Pinus flexilis) allow us to characterize NLR genes and related resistance gene analogs (RGAs) in host resistance against Cronartium ribicola, the causal fungal pathogen of white pine blister rust (WPBR) on five-needle pines throughout the world. We previously mapped a limber pine major gene locus (Cr4) that confers complete resistance to C. ribicola on the Pinus consensus linkage group 8 (LG-8). However, genetic distribution of NLR genes as well as their divergence between resistant and susceptible alleles are still unknown.Proteins with nucleotide binding site (NBS) and leucine-rich repeat (LRR) domains (NLR) make up one of most important resistance (R) families for plants to resist attacks from various pathogens and pests. The available transcriptomes of limber pine  and genetic mapping, a total of 541 SNPs from 155 genes were mapped across 12 LGs. Three putative NLR genes were newly mapped in the Cr4 region, including one that co-segregated with Cr4. The tight linkage of NLRs with Cr4-controlled phenotypes was further confirmed by bulked segregation analysis (BSA) using extreme-phenotype genome-wide association study (XP-GWAS) for significance test. Local tandem duplication in the Cr4 region was further supported by syntenic analysis using the sugar pine genome sequence. Significant gene divergences have been observed in the NLR family, revealing that diversifying selection pressures are relatively higher in local duplicated genes. Most genes showed similar expression patterns at low levels, but some were affected by genetic background related to disease resistance. Evidence from fine genetic dissection, evolutionary analysis, and expression profiling suggests that two NLR genes are the most promising candidates for Cr4 against WPBR.To identify NLR genes at the Cr4 locus as well as a set of NLR variants for marker-assisted selection in limber pine breeding. Novel NLR genes were identified at the Cr4 locus and the Cr4 candidates will aid deployment of this R gene in combination with other major/minor genes in the limber pine breeding program.This study provides fundamental insights into genetic architecture of the The online version contains supplementary material available at 10.1186/s12864-021-07885-8. The development of genomic resources potentially offers new avenues for speeding the development of resistant populations for restoration of tree species affected by highly virulent pathogens. Several next generation sequencing (NGS) approaches have been developed and widely used for the identification of genomic regions of interest: including whole-genome sequencing (WGS), whole-exome sequencing (WES), and targeted genomic sequencing (TS) , 2. CompPinus flexilis) is a keystone species in ecosystems of high elevation in western North America. However, it is highly susceptible to infection by Cronartium ribicola, a non-native, invasive fungal pathogen that causes white pine blister rust (WPBR) on native five-needle pines in North America. WPBR is also a serious forest disease in Europe and Asia, but to lesser extent due to a much longer history of co-evolutionary arms races between the pathogen and its host trees. Since its arrival in western North America in the early 1900s, WPBR has led to severe economic losses of several five-needle pine species, including limber pine. In past decades, screening and breeding programs have identified both major gene resistance (MGR) and quantitative disease resistance (QDR) against WPBR. These resistance resources have been employed in plantations and restoration plantings for enhanced resistance in native five-needle pines in both the USA and Canada [Cr1 to Cr4 in sugar pine (P. lambertiana), western white pine (P. monticola), southwestern white pine (P. strobiformis), and limber pine, respectively, in the USA [Cr4 has also been confirmed in seed families in Canada [Limber pine  during white pine-blister rust (WP-BR) interactions, and annotation and interactions of these genes in various biological processes portraying the molecular mechanisms underlying tree defense responses and disease resistance of five-needle pines [P. lambertiana) comprehensively revealed the organization and architecture of a very large conifer genome [P. balfouriana) by restriction site associated DNA sequencing (RADseq) [C. ribicola in sugar pine were shown to be involved in wide biological functions, including disease resistance and morphological and developmental processes, by a combination of genome-wide association study (GWAS) and quantitative trait locus (QTL) analysis [Recent advances in NGS technologies and other related genomics approaches have been applied to understand the genetics of host resistance to le pines –23. Wholr genome , providir genome , 25. Higr genome , 26, fox(RADseq) , and lim(RADseq) . SNPs asanalysis .Cr1, Cr2, and Cr4 were localized on the Pinus consensus LG-2, LG-1, and LG-8, respectively [Cr4 or a locus very close to Cr4 for limber pine MGR in seed families that originated in both USA and Canada [C. ribicola [C. ribicola in five needle pines.ectively , 26, 29.d Canada . These cribicola , 31. AltCr1 and Cr2 loci, few R gene analogs (RGA) of the NLR and RLK families were found to be clustered in the Cr4 locus [C. ribicola.Unlike r4 locus , hamperir4 locus , 33. ConFluidigm custom access arrays were designed for 480 RGASs, which were selected from a limber pine transcriptome shotgun assembly (TAS accession no. GHWC00000000.2), for construction of MiSeq libraries using 96 genomic DNA samples Table . FollowiR gene families were highly polymorphic in the seed family LJ-112. The highest number of SNPs was found in the M428660 gene, and its available sequence encoded a toll/interleukin-1 receptor (TIR) domain. Eight others had high levels of polymorphisms > 40 SNPs/Kb. It would be interesting to know if high levels of genetic polymorphism of the limber pine NLR genes reflect their evolutionary adaptation to abiotic or other biotic factors than C. ribicola, since limber pine was not previously exposed to WPBR prior to the last century.These polymorphic genes revealed SNP frequencies ranging from 2.8 SNPs to 52.5 SNPs per Kb Fig. , indicatPlotting SNP depth against the total SNPs in individual samples showed that about 90% of SNPs had a minimum depth of 10 times in 91 samples Fig. . The remSNPs were filtered for missing data at 10% and high distortion from the expected Mendelian segregation ratio of 1:1 at α ≤ 0.01, generating 728 SNPs of 217 polymorphic genes for genetic mapping Table . These SBecause the same reference transcriptome as described above was used in SNP calling, SNPs were directly compared for their types, nucleotide positions, and genetic mapping locations on the LGs between WES and Fluidigm amplicon-based TS. Compared to genes previously mapped by WES in the seed family LJ-112 , 79 addip < 0.001)  also showed similar consistency. Of 155 genes mapped here in family LJ-112, 82 genes were mapped previously by WES in family PHA-106 . Paired 01) Fig. . The phyCr4 region on the Pinus consensus LG-8 with two SNPs of each gene. M117450 co-segregated with Cr4 while M319779 and M581704 were localized within 4.45 cM of Cr4  by comparing allele frequencies between bulked resistant and susceptible samples. Compared to genetic mapping, significance testing using an extreme-phenotype genome-wide association study (XP-GWAS) detected more genes and SNPs significantly associated with the resistance phenotype, with nine, five, and two SNPs in M117450 (2.24E-05 ≥ p ≥ 4.90E-15), M581704 (1.16E-06 ≥ p ≥ 8.04 E-07), and M319779 (6.49E-20 ≥ p ≥ 9.26E-20), respectively. Although NLRs M257518 and M350981 were not genetically mapped, their SNPs also showed significant association with Cr4-controlled phenotypes , but significance levels were much lower compared to M117450 and M319779  were localized in the 779 Fig. . In addi112 Fig. .Fig. 3FCr4 region, all RGAs closely linked to Cr4 were anchored to the sugar pine genome sequences (v1.5) by syntenic analysis using BLASTn. Of six RGAs in the Cr4 region, one orthologous fragment was detected in the corresponding scaffolds of the sugar pine genome  independently by TS and WES approaches. Consistently, their corresponding orthologous regions were detected in the same scaffold (scaffold_12739) with 23.5 Kb physical distance as aligned to the sugar pine genome draft sequences  of M581704 was previously mapped on LG-2 by WES  were mapped at the WES Fig. . This inWES Fig. . PutativDNA and putative protein sequences of all 9645 gene sequences so far genetically mapped in limber pine populations, including those mapped in this study, as well as those mapped previously by Sequenom- and WES-based SNP genotyping approaches , 28, areCr4-co-segregated M117450 was the most complex with 32 NB-ARC sequences having long branches of divergence of up to 50% amino acid identity.Five main clusters were observed in the CNL subfamily and strongly supported by the bootstrap test, four of which were embedded with at least one rice NB-ARC sequence, indicating their ancient origins before the separation of angiosperms and gymnosperms. In contrast, the limber pine TNL clusters were clearly separated from those of Arabidopsis proteins. No Arabidopsis NB-ARC sequences embedded in any cluster of the limber pine TNL subfamily, suggesting that limber pine TNLs expanded after angiosperms separated from gymnosperms. It is noteworthy that the TNL cluster harboring p < 0.05), which indicated that most limber pine NLR genes (including M117450) were under purifying selection. Paralogous pairs of CNLs showed wider distributions of both the Ka/Ks ratios and values of either Ka or Ks compared to those of TNL paralogous pairs , suggesting that CNLs as a whole may have evolved earlier, but underwent relatively stronger diversifying selection and a faster evolutionary rate than TNLs in limber pine. Paralogous pairs of either TNLs or CNLs on the same LGs had Ka/Ks ratios significantly higher than pairs localized on different LGs   and synonymous (Ks) sites and ratios of Ka/Ks were calculated for each paralogous pairs in the same clusters of the phylogenetic tree. Almost all paralogous pairs except two CNL pairs had Ka/Ks < 1  and two WPBR susceptible families (MRO-3501 and UT-3359A). Most NLR genes were expressed at low levels (RPKM < 5), including the five genes mapped in the 47 and tw.84 Fig. .Fig. 6EC. ribicola, the present study applied an amplicon-based TS approach to re-sequence limber pine RGAs in a mapping seed family using the Fluidigm Access Array. Although several amplicon-based TS approaches are frequently used for SNP genotyping, few case studies have been reported that used Fluidigm Access Array in conifers [To search for candidate genes and new alleles for genetic resistance against conifers . The SNPCompared with the WGS- or WES-based mapping approach, TS is one alternative approach to obtain genotypic data of specific genomic regions more accurately due to greater sequencing depth but with minimal cost . TS thusCr4 candidates through both LG-based fine genetic mapping and NGS-based BSA with statistics of XP-GWAS. Non-consistent SNP pairs were observed in seven reference genes  in comparative genetic mapping, which might be caused by gene fusion, or amplification of multiple paralogous sequences that all align to the same reference gene targeted by one pair of PCR primers [Of more than 300 R genes functionally well characterized in plants, the majority belong to the NLR family . Therefo primers . The TS- primers , 26–28.Cr4 region.It is important to point out that the reference sequences we used for WES and Fluidigm amplicon-based TS were derived from transcriptome assembly of Illumina reads by the Trinity assembler, which is not an error-free process. Despite low levels of mis-assembly as compared to other short-read assemblers, mis-assembly rates of Trinity were 3.69% in Arabidopsis and 2.72% in human , but havCr4 in a genomic region of LG-8 where only a short fragment of a putative NLR gene was mapped [Cr4 region, with further verification by genotyping needle samples using TaqMan arrays and NGS-based BSA with significance test using XP-GWAS. These novel NLR genes and their variants added several Cr4-candidates to the Cr4-linked genes detected previously [Cr4-conferred resistance phenotypes with the fine genetic mapping.Host resistance to pests/pathogens is one of the most important focuses in plant breeding and conservation programs. Identification of functional candidates at R loci allows for comprehensive depiction of resistance mechanisms and evolutionary history, as well as development and application of DNA markers for marker-assisted or genomic selection. Characterization of the NLR family is a straight-forward avenue leading to genomic understanding of plant disease resistance in a species of economic or ecologic importance. Genome-wide NLR identification has in the past depended on gene predictions with available transcriptome and genome sequences. Previous studies mapped s mapped . Most NLs mapped , 38. Dess mapped , 40, ands mapped , 33. Thieviously , 28. MorCr4 candidates, but cannot exclude their potential roles in quantitative resistance against WPBR, or other pathogens as well as environmental stress [Although most NLR family members were actively expressed at low levels, five genes were differentially expressed between resistant and susceptible seed families. Long noncoding RNAs (lncRNAs) affect a series of biological processes by targeting genes of various families with diverse functions in the transcriptional and posttranscriptional mechanisms of gene regulation , 42. Lncl stress , 45, 46.l stress , which il stress , 48. A fCr4 in the absence of WPBR [Cr4 appears to be in Hardy-Weinberg equilibrium in the absence of WPBR, suggesting that there is little to no current directional natural selective pressure on Cr4 in these populations [Of most limber pine NLR genes (including M117450 and M287456), constitutive expression at low levels avoids the fitness cost associated with disease resistance in the absence of pathogens . Consist of WPBR . Cr4 appulations .At present over 9600 functional genes have been genetically mapped in limber pine , 28. Of Cr4 candidates, M117450 resides in a TNL cluster with 32 paralogs. A comparison of Ka/Ks ratios suggests locally organized NLR paralogs might have stronger diversifying selection, with genes originating from tandem duplication. This hypothesis is further supported by a syntenic analysis between limber pine and sugar pine. Because the limber pine genome sequence is not available, we took advantage of the recent release of the sugar pine draft genome [Cr4 region between these two closely related five-needle pine species, which are both highly susceptible to WPBR. At this point, five NLRs from the Cr4 region had orthologs in three distinct sugar pine genomic scaffolds. Each orthologous pair showed the highest degree of similarity to the other, but low similarity to other gene family members, indicating their origin possibly prior to the divergence of the subgenus Strobus.As expected, NB-ARC domain-based phylogenetic analysis divided limber pine NLRs into TNL and CNL subfamilies, with each further subdivided into several clusters. As one of the most promising t genome , and anaCr4 locus contained an NLR gene cluster with high complexity. Strikingly, M117450 and M287456 are predicted to be very near to one another – their orthologs separated by an intergenic distance of only 23.5 Kb in the sugar pine genome. Meanwhile, the low level of sequence divergence with each other implied that they might have evolved by local gene duplication, not by genomic translocation of a different family member. This is not surprising because in a number of angiosperms recent expansion of the NLR family appears to have occurred mainly through tandem duplication rather than ectopic or segmental duplication [Cr4 region provide a potential for accumulation of mutations to create novel R genes or alleles, allowing limber pine to adapt its immune system against an ever-evolving rust pathotypes. During the dynamic “arms race” between plants and pathogens [Cr4 for resistance against WPBR.Genomic collinearity and the genetic architecture revealed by both fine mapping and syntenic analysis demonstrated that the lication , 52. Theathogens , expansiathogens , 55. AltR genes or alleles against a spectrum of rust pathotypes. Undoubtedly, Cr4 is a very valuable MGR locus that confers highly effective resistance to those tested pathotypes. However, breeding for resistance to WPBR is quite challenging in five-needle pines; MGR can be easily overcome by virulent isolates in field tests of western white pine and sugar pine [Cr4 has not been discovered yet, extensive planting of limber pine using the Cr4 genotypes over a long time period would accelerate the proliferation of virulent C. ribicola races should they evolve. Thus, there is an urgent need to identity novel R genes, especially those with broad-spectrum resistance, and QDR loci in the host trees as well as the pathotypes targeted by them, making it possible to breed five-needle pines carrying different R genes for sustainable WPBR management. Until these genetic resources are available, management strategies to minimize the proliferation of a virulent rust strain to Cr4, should recommend that limber pine planting stock include a mix of both Cr4 and susceptible seedlings [To restore WPBR-disturbed ecosystems at high elevations where limber pine is the keystone species, it is necessary to plant seedlings carrying a set of gar pine , 57. Alteedlings , 59.Cr4 in wide ranging geographical regions indicates its complicated genetic background [Cr4-linked markers shared by different germplasms for development of molecular selection tools for wild stands. The present study detected sequence variations of Cr4 candidate alleles, useful for tracing Cr4 origin in limber pine populations. Genetic dissection of WPBR resistance would provide information and materials for a future study to develop tools and strategies for marker-assisted selection and genomic selection, facilitating the improvement of limber pine resistance to WPBR.The existence of ckground , 17, 30,Cr4/cr4) for major gene (Cr4) resistance to C. ribicola by segregation analysis of the Cr4-controlled canker-free trait in its progeny populations [C. ribicola basidiospores in September, 2014 at DGRC following a well-established protocol [Seed trees of families LJ-112, GE213, CH125, CH130, and PS1383 were identified as limber pine by Dr. Anna W. Schoettle’s research team. Seeds were collected in 2003 and voucher seed samples are stored at USDA Forest Service, Rocky Mountain Research Station. The International Union for Conservation of Nature (IUCN) lists limber pine as ‘least concern’. USDA Forest Service researchers are allowed to collect limber pine seeds from non-listed species without a permit on federal lands. Experimental research and field studies on limber pine in this work, including seed collection, complied with relevant institutional, national, and international guidelines and legislation. Open-pollinated seed family LJ-112 was used for targeted amplicon sequencing of the limber pine R families encoding NLR proteins and RLKs. The seed tree of family LJ-112, from northern Colorado , was previously identified with a heterozygous genotype , were included as diploid controls. Genomic DNA was extracted from megagametophyte and needle tissues using a DNeasy Plant Mini kit (QIAGEN). A set of 480 limber pine RGAs, including 474 NLR-encoding genes and six LRR-RLK-encoding genes  and of sugar pine  [Haploid megagametophyte samples from 91 seedlings of seed family LJ-112 were used for targeted amplicon sequencing. Needle tissues of five roteins) , 61. Sugroteins) . PCR wasIllumina MiSeq reads were demultiplexed using the sample-specific barcodes and trimmed for removal of the Fluidigm Access Array barcodes with a quality score of 0.05. A SNP calling pipeline as described previously  was used2 test and a check for missing data prior to mapping analysis. Genotypic segregation of SNP loci was tested for Mendelian ratio of 1:1 by X 2 (α = 0.05). SNP loci were filtered by significant segregation distortion (P < 0.01) and missing data at 10% for initial genetic mapping analysis. Because over 80% of all the SNPs had missing data levels of less than 10%, SNPs with > 10% of missing data were added later in the mapping analysis. In addition to the SNPs of the NLR family detected in the present study, other DNA markers available from previous studies  or different seed families (LJ-112 and PHA-106) and visualized using CIRCOS . RGAs maCr4 candidates. The Res-pool has MiSeq reads from 52 resistant seedlings, and the Sus-pool had MiSeq reads from 39 susceptible seedlings. Clean reads were mapped to the reference of RGA sequences using CLC genomics workbench (v5.5) with setting at mismatch cost = 2, insertion cost = 3, deletion cost = 3, length fraction = 0.9, similarity fraction = 0.95, auto-detect paired distances = yes, global alignment = yes, and non-specific match handling = ignore. SNPs were called by quality-based variant detection with settings at neighborhood radius = 5, maximum gap and mismatch count = 2, minimum neighborhood quality = 15, minimum central quality = 20, ignore non-specific matches = yes, ignore broken pairs = no, minimum coverage = 4, minimum variant frequency (%) = 1.0, and maximum expected alleles = 2. A total of 5608 SNPs of 354 RGAs were detected in both Res-pool and Sus-pool with coverage > 50 in each pool, and used for the significance test using extreme-phenotype genome-wide association study (XP-GWAS) [Cr4-candidates were predicted with difference of allele frequencies close to 0.5.NGS-based bulked segregation analysis (BSA)  was usedXP-GWAS) , 71. TheCr4 candidates were selected for verification of their SNPs by TaqMan assays  and two susceptible seed families (MRO-3501 and UT-3359A) were downloaded from GenBank (SRA accession numbers SRR3273741-SRR3273743) . Clean rhttps://www.ebi.ac.uk/Tools/hmmer/search/hmmscan). Predicted amino acid sequences of the conserved NBS domain (Pfam 00931) with a minimum length of 150-amino acids were aligned using Clustal Omega [Arabidopsis thaliana and Oryza sativa NB-ARC (PF00931) sequences were retrieved from the Pfam database at EMBL-EBI; and those sequences shown as top-hits when queried by limber pine NB-ARC sequences in BLASTp analysis were included in the phylogenetic analysis. Reliability of the interior nodes of the phylogenetic tree was evaluated by bootstrap analysis with 100 replicates. NLR clusters comprising multiple paralogous sequences were used to determine the synonymous (Ks) and non-synonymous (Ka) substitution rates for each paralog pair.BLASTp analysis was used to determine domain presence of the mapped limber pine RGAs by searching against the NB-ARC domain dataset (Pfam: PF00931) downloaded from the UniProtKB sequence database  and furtal Omega . Based oal Omega , 75. In .ParaAT (v 2.0) was used to align nucleotide and protein sequences for each pair of paralogs . The aliAdditional file 1: Fig. S1. Distribution of minor allele frequency (MAF) of SNP loci detected in limber pine resistant gene analogs (RGAs). Fig. S2. SNP frequencies of limber pine resistance gene analogs (RGAs). Fig. S3. Sequencing depth of SNP loci in individual samples. The percentages of total SNPs (x-axis) have a coverage of a certain depth (y-axis). SNP depth was assessed in each individual of seed family LJ-112. The plot displays data for SNPs (967) with MAF ≥ 0.3 and about 70% of them have a minimum depth at 10 x in all samples except one. Fig. S4. The extent of missing data for 967 SNP loci in 96 individual samples of seed family LJ-112. Individual samples with missing data were calculated as a percentage of the total (y-axis) and plotted across the cumulative total SNPs (x-axis). Over 80% of total SNPs showed missing data in less than 10% of total individual samples. Fig. S5. Correlation of SNP positions of the same NLR gene mapped on the same linkage groups (LGs) by both Fluidigm amplicon-bases targeted-seq (TS) and whole exome-seq (WES). (a) Comparison of TS and WES in seed family LJ-112; (b) comparison of TS and WES between seed families LJ-112 and PHA-106. Fig. S6. Physical distances (bp) of paired SNPs mapped by Fluidigm amplicon-bases targeted-seq (TS) and whole exome-seq (WES). Fig. S7. Identification of NLR alleles significantly associated with MGR-conferred phenotypes using extreme-phenotype genome-wide association study (XP-GWAS). (a) Quantile–quantile plot of the test statistic: 5608 SNPs detected in 354 RGAs with coverage > 50 were subjected to association analysis. (b) Manhattan plot: top SNPs were selected for each genes and plotted against genetic maps of 12 linkage groups, and those genes not mapped so far were included as a separate group. Significant threshold value (p = 1.69 × 10− 4) is presented by a horizontal dash line.Additional file 2: Table S1. PCR primers and gene sequences for amplicon-based targeted sequencing of limber pine resistance gene analogs (RGAs). Table S2. Summary of targeted-seq of limber pine RGAs and their SNPs detected for genetic map construction in the seed family LJ-112. Table S3. RGAs and their SNPs mapped by amplicon-based targeted genomic sequencing in seed family LJ-112. Table S4. Summary of functional genes and their representative SNPs genetically mapped in seed family LJ-112 in the present and previous studies. Table S5. Oligonucleotides designed for TaqMan-based SNP genotyping. Table S6. Syntenic analysis of Cr4-linked NLRs with sugar pine genome sequences. Table S7. DNA and protein sequences of functional genes so far genetically mapped in limber pine seed families.


Interactions of single nucleotide polymorphisms (SNPs) and environmental factors play an important role in understanding complex diseases' pathogenesis. A growing number of SNP-environment studies have been conducted in the past decade; however, the statistical methods for evaluating SNP-environment interactions are still underdeveloped. The conventional statistical approach with a full interaction model with an additive SNP mode tests one specific interaction type, so the full interaction model approach tends to lead to false-negative findings. To increase detection accuracy, developing a statistical tool to effectively detect various SNP-environment interaction patterns is necessary.SNPxE, a SNP-environment interaction pattern identifier, tests multiple interaction patterns associated with a phenotype for each SNP-environment pair. SNPxE evaluates 27 interaction patterns for an ordinal environment factor and 18 patterns for a categorical environment factor. For detecting SNP-environment interactions, SNPxE considers three major components: (1) model structure, (2) SNP’s inheritance mode, and (3) risk direction. Among the multiple testing patterns, the best interaction pattern will be identified based on the Bayesian information criterion or the smallest p-value of the interaction. Furthermore, the risk sub-groups based on the SNPs and environmental factors can be identified. SNPxE can be applied to both numeric and binary phenotypes. For better results interpretation, a heat-table of the outcome proportions can be generated for the sub-groups of a SNP-environment pair.https://github.com/LinHuiyi/SIPI).SNPxE is a valuable tool for intensively evaluate SNP-environment interactions, and the SNPxE findings can provide insights for solving the missing heritability issue. The R function of SNPxE is freely available for download at GitHub (The online version contains supplementary material available at 10.1186/s12859-021-04326-x. It is well known that genetic factors or environmental risk factors alone are not sufficient to explain the complexity of disease causality. It has been shown that gene-environment interactions play an important role in the etiology of complex diseases –6. SpeciMissing heritability of complex diseases is a well-known unsolved problem for genetic association studies. Using cancers as an example, the genome-wide association studies (GWAS) have successfully identified many inherited genetic variants or single nucleotide polymorphism (SNPs) associated with cancer risk and prognosis during the past decade. The majority of GWAS focuses on identifying SNP individual effects, but the GWAS-identified SNP individual effects can only explain a small portion of variations in complex diseases . For addThe conventional statistical method for testing SNP-environment interactions associated with a phenotype is the full interaction model with an additive SNP, an environmental factor, and their interaction (Full_AE_oo) –21. The This SNPxE is a new method that integrates the model-based and pattern-based search for testing SNP-environment interactions. The interaction is tested based on the significance of the interaction term in the model. The interpretation of these interaction patterns can be visualized using the 3 × 3 heat-tables. In SNPxE, the environmental factor can be an ordinal variable (such as low/medium/high level) or a categorical variable . In practice, many environmental factors are continuous in nature but are treated as an ordinal variable because of the similar impact of some values or easy interpretation purpose. Examples of ordinal environmental factors are cigarette smoking and heavy metal exposure levels (high/medium/high). The SNPxE interaction patterns are developed based on 3 major components: 1) model structure, (2) SNP’s inheritance mode, and (3) risk direction. The labels of these SNPxE patterns reflect these three components  are considered for a categorical environment factor.p = 3.6 × 10−4) per G allele for subjects in the Env1 = 2 group compared with the reference group. In addition, the effect of the additive G-allele effect was also significant for the Env1 = 3 group . For Fig. p = 6.0 × 10−11) for low environment level, and OR = 1.8, 95% CI = 1.2–2.7, and p = 5.3 × 10−3 for the medium environment level).In the 3 × 3 heat-tables, the outcome proportions are shown for the 9 sub-groups based on the selected SNP and environment factor status. For variable reduction and increase detection power, SNPxE selects the best interaction pattern among the designed patterns, allowing the sub-groups with similar risk profiles or a small sample size to be combined. The interpretation of the 27 SNPxE interaction patterns for an ordinal environment factor or 18 patterns for a categorical environment factor is shown in Additional file p-value of the interaction term. The BIC approach is the default method, because a parsimonious pattern is preferable for result generalization. This SNPxE function can adjust for continuous or categorical factors in modeling. In addition to the ‘SNPxE’ function, there are four related functions  that can be used in gene-environment interaction association studies. For an SNP-environment pair with a binary outcome, the “GridSNPxE” function can generate outcome proportions by combining a given SNP and environmental factor. The “plotSNPxE” function can generate a corresponding heat-table of the outcome proportions for better visualization. The “MAFinfo” function provides useful SNP information, including major and minor alleles, minor allele frequency/percentage, and missing value percentages. When identifying promising SNP-environment interactions, it is important to compare SNP-environment interactions with SNP individual effects. The “SNPmain” function can be applied to test a SNP associated with a phenotype by considering three inheritance modes . For better demonstration and practice purposes, an example dataset ‘simData2’ developed based on a real dataset is included in the SIPI R package. The example codes and outputs of this example are listed in Additional file https://github.com/LinHuiyi/SIPI.For SNPxE, the outcome can be a binary or continuous variable. For a continuous outcome, the linear-based SNPxE based on linear regression will be used. For a binary outcome, the logistic-based SNPxE based on logistic regression will be applied. The environmental factor can be an ordinal or categorical variable. The best pattern within each SNP-environment pair can be selected based on the smallest value of the Bayesian information criterion (BIC) or the smallest p-values of the 27 interaction patterns are listed in Table p-value = 0.425). The other two full interaction models were also not significant: Full_DE_oo (p = 0.905), and Full_RE_oo (p = 0.157). However, the p-value of SNP5-Env based on the SNPxE approach was 0.023 with the Int_RE_ro pattern. The p-values of the 27 patterns are in a wide range of 0.012–0.998. This example demonstrates that the selection of testing patterns plays an important role in testing SNP-environment interactions.Using the ‘SimData2’ dataset as an example, the outcome is the binary disease status (yes/no) with a sample size of 2000. The potential predictors are 5 SNPs snp1-snp5) and an environmental factor, an ordinal variable with three levels . We want to evaluate interactions between this environmental factor and the 5 SNPs associated with disease status. For a binary outcome, the logistic-based SNPxE was applied. The best pattern within each SNP-environment pair was based on the smallest BIC among the 27 interaction patterns. Using the interaction of SNP5 and the environmental factor associated with disease (SNP5-Env) as an example, the -snp5 andp < 0.01 are SNP2-Env_g3 (p = 0.006) and SNP3-Env_g3 (p = 0.0001) among the 5 interaction pairs. For the SNP2-Env_g3 interaction  per G allele for subjects in the env_g = 3 group compared with the reference group. However, the G-allele additive effect was not significant for the env_g = 2 group (p = 0.209). For the SNP3-Env_g3 interaction .For multiple comparison justification, the significance level of 0.01 (= 0.05/5 pairs) was applied based on the Bonferroni correction when testing 5 SNP-environment interaction pairs associated with the disease outcome. As shown in Table p = 0.576) but was significant for SNP3 . The SNP3 individual effect can be observed in Fig. p = 0.576 for SNP2 and p = 0.0005 for SNP3) and the environmental factor (p = 0.418). The disease prevalence by the status of the SNP and the environmental factor for these two pairs are listed in Fig. We further compared the performance of these two SNP-environment interaction pairs with the individual effect of their constituent SNPs and the environmental factor. The SNP individual effect associated with outcome was not significant for SNP2 , this complicated pattern is likely to be simplified to an interaction-only pattern (such as Int_AE_oo) due to a small sample size. For addressing this challenge, SNPxE has the flexibility to detect interaction signals by searching different patterns. The external validation using independent data is encouraged to verify the SNP-environment interaction and patterns. In addition, the individual effects of SNPs and environmental factors can influence the significance of the interaction terms, so it is important to compare them for identifying promising SNP-environment interactions.One limitation of SNPxE is that it does not search for all possible interaction patterns. Computation efficiency is an important issue in genetic association studies because of high dimensional data. Thus, it is not feasible to test all possible patterns within a SNP-environment pair, especially for testing thousands of SNPs. To increase detection power with computation feasibility, the design of SNPxE is to consider the 27 or 18 key biological meaningful interaction patterns associated with an outcome. For result interpretation, the point estimates of outcome proportions are shown in the heat-table so that users can get a close look at the risk profile of these sub-groups. When evaluating the similarity of their risk profile, the variances of outcome proportions should be considered. The sub-groups with a small sample size have a large variance. In addition to the SNPxE function, the SIPI R package includes other functions for testing SNP-environment interactions, including ‘GridSNPxE’, ‘plotSNPxE’,’ MAFinfo’, and ‘SNPmain’. This toolset can be used to visualize SNP-environment patterns, detect major/minor alleles, calculate minor allele frequency, and test SNP individual effects. Thus, we believe that the SNPxE related software provides a valuable statistical tool for gene-environment interaction studies.https://github.com/LinHuiyi/SIPI. Operating system(s): Platform independent. Programming language: R. Other requirements: SIPI requires the following R packages: Survival, mvtnorm, car, carData, lmtest, zoo, ggplot2, ggpubr. License: GNU General Public License v3.0. Any restrictions to use by non-academics: None.Project name: SNPxE. Project home page: Additional file 1. Supplementary Table S1 and Figures S1–S2.


Colossoma macropomum (tambaqui) and Piaractus mesopotamicus (pacu), the key native freshwater fish species produced in South America. The main objectives of this study were to design a dense SNP array for this fish group and to validate its performance on farmed populations from several locations in South America. Using multiple approaches based on different populations of tambaqui and pacu, a final list of 29,575 and 29,612 putative SNPs was selected, respectively, to print an Axiom AFFYMETRIX (THERMOFISHER) SerraSNP array. After validation, 74.17%  and 71.25%  of SNPs were classified as polymorphic variants in pacu and tambaqui, respectively. Most of the SNPs segregated within each population ranging from 14,199 to 19,856 in pacu; and from 15,075 to 20,380 in tambaqui. Our results indicate high levels of genetic diversity and clustered samples according to their hatchery origin. The developed SerraSNP array represents a valuable genomic tool approaching in-depth genetic studies for these species.Scarce genomic resources have limited the development of breeding programs for serrasalmid fish  Serrasalmids (round-shaped fish) are a diverse group comprising 101 valid species, represented by a variety of feeding strategies and associated morphological adaptations3. While piranhas are popularly known for voracious and carnivorous behavior, tambaquis and pacus are omnivorous, possessing special teeth to eat fruit and seeds, and have a great ability to harness natural food sources (zooplankton). Beyond their feeding habitats, these species have excellent traits of economic interest for aquaculture including high growth performance, ease of reproduction and market appreciation6.Tambaquis, pacus and piranhas are included in the family Serrasalmidae (Ostariophysi: Characiformes)Colossoma macropomum), pacu (Piaractus mesopotamicus) and pirapitinga (Piaractus brachypomus) are the major serrasalmid fish produced by the aquaculture industry in South America4. Tambaqui and pirapitinga are mainly farmed in the Amazon region , while pacu is produced at higher latitudes in South America (southern Brazil and northern Argentina). These species have also been introduced for farming purposes in several countries in Asia, including China, Indonesia, Malaysia, Myanmar and Viet Nam7. About 142 thousand tonnes of tambaqui was produced in 2016, of which 96.4% was produced by Brazil (approximately 137 thousand tonnes)8. Pacu is also primarily produced in Brazil, representing the second largest native fish species production in the country, with 12 thousand tonnes produced in 20199.Tambaqui  for tambaqui16 and RADseq (Restriction site-associated DNA sequencing) for pacu17, create new opportunities for the application of molecular markers to uncover the genetic basis of economically and ecologically important traits.Despite the potential for serrasalmid species in aquaculture, their production remains entirely based on genetically unimproved stocks, and there is enormous untapped potential for selective breeding in these species. Traditional pedigree-based breeding programs have only recently been initiated for these species19. In addition, the use of genomic selection has the potential to increase genetic gain21 and, consequently, boost emerging breeding programs of new species. A few GWAS have been performed for serrasalmid species. For example, suggestive Quantitative Trait Loci (QTLs) and genes have been associated with resistance against an important bacterial pathogen in pacu17, and to a lack of intermuscular bones in tambaqui22. One of the major limitations for application of GWAS and genomic selection in serrasalmid fish is the lack of a standard high-throughput and robust genotyping assay. Dense single nucleotide polymorphism (SNP) arrays have been developed for popular farmed fish species, such as Atlantic salmon24, rainbow trout25, coho salmon26, catfish27, tilapia28 and carp29. These arrays have been broadly used to study traits of economic importance and to apply genomic selection in aquaculture breeding programs31.Genome wide association studies (GWAS) are considered useful tools for dissecting complex traits in both natural and farmed populationsC. macropomum and P. mesopotamicus, using SNPs from multiple sources including different populations from commercial broodstocks and breeding programs. The SerraSNP array was developed using AFFYMETRIX Axiom (THERMOFISHER) technology and validated in samples from various populations of pacu and tambaqui. This dense SNP array was also tested in a closely related serrasalmid (P. brachypomus) to assess its potential use in other commercial species.In the present study, the main objective was to design and evaluate a multi-species SNP array for two key serrasalmid fish of economic importance, 16 passed the SNP filtering steps and were included in the final array design  and 13,046 SNPs (62.7%) were located into transcribed regions in pacu and tambaqui, respectively (Supplementary Table Piaractus brachypomus) was also tested. The samples passed the QC call rate when using a 94% threshold in the 30K SNP dataset of pacu, which demonstrates a high SNP conversion rate across this species. However, the polymorphic rate was much lower compared to pacu, resulting in 3042 SNPs   and pacu (RADseq17) revealed high and positive values for the relative amount  and density   and a range of r2 pruning thresholds were applied (from 0.1 to 0.9) to determine the number of markers remaining after each filtering step in each population of both species. Thus, we have a picture of the number of SNPs with different levels of redundant genotypic information. Generally, different profiles of increasing numbers of markers, at increasing levels of LD pruning, can be detected in populations of pacu. For instance, the lowest and the highest number of pruned SNPs from pacu hatchery1 and hatchery3 were at different levels of LD. Pacu hatchery2 and hatchery4 showed similar numbers of SNPs pruned at different LD levels. Assuming a r2 > 0.8, which is considered a strong LD threshold, the number of informative SNPs showing non-highly redundant genotypic information ranges from about 9K (hatchery3) to 19K (hatchery1), depending on the population of pacu analysed. In contrast, the number of pruned SNPs at different levels of LD from tambaqui populations, showed a similar trend between the different populations analysed. Assuming a r2 > 0.8, the number of informative SNPs showing non-highly redundant genotypic information ranges from about 11K (hatchery6) to 15K (hatchery5), depending on the population of tambaqui analysed.Due to the absence of a reference genome at chromosome level (at least not published yet) for pacu and tambaqui, the genome coverage of the polymorphic SNPs cannot be evaluated in the present study. However, correlation analysis between previously mapped SNPs included on the SerraSNP array and the corresponding linkage map for tambaqui  and also the implementation of genomic selection in fish species35. Although pacu and tambaqui represent the main native fish of continental aquaculture in South America, studies aimed at the incorporation of genome-wide SNP information to boost the breeding programs of these species are scarce. Therefore, the broad utility (multi-species) and open access format of the array presented here will allow the advancement of genomic studies in this fish group and support ongoing and emerging breeding programs in South America.Dense SNP arrays have been shown to facilitate genome-scale studies by allowing the simultaneous evaluation of thousands of SNPs in commercially important fish species, such as Atlantic salmon37, demonstrating the efficacy of our multi-strategy design and stringent filtering steps for robust SNP discovery. A balanced conversion rate was observed across the four main SNP discovery techniques, except the RNA-Seq-derived SNPs with lower conversion rate (33%) in tambaqui, which could be linked to the low coverage sequencing (454 Roche technology) and the small sampling used to generate the RNA-Seq database , or even to the absence of a reference genome resulting in false positive markers23. Otherwise, the previous published GBS-derived SNPs used in tambaqui registered the highest conversion rate (85%), as result of a particular strategy in SNP discovery and validation by using linkage mapping experiments16. In pacu, the larger dataset of candidate SNPs provided to AFFYMETRIX were derived from RNA-Seq, but only 10% were incorporated on the Axiom array. This high discrepancy compared to RADseq and ddRADseq (40–50%) is due to the larger size of the RNA contigs, which resulted in multiple SNPs per contig; therefore, as only one SNP per contig was selected, high drop out of SNPs derived from the RNA-Seq dataset was observed.After assessment of the cluster properties of each of the SNPs in the SerraSNP array, most of the markers on the Axiom platform were designated as high quality and polymorphic, with validation rates of 74.17% and 71.25% in pacu and tambaqui, respectively. This conversion rate is similar to previous SNP arrays developed for other aquaculture species38). Moreover, false positive or failed SNPs may also occur in RNA-seq-derived SNPs because Mendelian errors in pedigreed samples were not possible to be detected by this method and/or due to limitations in the genotyping technology (SNPs located close to exon–intron boundaries).Overall, although a high conversion rate of QC-filtered SNPs was reported, it is likely that a low proportion of false positive SNPs discovered in these sequencing experiments would remain, particularly in the ddRAD and RADseq dataset. This is due to unique potential sources of error and bias in the library preparation protocol, especially related to PCR duplicates  that can lead to downstream genotyping errors  in the latter study were mainly attributed to the RNA-seq technique (53%), which is particularly susceptible to false positive marker discovery23. Moreover, the conversion rate in the Atlantic Salmon array (46%) was relatively low comparing to the SerraSNP array (71–74%), mostly due to the duplicated genome nature in salmonids, in which was necessary to apply special strategies to avoid false positive SNPs, such as effective removal of putative paralogous variants by RR-seq (reduced-representation sequencing) of a haploid fish (conversion rate of 74%).It is also worth noting that the rate of monomorphic SNPs was low, with values of 5.98% and 4.96% in pacu and tambaqui, respectively, especially when compared to the array developed for Atlantic Salmon using similar techniques (12–53%)In the present study, a high fraction of polymorphic SNPs was identified in putative transcribed regions of the genomes of pacu and tambaqui  . For instance, most of the samples used for SNP discovery in tambaqui were from Brazil, creating bias for Colombia and Peru hatcheries. Otherwise, a similar rate of SNPs segregation at population level was detected across the different techniques of SNP discovery, even when using limited sampling source (only 3 families by ddRAD in pacu).vice-versa) or using an additional species (pirapitinga) is low. This situation is comparable to the 250K SNP array for catfish, in which the transferability for different species also resulted in a low number of polymorphic SNPs27. Only 10% of the 30K SNPs for pacu on the SerraSNP array were polymorphic to pirapitinga; thus, the probes designed from pacu sequences could hybridize to the genomic DNA of pirapitinga, but the level of polymorphic SNPs in the latter species was limited. However, as there is no SNP marker data available for pirapitinga to date, the 3K polymorphic SNPs identified are a valuable resource for the application of genomics to population genetic and evolutionary studies, and for selective breeding in in this species; especially given the high values of genetic diversity identified .The conversion rate for using SNP markers between species   and was approved by the Ethics Committee on Animal Use (CEUA numbers 19.005/17 and 19.006/17) of Faculdade de Ciências Agrárias e Veterinárias, UNESP, Campus Jaboticabal, SP, Brazil.The tambaqui samples used for SNP discovery were obtained from broodstocks of different commercial hatcheries in South America and from the breeding nucleus of the Aquaculture Center (CAUNESP) of São Paulo State University (UNESP), Brazil Table .Table 6DThe commercial hatcheries represented ten different populations, of which six were from Brazil, two from Peru and two from Colombia. The samples from the Caunesp breeding nucleus were composed of 29 selected breeders, plus the parents and offspring from 20 full-sib families generated with a hierarchical mating scheme using 6 dams and 17 sires. In total, the 20 full-sib family dataset consisted of 23 parents and 577 F1 individuals (≅ 30 fish per family). The SNP array was validated with 5 different populations of tambaqui . The three from Brazil were the same populations used for SNP discovery Table .The individuals of pacu used for SNP discovery were obtained from the breeding nucleus of CAUNESP and represented 17 full-sib families Table , which w43. Previous published databases using different techniques, such as GBS, RADseq and RNA-Seq (Table DNA extraction was performed using the DNeasy Blood & Tissue kit (QIAGEN). Purified DNA was quantified using the Qubit dsDNA BR Assay kit (INVITROGEN). There is no reference genome available for tambaqui and pacu, therefore, the ddRADseq approaches were used for SNP discovery because they are appropriate for genome reduction and de novo assemblyeq Table , were al43. Briefly, 25 ng of genomic DNA from each individual was double-digested (8 U/reaction) using one of two enzyme combinations, I (NlaIII and MluCI) or II (SphI and MluCI)  Table , and lig44). The subprograms of Stacks were implemented sequentially . The stacks populations module was used to generate genotype output data for the population samples. Initially, reads were demultiplexed and filtered using process_radtags, with parameters that removed reads with uncalled bases, discarded reads with low quality scores and excluded sequences in which barcodes and RAD cutsites were not found. Subsequently, the loci were constructed by de novo methodology due to the absence of reference genomes for both species, using a minimum read depth of three (m = 3). A loci catalog was constructed in cstacks using a subset of individuals for those derived from broodstocks or using the parental individuals for those resulting from family structure. The maximum number of mismatches between sample loci when building the catalog was set to three (n = 3). The sstacks program was used in order to match individual putative loci (constructed by ustacks) against the catalog, followed by the module populations for SNP discovery, with the filter parameters of minor allele frequency (MAF) > 0.05 and genotyping call rate > 0.7. To differentiate putative SNPs from sequencing errors, PLINK 1.9 software45 was used to exclude SNPs using Hardy–Weinberg equilibrium  and Mendelian error rate (me 0.1 for family structure). Moreover, individuals with high missing genotype rates (mind 0.3) were also discarded.After sequencing, data were analyzed using the software package Stacks for de novo SNP identification . The RNA-Seq raw data of tambaqui was obtained from 10 liver samples and 8 muscle and skin samples, all of them collected from random individuals, as described in detail by Ariede et al.14 and Gomes et al.47, respectively. High quality reads were used to assemble a de novo transcriptome using TRINITY v.2.9.1, adopting standard parameters48, such as a kmer value of 25 and contigs longer than 200 bp. The read mapping was performed by BOWTIE2 v.2.3.4.349. Redundancy was reduced using the CD-HIT-EST v.4.6.8 software, clustering sequences with 95% identity50. After mapping, the SAMTOOLS program51 was used to manipulate the BAM files for SNP discovery. The filtering was performed with a quality equal to or higher than 20 (Phred score) and variants with a minimum sequencing depth of 10 reads. Abundant and repetitive SNPs in small areas were excluded. The VCFtools program52 was used to eliminate SNPs with MAF values less than 0.05.The raw sequences from previous RNA-Seq databases were downloaded from the NCBI database (see Data Availability section) and the following steps were performed. The RNA-Seq database of pacu was originated from 36 individuals challenged against the bacteria 16.SNPs from GBS were the same used for linkage mapping in tambaqui described in detail by Nunes et al.p-convert value (probability of a putative SNP locus converting to a reliable assay on the Axiom array) was produced by THERMOFISHER team for each submitted SNP adjacent sequence. Probes were analysed for each SNP side (forward and reverse), and then classified as recommended, neutral, or not recommended, based on p-convert values and wobble criteria .A list consisting of 130,403 and 81,848 putative SNP markers for pacu and tambaqui was provided to THERMOFISHER (AFFYMETRIX) as 71-mer nucleotide sequences, with both alleles at the target SNP highlighted at position 36. A p-convert was selected.The initial analysis performed by THERMOFISHER resulted in a superior number of recommended markers than the total capacity of the Axiom MyDesign custom array. Therefore, a second step of filtering was carried out to obtain the best 30K SNPs for each species. Basically, the data was filtered to achieve only one SNP per RAD/ddRAD locus or RNA contig. When one RAD/ddRAD locus or RNA contig was identified with multiple recommended SNPs, the SNP marker with highest In total, 152 genomic DNA samples  were sent to THERMOFISHER  for genotyping using the SerraSNP array. The results were used to validate the performance of the array and to quantify the number of segregating SNPs in the sampled populations.Raw data consisting of intensity calculations (CEL files) was imported into the Axiom Analysis Suite  for genotype calling and quality control. Samples presenting a QC call rate > 0.97 and quality control (DQC) > 0.82 passed the quality control assessment (following the “Best Practices Workflow” recommended by AFFYMETRIX). Quality control analysis then classified the SNPs into categories according to their clustering performance in relation to Axiom quality control criteria: (i) “polymorphic high resolution” where the SNP passes all QC, (ii) “monomorphic high resolution” where the SNP passes all QC, but only one genotype is detected, (iii) “call rate below threshold” where genotype call rate is < 97%, (iv) “no minor homozygote” where the SNP passes all QC but only two clusters are observed, (v) “off-target variant” where atypical cluster properties arise from variants in the SNP flanking region, and (vi) “other” where the SNP does not fall into any of the previous categories. Only SNPs from categories (i) and (iv) were included and identified as polymorphic for further analyses, as they are most probably reliable and informative SNPs.e) and observed (Ho) heterozygosity; and IBS (Identity by State) analysis followed by multi-dimensional scaling (MDS), discriminant analysis of principal components (DAPC) and linkage disequilibrium (LD) were performed using PLINK 1.9 software45. A predefined tambaqui genome (not published) was adopted as reference to identify SNPs into transcribed regions using BEDOPS v2.4.4053. To evaluate SNPs from RNA-Seq with potential functional effects, the list of polymorphic SNPs selected for the array was annotated with SNPEff v 5.054 using Uniprot proteins database as reference.Descriptive statistics of minor allele frequencies (MAF), expected (H16 and pacu17 using 7192 and 17,453 SNPs, respectively. Of these total, 25.6% and 42.6% of mapped SNPs were included respectively in the SerraSNP array, and were correlated to the linkage maps to evaluate the level of coverage and genetic representativeness of the SerraSNP array for both species.At the moment, there are still no reference genomes at chromosome level available for both species. However, linkage maps were previously developed for tambaquiSupplementary Figure S1.Supplementary Figure S2.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.


HEMK1 (HemK methyltransferase family member 1) is involved in immune system development, and CHL1 (cell adhesion molecule L1 like) has an immunosuppressive effect during stress conditions. The results suggest housing system-specific breeding strategies in order to improve udder health in compost-bedded pack and conventional cubicle barns.New free walk housing systems such as compost-bedded pack barns might positively influence animal welfare. However, udder health can be negatively affected due to the microbial environment in the pack. Udder health depends on many factors, such as the environment, the feed, the pathogen species, and the genetic mechanisms of the cow’s immune system. For a more precise evaluation of udder health, we examined novel traits including specific mastitis pathogens and differential somatic cell fractions in milk. In order to identify possible candidate genes for udder health, a genome-wide association study, including single-nucleotide polymorphisms (SNP) by housing system interactions , was performed. We identified two potential candidate genes for the interaction effect in relation to udder health. The identified potential candidate gene HEMK1 plays a role in the development of the immune system, depending on environmental stressors. CHL1 is regulated in relation to stress level and influences immune system mechanisms. The significant interactions indicate that gene activity can fluctuate depending on environmental stressors. Phenotypically, the prevalence of mastitis indicators differed between systems, with a notably lower prevalence of minor bacterial indicators in compost systems.The aim of the present study was to detect significant SNP (single-nucleotide polymorphism) effects and to annotate potential candidate genes for novel udder health traits in two different farming systems. We focused on specific mastitis pathogens and differential somatic cell fractions from 2198 udder quarters of 537 genotyped Holstein Friesian cows. The farming systems comprised compost-bedded pack and conventional cubicle barns. We developed a computer algorithm for genome-wide association studies allowing the estimation of main SNP effects plus consideration of SNPs by farming system interactions. With regard to the main effect, 35 significant SNPs were detected on 14 different chromosomes for the cell fractions and the pathogens. Six SNPs were significant for the interaction effect with the farming system for most of the udder health traits. We inferred two possible candidate genes based on significant SNP interactions.  Genotype by environment interactions (GxE) in dairy cows have been reported widely ,2,3,4. TIn genomic studies, Lillehammer et al.  and HayeNew animal-friendly “free walk” housing systems, such as compost farming, reduce environmental stress, implying improved animal welfare . HoweverFurthermore, selection on mastitis resistance is hampered due to the trait definitions and the complex polygenic mode of inheritance ,14. HeriStaphylococcus aureus and Escherichia coli, possibly due to the different immune responses of these two pathogens. The highest correlation of 0.77 was detected between Streptococcus dysgalactiae and Streptococcus uberis. Both pathogens belong to the same genus and trigger a similar cascade of immune reactions in the udder [Coagulase-negative staphylococci (CNS) and Corynebacterium sp. (COR), as well as minor pathogens, are of increasing importance [Previous studies indicate that mastitis is caused by different pathogens to which the cow may respond differently . Sørensehe udder . The modportance ,21. Accoportance  suggesteportance .LY6K, LY6D, LYNX1, LYPD2, SLURP1, and PSCA are part of the lymphocyte antigen 6 complex and influence the regulation of PMN [LY75, NR4A2, and ITGB6, are involved in regulating immune response or were regulated during the presence of specific pathogens [RASGRP1 as a potential candidate gene based on differential gene expression studies, considering different levels of pathogen burden.In a previous GWAS without farming interaction effect, dense SNP marker panels were used to infer possible candidate genes playing a role in defense mechanisms against pathogens and mastitis resistance . Possibln of PMN . Furtherathogens . Kurz etathogens  identifiThe aim of the present study was to identify significant main and interaction effects for specific mastitis pathogens and differential somatic cell counts in conventional cubicle and compost-bedded farming systems. Significant SNP markers were annotated with potential candidate genes with the aim of improving mastitis resistance in specific farming systems, taking GxE into account. In this regard, our own software package, allowing GWAS with SNP by housing system effects, was applied.Data for this study considered milk samples from routine milking. No additional experiments were conducted. Thus, in concordance with German animal welfare legislation, no ethical approval was required for this study.Cow milk samples were collected from six selected Holstein dairy cattle herds. The herds were located in the German federal states of North Rhine-Westphalia and Hesse. Herd selection for cubicle and compost-bedded farming systems considered the herd criteria as defined in the collaborative EU FreeWalk project . The casTrait recording spanned the period from October 2018 to April 2019. The final dataset comprised milk samples from 2198 udder quarters from 537 first and second parity Holstein Friesian dairy cows. A total of 44% of the samples represented the compost system, while 56% of the samples represented the cubicle system.Aerococcus sp., Aesculin hydrolyzing streptococci, Candida krusei, Enterococcus sp., Escherichia Coli, Lactococcus sp., Staphylococcus aureus, Streptococcus dysgalactiae, Streptococcus uberis, mold fungus, and Proteus sp.; minor pathogens (MINOR) included Coagulase-negative staphylococci and Corynebacterium sp. The following pathogens were additionally analyzed separately: Coagulase-negative staphylococci (CNS), Corynebacterium sp. (COR), Aesculin hydrolysing streptococci (AESC), and Aerobic bacilli (AER). The pathogens were defined as binary traits. A cow received a score of 1 for the overall group definition (MAJOR or MINOR) or for CNS, COR, AESC, or AER when the respective pathogen was detected in at least one udder quarter, otherwise a score of 0 was assigned. At the individual cow level, 32.9% and 12.6% of all samples were culturally negative in the compost and in the cubicle system, respectively. The prevalence of minor pathogens was 57.2% in compost and 80.6% in cubicle. A fraction of 16.7% of cows kept in compost and 6.1% of cows kept in cubicle had infections with AER. The prevalence of AESC was 5.0% in compost and 4.8% in cubicle.Teats were disinfected with 70% ethanol before milking and the first five squirts of milk were discarded. A quantity of 10 mL milk per udder quarter was examined for specific mastitis pathogens and somatic cell count in the laboratory of Landesbetrieb Hessen, following the DVG guidelines . The speMicroscopic differential cell counting from a 50 mL sample was conducted in the laboratory of the Institute of Animal Breeding and Genetics, University of Gießen, according to the protocols by Sarikaya et al. and Pappenheim ,27. We cp ≥ 10−3) were discarded. After quality control, we considered 43,095 SNPs from 550 genotyped cows (235 genotyped cows from compost and 315 genotyped cows from cubicle). For the ongoing GWAS, 537 cows with genotypes and phenotypic information were included.The 273 Holstein cows from both systems were genotyped based on extracted DNA from hair samples in the laboratory of the veterinary institute (The Center for Molecular Diagnostics), Georg-August-Universität Göttingen, using the Illumina BovineSNP50 Bead Chip V3. The SNP genotypes from the Illumina BovineSNP50 Bead Chip V2 for the 277 cows (compost and cubicle) were provided by the Vereinigte Informationssysteme Tierhaltung w.V. (VIT). A total of 53,217 SNPs from Bead Chip V3 and 45,613 SNPs from Bead Chip V2 were available for quality control. In this regard, only the SNP available in both genotype datasets and located on Bos taurus autosomes were considered. The SNP positions were remapped according to the ARS1.2 assembly . Qualityhttps://jlupub.ub.uni-giessen.de/, accessed on 7 January 2021). The application of GWAInter.R focuses on the detection of possible GxE for SNP markers in a one-step approach. The approach simultaneously enables the estimation of main SNP effects, as well as of SNP by environmental interaction effects, for the two different farming systems—compost and cubicle. Compared with other approaches, it is possible to estimate main SNP effects and interaction effects in one model without splitting the data set. The corresponding statistical model was:snpi was a vector of genotypes for marker i; bsnpi was a regression coefficient for the ith SNP (the main SNP effect); xinteri was a vector of genotypes for cows; binteri was a regression coefficient of ith SNP (the interaction effect); and g was a vector of genetic effects following N and contained the polygenic effects, where G was a relationship matrix [2g was the genetic variance. The G matrix was constructed considering all SNPs, but we excluded the SNP markers from the same chromosome where the candidate SNP was located, i.e., the “leave one chromosome out strategy”; e was the vector of random residual effects following N, where I was an identity matrix and σ2e was the residual variance. X and Z were incidence matrices for b and g, respectively. The “leave one chromosome out approach”, as also implemented in most of the commercial GWAS software packages, was realized by connecting GWAInter.R with the R package “gaston” [p-value for each SNP was calculated by applying a test statistic which follows a chi-square distribution . The test statistic for the ith main SNP effect was: th interaction effect was: Single-trait GWAS with interaction effects between farming systems and SNP effects were conducted using the self-written R program “GWAInter.R”. All relevant data and the program source code are available at JLUdata at the University of Giessen . The number of independent SNPs was 4479 when setting the linkage disequilibrium as ≤0.15 (squared correlation between two SNPs) for consecutive genomic windows including 500 SNPs along the bovine genome.Significantly associated SNPs were detected according to the Bonferroni corrected significance threshold Potential candidate genes were queried and assigned to associated SNP markers using the current gene annotations from the ENSEMBL and NCBI databases, considering a window 50 kb upstream or downstream from the significantly associated candidate SNP ,33. PhysA total of 35 significant (according to the Bonferroni corrected genome-wide significance and less conservative threshold) SNPs for the main effect were detected on 14 different chromosomes for the traits PMN, sN, bN, MAJOR, MINOR, cultural negative, AER, and CNS . A subseThe chromosome including the largest number of significantly (Bonferroni corrected genome-wide significance and less conservative threshold) associated SNPs was chromosome 14. For the cell fractions PMN and sN, the same significant SNPs when considering the less conservative threshold were detected on chromosome 6. One significant (less conservative threshold) SNP on chromosome 28 was significantly associated with bN. For MAJOR, 18 significant (Bonferroni corrected genome-wide significance and less conservative threshold) SNPs were located on chromosomes 1, 2, 4, 5, 11, 15, 17, 18, 22, and 26. Five SNPs exceeded the Bonferroni significance threshold, and 13 exceeded the less conservative threshold. For MINOR, four significantly associated SNPs (according to the less conservative threshold) were detected, and three of them were located on chromosome 14. In addition, for cultural negative, all three significant SNPs (two passed the less conservative threshold and one passed the Bonferroni genome-wide significance) were located on chromosome 14. Four SNPs were identified on chromosomes 4, 5, and 15 for AER. For CNS, three SNPs were detected on chromosome 8.Manhattan plots from the GWAS indicate the most significant SNP (Bonferroni corrected genome-wide significance and less conservative threshold) for PMN as representative of the cell fractions and for MAJOR as representative of the specific mastitis pathogens. Hence, Manhattan plots for main effects and for interaction effects are given in Regarding SNP interaction effects, significances (Bonferroni corrected genome-wide significance and less conservative threshold) were detected for PMN, MAJOR, MINOR, cultural negative, AER, and AESC. In total, six SNPs were identified as displaying significant interaction effects with the farming system , whereasCOL25A1  gene is a protein-coding gene which is involved in protein digestion and absorption (KEGG entry: bta04974). Functional annotation of the bN candidate gene reveals the involvement of adherent junction (KEGG entry: bta04520), leucocyte transendothelial migration (KEGG entry: bta04670), and bacterial invasion of epithelial cells (KEGG entry: bta05100) for CTNNA3 . For some potential candidate genes for MAJOR, functional relationships and their roles in biological or functional pathways are not yet known, e.g., for CRISPLD2 (cysteine rich secretory protein LCCL domain containing 2), HACL1(2-hydroxyacyl-CoA lyase 1), and CAPN5 (caplain 5). Nevertheless, we inferred four other potential candidate genes for MAJOR. CHL1 (cell adhesion molecule L1 like) on chromosome 22 is a cell adhesion gene playing a role in cell migration and in the suppression of neuronal cell death (KEGG entry: bta04515). EVA1A (eva-1 homolog A) on chromosome 11 also mediates autophagy and apoptosis and acts in the regulation of programmed cell death [ABO .Among the 35 significant SNPs for main effects, 14 were located in different genes . With rell death . FurtherADAMTSL (ADAMTS like 1) and SAMD12 . The same SNP localized in SAMD12 was detected for the cultural negative group. Likewise, the same SNP in gene ADAMTSL was significantly associated with CNS. With regard to CNS, the candidate gene MLLT3 (MLLT3 super elongation complex subunit) was also identified for CNS, which is involved in the transcriptional misregulation mechanism of cancer (String). Functional annotation of AESC candidate genes indicates the involvement of the cAMP (adenylyl cyclase) signaling pathway for GRIA4 (glutamate ionotropic receptor AMPA type subunit 4) (KEGG entry: bta04024).For MINOR, two SNPs were located in the genes HEMK1 (HemK methyltransferase family member 1) gene is a protein-coding gene which is involved in the methylation of the mitochondrial translation release factor [CHL1 gene was significant for the main SNP effect, as well as for the interaction effect between SNPs and farming system. The gene BTB/POZ 8 (BTB (POZ) domain containing 8) was identified as a possible candidate gene for AER. For AESC, the TBCEL (tubulin folding cofactor E like) gene was detected on chromosome 15.Based on the significant SNP by housing system interaction effects, four different potential candidate genes carrying one significant SNP were detected . An assoe factor . The SNPThe new free walk housing system (compost) implies an enriched bacterial concentration in the bedded pack, depending on management . Thus, tStaphylococcus aureus and Escherichia coli, also explained by the different immune responses of these two pathogens. The highest correlation (0.77) was detected between Streptococcus dysgalactiae and Streptococcus uberis [Most of the previous genomic udder health studies focused on easier trait definitions such as producer-recorded clinical mastitis or overall somatic cell score. In a study based on specific pathogens , signifis uberis . Both pas uberis . Accordis uberis . Only foEscherichia coli and Staphylococcus aureus [ADAMTSL1, SAMD12, and MLLT3) for minor pathogens.The different pathogens, which can vary widely between housing systems (compost and cubicle), initiate different immune responses and thus modulate many different genes . These ds aureus . Howevers aureus ,21. Accos aureus ,40. HoweCTNNA3 as a possible candidate gene. CTNNA3 is part of the CTNNA family with three catenin subtypes  displaying close functional relationships [We identified ionships ,42. The ionships . It alsoionships .EVA1A is involved in autophagy and programmed cell death [EVA1A is also involved in this process.ll death ,45. Sincll death , it is pHEMK1 was identified as a possible candidate gene. The SNP located in HEMK1 was significant for the interaction effect, indicating alterations of gene activities with farming system particularities. HEMK1 acts as a regulator in the JAK-STAT  pathway. The JAK-STAT pathway is pivotal for the development and function of the immune system, displaying different mechanisms in different studies [HEMK1 is differently regulated in different housing systems, such as compost and cubicle, due to different environmental stressors. Previous studies demonstrated that immune-related genes such as IL17, IL17F, and LIF indicate GxE [ studies . Hence, cate GxE . Furthercate GxE .CHL1 gene on chromosome 22, belonging to the L1 family cell adhesion molecule [CHL1 plays a role in diseases triggered by chronic stress [CHL1 expression in monocytes was significantly downregulated in depressed patients with chronic stress [+ and CD20+ B cells. The downregulation of these two immune cells has a negative effect on the immune system and implies an increased disease susceptibility [CHL1 is associated with a positive effect on the immune system and therefore on udder health. A gene expression analysis was conducted in relation to different floor types in Holstein dairy cows [HSPA1A during stress was identified, with ongoing effects on immune system functions [The molecule , was alsc stress . Yang etc stress . In additibility . Due to tibility , we assuiry cows . Variousiry cows ,52. Consunctions .EVA1A and CTNNA3 which are involved in the pathways for autophagy and programmed cell death (EVA1A), in the MAPK pathway, and in the adherens-junctional complex in epithelial cells for cellular adherence (CTNNA3). Interestingly, for the traits PMN, MAJOR, MINOR, cultural negative, AER, and AESC, we identified significant SNP by farming system interactions, indicating opposite effects of the same SNP markers in cubicle and compost. Annotated potential candidate genes were CHL1 and HEMK1, which are involved in pathways regulating the immune system dependent on stress levels and in the development of the immune system. From a practical perspective, our results suggest farming system-specific breeding strategies in order to improve udder health in both systems. In addition, our new modelling approach considering SNP main and interactions effects simultaneously simplifies the estimations and removes the need to split the data. For future breeding strategies to improve udder health, a very precise definition of mastitis traits is imperative as we identified different genetic mechanisms in response to specific pathogens.Based on a novel modelling approach allowing GWAS with SNP by farming system interaction effects, we identified significant main and interaction effects for specific pathogens and cell fractions in milk. The detailed udder health phenotyping strategy, combined with the novel GWAS, contributed to the detection of significant SNPs for PMN, sN, bN, MAJOR, MINOR, cultural negative, CNS, AER, and AESC. For the traits PMN and sN, MINOR and CNS, and MINOR and cultural negative, we identified overlapping significant SNP effects, indicating similar genetic and physiological mechanisms. In this regard, we inferred the potential candidate genes


There is a long-term interest in investigating the genetic basis of the horned/polled phenotype in domestic goats. Here, we report a genome-wide association study (GWAS) to detect the genetic loci affecting the polled phenotype in goats.P = 5.15 × 10− 119) and 128,286,704–131,306,537 bp (P = 2.74 × 10− 15) on chromosome 1. The genotype distributions of the 14 most significantly associated SNPs were completely correlated with horn status in goats, based on the whole-genome sequencing (WGS) data from JT and two other Chinese horned breeds. However, variant annotation suggested that none of the detected SNPs within the associated regions were plausible causal mutations. Via additional read-depth analyses and visual inspections of WGS data, we found a 10.1-kb deletion (CHI1:g. 129424781_129434939del) and a 480-kb duplication  encompassing two genes KCNJ15 and ERG in the associated regions of polled and PIS-affected goats. Notably, the 10.1-kb deletion also served as the insertion site for the 480-kb duplication, as validated by PCR and Sanger sequencing. Our WGS genotyping showed that all horned goats were homozygous for the reference alleles without either the structural variants (SVs), whereas the PIS-affected goats were homozygous for both the SVs. We also demonstrated that horned, polled, and PIS-affected individuals among 333 goats from JT and three other Chinese horned breeds can be accurately classified via PCR amplification and agarose gel electrophoresis of two fragments in both SVs.We obtained a total of 13,980,209 biallelic SNPs, using the genotyping-by-sequencing data from 45 Jintang Black (JT) goats, which included 32 female and nine male goats, and four individuals with the polled intersex syndrome (PIS). Using a mixed-model based GWAS, we identified two association signals, which were located at 150,334,857–150,817,260 bp (Our results revealed that two genomic regions on chromosome 1 are major loci affecting the polled phenotypes in goats. We provided a diagnostic PCR to accurately classify horned, polled, and PIS-affected goats, which will enable a reliable genetic test for the early-in-life prediction of horn status in goats.The online version contains supplementary material available at 10.1186/s12864-021-08089-w. As a major form of sexual weaponry in intra-male competition, horns in natural populations of ruminants also serve the purpose of self-defense against predators . In modeRXFP2 is a major candidate gene influencing the horned and polled phenotypes [To date, the genetic basis of the horned and polled phenotypes has been extensively investigated in ruminants. Although their genomic positions are closely located on the proximal end of bovine chromosome 1, a total of four different genetic variants are associated with the polled phenotypes in cattle with diverse origins . As wellenotypes –12 and eenotypes , whereasenotypes , 15. In enotypes . Howeverenotypes , which aenotypes . More inenotypes . Based oenotypes . HoweverCompared to low-density SNP chips, millions of SNPs detected from whole-genome sequencing (WGS) data permit fine mapping of association signals in genome-wide association studies (GWAS), which make it more feasible to find candidate genes affecting complex traits  and evenThe alignment of short-read WGS data against the goat reference genome  yielded an average sequencing depth of 6.24× (ranging from 5.52× to 7.28×) and genome coverage of 98.80% over 45 JT goats  showed two sub-groups among the total of 45 animals, possibly due to familial relatedness. However, the total fraction of genetic variance explained by the first two principal components was low (12.01 and 1.68%)  on all 29 autosomes remained for the GWAS. We carried out a mixed-model association analysis using all 45 goats, including 32 JT female goats plus additional nine male goats and four PIS cases, which corrected for the population structure. We identified two association signals that consisted of 528 significant SNPs  that were all located on chromosome 1  at 150,363,2033–150,734,900 bp. The second association signal was located at 128,286,704–131,306,537 bp on chromosome 1, which included 73 genome-wide significant SNPs with the leading one at 130,095,365 bp (P = 2.74 × 10− 15).After the Hardy-Weinberg test (Fisher’s exact test) in PLINK, 13,980,209 biallelic SNPs (Capra aegagrus). The occurrence of heterozygous and homozygous variant genotypes at several sites was possibly due to breed differences. Although we did not know the phenotypes of the 21 Bezoars, these results suggested that the reference alleles at these loci were ancestral, while the variant ones were derived alleles.According to variant annotation, the 14 genome-wide most significantly associated SNPs all resided in intergenic regions Fig. a. ConsidKCNJ15 (potassium inwardly rectifying channel subfamily J member 15) and ERG (ETS transcription factor ERG)  and ERG , but they did not yet show significant associations.A total of 4389 biallelic and 37 multiallelic SNPs, as well as 383 Indels, were detected in the strongest associated region that encompasses two genes, r2 = 0.98) with the 14 most associated SNPs , and both adjacent insertions  were also found at the genomic positions 2 bp before the SNP963. Interestingly, the three sites were phased based on the variant calling using GATK, which resulted in that the reference haplotype ‘ACG’ changed into the mutant haplotype ‘ATTTCTT’. All 52 horned goats  and 21 Bezoars were homozygous for the three sites except for one goat with the missing genotype at the SNP963 locus, whereas all 18 polled goats and four PIS-affected cases were heterozygous based on the genotypes from WGS data  at 150,363,963 bp (hereafter referred to as the SNP963 for simplicity) on chromosome 1 showed the strongest linkage disequilibrium LD, r = 0.98 wr2 = 0.82) with an intergenic SNP at 129,548,618 bp. Furthermore, this associated region harbors 27 genes (21 protein-coding genes and 6 RNA genes), such as NMNAT3, CRBP1, PIK3CB, SOX14, and FOXL2 (ENSCHIG00000026523) . According to variant annotation, the leading associated SNP  resided in intergenic regions, and it showed the highest LD  within or around the two association peaks using WGS data from 74 domestic goats and 21 Bezoars. The read-depth analysis in 1-kb sliding windows first detected a near 10-kb deletion  on chromosome 1 in polled and PIS-affected goats Fig. . Visual KCNJ15  and ERG   that overlapped with the most associated region in polled and PIS-affected goats , which included the 74 goats that were whole-genome sequenced. The PCR results for both SVs showed, as expected, only the 369-bp segment was present  in a total of 269 horned goats from four Chinese breeds, whereas only the 1822-bp fragment was present  in four PIS-affected goats  in horn buds [FOXL2 is a well-known female sex-determining gene in mammals, supported by the studies using gene knockout technology in mice [FOXL2 was one of the two headgear-specific genes [ALPL, COL1A1, and ET-1) at the cell level [KCNJ15 increases in otosclerosis patients [ERG gene were significantly associated with multiple traits  in Holstein cattle [The two associated genomic regions encompass several genes, including orn buds . FOXL2 i in mice  and goat in mice . Notablyic genes , suggestll level , indicatpatients , which in cattle . These fn cattle . As discIn the associated genomic regions in polled and PIS goats, we also detected two SVs , while both variants did not exist in the genomes of horned goats and Bezoars. Here, the PIS deletion shortened from a previously known length of 11.7-kb  to 10.1-KCNJ15 and ERG, was inversely inserted at the location of the PIS deletion. Given the potential functions of KCNJ15, ERG, and FOXL2, there might be interactions among these genes, or the dosage effects of KCNJ15 and ERG might exist due to the duplication. However, the comparison of the 3D structures of the PIS variant between intersex and normal goats did not provide strong evidence of how the sequences interact [Consistent with previous studies , 19, ourThere is a long-term interest in finding the molecular genetic markers  for the accurate prediction of polled and horned phenotypes in ruminants. For example, a recent study demonstrated that an optimized genetic test containing five biallelic SNPs successfully predicted a genotype in 99.96% of 20,636 animals from multiple cattle breeds  . AlthougIn summary, our results demonstrated that two genomic regions located at the distal end of chromosome 1 are highly associated with the horned and polled phenotypes in goats. A diagnostic PCR enabled us to accurately classify horned, polled, and PIS-affected goats, which provided a reliable genetic test for the early-in-life prediction of horn status in goats.In this study, 32 female Jintang Black goats (JT) were randomly sampled from a breeding farm in Jintang County, Sichuan Province, China, including 17 horned and 15 polled animals. Additionally, four polled JT individuals with PIS were included. According to the PCR amplification of a 272-bp fragment within the SRY gene, the four PIS-affected goats were karyotypically XX individuals . Moreover, we included WGS data for 21 Bezoar ibexes, which were downloaded from NCBI  as reported in our previous work [We also used short-read WGS data for nine male JT goats (six horned and three polled), 15 male Chengdu Brown goats , and 14 Tibetan Cashmere goats from Coqen  that was generated in our previous work  and haveous work . In briehttps://asia.ensembl.org/index.htm) using the ‘mem’ algorithm of BWA [http://broadinstitute.github.io/picard/) was applied to remove the duplicated reads followed by local realignment around existing Indels and base quality score recalibration using GATK [After filtering the read pairs containing adapter sequence, we conducted quality control of the raw reads using Trimmomatic  (v0.36),m of BWA  (v0.7.12ing GATK  (v3.8–0)To obtain quality variants, we performed filtering of the raw variant calls (SNPs and Indels) using GATK with the following cut-offs: QUAL < 100.0, QD < 2.0, MQ < 40.0, FS > 60.0, MQRankSum <− 12.5, ReadPosRankSum < − 8.0 and DP < 100. The highest-confidence variant sites were then obtained after discarding the variants with minor allele frequency (MAF) < 0.05 and missing genotype > 10% at the meta-population/population level, using VCFtools . The biaWe also employed CNVcaller  (a read-The VCFtools program  was applWe performed single-maker GWAS to identify the loci of interest using a linear mixed model implemented in EMMAX (F-test) , given tGiven its potential biological implications, we first conducted PCR and Sanger sequencing to validate a stop-gained C > G mutation  detected from the short-read sequencing data. A primer pair . The PCR products were resolved by agarose gel electrophoresis.For the 10.1-kb deletion and the 480-kb duplication identified using WGS data, we carried out the PCR amplification to verify the presence of both the SVs using two primer pairs (Additional file Additional file 1: Table S1. Summary of mapping statistics for 45 JT goats in this study.Additional file 2: Fig. S1. PCA of the 45 sampled goats based on the identified biallelic SNPs. The red, green, and blue circles represented female, male, and PIS goats, respectively.Additional file 3: Table S2. Lists of the genome-wide significantly associated SNPs for the polled phenotype in 45 JT goats.Additional file 4: Table S3. Lists of genes within the two association signals.Additional file 5: Fig. S2. Validation of a mutation within the ERG gene through PCR and Sanger sequencing.Additional file 6: Table S4. Genotypes and normalized copy numbers of both SVs in 74 domestic goats and 21 Bezoars analyzed by WGS.Additional file 7: Table S5. The detailed information of validation of both SVs in 333 animals from four Chinese goat breeds.Additional file 8: Fig. S3. The validation of genetic sex for four PIS goats by PCR amplification and agarose gel electrophoresis.Additional file 9: Table S6. The information of PCR primer pairs used in this study.