FSHR, BMPR1B and OXT, were identified as being under selection. Specific missense mutations of the FSHR gene that differed from the reference genome were also identified in the population, and we found some SNP variations that may affect litter size. Our findings provide a theoretical basis for the conservation and utilization of Yunnan semi-fine wool sheep. Furthermore, our results reveal some changes common to sheep after domestication and provide a new opportunity to investigate the genetic variation influencing fecundity within a population evolving under artificial selection.Yunnan semi-fine wool sheep are among the most important cultivated sheep breeds in China. However, their population structure, genetic characteristics and traits of interest are poorly studied. In this study, we systematically studied the population characteristics and selection signatures of 40 Yunnan semi-fine wool sheep using SNPs obtained from whole-genome resequencing data. A total of 1393 Gb of clean data were acquired. The mapping rate against the reference genome was 91.23% on average (86.01%–92.26%), and the average sequence depth was 9.51X. After filtering, 28,593,198 SNPs and 4,725,259 indels with high quality were obtained. The heterozygosity rate, inbreeding coefficient and effective population size of the sheep were calculated to preliminarily explore their genetic characteristics. The average heterozygosity rate was 0.264, the average inbreeding coefficient was 0.0099, and the effective population size estimated from the heterozygote excess (HE) was 242.9. Based on the Tajima’s D and integrated haplotype score (iHS) approaches, 562 windows and 11,356 core SNPs showed selection signatures in the Yunnan semi-fine wool sheep population. After genome annotation and gene enrichment analysis, we found traces of early domestication in sensory organs, behavioural activity and the nervous system as well as adaptive changes in reproductive and wool traits under selection in this population. Some selected genes related to litter size, including  Yunnan semi-fine wool sheep are among the most important cultivated breeds in Yunnan Province, China. This breed has excellent wool quality, high adaptability and strong robustness. Currently, it is widely used to produce wool and meat products . From 19Selection signatures are the imprints left on the genome of a species under long-term natural and artificial selection during the process of evolution. Genome selection signatures are an effective tool for studying the adaptability of domestic animals in different natural environments and exploring the genetic mechanisms underlying phenotypic differences. Various methods are currently used to assess genetic diversity and detect selection signatures. These methods can be divided into three categories depending on the different types of examined genomic information: methods based on the allele frequency spectrum, e.g., Tajima’s D  and the With the development of sequencing technologies and the improvement of biological analysis methods, selection signature detection has been used to mine the imprints left by domestication and artificial selection and the genes associated with certain important traits . HoweverThe ethical committee of the Yunnan Animal Science and Veterinary Institute  approved all experiments in this study (201909006). In addition, during the study, all authors strictly complied with the Regulations on the Administration of Laboratory Animals  and the Regulations on the Administration of Experimental Animals of Yunnan Province . There was no use of human participants, data or tissues.A total of 40 female Yunnan semi-fine wool sheep with similar body weights (approximately 50 kg) and ages (3 years old) were sampled from the Laishishan sheep farm in Qiaojia City, Yunnan Province , which is one representative Yunnan semi-fine wool sheep farm in Yunnan Province, and genetic exchange (rams or sperm) was carried out with other Yunnan semi-fine wool sheep farms. The samples were unrelated according to the pedigree. Based on their litter sizes in two successive parities, these ewes were classified into two groups: ewes with a litter size of 1  and those with a litter size of 2 . In addition, the sequencing data of 4 Zhaotong sheep and 3 Romney sheep were downloaded from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database to further explore the population structure of Yunnan semi-fine wool sheep.Ear tissues of the selected sheep were collected, transferred to sterile centrifuge tubes, and stored in a freezer at −80°C. DNA was extracted from the sample of each individual and analysed following standard experimental procedures to guarantee quality. Sequencing libraries of each individual were constructed by Beijing BerryGenomics Biotechnology Co., Ltd., using the Illumina NovaSeq6000 PE150™ platform.Trimmomatic software (version 0.38)  was applTo ensure high quality of the variations for the following analysis, BCFtools (version 1.10.2)  was used2) between loci , admixture analysis and LD decay analysis were performed to explore the population structure of Yunnan semi-fine wool sheep. For PCA, PLINK (v1.90b4) software  was usedeen loci , and theFROH) of individuals were estimated based on runs of homozygosity (ROHs). PLINK software was used to detect ROHs with the following parameters: 1) minimum density of 1 SNP per 100 kb, 2) minimum ROH length of 1000 kb, 3) 1 heterozygote allowed per window, 4) 5 missing calls allowed per window, 5) minimum number of SNPs in a window of 50, and 6) 0.05 missing calls allowed per window. The following formula was used to calculate FROH:As important population parameters, the heterozygosity rate, inbreeding coefficient and effective population size were calculated in this study. PLINK (v1.90b4) software  was usedsoftware . NeEstimSelection signature detection within populations can reveal selection dynamics and the history of evolution. In this study, two metrics, Tajima’s D  and the iHS , were uthttp://asia.ensembl.org/biomart/martview/). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were performed for further gene function analysis using DAVID Bioinformatics Resources 6.8 (https://david.ncifcrf.gov/). The GO terms and KEGG pathways with P values less than 0.05 were considered significant.Based on the core SNPs or windows under selection detected based on the iHS and Tajima’s D analyses, selected regions were defined for the bioinformatics analysis. From each core SNP detected based on iHS analysis, the region was extended 20 kb upstream and downstream to define the selected region. Under the Tajima’s D approach, each selected 50-kb sliding window was extended 150 kb upstream and downstream to define the selected region. Gene information in the selected regions was obtained using the Biomart database  related to reproductive traits were studied. PLINK (v1.90b4) software was employed to perform a standard case /control  association analysis using Fisher’s exact test with the option plink --file gene --fisher.In this study, 40 Yunnan semi-fine wool sheep were classified into two groups on the basis of litter size: 1 lamb  or 2 lambs  in two successive parities. To investigate whether SNP mutations or frequency differences existed between the two groups, three significantly selected genes , and the average sequence depth was 9.51X (8.09X∼12.10X)  and 0.0099 (0.0004–0.0239), respectively , bone morphogenetic protein receptor type 1B (BMPR1B) and oxytocin/neurophysin I prepropeptide (OXT), were identified as being under selection in this study. OXT was identified by the Tajima’s D test, and BMPR1B and FSHR were identified by the iHS test. Based on the whole-genome sequencing results obtained for Yunnan semi-fine wool sheep, the polymorphic loci in the exons of the BMPR1B, FSHR and OXT genes were analysed by comparison with the reference genome , and 44 SNPs showed significant differences between the single- and double-lambing groups . Although none of the SNPs showed significant differences, we found that in the two-lamb group, the frequency of minor alleles increased de novo.In the g groups . In the FROH) is more accurate than estimating the inbreeding coefficient from pedigree data (FROH = 0.032) (FROH = 0.053) (FROH = 0.28) (FROH = 0.133)  , Italian= 0.053) ) and oth = 0.28)  and Laiw= 0.133) , indicat= 0.133) . AccordiThe breeding of Yunnan semi-fine wool sheep is carried out under a strict system: new genetic materials cannot be introduced into the core group, and sheep in propagation groups must be obtained from foundation seed farms, which maintains the good breeding status of these sheep. However, the herd size in breeding farms is small, e.g., only 1010 and 270 sheep in the Lashishan breeding farm and Xiaohai breeding farm in Qiaojia County, respectively, both of which are representative breeding farms of Yunnan semi-fine wool sheep in Yunnan Province. Such a small population size will lead to inbreeding, resulting in a decrease in the effective population size.Genetic variation profoundly affects phenotypic variation. When either natural or artificial selection occurs, it will leave traces in the genome. Therefore, we adopted intrapopulation detection methods (Tajima’s D and iHS) to discover genome-wide footprints caused by natural and artificial selection in Yunnan semi-fine wool sheep. In this study, overlap of the selected genes simultaneously identified by the two approaches was observed in a few cases  receptor family of transmembrane serine/threonine kinases and regulates animal cell growth and differentiation, embryonic development and reproductive performance (FecB [nonsynonymous substitution (Q249R)] form of the sheep BMPR1B gene was proven to be the main gene regulating high-fertility performance in Booroola sheep, researchers have attempted to identify the FecB gene in some indigenous breeds such small-tail Han and Hu sheep (FecB mutation was not detected in Yunnan semi-fine wool sheep, suggesting that this BMPR1B gene mutation may not affect the reproductive performance of the breed. Of course, it is also possible that the examined sample size was too small for detection of this mutation in this study. Nevertheless, we detected 44 SNPs with significantly different frequencies between the single- and double-lambing groups. However, these SNPs were mainly located in intronic regions. Many mutations in intronic regions have previously been found to (In previous studies on a segregated flock based on QTL analysis and GWAS mapping, some mutations such as FecB , FecX (GcB (FecX , and FeccX (FecG  that mayformance . Since tHu sheep . HoweverHu sheep . In thisfound to  lead to found to , so it iFSHR and OXT. The OXT gene encodes a precursor protein that is processed to produce oxytocin and neurophysin I. This precursor seems to be activated as it is transported along the axon to the posterior pituitary. This hormone causes the contraction of smooth muscle during parturition and lactation and functions as a neurotransmitter in the central nervous system, playing a role in cognition, tolerance, adaptation, and complex sexual and maternal behaviours (OXT may be related to litter size. Most relevant studies have shown that the FSHR gene is closely related to reproductive traits, such as ovarian function (FSHR are significantly correlated with fertility traits of the Chinese native pig breed Xiaomeishan (FSHR gene may also be associated with litter size (Two other genes that participate in reproduction-related hormone secretion are haviours . In the function  and testfunction , in mammomeishan . Mutatioter size . In thister size , but theIn conclusion, using second-generation sequencing technology, the population structure of Yunnan semi-fine wool sheep was detected, and strategies for the conservation of this breed were proposed. In addition, some genes related to adaptation and reproductive traits were shown to have experienced strong selection. The results of this study increase our knowledge of the genetic basis of litter size in Yunnan semi-fine wool sheep, shed light on the changes in heritable phenotypes during the processes of adaptation and artificial breeding, and provide a theoretical basis for the breeding and conservation of Yunnan semi-fine wool sheep.


Apis mellifera L.), a key managed pollinator species that has been severely affected by biological invasions. To fill the gap, here we identified single nucleotide polymorphisms (SNPs) in a wide range of honey bee immune genes and developed a medium-density assay targeting a subset of these genes. Using a discovery panel of 123 whole-genomes, representing seven A. mellifera subspecies and three evolutionary lineages, 180 immune genes were scanned for SNPs in exons, introns (< 4 bp from exons), 3’ and 5´UTR, and < 1 kb upstream of the transcription start site. After application of multiple filtering criteria and validation, the final medium-density assay combines 91 quality-proved functional SNPs marking 89 innate immune genes and these can be readily typed using the high-sample-throughput iPLEX MassARRAY system. This medium-density-SNP assay was applied to 156 samples from four countries and the admixture analysis clustered the samples according to their lineage and subspecies, suggesting that honey bee ancestry can be delineated from functional variation. In addition to allowing analysis of immunogenetic variation, this newly-developed SNP assay can be used for inferring genetic structure and admixture in the honey bee.With a growing number of parasites and pathogens experiencing large-scale range expansions, monitoring diversity in immune genes of host populations has never been so important because it can inform on the adaptive potential to resist the invaders. Population surveys of immune genes are becoming common in many organisms, yet they are missing in the honey bee ( The range and host shifts resulting from these introductions are threatening many organisms across the globe, from mammals3, birds4 amphibians5, to fishes6. Within insects, the honey bee Apis mellifera L. has been particularly impacted by introduced parasites and pathogens, most notably the mite Varroa destructor and the microsporidian Nosema ceranae10. These are both native to Asia and have rapidly spread worldwide, after a host shift from Apis cerana to Apis mellifera10. V. destructor suppresses the bee immunity, but most importantly it acts as a reservoir, incubator and transmission route for several viruses11, including one of the prime honey bee pathogens: the Deformed Wing Virus (DWV). N. ceranae is an intracellular parasite, which decreases colony longevity by inducing oxidative stress and by causing changes in metabolism and immune response12.The worldwide movements of humans and goods, coupled with climate change, have led to the introduction, and often successful spread, of many pathogens and parasites into novel environments, and this phenomenon is occurring at unprecedented temporal and spatial scales13 and DeGrandi-Hoffman and Chen14). Individual immunity entails different lines of defence, ranging from physical barriers (e.g. exoskeleton cuticle and peritrophic membranes lining the digestive tract) to cellular  and humoral responses  has been described as a major mechanism of antiviral defence in honey bees19.With emerging diseases becoming major selective pressures, colony survival will ultimately depend on how honey bees are able to successfully activate immune mechanisms to protect themselves against foreign pathogens, at both colony  and individual  levels. Social immunity arises from behavioural cooperation and includes removal of adult corpses (necrophoric behaviour), removal of diseased or parasitized larvae (hygienic behaviour), and over-production of heat  bind to its receptors (pattern recognition receptors—PRRs) activating different pathways, depending on the pathogen. These pathways include Toll, IMD (immune deficiency), Jak-STAT , and c-Jun N-terminal kinase (JNK). The honey bee has fewer immune genes than 24. Different studies show that this level of variation affects disease resistance in a wide range of taxa, from mammals26 to insects27. In fact, there is evidence that a small number of large-effect genetic variants has a substantial contribution to differential susceptibility to many diseases in humans28 as well as in arthropods29. In honey bees, studies on immune response to infection have been conducted at the gene-expression level for important pathogens and parasites (e.g.33). However, the link of these findings to inter-individual variability at the DNA level is lacking.Although immune genes are highly conserved across species, some of them also display substantial intra-specific genetic variation36) and to identify genes associated with susceptibility to pathogens and parasites in many organisms37 but not in honey bees. As a first step towards filling this gap, here we developed a medium-density-SNP assay for genotyping immune genes in the honey bee. This assay contains 91 quality-proved SNPs located in functional regions of 89 immune genes and these can be readily genotyped with the high-sample-throughput iPLEX MassARRAY system. To the best of our knowledge, this is the first medium-density assay tailored for genotyping SNPs involved in immune response in honey bees.Single nucleotide polymorphisms (SNPs) have been widely used to screen genetic diversity in immune genes  in protein coding, 36 in intronic < 4 bp from exons, 1030 in intergenic < 1 Kb upstream of the transcription start site, 183 in 5’ UTR, and 813 in 3’ UTR. These functional SNPs cover 177 genes, with GB50482 (serine protease) and GB55483 (immunoglobulin-like and fibronectin type III domain) genes harbouring the highest numbers with 233 and 399 SNPs, respectively , M- (A. m. mellifera and A. m. iberiensis) and A-lineages (A. m. intermissa and A. m. sahariensis), respectively, and 46 (43%) were fixed (FST = 1) between C- and M- lineages  within the M- and C- European lineages were retained, allowing further downsizing to 3723 SNPs. In the subsequent filtering step, which was linked to the MassARRAY MALDI-TOF genotyping system, the flanking regions of the 3723 SNPs were used as input in the AssayDesign software. Despite the high number of possible SNP combinations, none of the final designed multiplexes reached the desired 30-plex size, due to predicted hairpin and dimmer formation artefacts, with IA1 and IA3 containing 28 SNPs, IA2 27, and IA4 only 24 , 131 (6.8%) revealed to be inaccurate , Israel (Beit Dagan), Portugal (Bragança and the island of São Miguel) and Spain  split . This suggests that both populations are mostly of C-lineage commercial ancestry, although they also have a background assigned to the clusters of the reference subspecies A. m. mellifera (green cluster) and the north African A. m. intermissa and A. m. sahariensis subspecies (purple cluster). On the other hand, the remaining populations show negligible levels of C-lineage ancestry and they are assigned to clusters that match the historical distributional ranges of the subspecies, i.e. samples from Portugal and Spain are assigned mostly to the A. m. iberiensis cluster (blue) and samples from Ouessant (France) are assigned to the A. m. mellifera cluster (green).The MAF distribution for each SNP and population is shown in Supplementary Fig. lit Fig. . At K = lit Fig.  and Beitlit Fig.  share an47, many of which became major causes of colony losses48. In addition to be a vehicle for disseminating parasites and pathogens at the global scale, trade can also help exotic gene flow within the A. mellifera natural range, leading to introgressive hybridization or even displacement of native subspecies51. In previous studies, we developed SNP-based tools for estimating introgression of C-derived commercial honey bee stocks into M-lineage subspecies from Western Europe53. In this study, we expanded the existing SNP-tool box by developing a medium-density assay for screening polymorphisms in immune genes. Of note is that, in addition to allowing surveys of putatively functional variation in 89 immune genes56, this assay can discriminate subspecies belonging to different evolutionary lineages and detect C-derived introgression in colonies with diverse genetic background.In the last decades, international trade in honey bees and their products has facilitated global dissemination of exotic parasites and pathogensA. mellifera diversity, this discovery panel enabled detection of SNPs with low frequency of the minor allele (average MAF = 0.19), therefore minimizing ascertainment bias57. With the exception of the recently published study of Momeni, et al.58, this is the largest and broadest panel used for SNP discovery in the honey bee. Previous works screened narrower discovery panels, suited to the goals being addressed by the constructed assays. While Momeni, et al.58 screened 14 subspecies to construct a ~ 4 K SNP assay for diagnosing European subspecies, others used discovery panels with less than four subspecies to construct high- to medium-density SNP assays tailored for addressing multiple objectives, including analysis of variation linked to defensive behaviour against V. destructor60 genomic selection and breeding60, identification of Africanised honey bees62, and inference of C-lineage introgression into M-lineage subspecies63.Development of the medium-density assay begun with detection of SNPs across the honey bee immunome. The entire coding regions of 180 genes  were scanned on 123 whole genomes, representing seven subspecies and three evolutionary lineages . By sampling such an important fraction of the Nosema spp.45.Selection of SNPs for the medium-density assay was done in multiple steps using several criteria, with emphasis put on exonic SNPs while at the same time attempting a good representation of the immunome. Due to potential hairpin and dimmer artefacts, the AssayDesign software only allowed multiplexing 107 SNPs, covering 58% (104 in 180) of the polymorphic immune genes. After validation, the number of usable variation was downsized to 91 SNPs, representing only 89 immune genes (49%) but yet covering a wide array of important pathways, including IMD, RNAi, JAK/STAT. Besides, 23 of these genes might be of adaptive relevance because they are differentially expressed in bees infected with different viruses and A. mellifera because, by screening drones instead of the traditionally used workers , we were able to detect allelic drop-in artefacts in the erroneously typed heterozygous loci. Fourteen SNPs showed heterozygous calls in > 10% of the drones, which was the main source of mismatch (14 out of 16 problematic SNPs) between the two platforms. This 13% (14 out of 107 typed SNPs) allele drop-in error was considerably higher than that obtained  with a SNP assay designed in a previous study for detecting introgression in A. m. mellifera, which was also typed with the iPLEX MassARRAY system in an independent dataset52. Allele drop-in could not be due to contamination because the 14 problematic SNPs were spread across the four assays and the remaining loci were correctly typed as homozygous and matched the calls of the Illumina HiSeq 2500 platform. Instead, allele drop-in could be explained by gene homology52, although this is an unlikely source of error because the primers designed by the AssayDesign software were all verified in silico for potential binding to multiple targets. Nonetheless, this hypothesis cannot be completely ruled out because the honey bee genome is not fully curated. Given that the genotyping with the two different assays was outsourced to different laboratories52, which have distinct MassARRAY models, the difference in the error rates could be explained by a technical artefact. After removing the problematic SNPs, the remaining 91 were accurately called in the 18 drone samples and successfully genotyped in the 156 independent samples, as reflected by the low rate of missing data (3.5%). Among the 91 quality-proved SNPs, of particular interest are 36 that result in non-synonymous changes, as these are likely to have a functional effect. However, the remaining 55 are also promising as they may affect gene regulation or expression and thus also have a functional role64.The 107 selected SNPs were validated on 18 independent drones  by comparing the SNP calls generated by the iPLEX MassARRAY system with those obtained in the Illumina HiSeq 2500 platform. This endeavour was greatly facilitated by the haplodiploid sex determination system in 27. In honey bees, the interaction between the immune system and pathogens has been addressed only through employment of gene expression assays33. These assays showed differential expression in immune genes when honey bees are infected with SINV43, IAPV43, DWV40 and Nosema spp.45. But more importantly, these assays showed differential expression between tolerant and sensitive bee strains to infections of N. ceranae and V. destructor65. Despite the shallow coverage of the honey bee immunome, the SNP assay developed here can be used as a very first step towards unravelling the genetic basis of honey bee susceptibility to infections through case–control association tests, similar to studies on other organisms69. Our assay is promising for this type of analyses for different reasons. First, although the assay only partially samples the immunome (average one SNP per gene and 89 of the 180 annotated genes), it includes SNPs located in the main pathways, such as IMD, JAK-STAT, Toll and RNAi, and there is evidence that a small number of large-effect genes have a substantial contribution to the genetic variation of host susceptibility29, increasing the probability of finding associations. Second, 82% of the SNPs lay in protein-coding regions and the non-synonymous SNPs may directly change protein function. Third, while the remaining SNPs fall outside of protein-coding regions, they are not only mapped to other relevant regions, potentially affecting transcription regulation (5’UTR), messenger RNA stability (3’UTR), and binding of the transcription initiation complex to the promoter (< 1 Kb upstream of the transcription start site64), but also in linkage with the coding region. Finally, some of the SNPs are located in genes that showed to be differentially expressed in honey bees infected with SINV43, IAPV43, DWV40 and Nosema spp.45.Evidence from many organisms indicates that sequence variation in immune genes affects host susceptibility to infections62, is not recommended for standard population genetics analyses relying on allele frequency spectrum in different genomic regions or neutrality . This is because genetic inference can be distorted due to ascertainment bias71 as well as to biases introduced from choosing high-graded SNPs segregating among target populations72, or from targeting only functional regions73, which might not be neutral. Particularly, SNPs in immune genes are expected to be under selective pressure by the spread of new parasites and pathogens or by evolution of the existing ones75, which can act to increase the frequency of beneficial alleles or to maintain polymorphisms. Therefore, caution is warranted when using this biased subset of the honey bee genome to infer broader patterns of differentiation because, as shown for other organisms, disease-related genes may have excessive SNPs with lower or higher levels of population differentiation due to purifying76, balancing77 or directional selection76. Notwithstanding these biases, inclusion of non-neutral or highly informative markers in dedicated assays may be advantageous when attempting to (i) assign individuals to source populations, (ii) estimate genetic admixture or even (iii) infer structure on ecological (rather than evolutionary) time-scale . In this study, the ADMIXTURE analysis clustered the reference samples according to their lineage and subspecies, suggesting that our medium-density assay can be used to infer population structure, consistent with findings reported for other organisms with immune SNPs79. Furthermore, the admixture patterns inferred for the 156 samples are consistent with the conservation status and the genetic background that has been documented for the populations from where they were collected. Samples originating from the A. m. mellifera population of Ouessant and from A. m. iberiensis of mainland Portugal and Spain show a high genetic integrity as well as a shared ancestry, as expected for these sister subspecies81. In contrast, samples from the Azorean island of São Miguel and, more prominently, samples from mainland France and Israel are severely introgressed, as a result of beekeeper-mediated processes linked to recurrent use of commercial stock typically of C-lineage ancestry84.The medium-density immune assay developed here, as well as other dedicated assays85. The assay was tailored for assessing diversity in immune genes and allows for high-throughput screening of samples in a time- and cost-effective manner. In addition to potential application in studies searching for associations between molecular markers and response to pathogens, the assay could be useful for delineating ancestry in honey bees.Here, we detailed the steps involved in developing a medium-density assay for genotyping immune-related SNPs with the iPLEX MassARRAY system. We provided the genomic information along with the PCR and iPLEX primers for the 91 quality-proved SNPs, enabling immediate application of the immune assay and analysis of the output does not require powerful computational resources or advanced bioinformatics skills. The 91 SNPs are mapped to coding regions (and some even involve amino acid changes) or in their close vicinity. Whether they are directly associated to immune response to pathogens or in linkage with causative SNPs not included in our assay requires functional or expression studies52 , 19 for lineage A (12 A. m. intermissa and 7 A. m. sahariensis), to 9 for lineage C . Mapping and variant calling were performed following Henriques, et al.41. A total of 2,525,621 SNPs with a minor allele frequency (MAF) ≥ 0.05 were identified after the quality control step. The functional state  of each SNP was annotated using the reference genome Amel_4.5, the Official Gene Set 3.2 (BEEBASE) and the Entrez Gene of NCBI.The immune SNPs were identified from scanning whole genomes (WGs) previously re-sequenced for 123 individuals originating from a wide geographical area52 Fig. . This WG55 and Nosema spp. infections56 was compiled from the literature , we discarded those with MAF ≤ 0.05 identified in a subset comprising only C- and M-lineage subspecies  protein coding (synonymous and non-synonymous), (ii) intergenic < 1 Kb upstream of the transcription start site, where the promoter is expected to be located, (iii) 3’UTR, (iv) 5’UTR, and (v) intronic < 4 bp from exons, where alternative splicing occurshttp://agenabio.com/assay-design-suite-20-software), using the default settings. The 3723 SNPS were prioritized to assure that the final panel contained the highest possible number of non-synonymous SNPs, functional SNPs with FST = 1 between M- and C- lineages , immune genes, and chromosomes. The AssayDesign Suite searched for optimal areas within the 501-bp flanking region of the 3723 SNPs for designing the PCR primers, while constructing all possible multiplex combinations. The optimal 30-SNP multiplexing capacity was attempted by the software whilst preventing hairpin and dimmer formation. In addition to the PCR primers, the software designed the iPLEX extension primers placed immediately adjacent to each SNP. The quality of each designed primer was assessed using the Primer-BLAST tool available in NCBI. This additional step in the SNP selection process allowed identification of primers matching multiple targets in the honey bee genome, and subsequent deletion of potentially problematic SNPs. Lastly, the genomic positions of the SNPs incorporated in the final panel were upgraded in the latest honey bee genome assembly Amel_HAv3.1 by using the BLASTn tool in NCBI  was used to dephosphorylate any remaining dNTPs to render them unusable for ensuing polymerase reactions. A total of 0.5 U of SAP and buffer (Agena Bioscience) was added to the PCR tubes and incubated for 40 min at 37 °C, followed by 5 min at 85 °C. Single base extension reactions were performed on the PCR reactions with the iPLEX Gold Kit (Agena Bioscience) and 0.8 µl of the custom UEP (unextended primer) pool. The kit contains mass-modified terminator nucleotides that increase the mass difference between extended UEPs, allowing for greater accuracy in genotyping. The mass difference with unmodified terminator nucleotides ranges from 9 to 40 kDa, depending on the two nucleotides compared. With the mass-modified terminator nucleotides the mass difference increases to 16–80 kDa. The single base extension reactions were cycled with a nested PCR protocol that used five cycles of annealing and extension nested with a denaturation step in a cycle that was repeated 40 times for a total of 200 annealing and extension steps. The goal was to extend nearly all of the UEPs. Following single base extension, the reactions were diluted with 16 µl of deionized water and with 6 ng of resin. After a 20-min. deionizing step, the reactions were dispensed onto SpectroChipArrays with a Nanodispenser (Agena Bioscience). The speed of dispensation was optimized to deliver an average of 20 nL of each reaction to a matrix pad on the SpectroChip. An Agena Bioscience Compact MassArray Spectrometer was used to perform MALDI-TOF mass spectrometry according to the iPLEX Gold Application Guide88. The Typer 4 software package (Agena Bioscience) was used to analyse the resulting spectra and the composition of the target bases was determined from the mass of each extended oligo.The four newly-designed SNP assays were genotyped using the iPLEX MassARRAY MALDI-TOF system. Briefly, the PCR primers were pooled to a final concentration of 500 nM. The PCR primer pool was used to amplify 10 ng of DNA in a 5 µl volume reaction with 1 U of FastStart Taq Polymerase  and 4 mM of MgA. m. iberiensis, 3 A. m. mellifera, 4 A. m. ligustica, 3 A. m. carnica, 4 A. m. intermissa) were chosen from the WG set for assessing SNP calling quality and genotyping accuracy of the newly developed IA1, IA2, IA3, and IA4 assays , France (N = 36), Israel (N = 40), and Portugal  were inferred for the 156 pooled samples with ADMIXTURE91 for a number of K genetic groups ranging from one to seven, using 10,000 iterations in 20 independent runs. The convergence between iterations was monitored by comparing log-likelihood scores (LLS) using the default termination criterion set to stop when LLS increases by < 0.0001 between iterations. CLUMPAK92 was used to summarize and visualize the Q-plots. A subset of the whole-genome sequenced individuals  was used as a reference in the ADMIXTURE analysis. The most probable K was estimated for this dataset using the 5- fold cross-validation (CV) error.The allele frequencies were plotted for each SNP using the R script described in Jenkins, et al.Supplementary FiguresSupplementary Tables


Genetic variants which affect complex traits  are thought to be found in functional regions of the genome. Identifying causal variants would be useful for predicting complex trait phenotypes in dairy cows, however, functional regions are poorly annotated in the bovine genome. Functional regions can be identified on a genome-wide scale by assaying for post-translational modifications to histone proteins (histone modifications) and proteins interacting with the genome  using a method called Chromatin immunoprecipitation followed by sequencing (ChIP-seq). In this study ChIP-seq was performed to find functional regions in the bovine genome by assaying for four histone modifications  and one transcription factor (CTCF) in 6 tissues  from 2 to 3 lactating dairy cows. Eighty-six ChIP-seq samples were generated in this study, identifying millions of functional regions in the bovine genome. Combinations of histone modifications and CTCF were found using ChromHMM and annotated by comparing with active and inactive genes across the genome. Functional marks differed between tissues highlighting areas which might be particularly important to tissue-specific regulation. Supporting the cis-regulatory role of functional regions, the read counts in some ChIP peaks correlated with nearby gene expression. The functional regions identified in this study were enriched for putative causal variants as seen in other species. Interestingly, regions which correlated with gene expression were particularly enriched for potential causal variants. This supports the hypothesis that complex traits are regulated by variants that alter gene expression. This study provides one of the largest ChIP-seq annotation resources in cattle including, for the first time, in the mammary gland of lactating cows. By linking regulatory regions to expression QTL and trait QTL we demonstrate a new strategy for identifying causal variants in cattle. Finding the genetic variants which lead to different phenotypes has been the goal of geneticists for many years. Bridging the genotype to phenotype “gap” has linked genes to their functions and identified causes of disease. In the dairy industry, finding genetic variants which affect phenotypes would improve selective breeding using genomic selection . GenomicMost traits of interest to the dairy industry are complex traits which are predicted to have many causal variants of small effect . BecauseWhile genes can be broadly identified using sequence homology, other types of functional elements do not have easily identifiable features, lack sequence conservation and can be located far from genes . One stuth lysine  . ChromatFunctional regions have been identified in the bovine genome in liver , rumen eTissue from liver, lung, mammary gland, kidney, heart, and spleen from three lactating Holstein dairy cows were sampled after euthanasia . Ethics Tissues were dissected, snap frozen in liquid nitrogen and stored at −80°C until use. Frozen tissue was ground for 3 min in the Geno/Grinder (SPEX SamplePrep). Ground, frozen tissue was fixed for 10 min with 10% formaldehyde and chromatin prepared using the Magnify Chromatin Immunoprecipitation kit (ThermoFisher) as per the manufacturer’s instructions. Fixed chromatin was sheared to 200–500 bp using the Covaris S2 (Covaris). Mammary and liver chromatin was sheared for 3 min, duty cycle five, %intensity four and 200 cycles per burst 200. The remaining tissue were sheared at duty cycle two, %intensity three, 200 cycles per burst for 5–15 min.Chromatin immunoprecipitation was performed using the Magnify Chromatin immunoprecipitation kit (ThermoFisher) with some modifications. For the mammary and liver each sample was immunoprecipitated in three separate reactions with 10 ul of chromatin and 0.25 ug or 0.5 ug of antibody for the histone modifications and 10 μl of antibody for CTCF. Triplicate samples were combined after de-crosslinking using MinElute PCR purification kit (QIAGEN). For spleen, heart, kidney and lung, 1.5–10 μg chromatin was used for immunoprecipitation with 0.5–0.15 μg antibody  per reaction or 10 μl of CTCF antibody. The DNA obtained from ChIP was purified and concentrated using Monarch Genomic DNA Purification Kit (New England Biolabs).Sequence libraries for ChIP and a corresponding input sample were prepared with the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs) each with unique barcodes as per the manufacturer’s instructions and run on the HiSeq 3000 (Illumina) in a 150 cycle paired end run.q > 15 and duplicate reads removed. ChIP and input reads were used to call peaks with MACS2 default settings  taken from Ensembl . The prEnrichment of putative causal SNPs in peaks, peaks which correlated with gene expression, differentially bound peaks and ChromHMM states was calculated. Enrichment was calculated using the formula outlined in ChromHMM  and descEnrichment = (C/A)/(B/D) where: A is the number of positions under peaks, B is the number of positions that were putative causal SNPs, C is the number of positions under peaks and also a putative causal SNP and D is the number of positions in the genome.The significance of enrichment or depletion was calculated using a hypergeometric test in R. A variety of putative causal SNP datasets were used for enrichment analysis . There wWe tested whether enrichment of putative causal SNPs within peaks changed depending on location in the genome. Enrichment was calculated with only peaks and SNPs located within 100 kb from a TSS, 100–200 kb from a TSS and so on up to a million base pairs from a transcription start site. Transcription start sites for each gene were taken from Ensembl [Release 94 ]. The dir2 < 0.5. Enrichment was calculated with these filtered SNP datasets as described above.To account for SNPs in high linkage disequilibrium (LD), the GWAS SNP datasets were filtered for LD using plink . Within 86 ChIP-seq datasets were generated as shown in Quality of the ChIP-seq assay was assessed by calculating the Jensen-Shannon Distance (JSD) between the sample and input and cross strand correlation metrics. All but two samples exceeded ENCODE standards for cross correlation metrics  but was less strongly correlated in broad peaks . Number of peaks per dataset was also strongly correlated with the number of mapped reads   .Profile plots display the normalised ChIP-seq signal above input signal. As expected H3K4Me3 displayed the highest signal around the TSS, followed by H3K27ac. The profiles for H3K4Me3, H3K4Me1, and H3K27ac displayed a slight bimodal shape at the TSS . When coWe compared replicates within marks using Pearson correlations. In general, all samples were strongly correlated regardless of tissue or animal, however, there were some batch effects .P < 0.05) and two-fold higher or lower than the mean of the counts in all other tissues.Normalised ChIP-seq counts at consensus peaks in each tissue were compared for differential binding. Peaks were defined as differentially bound where the mean of the counts in one tissue was significantly different  correlation in 6–11% of peak-gene pairs, which is more than the 5% expected by chance. Similarly, most correlations were negative for H3K27Me3 and positive for all other marks which would not be expected if the correlations were random chance , there was a significant  is a gene known to be highly expressed in the mammary gland  with average enrichment between 1.17 (for SNP80k) and 3.82 (for QTL Protein Yield) for each SNP dataset . The narP < 0.001  were enriched for some causal SNP datasets . The staConsistent with results in peaks there was little variation in enrichment between tissues in most of the SNP datasets except for conserved regions and milk production QTL . ConservOne confounding factor of the enrichment analysis is that potential causal SNP may be found within peaks because both peaks and causal SNP may tend to be found close to genes. We tested whether enrichment of potential causal SNPs in peaks changed depending on the distance of the peaks from the nearest gene and whether this explained the high enrichment seen within peaks. Peaks and SNPs were split up into 10 groups of 100 kb increments , 12 depeAs distance from TSS increased the number of SNPs and peaks in these regions decreased , 12. In P < 1 × 10–7) were filtered to account for linkage disequilibrium (LD pruning) where SNPs were retained with r2 < 0.5  will be diluted by the non-causal variants in the dataset, but it could also mean that our functional regions were just enriched for SNPs in linkage disequilibrium with causal variants. However, the data shows that across multiple SNP datasets using different methodologies there was consistent enrichment for these SNPs within functional regions. This is consistent with studies in human and cattle .Another possible limitation is that the peaks were enriched because they and the SNPs tested were both near genes. This would mean they are more likely to intersect because of similar distributions in the genome rather than due to a causal variant affecting functional mark binding . To testPAEP . A genetic variant in this peak which altered its height therefore may also alter PAEP gene expression. Further study would be needed to verify this, but ChIP-seq data enabled us to filter 531 SNPs down to three genetic variants with a potential causal mechanism for altering gene expression.To highlight the utility of the data generated, an example of three SNPs which we hypothesise are good candidate causal variants for important milk traits is shown . In a gePAEP . In thisGenomic selection will be more accurate over time and across breeds if causal variants are included in predictive modelling . It is thttp://www.ebi.ac.uk/ena/data/view/PRJEB41939.The sequence data for this study is publicly available in the European Nucleotide Archive at The animal study was reviewed and approved by the Department of Jobs, Precincts, and Regions Ethics Committee (Application No. 2014-23).CP-W contributed to conception and design, acquisition of data, data analysis, and drafting of manuscript. JW contributed to acquisition of data. RX contributed to data analysis and critical revision. JG contributed to data acquisition. MG contributed to conception and design, data analysis, and critical revision. AC contributed to conception and design, data acquisition, data analysis, and critical revision. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


ST ≥ 0.20) to differentiate these cattle populations were identified, and utilized to partition genome of each animal into a predefined number of clusters. The structure of these cattle indicated shared ancestry of dairy breeds viz. Gir, Tharparkar and Sahiwal. Most of the animals (> 76%) of different populations under study except Vechur clustered into their own group of animals called breed. Vechur population retained highest rate of admixture, consistent with its crossing with other breeds. Ongole, Kangayam and Hariana shared comparatively less of their genome (≤ 15%) with other breeds. The study indicated that all seven breeds evolved from their independent ancestry but there was intermixing of these breeds in the recent past. The selection signatures identified between draft (Kangayam) and dairy breeds included several genes like FAM19A2, RAB31P, BEST3, DGKA, AHCY, PIGU and PFKP which are involved in immune response, metabolic pathway, transportation of glucose and sugars, signaling pathways, cellular processes, cell division and glycolysis regulation, respectively. Moreover, these genomic regions also harbour QTLs affecting milk performance traits. The signatures were also identified even between the dairy breeds. In comparison to large-sized cattle, there were significant differences in the number of QTLs affecting production  and morphological traits (height) in short-statured Vechur breed. The presence of HMGA2 gene in the selection signature on chromosome 5 may explain the variations in stature between these cattle.The genomic diversity and relationship among seven diverse cattle breeds viz. Sahiwal, Tharparkar, Gir, Vechur, Ongole, Kangayam and Hariana were investigated in 132 random samples based on high density SNP array comprising > 777 K SNPs. A total of 1993 SNPs  having greater power (F These breeds thrive in humid subtropical, semi-arid & arid and tropical wet/dry climatic regions, catering to a variety of specialized functions such as dairy, draft and dual (Dairy & draft) use. Therefore, these cattle would have gene content unique to such roles and adaptations in their genome1. Hence, they can serve as a great reservoir of genetic pool for identifying genes under selection for different traits that have evolved, as well as for determining genetic diversity.India has 50 registered cattle breeds adapted to different agro-climatic conditions, breeding and management practices  array and next-generation sequencing technologies in genomics and population genetics has resulted significant progress in deciphering the genetic structures17. The objectives of the present study were to: (1) assess genomic diversity and effective population size; (2) analyse admixture and structuring; (3) identify diversified selection signatures among breeds using SNP data.To better understand the genetic mechanisms underlying the local adaptation and functional characteristics of Indian zebu cattle, we investigated three dairy breeds  from sub-tropical and hot arid regions, two dual breeds  from sub-tropical and hot humid regions, and one draught breed (Kangayam) from the country's hot humid region. We have also included Vechur, a short statured cattle breed native to hot humid climate. This array of cattle breeds, each adapted to a different ecological niche and functional attribute, will help to uncover genomic diversity and natural and artificial selection footprints over centuries. Previously, we identified and characterized genome wide runs of homozygosity (ROH) signatures in these cattle breeds using Illumina BovineHD BeadChip19. Out of 735,293 autosomal SNPs genotyped in these cattle, 60%  were in Hardy Weinberg equilibrium and revealed higher degree of polymorphism (MAF > 0.05). It has also been suggested that if the number of markers is large enough, a sample size of 4–620 and polymorphic SNP filtration21 could offset the influence of ascertainment bias, as in the present work. The SNPs in strong LD (R = 0.5) were also filtered out to minimize the bias in estimating the genomic diversity. We were left with 165,021 informative SNPs after pruning for further genome diversity analyses. Minor allele frequency varied between breeds, ranging from 0.23 (Kangayam) to 0.26 (Vechur), with an average observed heterozygosity of 0.35 in all samples examined , and the average genotyping rate for the remaining 112 animals was 0.99. The final data on 112 cattle samples belonging to Sahiwal (13), Tharparkar (17), Gir (15), Ongole (17), Hariana (18), Kangayam (16) and Vechur (16) breeds were achieved after quality control measures (Table ST ≥ 0.20) were chosen for assessing genetic diversity, selection signature and breed structuring. Hence, out of 777 K, 1993 loci  were used for further analysis. The genetic differentiation (FST) of the breeds based on all the SNPs was just 0.05 and based on 1993 most differentiating loci was 0.23. The gene diversity among breeds (Dst), and Dest, a measure of population differentiation34, across the loci were 0.08 and 0.13, respectively.Initially, all filtered SNPs  were utilized to determine the genome diversity of seven cattle breeds: Tharparkar, Sahiwal, Gir, Vechur, Ongole, Kangayam and Hariana. After that, only differentiating loci with greater power to distinguish these cattle populations  having the higher degree of genetic differentiation (FST ≥ 0.40). The inbreeding coefficient  was zero, suggesting that the cattle from which the samples were taken were randomly mated. The genetic differentiation power of those informative SNP loci  in cattle breeds under study ranged from 0.20 to 0.51, with an average value of 0.23, indicating that these loci account for 23% of the genetic variation between the breeds. Table ST) and Nei's genetic distance35. Both estimates revealed higher genetic differentiation between Kangayam and rest of the cattle breeds (FST: 0.08 to 0.10), followed by Ongole and rest of the breeds (FST: 0.05 to 0.07). Dairy cattle breeds viz., Tharparkar, Sahiwal and Gir showed the least genetic differentiation (FST: 0.04 to 0.06). A moderate genetic differentiation36 where FST ranged from 0.05 to 0.15 have been recorded in Bos taurus dairy and beef cattle with an FST value of 0.08 across all SNPs14, as well as in African cattle breeds (FST : 0.04 to 0.08). Many SNPs with higher genetic differentiation power (FST > 0.5) have also been reported in Bos taurus dairy and beef cattle14. The degree of genetic differentiation among Indian Zebu cattle was comparable to that of African Zebu cattle30, but lower than that of Bos taurus and Bos indicus33, as anticipated given their historical divergence.Manhattan graph Fig.  depicts Ne) for seven Indian cattle breeds are presented in Fig. Ne showed a downward trend in recent generations across the populations. The most rapidly declining recent Ne was found in the KG and GR, whereas HR and TP showed a slowly declining trend. The estimated Ne at 13 generations ago in seven Indian native cattle breeds ranged from 48 to 74 , due to the intensive selection pressure or artificial insemination used for developing these breeds. In Kangayam cattle, we have observed maximum autozygosity attributable to both recent and ancient inbreeding17.Over the last 999 years, the Ne13 values ranging from 85 to 13237. The genotyping of Italian local beef breeds  and Italian Limousin (LIM) using the GeneSeek GGP-LDv4 33 k SNP chip containing 30,111 SNPs showed an average estimated historical effective population size (Ne13) of 45–310 38.The application of LD-based Ne estimation in developing countries for local breeds without pedigree information could offer new perspectives for the assessment of the actual gene pool available and the respective decision-making in conservation and management.Recently, a Bovine HD-SNPs array based screening of Chinese native cattle populations revealed a similar trend, with 17. Figure 17. Moreover, the structure and genomic relationship among these cattle was also studied using a small number of highly differentiated loci  and it was interesting to note that even 10 markers also revealed the same level of differentiation as with large sized battery of markers. Therefore, the structure and relationship between the cattle breeds were not significantly influenced by ascertainment bias, which was consistent with a previous study in sheep39 and cattle17.The principal component analysis (PCA) based on genomic relationship (IBS-Identity by state) matrix using 165,021 LD pruned autosomal SNPs was undertaken to assess breed composition of the animals. The first, second and third principal components accounted for 28.7, 11.9 and 6.8% of the total variation, respectively. However, the first three components based on runs of homozygosity explained 98.7% cumulative variation in these breeds17. The analysis of molecular variance also revealed significant (p < 0.05) differences in the proportion of variation (1.64%) due to their functional characteristics viz. dairy, dual, draft and small size. However, there were significant differences (5.44%) between breeds with specific characteristics such as dairy and dual  and hence differentiated dairy , dual , and draft (Kangayam) and small sized cattle (Vechur) Table . PCA basal Table . ROH reg40 to partition genome of each animal into a predefined number of clusters. The breeds were clearly grouped into draft and dairy/dual breeds  clustered together and 90% of the genome of draft cattle Kangayam grouped into separate cluster. At K = 3, Sahiwal, Tharparkar, Gir, Vechur and Hariana (> 72%) clustered together. Kangayam and Ongole clustered into their own group (89%). All major dairy breeds  shared > 80% of their genome. However, at K = 4, Vechur, was clustered apart from other breeds and at K = 5, Gir also clustered separately. At K = 7, most of animals (> 76%) of the different populations under study except Vechur clustered into their own group of animals called breed  with other breeds. The Fig. The population structure was studied using admixture model based clustering implemented in Structureeds Fig.  for pre- Ongole 8% clusterST value were identified. The most differentiated breed pairs based on their performance were chosen for the identification of selection signatures. Therefore, Kangayam, a draft breed, was used as control breed for the analysis of dairy breeds while, Vechur, a small sized breed for the rest of large sized breed to search for signatures that may be associated with stature. Genome wide significance level was set to 0.001 to represent a selection signatures. In these selection signatures between distinct breed pairs, 553 genes were found, 40 of which were shared with 412 genes previously identified in these breeds using runs of homozygosity17 , and thus may affect Kangayam and Sahiwal's health in different ways. Kangayam is a hardy draft breed compared to Sahiwal. There were significant differences in number of QTLs affecting milk yield and production traits when Kangayam paired with any of the dairy breed. For example, between Hariana and Kangayam, the marker BovineHD0500012581 lies in RAB31 gene which is involved in metabolic pathway (https://www.genecards.org/cgi-bin/carddisp.pl?gene=RAB31) and has also been annotated with QTLs affecting milk performance traits (Cattle QTL data base). Hence, may be responsible for variations in these traits.QTLs affecting milk yield, milk composition, reproduction, production, health status, and morphological traits were detected when the top five genomic regions in these cattle were searched in the Bovine QTL database (Supplementary Table BEST3) [https://www.genecards.org/cgi-bin/carddisp.pl?gene=BEST3&keywords=BEST3], Diacylglycerol Kinase Alpha (DGKA) [https://www.genecards.org/cgi-bin/carddisp.pl?gene=DGKA&keywords=DGKA], Adenosylhomocysteinase (AHCY) [https://www.genecards.org/cgi-bin/carddisp.pl?gene=AHCY&keywords=AHCY], Phosphatidylinositol Glycan Anchor Biosynthesis Class U (PIGU) [https://www.genecards.org/cgi-bin/carddisp.pl?gene=PIGU&keywords=PIGU] and Phosphofructokinase, Platelet (PFKP) [https://www.genecards.org/cgi-bin/carddisp.pl?gene=PFKP&keywords=Phosphofructokinase] which are involved in transportation of glucose and sugars, signaling pathways, cellular processes, cell division and glycolysis regulation, respectively. Moreover, these genomic regions also harbour QTLs affecting milk performance traits (Cattle QTL data base). The signatures were also identified even between the dairy breeds. For example, the genomic region, having BovineHD1300006213 locus, revealing high differentiation between Tharparkar and Gir (FST = 0.585) harbours QTLs affecting all the traits under study except milk yield (Cattle QTL data). There were significant differences in number of QTLs affecting production  and morphological traits (height etc.) of Vechur when paired with large sized cattle (Supplementary Table HMGA2 gene in the genomic region surrounding BovineHD0500013882 locus on chromosome 5 is responsible for explaining the variation in stature of cattle42 and human beings43. This locus with HMGA2 gene had high differentiating power (FST = 0.18) but didn’t appear in the common list of signatures with a threshold value of 0.25 in the present study. Genes such as FAM19A2, BEST3, AHCY, PIGU, PFKP and HMGA2 were previously identified while studying runs of homozygosity with the same set of data17, thereby validating these signatures.The other selection signatures identified between Kangayam and dairy breeds included several genes like Bestrophin 3 , Tharparkar , Gir , Ongole , Hariana , Kangayam  and Vechur  breeds of cattle were incorporated. Random blood samples were obtained from various farms across the country in accordance with the regulations and guidelines of the Institutional Animal Ethics Committee (IAEC), National Bureau of Animal Genetics Resources (ICAR-NBAGR), Karnal. Genomic DNA was extracted from the whole blood using HiPurATM SPP Blood DNA isolation kit. The quality of the genomic DNA was tested using an agarose gel electrophoresis, and the quantity of DNA was measured using a Nanodrop Spectrophotometer (Nanodrop ND-1000). The DNA samples were genotyped at Sandor Lifesciences Pvt. Ltd. in Hyderabad, India, using an Illumina BovineHD BeadChip with 777,962 SNPs and following the manufacturer's standard procedures. The data files including MAP and PED files were retrieved using Genome Studio. The analysis of the SNP data revealed the call rate ranged from 95 to 99%. The number of SNPs scored in a given sample / the number of SNPs available on chip * 100 was used to calculate the call rate of SNPs. The call rate indicated that the Illumina BovineHD BeadChip is useful in scoring SNP/genotypes in the Indian cattle population and could be used to assess breed signatures and diversity.45. The unmapped SNPs and SNPs present on X, Y chromosomes, and on mitochondrial DNA were removed and only the SNPs located on autosomes were considered for analysis. SNPs with call rate (CR) ≤ 95%, minor allele frequency (MAF) ≤ 0.05, and HWE (P ≤ 0.001) were excluded. Samples that had more than 10% missing genotypes were also excluded. The quality of SNPs genotyped were assessed based on Gene Call Score (< 0.2) and Gene Train Score (< 0.55) using Genome Studio. The gene train score, in general, was > 0.55 for the SNPs genotyped in these samples and hence good quality SNPs were obtained.The quality control procedure was carried out by using PLINK2) which depends upon the frequencies of the alleles at the loci under consideration. The r2 values were calculated using PLINK v 1.945 keeping the window size limit of 500 kb between pair-wise SNPs. Further, autosomal SNPs were pruned out with an r2 value of 0.5 using PLINK45. The minor allele frequency, heterozygosity and inbreeding in different breeds were also estimated using PLINK. Hierarchical F-statistics were computed to access genomic differences in different groups using Hierfstat in R : I) geographical distribution (North & South), II) body size , and III) Milk production . The genomic differences among the breeds were also calculated in terms of FST value46 as well as through analysis of molecular variance (AMOVA) using ARLEQUIN47.For handling and managing as well as analyzing the large size data on 777 K bovine Bead Chip, several in house computer scripts were written for making the suitable data formats for further downstream analyses using different genetic software including HierFstat in R and Structure. LD was measured for each breed as correlation between adjacent SNPs (rNe) was estimated using the SNeP v1.1 software as described earlier48. It inferred Ne based on linkage disequilibrium (LD) against past t generations, where t = 1/2c and c is the distance between SNPs in Morgans (100 Mb = 1 Morgan was assumed)49. The estimation was performed on the SNP data with correction of sample size, phasing and recombination rate.The historical and recent effective population size . The optimum number of K was determined based on mean value of log likelihood across the inferred clusters. The principal component analysis (PCA) based on genomic relationship (IBS-Identity by state) matrix using plink tools and R script was also undertaken to assess breed composition of the animals.The population structure was studied using admixture model based clustering implemented in StructureP < 0.0001). FST value of ≥ 0.25 was considered as selection sweep between two contrasting groups . The highly significant windows between two groups were annotated for their gene content. NCBI map viewer of the bovine UMD3.1.1 (https://www.ncbi.nlm.nih.gov/genome/gdv) was used to identify the genes underlying the selection sweeps. Genes underlying ± 1 MB of SNPs with FST value of ≥ 0.25 were identified.The diversifying selection signatures were identified among the breeds using F-statistics . Test of two proportions was worked out to find the significant differences between the numbers of QTLs affecting the two contrasting groups (dairy versus Draft) for six different traits using XLSTAT.The effect of the top 5 signatures on the underlying QTLs was assessed by exploring cattle QTL database (http://cpcsea.nic.in/WriteReadData/userfiles/file/Compendium%20of%20CPCSEA.pdf) and approved by the Institutional Animal Ethics Committee (IAEC) of ICAR-National Bureau of Animal Genetics Resources (ICAR-NBAGR), Karnal.Random blood samples were obtained from various farms across the country with written informed consent from the owner by qualified Veterinarian in accordance with the guidelines issued by the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA; Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


O-glucoside and quercetin O-acetylhexside are concentratedly located at 43.55 Mb on chromosome 5 and 26.9 Mb on chromosome 7, and two Lc were mined as candidate genes, respectively. However, the signals of luteolin 7-O-glucoside and kaempferol 3-O-glucoside were also detected at 14.36 Mb on chromosome 3, and five glycosyltransferase genes on this loci were deemed to regulate their content. Our work is the first research to use mGWAS in millet, and it paves the way for future dissection of complex physiological traits in millet.The plant metabolome is considered as a bridge between the genome and the phenome and is essential for the interaction between plant growth and the plant environment. Here, we used the liquid chromatography-tandem mass spectrometry method to perform a widely targeted metabolomics analysis of 150 millet germplasm and simultaneous identification and quantification of 330 annotated metabolites. Comparing the metabolic content of different millets revealed significant natural variation of both primary and secondary metabolites, including flavonoids, phenolamides, hydroxycinnamoyl derivatives, nucleotides, and lipids, in the millets from India and the north and south of China; among them, some of the flavonoids are the most prominent. A total of 2.2 TB sequence data were obtained by sequencing 150 accessions of foxtail millet using the Illumina platform. Further digging into the genetic basis of metabolites by mGWAS analysis found that cyanidin 3- Setaria italica) is an important grain crop. It is characterized by adaptation to unfavorable ecological conditions, such as irregular and untimely rainfall, high-salinity soil, and high temperatures (Setaria viridis), in Northern China for over 8000 years . The samples were sequenced on an Illumina HiSeq 4000 platform. To retain reads of high quality, reads with fewer than 5% N (missing) bases and with fewer than 50% of bases of base quality <5 were deemed as cleaned reads. All other reads were discarded.1 The water was doubly deionized with a Milli-Q water purification system . Authentic standards were purchased from ANPEL, Shanghai, China,2 BioBioPha Co., Ltd.,3 and Sigma-Aldrich, United States.4 Standards stock solutions were prepared using water, methanol, and/or dimethyl sulfoxide (DMSO) as the solvent and stored at −20°C. Combined standard solutions of chemicals were prepared just before use by mixing individual stock solutions and diluting these mixtures with 70% aqueous methanol.All the chemicals were of analytical reagent grade. Gradient-grade methanol, acetonitrile, and acetic acid were purchased from Merck Company, Germany.g for 10 min, the extracts were filtered  before LC-MS analysis.The freeze-dried leaves were crushed using a mixer mill  with zirconia beads for 1.5 min at 30 Hz. A 100 mg mass of powder was weighed and extracted overnight at 4°C with 1.0 ml of 70% aqueous methanol. Following centrifugation at 10,000 × 6; MS, Applied Biosystems 6500 Q TRAP7). The analytical conditions were as follows, HPLC: column, Waters ACQUITY UPLC HSS T3 C18 ; solvent system, water (0.04% acetic acid): acetonitrile (0.04% acetic acid); gradient program, 100:0 V/V at 0 min, 5:95V/V at 10.0 min, 5:95V/V at 11.0 min, 95:5 V/V at 11.1 min, 95:5 V/V at 15.0 min; flow rate, 0.35 ml/min; temperature, 40°C; and injection volume: 5 μl. The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap (Q TRAP)-MS.The sample extracts were analyzed using an LC-ESI-MS/MS system  scans were acquired on a triple quadrupole-linear ion trap mass spectrometer (Q TRAP) using an API 6500 Q TRAP LC/MS/MS System, which was equipped with an ESI Turbo Ion-Spray interface operated in a positive ion mode and controlled by Analyst 1.6.3 software (AB Sciex). The ESI source operation parameters were as follows: ion source, turbo spray; source temperature 550°C; ion spray voltage (IS) 5500 V; ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) were set at 55, 60, and 30.0 psi, respectively; and the collision gas (CAD) was high. Instrument tuning and mass calibration were performed with 10 and 100 μmol/L polypropylene glycol solutions in QQQ and LIT modes, respectively. The QQQ scans were acquired as MRM experiments with the collision gas (nitrogen) set to 5 psi. The DP and CE for individual MRM transitions were performed with further DP and CE optimization. A specific set of MRM transitions was monitored for each period according to the metabolites that were eluted within this period.Obtaining the highly reproducible metabolite signals with the production spectra. Based on the annotation, commercially available standards were purchased and analyzed using the same profiling procedure as the extracts.P-value. After Bonferroni correction , it was used to correct the threshold of localization, where the significant threshold of mGWAS after correction is P ≤ 1.8 × 10–6, and thus to locate the area significantly associated with the metabolic phenotype. Then, we used R to draw Manhattan graph and Q–Q graph in order to visually show the significant associated area and model effect.Only SNPs with minor allele frequencies R 0.05 and with the number of varieties having the minor allele in a (sub) population R6 were used to carry out GWAS. After obtaining the metabolome data, combined with the genotypes of 150 related populations, mGWAS analysis was first performed on the related population data. GWAS analysis used the compressed mixed linear model (cMLM), which integrates metabolic phenotype, genotype, population structure, and genetic relationship, to analyze, obtaining the association with each genotype (SNP) and phenotype 8 Identification of differential accumulation of metabolites between different tissue/varieties was determined by partial least squares-discriminate analysis (PLS-DA) with the VIP values (Variable Importance for the Projection) ≥0.8, followed by both ANOVA (p ≤ 0.05) and fold-change (≥1.5 or ≤0.67). PCA and PLS-DA were performed with SIMCA-P version 14.0.Principal component analysis (PCA) was performed with log2 transformed metabolite data to improve the normality. For hierarchical clustering analysis (HCA) in the study of developmentally controlled accumulation and natural variation of metabolites, metabolite data were firstly log2 transformed, followed by a min–max normalization. To construct the neighbor-joining tree, the data matrix was generated from 150 millet varieties and 1335 detected metabolites, which represented the contents of each metabolite in different populations. The pairwise population distance was used to construct a neighbor-joining tree using the software PHYLIP (version 3.69). The software TreeView and MEGA7 were used for visualizing the phylogenetic tree. For HCA, the “heatmap.2” function in the “gplot” R-package was utilized to generate the heatmap, and various R programming tools were used to plot data.F statistics, the fixation index (FST) is one of the most commonly used statistics in population genetics, and it is a measure of population differentiation due to genetic structure, which is frequently estimated from genetic polymorphism data, such as SNPs  was employed to quantify genetic differentiation (FST) for Indian and Northern Chinese accessions using the VCFtools software. Windows with FST values exceeding an empirical FST cutoff (top 5%) were regarded as highly differentiated regions. Windows separated by less than 200 kb were merged into a single non-overlapping region.Developed as a special case of Wright’s  as SNPs . EstimatLc transcription factors and glycosyltransferase were obtained from NCBI according to their accession numbers.9 Candidate gene information in this study was obtained from the draft assembly of the millet genome  using the SuperReal PreMix Plus (SYBR Green) kit  according to the manufacturer’s instructions. The PCR was executed under the following process: 95°C for 5 min for pre-degeneration, then 35 cycles of 94°C for 30 s, 56°C for 30 s, and 72°C for 90 s. Each experiment was performed in triplicate for biological and technical repeats. The sequences of primers for qRT-PCR are listed in 2 spectral tag (MS2T) library, we finally obtained the 1335 highly reproducible metabolite signals with the production spectra -based metabolic profiling method . By cons spectra , 3. Spec spectra . Among w9 others .P-value (p < 0.05) were used to evaluation of the metabolite contents and 617 metabolites showed significant variations among the millet varieties  greater than 50% , suggestarieties , among warieties . Overallholipids , disclosO-glycosides, 6 flavone C-glycosides, 5 flavanones, 5 flavonols, 6 anthocyanins, 6 hydroxycinnamoyl acid derivatives, 7 phenolamides, and 6 hydroxycinnamoyl quinates, were shown to be significantly higher in millet from the north of China, while 45 metabolites, including 7 flavone O-glycosides, 9 flavone C-glycosides, 4 flavanone, 7 nucleotides, and 6 glycerophospholipids, were shown to have significantly lower levels , kaempferol 3-O-glucoside (GZ2519), cyanidin 3-O-glucoside (GZ2192), and tricin 7-O-hexoside (GZ2643) displayed 5.9, 1.8, 5.0, and 2.0 times over-accumulation in millet from north of China than that from India, respectively , 1-O-beta-D-glucopyranosyl sinapate (GZ0627), N′-p-coumaroyl agmatine (GZ2195), and 3-O-p-coumaroyl quinic acid O-hexoside (GZ0446), also showed significantly higher levels in millet from the north of China with average levels up to 2.5, 12.0, 3.3, and 5.8-fold, respectively  and C-hexosyl-luteolin O-feruloylpentoside (GZ2521), and nucleoside phosphate and glycerophospholipids, such as adenosine 5′-monophosphate (GZ1824) and LysoPE 18:2 (GZ3304), in millet from the north of China than that in India, with average decreased levels up to 0.4, 0.6, 0.02, and 0.1-fold  is an example of a metabolite co-mapping with metabolites in different classes; it comapped with 1-O-p-coumaroyl quinic acid (GZ0548) and 3-O-p-coumaroyl quinic acid O-hexoside (GZ0446), primarily at 9.3 Mb on chromosome 9 , and flavonol O-glycosides, such as quercetin O-acetylhexoside , in Chinese millet is much higher than that of Indian millet according to GWAS analysis, and we found important identical loci related to their accumulation level  and quercetin O-acetylhexoside , similarly, another leading SNP 7:26896246 on chromosome 7 was significantly associated with the level of cyanidin 3-O-glucoside  and quercetin O-acetylhexoside . When searching for the candidate genes in the locus on chromosome 5, we found that the Si000845m gene located 0.13 kb downstream of lead SNP 5:43546119 and was annotated as an anthocyanin regulatory Lc protein. Furthermore, when focused on another locus at chromosome 7, we found the gene Si012401m located ±4.0 kb upstream of lead SNP 7:26896246 and is also annotated anthocyanin regulatory Lc protein  was significantly higher when compared with that in allele I (C), with p-values 2.5E−03  was observed between cyanidin 3-O-glucoside content and SNP7:26887726, which caused non-synonymous mutations and in Si012401m coding region  underlying the natural variation of anthocyanins and flavonol lucoside . Among wlucoside . This mulucoside . The mea 2.5E−03 . This is 2.5E−03 . Similarg region . Further regions .Si000845m and Si012401m as the candidate MYC transcription factors controlling the biosynthesis of both anthocyanins and flavonol O-glycosides, and we propose that genetic variants within their coding regions might contribute to the natural variation in levels of anthocyanins and flavonol O-glycosides in millet. We verified the expression levels of candidate genes in leaves of different genotype materials by using qRT-PCR and found that the expression level of Si012401m has the same trend as the content of cyanidin 3-O-glucoside (GZ2192)  .O-glycosides such as luteolin 7-O-glucoside (GZ0794) and kaempferol 3-O-glucoside (GZ2519) in millet from China was markedly higher than that in India. We found that two flavone-O-glycosides (luteolin 7-O-glucoside and kaempferol 3-O-glucoside) were identified at the same loci based on mGWAS  and kaempferol 3-O-glucoside . We next mined candidate genes in the loci on chromosome 3, and we found five genes downstream of lead SNP3:14349356 and annotated them as related functions of glycosyltransferase: Si024831m (7.03 kb downstream of SNP3:14349356), Si021855m (10.77 kb downstream of SNP3:14349356), Si021892m (14.54 kb downstream of SNP3:14349356), Si025159m (21.98 kb downstream of SNP3:14349356), and Si025028m (25.25 kb downstream of SNP3:14349356). Phylogenetic analysis showed that five candidate genes have similar sequences to the genes in rice that confer flavone-O-glucosyltransferase  and luteolin 7-O-glucoside (G) content and thus has a higher kaempferol 3-O-glucoside and luteolin 7-O-glucoside content , and its expression level in materials with high content is significantly higher than that in materials with low content  (Si021892m between high and low kaempferol 3-O-glucoside (GZ2519) content materials  (The expression level of 9.65E−4) . However = 0.32) .C-hexosides, flavone di-C,C-hexosides, C-hexosyl flavone-O-hexosides, flavone/flavanone/flavonol O-glycosides, flavone O-rutinosides, and anthocyanidin O-glycosides, coinciding with previous reports in rice and wheat  than that in indica in the relative lower latitudes (represented by India and the south of China), these metabolites, however, displayed significantly higher levels in the millet from the north of China than that in India, which is important for UV-B protection in plants  has obvious differences in the millet leaves of the three regions. Among them, the content of millet leaves in southern China is the highest, which is 596.75 times and 15.48 times that in India and northern China, respectively. Not only that, the content of it in millet leaves in northern China and India is also significantly different, with a multiple of 38.54  on chromosome 3, which are homologous to the three glycosyltransferases OsUGT705A1, OsUGT705A2, and OsUGT705A3 in rice, and these three genes in rice are also located in a small interval and have been confirmed to have flavonoid 7-O-glycosyltransferase and flavonoid 3′-O-glycosyltransferase activity  has been widely used in Arabidopsis, rice, and maize, deepening our cognition of the genetic basis of these crop metabolomes, greatly promoting the development of functional genomics. Our research fills the gap in the application of mGWAS in millet and paves the way for future dissection of complex physiological traits in millet.Luteolin 7-activity . So Si02O-glucoside and quercetin O-acetylhexside are concentratedly located at 43.55 Mb on chromosome 5 and 26.9 Mb on chromosome 7, and two Lc were mined as candidate genes, respectively. However, the signals of luteolin 7-O-glucoside and kaempferol 3-O-glucoside were also detected at 14.36 Mb on chromosome 3, and five glycosyltransferase genes on this loci were deemed to regulate their content. Then, we used phylogenetic analysis of candidate genes and reported genes to further determine their functions. Our work is the first research to use mGWAS in millet, and it paves the way for future dissection of complex physiological traits in millet.In the present study, LC-MS-based widely targeted metabolic profiling analysis was performed in a wide range of millet accessions. We observed differentially accumulated patterns of metabolites, including flavonoids and phenolamides, between the millets from India and the south and north of China. Moreover, we researched the genetic basis of metabolites by mGWAS analysis found that cyanidin 3-PRJEB45075.The data presented in the study are deposited in the European Variation Archive (EVA) repository, accession number ZZ and XW designed the research. WW and SL supervised this study. WW, XZ, GSh, GSo, WYZ, JP, DW, XL, YZ, TT, and CX participated in the material preparation. JS, WW, FZ, XW, FW, XF, and HL carried out the metabolite analyses. YW, QZ, BW, SL, WQZ, and GF performed the annotation of the metabolites and performed the data analysis. WW and SL discussed the results and wrote the manuscript. All authors contributed to the article and approved the submitted version.YW, BW, QZ, CX, TT, JS, WQZ, and HL were employed by the company Wuhan Metware Biotechnology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Thorough quality control is key to generating high integrity genotyping data and robust results. Quality control of genotyping array is generally a complicated process, as it requires intensive manual labor in implementing the established protocols and curating a comprehensive quality report. There is an urgent need to reduce manual intervention via an automated quality control process. Based on previously established protocols and strategies, we developed an R package GTQC  to automate a majority of the quality control steps for general array genotyping data. GTQC covers a comprehensive spectrum of genotype data quality metrics and produces a detailed HTML report comprising tables and figures. Here, we describe the concepts underpinning GTQC and demonstrate its effectiveness using a real genotyping dataset. R package GTQC streamlines a majority of the quality control steps and produces a detailed HTML report on a plethora of quality control metrics, thus enabling a swift and rigorous data quality inspection prior to downstream GWAS and related analyses. By significantly cutting down on the time on genotyping quality control procedures, GTQC ensures maximum utilization of available resources and minimizes waste and inefficient allocation of manual efforts. GTQC tool can be accessed at  Genotyping arrays are microarrays that exploit the DNA complementary hybridization principle to detect single nucleotide polymorphisms (SNPs) in human samples. Genotyping arrays are typically used for investigating known disease related variants in the human genome, and they have been the driving force of Genome-Wide Association Studies (GWAS) for the last 15 years. The evolution of genotyping arrays can be divided into two phases: before and after the introduction of High-Throughput Sequencing (HTS) technology. Before HTS was introduced and became popular, genotyping arrays existed as the only option to screen for variants on a genome-wide scale. After the recent boom of HTS, genotyping arrays remained a competitive alternative to HTS, due to the customizable array designs and the dramatically lowered assay cost. At ~1/20 cost compared to whole genome sequencing, the genotyping array is still a preferential platform for large scale GWASs, which typically involve thousands of samples per study and thus take account of the assay cost as a crucial factor. Furthermore, genotyping data has been used far beyond the traditional GWAS analysis. For example, recent advancements in utilization and curation of expression quantitative trait loci Quality control appears as one of the most important steps in genotyping data workflow, as it roughly takes half of the overall processing time Even though a multitude of insightful quality control strategies has been released, a satisfactory package is yet to be developed to take charge of the crucial quality control component of the genotyping data workflow. In the field, the existing protocols are composed of written recommendations and fragmented code chunks. Application of these established protocols requires extensive manual effort to tweak each step and then to string the outputs of discrete steps or multiple trials to a relatively comprehensive report. An automated quality control program not only significantly reduces the overall processing time for genotyping array data, but also dramatically suppresses random mistakes arising from frequent manual intervention. With the practical demand in mind, we designed and developed GTQC , an R package that automates the genotyping array quality control process for general genotyping data.We developed the genotyping quality control software GTQC purely in the R language and made it encompass the quality control strategies we established previously While the HTS technology is generally considered superior to microarrays, genotyping arrays managed to maintain a substantial influence within the biomedical research field. This lasting popularity of genotyping arrays can be best represented by the immense publications based on the very technology. According to PubMed publication records, the past two decades have witnessed much more publications based on genotyping array data than those based on exome sequencing, and the trend persisted even after the recent wide adoption of HTS Figure . For exaThe discrete components of GTQC are aimed at the following quality control metrics: Sample Call Rate, SNP call rate, MAF, gender check, race check, HWE, and Heterozygosity Ratio. We used the SNP data from TCGA's LIHC cohort as an example to demonstrate the utility of GTQC. The representative figure results on various quality control aspects are demonstrated in Figure Defined with respect to a sample, Sample Call Rate is formulated as the percentage of SNPs called in the particular sample over the union set of SNPs called from all samples. Defined with respect to a SNP, SNP Call Rate is formulated as the percentage of SNP-presenting samples over all samples of the study cohort. It was previously recommended that 98% and 95% could be used as threshold values for Sample Call Rate and SNP Call Rate, respectively Unlike Sample Call Rate and SNP Call Rate, MAF cannot be derived from a single sample; rather, it is defined with respect to a subject cohort or a population. Regarding the precise chromosome locus bearing a SNP, MAF is computed as the instances of minor alleles divided by the instances of all alleles, with “instances” referring to the whole cohort. Common SNP arrays typically include SNPs with MAF >0.2 in a population. In research practice, MAF is often used as a rudimentary filter to select a specific set of SNPs by rarity. As an illustrative example, we employed GTQC to calculate MAF values for all SNPs on chromosome 21  must be performed within a homogeneous gender sub-population. It is worth noting that Pseudo-Autosomal Regions (PAR) are existent on sex chromosomes. PARs are homologous regions that result from the pairing and recombining of chromosomes X and Y during meiosis in evolution. Often the so-called “chromosome XY” is tagged with SNPs on PARs, but, in some arrays, the PAR SNPs are simply labeled with Chromosome X. Thus far, there have been three PARs identified Like gender, race is another phenotypical variable that can be complicated with self-identification bias or data input error. Researchers often perform principal component (PC) analysis on ancestry informative markers (AIMs) to yield a genetically determined race and further use the genetically determined race as a surrogate for the self-reported race. AIMs are SNPs that exhibit considerable disparate allele frequencies between populations of different ethnicities. Most genotyping arrays encompass a number of AIMs. GTQC performs PC analysis and reports the first and second most accountable principal components, termed PC1 and PC2, respectively. In their association models, genetic association studies often adjust for the first few PC instead of the actual race, because the PCs can capture the intrinsic genetic difference more accurately even within a population ostensibly of a same race The HWE principle asserts that, in a randomly mating population devoid of genetic drift or other external disturbance, the genotype distribution among the population remains an equilibrium state in a long, cross-generation timespan. As indicated by the aforesaid constraints, when the equilibrium state is disturbed, it usually suggests unusual events including population stratification, genotyping errors, or association to the very trait under study For a genotyping dataset that involved a homogeneous sample population and covered a large number of SNPs, computing the Heterozygosity Ratio can help identify problematic SNPs. Heterozygosity Ratio is computed for a subject as the ratio between the number of heterozygous SNPs and the number of non-reference homozygous SNPs. The Heterozygosity Ratio was originally proposed by our group as a quality control parameter for SNP data, and we proved that it has a theoretically expected value of two GTQC is also capable of computing consistency between duplicated samples and duplicated SNPs. International GWAS and alike projects almost always include external control samples, such as those curated by HapMap High-throughput genomic technology has been the driving force for biomedical research for the last two decades. One early type of high-throughput genomic technologies are the DNA hybridization based genotyping array, which was the backbone of the GWAS era. A large amount of genotyping array data have been deposited into public genomic data repositories and are available for secondary analysis. For example, the TCGA project provides both blood and tumor genotyping data on all of their participants. As of February 2021, over 400 genotyping array datasets with millions of subjects have been curated in the most authoritative database of genotypes and phenotypes, dbGAP