list by performing Wilcoxon rank sum tests on each cell-type from the single nucleus dataset. A gene was defined as mapping to a specific kidney cell type if the expression fulfils all the following criteria: (i) present in the DEG list; (ii) expressed in >25% of the total cells in the specified cell-type; and (iii) log-fold change in expression was >0.25 in the specified cell-type when compared to all other cell-types49. Gene expression values for each cell were Z-score normalised. A new gene expression matrix with mean Z-scores for each gene was obtained by averaging the Z-scores from all individual cells in the same cluster. The Z-score normalized gene expression were presented as a heatmap using the heatmap.2 function in the R package gplots.We identified genes for which high-confidence SNVs mapped to introns and untranslated regions. We mapped the genes to cell-types from snRNA-seq data generated by 10x Chromium from a healthy human kidney 8); IgA nephropathy ; glomerular diseases ; CKD stage 5 ; hypertensive renal disease ; calculus of kidney and ureter ; DBP ; systolic blood pressure ; essential (primary) hypertension ; coronary heart disease ; myocardial infarction ; and ischemic stroke .We performed a lookup of association summary statistics for lead SNVs at each of the eGFR loci across a range of clinically-relevant kidney and cardiovascular outcomes from public and proprietary data resources. These included: CKD  and implemented in the R package TwoSampleMR88: weighted median regression90 and MR-EGGER regression91.We performed two-sample MR for each outcome using eGFR as the exposure and the extracted non-palindromic lead SNVs as instrumental variables. The lead SNVs were not in LD with each other, so that their effects on exposure and outcomes were uncorrelated. Analyses were performed separately in each of the three components of the trans-ethnic meta-analysis because allelic effect sizes were measured on different scales in each: COGENT-Kidney Consortium ; CDKGen Consortium ; and Biobank Japan Project . For each trait, we first accounted for heterogeneity in causal effects of eGFR via modified 51). We assessed the evidence for a causal association of eGFR on DBP in each component of the trans-ethnic meta-analysis using inverse variance weighted regression89, weighted median regression90 and MR-EGGER regression91, as implemented in the R package TwoSampleMR88.We performed an additional lookup of association summary statistics for non-outlying lead SNVs at each of the eGFR loci for DBP (150,134 individuals, published data from ICBPSupplementary InformationSource Data


Bayesian methods treat all markers as random effects, exhibit an advantage on dense markers, and offer the flexibility of using different priors. In contrast, genomic best linear unbiased prediction (gBLUP) is superior in computing speed, but only superior in prediction accuracy for extremely complex traits. Currently, the existing variety in the BLUP method is insufficient for adapting to new sequencing technologies and traits with different genetic architectures. In this study, we found two ways to change the kinship derivation in the BLUP method that improve prediction accuracy while maintaining the computational advantage. First, using the settlement under progressively exclusive relationship (SUPER) algorithm, we substituted all available markers with estimated quantitative trait nucleotides (QTNs) to derive kinship. Second, we compressed individuals into groups based on kinship, and then used the groups as random effects instead of individuals. The two methods were named as SUPER BLUP (sBLUP) and compressed BLUP (cBLUP). Analyses on both simulated and real data demonstrated that these two methods offer flexibility for evaluating a variety of traits, covering a broadened realm of genetic architectures. For traits controlled by small numbers of genes, sBLUP outperforms Bayesian LASSO (least absolute shrinkage and selection operator). For traits with low heritability, cBLUP outperforms both gBLUP and Bayesian LASSO methods. We implemented these new BLUP alphabet series methods in an R package, Genome Association and Prediction Integrated Tool (GAPIT), available at  One of the ultimate goals of genomic research is to predict phenotypes from genotypes. This line of research is named genomic prediction. Genomic prediction in the human has the potential to lead to better medical treatments. For example, disability or the need for hip replacement surgery could be prevented if an increased risk of developmental dysplasia of the hip could be determined at a young age  using a fixed and random effects mixed linear model (MLM). Individuals’ total genetic effects were treated as random effects with variance structure defined by the pedigree-based kinship (Henderson Inspired by marker-assisted selection (MAS) developed in the late 20th century, all available genetic markers were fit simultaneously as random effects to avoid the model over-fitting problem in MAS  was superior to gBLUP (0.64) for milk fat content; however, gBLUP (0.60) was superior to Bayes B (0.55) for milk protein content. In general, Bayes B has the accuracy advantage over gBLUP for traits controlled by smaller numbers of QTNs; otherwise, gBLUP has the advantage  the series retains the computational efficiency of gBLUP and (2) the series adapts to a variety of trait genetic architectures so that prediction accuracy improves. Accordingly, we added two new options within the BLUP framework—one option for traits controlled by a small number of genes and one for traits with low heritability.http://gscan.well.ox.ac.uk). This population was generated from crossing eight inbred lines, followed by 50 generations of random mating. The dataset contains 1940 individuals genotyped with 12,227 SNP markers  for Arabidopsis dataset. The rice data contains 374 inbred lines  was used to examine computing speed because it has the largest amount of SNPs. Each dataset contained both phenotypes and genetic markers. The mice data came from a heterogeneous stock population kept by The Welcome Trust Centre for Human Genetics (WTCHG)  for unknown random polygenic effects. These random effects follow a distribution with a mean of zero and a covariance matrix of K = 0.5 A is the pedigree-based kinship with element Kij  representing the relationship between individuals i and j, A = 2 K is additive numerator relationship, and X and Z are the incidence matrices for β and u, respectively; e is a vector of random residual effects that are normally distributed with a mean of zero and a covariance of I is the identity matrix and Although we did not use pedigree BLUP in our data analyses, it was the foundation for all the BLUP methods we developed in this study. Individuals’ genetic effects (u) were treated as random effects in a fixed effects and random effects MLM, originally developed for using pedigree to derive kinship among individuals. The model can be expressed asq is a rank of X and δ is solved to maximize (5) by using the EMMA algorithm.The full likelihood of observed y, given β, The estimate of β and prediction of u are solved as follows:pi is the frequency of the second allele for the ith marker . The size of bins (s) and number of bins selected (t) are optimized to maximize the following likelihood, which has the same format as formula (5):The difference between gBLUP and sBLUP is that gBLUP uses all the markers and sBLUP uses the associated markers to derive kinship in formula (7). In sBLUP, all associated markers are grouped into bins based on their locations on chromosomes  as W.The conventional gBLUP uses the kinship in formula (3), derived from using all the markers as In a previous study  are p by 1, g by 1, and p by g, respectively. Consequently, the kinship among groups has dimensions of g by g. Some groups may contain both types of individuals, with and without phenotypes. Some groups may contain only the individuals without phenotypes. Even in the latter case, the prediction formula is still the same as (6). The only difference is that kinship among individuals is replaced by kinship among groups.For With different settings of priors, the Bayesian alphabet series offers a variety of methods for performing GS. We chose Bayesian LASSO (Bayesian Least Absolute Shrinkage and Selection Operator) because, in addition to the criteria of computing speed and software availability, it has least assumptions among the Bayesian methods. The fundamental assumption is that the effect of a marker has its own distribution characterized by the variance of the genetic effect. The variance follows a double exponential distribution without further hyper-parameters. The other consideration in choosing Bayesian LASSO was that the differences between Bayesian LASSO and other Bayesian methods are well-documented . The number of individuals and markers in each dataset are summarized in Table For a specific trait, the kinship among individuals is best defined from only the true QTNs. With simulated traits, we compared the prediction accuracies that resulted from using four different sets of markers to define kinship: (1) true QTNs, (2) all available markers, (3) true QTNs plus 1000 Non-QTN Markers (NQMs) for dilution, and (4) estimated QTNs by using the SUPER algorithm under the assumption that their locations are unknown. The set of true QTNs mimics an ideal situation, which is unrealistic in practice. The set of all available markers corresponds to the kinship-derivation method in conventional gBLUP. The set of true QTNs plus NQMs demonstrates the impact of dilution as a consequence of including NQMs. The set of estimated QTNs corresponds to the method used in sBLUP.http://mus.well.ox.ac.uk/mouse/HS/). Model fit and prediction accuracy were evaluated through five-fold cross-validation. Model fit was evaluated as twice the negative log likelihood (−2LL) in the reference population. As both cBLUP and sBLUP do not change the number of fixed effects, other criteria, such as Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC), stay the same trend as likelihood. The lower the −2LL value, the better the model fit. Prediction accuracy was evaluated as the Pearson correlation coefficient between the observed and predicted phenotypes in the inference population. The phenotypes of inferences were not used for estimating QTNs to ensure independent tests on inferences.The simulated traits were controlled by 10 QTNs at three levels of heritability . The QTNs were randomly sampled from a total of 12,227 available markers on 1940 mice individuals from The Welcome Trust Centre for Human Genetics (WTCHG) dataset , we examined model fit and prediction accuracy on real traits in three species: Arabidopsis, mice, and maize Fig. . For theWe clustered individuals into groups based on kinship among individuals from both references and inferences; however, the phenotypes of inferences were masked. That is, only the phenotypes of references were used to evaluate the model fit, based on −2LL. Prediction accuracies were calculated as the correlations between predicted and observed phenotypes in the inference populations. The prediction for a group was used as the prediction for each individual within the group. We varied the number of groups from one to the total number of individuals. The average number of individuals per group was defined as the compression level in a previous study . This trend was the same as that of statistical power in GWAS in response to varying the compression level  and Bayesian LASSO with five-fold cross-validation.For a trait controlled by a few QTNs with high heritability, we found that the prediction accuracy of sBLUP is superior to any other method Fig. . For traAlthough ranking the genetic merit of individuals is the primary objective of genomic prediction, understanding the magnitude of predictions compared with their true values is also of academic interest. The phenomenon known as inflation of prediction, where BLUPs shrink as they approach zero, is well-documented. Thus, we were interested in exploring how the different BLUP methods performed relative to inflation under the different genetic architectures. We simulated a trait controlled by 5, 100, 500, and 1000 QTNs under different heritabilities .As expected, the regression coefficients of predicted breeding values over the true breeding values were less than one for all methods under all circumstances due to shrinkage of BLUPs. However, their magnitude of inflation were different. sBLUP demonstrated the least inflation when the number of QTNs was small (5 and 100), but the most inflation when the number was large (500 and 1000). This result suggested again that sBLUP performs best when traits are controlled by a small number of QTNs. For a trait controlled by a larger number of QTNs (500 and 1000), the advantage of gBLUP and cBLUP over sBLUP strengthened with increased heritability. In all circumstances, cBLUP outperformed gBLUP. This trend for inflation followed the same trend for prediction accuracies Figure .We examined the three BLUP methods  and Bayesian LASSO on a total of 157 real traits from three species: Arabidopsis (81), mice (41), and maize (35). The heritability of each trait was estimated as the proportion of estimated genetic variance among individuals to total variance. All traits are presented in Fig. The superiority of each method depends on both the complexity and heritability of the trait being analyzed. Among the 81 traits in Arabidopsis, 21 are complex traits associated with flowering time. The other 60 traits are relatively simple, relating to development and defense. For the 60 simple traits, we observed that sBLUP performed much better than Bayesian LASSO Fig.  and S2. Among the three species , mice had a clear family structure defined by the female parents. The 1940 individuals were from 160 female parents. This structure allowed us to create different levels of relatedness between reference and inference, which helped to investigate the relationship between prediction accuracy and level of relatedness. We chose the trait with the most phenotype records, weight growth intercept, to investigate the impact of relatedness on prediction accuracy through five-fold cross-validation Figure .Under the scheme of family selection with one individual from each family, the relatedness between reference and inference was defined as the average relatedness among families. No individuals in the inference had family members in the reference. As expected, both gBLUP and cBLUP had low prediction accuracies, and we found no significant difference between the two methods. When we increased the number of individuals per family to five, for a total of 800, the chance that individuals in the inference had family members in the reference greatly increased. Consequently, prediction accuracy improved for both cBLUP and gBLUP, with cBLUP’s improvement much higher than gBLUP’s. cBLUP’s advantage over gBLUP remained when individuals were sampled with an average of 5 individuals per family; that is, the number of individuals per family varied. However, when the average number of individuals per family was reduce to one, the prediction accuracies decreased and the advantage of cBLUP over gBLUP disappeared. As expected, the prediction accuracies that resulted from sampling exactly one individual per family were lower than sampling an average of one individual per family. In the latter case, because the selection of individuals was random, any one individual in the inference could have had family members in the reference.Results of our comparisons on simulated and real traits from the three species suggest that each of the BLUP methods and Bayesian LASSO has its own domain of superiority according to genetic architecture Fig. . These dThe sBLUP method is superior for traits with fewer genes. Bayesian LASSO performs well for traits with high heritability. The gBLUP method is best used with complex traits controlled by many genes. In most cases, cBLUP is superior to gBLUP, especially for traits with low heritability. With increased heritability and number of genes, the superiority of cBLUP over gBLUP disappears.O(mn2 + n3) with P3D (population parameter previously determined) algorithm . The time complexity of the MLM step is O(n3), required to produce the inverse of the kinship matrix. Although cBLUP and sBLUP increase the number of inversions, the complexity remains the same relative to n. Because the number of markers is irrelevant when solving MLM, cBLUP has the same level of computing time complexity as gBLUP, regardless of number of markers. During the optimization of grouping, computing time is reduced when more individuals are clustered into the same groups, compared to the initial stage, i.e., gBLUP.The complexity of computing time for the BLUP methods is O(mnl), where l is the length of the Markcov Chain (MC). For a given length of MC, the complexity of Bayesian LASSO is linear to both number of individuals and markers. However, the actual computing time is much greater than the BLUP methods with large numbers of markers because the MC Monto Caro (MCMC) length is long and also related to both m and n. Thus, with more markers, Bayes methods are slower than BLUP methods. In our datasets, the number of markers was much larger than the number of individuals. For example, ratios of markers to individuals (m:n) in Arabidopsis and rice reached 1086:1 and 2252:1, respectively. In such cases, BLUP methods are superior to Bayes methods. The observed computing times were 14 and 25 h with Bayesian LASSO for Arabidopsis and rice, respectively. In contrast, all the BLUP methods completed these analyses within 30 min  compared to BLUP methods. We compared the computing times of the four methods  on multiple synthetic datasets that were created from an original maize dataset with 282 individuals and 3082 markers. The original dataset was duplicated 5, 10, 15, and 20 times for both individuals and SNPs. The largest dataset (duplicated 20 times) contained 5640 lines and 61,640 SNPs. Bayesian LASSO was the most time-consuming. The most efficient method was gBLUP. Due to the additional computation necessary for clustering and selecting estimated QTNs, cBLUP, and sBLUP are less computationally efficient than gBLUP  method, in which kinship is based on pedigree. Pedigree-based kinship remains constant and represents the average kinship across traits. Pedigree-based kinship not only lacks the ability to adjust for different traits of interest, but also lacks the ability to differentiate among full sibs. Even when variation exists among full sibs, pedigree-based kinship does not distinguish pairwise kinship among them. In contrast, these magnificent variations became evident when we used genetic marker-based kinship methods. In this study, we not only changed the markers used to define kinship among individuals, but also changed the kinship among individuals into kinship among groups. These changes created extra flexibility for BLUP and, in turn, the opportunity to expand the BLUP alphabet series, adaptable to the different genetic architectures of complex traits.The kinship among some individuals, such as full sibs, are exactly the same based on pedigree. However, their kinship is highly likely to be different based on genetic markers. One pair of full sibs may share more common markers than another pair of full sibs due to multiple reasons, for example, Mendelian sampling. Studies have shown substantial variations in kinship among full sibs  QTNs to derive kinship. sBLUP also has the advantage of being able to fully use dense markers such as sequence data. In such cases, chances are greater for finding markers in strong LD with genes controlling the trait of interest. In contrast, genomic predictions with gBLUP is based on kinship derived from all markers. Because kinship calculated from all genetic markers remains stable after the number of genetic markers reaches a certain level, prediction accuracy with gBLUP does not increase with increased marker density. Consequently, dense genetic markers  provide more information than gBLUP can effectively use. In contrast, the use of dense markers enhances the prediction accuracy of sBLUP and the Bayesian methods.All of the above derived kinships—pBLUP, gBLUP, and sBLUP—define the genetic relatedness among individuals. Consequently, individuals are fit as random effects. Alternatively, for each of these kinships, individuals can be clustered (compressed) into groups and groups can then be fit as random effects instead of individuals. Therefore, the cBLUP procedure can be applied to any type of kinship among individuals. In this study, we applied cBLUP to all the kinships except the pedigree kinship. When we applied compression to kinship from sBLUP to create a combination, compressed SUPER BLUP (csBLUP), the compression failed to boost accuracy over sBLUP. This result makes sense because kinship in sBLUP is defined by a small number of estimated QTNs. That is, kinship has a much smaller number of different kinship elements, which is similar to kinship among groups.cBLUP works to boost accuracy when kinship is derived from all markers for two reasons. First, similar to why compression works for GWAS  is high. The individuals will share the same prediction. On other hand, the chance is small that an individual of inference will belong to a group in which no individuals have phenotypes. The prediction of the group effect is calculated based on other groups with individuals having phenotypes. This situation is true for every individual of inference by using regular gBLUP. Each individual must be predicted based on other individuals in reference.One could argue that the genetic differences among individuals in the same group might diminish under the compressed MLM. However, individuals are clustered into the same group for two reasons. First, these individuals are closely related; only minor genetic differences exist among them. Second, most of the phenotypic differences among individuals in the same group are caused by residual errors. In this case, individual-based BLUP, such as gBLUP, will result in different genomic predictions among individuals in the same group; but, these differences will be more likely due to error.The pBLUP method, based on pedigree-based kinship, has been successfully used for genetic evaluation in animals and plants for many decades. Many pBLUP software packages have been developed in both public and private sectors. Compared to the Bayesian methods, gBLUP, sBLUP, and cBLUP have the advantage of easily integrating into the existing genetic evaluation infrastructure that uses pedigree to derive kinship. The only modification required for gBLUP is the replacement of kinship based on pedigree by kinship based on genetic markers. Other minor modifications may also be required for the different types of marker-based kinship derivation used in the other BLUP alphabet methods. For example, the implementation of cBLUP is similar to gBLUP, but additional algorithms will be needed for the individual clustering analyses, group kinship calculations, and optimum grouping assignments.Genomic prediction with the BLUP alphabet series may involve GWAS, estimation of QTNs, and clustering individuals into groups. Yet, computing time complexity remains the same as conventional gBLUP in terms of number of individuals and markers. For the BLUP methods, computing time complexity is linear to number of markers. Currently, the number of markers is larger than the number of individuals; thus, BLUP methods have the advantage over the Bayesian methods. With the computer software package, Genome Association and Prediction Integrated Tool (GAPIT)  traits, the Bayesian LASSO method outperforms gBLUP and sBLUP outperforms Bayesian LASSO in terms of prediction accuracy. For low heritability traits, which are challenging for both Bayesian LASSO and gBLUP, cBLUP is superior to both. Expanding the BLUP alphabet series with sBLUP and cBLUP enriches prediction method options so that the best method can be matched to the specific genetic architecture of a given trait of interest.Supplementary Material


Understanding the genetic function of the forage quality-related traits, including crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), hemicellulose (HC), and cellulose (CL) contents, is essential for the identification of forage quality genes and selection of effective molecular markers in sorghum. In this study, we genotyped 245 sorghum accessions by 85,585 single-nucleotide polymorphisms (SNPs) and obtained the phenotypic data from four environments. The SNPs and phenotypic data were applied to multi-locus genome-wide association studies (GWAS) with the mrMLM software. A total of 42 SNPs were identified to be associated with the five forage quality-related traits. Moreover, three and two quantitative trait nucleotides (QTNs) were simultaneously detected among them by three and two multi-locus methods, respectively. One QTN on chromosome 5 was found to be associated simultaneously with CP, NDF, and ADF. Furthermore, 3, 2, 2, 5, and 2 candidate genes were identified to be responsible for CP, NDF, ADF, HC, and CL contents, respectively. These results provided insightful information of the forage quality-related traits and would facilitate the genetic improvement of sorghum forage quality in the future. Sorghum bicolor L.) is a popular crop worldwide, which is used a food source, animal fodder, and raw material for alcoholic beverages and biofuels in industries  content are the main focus for forage sorghum breeding , which is based on linkage disequilibrium (LD) and provided sufficient genetic background information, have become a powerful alternative for the investigation of quantitative traits. There are three main strategies for GWAS. Firstly, a generalized linear model (GLM) was proposed for the genetic analysis of the quantitative traits  and phenotyped them in the four environments. The data were analyzed by the multi-locus GWAS software, mrMLM.The 245 sorghum accessions Table  includedSeven hundred and thirty-five sorghum samples (3 replicates) were measured for CP, CL, HC, NDF, and ADF using the traditional chemical methods, and simultaneously scanned for near-infrared (NIR) spectra with an Antaris™ II FT-NIR Analyzer . A model was established using TQ Analyst software based on the NIR spectra and the results of the chemical analysis. The samples were then scanned for NIR spectra, and their CP, CL, HC, NDF, and ADF were calculated using the model. The mean of the phenotypic data and the correlation coefficients were calculated using Microsoft Excel.Total DNA was extracted using the DNAsecure Plant Kit . All the samples were standardized to 50 ng/μL, and 10 μL of each sample was digested with the enzymes, PstI (CTGCAG) and MspI (CCGG), at 37°C for 2 h and then at 65°C for 20 min. The digested samples were ligated with the adapters from Illumina . The ligated samples were then pooled using the same volume (10 μL) for PCR-amplification in a single tube. The fragment length was analyzed using a Bioanalyzer (Agilent), and the PCR products were quantified by a Qubit3.0 fluorometer (Invitrogen). The GBS library was run on an Illumina Hiseq2500 .http://hannonlab.cshl.edu/fastx_toolkit/). The high-quality sequencing data were aligned using BWA MEM  of fastx_toolkit-0.0.13.2  is an n × c matrix of covariates (fixed effects), including a column vector of 1; the population structure or principal components can be incorporated into W. Moving on, α is a c × 1 vector of fixed effects, including the intercept, and X is an n × 1 vector of marker genotypes. Z is an n × m design matrix, and m × 1 vector of polygenic effects. K is a known n × n relatedness matrix. n × 1 vector of residual errors, and In is an n × n identity matrix, and MVN denotes multivariate normal distribution. An LOD score of 3 was used as the critical threshold for significant QTNs for all the six methods.The GWAS for the five forage quality-related traits  was performed using six methods, including mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO in the mrMLM software. The main model used in this study in the mrMLM software is as follows :https://www.ncbi.nlm.nih.gov/genome/108). The detailed functions of the corresponding genes were annotated by performing BLASTP search at the NCBI website, and the candidate genes were assigned to different biological processes based on the function of their homologs in other species in literature or with the help of data in the Conserved Domains Database. The selected candidate genes were associated with the main QTNs of the five traits if they made a contribution (r2) greater than 5%.Genes that were hit directly by the associated QTNs within a 50-kb stretch were selected to choose the candidate genes as described in Upadhyaya et al. . The phyExtensive phenotypic variations of CP, CL, HC, NDF, and ADF were observed in the 245 sorghum samples in the four environments, including two locations in 2 years  across the four environments using six methods Table . There wr2 > 5%). Besides, 3 QTNs  were detected simultaneously by 3 methods, and another 5 QTNs  were identified simultaneously by 2 methods. The remaining QTNs were detected by a single method, but most of them were considered as reliable because of the high thresholds at which they were detected.Among the above six methods, pLARmEB was the most powerful and accountable for the identification of the 24 QTNs that mainly contributed to the NDF content trait (17 QTNs); however, their contributions were less than what were detected by other methods, except for one major QTN RSS17673), whose contribution was greater than 5%  encoded a serine/threonine-protein kinase (Sobic.002G217100), which was consistent with a previous study that concluded that serine/threonine-protein kinases are involved in signal cascade for nitrogen metabolism in plants (Champigny, Genome-wide association study is an important alternative for mapping quantitative traits. It has been applied rapidly and extensively in plant research. These methods have been widely adopted, but only a few QTNs for each complex trait have been identified. In this study, we implemented the latest multi-locus GWAS methods available in mrMLM (Wang S. B. et al., According to the GWAS analysis, 5, 3, 3, 7, and 24 QTNs were identified for CP, CL, HC, ADF, and NDF content, respectively. Of the 5 candidate loci for the CP content, 2 were already identified in the previous studies. The locus on chromosome 9 was mapped in the same region by Murray et al.  and Li eThe QTLs for the NDF or ADF content co-localized with those for the CL or HC content, which has been reported previously in sorghum. Cardinal et al.  reportedBoth NDF and ADF include CL and lignin. There are a series of reports about the biosynthesis and signaling pathways of CL and lignin in plants (Kim et al., Lignin, CL, and HC are the main components of secondary cell walls (Zhong et al., JL, LW, and S-QX designed and conceived the experiments. Y-WZ, K-NC, and S-BW performed the computational analysis. WT and JL extracted the DNA and performed the experimental analysis. CCW and YL assisted with experiments in data collection and analysis. QZ and CW participated in the design and supervised the study. S-QX and JL discussed the results and interpretation of the final data. S-QX and JL drafted the manuscript. All authors read and approved the final manuscript.The reviewer ML declared a shared affiliation, though no other collaboration, with several of the authors WT, CW to the handling Editor. The remaining 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.


Drosophila mojavensis. We created an F8 mapping population of 1,500 individuals from advanced intercross lines and sequenced the 10% of individuals with the highest and lowest levels of locomotor activity. Using three alternative statistical approaches, we found strong evidence for two relatively large-effect QTL that is localized in a region homologous to a region of densely packed behavior loci in Drosophila melanogaster, suggesting that clustering of behavior genes may display relatively deep evolutionary conservation. Broadly, our data are most consistent with a polygenic architecture, though with several loci explaining a high proportion of variation in comparison to similar behavioral traits. We further note the presence of several antagonistic QTL linked to locomotion and discuss these results in light of theories regarding behavioral evolution and the effect size and direction of QTL for diverging traits in general.Behavior is frequently predicted to be especially important for evolution in novel environments. If these predictions are accurate, there might be particular patterns of genetic architecture associated with recently diverged behaviors. Specifically, it has been predicted that behaviors linked to population divergence should be underpinned by a few genes of relatively large effect, compared to architectures of intrapopulation behavioral variation, which is considered to be highly polygenic. More mapping studies of behavioral variation between recently diverged populations are needed to continue assessing the generality of these predictions. Here, we used a bulk segregant mapping approach to dissect the genetic architecture of a locomotor trait that has evolved between two populations of the cactophilic fly  Behavior has been traditionally thought to play an especially important role in adaptation to novel environments . In partIn general, behaviors are considered to be highly polygenic and controlled mainly by loci of small effect . HoweverDrosophila mojavensis is endemic to the southwestern US and northwestern Mexico presents an ideal system for examining behavioral divergence on recent evolutionary timescales. Beginning 0.23-0.27 MYA, D. mojavensis has diverged from a population in Baja California , of whijavensis . Beyond r larvae . If so, D. mojavensis locomotor behavior, we performed a bulk segregant analysis. Bulk segregant analysis (BSA) maps quantitative traits by comparing allele frequencies between pools of individuals taken from the tails of the phenotypic distribution . We obtained activity phenotypes for 1,484 larvae, with 16 discarded due to a complete lack of movement or other measurement discrepancies. Immediately after activity trials, we froze each larva whole at -80°. All trials were performed between 12:00 and 3:00 pm under identical conditions as described above. Following a previous BSA study of aggression in D. melanogaster . We recorded trials using a Point Gray video camera  for five minutes, retaining images every 2.5 sec. To avoid using a period of inactivity observed immediately after transferring larvae to the petri dish, we analyzed activity only during the last 50 sec before each larva reached the edge of the dish. We calculated the mean speed of each larva in cm/min during this 50 sec period using the TrackMate plugin for ImageJ . We then quantified the DNA concentration of each sample using a NanoDrop  and combined equal amounts of DNA from each sample into single pooled samples for low and high activity. Each bulk contained 4 μg of total DNA, and libraries for each were prepared using Illumina Truseq kits. Each sample was sequenced as paired end 150 bp reads using an Illumina NextSeq Mid Output Flowcell at the University of Arizona Genetic Core (UAGC) facility.D. mojavensis genome  performing forward simulations to assess the likelihood that peaks in close physical proximity are actually separate QTL or artifacts due to linkage, and which could lead to overestimating QTL effect size. For this analysis, we used the same filtered set of individual SNPs as above. We defined non-overlapping windows to contain 50 SNPS each, generating an average window size of 8.1 kb to roughly match initial testing of SIBSAM  from this analysis. Additionally, we stitched together separate chromosome scaffolds for chromosomes 1 and 4 to facilitate chromosome-wide simulations of recombination.Lastly, we analyzed our data using SIBSAM , a set of SIBSAM . We thenhttps://ncbi.nlm.nih.gov/sra/PRJNA498146). Behavioral data are publicly available at OSF (https://doi.org/10.17605/OSF.IO/JXVZW). Supplemental figures are available at figshare. Supplemental material available at Figshare: https://doi.org/10.25387/g3.7840256.Raw reads are available at NCBI Bioproject PRJNA498146 . These means are also significantly more extreme than the trait means for both the parental Catalina Island line  and the Sonoran desert line , indicating that the advanced intercross lines have captured the natural range of phenotypic expression for this trait.The complete distribution of F8 We obtained an initial set of 1,598,192 SNPs segregating in the five replicate AIL populations and analyzed 931,281 SNPs after filtering. The distribution of SNPs across the genome is presented in Sliding window analysis using a G’ statistic identified a large region on the first half of scaffold 6500 (chromosome 3) as significantly associated with phenotypic variation between our bulks . OutsideTo complement the analysis presented above, we analyzed the same dataset on an individual, SNP-by-SNP basis. The same region identified in the sliding window analysis on chromosome 3 remained the most notable region of association . Of 3,89Lastly, we assessed our data using a markedly different approach, performing null simulations of statistical associations given our experimental design and an estimated recombination rate. This methodology suggested eleven significant QTL , locatedD. mojavensis, inversions have been previously identified on chromosomes 2 and 3 (2) has been estimated to be occurring between 6.9 Mb and 16.7 Mb of scaffold 6500 , the content of ∼7mb near the centromeric end is reasonably conserved between the two species (D. mojavensis may be in part controlled by an evolutionarily conserved behavioral “hotspot”. The presence of such clusters is also found in mice, where numerous behavioral phenotypes map to a single chromosomal segment (D. mojavensis behavior, as previous QTL studies on this species (Our most robust result demonstrates evidence for two relatively large-effect QTL controlling ulations , includi species . This ra segment , wherein species ; 2010 utBoth of our methods which identified multiple QTL found support for a minority of these as antagonistic  and empiD. mojavensis locomotion, the consensus between them point to a polygenic architecture, with potentially up to eleven loci spread across all chromosomes contributing to phenotypic variation. Furthermore, though effect size estimates from SIBSAM simulations are coarse (D. mojavensis are distantly geographically separated, and therefore unlikely to have experienced much migration, as evidenced by measures of gene flow between populations (Though the different statistical methodologies we used painted different pictures of the overall genetic architecture of e coarse , they are coarse . This sue coarse . The Sanulations , biasingulations . What re


An individual's dietary and supplement strategies can influence markedly their physical performance. Personalized nutrition in athletic populations aims to optimize health, body composition, and exercise performance by targeting dietary recommendations to an individual's genetic profile. Sport dietitians and nutritionists have long been adept at placing additional scrutiny on the one-size-fits-all general population dietary guidelines to accommodate various sporting populations. However, generic “one-size-fits-all” recommendations still remain. Genetic differences are known to impact absorption, metabolism, uptake, utilization and excretion of nutrients and food bioactives, which ultimately affects a number of metabolic pathways. Nutrigenomics and nutrigenetics are experimental approaches that use genomic information and genetic testing technologies to examine the role of individual genetic differences in modifying an athlete's response to nutrients and other food components. Although there have been few randomized, controlled trials examining the effects of genetic variation on performance in response to an ergogenic aid, there is a growing foundation of research linking gene-diet interactions on biomarkers of nutritional status, which impact exercise and sport performance. This foundation forms the basis from which the field of sport nutrigenomics continues to develop. We review the science of genetic modifiers of various dietary factors that impact an athlete's nutritional status, body composition and, ultimately athletic performance. Sport and exercise performance are significantly influenced by nutrition, yet individuals respond differently to the same foods, nutrients and supplements consumed. This holds true for a variety of ages, ethnicities, and level of skill, and whether the goal is optimizing physical activity for health and fitness or for high performance sport. The importance of a personalized sports nutrition plan was highlighted in the recent “Nutrition and Athletic Performance” Joint Position Statement by the American College of Sports Medicine, the Academy of Nutrition and Dietetics and the Dietitians of Canada, which states that “Nutrition plans need to be personalized to the individual athlete… and take into account specificity and uniqueness of responses to various strategies” . These sThe paradigm shift, away from the one-size-fits-all group approach and toward personalization for the individual, is moving nutrigenomics research from basic science into practice. While it has long been recognized that genetics play an influential role in determining how an athlete responds to foods and nutrients, the surge in research into gene-diet interactions over the past decade has provided a scientific basis for this hypothesis through various research initiatives and the corresponding increase in published studies. Genetic variants affect the way we absorb, metabolize, utilize and excrete nutrients, and gene-diet interactions that affect metabolic pathways relevant to health and performance are now widely recognized . PersonaThe practical application of the scientific knowledge gained from research on health and performance is to enable athletes to utilize genetic test results for personalized nutrition in an actionable manner. The demand for genetic testing for personalized nutrition and associated performance outcomes by athletes and active individuals is growing, and there is an increased need for dietitian-nutritionists, fitness professionals, coaches, and other sports medicine practitioners to understand the current evidence in this developing field –14. The Personalized nutrition, based on an individual's genotype, is not a novel concept, and there are several examples of rare  and common  genetic variants that require specific dietary strategies to manage . AlthougPersonalized dietary and supplement advice derived from genetic testing should be based on clear and defensible interpretations of relevant research studies. Traditional genome-wide association studies (GWAS) can be used to identify associations between genotypes and outcomes of interest such as blood levels of a micronutrient. However, the utility of such markers in providing actionable information on dietary advice is limited because it is not known what dietary intakes are required to counter effects of the genetic variant(s). For example, although a genetic variant that has been associated with low serum values of a vitamin is identified, a specific recommendation for intakes to prevent the risk of deficiency or to alleviate low levels of this micronutrient may remain undetermined. Such studies require the appropriate design that demonstrates how a genetic variant modifies the response to dietary intake on the outcome trait of interest and perhaps identify responders and non-responders. Genetic markers related to a performance trait, such as aerobic capacity or power, also provide little information on what factors could be used to improve the trait of interest.With the exception of investigations exploring genetic variation and supplemental caffeine, which have been shown to modify endurance exercise outcomes , 20, theIn an effort to achieve specific sport goals, there is generally considerable overlap in the development of complementary training and dietary plans for athletes –29. HoweSome commercial genetic tests claim to use proprietary algorithmic approaches to prescribe training protocols based on evidence reported in peer-reviewed research . AlthougThere is a considerable amount of ongoing research investigating individual variation in response to exercise training, however, sport and exercise genomics is still in its early stages and clinical or sport utility is lacking , 40–44. The objective of this review is to examine the scientific evidence on specific nutrients and food bioactives whereby genetic variants appear to modify individual responses related to athlete health and athletic performance. Although many studies reviewed herein have not been studied in athletes exclusively, they have been carried out in healthy individuals. Accordingly, several studies outlined reflect optimal health, body composition and nutritional status, which for athletes, provides the foundation for athletic success. Genetic variation impacting response to various micro- and macronutrients, as well as bioactives such as caffeine, on performance-related traits will be reviewed .Caffeine, found naturally occurring in several plant species including coffee, tea, cocoa, and guarana, is widely used in sport as a performance enhancer or ergogenic aid often in the form of caffeinated tablets, gels or chews.CYP1A2 and possibly ADORA2, which are associated with caffeine metabolism, sensitivity and response  or 4 mg (moderate dose) per kg body mass. There was a 3% improvement in cycling time in the moderate dose in all subjects, which is consistent with previous cycling time trial studies using similar doses  on the ergogenic effects of caffeine under exercise conditions  than C allele carriers after ingesting 400 mg caffeine during a sustained vigilant attention task after sleep loss  C allele have greater sensitivity toward caffeine-induced sleep disturbance compared to those with the TT genotype  sleep, reduces sleep efficiency, and alters the waking and sleep electroencephalogram (EEG) frequencies, which reliably reflect the need for sleep –91. Althgenotype . Taken tNo studies have examined the role of genetic modifiers of vitamin A status directly on athletic performance, however, there are several important functions of this micronutrient that are associated with optimal health, immunity and performance in athletes.Vitamin A is a fat-soluble vitamin, which plays a key role in both vision  and immuHigh-performance athletes appear to have superior visual abilities based on their capacity to access distinct visual skills, such as contrast sensitivity, dynamic acuity, stereoacuity, and ocular judgment, needed to accomplish interceptive actions  and resolve fine spatial detail, which is required by many sports , 95. In Deficiencies of certain micronutrients such as vitamin A decrease immune defense against invading pathogens and can cause the athlete to be more susceptible to infection. Low energy availability (dieting), poor food choices, jetlag, physical and psychological stress, and exposure to pollution and foreign pathogens in air, food and water while traveling can result in a deterioration in immune function and increased susceptibility to illness . AthleteBCMO1 gene affects circulating plasma carotenoid levels by impacting the conversion of dietary provitamin A carotenoids to active forms of vitamin A in the small intestine  (Methylene tetrahydrofolate reductase (MTHFR) is the rate-limiting enzyme in the methyl cycle, and is encoded by the HFR gene . The C67se (CVD) , 114. Sese (CVD) –118.MTHFR genotypes or dietary folate intake, hyperhomocysteinemia has been shown to be associated with diminished muscle function  AA genotype, which is associated with an increased risk for hemochromatosis, may be at a genetic advantage to excel in sport if iron levels are at the high end of the normal range, but not excessive to cause tissue damage. Notably, several studies have found that certain variants of the HFE gene that increase risk of iron overload are more common in elite-level athletes compared to the general population, suggesting this may be beneficial for performance  can be used to predict risk of hereditary hemochromatosis. Based on the combination of variants from these two SNPs, individuals can be categorized as having a high, medium, or low risk for iron overload , TFR2 (rs7385804), TF (rs3811647) can be used to assess genetic risk for low iron status, primarily due to their involvement in regulating the expression of hepcidin, which is a peptide hormone that controls iron absorption  has a significant impact on serum B12 levels where individuals with GG or GA genotypes possess the greatest risk for low serum vitamin B12 levels, but only when the diet is low in bioavailable sources of vitamin B12  levels in response to intake. These low circulating ascorbic acid levels may, in turn, diminish performance through an increased risk of high ROS and diminished muscle or connective tissue repair. Although studies have identified associations between circulating ascorbic acid concentrations and vitamin C transporters, SVCT SLC23A2 , there i SLC23A2 . As suchThere are no studies that link genetic modifiers of vitamin D status on athletic performance outcomes; however, there are several functions of this vitamin that are associated with bone health, immunity, recovery from training and various performance variables. Genetic determinants of circulating 25-hydroxyvitamin D (25(OH)D) can influence each of these factors thereby influencing performance.Vitamin D is essential to calcium metabolism, increasing calcium absorption for optimal bone health , which iGC gene and the CYP2R1 gene (GC and CYP2R1 genes are associated with a greater risk for low serum 25(OH)D. In one study (CYP2R1 (rs10741657) and GC (rs2282679) were significantly associated with vitamin D status. Specifically, participants with the GG or GA genotype of CYP2R1 (rs10741657) were nearly four times more likely to have insufficient vitamin D levels. Those with the GG genotype of the GC gene (rs2282679) were significantly more likely to have low vitamin D levels compared to those with the TT genotype (CYP2R1 (rs10741657) and GC (rs2282679) on vitamin D status. Not surprisingly, the number of risk variants that the participants possessed was directly related to their risk for vitamin D insufficiency  were genotyped for two SNPs in the GC gene, rs7041 and rs4588, and calcium intake was assessed in relation to the participants' risk for bone fracture (GC (rs7041) and the C allele of GC (rs4588). However, in participants with low dietary calcium intake (<1.09 g/day) and who were homozygous for the G allele of rs7041 and the C allele of rs4588, there was a 42% increased risk of fracture compared to other genotypes. No differences between genotypes were found in participants with high dietary calcium intakes  in 1998 . Cholinel health , 175. Thl health , 177. Sil health , 179, anl health , 180. Rel health , 182. Acl health –183. Chol health  and the l health .PEMT gene) and a folate pathway enzyme (MTHFD1) have been shown to impact the metabolic handling of choline and the risk of choline deficiency across differing nutrient intakes  dysfunction compared to those with the GG genotype  pathway, a SNP in the PEMT gene (rs12325817) has been shown to influence the risk of choline deficiency and the partitioning of more dietary choline toward PC biosynthesis at the expense of betaine synthesis (used a methyl donor) (PEMT gene have been shown to develop signs of choline deficiency and organ (liver and muscle) dysfunction compared to those with the GG genotype (While humans can make choline endogenously l donor) . Individgenotype .Athletes by nature experience muscle damage through high volume and high intensity training . A deficSeveral aspects of physique such as body size, shape and composition contribute to the success of an athlete, in most sports. In the athletic population, body composition is often the focus for change, as it can be easily manipulated through diet as both total energy intake and macronutrient composition , 196. VaAlthough research examining dietary factors and genetics has revealed that manipulation of dietary fat and protein intakes may have greater modifying effects on body composition than carbohydrates, all macronutrients serve a critical purpose. Carbohydrates provide a key fuel for the brain, CNS and working muscles, and the amount and timing of intake impacts sport performance over a large range of intensities , 201. AdFTO gene is also known as the ‘fat mass and obesity-associated gene’ since it has been shown to impact weight management and body composition  multicenter trial found that carrying an A allele of the FTO gene (rs1558902–a surrogate marker for rs9939609) and consuming a high protein diet was associated with a significantly lower fat mass at the 2-year follow up period compared to carrying two T alleles. Importantly, participants with the AA genotype (lesser effects in those with AT genotype) who were following the high protein diet protocol had significantly greater losses of total fat mass, total adipose tissue, visceral adipose tissue, lower total percent fat mass and percent trunk fat, compared to those following a lower protein diet protocol (FTO risk variants on BMI and waist circumference (FTO gene at rs1558902 would benefit the most in terms of consuming a moderate-to-high protein diet (at least 25% of energy from protein) to optimize body composition. Greater lean mass in athletes has been associated with improved performance in strength and power sports, as well as some endurance events, and a decreased risk for injuries  to optimize body composition  compared to those with the GG or GC genotype who consume < 56% of total fat from MUFAs. These results have not been found in those with the CC genotype of PPARγ2 at rs1801282  to polyunsaturated fatty acids (PUFA) influenced the risk of obesity associated with the TA and AA variants of the s9939609 . Specifis9939609 . These fs9939609 .This paper provided an overview of the current science linking genetic variation to nutritional or supplemental needs with a focus on sport performance. One of the ultimate goals in the field of personalized sport nutrition is the design of tailored nutritional recommendations to improve direct and indirect factors that influence athletic performance. More specifically, personalized nutrition pursuits aim to develop more comprehensive and dynamic nutritional and supplement recommendations based on shifting, interacting parameters in an athlete's internal and external (sport) environment throughout their athletic career and beyond.Currently, there are few gene-diet interaction studies that have directly measured performance outcomes and been conducted in competitive athletes, so this should be a focus of future research. However, it has been established that serum levels and/or dietary intakes of several nutrients and food bioactives can impact overall health, body composition and in turn result in modest to sizable modifying effects in athletic performance. The strongest evidence to date appears to be for caffeine on endurance performance with several trials demonstrating the modifying effects of genetic variants with sports performance outcomes. Genetic testing for personalized nutrition may, therefore, be an additional tool that can be implemented into the practice of sport clinicians, nutritionists and coaches to guide nutritional counseling and meal planning with the aim of optimizing athletic performance.NG and AE-S conceived of the original idea for the review and NG wrote the first draft. All authors contributed to the writing and editing of the manuscript. AE-S secured funding.AE-S is the Founder and holds shares in Nutrigenomix Inc. NG is on the Science Advisory Board of Nutrigenomix Inc. SV was a paid employee and remains a paid consultant with Nutrigenomix. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.