Genome wide association studies (GWAS) are a powerful tool for identifying quantitative trait loci (QTL) and causal single nucleotide polymorphisms (SNPs)/genes associated with various important traits in crop species. Typically, GWAS in crops are performed using a panel of inbred lines, where multiple replicates of the same inbred are measured and the average phenotype is taken as the response variable. Here we describe and evaluate single plant GWAS (sp-GWAS) for performing a GWAS on individual plants, which does not require an association panel of inbreds. Instead sp-GWAS relies on the phenotypes and genotypes from individual plants sampled from a randomly mating population. Importantly, we demonstrate how sp-GWAS can be efficiently combined with a bulk segregant analysis (BSA) experiment to rapidly corroborate evidence for significant SNPs.P ≤ 0.00001) for PH. The results from our single-plant GWAS were further validated by bulk segregant analysis (BSA) for PH. BSA sequencing was performed on the same population by selecting tall and short plants as separate bulks. This approach identified 37 genomic regions for plant height. Of the 25 significant SNPs from GWAS, the three most significant SNPs co-localize with regions identified by BSA.In this study we used the Shoepeg maize landrace, collected as an open pollinating variety from a farm in Southern Missouri in the 1960’s, to evaluate whether sp-GWAS coupled with BSA can efficiently and powerfully used to detect significant association of SNPs for plant height (PH). Plant were grown in 8 locations across two years and in total 768 individuals were genotyped and phenotyped for sp-GWAS. A total of 306 k polymorphic markers in 768 individuals evaluated via association analysis detected 25 significant SNPs  contains supplementary material, which is available to authorized users. Zea mays. L.) is one of the most widely grown crops worldwide because of its importance for food, feed, fuel, and raw material for industry  using tnce.org/ . This rence.org/ . After tlm function in R [The phenotypic data were standardized across years using a linear model where locations were treated a fixed effect with the ion in R . The resion in R . First, ion in R .P < 0.00001. Since approximately 300,000 SNPs were tested, this threshold means that we expect fewer than three false positives across the entire set of markers. Moreover, this threshold is more conservative than others that have been used for GWAS for plant height in maize [http://ensembl.gramene.org/Zea_mays/Info/Index) and the MaizeGDB database (http://www.maizegdb.org/gbrowse).There are many statistical models used for association analysis, a common one being the Mixed Linear Model (MLM). Incorporating kinship and population structure in the MLM can control the false positives, but can compromise the true positives as well . Fixed ain maize , 15, 31., et al. [p-value) over the outlier threshold of 0.5% [A modified form of bulk segregant analysis (BSA) was performed by evaluating the 384 plants observed in Generation-1.While the original method of RW Michelmore, et al.  used bi-, et al. . Signifi of 0.5% . Then a  of 0.5% .Additional file 1:2S, where S is the difference between the population average and average of selected individual from Gen-0, R is the difference between the population average from Gen-0 and the average of the individuals in Gen-1 and h2 is the heritability. (DOCX 12 kb)Estimation of heritability using the breeder’s equation: R = hAdditional file 2:Population structure based on the principal component analysis (PCA) for 768 Shoepeg plants used in the association analysis. Panel (a) shows the relationship of PC1 vs PC2 for the two generations and panel (b) shows the relationship of PC1 vs PC2 using selection regime. (TIFF 362 kb)Additional file 3:Quantile-quantile (Q-Q) plots for Plant Height GWAS using FarmCPU. The red line is the 1:1 identity line. (PNG 73 kb)


By determining how three different genetic perturbations give rise...To better understand cryptic genetic variation, Lee  Saccharomyces cerevisiae colony phenotype that is typically suppressed in a cross of the laboratory strain BY4716 (BY) and a derivative of the clinical isolate 322134S (3S). To do this, we comprehensively dissect the trait’s genetic basis in the BYx3S cross in the presence of three different genetic perturbations that enable its expression. This allows us to detect and compare the specific loci that interact with each perturbation to produce the trait. In total, we identify 21 loci, all but one of which interact with just a subset of the perturbations. Beyond impacting which loci contribute to the trait, the genetic perturbations also alter the extent of additivity, epistasis, and genotype–environment interaction among the detected loci. Additionally, we show that the single locus interacting with all three perturbations corresponds to the coding region of the cell surface gene FLO11. While nearly all of the other remaining loci influence FLO11 transcription in cis or trans, the perturbations tend to interact with loci in different pathways and subpathways. Our work shows how layers of cryptic genetic variation can influence complex traits. Here, these layers mainly represent different regulatory inputs into the transcription of a single key gene.Cryptic genetic variation may be an important contributor to heritable traits, but its extent and regulation are not fully understood. Here, we investigate the cryptic genetic variation underlying a  MOST research on complex traits focuses on characterizing the genetic basis of phenotypic diversity that is visible within populations . Thus, iSaccharomyces cerevisiae , a negative regulator of Ras signaling, had occurred during the generation of BYx3S segregants and enabled ∼2% of cross progeny to express the phenotype  sequences were cloned into the pML104 plasmid vector, which carries Cas9 and URA3  media lacking uracil to select for retention of the pML104 plasmid. Plasmids were then eliminated from the transformants by plating on 5-fluoroorotic acid (5-FOA). Presence of the intended genetic modifications was checked in the transformants using Sanger sequencing.The and URA3 . Oligos 2 segregant included in the current study is discussed in 2 segregant used in this study was described in 2 segregants recovered from screens of BYx3S crosses, performing genetic mapping in backcross populations obtained by mating the respective rough F2 segregants to both BY and 3S, and analyzing whole-genome sequencing data for backcross segregants. Here, to obtain a rough F2 segregant with GPc, flo8∆ sfl1∆ BY and 3S strains were mated to one another. The resulting diploid was sporulated and plated at low density (∼300 colonies per plate) onto YNB containing canavanine to select for MATa haploids using the Synthetic Genetic Array (SGA) system  DNeasy 96 Blood and Tissue kit. Illumina sequencing libraries were then prepared using the Illumina Nextera kit, with a unique pair of dual-indexed barcodes for each individual. Between 48 and 156 segregants from each combination of GP, backcross, and temperatures sensitivity were sequenced. Sequencing was performed at the Beijing Genomics Institute on an Illumina HiSeq 4000 using paired-end 100 × 100-bp reads. Each segregant was sequenced to an average per site coverage of at least 2.5×. Reads were aligned to either a BY or 3S reference genome using the Burrows–Wheeler Aligner version 7 with options mem -t 20 . MpileupP < 0.01. Multiple-testing correction was performed on each backcross on its own, as the number of unique tests in each mapping population varied from 837 to 1526. We delimited the interval surrounding a locus by computing the −log10 at each linked SNP and determining the SNPs at which this statistic was 2 lower than the peak marker. These bounds were used in fine-mapping, as well as in comparison of loci detected in different combinations of GP, backcross, and temperature sensitivity.Loci associated with the trait for each combination of GP, backcross, and temperature sensitivity were identified using binomial tests. Sites were considered statistically significant at a Bonferroni-corrected threshold of 2 tests in a custom Python script. Expected two-locus genotype frequencies were calculated as the product of the individual allele frequencies at each of the two sites. To reduce the number of statistical tests, SNPs containing the same information across all segregants in a given backcross population were collapsed into a single marker. The Benjamini–Hochberg method for false discovery rate (FDR) (Observed and expected two-locus genotype frequencies for each pair of SNPs were compared using χte (FDR)  was thente (FDR) . A strin2 test with d.f. equal to one less than the number of possible genotypes. These analyses only included loci where both the BY and 3S allele were present among the rough segregants obtained from a backcross population, and were only performed on backcross populations in which at least two detected loci segregated.Using genotype data for rough backcross segregants that only express the trait at 21°, the only temperature at which all three GPs enable trait expression, we attempted to determine the extent of additivity and epistasis among detected loci. To do this, we tested for significant differences between the expected and observed frequencies of multi-locus genotypes among loci identified using individuals that only express the trait at 21° in each backcross. Expected multi-locus genotype frequencies were calculated as the product of the individual BY or 3S allele frequencies among segregants expressing the rough phenotype in a particular backcross, while the observed frequencies were measured directly from the genotype data. For each population, observed and expected genotype frequencies were compared using a χkanMX cassette . We then employed random spore techniques to generate and screen > 100,000 flo8∆ sfl1∆ (GPc) cross progeny for the trait at 21° . We used this condition because the trait is less genetically complex at 21° than at higher temperatures, making it easier to screen for rough segregants . We observed varying degrees of temperature sensitivity among the rough GPb segregants, with only a minority of rough individuals capable of expressing the trait at all examined temperatures . In our past work on GPa .To map loci involved in expression of the rough phenotype across temperatures, we generated > 60,000 and > 12,000 GPb and GPc backcross segregants, respectively. First-generation backcross segregants were used exclusively in mapping, except in the case of the 3S backcross of GPb at high temperature. Because expression of the rough phenotype at 30 and 37° is rare among GPbx3S backcross segregants , determining the genetic basis of the trait in this backcross required combining information from the first-generation backcross and a second-generation backcross. Specifically, a rough first-generation GPbx3S backcross segregant was mated to 3S , which increased the frequency of the trait among backcross progeny by reducing the number of segregating loci. GPb segregants were phenotyped at 21, 30 and 37°, while GPc segregants were only phenotyped at 21°. Low-coverage whole-genome sequencing was performed on between 48 to 151 individuals for each GP–backcross–temperature combination, and loci associated with the trait were identified based on their enrichment among genotyped segregants . Similar to our work on GPa . If loci act in a predominantly additive manner in the presence of a given GP, observed multi-locus genotype frequencies should match expected multi-locus genotype frequencies, which can be calculated as the product of the frequencies of every individual allele involved in a multi-locus genotype. In contrast, if epistasis meaningfully contributes to the trait, observed multi-locus genotype frequencies should depart from expected multi-locus genotype frequencies. Based on these tests, we observed significant deviation from expected frequencies for GPa . The 3S allele of Flo11 produces a longer protein that should aid in the expression of cell-cell and cell-surface adhesion traits in yeast , a protein kinase A subunit (TPK3), a target or rapamycin (TOR) pathway component (MDS3), and IRA2. Two of the loci found in the current study among GPb segregants were located on chromosomes II and XIV, and had never been detected in our past work , which encodes a post-translational activator of adenylate cyclase, another component of the Ras pathway. At the chromosome II locus, the most likely candidate is SRB6, which encodes an essential subunit of the RNA polymerase II mediator complex. Previously, we showed that mutations disrupting other mediator components, Srb10 and Srb11 , can induce the rough phenotype by interacting with a subset of the alleles that have a phenotypic effect in the presence of GPa . Knockout and replacement of PMD1, GPA1, SDC25, GPI8, GCN4, and YPT6 resulted in loss of the phenotype , implying that these six genes play positive roles in the trait’s regulation. In contrast, deletion of DIG1, a protein that directly inhibits the transcriptional activator Ste12, enhanced the trait’s expression . These results suggest that genetic variation in these genes plays a causal role in the rough phenotype.Lastly, we sought to determine the genes harboring cryptic variants that interact with GPc. Regarding GPc, 3 of the 10 identified loci also interact with either GPa or GPb. These loci correspond to  mutated . To obtaEND3, IRA1, and IRA2, in the absence of Flo8 and Sfl1, indicates that the Ras pathway still contributes to FLO11 regulation when these transcription factors are not present, possibly by impacting the activities of other transcription factors ; members of the MAPK signaling cascade (DIG1 and GPA1) (GCN4); a Rab GTPase that influences Ras (Rim101 (YPT6); a stress-responsive guanine exchange factor (SDC25); and an enzyme that post-translationally modifies proteins to help them anchor into the cell wall (GPI8). Of particular note for GPA1, the BY strain is known to carry a laboratory-derived allele that is an expression QTL hotspot (GPA1 allele state might also impact expression of the rough phenotype in the presence of GPc. Additionally, GPA1BY was previously shown to influence other FLO11-dependent traits through its downstream transcriptional activator Ste12 (FLO11 transcription. The lone exception is GPI8, which could still influence Flo11 at the protein level, as it is responsible for adding glycosylphosphatidylinositol anchors to new proteins and could affect Flo11’s binding to the cell surface (FLO11 and the rough phenotype.Detection of  factors . The remTo better understand the extent of cryptic variation within a population, as well as the mechanisms regulating this cryptic variation, we comprehensively determined the genetic basis of a model phenotype that is only expressed in the presence of particular GPs. By doing this, we identified 21 loci harboring cryptic variants that can contribute to the rough phenotype. Notably, all but one of these loci show phenotypic effects that depend on the GP that is present. In addition, because the cryptic variants that are uncovered by each GP vary in their degree of epistasis with each other and interaction with the environment, we find that the trait’s genetic architecture significantly differs across the GPs. This results in a multitude of genotype, environment, and phenotype relationships that produce the same trait.FLO11. Supporting this point, the single locus common to all three GPs is the coding region of FLO11, suggesting that regulation of Flo11 levels and stability is the central determinant of the rough phenotype’s expression across GPs, combinations of segregating loci, and temperature. Bolstering the importance of variability in FLO11 regulation to our findings, most of the loci that exhibit phenotypic effects influence FLO11 in trans. Moreover, many of these loci are only visible in the absence of a repressive Rim101-binding site in the FLO11 promoter. Thus, our results also highlight the potentially important role that cis-regulatory polymorphisms can play in enabling trans-regulatory polymorphisms to exert phenotypic effects. Indeed, cis-regulatory polymorphisms have been shown to modify the effects of trans variants in other systems (Given the detailed understanding that we have obtained for the colony morphology system, a major question is how might our findings relate to other traits and species? One major insight from our study that may apply to other systems is that most, if not nearly all, of our findings connect to the transcriptional regulation of a single key gene,  systems .FLO11.In addition to showing that different GPs enable distinct cryptic variants to have phenotypic effects, we also demonstrated that the GPs modify how genotype–environment interactions influence the trait. For both GPa and GPb, we find that certain combinations of epistatic alleles enable expression of the trait at a permissive temperature of 21°, as well as provide the genetic potential for the phenotype at higher temperatures. However, different loci and multi-locus genotypes allow GPa and GPb segregants to express the trait at higher temperatures. In contrast, GPc individuals are unable to express the phenotype at temperatures above 21°, implying that their potential to express the trait across environments is constrained. These findings support the concept that not all genotypes specifying the same trait possess comparable environmental robustness . In the Flo8 and Sfl1, the main transcriptional regulators of FLO11, we not only uncovered a previously undetected set of cryptic variants but also converted the trait’s genetic architecture from mainly epistatic to additive. Perhaps this additivity at the phenotypic level reflects the cumulative effect of multiple pathways influencing FLO11 expression at the molecular level. While the majority of the loci that interact with GPa and GPb correspond to components of the Ras pathway, many of the loci found in the presence of GPc are involved in other signaling pathways that may have compensatory functions (Flo8 and Sfl1 might enable other pathways to play a stronger role in the expression of FLO11 and the rough colony phenotype.Furthermore, whereas the rough phenotype in GPa and GPb segregants involves complex epistatic effects, we find no evidence for epistasis in GPc rough segregants. This implies that by eliminating unctions . This isFLO11 regulation, consistent with theoretical work suggesting that how GPs affect transcriptional regulation can impact whether loci show additive or epistatic effects (Our results also provide valuable insights into genetic background effects, the phenomenon in which GPs show different phenotypic effects in distinct individuals . A numbeIn summary, our study demonstrates the large amount of cryptic variation that can underlie a single phenotype and strongly implicates multifactorial changes in transcription as a mechanism regulating this cryptic variation. Further, these results suggest a broader insight into the genetic architecture of complex traits. Although it has long been known that traits can vary in genetic architecture depending on the different populations and environments in which they are measured, our findings illustrate that even the same phenotype examined within a single population can exhibit a spectrum of genetic architectures. As shown by the GPs in our study, which of these architectures is visible may depend on only one or two alleles that modify how the rest of the genetic variation in the population behaves. This suggests that characterizing the molecular mechanisms that shape genetic architecture will be an important step in improving our basic understanding of the relationship between genotype and phenotype.


Dual-purpose cattle are more adaptive to environmental challenges than single-purpose dairy or beef cattle. Balance among milk, reproductive, and mastitis resistance traits in breeding programs is therefore more critical for dual-purpose cattle to increase net income and maintain well-being. With dual-purpose Xinjiang Brown cattle adapted to the Xinjiang Region in northwestern China, we conducted genome-wide association studies (GWAS) to dissect the genetic architecture related to milk, reproductive, and mastitis resistance traits. Phenotypic data were collected for 2410 individuals measured during 1995–2017. By adding another 445 ancestors, a total of 2855 related individuals were used to derive estimated breeding values for all individuals, including the 2410 individuals with phenotypes. Among phenotyped individuals, we genotyped 403 cows with the Illumina 150 K Bovine BeadChip.BovineHD1600006691, was significantly associated with both age at first service and age at first calving. This SNP directly overlapped a QTL previously reported to be associated with calving ease. Within 160 Kb upstream and downstream of each significant SNP identified, we speculated candidate genes based on functionality. Four of the SNPs were located within four candidate genes, including CDH2, which is linked to milk fat percentage, and GABRG2, which is associated with milk protein yield.GWAS were conducted with the FarmCPU (Fixed and random model circulating probability unification) method. We identified 12 markers significantly associated with six of the 10 traits under the threshold of 5% after a Bonferroni multiple test correction. Seven of these SNPs were in QTL regions previously identified to be associated with related traits. One identified SNP, These findings are beneficial not only for breeding through marker-assisted selection, but also for genome editing underlying the related traits to enhance the overall performance of dual-purpose cattle. The Xinjiang Brown was recognized as a new dual-purpose cattle breed in China in 1983 . SimilarDGAT1 gene associates with milk composition and yield traits [FASN has a significant effect on milk fat component traits [BRCA1 has an effect on somatic cell score (SCC), which influences mastitis disease in dairy cows [GH-L127 V mutation was reported to be associated with calving interval in a Jersey cattle population [With the development of genotyping technologies and new genetic analysis methods, the genetic architecture of economically important traits have been explored across different cattle breeds and populations. Substantial genomic regions have been identified –6. Accord traits , 9 and hd traits . FASN hat traits , 12. BRCiry cows , 14. Forpulation .Although many genome-wide association studies (GWAS) and genomic functional validation studies on dairy and beef cattle traits have been performed, few studies have focused on dual-purpose breeds and populations. For Xinjiang Brown, only a few genetic polymorphisms have been reported for milk composition, somatic cell score, and early growth traits –19. StudIn this study, we evaluated five milk production traits: milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), and protein percentage (PP); four reproductive traits: age at first service (AFS), age at first calving (AFC), gestation length (GL), and calving interval (CI); and one health trait: somatic cell score (SCS) in the Chinese dual-purpose cattle breed, Xinjiang Brown. We used milk production, reproductive, and health data records, collected during 1995–2017 on 2410 individuals, from four different breeding herds raised in the Xinjiang region of northwestern China. We used another 445 ancestors to obtain a total of 2855 individuals connected by pedigree to estimate variance components and breeding values. Ultimately, a total of 403 cattle were selected for genotyping with the 150 K Bovine BeadChip, which resulted in a total of 139,376 markers. Our objective was to identify SNPs associated with milk, reproductive, and health traits in the Xinjiang Brown for the benefit of marker-assisted selection and dissection of genetic architecture of these complex traits.A total of 2410 individuals with 6811 reproductive records and 5441 milk records were used in this study. The descriptive statistics results of milk, health, and reproductive traits in Xinjiang Brown Cattle are shown in the Table Somatic cell score (SCS) was used as an indicator trait for udder health; the smaller the SCS, the lower the risk for mastitis . SCS is Most reproductive traits are difficult to measure and vary across environmental conditions . We seleThe correlations and distributions of phenotypes, estimated breeding values (EBV), and residuals for each of the 10 study traits are shown in Additional file The PCA scatterplots illustrate a clear population structure for the 396 individuals in the four cattle herds that comprised our study population Fig. . In the BovineHD2400007916) and BTA 7 (BTB-01731924) and were associated with FP and PY, respectively. For the health trait, mastitis resistance, three significant SNPs were found on BTA 8 (BovineHD0800007286), BTA 22 (BovineHD2200012261), and BTA 5 (BovineHD0500013296) and were associated with SCS. For reproductive traits, three SNPs located on BTA 14 (BovineHD1400016327), BTA 3 (BovineHD0300035237) and BTA 16 (BovineHD1600006691) were significantly associated with AFS; two SNPs located on BTA 14 (BovineHD1400021729) and BTA 17 (ARS-USMARC_528) were significantly associated with GL; and two SNPs located on BTA 19 (Bovine HD1900002007) and BTA 25 (BovineHD2500003462) were significantly associated with CI. We found no significant markers associated with MY, FY, PP, or AFC  at 24.2 Mb on BTA 16 that associated with both AFS and AFC. This SNP has also been reported in a QTL region associated with calving ease [To check for overlaps among the SNPs significantly associated with milk, reproductive, or health traits, we created a heat map using different bin sizes and several significant ing ease . AdditioPopulation stratification is an important issue in population-based association studies , 36. BecCryptic relationships among individuals is another major source of spurious associations. Several methods have been developed to correct both population stratification and cryptic relationships to screen markers across genomes. Ideally, a one-step approach would perform the best by optimization over population structure, cryptic relationships, and genetic markers simultaneously; however, the associated computational burden prevents full optimization for practical uses. Furthermore, robust approximation was achieved with a dramatic reduction in computing time. For example, the EMMAx and P3D algorithms deliver almost identical results for full optimization of genetic and residual variance estimates for every testing marker, using the fixed and random effects mixed linear model (MLM).The computing time of the MLM was further improved by splitting the model into a fixed effect model and a random effect model. The fixed effect model is used for testing markers, one at a time. The random effect model is used to select markers that are used as covariates in the fixed effect model. The fixed effect model and the random effect model are used iteratively until no change occurs in the covariates. Compared to the kinship based on all the available markers, the kinship based on the selected markers has the best likelihood for the specific trait of interest. This method was named the Fixed and random model Circulating Probability Unification (FarmCPU). Both simulation and analyses on real traits demonstrated that FarmCPU has higher statistical power than the regular mixed method using all available markers to build kinship.Given this population stratification, we used two models to perform GWAS using FarmCPU, with and without the first three PCs as covariates. Without including the PCs, we found 20 significant markers associated with eight of the 10 traits , PP (Bovine HD2300015096), and AFC (Bovine HD1600006691) are the most significant SNPs in both models. Our study is the first GWAS on milk, reproductive, and mastitis resistance traits in the Xinjiang Brown dual-purpose cattle breed. Only a limited number of studies have reported on similar traits in other dual-purpose breeds [We found 12 significant markers associated with six important, complex traits in Xinjiang Brown cattle, based on a high-density SNP chip. Among them, two SNPs overlapped in both the SNP model and the combined SNP-PCA model. One SNP is seated on BTA 8 and significantly associated with SCS; the other SNP is on BTA 16 and significantly associated with AFS. Four SNPs were significantly associated with MY, FY, PP, and AFC when we used the SNP model, but these SNPs failed to pass the 5% threshold after a Bonferroni correction in the combined SNP-PCA model. Still, SNPs associated with FY  gene at 29.1 Mbp on BTA 24. CDH2 is a protein encoding gene and participates in adipogenesis [CDH2 to block the epithelial-mesenchymal transition-like response could weaken adipocyte lineage commitment [Milk composition traits are important breeding traits in both dairy and dual-purpose cattle breeds, especially in modern animal husbandry environments. We found two highly significant SNPs associated with milk composition traits. One SNP is associated with FP and is positioned within the cadherin-2 . GABRG2 primarily contributes to gamma-aminobutyric acid (GABA)-gated chloride ion channel activity and participates in GABA-A receptor activity [The other milk-related SNP we identified was significantly associated with PY and mapped at 75.8 Mbp on BTA 7, which is within a gene named Gamma-aminobutyric Acid Type A Receptor Gamma2 Subunit , was reported to be related to udder support score trait in crossbred Bos indicus-Bos taurus cows [SCS is highly correlated with mastitis in cattle populations , 57 and rus cows . Many QTrus cows . And, inrus cows . Two seprus cows  and Red rus cows . The posKFM1A). The second SNP is seated at 58.7 Mbp on BTA 14; the closest gene is a pseudo gene LOC511981. The third SNP is located at 24.2 Mbp on BTA 16 and within the Glutamyl-prolyl-tRNA Synthetase (EPRS) gene. Several QTL on BTA 16 contain the AFS-associated SNP we found. One of these QTL was previously reported to be related to calving ease in US Holstein cattle [Before reproductive traits became important breeding objectives, most breeders focused on production traits . Howevern cattle , birth wn cattle , and hipn cattle . Both caSPRY1) gene on BTA 17. The two SNPs we found significantly associated with CI were located at 7.6 Mbp on BTA 19 and at 12.4 Mbp on BTA 25. The nearest genes to these SNPs are Ankyrin-repeat and Fibronectin Type III Domain Containing 1 (ANKFN1) on BTA 19 and Shisa Family Member 9 (SHISA9) on BTA 25. A previously reported QTL region at 6.3–13.8 Mbp on BTA 25 was found to affect dystocia in a dairy population [For GL, we found two significant SNPs, one mapped at 77.5 Mbp on BTA 14 and the other mapped at 34.8 Mbp within the Sprouty RTK Signaling Antagonist 1 minor allele frequency (MAF) > 0.05. After quality control, 396 cows and 139,376 markers remained. The genotypes in A/T/G/C format were converted to numeric genotypes by iPat software [In total, 403 female cattle were selected for genotyping by using the Illumina 150 K Bovine BeadChip. Quality control was conducted by using Plink software  with crisoftware . The disThe experimental Xinjiang Brown population came from four breeding herds. We used the Prcomp function in R to perform a principal component analysis (PCA). The PCA showed a clear population structure Fig. . PC 1 shGenetic analysis was carried out using DMU  softwareyijklm is the phenotype in the jth year, kth season, and lth parity of the mth individual from ith herd; u is overall mean of population, Herdi is the herd effect according to a cow’s origin from one of the four herds; Yearj is the jth year effect, Seasonk is the kth season effect, and Parity is the effect of lth parity; a is the additive effect of mth individual and e is the residual in the jth year, kth season, and lth parity of the mth individual from ith herd. All effects were treated as random except the overall mean.where The fixed and random model circulating probability unification (FarmCPU) method was used to carry out the genome-wide association analysis in this study . The metAdditional file 1: Figure S1. Correlations and distributions of phenotypes, EBVs , and residuals. The histograms on the diagonal are the distributions for each trait: MY = milk yield, FY = fat yield, PY = protein yield, FP = fat percentage, PP = protein percentage, SCS = somatic cell score, AFS = age at first service, AFC = age of first calving, GL = gestation length, and CI = calving interval. The upper triangle is comprised of the correlation coefficients among traits. The lower triangle is comprised of the pairwise scatter plots. Graphs a, b, c illustrate milk trait phenotypes, EBVs, and residuals, respectively. Graphs d, e, f illustrate reproduction trait phenotypes, EBVs, and residuals, respectively.Additional file 2: Figure S2. Heatmap of milk and reproductive traits. Individuals are sorted row wise and traits column wise based on their similarity. The trait values were standardized and illustrated as heat map with red indicating highest and yellow the lowest. MY = milk yield, FY = fat yield, PY = protein yield, FP = the fat percentage, PP = protein percentage, SCS = somatic cell score, AFS = age at first service, AFC = age of first calving, GL = gestation length, and CI = calving interval.Additional file 3: Figure S3. Display of significant markers as visible bands at different width. The number significant markers were determined by the P-value cut off with three levels illustrated on the left. The significant markers are displayed as bands with width indicated on the top starting from 100 kb to 10,000 kb. More bands are visible with wider bands than narrow bands. Wide band and less stringent P value threshold (e.g. to right) demonstrate pleiotropy of significant markers across traits. These traits include milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), protein percentage (PP), and somatic cell score (SCS). Reproductive traits include age at first service (AFS), age at first calving (AFC), gestation length (GL), and calving interval (CI).Additional file 4: Figure S4. Scatter plot between principal components and trait phenotypes. We used these plot to determine which traits were correlated with population structure, represented by principal components (PC). Columns represent the first three principal components, rows represent each trait. Milk traits include milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), protein percentage (PP). and somatic cell score (SCS). Reproductive traits include age at first service (AFS), age at first calving (AFC), gestation length (GL), and calving interval (CI).Additional file 5: Figure S5. Manhattan and Q-Q plots of non-significant GWAS results. GWAS was performed with FarmCPU software and a significant p-value threshold set at P = 10–7. Four of the 10 traits studied, milk yield (MY), fat yield (FY), protein percentage (PP), and age at first calving (AFC), resulted in no SNPs passing the Bonferroni threshold, as illustrated by the Manhattan plots on the left. On the right, Q-Q plots are displayed as scatter plots of true and expected log p-values.Additional file 6: Figure S6. Manhattan and Q-Q plots of GWAS results, without considering population structure. These Manhattan plots (left) illustrate results from an association analysis model that did not consider population structure. GWAS was performed with FarmCPU software and a significant p-value threshold set at P = 10–7. Q-Q plots (right) are displayed as scatter plots of true and expected log p-values. Milk traits include milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), protein percentage (PP), and somatic cell score (SCS). Reproductive traits include age at first service (AFS), age at first calving (AFC), gestation length (GL), and calving interval (CI).Additional file 7: Table S1. Genomic inflation factor (lambda) of each trait.


Poecilia reticulata, and apply a multivariate approach to test for GxS interactions. We also quantify the among-individual and genetic covariances between personality and size and growth, which are known a priori to differ between the sexes. We found significant sexual dimorphism in three of the four behaviours, although rmf between sex-specific homologous traits was significantly <+1 for only one behaviour. Using multivariate models, we then estimated sex-specific genetic (co)variance matrices (Gm and Gf) and tested for asymmetry of the cross-trait cross-sex genetic covariance structure (submatrix B). While Gm and Gf were not significantly different from each other overall, their respective leading eigenvectors were poorly aligned. Statistical support for asymmetry in B was found, but limited to a single trait pair for which the cross-sex covariances differed  ≠ COVA). Thus, while single- and multi-trait perspectives evidence some GxS, the overall picture is one of similarity between the sexes in their genetic (co)variance structures. Our results suggest behavioural traits related to risk-taking may lack the sex-specific genetic architecture for further dimorphism to evolve under what is hypothesised to be antagonistic selection.Sexual dimorphism in behaviour and personality has been identified in a number of species, but few studies have assessed the extent of shared genetic architecture across the sexes. Under sexually antagonistic selection, mechanisms are expected to evolve that reduce evolutionary conflict, resulting in genotype-by-sex (GxS) interactions. Here we assess the extent of sexual dimorphism in four risk-taking behaviour traits in the Trinidadian guppy,  Poecilia reticulata.Traits under selection should evolve in a manner dependent on the genetic variance present, the genetic covariance structure with other traits and the strength of selection Lande . While hrmf) between homologous male and female traits is most commonly used to quantify the extent of sex-specific genetic variance, whereVAm and VAf are the sex-specific (additive) genetic variances and COVAmf is the cross-sex genetic covariance. Typically, an rmf of +1 is viewed as maximally constraining for sex-specific adaptation under SA selection as any increase in fitness of one sex will result in a reduction in fitness of the other sex , which need not be equal when rmf = +1. Only in the complete absence of GxS does it follow that both rmf = 1 and VAm = VAf  and compare them, using techniques, such as eigenvector analysis. For instance, if Gf and Gm differ in orientation and/or magnitude of their leading eigen vectors (gmax), then continued phenotypic divergence can be possible, even if homologous traits have high pairwise rmf  selection gradients. The ½ coefficient accounts for both parents making equal genetic contributions to offspring of both sexes and Gmf is the block matrix , but also contains the between sex genetic covariances for each pair of non-homologous traits. Note that B may be asymmetric . In Eq. Amx,fy ≠ COVAfx,my). Asymmetry in B leads to predictions of unequal multivariate response to selection between the sexes  and a simple open field trial (OFT) paradigm commonly used to characterise shy-bold type personality variation in fishes  more risk-prone or ‘bold’ behaviours, before testing for dimorphism in the multivariate phenotypic  covariance structure itself variation in risk-taking behaviours?). Second, we test for GxS interactions using both single trait analyses and the fully multivariate approach outlined above. While our principal focus is on risk-taking behaviours, we also expand our analyses to include size and growth traits, noting that these are known a priori to exhibit strong dimorphism in guppies, and that shy-bold type behavioural variation has been generally linked to body size across many taxa  have been described elsewhere  at the University of Exeter, Penryn campus fish facility. After initial rearing in family groups, adult fish (average age 132 days) were tagged using visible implant elastomer (anaesthetised in buffered MS222) and put into mixed family groups of 16 . The composition of tagged groups varied according to the availability of adult fish of suitable size for tagging, but all contained representatives of at least four families. Mixing individuals from different families during development reduces the risk of common environment effects biasing additive genetic (co)variance estimates but is not possible initially as the small size of juveniles precludes safe tagging for identification.www.biobserve.com) and a camera positioned above the tank. We chose four traits for analysis, activity , area covered , time in middle zone  and freezings . A fifth trait (track length) described in White and Wilson  over the course of 2 weeks. Each OFT comprised transferring a fish into an empty tank filled to 5 cm depth with water. Movement was tracked for 4 min 30 s (following a 30 s acclimation period) using Viewer software . Order of capture within each group was recorded, as was water temperature at the end of each behavioural trial (mean of 23.7 °C). Water in the OFT tank was changed between groups. Standard length  measures were taken at tagging, at each OFT, and 1 month after the last behavioural trial. For a subset of fish, we opportunistically collected additional size data on known age individuals at monthly intervals for up to 13 months after the last OFT. This was not possible in all cases as tanks housing groups were required for other projects in the facility. A total of 2594 behavioural trials and 4493 body size measurements were collected on 831 adults  in a three generation pedigree structure.m, Activityf, etc.).Behavioural traits activity, area covered, time in middle zone and freezings were mean centred and rescaled into standard deviation units . For time in middle zone and freezings this was done after a square-root transform to reduce positive skew and increase normality of residuals. Scaling to overall standard deviation units allows better comparison of parameters among traits and facilitates convergence of multivariate mixed models, while still preserving within-trait differences across sexes (in mean and/or variance). We denote traits by subscript m or f, when referring to male or female values specifically (e.g. Activitywww.vsni.co.uk). Conditional F statistics were used to test for significance of fixed effects where pertinent to biological hypotheses (e.g. to test for trait dimorphism). Note, however, that in most cases fixed effects were included principally to control for potential sources of variance not directly relevant to our hypotheses. In all behavioural models, fixed effects included temperature (of the tank water taken following each OFT), age (in days), repeat , order caught  and generation . Random effects of group  and fish ID were fitted to all traits in all models unless otherwise stated. To estimate genetic (co)variance parameters we used animal models (Kruuk Significance of random effect (co)variance components was assessed using likelihood ratio test (LRT) comparisons of nested models, with twice the difference in log-likelihoods assumed to be ls Kruuk  further ls Kruuk  showed m2 and age3 were fitted, the latter to allow a curvilinear average relationship between length and age.To model growth rate, we fitted random regressions of standard length over age in mixed model and animal model formulations, resulting in estimates of among-individual and additive genetic variation in both length (at average age) and growth. This reaction norm approach fits a random-by-covariate effect, allowing each level of a random effect to vary across a covariate and is an established technique in both behavioural and life-history studies  was considered evidence of average trait dimorphism. We refitted the behavioural models with length as an additional covariate to determine whether average differences between the sexes in behaviour could, at least in principle, be explained entirely by size effects .To ascertain whether our traits were dimorphic on average, we fitted univariate mixed models for each behaviour and for the length/growth random regression (sexes pooled), with an additional fixed effect of sex. A significant sex effect coefficient , allowing test for heterogeneity of total phenotypic variance between sexes for behavioural traits and length. Note it is not possible to estimate the total phenotypic variance of growth from the random regression framework used here therefore this comparison was not done for growth. Models including fish ID and group as random effects were then fitted to test for differences in among-fish variance (Group was fitted to control for among-group variation). LRTs were used to compare the unconstrained vs. constrained (homogeneous variance across sexes) models on 1 degree of freedom (DF) for the behavioural traits and 3 DF for the length random regression.We then fitted a series of models to test for sexual dimorphism in the variance components of observed traits (as opposed to their means). For each trait (X), we fitted bivariate mixed models with XID matrix variance matrix) of OFT behaviours differs significantly between the sexes. We fitted a multivariate model with all eight sex-specific behaviours allowing estimation of IDm and IDf submatrices  and compared this to a refitted model in which we imposed the condition that IDm = IDf. For a more qualitative comparison, eigenvector decomposition was applied to the estimates of IDm and IDf matrices to see if the major axes of among-individual variation were broadly similar in males and females. More specifically, any differences in trait loadings on the first eigenvector (idmax) were noted as well as the angle between idmax (the first eigen vector of ID) in males and females.We next asked whether the We sought to determine whether phenotypic associations between behaviour and size and/or growth differed between the sexes. Further expansion of the multivariate behavioural model to include male and female length as additional responses proved difficult, so we estimated the among-individual covariances (and corresponding correlations) with each sex-specific behaviour using a series of bivariate models. Statistical inference was by LRT comparison to constrained models in which among-individual covariance between behaviour and both size (random intercept for length) and growth (random slope) were fixed to zero.VAm and VAf) and genetic correlation between them (rmf). This was then compared to a model in which GxS interactions was assumed absent . We also compared model fits to two intermediate models, one where sex-specific VA were constrained to be equal but rmf was free to be <+1, and a second with rmf constrained to be +1 but sex-specific VA free to vary. Since these intermediate models are not nested, AIC values were calculated for each model and used for additional comparison.Previous analysis of the OFT data with univariate animal models has shown all behaviours are significantly heritable in adults , such that we estimated Gmf as:Cross-sex multivariate animal models were fitted with the eight sex-specific OFT sub-traits. First we compared the sex-specific Gm = Gf (using a LRT on 10 df). As in our comparison of IDm and IDf, we also subjected the sex-specific submatrices to eigenvector decomposition to facilitate a qualitative comparison of trait loadings and also the angle between gmax of males and females. We then fitted the full multivariate model including all cross-sex cross-trait terms such thatThis model was compared to one in which we impose the condition that B can offer additional opportunities for sexual divergence under sex-specific selection as well as constraint. Ideally, we would have compared the log-likelihood of our full multivariate model to a constrained fit in which symmetry of B was imposed. We were, however, unable to obtain a stable model convergence with the latter constraint imposed. Therefore, to test for symmetry we calculated an estimate of B–BT as a square matrix, denoted as ∆B, noting that if B is symmetrical, then B–BT = ∆B = 0. In order to generate approximate 95% confidence intervals on each element of ∆B we performed a 5000 draw parametric bootstrap on the Gmf matrix . This was to determine whether behaviour-length/growth associations differed between males and females at the genetic level. As with the corresponding phenotypic analysis, the significance of genetic covariance with size/length was determined for each sex-specific behaviour using LRT and genetic covariances were standardised to correlations for easier interpretation.Visual inspection of raw data shows broadly overlapping distributions of male and female behavioural trait observations Fig. . Nonetheχ21 = 9.68, P = 0.002) and for length in females , but the sex-specific estimates of VI are very similar for each OFT trait .Bivariate mixed models indicate significantly more total phenotypic variation  for time in middle in males .Sex-specific behavioural There is support for among-individual covariance between OFT behaviours and standard length (modelled as a random regression comprising size at average age and growth rate) although patterns are at least qualitatively different between the sexes. Area covered is the only male behaviour to significantly covary with length Table . Time inχ27 = 61.92, P = <0.001) and time in middle  but not the other behaviours . However, AIC-based comparison with intermediate models in which the constraints of homogeneous VA and rG = +1 were relaxed separately provided a slightly more nuanced picture . The fully unconstrained model (full GxS) is clearly the best fit for length/growth however, with large ∆AIC between this and all other constrained models  model for length/growth . While reiterating the lack of significant matrix differentiation overall, visual inspection of these two submatrices of our Gmf estimate (Table Gm and Gf shows that the first (gmax) and second eigenvectors explain 54 and 40%, and 68 and 27% of the additive genetic variation in males and females respectively .When modelled as sex-specific behaviours we found no evidence of overall significant differences between te Table  is suggely Table . In maleGmf also yields B, the cross-sex, cross-trait genetic covariance matrix. Our estimate of B shows that the cross-sex genetic correlations are all positive but low for time in middle (rmf = 0.110 (0.282)), higher for activity (rmf = 0.773 (0.147)) and area covered (rmf = 0.677 (0.199)) and close to +1 for freezings (rmf = 0.974 (0.124); Table rmf < +1 in activity and area covered. Calculation of ∆B provided some evidence for asymmetry in B although this is limited. Specifically, approximate 95% confidence intervals span zero for all the cross-sex elements of ∆B except activity time in middle (95% CI = 0.005–0.245). The activitym—time in middlef correlation being 0.177 (0.285), whereas the activityf—time in middlem being −0.367 (0.202) .The full estimate of 0.773 0.17) and arFinally, bivariate animal models revealed no support for significant genetic correlations between sex-specific behaviours and length/growth in either males or females Table .Here we investigated whether personality, characterised as among-individual differences in risk-taking behaviours, is sexually dimorphic in a captive population of guppies. We also scrutinised the relationship between behaviour and length and growth—traits known to be sexually dimorphic in this species—before employing quantitative genetic analyses to assess the extent of GxS. We find statistical support for sexual dimorphism in behaviour and discuss this first before addressing the evidence for GxS provided by both the single trait and multivariate approaches used. In what follows, we put our results into the context of the wider quantitative genetic literature and also seek to highlight the benefits of taking a multivariate view of sexual dimorphism in behavioural traits.Sexual dimorphism was present in OFT behaviours (except for freezing), as well as in length and growth. The latter result is already well known in guppies, with female fish tending to be larger, and having higher growth rates post maturity, while males preferentially invest in mating opportunities over growth  between males and females, for the most dimorphic behaviour (freezings) the difference was only 0.5 SDU. In addition, behavioural dimorphism was only partially in line with our prediction that males would, on average, exhibit more risk-prone or ‘bold’ type behaviours than females within the novel OFT environment. We found that males tended to explore the tank more and spend more time in middle zone. This tendency fits with previous studies, for instance, Lucon-Xiccato and Dadda  found thOur own previous work on female guppies  suggests that this could partially be explained by stress response. Although this interpretation is tentative (and perhaps subjective), high activity sometimes occurs because individuals swim rapidly and up and down one or two sides of the arena following introduction into the OFT. This is probably a general escape response found in many fish, with a fast-start swim profile consisting of rapid movement presumed to aid in predator escape  would therefore suggest that males inhabit the more exploratory or bold end of this axis, whereas females are closer to the escape response end of this axis.While we found that males had higher among-individual variation in time in middle zone, there was no evidence that among-individual variation was greater in males for the other traits. Overall, trait repeatabilities were similar across sexes for homologous traits. Furthermore, multivariate analysis showed strong similarity of full ID matrices were strikingly similar here, we suggest wider estimation of these structures will be generally useful to understand among-individual (co)variation and multivariate sexual dimorphism. Certainly sexes can differ greatly in selection pressure, and in the contributions of social and abiotic factors to variation among individuals at single behavioural traits , Carter et al. (ID matrix) and so does not explicitly separate within- from among-individual covariance structure  have been reported previously in fish (Brown and Braithwaite Treating standard length as response variable (rather than a ‘nuisance’ predictor of behaviour), we found some limited support for sex differences in among-individual correlations between size and behaviour. In males, individuals that cover more area in the OFT are smaller and grow less. In a previous study we also detected a negative correlation between area covered and growth in females from this population  and some support for the presence of sex-specific genetic variance in OFT behaviours. The former result suggests that length and growth have scope for further sexual divergence if SA selection is acting, and mirrors recent findings for size at maturity in another poeciliid (Parus major), van Oers et al. (Gryllus bimaculatus), as well as a low value of rmf for the latter behaviour. While this suggest that importance of GxS interactions may vary across behaviour and species, it is clearly too early to generalise and more empirical studies are needed.GxS interactions on OFT behaviours were detected, notably in relation to time in middle. However, across behaviours they were generally weak and less well supported statistically than GxS on size. In general the literature contains sparse estimates of GxS interactions for behavioural traits. However, in a study on selected lines of great tit , while time in middle was the most dimorphic behaviour with the weakest correlation estimate (sex difference of −0.507 SDU and rmf of 0.110).If contemporary selection favours further divergence of male and female behaviour, then the cross-sex genetic architecture is likely to be largely constraining in our behavioural traits. Sexual dimorphism coupled with moderate to high  Meagher . Nonethermf would lead us to conclude that the scope for further behavioural dimorphism to evolve under SA selection is limited. However, a multivariate approach can reveal either additional avenues for the sexes to diverge or additional constraints on independent evolution , the behavioural trait for which GxS was best supported in single-trait models. Furthermore, we also estimate a large angle between male and female gmax vectors consistent with the two matrices differing in ‘shape.’ In fact, while gmax in males is similar to idmax in both sexes (described above), in females gmax trait loadings actually correspond to our a priori expectations for a shy-bold continuum . Reiterating the caveat that Gm and Gf are not significantly different from each other (and both estimates have high uncertainty), it is interesting that ID is at least a qualitatively better proxy for G in males than in females.From a single trait perspective, a moderate to high B, the submatrix of Gmf that describes the cross-sex genetic covariance structure. Though largely symmetrical, we found a difference in genetic association between activityf—time in middlem (negative) and activitym—time in middlef (weakly positive). Predictions of (multivariate) sex-specific selection responses can be drastically altered by asymmetry in B, though how this manifests is necessarily dependent on the relative angles of SA selection  to generalise from the literature. However, . Here we show that guppies exhibit sexual dimorphism in size and growth, but also in average expression of heritable traits linked to risk-taking behaviour or shy-bold type personality variation. Although the structure of among-individual behavioural (co)variation (as measured by ID) is similar in males and females, single trait and multivariate analyses also provide evidence of some GxS interactions. These are detected as cross-sex genetic correlations of <1 in single trait analyses. In the multivariate analyses, the covariance structure of Gm and Gf were not significantly different from each other, although gmax was close to orthogonal. While there was one component of B that was asymmetrical, it was largely symmetrical on the whole. Lacking knowledge of (sex-specific) multivariate selection we cannot comment directly on how these genetic covariances will shape future evolution trajectories, although we broadly expect GxS to facilitate dimorphism under SA selection.Despite strong interest in sexual dimorphism this is, to our knowledge, the first study to estimate The research data supporting this publication are openly available from the University of Exeter’s institutional repository at: 10.24378/exe.224.Supplemental table 1Supplemental table 2Supplemental table 3Supplemental table 4Appendix 1Appendix 2


CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near  GWAS have identified more than 500 genetic loci associated with blood lipid levels. Here, the authors report a genome-wide analysis of interactions between genetic markers and physical activity, and find that physical activity modifies the effects of four genetic loci on HDL or LDL cholesterol. However, there is individual variation in the response of blood lipids to PA, and twin studies suggest that some of this variation may be due to genetic differences2. The genes responsible for this variability remain unknown.Circulating levels of blood lipids are strongly linked to the risk of atherosclerotic cardiovascular disease. Regular physical activity (PA) improves blood lipid profile by increasing the levels of high-density lipoprotein cholesterol (HDL-C) and decreasing the levels of low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG)12. At present, it is not known whether any of these main effect associations are modified by PA. Understanding whether the impact of lipid loci can be modified by PA is important because it may give additional insight into biological mechanisms and identify subpopulations in whom PA is particularly beneficial.More than 500 genetic loci have been found to be associated with blood levels of HDL-C, LDL-C, or TG in published genome-wide association studies (GWAS)CLASP1, LHX1, SNTA1, and CNTNAP2, are associated with circulating lipid levels through interaction with PA. None of these four loci have been identified in published main effect GWAS of lipid levels. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of PA interactions in the genetic contribution to blood lipid levels.Here, we report results from a genome-wide meta-analysis of gene–PA interactions on blood lipid levels in up to 120,979 adults of European, African, Asian, Hispanic, or Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We show that four loci, in/near 13. PA was harmonized across participating studies by categorizing it into a dichotomous variable. The participants were defined as inactive if their reported weekly energy expenditure in moderate-to-vigorous intensity leisure-time or commuting PA was less than 225 metabolic equivalent (MET) minutes per week (corresponding to approximately 1 h of moderate-intensity PA), while all other participants were defined as physically active  Gene-Lifestyle Interactions Working Groupn = 84,902], African , Asian [n = 6403], Hispanic [n = 4749], or Brazilian [n = 4438] ancestry ; (ii) we screened for genome-wide significant 2 degree of freedom (2df) joint test of SNP main effect and SNP × PA interaction14 (PJOINT < 5 × 10−8); and (iii) we screened all previously known lipid loci12 for significant SNP × PA-interaction effects, Bonferroni-correcting for the number of independent variants tested .We identified lipid loci interacting with PA by three different approaches applied to the meta-analysis of Stage 1 and Stage 2: (i) we screened for genome-wide significant SNP × PA-interaction effects (r2 < 0.1 with any previously identified lipid locus) were identified: in CLASP1 , near LHX1 , and in SNTA1 , which showed a genome-wide significant SNP × PA interaction on HDL-C in all ancestries combined  was genome-wide significant in the joint test of SNP main effect and SNP × PA interaction (PJOINT = 4 × 10−8) and showed moderate evidence of SNP × PA interaction (PINT = 2 × 10−6) in the meta-analysis of LDL-C in all ancestries combined  for SNP × PA interaction alone , suggesting that the beneficial effect of PA on lipid levels may be independent of genetic risk15.Of the previously known 260 main effect loci for HDL-C, 202 for LDL-C, and 185 for TG17. Of the four loci we found to interact with PA, three, in CLASP1, near LHX1, and in SNTA1, harbor genes that may play a role in muscle function19 and lipid metabolism21.While the mechanisms underlying the beneficial effect of PA on circulating lipid levels are not fully understood, it is thought that the changes in plasma lipid levels are primarily due to an improvement in the ability of skeletal muscle to utilize lipids for energy due to enhanced enzymatic activities in the muscleCLASP1 locus which interacts with PA on HDL-C levels is an intronic SNP in CLASP1 that encodes a microtubule-associated protein  and is in strong linkage disequilibrium (r2 > 0.79) with rs13403769 variant that shows the strongest association with CLASP1 expression in the region (P = 7 × 10−7). Another potent causal candidate gene in this locus is the nearby GLI2 gene which has been found to play a role in skeletal myogenesis18 and the conversion of glucose to lipids in mouse adipose tissue20 by inhibiting hedgehog signaling.The lead variant rs2862183  22%) in the LHX1 interacts with PA on HDL-C levels. However, the more likely causal candidate gene in this locus is acetyl-CoA carboxylase (ACACA), which plays a crucial role in fatty acid metabolism21  variant near 8% varianSNTA1 locus (rs141588480) interacts with PA on HDL-C and is an insertion only found in individuals of African (MAF 6%) or Hispanic (MAF 1%) ancestry. The rs141588480 insertion is in the SNTA1 gene that encodes the syntrophin alpha 1 protein, located at the neuromuscular junction and altering intracellular calcium ion levels in muscle tissue  intronic and no other genes nearby   in the CNTNAP2 locus is four times more frequent in African-ancestry than in European-ancestry. Our findings highlight the importance of multi-ancestry investigations of gene-lifestyle interactions to identify novel loci.The inclusion of diverse ancestries in the present meta-analyses allowed us to identify two loci that would have been missed in meta-analyses of European-ancestry individuals alone. In particular, the lead variant (rs141588480) in the 12, and none of them showed evidence of SNP × PA interaction. The 2df joint test bolsters the power to detect novel loci when both main and an interaction effect are present14. The lack of novel loci identified by the 2df test suggests that the loci showing the strongest SNP × PA interaction on lipid levels are not the same loci that show a strong main effect on lipid levels.We did not find additional novel loci when jointly testing for SNP main effect and interaction with PA. While 101 loci reached genome-wide significance in the joint test on HDL-C, LDL-C, or TG, all of these loci have been identified in previous GWAS of lipid levelsSNTA1 and CNTNAP2 loci interacting with PA was made possible by the inclusion of diverse ancestries in the analyses. The gene regions that harbor loci interacting with PA involve pathways targeting muscle function and lipid metabolism. Our findings elucidate the role and underlying mechanisms of PA interactions in the genetic regulation of blood lipid levels.In summary, we identified four loci containing SNPs that enhance the beneficial effect of PA on lipid levels. The identification of the The present study collected summary data from 86 participating cohorts and no individual-level data were exchanged. For each of the participating cohorts, the appropriate ethics review board approved the data collection and all participants provided informed consent.13. Stage 1 meta-analyses included 42 studies with a total of 120,979 individuals of European , African , Asian (n = 6403), Hispanic (n = 4749), or Brazilian ancestry (n = 4438) , African (n = 5384), Asian (n = 6590), or Hispanic  ancestry . We adjusted LDL-C levels for lipid-lowering drug use if statin use was reported or if unspecified lipid-lowering drug use was listed after 1994, when statin use became common. For directly assayed LDL-C, we divided the LDL-C value by 0.7. If LDL-C was derived using the Friedewald equation, we first adjusted total cholesterol for statin use  before the usual calculation. If study samples were from individuals who were nonfasting, we did not include either TG or calculated LDL-C in the present analyses. The HDL-C and TG variables were natural log-transformed, while LDL-C was not transformed.The levels of LDL-C were either directly assayed or derived using the Friedewald equation  . Only autosomal variants were considered. Specific details of each participating study’s genotyping platform and imputation software are described in Supplementary Tables R30. For each study-specific results file, we filtered out genetic variants for which the product of minor allele count (MAC) in the inactive and active strata and imputation quality [min × imputation quality] did not reach 20. This removed unstable study-specific results that reflected small sample size, low MAC, or low-imputation quality. In addition, we excluded all variants with imputation quality measure <0.5. To identify issues with relatedness, we examined QQ plots and genomic control inflation lambdas in each study-specific results file as well as in the meta-analysis results files. To identify issues with allele frequencies, we compared the allele frequencies in each study file against ancestry-specific allele frequencies in the 1000 Genomes reference panel. To identify issues with trait transformation, we plotted the median standard error against the maximal sample size in each study. The summary statistics for all beta-coefficients, standard errors, and P values were visually compared to observe discrepancies. Any issues that were found during the QC were resolved by contacting the analysts from the participating studies. Additional details about QC in the context of interactions, including examples, may be found elsewhere13.The participating studies excluded variants with MAF < 1%. We performed QC for all study-specific results using the EasyQC package in Y is the HDL-C, LDL-C, or TG value, PA is the PA variable with 0 or 1 coding for active or inactive group, and G is the dosage of the imputed genetic variant coded additively from 0 to 2. The C is the vector of covariates which included age, sex, study center (for multi-center studies), and genome-wide principal components. From this model, the studies provided the estimated genetic main effect (βG), estimated interaction effect (βGE), and a robust estimate of the covariance between βG and βGE. Using these estimates, we performed inverse variance-weighted meta-analyses for the SNP × PA interaction term alone, and 2df joint meta-analyses of the SNP effect and SNP × PA interaction combined by the method of Manning et al.14, using the METAL meta-analysis software. We applied genomic control correction twice in Stage 1, first for study-specific GWAS results and again for meta-analysis results, whereas genomic control correction was not applied to the Stage 2 results as interaction testing was only performed at select variants. We considered a variant that reached two-sided P < 5 × 10−8 in the meta-analysis for the interaction term alone or in the joint test of SNP main effect and SNP × PA interaction, either in the ancestry-specific analyses or in all ancestries combined, as genome-wide significant. The loci were defined as independent if the distance between the lead variants was >1 Mb.All participating studies used the following model to test for interaction:4 by searching PubMed and Google Scholar databases and screening the GWAS Catalog. After LD pruning by r2 < 0.1 in the 1000 Genomes European-ancestry reference panel, 260 independent loci remained associated with HDL cholesterol, 202 with LDL cholesterol, and 185 with TG  with the peak SNP associated with the same gene transcript.We examined published associations of the identified lipid loci with other complex traits in genome-wide association studies by using the GWAS Catalog of the European Bioinformatics Institute and the National Human Genome Research Institute. We extracted all published genetic associations with Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10


Genomic and pedigree accuracies were moderate to high  for important starch-related traits such as DY and FRY; when considering one breeding stage (CET) with the aim of early selection, the genomic accuracies ranged from 0.60 (DMC) to 0.71 (HI). Moreover, the correlations between the genomic estimation breeding values of one-stage genomic analysis and the estimated breeding values of the four-stage (full data set) pedigree analysis were high for all traits as well as for a selection index including all traits. The results indicate great possibilities for genomic selection in cassava, especially for selection early in the breeding cycle (saving time and effort).Cassava improvement using traditional breeding strategies is slow due to the species’ long breeding cycle. However, the use of genomic selection can lead to a shorter breeding cycle. This study aimed to estimate genetic parameters for productive traits based on pedigree (pedigree and phenotypic information) and genomic (markers and phenotypic information) analyses using biparental crosses at different stages of selection. A total of 290 clones were genotyped and phenotyped for fresh root yield (FRY), dry matter content (DMC), dry yield (DY), fresh shoot yield (FSY) and harvest index (HI). The clones were evaluated in clonal evaluation trials (CET), preliminary yield trials (PYT), advanced yield trials (AYT) and uniform yield trials (UYT), from 2013 to 2018 in ten locations. The breeding stages were analyzed as follows: one stage (CET), two stages (CET and PYT), three stages  and four stages . The genomic predictions were analyzed via  Manihot esculenta Crantz) is an important starchy root crop grown predominantly in the tropics that serves as a source of raw material for industrial applications and a food source for millions of people, mainly in tropical and developing regions  is the number of locations and i =  is the number of stages of evaluation.The mixed model equations for best linear unbiased prediction (BLUP) (1) are as follows:A is 290 × 290, regardless of the number of stages included in the analysis. The size of the AL matrix is 457 × 457, 581 × 581, 698 × 698 and 757 × 757 when one, two, three and four stages are included in the analysis, respectively.The size of G is the genomic relationship matrix, pi and qi are the allele frequencies of marker i, and M is the markers incidence matrix centered by the mean 2pi. GL is a block-diagonal matrix of Gni matrices in each location:i =  is the number of locations and i =  is the number of stages of evaluation.The equations of mixed genomic best linear unbiased prediction (GBLUP) models (2)  are giveG is 290 × 290 regardless of the number of stages included in the analysis. The size of the GL matrix is 457 × 457, 581 × 581, 698 × 698 and 757 × 757 when one, two, three and four stages are included in the analysis, respectively.The size of The SNP effects  prediction procedure with the REML  estimation of variance components. A Likelihood Ratio Test (LRT) and deviance analysis were undertaken to test the random effects, which were tested using the chi-square statistic .fold, and the clones were randomly assigned to each fold. The training set, composed of 9 of the 10 subsets, was used to estimate marker effects and the remaining subset was the validation set. These marker effects estimates were used to predict the genomic breeding values of the validation set individuals. This process was repeated until all 10 subsets had served as the validation population once. Trait estimates of predictive ability, accuracy and bias were calculated from cross-validation with the training set for pedigree and genomic analyses. The selection efficiency measures were estimated at each fold, and the value presented in this study is the mean of the folds.The cross-validation method used was the ten-The predictive ability was given by the correlation coefficient between predicted genetic values (PGVs) and predicted genetic values added to the mean of the residuals in the validation population. PGVs were the estimated breeding values (EBVs) for the pedigree analysis and the genomic estimated breeding values (GEBVs) for the genomic analysis. Predicted genetic values added to the mean of the residuals for each clone can be interpreted as pseudo-phenotypes (PPs). We adopted this type of cross-validation due to the considerable imbalance of the dataset. For example, the control clones (some of which were parents of other clones to be tested) were replicated at several locations and through the years, whereas many other clones to be tested were replicated only a few times  .The accuracy, one of the main measures to compare models and methods in genomic selection, was calculated as the ratio between the predictive ability and the square root of the phenotypic trait heritability. This measure indicates how accurate the model is in estimating genetic values.sommer [The PPs were linearly regressed on the PGVs, and the regression coefficient e sommer  in the se sommer .After obtaining the EBVs and GEBVs of the clones, we calculated an empirical selection index (SI) that integrates five relevant traits, assigning them weights based on the breeding program’s objective:SI = (FRY*10) + (DMC*10) + (DY*10) + (FSY*5) + (HI*3) p ≤ 0.01). In relation to the genotype × location interaction effect (GxL), when only one stage was evaluated, the clones presented different behaviors in relation to environmental changes for FRY, DY and FSY (p ≤ 0.01); however, when four stages were included in the analysis, the additive GxL interaction effect was significant (p ≤ 0.01) for all of the traits , while heritability of the other traits varied across the analyzed data sets . The varThe coefficient of residual variation (CVe), which is a measure of dispersion and relative variability, ranged from 0.04 to 0.82 across traits and datasets (number of stages included), being higher for FSY (0.62–0.82) and lower for DMC (0.04–0.05). In general, for all traits, the CVe decreased when a greater number of stages was included in the analysis.Trait predictive abilities ranged from 0.42–0.54, 0.45–0.61, 0.46–0.63 and 0.53–0.61 when one, two, three and four stages were included in the analyses, respectively. For FRY, the predictive ability was 0.44, 0.51, 0.49 and 0.53, when one, two, three and four stages were included in the analyses. For DY, the predictive ability also increased continuously , with values of 0.42, 0.51, 0.50 and 0.53 when one, two, three and four stages were included in the analyses. That increasing pattern occurred for all traits with the exception of HI, for which the predictive ability did not alter much, ranging from 0.50 to 0.53.Trait accuracies ranged from 0.62–0.69, 0.67–0.78, 0.69–0.75 and 0.69–0.77 when one, two, three and four stages were included in the analyses, respectively . FRY, DMThe bias ranged from -0.06  to 0.09 , with the lowest amplitude  occurring when four stages were evaluated. In general, the traits presented little bias, ranging from -0.03 to 0.07 for FRY, from -0.06 to 0.01 for DMC, from -0.03 to 0.05 for DY, from -0.02 to 0.07 for FSY and from 0.08 to 0.09 for HI. DY presented zero bias when four stages were included in the analysis. HI was the trait that presented the highest values of bias; therefore, the estimates of the genetic values for HI were slightly overestimated.Indirect selection is an option when there is a linear relationship between two traits and one trait either has low heritability or is difficult to measure compared to the other trait. For example, FSY is relatively easier to measure than root-related traits, and it had moderate positive correlations with FRY (0.41–0.48) and DY (0.43–0.49) and negative and moderate to high correlations with HI ((-0.59)–(-0.62)). FRY was highly and positively correlated with DY (0.99) . HI was p ≤ 0.01), as was shown for the pedigree analyses. The GxL interaction effect was significant (p ≤ 0.01) for all traits with the exception of DMC  and lower in the one-stage analyses ; for FSY, the predictive ability was higher in the four-stage analysis (0.50) but lower in the two-stages analysis (0.44) compared to one stage. For HI, predictive ability was also higher for the one-stage analysis (0.54) than for the two-stage analysis (0.42).The accuracies ranged from 0.61–0.69, 0.60–0.70, 0.63–0.72, 0.68–0.72 and 0.56–0.71 for FRY, DMC, DY, FSY and HI, respectively . Across In general, the traits presented no or very little bias, with the exception of HI when two, three or four stages were included in the analyses. As shown in the pedigree analysis, the genetic values for HI were overestimated. The bias in the genomic analyses varied slightly more, in terms of amplitude, when compared to the biases from the pedigree analyses .The estimates of the correlations based on the GEBVs  were simThe SNP effects for the five traits, considering one and four stages of evaluation, are shown in If the results of the one-stage and four-stage evaluations are comparable, it would verify the possibility of using the results from genomic analysis to select clones at the initial stage of a cassava breeding program. To perform this comparison, we compared the rankings of the top 10 clones based on the one-stage genomic analysis  and the four-stage pedigree analysis (complete data set) for each trait as well as for the selection index. We also calculated the correlations between GEBVs  and EBVs , which were high for all traits, ranging from 0.82 (FRY and DY) to 0.87 (FSY).For FRY, all clones in the top 10 ranking based on the one-stage genomic analysis were from the cross of Fécula Branca and BRS Formosa, indicating that their progeny represent an important full-siblings family for this trait. There was a good correspondence between the GEBVs from the one-stage genomic analysis and the EBVs from the four-stage pedigree analysis, with a correlation of 0.82 . Many ofThere was a good correspondence between GEBVs and EBVs for DMC (correlation of 0.83), and the one-stage and four-stage top 10 lists shared five clones. Progeny from the cross between Fécula Branca and BRS Formosa also stood out for this trait, as well as crossings between the clones Equador72 and BGM-0728, Equador72 and BGM-1124, Cascuda and BGM-0728, BGM-1662 and Fécula Branca and the BRS Mulatinha self-crossing .For DY, all of the clones in the rankings originated from the cross between Fécula Branca and BRS Formosa, and there was a good correspondence between the GEBVs and the EBVs (correlation of 0.82) . There wFresh shoot yield was the trait that presented the highest amplitude in the top 10 ranking list. The amplitude of GEBVs  for the top 10 clones was 20.90; for EBVs , the amplitude was 8.62. This trait also presented a high correlation between the GEBVs and the EBVs (0.87) .The same pattern was observed for HI, which presented a correlation of 0.84 between GEBVs and EBVs. Moreover, for this trait, all clones in both rankings were from the crossing of Fécula Branca and BRS Formosa .Assuming that it is a good idea to select clones based on their GEBVs obtained from the one-stage genotypic analysis, the genetic gains from selecting the top 10% of clones (29 clones) were calculated for each trait. The genetic gains were as follows: 15.91% for FRY, 5.15% for DMC, 14.76% for DY, 43.11% for FSY, and 8.47% for HI.For the selection index, the correlation between GEBVs and EBVs was 0.83 . CompariCassava breeding is usually based on a recurrent phenotypic selection with several stages. For some traits, the improvement rate in cassava is slow due to a combination of several problems related to the biology of cassava. Genomic selection could be one promising approach to reduce the selection interval by predicting the agronomic potential of clones earlier in the process , 15, 10.p ≤ 0.01), with the exception of DMC  and HI .In our study, we evaluated 290 cassava clones from biparental crosses, which were genotyped and phenotyped for productive traits. Our major objective was to characterize the set of clones, determining genetic parameters relying on pedigree and marker information, as well as to compare the results of one and four selection stages to verify the feasibility of genomic selection at the program’s early stage. We found substantial genetic variation among the 290 cassava clones for all traits evaluated. Moreover, they showed intermediate to high heritability, and consequently, good progress can be made in selecting clones for those traits. The majority of traits presented significant genotype × location interaction effects .-1 (mean of 34.37 t ha-1) and for DMC from 20.65 to 41.25% (mean of 33.47%). In the study by [-1, 33.57% and 58%, respectively. In the present study, when only the one-stage pedigree analysis (CET) was assessed, we found means of 29.24 t ha-1, 33.49% and 70.62%, for FRY, DMC and HI, respectively ; therefore, there were half-sibling and full-sibling populations . According to , using Gp ≤ 0.01) genotype × location interaction effect , but the heritability of DMC (0.66) was higher than those of FSY and HI , represent great perspectives for cassava breeding .  calculatIt is worth mentioning that although many of the clones with high selection index values come from the Fécula Branca × BRS Formosa crossing, many others came from other crossings. According to , this vaTraditional assessments based solely on phenotypic information are still very effective and work well for cassava breeding since most of the traits have high heritability. However, here, we wanted to present genomic selection as an option and a great tool to obtain accurate estimates by assessing clones’ genomic estimated breeding value earlier in the cassava breeding cycle, thus saving resources such as time, planting area, and direct and indirect costs. The evaluated clones that stood out in the present work could be new varieties to be released in the future and/or could be used as a germplasm source for the development of new varieties with high allele frequencies for starch-related productive traits.The heritability and accuracy values obtained from genomic and pedigree analyses were moderate to high for important traits related to starch production. The results indicate excellent potential for breeding and genomic selection in these cassava populations aimed at increasing starch production for commercial use. In addition, they indicate an optimal potential for selection at early stages of a cassava breeding program (CET), since the correlations between the GEBVs of one-stage genomic analysis and the EBVs of the four-stage pedigree analysis were high for all traits. Moreover, the GEBV-based and EBV-based rankings of the top 10 best clones overlapped by 5 to 9 clones for individual traits, and the rankings of the 10% best clones exhibited 72% overlap for the selection index. The estimated genetic gains obtained by using the 10% best clones identified by the selection index with the GEBVs from the one-stage genomic analysis were 15.13%, 0.39%, 14.38%, 24.95% and 1.84% for FRY, DMC, DY, FSY and HI, respectively. Thus, genomic selection enables effective early selection in the first stage of clonal evaluation.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S1 Dataseti) estimated breeding values (EBVs) and genomic estimated breeding values (GEBVs) based on the pedigree and genomic analysis, respectively, for the different breeding stages were analyzed as follows: one stage , two stages , three stages  and four stages ; ii) phenotypic raw data; iii) SNPs effects for the two breeding atges (one and four), and iv) SNPs location on the cassava chromosome.(RAR)Click here for additional data file.