Chronic diseases are becoming a critical challenge to the aging Chinese population. Biobanks with extensive genomic and environmental data offer opportunities to elucidate the complex gene–environment interactions underlying their aetiology. Genome-wide genotyping array remains an efficient approach for large-scale genomic data collection. However, most commercial arrays have reduced performance for biobanking in the Chinese population.Deep whole-genome sequencing data from 2 641 Chinese individuals were used as a reference to develop the CAS array, a custom-designed genotyping array for precision medicine. Evaluation of the array was performed by comparing data from 384 individuals assayed both by the array and whole-genome sequencing. Validation of its mitochondrial copy number estimating capacity was conducted by examining its association with established covariates among 10 162 Chinese elderly.The CAS Array adopts the proven Axiom technology and is restricted to 652 429 single-nucleotide polymorphism (SNP) markers. Its call rate of 99.79% and concordance rate of 99.89% are both higher than for commercial arrays. Its imputation-based genome coverage reached 98.3% for common SNPs and 63.0% for low-frequency SNPs, both comparable to commercial arrays with larger SNP capacity. After validating its mitochondrial copy number estimates, we developed a publicly available software tool to facilitate the array utility.Based on recent advances in genomic science, we designed and implemented a high-throughput and low-cost genotyping array. It is more cost-effective than commercial arrays for large-scale Chinese biobanking. SNP arrays focus on more informative variants among the genome to achieve higher throughput at a lower cost. Together with imputation methods, SNP arrays can generate a relatively accurate genotype, except for extremely rare variants.9 Imputed genotypes derived from SNP arrays can provide similar statistical power to those from WGS for genome-wide association studies (GWAS).11 However, most commercial SNP arrays were designed to maximize genome coverage and imputation accuracy in populations of European ancestry. These arrays include a significant proportion of SNPs that are monomorphic while genotyping samples from the other ethnic groups, resulting in a loss of valid information content. That is why most national biobanks worldwide have chosen to design a customized SNP array for genomic data collection.High-throughput and cost-effective genomic techniques have advanced dramatically. Whole-genome sequencing (WGS) can identify genetic variations accurately with any allele frequency across the whole genome.16 With the recent advance in large-scale population sequencing in the Chinese population,17 genomic mapping with higher resolution offered an opportunity to design a more efficient SNP array for Chinese biobanks.As large-scale biobanks of Chinese cohorts are currently underway, an SNP array optimized for large Chinese prospective cohort studies is urgently needed. The existing SNP arrays designed for the Chinese population were mostly based on small global genome reference panels such as the 1000 Genomes Project (1kGP) or the HapMap established more than a decade ago.19 Studying MCN in large cohorts and biobanks was made possible by the development of methodologies that could estimate MCN through analysing raw genotyping intensity data from existing SNP arrays.22 Therefore, future SNP arrays could be designed to include more mitochondrial markers to facilitate MCN estimation as an extra type of genetic biomarker content for studies of ageing-related outcomes.Another recent advance in human genetics is the confirmation of mitochondrial DNA copy number (MCN) as a novel biomarker of aging-related diseases and all-cause mortality.Here we describe the design and assessment of a genome-wide SNP array, the CAS Array, specifically optimized for cost-effective whole genome genotyping in the Chinese population. The array design took advantage of a large high-quality Chinese genomic reference panel and incorporated the latest methodological developments for MCN estimation, providing an efficient tool for precision medicine in Chinese individuals.17 It was mainly used to construct two reference panels for SNP selection and imputation validation. The evaluation dataset consists of another 384 Chinese individuals with both WGS and CAS Array data available.23 This was used for evaluating the genotyping accuracy and imputation performance. The validation dataset came from a large population cohort, which includes 10 162 elderlies recruited from Kunshan City, Jiangsu, China. These individuals were genotyped with the CAS Array to validate the MCN estimates by assessing their association with established age-related biomarkers recorded in the electronic health records.Three main datasets were used for the development and assessment of the CAS Array. The development dataset is part of the NyuWa reference panel, which includes deep (30x) WGS data of 2 641 Chinese individuals across China.P value > 10−6] was conducted by VCFtools.24 A total of 17.3 M SNPs, including 5 M common  ≥ 0.05) SNPs and 71 k rare (0.001 < MAF < 0.05) coding SNPs passed the quality control and were used for GWAS tagging marker selection.For array design, a tagging reference marker panel was constructed from the development dataset of 2 641 Chinese individuals with WGS variant calls. Quality control [Variant Quality Score Recalibration (VQSR) passed, SNPs only, missing rate < 0.05, minor allele count ≥ 3, quality value ≥ 30, read depth (DP) ≥ 3, and Hardy–Weinberg equilibrium (HWE), P value < 10−6 or minor allele count < 3 were excluded. Samples that were probably contaminated (deviate ± 3 SD from mean heterozygosity rate), relatives within the third degree or abnormally recorded data were excluded. The sex of each individual was inferred by F coefficient and SNP observation on the Y chromosome. A putative XO type sample was marked as male to match the haploid state of the X chromosome. The relationship inference was done by KING software and other quality control steps were done by PLINK.26 The genotype was phased and converted to IMPUTE2 reference panel format by SHAPEIT2 software with a 0.5 Mb window size as recommended for WGS data.27 The genetic maps used for phasing were obtained from SHAPEIT4.28 The final reference panel contains 2 562 samples with 17.9 M SNPs.To derive the reference panel for imputation, slightly different quality control steps were applied to the development dataset. Among the SNPs passing VQSR, those with missing rate > 0.05, HWE 29 They were then complemented by greedy tagging on our reference panel to cover all the common (MAF > 0.05) SNPs. The second priority was to directly type as many coding variants with MAF > 0.001 as possible in our reference panel that Axiom technical efficacy allowed. Finally, a total of 776 mitochondrial markers were selected to enable more accurate MCN estimation. Additional markers were added to the array for a wider range of applications in medical research. Markers in the human leukocyte antigen (HLA) region, pharmacokinetic variants in drug absorption, distribution, metabolism, and excretion (ADME), ancestry informative markers (AIMS), and mitochondrial markers were selected based on the reference set validated by Illumina and Affymatrix. HLA markers, ADME markers and AIMS with MAF > 0.01 in our development dataset were included while all available mitochondrial markers were included on the array.As for genotyping arrays chosen by most national biobanks, the CAS Array utilized a ThermoFisher Axiom custom array harboring up to 675 k markers. The SNP markers were selected according to three priorities. Firstly, to achieve adequate coverage of common variants for imputation-based GWAS, common SNPs on the Axiom APMRA with proven technical efficacy were anchored.30 However, clinical translation of such knowledge of precision medicine requires high genotyping accuracy to maintain reasonable sensitivity and specificity, which could be better achieved by directly genotyping rather than using imputed genotypes. The coverage of coding variants with MAF > 0.001 was examined on the latest ChinaMAP reference panel.31 Variants position, alleles labels, and frequencies derived from WGS data of 10 588 Chinese individuals were downloaded and annotated with ANNOVAR.32 There were 107.4 k variants marked as coding variants with MAF > 0.001 in ChinaMAP. The coding variants coverage of the CAS Array was defined as the proportion of variants having matched position and alleles with the designed markers on the arrays relative to the total of 107.4 k variants on ChinaMAP.Coding variants were more likely to be identified as clinically relevant.33 Five samples having inconsistent sex or that were duplicated were removed by PLINK.26 The array genotyping call rate was defined as the proportion of recommended variants relative to the total number of designed markers on the array. Within these successfully called SNPs on the array, concordance rate was calculated as the proportion of concordant genotypes relative to all non-missing variant calls from WGS.Genotyping accuracy of the CAS Array was evaluated by calculating the concordance rate between WGS calls and array genotyping results in the array evaluation dataset. Quality control of WGS data was the same as that applied to the imputation reference panel. Array genotyping SNPs were called by APT software following the manufacturer's instructions.34 Genotypes with matching physical position and alleles were extracted from the WGS dataset as simulated genotyping calls. Low-quality variants including those with call rate < 0.95, MAF < 0.01, or HWE P value < 10−6 were excluded before imputation. Autosomes and chromosome X genotypes of each array were phased by SHAPEIT2 using the genetic map from SHAPEIT4.28 The reference strands were aligned to our Chinese reference panel derived from the NyuWa reference panel by Genotype Harmonizer.35 Imputation was performed by IMPUTE2 with the same reference panel.36The evaluation dataset was also used to evaluate the imputation performance of the CAS Array as compared to eight commonly used commercial arrays, including Genome-Wide Human SNP Array 6.0 (Affy SNP6), Axiom Precision Medicine Research Array (Axiom PMRA), Axiom Asia Precision Medicine Research Array (Axiom APMRA), Infinium Global Screening Array (Illumina GSA), Infinium Asian Screening Array (Illumina ASA), Infinium HumanOmni1 (Illumina Omni1), Infinium OmniExpress (Illumina OE), and Infinium OmniZhongHua (Illumina OZH). Manifest files were downloaded from the respective official websites of these arrays and the positions of the markers were converted to genome build hg38 by UCSC liftOver.r2, discordance rate, and imputation-based genomic coverage to assess the performance of the arrays as in previous studies.37 The imputation r2 was defined as the squared Pearson correlation r2 between the allele dosages of WGS and imputed genotypes. The discordance rate was defined as the proportion of the mismatching genotypes between WGS results and the most possible genotypes at each site generated by imputation. Coverage was defined as the proportion of the variants having imputation r2 greater than a given threshold . Average imputation r2 and discordance rate was calculated for each array. Coverage of common SNPs (MAF ≥ 0.05) and low-frequency SNPs (0.01 ≤ MAF < 0.05) were calculated separately for the arrays.The imputation performance of each array was evaluated by comparing the imputed genotypes with the original WGS outputs. We used imputation 21 In brief, the MCN was estimated by the intensity of fluorescent signal of mitochondrial markers indicating the segments of mitochondrial DNA captured by the corresponding probes. The intensities of autosomal markers were used as a reference to capture latent confounding factors such as batch effects and variation in DNA concentrations. Firstly, raw genotyping intensity files were processed for quality control by APT Software.33 Genotype calls and normalized signal intensity were also generated by APT. Log R ratios (LRRs) were calculated as an intensity measure and corrected for GC content to adjust for genomic waves by PennCNV.39 To select high-quality markers for MCN estimation, PLINK and BLAST+ were used for quality control.40 Markers with multiple alignment of percentage of identical matches > 80% were excluded for potential off-target. For autosomal markers, additional quality control including call rate > 95%, HWE P-value > 10−6, linkage disequilibrium (LD)-pruning (r2 < 0.3), and maximum spacing was done. After filtering, 47 102 autosomal markers and 166 mitochondrial markers were left as high-quality markers for MCN estimation. Principal component analysis (PCA) was applied on the LRRs of high-quality autosomal markers generating 80 PCs using R.41 The LRRs of high-quality mitochondrial markers were adjusted by regressing out the PCs of the autosomal markers. The final MCN estimates were extracted from the adjusted mitochondrial LRRs by PCA and converted to a standard normal distribution. After excluding samples with low genotyping quality , fluctuating LRR (LRR SD > 0.35), inconsistent sex calling, or without available phenotype data, the validation data set was finally used to examine the association between estimated MCN and age-related biochemical traits such as white blood cells count (WBC), haemoglobin (HEMO), and platelets (PLT). The same pipeline was also applied on the evaluation dataset, where MCN estimated from array data could be compared directly with MCN estimated from WGS as twice the ratio of the sequencing depth between mitochondrial reads and autosomal reads.MCN estimation was conducted in a similar manner as implemented by two previous MCN estimation pipelines, MitoPipeline and AutoMitoC.17 The CAS Array includes a total of 652 429 SNPs selected for different purposes , 645 327 were genotyped and passed quality control, resulting in a raw call rate of 98.89%. Of the 582 342 non-ambiguous variants that overlapped between CAS Array and WGS, the average concordance rate across samples was 99.89%. These results indicate that the in-house genotyping accuracy of the CAS Array was comparable to most commercial SNP arrays.31 Out of the 107 403 coding variants with MAF > 0.001 in the ChinaMAP, 74 470 (69.3%) were directly captured by the CAS Array and passed quality control. Compared to other commonly used commercial SNP arrays, CAS Array has a much higher direct coverage of coding variants that are more relevant to precision medicine  and 1.6 M low-frequency SNPs (0.01 ≤ MAF < 0.05). Figure accuracy . When imP < 2.2 × 10−16). For the validation dataset, a total of 8 584 individuals passed quality control and their MCN was estimated by 47 268 high-quality markers, including 166 mitochondrial markers. As shown in Fig. 44 The pipeline was packed into an R package available on GitHub (https://github.com/Zijian-Tian/CASMCN).We developed a pipeline to estimate the MCN from raw genotyping intensity data of the CAS Array and applied it to the validation dataset. After quality control, 378 individuals in the evaluation dataset had their MCN estimated by 47 878 high-quality markers, including 134 mitochondrial markers. The MCN estimated from CAS Array was positively correlated with the MCN estimated from WGS  end of SNP distribution, but only at the cost of ∼150 k extra rare markers on the array (As with other custom-designed genotyping arrays for biobanks, the main purpose of CAS Array is to facilitate cost-effective large-scale GWAS via imputation.he array . At the 45 especially on low-frequency SNPs (Fig. 45 In contrast, the NyuWa reference panel consists of 2 562 Chinese individuals from 23 of 34 administrative divisions in China.17 Therefore, the CAS Array would serve genotyping of Chinese individuals better, especially with the large Chinese imputation reference panels that are increasingly available.In addition to facilitating the marker selection on the CAS array, the high-quality Chinese reference panel also played an important role in improving its imputation performance. Our results show that all SNP arrays had better imputation performance when using the large NyuWa Chinese reference panel compared to the widely-used 1kGP reference panel,NPs Fig. . This ad46 The direct calling of these variants would enable more accurate genotyping than imputation. In turn, the downstream association analyses and genetic risk profiling would be more powerful and accurate with these directly assayed genotypes. More importantly, these more accurate genotype calls would also benefit the translation of genomic knowledge into potential clinical practice. As suggested by multiple biobanks around the world, pre-emptive genotyping of key pharmacogenetic variants, which are mostly coding variants, would benefit from more reliable genotype data to achieve high specificity.47The designing priority to directly genotype more coding variants is another key feature of CAS Array. This group of variants has been proven by accumulating GWAS results to be the most likely type of causal variants for a wide range of complex phenotypes.CAS Array is the first genotyping array designed with MCN estimation in mind, aiming to better serve the investigations into complex age-related diseases. Compared to other commonly used arrays with dozens to 300 mitochondrial probes, CAS Array harbors 776 mitochondrial SNP markers. Therefore, it has more comprehensive data and statistical power to estimate MCN. We also implemented an array-specific pipeline to estimate MCN from raw genotyping intensity signals. Using the large validation dataset, we further demonstrated that the MCN estimated from CAS Array was indeed associated with established biomarkers, paving the path to use the array for more precision medicine research in the elderly.The CAS Array design is inherently limited by the total number of markers it can carry, in order to meet the requirement of cost-effective genotyping. However, with the support of more comprehensive Chinese reference genome panels, the CAS Array outperformed most commercial arrays in terms of imputation-based GWAS for complex trait gene mapping. Although coding variants were prioritized on the CAS Array, higher coverage of variants with translational potential is still limited. A more purpose-built translation-oriented genotyping array will become a useful tool when more Chinese-specific functional variants are discovered by large-scale biobank studies. It is also worth noting that the accuracy of array-based MCN estimation is prone to technical fluctuations, and is thus more appropriate for large sample investigations.In conclusion, we designed and implemented the CAS Array based on a large comprehensive Chinese reference genome panel. Albeit restricted by the SNP content, its relatively high genotyping accuracy and imputation performance, high coverage of coding variants, and convenient MCN estimation, together make the array a cost-effective tool for large Chinese biobanking and precision medicine studies.pbad002_Supplemental_Tables_and_FiguresClick here for additional data file.


Apis mellifera, drones are typically haploid, developing from an unfertilized egg, inheriting only their queen’s alleles and none from the many drones she mated with. Thus the ordered combination or ‘phase’ of alleles is known, making drones a valuable haplotype resource. We collated whole-genome sequence data for 1,407 drones, including 45 newly sequenced Scottish drones, collectively representing 19 countries, 8 subspecies and various hybrids. Following alignment to Amel_HAv3.1, variant calling and quality filtering, we retained 17.4 M high quality variants across 1,328 samples with a genotyping rate of 98.7%. We demonstrate the utility of this haplotype resource, AmelHap, for genotype imputation, returning >95% concordance when up to 61% of data is missing in haploids and up to 12% of data is missing in diploids. AmelHap will serve as a useful resource for the community for imputation from low-depth sequencing or SNP chip data, accurate phasing of diploids for association studies, and as a comprehensive reference panel for population genetic and evolutionary analyses.Honey bee,  Apis mellifera, is the most important managed pollinator for crop and wild flora worldwide2. Since the publication of the honey bee genome in 2006 it has received much attention by the scientific community, resulting in an increasing number of papers on honey bee genomics3. In fact, numerous whole-genome sequencing studies have been published since, studying honey bee population structure and diversity8, evolutionary history10, and investigating signatures of selection13 not only in extant populations, but also museum specimens14. Mining whole-genome sequence data sets has also been used to identify ancestry informative markers, for instance, and to quantify admixture15 and identify subspecies6 for breeding or conservation purposes. Besides, whole-genome sequencing has also been used for quantitative-trait loci (QTL) mapping of economically important traits18, as well as genome-environment associations to identify local adaptations21.The honey bee, 7. Large sample sizes are especially important to increase power for genotype-phenotype associations23. Moreover, accuracy of QTL mapping and genome-wide associations studies greatly benefit from a haplotype-resolved dataset25, in particular, to identify structural variation27. For honey bees, structural variation has been shown to be characteristic in the differentiation of subspecies and local adaptation28, but accurate phasing empowered by a large haplotype reference is necessary - in particular due to the honey bee’s high recombination rate29. More recently in other species, pangenomes are emerging as a powerful tool to characterise structural variation opening up previously inaccessible genomic regions31. The development of a pangenome for the honey bee would therefore benefit from a large and diverse dataset of reference haplotypes.Despite the decreasing costs of genome-sequencing, large-scale population genomics studies are still not feasible for all labs, and thus, studies are often limited by sample size32. Significant advantages of A. mellifera for such a project include its small genome size  and the availability of haploid drones whose alleles are naturally phased. The resource will be useful for a variety of applications, including for example: imputation of low-depth sequence or SNP array data to enable cost-efficient large-scale studies; accurate phasing of diploid genomes to facilitate haplotype-based analyses such as XP-EHH;33 recovery of host genomic data from suboptimal samples ; identification of ancestry informative markers or tag-SNPs; validation of reduced SNP panels; and to serve as a comprehensive reference panel to support studies on population and evolutionary genetics.For these reasons, we have collated and curated whole-genome sequence data from 1,407 drones representing a diverse range of subspecies, habitats and geographic origins, into an accessible genomic resource: AmelHap. The key aims of AmelHap are to support genotype imputation, haplotype phasing and act as a comprehensive reference panel, similar to the human International HapMap Projecti) describe the details of the AmelHap dataset including sample origins and sample strategies, ii) outline step-by-step our sequence processing pipeline, and iii) validate AmelHap on its performance for genotype imputation in a haploid and diploid dataset. We have created a Zenodo community (https://zenodo.org/communities/amelhap) for the ongoing development of AmelHap, where we have released the metadata, raw and filtered variant data from this study.Herein, we A. m. capensis, A. m. scutellata and A. m. unicolor from the African (A) lineage; A. m. carnica and A. m. ligustica from the central and southern European (C) lineage; A. m. iberiensis and A. m. mellifera from the northern and western European (M) lineage; and A. m. caucasia from the Eastern European lineage (O). The drones comprised 1,156 samples from Europe36, 73 from South Africa and the South West Indian Ocean36, 125 from North America37, 13 from Asia, and 40 from Oceania8. In addition to representatives of the 8 subspecies listed, the dataset comprises various hybrids including Buckfast and experimental populations from Canada and Gotland. An overview of the sample origins can be found in Supplementary Table We processed publicly available Illumina sequencing data for 1,362 honey bee drones representing 19 countries, 4 distinct evolutionary lineages and 8 subspecies: PRJNA31127438: This Bioproject includes a large number of drones sequenced for the French research project SeqApiPop - an initiative to characterise the genetic diversity of French honey bees that not only included populations from France but also reference samples from several other countries. To date, 3 studies have been published within the scope of SeqApiPop. An initial pilot study investigated selection signatures in commercial honey bee populations11. This included sequence data for 30 unrelated drones selected for royal jelly and another 32 for honey production, as well as 30 A. m. mellifera drones from the island of Ouessant, 12 A. m. carnica from Germany and 18 A. m. carnica from Slovenia. A second study, explored admixture between European and African honey bees in the South West Indian Ocean islands5. Sequence data sourced from this study included 6 unrelated drones from Reunion, 2 from Rodrigues, 6 from Madagascar, and 2 from Mauritius. Drones from Madagascar and Mauritius are reported to be typical examples of the endemic A. m. unicolor, while those on Rodrigues and Reunion are hybrids. In addition, data was generated for 10 A. m. ligustica drones from Italy, and 10 A. m. caucasia drones from a breeder that had imported colonies to France from Georgia. Finally, the most recent study from the SeqApiPop to characterize French bee populations provides sequences data for a further 681 drones from independent colonies across Europe including France, Spain, Germany, Switzerland, Italy, the UK, Slovenia, Poland, Denmark, China, and New Caledonia8. These drones include A. m. mellifera from conservatories, A. m. carnica from breeders and breeder organizations, A. m. caucasia and Buckfast from breeders in France, A. m. iberiensis from beekeepers in Spain, A. m. ligustica from breeders in Italy, as well as a large number of drones of hybrid genetic ancestry. These samples also include a large number from a Varroa-resistance study39 in Vaucluse (France) labelled the MOSAR experiment.PRJNA59607140: Sequence data of 61 unrelated drones obtained from this study were sampled across Austria, Germany, Slovenia, Switzerland and Norway16. These include representatives of A. m. carnica and A. m. mellifera from international breeding and conservation programmes. The drones were sequenced in the frame of the development of a SNP array for genomic selection.PRJNA36303241: Sequence data for 125 drones from Canada was sourced from a study of hygienic behaviour37. Drone larvae were sampled randomly from selected and control populations, averaging 3.1 drones per colony, totaling 41 colonies. Canadian honey bees are highly admixed, but derive predominantly from the C and M lineages42. Drones were sampled from the third generation of two artificially selected populations based on increased hygienic behaviour, as well as from an unselected baseline population.PRJNA51667843: Sequence data for 158 drones from 8 colonies each on the islands of Gotland (Sweden) and Åland (Finland), in addition to 57 drones from 6 colonies in South Africa , was sourced from a study of recombination36. Between 8 and 10 drones were sampled per colony across 22 colonies. The samples from Gotland originate from a natural selection experiment between 1997–1999, in which 150 colonies were introduced to the island, artificially infested with the mite Varroa destructor, and left unmanaged in a “live and let die” experiment referred to as the “Bond Project”45. The bulk of colonies introduced from Sweden were described as Buckfast, a hybrid strain mainly derived from the C lineage, in addition to 10 queens each sourced from Swedish A. m. carnica and A. m. mellifera breeders44. The Åland bees were described as typical managed honey bees, containing a mixture of European ancestry36.PRJEB1653346: Sequence data of 119 unrelated drones, each sampled from a different colony, was retrieved from a study on conservation genomics in Switzerland4. Of these, 39 A. m. mellifera drones were sampled from Swiss conservation areas, while A. m. carnica drones (n = 33) and Buckfast drones (n = 14) were obtained from breeders and breeder associations of the respective strains.PRJNA57823347: This project accession, which has no associated publication, comprises three A. m. mellifera drones collected from the South Urals , where a large population of pure dark honey bees are found48.49). DNA was extracted from the thorax of individual drones with Zymo Quick-DNA MiniPrep Kits, and quantified by Qubit. DNA samples were sequenced by Novogene (UK) on the IlluminaTM NovaSeq platform, following library preparation for 150 bp paired end reads, generating ~4 Gb of raw data per sample.In addition to publicly available data, we collected 5 drone pupae from each of 9 colonies from the Lothians in Scotland, totaling 45 samples of undefined ancestry (Bioproject: PRJEB3936950 using BWA-MEM51 v0.7.17. Reads were sorted with SAMtools52 v1.9 and duplicates marked (MarkDuplicates) with GATK53 v4.0.11.0. Variants for each sample were called using GATK’s HaplotypeCaller54 with the following non-default parameters–ERC GVCF,–sample-ploidy 1 and -A AlleleFraction. Joint variant calling was performed across all samples using GATK’s GenomicDBImport and GenotypeGVCFs with–sample-ploidy 1 and a window size of 2.5 Mb. The pipeline is implemented in BAGPIPE . Across the 1,407 drones we identified 21,203,582 variants. This included 15,847,428 SNPs, 6,316,757 INDELs, 1,139,661 other variant types (e.g. complex substitutions), and 5,126,225 multiallelic sites of which 2,324,214 were multiallelic SNPs. The mean genotype depth of coverage (DP) for these variants across sample-level averages was DP = 7.4 ± 7.5 , Root Mean Square Mapping Quality (MQ), Strand Odds Ratio (SOR), and genotype depth of coverage and quality Fig. .11. This is because two peaks are typically observed from a diploid QD distribution , reflecting variants that are mostly observed in either the heterozygous (QD peak between 12–15) or homozygous state  divided by the unfiltered depth of homozygous non-reference samples and is a more robust metric for filtering than either QUAL or depth (DP) alone. QD values generally fall within the range 0 to 40, however, where DP < 1 values of QD > 40 can be observed, thus, variants exceeding this threshold were filtered representing low-depth low-quality variants. We also set a lower end threshold for this metric, i.e. QD > 20 in order to remove spurious heterozygous variantsMQ is the root mean square mapping quality over all the reads at the site and is used to evaluate deviations in mapping quality. An MQ of 60 indicates good mapping qualities at the site, and the general recommendation for hard-filtering is to exclude sites with MQ < 40 . Within our data we observe a median MQ of 60, with 411,360 records (1.9%) having MQ < 40 and 882,951 (4.2%) having MQ < 50. Given that fewer than 5% of variants had an MQ < 50 we chose to filter on this higher stringency, rather than the generic MQ < 40.SOR is a means of measuring strand bias, whereby one DNA strand is favoured over the other during sequencing, potentially resulting in a bias in evidence for one allele over the other. Intuitively strand bias should not present a problem with haploid data, however there is the potential for variants to fall within regions containing duplications which might then be influenced by such a bias. Removing the long tail of the SOR distribution seeks to remove sites demonstrating such bias. By filtering on SOR > 3 we remove 356,473 records (1.7%) from our data Fig. .Overall, after evaluating the distributions of these quality metrics, and applying appropriate filtering , there remained 18,154,924 records, comprising 13,777,414 SNPs and 5,159,405 INDELs, including 2,974,414 multiallelic sites of which 298,587 were multiallelic SNPs.−09. GQ values were extracted with vcftools55 v0.1.13 (–extract-FORMAT-info GQ) for all sites that passed the initial filter . To evaluate the relationship between GQ and depth of coverage for a genotype, we also extracted DP values (–extract-FORMAT-info DP). Although we observe genotype depths in excess of 1,000, the significant majority (99.9%) have DP ≤ 704, equivalent to half the sample size, and corresponds to the elbow at the upper end of the distribution  across the 25,543,978,068 genotypes Fig. . GQ reprion Fig. . We alsoion Fig. , with a 2 > 0.1) using bcftools + prune by considering pairs of variants up to 10 kb apart (–max 0.1 –window 10 kb). This left 660,258 records comprising 513,322 SNPs and 152,810 INDELs, including 31,921 multiallelic sites of which 2,980 were multiallelic SNPs. Using the LD filtered dataset, we generated an IBS matrix with Plink56 v1.90p (–distance square gz ibs). From this, we observe clear clustering of samples corresponding to C, M, and A lineage ancestries, as reported within the respective studies from which the samples were sourced  and genetic admixture to validate if these corroborated with reported or expected relationships. Prior to performing these analyses, we first filtered to retain variants with a minor allele frequency (MAF) >1% , then removed variants in strong linkage disequilibrium r > 0.1 usi.e. pairs of drones from unrelated colonies or whose relatedness is unknown (blue), and pairs of drones reported to be siblings/sampled from the same colony (red) .To further investigate this point, we plotted the distribution of pairwise IBS values and colored them according to their reported relationship, ed) Fig. . We finded) Fig. . For end9.Pairwise IBS values between the unrelated drones are roughly distributed across two dominant peaks Fig. , blue. C58  using default settings. ALStructure unifies PCA-based and likelihood-based methods for estimating ancestry by first clustering the data using latent subspace estimation (LSE), a method similar to PCA, and then estimating a sample’s global ancestry proportions using the alternating least squares (ALS) method to transform the allele frequencies obtained from LSE. We ran ALStructure on the LD filtered dataset for K = 2 to 6 dimensions and applied the structural Hardy-Weinberg Equilibrium (sHWE) test59 to generate p values from each K run using 3 null datasets. For the purpose of the sHWE test, missing genotypes were set to 0.5 as the function cannot process matrices with missing data. The resulting sHWE p values were applied to the entropy-based procedure described by Hao and Storey59, in order to determine the optimal K. The resulting entropy values for K = 2 to 6, respectively, were 763, 766, 768, 769, and 770, which, although indicating K = 2 to be optimal, demonstrates only marginal difference at these K levels. We present the results of K = 3 and K = 4  having mixed ancestry estimates of C (~56%) and A (~44%) lineages. At K = 4 a distinct background emerges for these A. m. caucasia samples. We also observe a number of samples whose admixture-inferred ancestry is inconsistent with their reported lineage, highlighting the need to apply caution when processing third party data.We next evaluated the overall genetic structure of AmelHap by calculating global ancestry estimates (Q) with ALStructure49. ENA accession numbers to individual sequence data are deposited at Zenodo57, listing all 1407 drone sequences processed in this paper alongside sample metadata  and variant filtering metrics (i.e. genotype depth and missingness). Raw gVCF files for all samples grouped by project accession67, along with joint-called raw variants74 across samples from these project accessions are available from the AmelHap community on Zenodo  and individual FORMAT (GT:AD:DP:GQ) annotations allowing users to further filter the dataset according to their needs. This VCF file has also been deposited at the European Variation Archive76 (Project Accession PRJEB5991277). Sample metadata is also available from the AmelHap community78, where revisions will be uploaded as the resource grows. The genetic maps used for imputation and example code to process sequence data, filter variants, and generate plots is available at https://bitbucket.org/gibberwocky/amelhap.Sequence data generated in this study have been deposited under project accession code PRJEB39369One of the key values of a HapMap is its utility for imputing missing genotypes. To evaluate AmelHap in this regard, we tested its imputation performance on a subset of drones and an independent diploid dataset.79 v4.2 (–sequencing) using genetic maps generated by Wragg et al.8. We generated a “truth” dataset by self-imputing missing genotypes in AmelHap. Then, we randomly selected for imputation 100 unrelated samples (see metadata57). Each sample was extracted from AmelHap and processed independently with bcftools + prune to randomly remove N SNPs in 1 Mb windows (-w 1000000 bp -n ${N} -N rand). We repeated this for N = 12.5 K, 25 K, 37.5 K, 50 K, 62.5 K, 75 K, 87.5 K, 100 K, 250 K, 500 K, and 750 K SNPs. For each N, the pruned samples replaced their equivalent selves in a copy of the AmelHap truth dataset and self-imputation was performed. We then calculated genotype concordance by comparing the imputed versus truth genotypes with bcftools stats. Figure For the imputation of a subset of haploid drones, we first subset and filtered AmelHap to retain bi-allelic SNPs which left more than 11 M SNPs positioned along the 16 linkage groups. Imputation analyses were conducted with SHAPEIT7 comprising samples from 13 subspecies representing 4 evolutionary lineages  was sourced from the CNGB Sequence Archive (CNSA) of the China National GeneBank DataBase (CNGBdb accession number CNP000198680). The sequence data was processed to generate gVCF files82 as per the drone data with a single exception - variant calling was performed with–sample-ploidy 2. Joint calling of this dataset included AmelHap as the target intervals . Each sample within the diploid dataset was processed independently with bcftools + prune to randomly remove N SNPs in 1 Mb windows (-w 1000000 bp -n ${N} -N rand). We repeated this for N = 10 K to 100 K SNPs, in 10 K increments. For each N, the pruned samples were merged with the AmelHap truth dataset and self-imputation was performed. We then calculated genotype concordance in the diploids by comparing their imputed versus original genotypes with bcftools stats. While not as effective at imputation in the diploid dataset, >95% genotype concordance is achieved when up to 10% of data is missing , a call rate of >90% is typically achieved in samples with >3X genotype depth of coverage , sequencing to at least 3X coverage should achieve a 90% call rate , very high genotype concordance ( > 95%) can be achieved in drones. We also demonstrate the resource to be effective at imputing moderate levels of missing data (12%) in an independent diploid dataset. We have not extensively investigated the parameter space for imputation, or the full range of tools available, and so further improvements on imputation performance are likely achievable. Moreover, restricting the reference panel to lineages relevant to the target panel may further increase accuracy. Based on the AmelHap filter 3 metrics  to enable researchers to easily replicate the sample processing steps, to generate a haploid gVCF file from raw fastq data, using the Nextflow sarek workflow (https://nf-co.re/sarek). We aim to integrate gVCF files uploaded to the AmelHap community on a regular basis, enabling the community to grow the resource for the benefit of all. Looking ahead, the data underpinning AmelHap can be leveraged further to support the development of a pan-genome86. A graph genome incorporating high quality assemblies from each of the evolutionary lineages, integrating haplotypes representative of variation within those lineages, would enable a greater range of genetic diversity to be captured. In particular, it would allow structural variations to be genotyped which, to date, remain largely understudied in the species.There remains an ongoing need to improve on the limited genomic resources for the species. As has been demonstrated in other species, increasing the sample size and diversity of AmelHap will yield improvements in genotype imputationSupplementary Information


Salmonella pullorum (S. pullorum), which can result in a decrease in the reproductive performance of laying hens, thus causing considerable economic losses. However, studies about the characteristics of intestinal microbiota with pullorum and their potential association with reproductive performance in hens are still limited. This study was to identify the gut microbiota associated with S. pullorum in poultry.Pullorum disease is one of the common bacterial infectious diseases caused by S. pullorum-negative (PN) and 30 hens with S. pullorum-positive (PP) were analyzed for hatching eggs laid in 2 weeks (HEL), fertilization eggs (FE), chick number (CN), and microbial structure.A total of 30 hens with p < 0.01), FE (p < 0.01), and CN (p < 0.01) between PP and PN. Histomorphological observations showed abnormal morphology of the ovaries and fallopian tubes and low integrity of epithelial tissue in the ileum and cecum in PP. 16S rRNA gene sequencing revealed that beneficial cecal microbes, such as Bacteroides, Desulfovibrio, and Megamonas, were positively correlated with reproductive performance and had lower abundance in PP (p = 0.001). Furthermore, diminished phosphotransferase system (PTS) and pentose phosphate pathway, butanoate metabolism and oxidative phosphorylation were also found in PP.There were significant differences in HEL (S. pullorum and preliminarily explored the potential association between cecal microbiota and reproductive performance in hens. Our data may provide a reference for revealing the intestinal microbial characteristics of hens in resisting pullorum and exploring novel approaches to infection control in future studies.Taken together, this study clarified the morphological characteristics of the reproductive tract and intestines of chickens infected with  Salmonella pullorum (S. pullorum), is transmitted both vertically and horizontally in chickens .S. pullorum infections in chickens were diagnosed by S. pullorum and S. gallinarum polyvalent antigen rapid slide agglutination test reagents . On a clean glass slide, 50 μl of polyvalent antigen and 50 μl of venous blood were placed. The samples were deemed positive if 50% or more agglutination occurred in the mixture within 2 min, and samples without agglutination were considered negative. In this study, two chicken groups were obtained after three times of the slide agglutination tests, and the results of the S. pullorum infection texts were positive or negative all three times. A total of 60 New Pudong chickens [samples from 30 hens with S. pullorum-negative (PN) and 30 hens with S. pullorum-positive (PP)] were selected from the experimental farm of the Shanghai Academy of Agricultural Sciences, Shanghai, China. These 60 hens were artificially inseminated at the age of 47 weeks. Recording the number of eggs laid, fertilized, and hatched chicks per hen and the performance, including HEL, FE, and CN, were analyzed during chick hatching processes. All eggs received disinfection before hatching. All hens were in the same poultry house (PN hens were distributed in the east of the poultry house and PP hens were distributed in the west of the poultry house), selected according to a unified breed standard, and fed antibiotic-free corn-soybean diets . Morphological observations were conducted by a Nikon ECLIPSE 80i light microscope with a computer-assisted morphometric system .S. pullorum-negative (n = 30) and S. pullorum-positive (n = 30) was analyzed by 16S rRNA gene sequencing, respectively. Microbial community genomic DNA was extracted from cecum samples using the E.Z.N.A.® soil DNA Kit  according to the manufacturer's instructions. The DNA extract was checked on 1% agarose gel, and the DNA concentration and purity were determined using NanoDrop 2000 UV-vis spectrophotometer . The hypervariable V3-V4 region of the 16S rRNA gene with a length of ~468 bp was targeted for sequencing. PCR amplification was performed with gene-specific primers 338 F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806 R (5′-GGACTACHVGGGTWTCTAAT-3′) under the following conditions: initial denaturation at 95°C for 3 min, followed by 27 cycles of denaturing at 95°C for 30 s, annealing at 55°C for 30 s, extension at 72°C for 45 s, single extension at 72°C for 10 min, and end at 4°C.The gut microbiota population in the hens with 2O up to 20 μl. PCR reactions were performed in triplicate. The PCR product was extracted from a 2% agarose gel, purified using the AxyPrep DNA Gel Extraction Kit  according to the manufacturer's instructions, and quantified using the Quantus™ Fluorometer . Purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina MiSeq PE300 platform  according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. . All obtained raw sequence datasets have been deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) with the accession number PRJNA799073.The PCR mixtures contain 5 × TransStartFastPfu buffer 4 μl, 2.5 mM dNTPs 2 μl, forward primer (5 μM) 0.8 μl, reverse primer (5 μM) 0.8 μl, TransStartFastPfu DNA Polymerase 0.4 μl, template DNA 10 ng, and finally ddHThe raw 16S rRNA gene sequencing reads were demultiplexed, quality-filtered by the FastP version 0.20.0 , average fertilization eggs (FE), and average chick number (CN) of S. pullorum-positive hens  analysis was performed to investigate the functional properties of microbiota. Using the KEGG pathway annotation information, we found that among the major microbial functions (top 100), the PP cecum microbial functions were generally weaker than the PN group , where tp < 0.01, p < 0.05, Bacteroides, Desulfovibrio, and Megamonas) in PN  of hens  in PN . Additiond in PP . NotablyFirmicutes is the core microbe in the co-occurrence network and has extensive connections with microbes from other phyla , Fusobacteria (16.3% in group N and 18.7% in group P), and Proteobacteria (9.37% in group N and 9.95% in group P); the preponderant genera were Lactobacillus, Fusobacterium, Peptoclostridium, and Gallibacterium  by fermenting carbohydrates to provide energy for the gut and promote the growth performance of animals   on the prevention of pullorum disease in commercial breeder chickens and demonstrated that SYF supplementation could significantly decrease SP and SG infection rates and improve the body weight of birds challenged with S. pullorum  and pentose phosphate pathways, butanoate metabolism, and oxidative phosphorylation were also found in PP. Taken together, this study clarified the morphological characteristics of the reproductive tract and intestines of hens infected with S. pullorum and preliminarily explored the potential association between cecal microbiota and reproductive performance in hens.Pullorum disease reduced reproductive performance. Abnormal morphology of the ovaries and fallopian tubes and low integrity of epithelial tissue in the ileum and cecum were found in PP. Pullorum disease reduced the cecal microbial alpha diversity and relative abundance of https://www.ncbi.nlm.nih.gov/, PRJNA799073.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: The animal study was reviewed and approved by the Ethics and Animal Welfare Committee of Shanghai Academy of Agricultural Sciences (No. SAASPZ0521009). Written informed consent was obtained from the owners for the participation of their animals in this study.QN and QH conceived and designed the experiments. CG, ZZ, and KY raised the experimental animals. XW, CC, XQ, and QN participated in the sample collection. XQ and QN participated in the data analysis. QN wrote the article. QH, XW, CC, XQ, CG, KY, and ZZ assisted with experiments and provided advice on manuscript content. All authors read and approved the final manuscript.


The phenotypic variation explained by these markers are in the range of 4.1–37.6 in the FIGS population. High throughput PCR‐based genotyping using new markers and association with phenotypes in FIGS wheat set and DH population validated the effect of functional SNP on closely associated genes—calcineurin B‐like‐ and dirigent protein, basic helix–loop–helix (BHLH‐), plant homeodomain (PHD‐) and helix–turn–helix myeloblastosis (HTH myb) type ‐transcription factor. Further, genome‐wide SNP annotation using SnpEff tool confirmed that these SNPs are in gene regulatory regions  modifying gene expression and protein‐coding. This integrated approach of marker design for t‐GBS alleles, SNP functional annotation, and high‐throughput genotyping of functional SNP offers translation solutions across crops and complex traits in crop improvement programs.Targeted exome‐based genotype by sequencing (t‐GBS), a sequencing technology that tags SNPs and haplotypes in gene‐rich regions was used in previous genome‐wide association studies (GWAS) for sodicity tolerance in bread wheat. Thirty‐nine novel SNPs including 18 haplotypes for yield and yield‐components were identified. The present study aimed at developing SNP‐derived markers by precisely locating new SNPs on ~180 bp allelic sequence of t‐GBS, marker validation, and SNP functional characterization based on its exonic location. We identified unknown locations of significant SNPs/haplotypes by aligning allelic sequences on to IWGSC RefSeqv1.0 on respective chromosomes. Eighteen out of the target 39 SNP locations fulfilled the criteria for producing PCR markers, among which only eight produced polymorphic signals. These eight markers associated with yield, plants m The three sub genomes  have a high level of coding sequence similarity (~95%) between homoeologous genes  platform enables high‐throughput identification of new variants throughout the genome, contributing to the trait of interest  file after quality filtering resulted in 25,448 variants. The variants were annotated into 33 different classes based on their predicted effects on protein function Figure  were fur2.3YD52 (1B: 411775364), YD6 (2B: 34083794); plants m−2—PM7 (6B: 486517837), PM2 (1B: 53737652); heads m−2—HM14 (1D: 356142374); harvest index—HI3 (2B: 34083794), HI4 (1D: 41415831) produced allele‐specific polymorphic clusters. Scepter and IG107116, the parents of DH population, produced polymorphic allelic signals for yield , plants m−2   having both homozygous and heterozygous calls. The marker for PM2 on chromosome 1B was non‐homoeologous (single copy) having two clear homozygous clusters.FIGS wheat set 192) was genotyped with 18 allele‐specific markers Table . The all) Figure . The suc was geno2.4, plants m−2(PM7), heads m−2(HM14), harvest index  except, for plants m−2 (PM2) were less than the absolute phenotypic difference between alternative allele groups, indicating the significance of favorable allele contributing to yield and yield‐components. The average phenotypic values of favorable allele and alternative allele groups in DH lines for PM2, PM7, YD6, and YD52 are presented in Table p < .05. Overall, the presence of favorable alleles increased grain production.Genotyping full FIGS set and DH population using new SNP markers resulted in homozygous and heterozygous allelic signals Figure , which ws m−2HM1, harvestYD6) and harvest index (HI3), and haplotype for grains/head (GH1) on chromosome 2B: 34083794, is 14.8 kb away from the gene encoding dirigent proteins and lines having favorable allele CC had improved yield and yield‐components compared to alternative allele TT. Marker for yield (YD52) on chromosome 1B: 411775364, is 45.8 kb away from the gene encoding calcineurin B‐like protein and the FIGS lines having favorable allele CC had improved yield compared to lines having alternative allele GG. The marker for plants m−2 (PM7) is an overlapping intron gene regulatory variant, encoding BHLH domain TF and the lines having favorable allele TT improved wheat establishment compared to alternative allele AA. The marker for plants m−2 (PM2) is associated with uncharacterized protein and favorable allele AA on chromosome 1B: 53737652 seemed beneficial to allele TT for greater wheat establishment on sodic soils. The marker for heads m−2 (HM14) is an overlapping 3′‐UTR gene regulatory variant, encoding PHD‐type domain TF and the lines having favorable allele AA improved heads m−2 compared to alternative allele GG. The marker for harvest index (HI4) is an upsteam gene regulatory variant, encoding HTH myb‐type TF and the lines having favorable allele TT improved harvest index compared to alternative allele CC. In silico gene expression analysis, it is revealed that most of the genes have the highest expression at critical wheat growth stages   closely associated with functional SNPs are presented in Table  yield YD2 on chro33.1The integration of exome‐based wheat genome sequences and genome‐wide variant annotation and characterization has enabled tagging new functional variants (SNPs/haplotypes) in gene‐rich regions contributing to yield and yield‐components. The high linkage disequilibrium (LD) novel haplotypes (spanning ~180 bp scaffolds) infer maximum genotypic variation for a given gene, without any computation estimation. The local patterns of LD and variant loci in gene‐rich regions are rich genetic resources to improve gene expression contributing to yield and yield‐components on sodic soils. Haplotype‐based breeding has a scope of incorporating desirable alleles of candidate genes either by enhancement of gene expression rate or change in desired protein characteristics contributing to complex trait expression  is an allopolyploid that was formed through hybridization and successive chromosome doubling of three ancestral diploid species (2n = 14), T.urartu (AA), Aegilops speltoides (BB), and Ae. squarrosa (DD) , with a single gene copy on the chromosome 1B, and clear homozygous clusters could be visualized  in full FIGS set and correlations with phenotypic values Figure , Table 3+) and clay. The negatively charged clay particles attract positively charged Na+ ions, forming a massive duplex and dispersive soil, with higher pH (>8)  were used in the present study. FIGS are sorted lines from 24 countries for tolerance to subsoil challenges including high salinity and ‐pH  for Chinese Spring v1.0  at different unknown locations on ~180 bp allelic sequences for yield and yield‐components Table . The pos4.3http://www.polymarker.info/; Ramirez‐Gonzalez et al., Chromosom‐specific primers were designed using the PolyMarker bioinformatics pipeline  assay  for allele‐specific markers, for plantsm−2 and yield. YD6/HI3/GH1 are pleiotropic SNPs for yield, Harvest index and Grains/head.Click here for additional data file.Table S1. Significant Quantitative trait SNPs and haplotypes identified for yield and yield‐components  location with respect to reference sequence (IWGSC RefSeqv1.0) for marker design.Table S3. Characterization of 25,471 genome‐wide SNPs identified in 206 FIGS population using SnpEff tool.Table S4. Successfully designed allele‐specific primers for SNPs and haplotypes for sodic‐dispersive soil tolerance.Click here for additional data file.Data S1. Peer Review.Click here for additional data file.


Triticum aestivum) breeding. The internode length of individual stems and tiller length of individual plants are important for the determination of plant architecture. However, few studies have explored the genetic basis of these traits.Plant architecture associated with increased grain yield and adaptation to the local environments is selected during wheat  to dissect the genetic basis of geographical differentiation of these traits in 306 worldwide wheat accessions including both landraces and traditional varieties. We determine the changes of haplotypes for the associated genomic regions in frequency in 831 wheat accessions that are either introduced from other countries or developed in China from last two decades. We identify 83 loci that are associated with one trait, while the remaining 247 loci are pleiotropic. We also find 163 associated loci are under strong selective sweep. GWAS results demonstrate independent regulation of internode length of individual stems and consistent regulation of tiller length of individual plants. This makes it possible to obtain ideal haplotype combinations of the length of four internodes. We also find that the geographical distribution of the haplotypes explains the observed differences in internode length among the worldwide wheat accessions.This study provides insights into the genetic basis of plant architecture. It will facilitate gene functional analysis and molecular design of plant architecture for breeding.The online version contains supplementary material available at 10.1186/s13059-023-02932-x. Triticumaestivum L) is a widely cultivated crop on over 200 million hectares with an annual production of approximately 700 million metric tons of grain (http://www.fao.org/faostat/). Wheat contributes to nearly 20% of the total dietary calories and protein consumed worldwide ).TEOSINTE BRANCHED1 (TB1) as a control, because recent work reported that TB1 regulates height and stem internode length in bread wheat  > 5.0) associated with the traits and located in the gene TraesCS1A02G064800. Therefore, we identified TraesCS1A02G064800 as a candidate gene that controlled peduncle length. The orthologue of TraesCS1A02G064800 in Arabidopsis is Trehalose-6-P synthase 1. We identified four major haplotypes at TraesCS1A02G064800 in the 306 worldwide wheat accessions  of TraesCS1A02G064800 resulted in the identification of a nonsynonymous A/G SNP  between the cultivars Zhongmai175  and Yanzhan4110 . The nonsynonymous SNP caused a change from aspartate (Zhongmai175) to glutamate (Yanzhan4110). Asian accessions exhibited a higher proportion of the allele with longer main shoot and peduncle , whereas the allele with shorter main shoot and peduncle was more heavily represented in Oceania   and Yanzhan4110 . On average, the RILslong  had taller plants than the RILsshort   generated from the parents Zhongmai175  encodes a gibberellin 2-oxidase and specifically controls the elongation of basal internodes in rice [Solanum lycopersicum), a gene controlling internode elongation was mapped to GA2oxidase 7, a class III GA 2-oxidase in the gibberellin biosynthetic pathway [Lodging is a serious concern that leads to lower grain yield in most cereal crops. The length of basal internodes is closely connected to lodging resistance.  in rice . Similar pathway . We idenThe spike number per unit area is one of the key factors that determine grain yield in wheat , 37. TheIn summary, our work explored the genomic basis of geographical differentiation and breeding selection for wheat plant architecture traits. We revealed the genetic association networks across different traits as well as the loci specifically controlling individual traits. This information will be helpful for the improvement of cultivars during wheat breeding.Seeds for 306 worldwide wheat accessions were sown with two replicates to ensure data repeatability in Zhaoxian, Hebei province, China  in 2020–2021 and 2021–2022. For each replicate, six rows were planted per accession, with each row (1.5 m in length) including 15 plants, with 10 cm between rows. For the sowing, we used a self-made hole punch to ensure the consistency of plant spacing and sowing depth. The punch is welded on a 1-m-long steel plate, on which steel columns with equal spacing are evenly distributed. These steel columns are thick at the top and thin at the bottom and have equal length, which allowed us to ensure that the hole depth is consistent.The phenotypic data of the eight traits exhibited differences and similarity between the two locations, and closely correlated with each other  values, the values of eight plant architecture traits in all environments were calculated using the age in R . The forSNP discovery and genotyping were performed through a well-established pipeline used for the construction of version 2.0 of the whole-genome genetic variation map of wheat (VMap 2.0) . A totalThe GWAS was performed for eight plant architecture traits on 306 worldwide wheat accessions using 40,710,923 SNPs . The algorithm efficient mixed model association expedited (EMMAX) can efficiently correct for a wide range of population structures, which would otherwise lead to spurious genotype–phenotype associations in a GWAS . TherefoK) matrix. Genetic relationship between accessions was modeled as a random effect using the K matrix. We used significant P-value thresholds (P < 10–5) to control genome-wide type I errors according to previous study that included identical SNP number in wheat [The top three principal components (PCs) from principal component analysis (PCA) were used to build the matrix for population structure correction using Plink  with thein wheat .10 = 5 were combined and the duplicates removed with vcftools. LD between SNPs was calculated by PLINK [ r2 > 0.1 being included in the same interval. If the distance between the peak SNPs of two adjacent loci was less than 5 Mb, these two loci were merged. The number of significant SNPs contained within each genomic region was counted. If the SNP number in the corresponding genomic region was more than 5, the region was defined as a QTL. The software LDBlockShow was used to conduct the pairwise LD analysis of the associated genomic region for TraesCS1A02G064800 [To determine the genomic regions of interest, the SNPs associated with all traits above a significance threshold of −Logby PLINK , with th2G064800 .Cytoscape [r2 ≥ 0.5). The effective scores for each locus are represented by P-values of the most significant SNPs associated with the corresponding traits in the GWAS. The link between pairs of loci was represented as their average LD. Here, the LD was calculated according to previous work [r2) between all SNPs of locus1 and all SNPs of locus2; PmaxLD(locus1)/PmaxLD(locus2) is the largest possible LD value within the locus1/locus2 locus, obtained by calculating the average r2 of each SNP against all SNPs from the locus1/locus2 locus. The maximum average LD value represents this locus’s PmaxLD. Pairwise r2 values were calculated between all significant SNPs using PLINK [The analysis of association network was conducted using the software ytoscape   for each block, as well as the number of tagged SNPs. If the SNP was the same as in the reference type (Chinese Spring), it was given a value of 0; if the SNP was different from that of Chinese Spring, it was given a value of 1. Heterozygous and missing SNP were given a value of 0.5. Full cluster analysis was performed on all accessions using Euclidean distance using Pheatmap (version1.0.12) in R. The LDheatmap package in R [The SNP-based haplotype construction for each locus was evaluated using the age in R  was usedThe XP-CLR test  was usedgeij is the interaction between genotypes and environemts, yij is the phenotypic values of genotype i in environment j, gi is the effects of genotype i, ej is the effects of environment j, and μ is the mean of the corresponding traits for all the genotypes in all the environments.The Shukla model  was usedfd statistic can estimate the proportion of introgression in a given window [fd values across the genome using the python code available at https://github.com/simonhmartin/genomics_general. The sliding window was set with a window size of 100 SNPs and a step size of 5 SNPs. We converted the fd statistic value to 0 for windows of D < 0 because the negative fd statistic value is meaningless. We estimated the fd statistic value using Indian dwarf wheat as P1, Mengxian201 as P2, rye as outgroup in four-taxon topology , P3, O), the P3 are diploid and tetraploid relatives of bread wheat, including 28 urartu, 31 wild einkorn, 31 domesticated einkorn, 26 wild emmer, 29 domesticated emmer, 41 free-threshing tetraploids.The n window . We estiAdditional file 1: Figure S1. Phylogenetic relationships and population structures. Figure S2. Geographical distribution and breeding selection of the haplotype blocks associated with the length of the second internode on chromosome 1A. Figure S3. Geographical distribution and breeding selection of the haplotype blocks associated with the length of the third internode on chromosome 3D. Figure S4. Geographical distribution and breeding selection of the haplotype blocks associated with the length of the fourth internode on chromosome 2A. Figure S5. The evolutionary relationship of the haplotypes for the loci for the length of second internode, third internode, four internode.Additional file 2: Table S1. Summary of the 306 worldwide wheat accessions. Table S2. Comparison of the eight plant architecture traits between two environments. Table S3. Detailed information of all significantly associated SNPs for the investigated traits> 5). Table S4. The 330 identified loci associated with investigated traits in this study. Table S5. Overlap between the known genes/QTLs and 330 identified loci in this study. Table S6. XP-CLR scores for the known genes. Table S7. The haplotypes of four major loci for the length of the four internodes in 831 Chinese wheat accessions. Table S8. The phenotypic data for the varieties from the seven continents/regions. Table S9. The phenotypic data of the haplotypes of the four major loci for the length of the four internodes. Table S10. The distribution of haplotypes of the four internodes in 306 worldwide accessions. Table S11. The genotypic data of all the varieties in the first pedigree. Table S12. The genotypic data of all the varieties in the second pedigree. Table S13. The interaction between environments and haplotypes for the length of the four internodes. Table S14. The interaction between environments and haplotypes for the length of the four internodes. Table S15. The 432 wheat varieties used for the analysis of the evolutionary relationship of the haplotypes. Table S16. The haplotype combinations for the length of the four internodes in 306 worldwide wheat accessions. Table S17. The haplotype combinations for the length of the four internodes in 306 worldwide wheat accessions. Table S18. The distribution of haplotype combinations for the length of the four internodes in 831 Chinese wheat accessions. Table S19. The phenotypic data of the four major haplotypes of TraesCS1A02G064800 in 306 worldwide wheat accessions. Table S20. The allele distribution of the SNP used in the RILs in the 306 worldwide wheat accessions. Table S21. The phenotypic data of RILs.Additional file 3. Review history.


ADAM‐10 on oral squamous cell carcinoma (OSCC) remain unclear. The aim of this study was to investigate the influence of SNP of ADAM‐10 on the clinical features of OSCC in male Taiwanese. Five loci of ADAM‐10 SNPs including rs653765 (C/T), rs2305421 (A/G), rs514049 (A/C), rs383902 (T/C) and rs2054096 (A/T) were genotyped by TaqMan allelic discrimination in 1138 OSCC patients and 1199 non‐OSCC individuals. The ADAM‐10 SNP rs2305421 GG  and G allele  illustrated a significantly higher genotypic frequencies in the OSCC group compared to the distribution of the ADAM‐10 SNP rs2305421 AA wild type. In the subgroup analysis, the ADAM‐10 SNP rs383902 TC+CC was significantly correlated to tumour size larger than T2 in betel quid chewer , while the ADAM‐10 SNP rs653765 CT+TT was significantly associated with tumour size larger than T2 in cigarette smoker . The results from The Cancer Genome Atlas revealed highest ADAM‐10 mRNA level in T2 stage of current smokers with head and neck squamous cell carcinoma (HNSCC). In conclusions, the ADAM‐10 SNP rs2305421 G allele is associated with the presence of OSCC, and the ADAM‐10 SNP rs383902 TC+CC and ADAM‐10 SNP rs653765 CT+TT correlates to large tumour size in specific conditions.A disintegrin and metalloproteinase domain‐containing protein 10 (ADAM‐10) involves in the tumour progression, but the impacts of single‐nucleotide polymorphism (SNP) of  Because the SNP of ADAM‐10 could influence the clinical course of other disease and ADAM‐10 itself is related to OSCC development,A disintegrin and metalloproteinase domain‐containing protein 10 (ADAM‐10) belong to a group of transmembrane and secreted proteins and involves in the pathway of cell migration, cell adhesion, proteolysis and cell signalling.ADAM‐10 SNPs  might influence the susceptibility of OSCC in an Asian male population. The effects of several risk factors of OSCC were also considered in our analytic model.In this study, we hypothesized that 22.1ADAM‐10 genes and associated SNPs, we draw venous blood in the study population and the venous blood samples were preserved in ethylenediaminetetraacetic acid‐containing tubes. Immediately after the sample acquisition, those venous blood were centrifuged and placed in a laboratory refrigerator at −80°C.This study was executed in the Chung Shan Medical University Hospital which locates in the central Taiwan region. After obtaining the informed consents, 1138 men with the diagnosis of OSCC participated this study. The diagnosis standard of OSCC in our study is according to the result of incisional biopsy which was judged by an experienced oncologist/otolaryngologist. For the comparison, another 1199 non‐cancer male constituted the control group. We obtained the demographic data such as the age, betel quid chewing, cigarette smoking and alcohol drinking from the medical documents. In addition, the clinical stage, tumour–node–metastasis (TNM) stage and degree of cell differentiation were judged with the assistance of AJCC 7th chart. Our study was approved by the Institutional Review Boards of Chung Shan Medical University Hospital (project identification code: CS1‐21151) and written informed consents were signed by all the patients in our study before the start of our project. For the processing of 2.2ADAM‐10 SNPs like the rs653765 (C/T), rs2305421 (A/G), rs514049 (A/C), rs383902 (T/C) and rs2054096 (A/T) were enrolled in the analysis model since the previous studies illustrated the potential correlation between these ADAM‐10 SNPs and other carcinoma.ADAM‐10 SNPs rs653765 (C/T), rs2305421 (A/G), rs514049 (A/C), rs383902 (T/C) and rs2054096 (A/T) were detected via the usage of ABI StepOne Real‐Time PCR System , and the ADAM‐10 SNPs were further evaluated and optimized by the application of SDS version 3.0 software.Five 2.3The Cancer Genome Atlas (TCGA) is a nationwide program held by National Cancer Institute and National Human Genome Research Institute in the United States. This genome database enrolled more than 20 thousands of participants from 33 cancer types, and the results analysed from blood sample and pathological tissue are available in public. We obtained the ADAM‐10 mRNA levels and clinical characters of head and neck squamous cell carcinoma (HNSCC) from TCGA database, and a subgroup analysis about the correlation between ADAM‐10 mRNA levels and OSCC clinical stage was made in non‐smoker and current smoker.2.4U test or Fisher's exact test was used to evaluate the difference of demography between the two groups. Then, the genotypic frequencies of each ADAM‐10 SNP was compared between the OSCC group and control group via the application of multiple logistic regression models after controlling for age, betel quid chewing, cigarette smoking and alcohol drinking. The adjusted odds ratio (AOR) and confidence intervals (CI) were produced in this process. We divided the OSCC patients into those with betel quid chewing and those without such habit, and we estimated the AOR and 95% CI between OSCC clinical characters and genotypic frequencies of ADAM‐10 SNP rs383902 in each subgroup by multiple logistic regression models after controlling for age, alcohol drinking and cigarette smoking. Similarly, the AOR and 95% CI of ADAM‐10 SNP rs653765 genotypic frequencies for OSCC clinical statuses were estimated by multiple logistic regression models after controlling for age, betel quid chewing and alcohol drinking in cigarette smokers and non‐cigarette smokers of OSCC. A p value lower than 0.05 was defined as statistical significance.We used the SAS version 9.4  for the analyses included in our study. Firstly, the descriptive analyses were used to present the demographic data between the OSCC group and control groups, and Mann–Whitney 33.1p = 0.670), while the ratio of betel quid chewing, cigarette smoking and alcohol drinking were all significantly higher in the OSCC group than the control group . The detail of OSCC characters are also presented in Table The demographic data between the two groups are presented in Table 3.2ADAM‐10 SNPs between OSCC patients and control group are demonstrated in Table ADAM‐10 rs653765, rs2305421, rs514049 and rs2054096 were in Hardy–Weinberg equilibrium (p > 0.05). The ADAM‐10 SNP rs2305421 GG revealed a significantly higher genotypic frequencies  in the OSCC group compared to the distribution of the AA wild type . Also, the ADAM‐10 SNP rs2305421 G allele owned a higher distribution in the OSCC group  compared to the ADAM‐10 SNP rs2305421 A allele wild type        Figure . The ADA) Figure  but did ) Figure . About t) Figure  and life) Figure , while t) Figure .4ADAM‐10 SNP rs2305421 GG variant and G allele in the individuals with OSCC than the control group. Furthermore, the ADAM‐10 SNP rs383902 TC+CC variant showed a strong correlation to higher OSCC T status in betel quid chewer. On the other hand, the ADAM‐10 SNP rs653765 CT+TT variant related to the larger OSCC T status in the cigarette smoker which may correspond to the findings of HNSCC and ADAM‐10 mRNA level in the TCGA.Briefly, our study demonstrated the higher distribution frequency of ADAM‐10 SNP rs2305421 is related to late‐onset Alzheimer's disease.ADAM‐10 SNP rs653765 was correlated to the formation of atherosclerotic cerebral infarction, thus may own therapeutic implications.ADAM‐10 SNPs rs514049 and rs514049 have a higher chance to develop hepatocellular carcinoma in more advanced TNM stage.ADAM‐10 SNP can affect the clinical course of some cancers and the OSCC owns correlation to the ADAM‐10 expression.ADAM‐10 SNP rs653765 in the promoter region were presented with higher ADAM‐10 mRNA levels in a Chinese population.ADAM‐10 may alter the clinical condition of OSCC. This hypothesis was partially supported by the findings of our study and the TCGA database.The ADAM‐10 and its genetic polymorphism had been proven to influence various diseases including neoplasms according to previous literatures.ADAM‐10 SNP rs2305421 GG variant and G allele demonstrated a higher distribution in the individuals with OSCC than the individuals without OSCC. To the best of our knowledge, few study survey the relationship between the genotypic frequencies of ADAM‐10 and the presence of OSCC. Moreover, the multiple logistic regression of our study considered the effects of several known risk factors of OSCC which including the age, betel quid chewing, cigarette smoking and alcohol drinking. Consequently, the presence of ADAM‐10 SNP rs2305421 GG and G allele may independently correlate to the development of OSCC in the Taiwanese male population. About the effect of this ADAM‐10 SNP in other disease, a previous study revealed the correlation between ADAM‐10 SNP rs2305421 G variant and late‐onset Alzheimer's disease,ADAM‐10 SNP rs2305421 G and Alzheimer's disease.ADAM‐10 SNP rs2305421 G allele may be a risk factor for many diseases. According to the data from the TCGA, the mRNA expression of ADAM‐10 was significantly higher in the HNSCC group than that in the non‐HNSCC individuals. In general, the OSCC is a subtype of HNSCC and both cancers share similar predisposing factors and histological features.ADAM‐10 SNP rs2305421 G allele may cause a higher expression of ADAM‐10 mRNA and increase the possibility of OSCC. Still, further experiment is warranted to prove this concept.In this study, the ADAM‐10 SNP rs383902 TC+CC and ADAM‐10 SNP rs653765 CT+TT variants are associated with higher OSCC T status in the betel quid chewer and cigarette smoker respectively. Similar to the multiple logistic regression for the comparison between OSCC and control groups, several predisposing factors of OSCC were adjusted in the two subgroup analyses. The two ADAM‐10 SNPs may be independent predictor for higher T status in the two subgroups. The betel quid chewing can also affect the clinical course of oral cancer under the expression of dipeptidyl peptidase IV SNP rs2268889,ADAM‐10 also owns the cell migration ability that is similar to dipeptidyl peptidase IV, thus the SNP of ADAM‐10 may show similar effect.ADAM‐10 SNP rs653765 also showed certain effect on the atherosclerotic cerebral infarction.ADAM‐10 variant caused by ADAM‐10 SNP rs653765 have synergic effect on various disease developments. Moreover, previous studies also showed that ADAMs was involved in betel quid or tobacco‐related carcinogenesis.ADAM‐10 SNP rs653765 CT+TT variant trigger a larger tumour size of OSCC in smoker, but the expression of ADAM‐10 mRNA was down‐regulated by the presence of this genetic polymorphism and the cigarette smoking. Since the higher expression of ADAM‐10 mRNA was correlated to lower 5‐year survival rate of HNSCC, the role of ADAM‐10 SNP rs653765 CT+TT variant in the prognosis of OSCC and HNSCC need additional evaluation.In the subgroup analyses, the ADAM‐10 SNP rs2305421 G allele is higher in the OSCC patients compared to non‐OSCC group. Furthermore, the ADAM‐10 SNP rs383902 and ADAM‐10 SNP rs653765 variants are correlated to larger tumour size of OSCC in specific population. Consequently, a survey of ADAM‐10 genetic variant might be recommended for OSCC men especially for patients with betel quid chewing and cigarette smoking. Further large‐scale prospective study to evaluate whether the genetic polymorphism of ADAM‐10 would affect the treatment outcome of OSCC in male is mandatory.In conclusion, the Yi‐Tzu Chen: Conceptualization ; writing – original draft ; writing – review and editing . Chiao‐Wen Lin: Writing – original draft . Ying‐Erh Chou: Methodology . Shih‐Chi Su: Methodology . Lun‐Ching Chang: Methodology . Chia‐Yi Lee: Writing – original draft . Ming‐Ju Hsieh: Conceptualization ; writing – original draft ; writing – review and editing . Shun‐Fa Yang: Conceptualization ; writing – original draft ; writing – review and editing .This study was supported by Chung Shan Medical University Hospital, Taiwan (CSH‐2021‐C‐016).The authors declare that there is no conflict of interest.


Improving the reproductive performance of sows is a paramount goal in breeding programs. The number of teats is significantly associated with the reproductive performance of sows, with quantitative trait loci (QTL) related to porcine papilla number traits identified across almost all chromosomes, and with candidate genes proposed. In this study, we analyzed the total teat number traits of 1518 Duroc × (Landrace × Yorkshire) (DLY) pigs and performed genotyping using a 50K chip. By screening SNP loci and related genes, this study aimed to identify genetic factors affecting the number of teats in DLY pigs and to facilitate future breeding programs aimed at improving this trait.ABCD4 and VRTN genes on porcine chromosome 7. Our results establish SNP variants of ABCD4 and VRTN as new molecular markers for improving the number of teats in DLY pigs. Furthermore, the most significant noteworthy single nucleotide polymorphism (SNP) (7_97568284) was identified within the ABCD4 gene, exhibiting a significant association with the total teat number traits. This SNP accounted for a substantial proportion of the genetic variance, explaining 6.64% of the observed variation. These findings reveal a novel gene on SSC7 for the number of teats and provide a deeper understanding of the genetic mechanisms underlying reproductive traits.The number of teats is a crucial reproductive trait with significant economic implications on maternal capacity and litter size. Consequently, improving this trait is essential to facilitate genetic selection for increased litter size. In this study, we performed a genome-wide association study (GWAS) of the number of teats in a three-way crossbred commercial Duroc × (Landrace × Yorkshire) (DLY) pig population comprising 1518 animals genotyped with the 50K BeadChip. Our analysis identified crucial quantitative trait loci (QTL) for the number of teats, containing the  In pig breeding, reproductive traits are paramount for economic productivity. A higher reproductive performance in the pig herd is directly related to increased production efficiency . To promVRTN on SSC7 (Sus scrofa chromosome) has been identified as a possible candidate gene for NTE [Genome-wide association studies (GWAS) are a widely used approach for analyzing the associations between genotypes and phenotypes by testing differences in the allele frequency of single nucleotide polymorphisms (SNPs) between individuals ,7,8,9. T for NTE ,12,13,14 for NTE ,11. Thus for NTE .To achieve this goal, we conducted a GWAS to identify genomic regions that affect NTE traits in a three-way crossbred commercial DLY pig population. Furthermore, we initially investigated the expression levels of putative candidate genes at various developmental stages. Through our comprehensive investigation, we have discerned multiple statistically significant SNPs and compelling candidate genes associated with NTE. These findings not only expand the current knowledge of the genetic architecture of NTE, but also underscore the complex nature of this trait.All animals used in the current study were managed in accordance with the guidelines of South China Agricultural University, Guangzhou, China, with approval number 2018F098. The experimental animals were obtained from the breeding pig farm Guangdong Wen’s Foodstuffs Group Co., Ltd., located in Guangdong, China.p < 10−6), and those unmapped or located on sex chromosomes were also excluded. Following QC, 28,065 SNPs for 1518 pigs remained for further analyses.The experimental animals were sourced from a three-way crossbred commercial pig population, which involved the breeding of 84 Duroc boars with 397 Landrace × Yorkshire sows to produce a large-scale offspring. These pigs were subjected to uniform feeding conditions and were raised in farms owned by Wen’s Foodstuffs Group Co., Ltd. . Following the fattening period, 1518 individuals were humanely slaughtered for phenotype recording. Teat numbers were collected by separately counting the left and right teats after birth, and teat number calculation involved the summation of both sides. Genomic DNA was extracted from ear tissue samples, using the standard phenol/chloroform method, quantified, and diluted to 50 ng/μL. The 1518 DLY pigs were genotyped using the GeneSeek Porcine 50K BeadChip , which contained 50,703 SNPs. Data quality control (QC) was performed using PLINK v1.07 . IndividPrincipal component analysis (PCA) was performed using the qualified SNPs to investigate the population structure of the DLY pigs. PCA analysis was conducted using GCTA software (version 1.93.2 beta).p-values for single-locus GWAS [p-value was defined as p = FDR × N/M, where N represents the number of SNPs with p-values < 0.01 in the GWAS results and M refers to the total number of SNPs qualified from the populations.The univariate mixed linear model implemented in GEMMA software (version 0.98.5)  was usedcus GWAS . SpecifiTo estimate the phenotypic variance explained by genome-wide SNPs and the proportion of phenotypic variance explained by each significant SNP, we implemented a model using GCTA software . The modhttp://ensemble.org/Sus_scrofa/Info/Index, accessed 20 September 2022). Genes located nearest to significant SNPs were listed in the Tables. We used GeneCards, MGI, and NCBI public network databases to query the function of these genes. Additionally, we conducted a thorough literature review to gather information on the association between all candidate genes nearest to SNPs and then analyzed the NTE traits.The potential functional genes were identified using the Ensemble Sus scrofa11.1 reference genome version (The phenotypic statistics of NTE in DLY pigs are presented in p = 1.19 × 10−4) at an FDR = 0.01. Based on the figure, it can be seen that the primary sites of correlation between the population and teat number phenotype are situated on SSC7, indicating that this site mainly influences the phenotypic variation of pig teat number. Notably, we detected one consistent QTL on SSC7 for teat number, with the top SNP demonstrating the highest phenotypic variance of teat number in this QTL, at 6.64%. Of the four SNPs located on SSC7, three were located within the ABCD4 gene. ABCD4 is a protein-coding gene belonging to the ATP-binding cassette transporter superfamily, and it participates in the intracellular processing of vitamin B12 transport [ABCD4 plays a role in the intracellular processing of vitamin B12 transport, and mutations in the ATPase domain of this protein have been demonstrated to modify intracellular vitamin B12 trafficking [ABCD4 could potentially alter the expression or function of this gene. Further analyses could help to better characterize the variants in this region and identify important functional mutations. Therefore, ABCD4 should be regarded as a critical candidate gene for teat number. Only one SNP on SSC7 was located within the VRTN gene. VRTN is a novel DNA-binding transcription factor that exclusively localizes in the nucleus, binds to DNA on a genome-wide scale, and regulates the transcription of a set of genes harboring VRTN binding motifs [After inspecting the 50K SNP information of the DLY pigs, we conducted GWAS analysis on traits related to teat number. This analysis identified seven significant SNPs located on SSC7, SSC8, and SSC9 ; Table 1ransport . It is tfficking . Vitaminfficking ,23.The mg motifs . The funVRTN. Building on this prior research, the present study provides a more detailed understanding of the genetic factors that affect teat number traits in pigs. The results of this study may have implications for pig breeding programs, as they provide insights into the genetic basis of reproductive performance in DLY pigs. By identifying SNP loci and related genes that influence teat number, this study may help breeders select animals with better reproductive performance and ultimately improve the overall efficiency and profitability of pig farming.This study utilized a 50K SNP chip to identify SNP loci and associated genes responsible for the number of teats in DLY pigs. In addition, the QTL regions in the current study were significantly reduced in length relative to previous investigations. In this study, the genetic mechanisms governing mammary gland development in pigs were investigated through a GWAS on various measures of teat number in a composite population of commercial pigs. The study aimed to identify candidate genes that could influence mammary gland development and enhance lactation capacity through selective breeding. Individual counts of teats on each side were collected to evaluate bilateral symmetry and determine the most effective measure for selection, which was found to be the total number of teats. The study used advanced genetic analysis methods to identify SNP loci and related genes that influence teat number traits in DLY pigs. Previous studies have identified QTL related to porcine teat number traits on chromosomes and proposed candidate genes such as ABCD4 and VRTN are two related genes which are likely responsible for the variation observed among individuals. Moreover, a most significant noteworthy SNP (7_97568284) was identified within the ABCD4 gene, demonstrating a significant correlation with the total number of teats. The SNP explained the 8.68% phenotypic variance of teat number in the DLY pigs. SNP (7_97568284) was associated with TNE. This SNP is located in the 3′ UTR of the ABCD4 gene. ABCD4 has been reported to participate in the intracellular processing of vitamin B12 transport, and alterations in the ATPase domain of this protein have been shown to modulate intracellular vitamin B12 trafficking [VRTN gene. VRTN is an intriguing DNA-binding transcription factor characterized by its exclusive nuclear localization. It exhibits genome-wide DNA binding capacity and effectively governs the transcriptional activity of a distinct set of genes that contain VRTN binding motifs [In this study, we conducted a GWAS for NTE in a population of 1518 DLY pigs and identified a classic QTL on SSC7 that affects teat number. fficking . SNP  transporter family responsible for processing vitamin B12 in cells. A previous study reported differential expression of VRTN in pig embryos at 17.5 dpc [So far, it is evident that a dominant QTL on SSC7 significantly impacts the NTE. However, the causal genes and specific mutation sites in this QTL remain unidentified. To identify the function of the gene in the dominant QTL for NTE, we combined the findings of this study with previous reports, detecting the Mammalia , locatioMammalia . MammaryMammalia . Mammary17.5 dpc . The for17.5 dpc ,27.ABCD4 and VRTN genes. Subsequently, it is imperative to analyze the potential effects of these variants on the expression or regulatory patterns of host loci. This line of research will shed light on the functional implications and molecular mechanisms associated with these genetic variations, providing valuable insights into their influence on porcine traits and population dynamics [With the discovery of a repertoire of potential single nucleotide variant (SNV) genes derived from the population, which could potentially impact porcine populations, the clear trajectory for future investigation lies in identifying pigs harboring diverse SNV genotypes within or in close proximity to the dynamics . The genVRTN, which have been shown to influence the number of vertebrae in pigs, are also consistent with the candidate genes that are known to influence the number of nipples in pigs. This suggests that there may be a common genetic mechanism underlying the development of these two traits in pigs [It is interesting to note that the candidate genes  in pigs ,28. The  in pigs ,30,31,32The knowledge gained from these findings can potentially be used to improve pig breeding programs and increase efficiency in pig farming. It will be important for future research to further investigate the relationship between these candidate genes and the development of both vertebrae and nipples in pigs. This will help to provide a more comprehensive understanding of the genetic factors that influence these important traits and may lead to the development of new breeding strategies to improve the reproductive performance of pigs. Unraveling and validating causative mutations associated with oligogenic or polygenic traits, particularly non-coding variants, presents a formidable challenge in the absence of functional laboratory investigations. The task of identifying and confirming such mutations remains exceedingly difficult ,34,35,36VRTN and ABCD4 genes speculatively linked to the number of teats in this population. Our results provided novel insights into the genetic basis of porcine NTE. The study further supports the role of the VRTN and ABCD4 gene regions in influencing the variability in the number of teats recorded in the DLY pig population. The next area of research will be to increase the marker density to search for the mutation sites of these genes and regions. Our findings provide a reference for understanding the genetic mechanisms and breeding improvement of the number of teat traits.In summary, the number of teats in DLY pigs was analyzed using a 50K SNP chip, followed by GWAS analysis for the number of teats. Significant signal loci that affect the number of teats were located on chromosome 7, with the