Breeding for new maize varieties with propitious root systems has tremendous potential in improving water and nutrients use efficiency and plant adaptation under suboptimal conditions. To date, most of the previously detected root-related trait genes in maize were new without functional verification. In this study, seven seedling root architectural traits were examined at three developmental stages in a recombinant inbred line population (RIL) of 179 RILs and a genome-wide association study (GWAS) panel of 80 elite inbred maize lines through quantitative trait loci (QTL) mapping and genome-wide association study.v3-1-1 and qRDW/SDWv3-1-1), 2 (qRBNv1-2-1), 4 , and 10 . GWAS analysis involved three models  for a set of 1,490,007 high-quality single nucleotide polymorphisms (SNPs) obtained via whole genome next-generation sequencing (NGS). Overall, 53 significant SNPs with a phenotypic contribution rate ranging from 5.10 to 30.2 % and spread all over the ten maize chromosomes exhibited associations with the seven root traits. 17 SNPs were repeatedly detected from at least two growth stages, with several SNPs associated with multiple traits stably identified at all evaluated stages. Within the average linkage disequilibrium (LD) distance of 5.2 kb for the significant SNPs, 46 candidate genes harboring substantial SNPs were identified. Five potential genes viz. Zm00001d038676, Zm00001d015379, Zm00001d018496, Zm00001d050783, and Zm00001d017751 were verified for expression levels using maize accessions with extreme root branching differences from the GWAS panel and the RIL population. The results showed significantly (P < 0.001) different expression levels between the outer materials in both panels and at all considered growth stages.Using inclusive composite interval mapping, 8 QTLs accounting for 6.44–8.83 % of the phenotypic variation in root traits, were detected on chromosomes 1 (qRDWThis study provides a key reference for uncovering the complex genetic mechanism of root development and genetic enhancement of maize root system architecture, thus supporting the breeding of high-yielding maize varieties with propitious root systems.The online version contains supplementary material available at 10.1186/s12864-021-07874-x. With thhe world –4. Planthe world . As the he world , and infhe world –8, root-he world , and nuthe world –13 and rhe world , 14, 15.he world . Besideshe world  especialhe world . Furtherhe world . Neverthhe world –22. Due he world , 23 and he world . Therefohe world –28. Owinhe world –34. Thushe world , EZ-Rhizhe world , Smart Rhe world , WinRhizhe world , Optimashe world , Root Nahe world , IJ_Rhizhe world , Root Syhe world , and Roohe world  have beeTo date, several Quantitative Trait Loci (QTL) studies have been conducted to locate root-trait QTLs under various conditions of growth, at diverse developmental stages and involving various genetic populations , 22, 24.P < 0.05; P < 0.01; P < 0.001) for all the measured seedling traits and at all the three stages except RDW/SDW at V1 stage  while RDW/SDW showed very weak correlations with all other traits  while RDW/SDW is weakly correlated to all other traits  located within the marker interval Snp3292_Snp3298 was associated with RDW and RDW/SDW at the genetic region 92.5- 95.5 cM. The Cluster on chromosome 10 (qRBNv1-10-1 and qTRLv1-10-1) detected within the marker interval Snp62466_Snp62578 was significantly associated to RBN and TRL and spanned 50.5–51.5 cM genetic region. This region harbored two candidate genes GRMZM2G116542 and GRMZM2G016477 predicted to encode a putative Spc97 / Spc98 family of spindle pole body (SBP) component and a putative leucine-rich repeat receptor-like protein kinase, respectively. The three QTLs detected on chromosome 4  were significantly associated with SUA and ROV and spanned the genetic region 89.5–102.5 cM. QTL qROVv2-4-1  associated with ROV was the most significant QTL detected in this study . The averages sequencing depth and coverage were 17.62 and 88.39 %, respectively. With reference to the B73 genome (RefGen_v3), the average similarity rate was 98.82 %.High-quality genomic data consisted of 3230.75 Gb with an average of 40.38 Gb per sample were obtained from the whole-genome sequencing of the 80 inbred maize lines. All related sequences were made available under the accession number PRJNA495031 in the Sequence Read Archive (2 = 0.1. Chromosome 1 with a distance of 10.7 kb showed the highest LD decay while the shortest decay distance (3.7 kb) was observed on chromosome 2.Based on phylogenetic and PCA analysis, the 80 inbred maize lines were subdivided into three subgroups Fig. A, B. Sub10(P) ≥ 6.0 were obtained (Table 10(P) = 14.95, R2 = 30.2 %), and was associated with RBN and SUA. This SNP was detected by FarmCPU stably across V1 and V3 stages. The SNP on chromosome 2  = 8.36, R2 = 25.1 %) was simultaneously detected by two different methods  at V2 stage. In regards of significant SNPs controlling multiple traits, two SNPs located on chromosomes 1 and 5  were substantially linked to three root traits including ROV (-log10(P) = 6.06, 14.10, R2 = 14 %, 22.8 %), RDW(-log10(P ) = 6.10, 6.87, R2 = 14 %, 6.3 %), and SUA (-log10(P) = 7.02, 14.10 R2 = 12.1 %, 22.8 %), respectively. Another SNP on chromosome 2  = 6.89, R2 = 11 %) was also significantly linked with three different root traits, including RBN, ROV and SUA. The Q-Q (quantile-quantile) plots of all traits at all stages are shown in Additional file In this current analysis, three GWAS approaches including EMMAX, FarmCPU, and MLM were used to scan significant SNPs associated with seven root traits namely RDW, RDW/SDW, TRL, SUA, ARD, ROV, and RBN across three vegetative stages . The detailed list of all significant SNPs detected in this study and their associated genes is presented in Additional file ed Table . These aed Table . Our stued Table . The SNPA total of 46 genes, along with 41 genes with SNPs inside, were found showing associations with the seven root architectural traits Table . The canP < 0.05, P < 0.01, P < 0.001) than those of accessions with low root branching number at all V1 and V3 stages  within exonic regions were tested for expression levels using maize accessions with extreme root branching number differences from both GWAS and mapping panels at two growth stages . The relative expression level results obtained through qRT-PCR revealed that three candidate genes viz. Zm00001d038676, Zm00001d015379, and Zm00001d018496 acted as positive regulators for root branching number while two genes viz. Zm00001d050783 and Zm00001d017751 acted as negative regulators for root branching in both GWAS and mapping accessions  ≥ 6.0) identified within candidate genes or across at least two stages/methods simultaneously, 53 potential SNPs were detected. Comparatively, EMMAX and FarmCPU were the most efficient and accurate models for detecting 34 SNPs and 19 SNPs, respectively, while MLM model was the least efficient by detecting only one significant SNP. Due to its high stringency, MLM was earlier noted to create type II errors and cause false negatives [v3-1-1 and qRDW/SDWv3-1-1 in addition to qTRSA21-1, qTRL21-1 [9dRTN1-1 [v1-4-1, qSUAv2-4-1, and qROVv2-4-1 in addition to the gene model GRMZM2G32186 [qARL26-1, qARN26-1 [qTRL17-1 [9dLRL7-1 [v1-10-1 and qRBNv1-10-1 associated with total root length and root branching number at V1 stage collocated consistently with the QTL qTRL5d-10-1 regulating total root length detected recently by Moussa et al. [Most of the earlier root-related studies have been carried out using low-density genetic linkage maps involved commonly simple sequence repeat (SSR) or restriction fragment length polymorphism (RFLP), resulting in scanty inter-marker intervals not efficient enough for accurate QTL and gene identification . In cropegatives , 44. Recegatives –56. A laegatives . Interesegatives . SNP S1_egatives –60. This9dRTN1-1 . SNP S4-M2G32186 . FurtherM2G32186  which coM2G32186 . SNPs S6M2G32186 , 61. TheqARN26-1 . SNP S7_9dLRL7-1 . Remarkaa et al.  within tWhen looking for SNPs within identified mutants or cloned genes for maize root development, rth6  was putaGene expression profile can show whether the gene possesses a biological function . Howeverv3-1-1 and qRDW/SDWv3-1-1), 2 (qRBNv1-2-1), 4 , and 10 , and each QTL explained 6.44–8.83 % of phenotypic variation. Genome-wide association study analysis identified 53 substantial SNPs through three GWAS models . The detected SNPs showed individual phenotypic contribution rates ranging from 5.10 to 30.2 % and were significantly associated with RBN (17), ROV (10), SUA (8), RDW (6), TRL (5), ARD (2), and RDW/SDW (1). Within the LD region of 5.2 kb for the significant SNPs, 46 candidate genes were identified. Five promising genes viz. Zm00001d038676, Zm00001d015379, Zm00001d018496, Zm00001d050783, and Zm00001d017751 were identified and successfully verified for expression level. The evaluated genes were shown to serve as positive/negative regulators for root branching in the spring maize lines. Thus, SNPs and underlying genes discovered in the present study could be critical for future marker-assisted breeding programs of high-efficient root systems in maize, as well as supporting the breeding of high-yielding maize varieties.This study provides a comprehensive analysis of the genetic architecture of root traits in elite maize lines. A recombinant inbred line population and a genome-wide association study panel were used for mapping loci associated with maize root architectural traits at three developmental stages under standard conditions. QTL mapping using inclusive composite interval mapping identified eight QTLs for root traits on chromosomes 1(qRDW− 2 s− 1. The seeds from both GWAS and mapping populations were directly planted in polyvinyl chloride (PVC) pipes  containing a mixture of sandy soil and vermiculite (2:1 ratio). Root architectural traits were investigated at three vegetative stages of maize growth namely V1 (one fully expanded leaf), V2 (two fully developed leaves), and V3 (three fully developed leaves) which approximately corresponded to 5, 15 and 25 days after emergence, respectively. For data collection, each PVC pipe consisted of one seedling was considered an experimental unit and the growth chamber independent trials were completed in February, April, and July 2020. All the experiments were repeated three times to increase the reliability of root quantitative traits measurements. The seedlings were cautiously removed from soil at each specific stage and washed with running water to eliminate the soil residues. For each seedling, the root system was separated from the shoot, and roots were scanned using a root scanner-based image ) then analyzed via DJ-GXG02 software [In this study, two genetic populations were used. The GWAS panel comprised 80 natural maize inbred lines covering more than 80 % planting area in Jilin Province (China). All the accessions are the same as defined in our earlier study . The mapsoftware . If the software . A total2) was determined using variance components obtained via ANOVA as described by Pace et al. [The descriptive analyses were carried out using Minitab17 program . For every root related trait at each stage and in both populations, descriptive statistics including mean, standard deviation, maximum, minimum, skewness, kurtosis, and coefficient of variation were calculated. The broad-sense heritability H was deteThe 179 RILs were genotyped by sequencing (GBS), and MSTmap was used for linkage analysis of marker data . Linkagehttps://novogene.com/, Beijing, China). Briefly, for genomic libraries construction, the DNA samples were digested by sonication to a size of 350 bp, the genomics fragments were then subjected to end polishing and A-tailing afterward ligated to the full-length adapter for Illumina HiSeq PE150 platform . Subsequently, the libraries were analyzed using Agilent 2100 Bioanalyzer and high-consistent sequencing data that show polymorphisms were then mapped to the B73 maize reference genome (RefGen_v3) using BWA software [Genomic DNA was extracted from leaves for the 80 maize inbred lines using a cetyltrimethylammonium bromide (CTAB) protocol for plant tissues . The difsoftware . Duplicasoftware . Thus, 3http://treesoft.sourceforge.net/treebest.shtml/). Using PLINK [2 value of 0.1 as a cut-off.Based on a total of 1,490,007 high-consistent SNP markers, the principal component analysis (PCA) of individuals was performed using genome-wide complex trait analysis software tool (GCTA) . The disng PLINK , the decP ≤ 0.000001(-log10 (P) ≥ 6) was set as the critical threshold to detect SNPs significantly associated with the different root morphological traits.In this study, to control false positive or spurious associations, three GWAS models were implemented, viz.: (1) Mixed Linear Model (MLM) , where Phttp://www.maizegdb.org/) and Gramene (https://www.gramene.org/) databases were used for predicting functional annotations of the candidate genes with reference to B73_RefGen_v4 [Candidate genes with SNP in coding regions and which could promote mutation were considered as high priority candidate genes. MaizeGDB   method [High priority candidate genes were chosen based on the existence of substantial SNPs within the exon of candidate genes and phenotypic contribution rates greater than 12 %. For expression analysis, ten maize accessions with extreme root branching number differences from both GWAS and biparental populations were chosen. There were M08, M40, M25, M06, M35, M44, M63, M52, M56, and M54 from the GWAS population. From the mapping population, there were P014 , E1312 , C055, C039, C0143, C096, C004, C070, C152, and C010. The roots were sampled at two different stages (V1 and V3). Total RNA was extracted using the Trizol method, cDNA was reverse transcribed the All-in-One First-Strand cDNA Synthesis kit  following the standard protocol. Oligonucleotide primers were designed with PrimerPremier5.0  method . All expAdditional file 1: Table S1. Phenotypic data of the seven root-related traits from the mapping population at V1, V2 and V3 stages.Additional file 2: Table S2. Phenotypic data of the seven root-related traits from the GWAS panel at V1, V2 and V3 stages.Additional file 3: Table S3. Physical positions of the identified QTLs.Additional file 4: Table S4. List of all significant SNPs detected in this study and their associated genes.Additional file 5: Figure S1. Q-Q (quantile-quantile) plots of all traits at V1, V2 and V3 stages.Additional file 6: Table S5. List of oligonucleotide primers used for the qRT-PCR assay for the evaluated candidate genes.


Since the domestication of chicken, various breeds have been developed for food production, entertainment, and so on. Compared to indigenous chicken breeds which generally do not show elite production performance, commercial breeds or lines are selected intensely for meat or egg production. In the present study, in order to understand the molecular mechanisms underlying the dramatic differences of egg number between commercial egg-type chickens and indigenous chickens, we performed a genome-wide association study (GWAS) in a mixed linear model.NELL2 , KITLG (KIT ligand), GHRHR (Growth hormone releasing hormone receptor), NCOA1 (Nuclear receptor coactivator 1), ITPR1 , GAMT (guanidinoacetate N-methyltransferase), and CAMK4  deserve our attention and further study since they have been reported to be closely related to egg production, egg number and reproductive traits. In addition, the most significant genomic region obtained in this study was located at 48.61–48.84 Mb on GGA5. In this region, we have repeatedly identified four genes, in which YY1 (YY1 transcription factor) and WDR25 (WD repeat domain 25) have been shown to be related to oocytes and reproductive tissues, respectively, which implies that this region may be a candidate region underlying egg number traits.We obtained 148 single nucleotide polymorphisms (SNPs) associated with egg number traits . Among them, 4 SNPs overlapped with previously reported quantitative trait loci (QTL), including 2 for egg production and 2 for reproductive traits. Furthermore, we identified 32 candidate genes based on the function of the screened genes. These genes were found to be mainly involved in regulating hormones, playing a role in the formation, growth, and development of follicles, and in the development of the reproductive system. Some genes such as Our study utilized the genomic information from various chicken breeds or populations differed in the average annual egg number to understand the molecular genetic mechanisms involved in egg number traits. We identified a series of SNPs, candidate genes, or genomic regions that associated with egg number, which could help us in developing the egg production trait in chickens.The online version contains supplementary material available at 10.1186/s12864-021-07755-3. Reproduction traits, especially egg production, are the most important economic cares in chickens . Laying It is possible to analyze the genetic mechanisms of complex traits by using GWAS with the development of sequencing technology. GWAS can not only take full advantage of molecular markers at the genome level, but, owing to the use of whole genome sequences, avoid the effects of linkage imbalance between SNPs and underlying genes . At presSome commercial lines or populations are intensively selected for their production traits. Rhode Island Red and White Leghorn chickens are well known for their distinguished egg productivity. Dwarf chickens in China have also been developed for egg production. The average annual egg number of them is approximately about 300 eggs. Chinese indigenous chickens grow relatively slowly and are known to produce less than 200 eggs per year. Therefore, we performed a GWAS based on the differences of egg number between egg-type chickens and local chickens to explore the underlying molecular genetic mechanisms and identify candidate genes or genomic regions related to egg number traits. The results of this study are supposed to be beneficial for layer breeding.Principal component analysis (PCA) using the first two principal components showed that there was an obvious stratification phenomenon between Chinese indigenous chicken breeds (black circle) and commercial egg-type chickens. At the same time, we found a Tibetan chicken was mixed with White Leghorn, as shown by the red circle in Fig. Admixture software was used to analyze the population structure. We displayed a bar plot based on the cross-validation error rate ) for all SNPs was represented by a Manhattan plot, as shown in Fig. NELL2, KITLG, GHRHR, NCOA1, ITPR1, GAMT, and CAMK4, which have been proved to be related to egg number, egg production, litter size, or reproductive traits, are worth a deeper exploration [After correction, we found 148 SNPs that could be associated with egg number   is involved in the transport of amino acids. WDR25 may be related to the reproductive tissues [BEGAIN) is a gene specifically expressed in the brain and is involved in the regulation of postsynaptic neurotransmitter receptor activity [In addition, the most significant peak in this study was located at 48.61–48.84 Mb on chromosome 5. The chi-square test was carried out to compare the allele frequencies of the significant SNPs identified in this region between the high and low productivity groups. The results showed that the allele frequencies of these 20 SNPs were significantly different between the two groups  gene on chromosome 4. Although these genes have not been very well studied in chickens, and their functions have not been fully elucidated, they provide a reference and idea to understand the molecular mechanism for egg number traits.Four of the 148 QTLs in this study were those identified in previous studies. Among these overlapping QTLs, 2 QTLs were associated with egg production. This reinforces the results of our study. AX-75745363 is located the NELL2 and KITLG are located on GGA1. NELL2 not only affects the synthesis and secretion of GNRH [KITLG plays an important role in the growth and development of follicles [KITLG has an important impact on egg number traits. GHRHR located on GGA2 participates in the secretion and synthesis of GH. It is believed to be involved in the growth and reproduction of livestock [GHRHR promoter that are significantly related to egg number traits in Beijing You chickens [NCOA1, located on GGA3, is involved in regulating signal pathways mediated by TH and estrogen. It has not only been shown to be an important gene that influences reproductive traits in pigs and sheep, but also related to egg production, fertility, and reproductive traits in chicken [ITPR1 repeatedly identified in the 18.56–18.57 Mb on chromosome 12 can not only participate in the signaling pathway of GnRH, estrogen, and the synthesis and secretion of GH and TH, but also affects the growth and differentiation of follicles. In addition, ITPR1 has been reported to be involved in the transport of Ca2+ and may be associated with egg number [ITPR1 gene may be important for chicken egg number traits. GAMT located on GGA28 has been shown to be associated with the reproductive system and development [CAMK4 located on chromosome Z is involved in the signaling pathway of OXT and may play a role in the development of follicles and ovulation [As far as we know, this study has the largest variety of breeds so far in the research of reproductive traits in chicken, which not only improves the detection ability of related QTLs, but also allows us to detect some QTLs related to fat and heat resistance. We speculate that this was due to the relatively slow fat formation and deposition , 39 and  of GNRH , but has of GNRH . KITLG pollicles , 48. It ollicles –23. Therivestock . Liu et chickens . NCOA1,  chicken –30. ITPRg number . This suelopment . CAMK4 lvulation . It is bvulation .NELL2, GAMT, and CAMK4 have not been studied before in chickens, and the results of this study may pave the way for future researchers to explore the relationship between these genes and egg number traits. Also, the specific functions of these genes need to be further verified.However, YY1, SLC25A29, WDR25, and BEGAIN were annotated. Among them, YY1 and WDR25 have been shown to be related to oocytes and reproductive tissues, respectively [In this study, the most significant peak obtained was located at 48.61–48.84 Mb region on GGA5. In this region, ectively , 35. Howectively , 15. TheIn this study, we performed a GWAS based on the difference of egg number between high productivity layers and Chinese indigenous chickens and identified a series of SNPs and candidate genes related to reproductive traits. Four of the SNP effects overlapped with previously reported QTL regions, which supports the results of this study. These results may help us to better understand the molecular mechanisms underlying reproductive traits in chickens and even other species.For this study, 442 chicks were available. Among them, White Leghorn and Rhode Island Red are intensively selected commercial breed, Dwarf Chicken is a synthetic layer line. Both they are egg-type chickens and produce about 300 eggs per year. They were placed in the high egg productivity group in this study. The other ten Chinese indigenous breeds laying less than 200 eggs annually were classified into the low egg productivity group. The details of the samples are presented in Table P < 10− 6 were eliminated. After filtering, 439 chickens, including 218 in high and 221 in low egg productivity groups, and 456,647 SNPs were retained for further analyses.In total, blood samples from 442 chickens from the high and low egg productivity groups were collected by standard venipuncture. After DNA extraction using the standard phenol/chloroform method , the chiPrior to GWAS, population structure was examined by PCA. Plink 1.9 was used to determine the population structure and generate eigenvectors and eigenvalues, and the “ggplot2” package in R studio was used to visualize the results of PCA. We selected the first two principal components with the largest variance interpretation rate as the horizontal and vertical coordinates to create a PCA plot. At the same time, we calculated the principal component contribution rate based on 439 eigenvalues.http://pophelper.com/) [We retained relatively dependent SNPs with the plink ‘--indep-pairwise 25 5 0.2’ command. The genetic structure was estimated using Admixture software . We calcer.com/) .GWAS analyses of the egg number were performed using a univariate mixed linear model in GEMMA . In the y denotes a phenotypic value vector of 439 individuals, W is a matrix of covariates , α represents a vector of the corresponding coefficients consisting of intercepts, x is a vector of marker genotypes, β is the effect size of a marker, u is a vector of random effects with a covariance structure that follows a normal distribution as u ~ N , where K is a genetic relationship matrix and Vg is the polygenic additive variance; and ɛ is a vector of random residuals. In this study, the Wald statistic was used to test each SNP.where p-values from GWAS to judge the degree of false-positive [Manhattan and Quantile-Quantile (QQ) plot were made by R package “CMplot” and “qqman” respectively, and we also calculated λ based on positive . λ was cP value was adjusted to 3.91 × 10− 7 for a genome-wide significance level, and 7.82 × 10− 6 for a genome-wide suggestive significance level. This means that SNPs with P values below 7.82 × 10− 6 are considered and may be associated with egg number traits.The traditional Bonferroni correction is too strict, resulting in a higher false-negative rate and omission of some SNPs truly associated with the target trait . Therefohttp://www.ensembl.org/index.html) databases. We then checked the biological functions of these genes in PubMed (https://pubmed.ncbi.nlm.nih.gov).We identified candidate genes by searching for the nearest genes located within 400 bp upstream or downstream of the significant associated SNPs and annotated based on the Galgal 5.0 assembly supported by Ensembl .In addition, regions within 100 kb of a candidate SNP were searched for previously reported QTLs with egg number, egg production or reproductive traits in the chicken QTL database .


ITPR1, ITPR2, and GRIA4) and neuroactive ligand–receptor interaction functions for heat tolerance , providing a novel insight that can help to develop genetic and management approaches to combat heat stress.While understanding the genetic basis of heat tolerance is crucial in the context of global warming’s effect on humans, livestock, and wildlife, the specific genetic variants and biological features that confer thermotolerance in animals are still not well characterized. We used dairy cows as a model to study heat tolerance because they are lactating, and therefore often prone to thermal stress. The data comprised almost 0.5 million milk records  of 29,107 Australian Holsteins, each having around 15 million imputed sequence variants. Dairy animals often reduce their milk production when temperature and humidity rise; thus, the phenotypes used to measure an individual’s heat tolerance were defined as the rate of milk production decline (slope traits) with a rising temperature–humidity index. With these slope traits, we performed a genome-wide association study (GWAS) using different approaches, including conditional analyses, to correct for the relationship between heat tolerance and level of milk production. The results revealed multiple novel loci for heat tolerance, including 61 potential functional variants at sites highly conserved across 100 vertebrate species. Moreover, it was interesting that specific candidate variants and genes are related to the neuronal system ( In livestock, the annual temperature–humidity values that rise above thresholds considered to be comfortable have been increasing in many regions including Australia, the USA, Canada, and parts of Europe4, making heat stress a multimillion-dollar issue in the livestock industry that compromises production  and reproduction leading to economic losses5.Heat stress from rising global temperatures is an issue of growing importance across tropical and temperate zones affecting humans, livestock, wildlife, and plants. A recent studyBos indicus), have a better tolerance to temperature and humidity than cattle from temperate zones , in part, due to the lower productivity of Zebu cattle6. Temperate breeds also show genetic variation in heat tolerance; for example, New Zealand Holsteins appear to exhibit higher reductions in milk yield in hotter climates than Jerseys or crossbreds7. While it is not fully understood why animals differ in their thermotolerance, it is hypothesised to be due to a myriad of biological mechanisms; including cellular, morphological , behavioural , as well as neuro-endocrine systems. See comprehensive review by8 for more information. Notably, the molecular basis for differences in these adaptive responses within various mammalian species is still largely unknown.The thermoregulatory capacities of mammals and plants to cope with extreme heat have been studied for decades. Genetic variation of thermoregulation during heat stress exists within species, including cattle breeds, with the literature indicating that tropical breeds, such as Zebu  large phenotype datasets needed to study heat tolerance, as well as extensive genomic information, are available; (2) they have been genetically selected mainly for high milk production over many years, offering an opportunity to understand the genetic basis for coping with both environmental and elevated metabolic-heat stress associated with increased milk production.9 pioneered using daily milk yield and temperature–humidity data to measure variability in the rate of decline in milk yield associated with variability in response to heat stress. This method has been widely adopted due to the availability of large datasets from routine recording in dairy farms, e.g.,3. Heat tolerance in dairy cattle measured using rectal temperatures or the rate of milk yield decline is partly under genetic control, having a low (0.1) to moderate heritability (0.30)10, which makes it amenable to selection. As such, considerable research has been undertaken to provide breeding solutions for heat stress, which is already a feature of dairy cattle breeding programmes in some parts of the world, e.g., Australia3. Identifying specific genetic variants that increase tolerance to heat may help to improve dairy breeding programmes in addition to improving our knowledge of the thermal biology in other mammals. However, except for mutations in the SLICK locus11, the identification of the specific genetic variants for heat tolerance in cattle and other species has, in most cases, remained elusive, in part due to many reasons, including the sample size used in past studies15.The development of methods to describe heat tolerance in cattle has been an active research area for many years. Measuring changes in core body temperature , thermal indices ) are some of the ways to assess thermal adaptations and performance in animals. Ravagnolo et al.16. Several selection signature work e.g.,18 and genome-wide association studies (GWAS) using Single nucleotide polymorphisms (SNPs) have been conducted over the last decade to identify candidate causal genes for various heat tolerance traits  in dairy cattle15 and pigs19. However, these GWAS were underpowered, with the largest sample size to date of around 5000 animals13. These studies have also used standard industry SNP panels of random genome-wide markers, either 50 k or 600 k SNPs, leading to inconsistencies and poor replication of the results. Although these studies have identified multiple significant variants associated with heat stress in animals, none were established to be causal mutations.Having a large sample size is particularly important for identifying rare causal variants with medium-sized effects and common variants with small effects. As sample size increases, the loci significantly associated with complex traits are expected to increase, as demonstrated for the human height20. The specific aims of the current study were to: (1) perform single-trait GWAS to identify genomic variants associated with sensitivity of milk traits  to heat stress; (2) combine single-trait GWAS results in a multi-trait meta-analysis to boost the power and identify pleiotropic variants associated with all the milk traits; and (3) conduct post-GWAS pathway analysis using the list of candidate genes identified in single-trait GWAS and meta-analysis to elucidate biological mechanisms underlying heat tolerance.Here, we performed a GWAS using milk production records of 29,107 Holstein cows, each having over 15 million sequence variants that were imputed from various lower density SNP chips to whole-genome sequence using a reference dataset of sequences from the Run7 of 1000 Bull Genome ProjectThe average yield and their corresponding standard deviation (in brackets) of milk (in liters), fat (kg), and proteins (kg) used in our study was 25.85 8.19), 0.98 (0.30), and 0.85 (0.26), respectively. The heat tolerance proxy-phenotypes  and intercepts (representing level of milk production) that were derived from the milk traits are in Table 9, 0.98 ”. The gep value thresholds tested  varied between 0.02 and 0.03 for intercept and 0.02 and 0.05 for slope traits. The number of significant independent QTL (based on the number of 5 Mb non-overlapping windows across the chromosome with at least one significant SNP) ranged from 28 to 72 for intercept traits and from 21 to 37 for slope traits. At a relaxed cut-off threshold, where the FDR was < 0.10, the number of significant QTLs from single-trait GWAS ranged from 78 to 188 (intercept traits) and from 51 to 109 (slope traits).The number of significant SNPs was generally higher for intercept than slope traits at the ed Table . At a stp < 1E–05) QTLs  varied across the three slope traits with greater overlap between HTMYslope and HTPYslope  compared to HTMYslope and HTFYslope   was much higher for a multi-trait meta-analysis  and 65 . Of the significant QTLs  for meta-analysis of slope traits, 35% (N = 23) overlapped with the candidate QTLs for single-trait GWAS analysis based on whether the lead SNP (most significant) within overlapping QTLs were close (within 1 Mb).Meta-analysis of GWAS results could increase the power of detecting informative variantssis Fig. ; Table 2p value cut-off (p < 1E–05) are in the Supplementary Table The lead SNPs were defined as the most significant SNPs within an independent QTL . Detailed annotation of all the lead SNPs for single slope traits and the meta-analysis (N = 118) detected at the most stringent 2 < 0.5) with nearby (within 1 Mb region) lead SNPs for intercepts, indicating that they are not strongly associated with the level of milk production. Some lead SNPs mapped within or close to several candidate genes, which have been linked to environmental stress or heat tolerance in animals in previous studies, including REG3A23, NPFFR224, and CLSTN225. Several other lead SNPs mapped close to novel candidate genes that, to our knowledge, have not been described for thermotolerance in previous studies.About half the lead SNPs (51%) for slopes were in relatively low LD (r2 > 0.50) with nearby (within 1 Mb) lead SNPs identified for intercept traits  associated with the level of milk production (intercept traits) mapped close to or are within genomic loci previously reported to have pleiotropic effects on bovine milk production traits, including the DGAT127, MGST128, and GHR gene29.However, the remaining lead SNPs (49%) for slopes were in medium to strong LD  and HTPYslope  traits were upstream to SLC52A2 and a synonymous variant in the CPSF1 genes, respectively.We performed two conditional GWAS for slope traits to confirm whether the top hits (lead SNPs) detected in the first-round of GWAS for the slope traits were in fact discoveries of heat tolerance rather than indicators of milk yield . Of interest was the conditional GWAS analysis on chromosome 14, since the highly significant QTL around 0.5 Mb harbours the DGAT1 gene and the HSF1 (heat shock factor 1) gene, for which the latter has been linked to thermotolerance in Holstein cattle in different countries, including Australia2 > 0.8) with several other variants around this QTL spanning over 10 genes  SNPs remaining for the HTFYslope trait  difference in the distribution of the GWAS p values across slope traits. This is, in part, due to the difference in the two conditional GWAS approaches regarding the covariate fitted in the linear model. We also observed similar findings for the conditional GWAS analysis on chromosome 20  were generally comparable regarding the strength of the GWAS signals Fig. , we obsep < 1E–05  with FDR < 0.10, the strongest GWAS signal was in BTA 14–1.7 Mb, of which the lead SNP  mapped to the downstream region of JRK (Jrk helix-turn-helix protein). Notably, this gene was found to regulate behavioural rhythms in Drosophila flies, which is crucial for adaptive response to environmental changes such as temperature variations31.By conducting a conditional analysis of slope traits on the intercepts, we detected multiple additional QTL signals (lead SNPs) across the genome at p < 1E–05), all of which associated with low FDR < 0.10 , confirming that the conditional analysis was successful in identifying additional candidate variants for heat tolerance (besides the QTL detected from the first-round of GWAS) that are not strongly associated with the level of milk production. The most significant lead SNP  mapped to the upstream region of the SLC39A4 gene, a member of the solute carrier family, required for intestinal zinc uptake.When combining conditional GWAS results for slope traits (conditioning on the intercept traits) in the meta-analysis approach, we detected 40 lead SNPs (2 > 0.8) with the lead SNP, 500 kb up or downstream of the chromosome. We identified a total of 3010 candidate causal variants for heat tolerance (slope traits) across all the analyses: single-trait GWAS; a meta-analysis of single-trait GWAS results; and meta-analysis of conditional GWAS results for slope traits, most of which were intergenic  followed by intronic  and upstream  variants  plus other significant SNPs in strong LD  in the upstream gene regions  gene and (b) a frameshift mutation  in STX5 (syntaxin-5) gene. The two candidate mutations appear to have a stronger effect on milk production compared to heat tolerance. This is evidenced by a smaller (p = 1.39E–19) p value for the stop-gain mutation  observed in the meta-analysis of intercept traits compared to the meta-analysis of slope traits (p = 4.08–12). Similarly, the p value for the frameshift mutation  in the STX5 gene was smaller (p = 2.06E–16) for the meta-analysis of intercept traits than the meta-analysis of slope traits (p = 5.06E–06). None of these two candidate stop-gain mutations were significant (p < 0.05) following conditional GWAS for slope traits on intercept traits  in the conserved regions of the genome.Using data fromSCD35, KIAA132436, and TONSL14. The SCD (stearoyl-CoA desaturase) gene encode fatty acid metabolic enzyme and perhaps is required for metabolic homeostasis during heat stress in mammals. Other putative candidate genes for heat tolerance include KIFC2, VPS13B, and USP3. For example, Fang et al.37 demonstrated that the USP3 gene, a member of the ubiquitin-specific proteases (USPs) family, is required for eliminating misfolded proteins under heat stress conditions in Yeast.Table CALCR, PTGER2, THRB, GRIK2, NPY2R, F2RL1, GRIN2A, NR3C1, CHRM3, GRM8, GRM7, GRID2, NPFFR2, MC4R, GHR). A total of 8 genes were enriched (p = 4.0E–03) in the glutamatergic synapse pathway . The citrate cycle pathway was also enriched (p = 1.87E–03), comprising of 5 candidate genes for heat tolerance .We generated a list of candidate genes mapping within or near lead SNPs detected at FDR < 0.10 for each trait for the pathway enrichment analyses. We found that the candidate gene-list for slope traits were highly enriched for the KEGG pathways related to the neuronal system (neuroactive ligand–receptor interaction and glutamatergic synapse) and metabolism system (citrate cycle) Fig. . Interesp < 1E–05) evidence of association for heat tolerance, separately  within an independent QTL plus other significant SNPs in strong LD (r2 > 0.80) with the lead SNP, 500 kb up or downstream), to see enriched biological pathways. Interestingly, we observed enrichment (p = 0.02) of the genes in the neuroactive ligand–receptor interaction pathway, which provides strong support that this neuronal pathway is relevant for heat tolerance comprising of 8 genes .We also analysed a smaller set of genes (N =  ~ 230) with the strongest (p = 3.19E–04) and HTPYslope (p = 7.79E–03) traits  for the axon guidance pathway comprising four genes  and metabolic (p = 0.06) pathways.When examining the candidate gene-list from single-trait GWAS analyses for slope traits separately, the neuroactive ligand–receptor interaction pathway was overrepresented for candidate gene-list for HTMYslope  in any neuronal pathways; thus, providing further favourable support that neuronal pathways are relevant for heat tolerance in mammals.To further test whether the neuronal pathways are real and not an artifact of our analyses for heat tolerance traits (slopes), we performed enrichment analyses for the significant candidate gene-list for intercepts traits (level of milk production traits). In the candidate gene-list for intercept traits, we found no evidence for enrichment  with milk production records and imputed sequence data (~ 15 million SNPs) to identify candidate causal variants and functional genes and pathways associated with heat tolerance. Australia’s dairy cattle are uniquely placed for studying heat tolerance in mammals for two main reasons: (1) they are subjected to a wide range of seasonal climatic variations across diverse dairying regions spanning one of the geographically largest countries in the world, and (2) Australia’s dairying is predominantly pasture-based with limited heat stress mitigation measures in contrast with those, for example, in North America, where extensive managerial strategies are used more to reduce thermal stress. Overall, we have identified novel candidate causal variants in the neuronal pathways that contribute significantly to heat tolerance in animals.38 and cattle34 have demonstrated that the conserved genomic sites have strong enrichment of trait heritability. Moreover, the results revealed specific candidate causal variants and genes related to neuronal functions for heat tolerance in animals, which we now discuss in more detail.We leveraged two statistical approaches to identify genetic loci and pathways for heat tolerance: single-trait GWAS linear models and multi-trait meta-analysis. Single-trait GWAS is based on regressing phenotypes on each SNP one at a time. On the other hand, a meta-analysis that combines results of the single-trait GWAS allowed us to discern putative pleiotropic genetic variants for heat tolerance. Consequently, we identified multiple novel loci for heat tolerance, including 61 potential functional variants at genomic sites highly conserved across 100 vertebrates Table , which c39. Among the candidate gene-list that contribute significantly to heat tolerance in the study animals (Holstein cows), the neuroactive ligand–receptor interaction and glutamatergic synapse pathways  biological features.Heat stress responses are complex adaptations in animals involving many biological pathways, including the nervous system, which connects the internal and external environment to maintain stable core body temperatureays Fig. , as compITPR2  and GRIA4  genes in the glutamatergic synapse pathway could be potential targets for resilience to environmental stress in animals. ITPR2 gene was associated with heat stress in the US Holsteins14 or sweating rate in humans and mice40, while the GRIA4 gene has been linked to thermoregulation in the Siberian cattle41. Another candidate variant  detected for heat tolerance milk (p = 3.87E–05) and protein (7.15E–05) yield slope traits mapped to the intronic region of ITPR1—a gene associated with environmental adaptation in the domestic yak42. These three lead SNPs for slope traits overlapped with those for intercept traits, with opposing effect direction, suggesting that selecting for these variants may negatively impact milk production.At least two candidate variants in the intronic region of 43. As protein production is the most valuable output from dairy farms, the focus of breeding programs has been traits associated with yield, with the average milk volume per cow/year almost doubling within the past three decades in Australia44. The environmental heat stress, coupled with the elevated metabolic-induced thermogenesis, means that the genetic and cellular reprogramming of pathways such as the nervous system may be necessary to regulate a cascade of hormonal processes such as growth factors, insulin, serotonin, thyroid, prolactin, and mineralocorticoids associated with milk synthesis45. We identified 15 genes (FDR < 0.10) associated with the neuroactive ligand–receptor interaction, which could be relevant for metabolic homeostasis in cattle during thermal stress, of which three candidate genes  showed the strongest evidence (p < 1E–05).Previous studies show that the neuroactive ligand–receptor interaction is involved in maintaining energy homeostasis during heat stress in ducks24 demonstrated that the NPFFR2 (neuropeptide FF receptor-2) gene, which is mainly expressed by neurons in the brain, plays a crucial role in regulating diet-induced thermogenesis and bone homeostasis in mice. In this study, two lead SNPs , detected from single-trait GWAS for HTMYslope and HTPYslope (p < 1E–05) mapped to the intergenic and intronic regions of NPFFR2 gene in BTA 6, respectively. Physiological studies suggest that NPFF family genes regulate feeding behaviour and energy expenditure in mammals reviewed in46. During heat events, dairy cattle typically reduce their dry matter intake by up to 30%, perhaps as part of an adaptive mechanism to depress metabolic heat production47. Other studies, e.g.,48 show that inhibition of NPFF receptors induces hypothermia in mice. A recent review by Nguyen et al.49 indicates that NPFF and its receptors have many promising therapeutic applications including pain, cardiovascular, and feeding regulations in mammals. By examining the genomic region around the NPFFR2 gene  than the intercept , suggesting that this SNP is a good candidate for heat tolerance. Besides, this lead SNP  remained significant (p = 6.36E–06) following single-trait conditional GWAS analysis for HTMYslope trait (conditioning slopes on the intercept traits) as well as in the meta-analysis of single-trait conditional GWAS results for slope traits (p = 3.74E–06).Zhang et al.50. In this study, the lead SNP  was intronic in the CALCR  gene, perhaps indicating that it could be relevant for animals experiencing recurrent or chronic stress, such as in Australian seasonal summers. The strong GWAS signal around this QTL  gene is down-regulated during heat stress in livestock, including dairy cows51 and avian species43. The adaptive physiological significance of this down-regulation is not well understood, and it is partly independent of the nutritional level of the animal51. In this study, the lead SNP  identified only in one slope trait (HTMYslope) based on significant cut-off of p < 1E–05 mapped to intronic region of GHR gene  mapping to the intergenic (~ 22 kb) region of the GHR gene, which confirms the pleiotropic effect of this QTL22. Also, we observed no significant SNP (p < 1E–05) around this QTL following single-trait conditional analyses, but a somewhat strong signal emerged when we combined single-trait conditional GWAS results in the meta-analysis, for which the lead SNP  mapped to the intergenic region (~ 47 kb) of GHR. This further supports a possible second QTL that is independent of the level of milk production and shows pleiotropy for the heat tolerance traits. Other published GWAS have also reported an association of the GHR gene with milk production in heat-stressed cows14 and respiratory rates in pigs during heat stress19. Several studies have also implicated the GHR polymorphisms to milk production in cattle, e.g., Chr20:31,888,449 phenylalanine-to-tyrosine missense mutation29. This mutation was not in strong LD (r2 > 0.8) with the lead SNP detected for slope traits in our study. Taken together, polymorphisms around the GHR gene could be candidate targets for improving thermotolerance in livestock, although with possible antagonistic effect on milk production considering, for example, the opposing effect direction observed for the lead SNP  within this QTL on the slope (HTMYslope) and intercept (MYint) traits.The expression of the Here we discuss the evidence for each of these three candidate genes:47. The molecular control and pathways for individual milk traits during heat stress are scarce and inconclusive. In this study, the QTLs detected for the heat tolerance traits varied across the three milk traits , suggesting that they are, in part, regulated by different genes in heat-stressed cows. The greater overlap of candidate genes observed for HTMYslope and HTPYslope traits was expected due to their relatively high correlation (0.90) compared to HTMYslope and HTFYslope (0.56) or HTPYslope and HTFYslope (0.62). These correlations appear to mirror the proportions of SNPs with the same or inconsistent effect direction observed for significant SNPs between slope traits. Considering that heat stress alters carbohydrate, lipid, and amino acid metabolism52, the large proportion of SNPs with inconsistent effect direction, particularly between HTPYslope and HTFYslope, suggest that these traits are somewhat differently regulated in heat-stressed dairy cows.There is general agreement that heat stress decreases milk yield  in dairy cattle. However, the genetic and biological basis for this reduction is still unclear. Evidence suggests that the reduced feed intake in heat-stressed dairy cows partially explains (35–50%) reduced milk yield and composition54. We found that the candidate genes for HTMYslope and HTPYslope traits were overrepresented (p < 0.005) in the neuroactive ligand–receptor interaction pathway. This agrees with Pegolo et al.55 that genes associated with milk proteins are involved in neuronal signaling pathways in dairy cattle. However, it remains unclear how this pathway is regulated during heat stress conditions in dairy cows to impact protein production.Several pair-fed studies suggest that pathways related to the mammary gland protein synthesis govern protein production under heat stress in dairy cows, in part, via reduced amino acid supply to the mammary gland, e.g.,56 suggest that the reduced activation of PPAR (peroxisome proliferator-activated receptor) signaling pathways leads to decreased expression of genes associated with fat metabolism. Candidate genes for HTFYslope identified in this study are associated with the KEGG term “metabolic pathways”  within this pathway may be involved with alleviating oxidative stress in heat-stressed cows. In line with these findings, we found that the candidate genes for heat tolerance , different QTLs may be captured by different traits used in GWAS. Notably, although heat tolerance traits (slopes) used in earlier studies in Australia58 were comparable to those used in our study, we could not confirm most candidate genes (except HSF1 gene). This is likely due to the reduced power in earlier GWAS studies . Third, differences in the patterns of LD among study populations used and imputation quality may have implications on GWAS, particularly in the detection of putative causal mutations59. Here we explored QTLs for heat tolerance in purebred Holstein cows, while some other studies, e.g.,15 have used crossbred cattle. Collectively, these factors likely impacted the replication of previous GWAS candidate genes for heat tolerance.We could not replicate most of the candidate genes with published GWAS results for heat tolerance in cattle, likely for several reasons. First, all comparable earlier studies were much smaller  and therefore were under-powered, and the marker density used was typically 50 k or 600 k SNP array e.g.,16. For example, many of the lead SNPs (most significant) for heat tolerance were tagged by none or very few significant SNPs (Table p < 1E–05).Although we detected multiple candidate causal variants for heat tolerance in this study, it appears that larger sample size  would be beneficial considering the polygenic architecture of this trait. Larger sample size is required to detect causal variants with very small effects and the effects of rare causal variantsPs Table , which m60. Furthermore, we used conditional analyses of slope traits in a bid to separate production and heat tolerance genes. It may be useful to consider alternative heat tolerance traits in the future GWAS (besides milk decays) that are independent of production, such as those derived from milk yield based on the principal components (PC)61 or eigen-functions62. Overall our results support the highly polygenic nature of heat tolerance characterised by multiple small-effect variants, suggesting that this trait is more amenable to genomic selection tools such as those currently implemented in the Australian dairy industry63 rather than approaches that exploit few QTLs with large effects. The significant variants detected in this study will be tested in a follow-up study to assess their benefits in the genomic prediction of heat tolerance in dairy cattle.With the increasing availability of high-throughput data from automatic sensor devices such as activity-based collars or tags, it is now feasible to obtain large-scale data for thousands of animals; if genotyped, it would allow a comprehensive genetic evaluation of heat tolerance from a wide array of phenotypes e.g., mid-infrared (MIR) predicted traits from milk recording dataIn conclusion, we performed GWAS for heat tolerance using large sample size and genotype dataset for dairy cattle. The increased sample size and high-resolution SNP data in our study compared to previous reports allowed us unprecedented power and precision of the GWAS to pinpoint multiple putative causal mutations, including 61 potential functional variants at genomic sites highly conserved across 100 vertebrate species. Also, results indicate that different genes and pathways, in part, regulate different milk production traits  in heat-stressed dairy cows with a substantial overlap of genes for heat tolerance milk and protein yield. Overall, the results revealed the importance of variation in genes related to the neuronal functions for heat tolerance in mammals, which is of interest for future research towards understanding and managing heat stress for warm climates and particularly in view of the anticipated rising global temperatures.64 obtained from DataGene —the organisation responsible for genetic evaluation of dairy animals in Australia. The phenotypes were test-day milk, fat, and protein yield for Holstein dairy cows collected from dairy herds that were matched with climate data (daily temperature and humidity) obtained from weather stations across Australia’s dairying regions. The distribution of dairy herds and weather stations; and the calculation of environmental covariate ) used here were described in our earlier studies64.No live animals were used in this study. Phenotypes used for GWAS were part of our previous study64, comprising a total of 424,846 test-day milk records for first, second and third lactations from 312 herds and 15,906 herd-test days (HTD) collected over 15 years (2003–2017). A summary of the final dataset is given in Table 64. In these models, data on milk, fat, or protein yield were adjusted for the fixed effects, including herd test day, year season of calving, parity, Legendre polynomials (order 3) on the cow age on the day of test, and the Legendre polynomials (order 8) on the interaction between parity and DIM. Random effects fitted in the model included a random regression on a linear orthogonal polynomial of THI, where the intercept represents the level of mean milk yield and the linear component represents the change in milk yield (slope) due to heat stress for each cow and a residual term. In the model, the threshold of THI was set to 60 following65. The analyses to derive trait deviation (TD) which represents a phenotype adjusted for all fixed effects  were conducted using ASReml v4.266. To facilitate convergence, milk, fat and protein yield traits were scaled by a factor of 10, 100, and 100, respectively. The description of heat tolerance traits  used in this study are comparable to those used in previous GWAS in Australia58.The dataset used to calculate heat tolerance phenotypes for cows was similar to that used byWe refer to milk intercept traits as  and the slopes traits as , respectively.https://www.ncbi.nlm.nih.gov/assembly/GCF_002263795.1/)67 positions from reference genome UMD3.1 and imputed using Fimpute368. The WGS was imputed from the HD genotypes using a reference set of 3,090 Bos taurus sequences in the Run7 of the 1000 Bull Genome Project (http://1000bullgenomes.com/)20 aligned to the ARS-UCD1.2 reference genome. Only bi-allelic sequence variants with a minor allele count (≥ 4) and GATK69 quality tranche 99.0 or better were retained for imputation. Pre-imputation, we also removed sequence variants from the imputation reference that had a higher than expected proportion of heterozygous calls (> 0.5) if these variants fell in a 500 kb window enriched for variants showing excessive heterozygosity . A total of 31,994,954 sequence variants remained for imputation. Minimac370 was used for WGS imputation, having first pre-phased both the HD genotypes and the WGS reference using Eagle v271. For the analysis, we retained only the variants with Minimac3 imputation accuracy, R2 > 0.4 and MAF > 0.005 .Two genotype datasets were analysed for 29,107 Holstein cows with the above phenotypes: 50 k SNP chip and 15,098,486 imputed whole-genome sequence variants (WGS). Most of the cows were originally genotyped with a custom low-density 10 k SNP panel or a standard medium density 50 k SNP array (BovineSNP50k BeadChip: Illumina Inc). The low-density genotypes were imputed to the 50 k array using a reference set of approximately 14,000 animals with real 50 k genotypes, with approximately 7,000 SNPs of the low-density SNP panel overlapping the 50 k SNP array. The 50 k genotypes were then imputed to the high-density Bovine SNP array  using a reference set of 2,700 animals with real HD genotypes. All SNP BeadChip genotypes were first converted to the ARS-UDC1.2 reference genome  > 0.005, the fitted model per trait was,β was the allele substitution effect of SNP i, x was the vector of genotype dosages  for SNP i, g was the vector of polygenic effect with ε was a vector of the residual effect with n × n identity matrix. The variance of y was 2g and σ2e were the genetic and residual variances. For animal j and k relationship was calculated using GCTA72 as follows:ith SNP; and ith SNP.A genome-wide association analysis (GWAS) using a mixed linear model was used to test associations between individual SNP and cows' slope  and intercept  traits using GCTA softwareGenomic heritability was calculated for each trait using variance component estimates from –reml option of GCTA for 50 k SNP array  data of cows : 21. The multi-trait chi-squared .Conditional analysis of slope traits on lead SNP —aimed at identifying additional or secondary putative causal variants beside those detected from first-round GWAS. We performed a conditional analysis strategy on two chromosomes (BTA 14 and BTA 20), which showed the strongest GWAS signal for slope traits in the first-round GWAS  from the three analytical approaches .p  < 1E–05 (FDR < 0.10).For each trait, select all SNPs with Split each chromosome (N = 1…29) into 5 Mb non-overlapping windows from the start to the distal end of the chromosome.ith 5 Mb window, select the most significant SNP  defined as the ‘lead SNP’. We chose this arbitrary 5 Mb window size to obtain a small set of significant lead SNPs representing independent QTL  for further detailed examination.Within the 73.Calculate the LD between each lead SNP and all the other SNPs within 500 kb up and downstream of the lead SNP using Plink v1.9p < 1E–05) in strong LD (r2 > 0.80) with the lead SNP within 500 kb up or down downstream – to account for the fact that the lead SNP (most significant) is not necessarily the causal variant.For each lead SNP, extract all the significant SNPs  into 9 classes  and performed enrichment analysis using phyper in R v3.6174. The class “Other” comprised variants including 5_prime_UTR_premature/_start_codon_gain, frameshift, missense&splice, splice&intron, stop_gained, etc. Supplementary Table Annotation of all variants (~ 15 million SNPs) was performed using SnpEff34. Briefly, these authors documented over 300 k sequence variants at conserved sites in cattle based on the LiftOver (https://genome.ucsc.edu/cgi-bin/hgLiftOver) human sites with conservation scores (PhastCon score) > 0.9 calculated across 100 vertebrate species .We identified candidate causal variants for heat tolerance at highly conserved genomic sites using data fromp < 1E–05 to include genes associated with smaller effects while guarding against false positives. We then performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using DAVID75.We generated candidate gene-list mapping near or underlying lead SNPs (most significant SNPs within 5 Mb QTL windows) identified at FDR < 0.10 cut-off threshold from both single-trait and multi-trait analyses of intercept or slope traits. For intergenic lead SNPs, we selected the closest gene on either side of the SNP. We chose this cut-off (FDR < 0.10) instead of a more stringent p < 1E–05) evidence of association defined as the candidate causal variants  with the lead SNP within 500 kb up or downstream passing the cut-off p value of 1 < 1E–05). For all the analyses, we considered functional pathways with Fisher’s p < 0.05 as significantly enriched.We also performed enrichment test separately for the gene-list associated with potential major effects on heat tolerance identified across all analyses (i.e., gene-list with the strongest (Supplementary Figures.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.


Mycobacterium bovis, these tools offer a high-resolution alternative to traditional molecular methods in the study of herd breakdown events. However, despite the increased use and efforts in the standardization of WGS methods in human tuberculosis around the world, the application of these WGS-enabled approaches to control TB in livestock is still in early development. Our study pursued an initial evaluation of the performance and agreement of four publicly available pipelines for the analysis of M. bovis WGS data  on a set of simulated Illumina reads generated from a real-world setting with high TB prevalence in cattle and wildlife in the Republic of Ireland. The overall performance of the evaluated pipelines was high, with recall and precision rates above 99% once repeat-rich and problematic regions were removed from the analyses. In addition, when the same filters were applied, distances between inferred phylogenetic trees were similar and pairwise comparison revealed that most of the differences were due to the positioning of polytomies. Hence, under the studied conditions, all pipelines offer similar performance for variant calling to underpin real-world studies of M. bovis transmission dynamics.Whole genome sequencing (WGS) and allied variant calling pipelines are a valuable tool for the control and eradication of infectious diseases, since they allow the assessment of the genetic relatedness of strains of animal pathogens. In the context of the control of tuberculosis (TB) in livestock, mainly caused by  Mycobacterium tuberculosis complex (MTBC)  is a chronic infectious disease that affects a wide variety of mammalian species, which is caused by members of the x (MTBC) . The priBovine TB is subjected to control and eradication programmes in many countries, not only due to its economic impact, as a result of reduced yields and animal mortality, but also because of the risk of zoonotic transfer of infection from affected animals to humans . EradicaTraditionally, molecular epidemiological studies of aTB are based on techniques that analyse small fragments of the microbial genome, such as spoligotyping or mycobacterial interspersed repeat unit-variable number of tandem repeats (MIRU-VNTR) , 9. AlthThe advent of Whole Genome Sequencing (WGS) has revolutionized the study of microbial populations. When applied to epidemiological studies, the availability of the whole genome of the microorganism of interest allows for a much higher resolution than that obtained with previous molecular techniques . As a reDue to the limited genetic diversity in MTBC genomes, the standard workflow in MTBC studies is based on the alignment of genomic sequences to a reference genome followed by the detection of genomic variants, usually single nucleotide polymorphisms (SNPs) . The proThere are several variant calling pipelines for human tuberculosis and, recently, several efforts have been made to assess their performance in human TB outbreak investigations –21. RegaM. bovis AF2122/97 genome (NCBI RefSeq accession number: NC_002945.4) using simuG 1.0.0  files were selected from an already published dataset from a bTB high prevalence setting in the Republic of Ireland . A totaluG 1.0.0  . In SNiPgenie, minimum mapping quality was set to 60 in variant calling and the minimum MQ was set to 30 in posterior filtering steps. Amended variant tables returned by MTBseq were converted to the VCF format using an in-house script for further comparisons. VCF files were generated from excel tables output by vSNP's step 2 script using an in-house script and including a zero coverage VCF of M. bovis AF2122/97 in order to include non-parsimonious SNPs.The artificially generated reads were analyzed with four variant calling pipelines: vSNP 2.03 , SNiPgenAs well as a FASTA file containing the artificial genome, SimuG generates a VCF file that contains all the variants included in the generated genome. These artificial VCF files were used as a reference standard to compare the VCF files output from the variant calling pipelines using the Haplotype Comparison Tools 0.3.12 (Som.py).Variants occurring in locations where no mutations existed in the simulated genome were considered “false positive SNPs,” while mutations not detected by a given pipeline were considered “false negative SNPs”.The evaluated performance parameters were relative sensitivity or recall rate  and relative specificity or precision . In addition, alternative (ALT) alleles were extracted from all sample VCF files obtained from each pipeline and combined to obtain the total amount of alleles identified per pipeline. The agreement between the different pipelines was then evaluated using Venn diagrams generated using VennDiagram v1.6.20 in R 3.6.3 .pks12 gene), and their positions in the reference genome were extracted from the GFF3 annotation file available at the NCBI.In order to identify groups of genetic elements that usually give rise to false positive and negative calls, all VCF files were annotated using SnpEff 4.3t . These gPerformance was re-evaluated using different levels of hard filtering: (A) unfiltered, (B) a proximal window distance of 10 bp , (C) 10 In addition, the effect of filtering on the number of identified homoplasies was assessed using HomoplasyFinder .All pipelines, except for BovTB, generate a multi-FASTA alignment containing the concatenated variants. The SNPs in the alignment files obtained from vSNP and SNiPgenie only include polymorphic sites, whereas MTBseq alignments also include monomorphic sites. BovTB yields a consensus genome generated from the VCF files using the BCFtools consensus caller. In order to compare the different methods, core polymorphic SNPs were extracted from these consensus genomes using SNP-sites 2.5.1 . In addiMaximum-likelihood trees were reconstructed from the resulting multi-FASTA alignment files using RAxML 8.2.12 with 100 bootstraps and the GTRCATI model . The bip6 reads (coefficient of variation or C.V. = 0.09%) were generated with an average depth of coverage of 145, with a minimum of 0 and a maximum of 310 reads per site.An average of 2.5 x 10Excluding the outgroup isolates, the average observed differences between isolates in the unfiltered simulation was 38.61, with an inter quartile range of 30–47, and a minimum and maximum number of 12 and 64 nucleotide differences, respectively.Recall rates were highest for SNiPgenie and BovTB when base parameters were employed, followed by vSNP and MTBseq .Sensitivity increased when increasing levels of hard filtering were applied for vSNP, SNiPgenie, and MTBseq, and remained similar for BovTB . The posn = 4). Adjusting this threshold increased recall rates above 99%  calls was produced by strand bias introduced by the artificial read generation, leading to forward or reverse read coverage being below the default minimum threshold  SNPs were identified by BovTB (43 SNPs across 37 positions) but, nevertheless, precision was high (>99%) for all of the evaluated pipelines . Approxipks12 gene (data not shown). A similar proportion (6.70–7.45%) of homoplasies was identified in the alignments obtained from all of the evaluated pipelines. The removal of proximal SNPs reduced homoplasies to an average of 2%, similarly to what was observed for the removal of all of the problematic regions (filter G). Filtering of problematic regions with the proximity filter produced an additional reduction to 1%; most of the reduction was obtained with the removal of PE/PPE proteins alone and additional filters did not decrease the proportion significantly. Once all filters had been applied, all pipelines presented a reduced proportion of homoplasies compared to the ones present in the simulation. Finally, the use of default filters had a varying effect in the proportion of homoplasies, with vSNP and SNiPgenie obtaining the highest reduction in homoplasic positions.HomoplasyFinder identified 64 (7.65%) homoplasic positions among the generated sequences , mostly There was a high agreement between the SNPs identified by the different pipelines and those in the simulated genomes, with the majority of simulated SNP positions being appropriately detected . When prThe analysis of RF distances from best trees and bootstrap replicates revealed that trees output by the different pipelines clustered together with their simulated counterpart . In addiIn order to further evaluate topological differences among trees, a pairwise comparison of best trees obtained from each of the pipelines was carried out against their simulated counterpart . In geneThree major groups of taxa could be identified in all trees and no inter-cluster exchange was observed between pipelines. Among unfiltered trees, several isolates presented a small change in their relative location within the tree in the different pipelines , sharing their Most Recent Common Ancestor (MRCA) with a different group of isolates to the one observed in the simulation. The main differences among filtered trees were produced by small topological variations among highly related taxa  and the appearance of polytomies further contributed to the topological differences observed with the simulated tree. When compared against filter F, filter G resolved a small number of polytomies .The application of WGS technologies in the study of aTB has increased in the last decade around the world. Despite its great promise as a higher resolution alternative to traditional molecular techniques in phylogenetic and epidemiological studies, its implementation in the eradication of bTB is still in development.The digital nature of the data produced by WGS platforms and tools facilitates the exchange of information between laboratories, fostering collaboration between countries and organizations tasked with aTB control. However, the plethora of tools, parameters, protocols and types of analyses available may introduce variations that hamper this process of communication. Standardized procedures and parameters are needed in order to reduce the effect of these variations.Prior to any standardization taking place, there is a need to evaluate the currently available techniques. None of the variant calling pipelines designed up to date for aTB have been benchmarked in the scientific literature, leading to uncertainty regarding the best method to implement in laboratories that are considering incorporating WGS analyses into their workflows. The aim of this study was to carry out an evaluation of the performance of the currently available aTB variant calling pipelines and assess the degree of between-pipeline agreement in order to inform animal health authorities and laboratories.The four pipelines evaluated in this study follow a similar procedure to other variant calling pipelines and employ bioinformatics tools widely used in the study of microorganisms. All pipelines use BWA as their sequence aligner , and theM. tuberculosis sequences . This suggests that differences in FN calls could be due to how sample-specific filters deal with low quality regions.BovTB was the only pipeline to identify FP SNPs and an evaluation of these positions revealed that they were a result of being identified as mixed positions. These were, in addition, partially responsible for a small proportion of the FN SNPs and were effectively removed from the analysis during the consensus calling. As a result, these positions did not have any effect in posterior analyses.Identified variants are usually translated into phylogenetic trees as a visual aid to assess the genetic relatedness between strains, which can help to identify epidemiologically related isolates suggestive of transmission. In this study, the phylogenetic trees obtained from the different pipelines clustered together with their respective simulated trees and a pairwise inspection revealed a high level of agreement between simulated and pipeline-specific trees, especially on those obtained from BovTB. The phylogenetic trees inferred from the unfiltered SNP alignments obtained from each pipeline were compared against the reference phylogeny in order to assess the effect of pipeline performance in phylogenetic inference. Small divergences were identified in vSNP, SNiPgenie, and MTBseq with respect to the simulation, which are probably a consequence of a reduced sensitivity due to the application of stringent quality filters alone.pe/ppe genes and the pks12 gene.Despite the abovementioned effects of quality filters in performance, these are rarely the only parameter taken into consideration when carrying out variant calling in MTBC species. Repeat-rich regions, such as PE/PPE family proteins, mobile genetic elements or direct repeats, are generally considered low confidence regions either due to a higher error rate or mapping issues , 38, whiM. bovis strains, this could be especially relevant in closely related isolates and could potentially alter the epidemiological conclusions drawn from outbreak investigations   . InteresM. bovis isolates in the original publication presented a maximum of 35 SNPs with respect to each other and a median distance of 14 SNPs once all filters were applied , limited command requirements (vSNP or BovTB) or by a straightforward data representation (vSNP and MTBseq) which could allow for more inexperienced users to access the bioinformatics analyses. In addition, the inclusion of additional analyses, such as antibiotic resistance profiling and cluster analysis (MTBseq), detection of INDELs and Regions of Difference (SNiPgenie), or lineage definition  could also be of interest for certain studies. However, in a similar manner to pipeline parameters, there is currently no standardized M. bovis , 49, effpolymers . TherefoM. bovis AF2122/97 as a scaffold for the generation of simulated genomes meant that there were no sample-specific deletions, and therefore the capacity of these pipelines in calling SNPs near deletion events could not be evaluated. Furthermore, as is the case in human TB with M. tuberculosis, the choice of reference genome could also have an important effect in the WGS analysis of aTB due to differences in gene content between lineages, which could be masked by an inappropriate reference selection  complex, or that certain genomic deletions may occur independently, such as the RD900 deletion. This study focused on the use of M. bovis AF2122/97, an Eu1 complex strain which is the default genome used by the evaluated aTB pipelines and the most extensively used M. bovis reference genome. However, the use of this reference genome in regions in which other clonal complexes are prevalent, such as the African 1 in western Africa, may lead to a loss of phylogenetic information. Therefore, other reference genomes may be better suited for different countries or regions and should be evaluated in the future.Furthermore, the use of election . This coFinally, it is important to note that, unlike other pipelines, manual and visual curation of SNPs is an important component of vSNP's design and functioning. As a result, a more detailed evaluation of this pipeline's results may have led to a reduced number of inconsistencies but would have added subjectivity to this comparison and was therefore avoided.In conclusion, despite the above-mentioned limitations, the results of our comparison show that all evaluated pipelines perform well as long as similar hard filters are used, with minor differences amongst them with regard to performance and phylogenetic inference. This highlights the importance of standardizing and appropriately annotating filtering files when analyses are carried out between different laboratories or countries, and in particular in the context of aTB disease control.https://doi.org/10.5281/zenodo.5179838, 5179838; https://github.com/Viloleal/bTB-pipeline-comparison-data-and-tools, None.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: VL-L was involved in conceptualization, data curation, formal analysis, investigation, software, validation, and writing of the original draft. DF was involved in conceptualization, data curation, methodology, software, validation, and review and editing of the original draft. BR and LJ were involved in conceptualization, funding acquisition, and review and editing of the original draft. JÁ was involved in conceptualization and resources and review and editing of the original draft. SG was involved in conceptualization, funding acquisition, resources, validation, supervision, and review and editing of the original draft. All authors contributed to the article and approved the submitted version.The author VL-L was funded with a predoctoral grant from the Complutense University of Madrid and Banco Santander 2017–2018 (CT17/17—CT18/17). This research project was made possible thanks to an international fellowship grant from the Complutense University of Madrid and Banco Santander 2020 (EB25/20). DF and SG acknowledge funding from the Department of Agriculture Food and the Marine (DAFM) award 2019R404 (BTBGenIE). This project was partially funded by the EU-RL for Bovine Tuberculosis.SNiPgenie was developed by DF and SG. 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Genotype data were obtained for ~440,000 SNPs from 343 border collies (168 BCC cases and 175 controls). h2SNP was calculated to be 49–61% depending on the estimated BCC prevalence. A total of 2407 SNPs across the genome accounted for nearly all the h2SNP of BCC, with an estimated 2003 SNPs of small effect, 349 SNPs of moderate effect, and 56 SNPs of large effect. Genome-wide association analyses identified significantly associated loci on chromosomes 1, 6, 11, 20, and 28, which accounted for ~5% of the total BCC h2SNP. We conclude that BCC is a moderately- to highly-heritable complex polygenetic disease resulting from contributions from hundreds to thousands of genetic variants with variable effect sizes. Understanding how much the BCC phenotype is determined by genetics and whether major gene mutations are likely to exist inform veterinarians and working/stock dog communities of the true nature of this condition.An episodic nervous system disorder triggered by strenuous exercise, termed border collie collapse (BCC), exists in border collies and related breeds. The genetic basis of BCC is unknown but is believed to be a complex genetic disorder. Our goal was to estimate the heritability (h An episodic nervous system disorder triggered by strenuous exercise, termed border collie collapse (BCC), exists in border collies and border collie mixes. BCC is recognized worldwide in dogs used for working livestock, participating in agility and fly-ball competitions, or repeatedly retrieving a ball. Individuals susceptible to BCC can show clinical signs after 5 to 15 min of strenuous activity, particularly in warm weather ,3,4. TheDNM1 gene mutation responsible for a phenotypically dissimilar form of an exercise-induced collapse in Labrador retrievers and related breeds , whereas 14.5% (n = 349 SNPs) were of moderate effect [10 × 10−3 × genetic variance], and 2.3% (n = 56 SNPs) were of high effect [10 × 10−2 × genetic variance]. The model estimated that these 2407 SNPs accounted for virtually all the h2SNP of BCC, with the 2003 SNPs of small effect accounting for 21.0% of the genetic variation, the 349 SNPs of moderate effect accounting for 36.9% of the genetic variation, and the 56 SNPs of large effect accounting for 34.5% of the genetic variation. The proportion of genetic variation explained by the SNPs on the array was then assessed [−3) was explained from SNPs located on chromosomes 11, 28, and 33 with a small percentage of the genetic variance (posterior variance >5.0 × 10−4 and <1.0 × 10−3) explained by SNPs on chromosomes 1, 6, 8, 9, 13, 15, and 17  were included as fixed effects due to regional breeding preferences and distinct population stratification which has been previously reported between show and working dog lines . Array w results .2SNP due to SNPs in linkage disequilibrium. When the results obtained with the wGRM were compared to those with the standard GRM we found that the estimates only differed by 2%, suggesting that linkage disequilibrium had a minimal impact on our estimates  were predicted to have a small effect whereas the remaining 405 SNPs were predicted to have a moderate or high effect, which is consistent with the genetic architecture of complex traits [Knowledge of the genetic architecture of a trait is an important step for determining appropriate genomic methods for identification of the specific causal variants, as well as understanding the number of variants having a moderate to high impact, as these have the most relevance for predicting an individual’s genetic risk and are more likely to result in targeted therapies. BayesR determines the genetic architecture of a trait by assigning priors to bins representing SNPs of null, low, moderate, or high effect. Gibbs sampling is then used to determine the posterior probability that an SNP is assigned to each bin, and the final model parameters are determined based on the posterior mean across iterations. We determined that virtually all of our conservative estimates of hx traits . Thus, tSimple dominant or recessive (single-gene Mendelian) traits, with heritabilities approaching 1.0 (100%), can frequently have their causative mutations identified by GWA studies to define the causative locus, combined with Sanger or whole-genome sequencing (WGS). Due to limitations in sample population sizes and structure, and in some cases SNP genotyping array densities, our ability to investigate canine complex/polygenic genetic diseases with moderate heritabilities is far more limited when using software programs optimized for human data (sample sizes in the thousands with reduced relatedness). Thus, it is not surprising that we were not powered to detect loci exceeding our genome-wide significant threshold with GEMMA. However, we have previously described an improved linear mixed model for the analysis of complex traits with small sample sizes . This ap−3) was explained from SNPs located on chromosomes 11, 28, and 33 with a smaller percentage (posterior variance >5.0 × 10−4 and <1.0 × 10−3) explained by SNPs on chromosomes 1, 6, 8, 9, 13, 15, and 17. Combined, these loci explained 41% of the total h2SNP. Intriguingly, the majority of the SNPs identified on this analysis were also SNPs that exceeded the suggestive or genome-wide threshold in the GWA using the improved linear mixed model, accounting for approximately 5% of the total h2SNP. The SNP on chromosome 33 had the largest individual SNP posterior variance (~0.07%). This SNP also exceeded the suggestive threshold for our analysis on GEMMA; however, single SNP regions are often spurious associations and further analysis is required to determine its validity.In BayesR, the objective is not to estimate individual SNP effects, but to determine the number of SNPs contributing to a phenotype based on a distribution of probabilities that an SNP falls into one of four effect sizes. However, a recent modification of the BayesR code allows for the extrapolation of the posterior variance of the per-SNP effects at each iteration, providing an estimate of the percentage of total genetic variance explained by each SNP. In our dataset, the largest percentage of genetic variance  the presence of the true quantitative trait loci (QTL) on the genotyping platform, (2) the effect size of the QTL, and (3) the number of SNPs in linkage disequilibrium with the QTL. Further caution must be taken when comparing analyses across different approaches, particularly Bayesian and frequentist statistics. Nevertheless, this provides compelling evidence for prioritizing further exploration of the LD-defined regions identified on chromosomes 6, 11, and 28 as these are likely harboring variants of high effect.2SNP in an Australian shepherd population of 41 cases and 125 controls. Our estimates were similar to those found in the border collies, albeit with large standard errors due to the small population size in this cohort (Episodic collapse, identical or highly similar to BCC, also exists in border collie mixes, and related breeds, including Australian shepherds, kelpies, bearded collies, Shetland sheepdogs, and whippets. SNP data have demonstrated the close genetic relationship of these breeds to border collies , supports cohort . AlthougOur descriptions of typical signalment, clinical features, laboratory criteria ,2,3,4,5,BCC is a common condition affecting the health and well-being of many related herding breeds that work stock, participate in agility and fly-ball competitions, or repeatedly retrieve a ball. Our analyses determined that the heritability of BCC in border collies is in the range of 49–61% and that hundreds of alleles of moderate to large effect contribute to BCC risk. This makes BCC a moderately- to a highly heritable complex genetic disorder, where many genetic variants, in combination with environmental factors, contribute to the likelihood of developing the condition.