Bos taurus cattle breeds have been resequenced and scanned for genome-wide polymorphisms. As a result, a substantial number of single nucleotide polymorphisms (SNPs) have been discovered across European Bos taurus genomes, whereas extremely less number of SNPs are cataloged for Bos indicus breeds. In this study, we performed whole-genome resequencing, reference-based mapping, functional annotation and gene enrichment analysis of 20 sires representing eleven important Bos indicus (indicine) breeds of Pakistan. The breeds sequenced here include: Sahiwal, Red Sindhi, Tharparkar and Cholistani , Achai, Bhagnari, Dajal and Lohani ; Dhanni, Hisar Haryana and Gabrali (dairy and light drought purpose breeds). A total of 17.4 billion QC passed reads were produced using BGISEQ-500 next generation sequencing platform to generate 9 to 27-fold genome coverage (average ~16×) for each of the 20 sequenced sires. A total of 67,303,469 SNPs were identified, of which 3,850,365 were found novel and 1,083,842 insertions-deletions (InDels) were detected across the whole sequenced genomes . Comparative analysis using coding region SNPs revealed a close relationship between the best milking indicine breeds; Red Sindhi and Sahiwal. On the other hand, Bhagnari and Tharparkar being popular for their adaptation to dry and extremely hot climates were found to share the highest number of SNPs. Functional annotation identified a total of 3,194 high-impact (disruptive) SNPs and 745 disruptive InDels (in 275 genes) that may possibly affect economically important dairy and beef traits. Functional enrichment analysis was performed and revealed that high or moderate impact variants in wingless-related integration site (Wnt) and vascular smooth muscle contraction (VSMC) signaling pathways were significantly over-represented in tropically adapted heat tolerant Pakistani-indicine breeds. On the other hand, vascular endothelial growth factor (VEGF) and hypoxia-inducible factor 1 (HIF-1) signaling pathways were found over-represented in highland adapted Pakistani-indicine breeds. Similarly, the ECM-receptor interaction and Jak-STAT signaling pathway were significantly enriched in dairy and beef purpose Pakistani-indicine cattle breeds. The Toll-like receptor signaling pathway was significantly enriched in most of the Pakistani-indicine cattle. Therefore, this study provides baseline data for further research to investigate the molecular mechanisms of major traits and to develop potential genomic markers associated with economically important breeding traits, particularly in indicine cattle.The primary goal of cattle genomics is the identification of genome-wide polymorphism associated with economically important traits. The bovine genome sequencing project was completed in 2009. Since then, using massively parallel sequencing technologies, a large number of  Livestock contributes 40% of the worldwide estimation of agricultural yield . It provBos taurus (taurine) and Bos indicus (indicine) [Based on a number of phenotypic differences, nearly 800 domestic cattle breeds are divided into two main types, regarded as two subspecies of a species, ndicine) . Along wndicine) . More hendicine) . These pndicine) , 10.In Pakistan and India, there are 35 recognized zebu breeds . Cattle Ruminantia, bovine genome was among the first mammalian genomes sequenced [Being the key farm animal that provides major source of essential nutrients and because of their phylogenetic position, representing equenced . The bovequenced , 16, Theequenced .Gibbs and his team studied the HapMap Consortium of different cattle populations and provided the foundation for understanding the cattle genetic diversity by scanning 37,000 probes (SNPs) in 497 cattle from 19 phenotypically different and geographically distant breeds , 17, 18.All cattle whole genome resequencing efforts for assessing genomic variations such as the 1000 bull genome project, the international bovine genome sequencing and HapMap projects have catalogued sufficient number of SNPs and small InDels in publicly accessible dbSNP database , 30, 31.High density SNP arrays have been constructed for cattle genome wide association (GWAS) and genotyping studies using the available SNPs in dbSNP databases , 33. Forhttp://www.ncbi.nlm.nih.gov/snp/), suggesting that more than 96% SNPs are published for taurine breeds. Furthermore, currently available SNP arrays for genotyping, GWAS and genomic selection are constructed from the available dbSNP data, hence they are found biased towards taurine breeds, limiting genome-wide studies of indicine breeds [As of February 2017, only 4.38 million SNPs out of total 99.71 million across the whole bovine genome have been deposited for the indicine breeds in dbSNP database , Faisalabad, Pakistan has approved the protocols and all animal procedures used for the collection of blood samples from cattle.Whole-blood samples (10 mL) were collected from two Achai, three Bhagnari, two Cholistani, two Dhanni, one Dajal, two Gabrali, two Hisar Haryana, two Sahiwal, two Tharparkar, one Red Sindhi and one Lohani proven bulls. Next generation sequencing (NGS) pair-end reads were generated using BGISEQ-500 sequencing platform. Genomic DNA (gDNA) was isolated from each sample using FavorPrep gDNA extraction kit  according to the manufacturer’s protocol. The quantity and quality of each recovered gDNA sample were evaluated using agarose gel electrophoresis scans of fluorescently tagged (ethidium bromide) gDNA, spectrophotometer absorbance values, and by measuring double-stranded DNA concentration using qubit dsDNA high sensitivity assay . All high quality recovered gDNA samples were randomly fragmented in a separate microtube by ultra-sonication using Covaris S2 . Sheared gDNA fragments ranging 500–800 bp in size, were gel purified and for each of the sample, the average fragment size was evaluated using Agilent bio-analyzer 2,100 .BGISEQ-500 libraries were constructed using slight modification in the Illumina libraries construction method adopted from the previously reported single tube “BEST” procedure, mainly pursuing Carøe et al . To reduAfter the final best fill-in step in gDNA library construction, all gDNA libraries were mixed with Qiagen PB binding buffer (1:5 volume) and cleaned by employing Monarch DNA clean-up columns . For washing, size-selected gDNA libraries were first incubated at 37◦C for 5-minutes in 750 μL PE buffer (Qiagen) and finally purified gDNA libraries were eluted using 40 μL Qiagen EB.For all libraries the number of indexed PCR cycles were determined in qPCR-quantification by using common primer, BGI-forward primer and one of the eight index ligated reverse primers (using the Agilent MX3005 qPCR machine). All libraries were subsequently amplified in 9 to 17 index PCR cycles by using common primer, the index ligated reverse primers, and BGI-forward primer. Each purified BGISEQ-500 library was subjected to an additional refinement step to remove any remaining impurities and primer dimers. All amplified libraries were sent to BGI sequencing facility for independent circularization and high throughput sequencing on BGISEQ -500 NGS platform. Sequences are available from CNGB Nucleotide Sequence Archive (CNSA) with the Bioproject accession number CNP0000189.After verifying read qualities using FastQC, SOAPduke filter is used to remove adapter sequences, low-quality base calls and N’s from both ends of sequenced reads, and finally clean reads were subjected to reference genome mapping. Clean pair-end reads were mapped to the bovine reference genome assembly UMD 3.1  using thPicard Tools—By Broad Institute. https://broadinstitute.github.io/picard/) for bam files. Mapped reads were then analyzed to resolve the mapping issues emerged from the presence of small InDels at the sequenced reads ends by using “RealignerTargetCreator” and “InDelRealigner” command line tools of the Genome analysis toolkit v. 3.3.0 (GATK) [The PCR duplicates were removed using the Picard (v. 1.105) “MarkDuplicate” command line utility (0 (GATK) , 55. Aft0 (GATK) . Prior t0 (GATK) , 56.de-novo assembly of haplotypes in the regions showing deviation [The GATK command-line tool “HaplotypeCaller” was adopted to call SNPs together with InDels through local eviation . All SNPeviation . Varianteviation , 46.After variant filtration, SNPs and InDels were functionally annotated using SnpEff_4.3  to attriPANTHER enrichment analysis was conducted to identify over-represented biological process categories using all 265 known genes harboring high impact non-synonymous variants in all resequenced samples in this study (GO Ontology database Released 2018-04-04) . BinomiaB. indicus breeds were sequenced to an average of ~16× coverage each (9× to 27× coverage range). To facilitate comparisons with African cattle, the sequenced cattle data were jointly analyzed with publicly available whole genome sequenced data of African cattle breeds . The B. indicus breeds from Pakistan comprise of dairy breeds , draught breeds  and dual purpose breeds   . In totalistani) . The mapIn this study, a total of ~67.3 million SNPs and ~1.08 million InDels were finally retained across all sequenced genomes, with an overall average change rate of 1 in 827 base pairs . All SNPThe 3,850,365 SNPs and 491,247 InDels were found novel when compared against dbSNP build 148. The proportion of novel SNPs is lower than reported in previous cattle resequencing studies, such as in Fleckvieh, Holstein, Black Angus, Kuchinoshima Ushi, Boran, Ogaden, Kenana, Ankole, N’Dama and Chikso breeds , 62. TheAmong the total SNPs and InDels, the homozygous and heterozygous ratios were 1:1.3  and 1:1.27 , respectively. To evaluate the quality of identified SNPs, the transition (Ts) versus transversion (Tv) ratio of all detected SNPs was measured and on an average, we found 2.3 Ts versus 1 Tv (Ts: Tv 2.3:1) across the entire resequenced genome of all samples under study. The Ts: Tv ratio value is analogous to the ratios documented in other related studies for cattle and human. , 28, 63.Among 1,083,842 InDels, 573,528 (52.9%) are deletions. The lengths of InDels ranges from -26 (deletion) to 20 (insertion). However, most of the determined InDels are short: approximately 80.48% of InDels are less than and equal to 3bp  which isIn this study, a large number of SNPs were found being shared among all sequenced breeds. We compared genic and regulatory  region SNPs among top five important indicine breed of Pakistan; Sahiwal, Red Sindhi, Bhagnari, Lohani and Tharparkar. A total of 106,460 (1.7%) coding region SNPs were common to all five breeds. While breed specific SNPs (Private SNPs) represented 32.6% in Tharparkar, 31.6% in Bhagnari, 13.9% in Sahiwal, 12.8% in Lohani and 9.9% in Red Sindhi .Breed specific SNPs could possibly be helpful in further research concerning breed characterization, whereas the extent of relations between breeds may be determined by the number of shared SNPs between them. Although Bhagnari is a heavy drought breed mostly raised for beef production, while Tharparkar is a dual purpose breed and fairly good milk producer, we identified larger number of shared coding SNPs common to Bhagnari and Tharparkar  may be due to fact that both are adopted to extreme hot and dry tropical regions  and share similar grey to white coat color—than those common to Sahiwal and Bhagnari , or to Red Sindhi and Bhagnari  or even to Lohani and Bhagnari . Similarly, we found that Red Sindhi, being the second best milking indicine breed shares the highest number of coding SNPs with the best milk producer Sahiwal breed , both breeds have also been reported to withstand heat and resist several infectious diseases .The final variant set was extensively investigated to functionally annotate every single SNP and InDel with their particular attributes by using SnpEff_4.3 . Of the B. indicus breeds from Pakistan . A total of 157,690 (0.234%) genic SNPs are found to introduce non-synonymous (nsySNPs) mutations; of which 155,251 (0.23%) SNPs were predicted to introduce amino acid substitutions (missense) and 1132 (0.00168%) SNPs were detected to change amino-acids specifying codons into stop codons (nonsense), whereas many other nsySNPs, such as stop-gain, start-loss and stop-loss  were also detected in this study. Such nsySNPs may influence phenotypic variation in economically important traits in cattle. The identified variants were annotated with the latest available dbSNP (Build 148), revealing 3,850,365 novel SNPs. Following annotation, we further investigated nsySNPs using SnpEff_4.3 and identified 3194 high (disruptive), 162,740 moderate and 316,266 low impact SNPs in all sequenced Pakistan .InDels events were also searched in all sequenced samples and detected 1,083,842 InDels in total, 592,595 (54.74%) of which were found in the dbSNP database, whereas the remaining 491,247 (45.32%) are novel. The functional annotation using SnpEff_4.3 found a total of 769,078 (67.14%) and 349,596 (30.52%) InDels in intergenic and intronic regions, respectively .Moreover, 9,316 (0.85%) 11,370 (0.99%) InDels are located within the 1000 base pair upstream and downstream genic region, respectively. Following annotation, we investigated the length distribution of all discovered InDels and InDels in the coding regions. As shown in Generally, indicine cattle can withstand high temperature compared to taurine breeds. In Pakistan, indicine cattle inhabit hot regions of Punjab, Sindh and Baluchistan provinces, and have evolved to adapt harsh environmental conditions, such as high temperature and solar radiation, infectious diseases, and poor diet. These environmental conditions dominate across most of the indicine populated regions of Pakistan. Through comparisons of the genomes of indicine breeds inhabiting extreme environments in Pakistan with the reference taurine (Herefords breed) genome from contrasting environments, we aimed to identify the functional gene ontology (GO) biological processes and KEGG signaling pathways responsible for the adaptations of indicine breeds to hot, humid and plateau environments of Pakistan. To investigate this; all identified SNPs are functionally annotated by SnpEff_4.3 to predict high (triggering loss/gain of function or protein truncation) and moderate (a non-disruptive variant that could change protein effectiveness) impact variants that could possibly alter economically important protein coding genes. Such genetic variants of high or moderate severity provide a useful resource for further studies to phenotypically differentiate taurine and indicine breeds. Our analysis showed a total of 462 genes (256 known genes) in all Pakistani-indicine population harboring at least one SNP of high or moderate severity. The GO enrichment analysis was performed for high or moderate impact SNPs harboring 256 known genes  using PANTHER overrepresentation test (Released 2017-12-05) . The GO  pathways were identified as being significantly over-represented (p < 0.05) by DAVID in at least two of the Pakistani indicine breed . The Wnt.Wnt proteins are specialized secretory proteins that play role in different cellular developmental processes by promoting morphogenesis, differentiation of progenitor-cells into more specific types, cell fate specification, and controlling irregular cell divisions in different tissues and organs. Whereas, the VSMC is a specialized contracting cell required for the regulation of blood flow and pressure in different stress conditions, by adjusting the diameter of the blood vessels. The body’s core internal temperature controlled by skin blood flow is critical during external and internal challenges to thermal homeostasis . The Wntex-vivo low oxygen treated rats. In 2016, Yang and colleague [VEGF signaling pathway is a VEGF to VEGFR-2 binding dependent cascade of chemical reactions that plays a crucial role in both pathologic and physiologic formation of blood vessels, called angiogenesis. VEGFR-2 is a major mediator of VEGF driven response in endothelial cells. The VEGFR-2 to VEGF binding initiates various signaling transduction pathways leading to endothelial cell proliferation and subsequent migration to ensure their survival and vascular permeability. Whereas, the HIF-1 is a transcription factor that functions as a master gene regulator of numerous hypoxia-inducible protein-coding genes under hypoxic condition. The HIF-1 targeted genes encode proteins that facilitate oxygen supply in response to oxygen depletion. Many human and rodent genes from VEGF and HIF-1 pathways are found associated with the regulation of cardiovascular system under hypoxic conditions –73. Amonolleague  reportedolleague . The resThe ECM is a mixture of complex structural and functional macromolecules that maintain the structure and function of cells and tissues, with an essential role in the morphogenesis of tissue and organ. Its interaction with the transmembrane cellular molecules can directly or indirectly control the cellular migration, adhesion, proliferation, apoptosis and differentiation. The ECM-receptor interaction pathway was significantly enriched in Pakistani dairy cattle breeds  by sixty-five genes harboring high or moderate impact variants. Gao et al.  reportedIn mammals, the Jak-STAT pathway is the major signaling mechanism for a wide array of cytokines and growth factors that transduces a multitude of signals and control the expression of target genes for the development and homeostasis in animals, from humans to flies. In this study, the Jak-STAT signaling pathway was found significantly over-represented in meat purpose Pakistani-indicine breeds  by a total of fifty high or moderate impact variation harboring genes. Doran, et al.  also fouToll-like receptors (TLRs) are specific families of pattern recognition receptors, responsible for detecting microbial pathogens and produce innate immune responses to infectious microbes. Mammalian TLRs are membrane-bound receptors, expressed on macrophages and dendritic cells that respond to the antigenic microbial components. TLR showed to play a crucial role in invoking innate immunity against the pathogenic invasion by inducing the production of cytokines upon binding to bacterial lipopolysaccharide in bovine mammary and endometrium epithelial cells , 80. TheThe GO over-representation results should be very carefully evaluated because of the small number of samples used for each breed in this study. However, these results provide important genomic information regarding the economically important over-represented biological processes in all sequenced breeds.This study presented extensive genome analysis of eleven indigenous Pakistani cattle breeds following whole-genome resequencing using BGISEQ-500 sequencing platform. The selected low to medium coverage resequencing method lead to detection of 67,303,469 SNPs and 1,083,842 InDels in all studied cattle samples. The novel SNPs deposition to the dbSNP has considerably increased the number of indicine variants, which could play an important role towards the development of biased free SNP-array for genomic selection and genome-wide-association studies in indicine cattle. Coding and regulatory SNPs based genome comparison of five major and geographically diverse indicine breeds from Pakistan indicated that, the second best milking breed Red Sindhi is closely related to the best milking breed Sahiwal by sharing highest number of SNPs . On the other hand, the indicine breeds adapted to dry and extremely hot climate, Bhagnari and Tharparkar (white to gray coat color) shared highest number of SNPs . For all samples, variant function annotation revealed only 3,194 and 745 high impact (disruptive) SNPs and InDels, respectively. A total of 531 disruptive genes (256 genes harboring LoF SNPs and 275 harboring frame-shift) were used separately for GO enrichment analysis. The GO enrichment analysis revealed that most of the altered genes are significantly enriched in economically important biological processes, such as immune responses, heat tolerance, signaling pathways, cellular development and sensory perceptions. Therefore, this study provides baseline data for further research to reveal molecular mechanisms and identify potential genomic markers associated with economically important cattle traits for genomic selection (breeding), particularly in indicine breeds.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


HSPA8) and erb-b2 receptor tyrosine kinase 2 (ERBB2) were the promising candidates for milk protein concentration in dairy cattle revealed through previous RNA sequencing (RNA-Seq) study. The objective of this post-RNA-Seq study was to confirm genetic effects of HSPA8 and ERBB2 on milk protein concentration in a large Chinese Holstein population and to evaluate the genetic effects of both genes on other milk production traits. There were 2 single-nucleotide polymorphisms (SNPs) identified for HSPA8 and 11 SNPs for ERBB2 by sequencing 17 unrelated Chinese Holstein sires. The SNP-rs136632043 in HSPA8 had significant associations with all five milk production traits (p = 0.0086 to p < 0.0001), whereas SNP-rs132976221 was remarkably associated with three yield traits (p < 0.0001). Nine  of 11 SNPs in ERBB2 were significantly associated with milk protein percentage (p = 0.0177 to p < 0.0001). A 12 Kb haplotype block was formed in ERBB2 and haplotype associations revealed similar effects on milk protein traits. Our findings confirmed the significant genetic effects of HSPA8 and ERBB2 on milk protein concentration and other milk production traits and SNP phenotypic variances above 1% may serve as genetic markers in dairy cattle breeding programs.Heat shock 70 kDa protein 8 ( HSPA8) and erb-b2 receptor tyrosine kinase 2 (ERBB2) were candidate genes affecting milk protein traits in dairy cows [HSPA8  and ERBB2  were significant differentially expressed in bovine mammary tissues of cows in high and low milk protein percentage comparisons [Molecular selective breeding strategies have been widely applied in animal breeding to improve the important economic traits of livestock. Identification of key genes or causal variations for economic traits is prerequisite to perform molecular selective breeding strategies ,2. Milk iry cows . It was HSPA8 is located in BTA15 with a total length of 4426 bp, containing 9 exons and 8 introns, and encoding 650 amino acids. HSPA8 is an important gene in the MAPK pathway [HSPA8 is highly expressed in the lactating mammary gland [ERBB2 encodes a receptor of tyrosine kinases [ERBB2 could activate PI3K signaling pathway by directly binding of PI3K regulatory subunit p85 to phosphorylated tyrosine residues, which is known to regulate milk protein synthesis [HSPA8 is known to be downstream of ERBB2 in human [ERBB2 to HSPA8 and working together in specific biological processes. pathway , which h pathway . HSPA8 iry gland ,13, whicry gland , likely ynthesis . Most imin human ,18, indiHSPA8 and ERBB2, to explore the linkages among single-nucleotide polymorphisms (SNPs), and to conduct the genetic effects analyses in a large Chinese Holstein population.Genetic analyses between selected genes and bovine milk production traits can provide valuable molecular information for the genetic improvement program for milk quality of dairy cattle. Therefore, the aims of this study were to identify genetic polymorphisms in A total of 1027 Chinese Holstein cows from 17 sire families were used to construct the study population. Family size ranges from 25 to 187 daughters with an average of 60 daughters per sire . Cows weBlood samples were collected from 1027 Chinese Holstein cows via coccygeal vein and stored at −20 °C. The tubule frozen semen samples of 17 sires were collected from Beijing Bull Station. Genomic DNA was extracted from blood samples with a TIANamp Blood DNA kit  and from the semen samples using a standard phenol-chloroform procedure . The quaHSPA8 and ERBB2 genes. A total of 13 and 26 pairs of primers (HSPA8 (NCBI Reference Sequence: AC_000172.1) and ERBB2 (NCBI Reference Sequence: AC_000176.1) referring to Bos_taurus_UMD_3.1 assembly using Primer3 web Program v.0.4.0 (http://primer3.ut.ee), respectively.A DNA pool was constructed from aforementioned 17 Holstein bulls  to identify potential SNPs that were involved in  primers  were deshttp://blast.ncbi.nlm.nih.gov/Blast.cgi) to identify potential SNPs. The subsequent genotyping analysis of the 1027 Chinese Holstein cows were performed with matrix-assisted laser desorption/ionization time of flight mass spectrometry  assay.The polymerase chain reaction (PCR) was performed to amplify the pooled DNA from 17 sires with a final reaction volume of 25 μL, comprising of 50 ng genomic DNA, 0.5 μL of each primer, 2.5 μL 10× PCR buffer, 2.5 mM each of dNTP, and 1 U of Taq DNA polymerase . The PCR protocol was 5 min at 94 °C for initial denaturing, followed by 34 cycles at 94 °C for 30 s, 56 °C for 30 s, 72 °C for 30 s, and a final extension at 72 °C for 7 min. The amplification products were visualized by gel electrophoresis on 2% agarose gels, followed by photography under UV light. After that, 40 μL of each PCR product from the pooled DNA was bi-directionally sequenced using the ABI3730XL , and the sequences were aligned to the bovine reference sequences (UMD3.1) using BLAST (HSPA8 and ERBB2 genes based on the criterion of D prime (D′) using Haploview [Pair-wise linkage disequilibrium (LD) was measured for each pair of SNPs genotyped within the aploview . Genotypaploview . Brieflyaploview . A haplon = 1027 for each trait); μ was the overall mean; a2), with the additive genetic relationship matrix A and the additive genetic variance σa2; and e2), with identity matrix I and residual error variance σe2. The overall reliability of the whole model was symbolized as ‘Goodness of Fit’, and was calculated by Hardy–Weinberg equilibrium test was conducted on each identified SNP. Chi-square was used to compare the number of expected and observed genotypes with a significance level of 0.05. The genetic effects of each candidate SNP or haplotype on five milk production traits were analyzed with the mixed procedure of SAS  with the following statistical model:P value divided by number of tests. The additive (a), dominance (d) and allele substitution (α) effects were estimated according to the equation proposed by Falconer & Mackay [The differences among the effects of single SNPs or haplotypes on each trait were compared with Bonferroni correction. The significant level of the multiple tests was equal to the raw & Mackay , i.e., aThe proportion of phenotypic variance of the trait explained by a SNP was symbolized to show the effect of a SNP on a specific trait. The calculation formula is:All protocols for collection of the blood and frozen semen samples of experimental individuals were approved by the Institutional Animal Care and Use Committee (IACUC) at China Agricultural University (Permit Number: DK996). We obtained written agreements from the cattle owners to use the samples and data.HSPA8 gene, with one located in the 3′ regulatory region (3′-UTR) and the other located in the intron  were found within introns, one (rs133724008) was in the 5’ regulatory region (5′-UTR), and two SNPs (rs110735562 and rs109017161) were synonymous substitutions located in exons. All 13 identified SNPs of HSPA8 and ERBB2 genes were in Hardy–Weinberg equilibrium . There were six significant pairs of SNP-trait explaining phenotypic variations with greater than 1%, a range from 1.70 to 5.13% , whereas SNP of rs133724008 was also significantly associated with milk protein percentage  in to 2.05% . Nine SNto 2.05% . SNP-rs1to 2.05% .ERBB2 gene was insertion/deletion (InDel), whereas the remaining 10 SNPs were used to perform LD analysis. Pair-wise D′ measures showed that nine SNPs in ERBB2 were highly linked (D′ = 0.99~1.00) and one 12 Kb haplotype block comprising these nine SNPs were inferred  identified for inferred , in whicinferred . SubsequHSPA8 and ERBB2 genes on milk protein traits in a large population of dairy cattle. The two candidate genes also had remarkable impacts on other milk production traits, which may provide new insights into the enhancement of milk profiles via selection strategies.The results of the present study confirmed the significant genetic effects of ERBB2 showed significant associations with milk protein traits are synonymous variations, which could modify mRNA stability and further affect protein expression [SAA2 (serum amyloid A2) gene impact the phenotype through altering the promoter activity [ERBB2 gene on milk production traits was likely due to the impacts on its transcription. Generally, mature miRNAs are bound to mRNA ribosomal complex and regulate the expression of target genes by complementary recognition of the 3′-UTR of the mRNA [HSPA8 including identified SNPs were predicted by RNAhybrid software [HSPA8 with milk production traits probably resulted from miR-301a or unknown potential regulatory mechanism. Although an intron does not hold a sequence for coding protein, an important function of SNPs in introns in altering gene transcriptional level has been elucidated [Two SNPs, rs110735562 and rs109017161 in exonic region of pression ,25. The pression , and a pactivity . Hence, the mRNA ,29. Thussoftware . The resucidated ,32. AddiERBB2 on milk production traits has been also identified as positional candidate gene for lactation persistency in Canadian Holstein cattle [HSPA8 and ERBB2 are promising candidate genes that have strong genetic effects on milk production traits. In addition, both single SNP-based and haplotype-based association analyses also suggest that the novel SNPs in these two genes may be used as potential genetic markers for genetic improvement in dairy breeding schemes.As a group of highly conserved and widely expressed proteins, heat-shock proteins (HSPs) play important physiological functions . For exan cattle . TherefoHSPA8 and ERBB2, respectively. These results suggest that the subset of these large-effect SNPs  could be used as potential genetic markers for further marker-assisted selection (MAS) in milk production traits, especially for milk protein traits. All identified SNPs in HSPA8 and ERBB2 genes could be incorporated into the SNP panels for genomic selection of dairy cattle breeding schemes and could be used to improve frequencies of the genetic markers that are positively related to milk production traits of interest.Generally, a small proportion with less than 1% of the phenotypic variance was explained by polymorphisms underlying complex traits in livestock animals . In the


DNA sequence reads contain information about the genomic variants located on a single chromosome. By extracting and extending this information using the overlaps between the reads, the haplotypes of an individual can be obtained. Using parent-offspring relationships in a population can considerably improve the quality of the haplotypes obtained from short reads, as pedigree information can be used to correct for spurious overlaps (due to sequencing errors) and insufficient overlaps . We developed a novel method, PopPoly, to estimate polyploid haplotypes in an F1-population from short sequence data by taking into consideration the transmission of the haplotypes from the parents to the offspring. In addition, this information is employed to improve genotype dosage estimation and to call missing genotypes in the population. Through simulations, we compare PopPoly to other haplotyping methods and show its better performance. We evaluate PopPoly by applying it to a tetraploid potato cross at nine genomic regions involved in tuber formation. Genetic polymorphism is the key to understanding inheritance patterns of traits and to identifying genomic regions that affect a trait. While the traits of interest usually have medical importance in human genetics, in plant sciences these traits are often of importance for breeding and selection of the best varieties. Therefore, polymorphic genomic loci are used as genetic markers to investigate co-segregation of genetic variants  with qualitative traits, e.g., flower color, in populations from crosses or in natural populations. These markers can also be used to investigate the genetic components of quantitative traits such as yield and the degree of tolerance to biotic or abiotic stresses.haplotype, of which a diploid organism possesses k = 2 versions while a polyploid has k > 2. To phase markers means to determine these k haplotypes, which might be identical  or different .The sequence of DNA marker alleles along a single chromosome is called a Among various types of genetic markers, Single Nucleotide Polymorphism (SNP) markers Brookes,  are the cis or trans with other alleles  methods use DNA-sequence reads to phase the SNPs of a single organism at positions covered by the reads, using the fact that the sequence of called alleles should be the same in the reads that originate from the same chromosome. To deal with sequencing errors, which can cause spurious differences between reads of the same chromosome and therefore can influence variant calling and haplotyping especially at low sequencing depths, these methods use probabilistic models or cost functions to prefer a certain phasing to others based on the observed reads  criterion , we selected haplotype tagging SNPs (htSNPs) for the eight haplotypes proposed by PopPoly and developed a KASP assay  parental haplotypes.Hbm and Hbf  at each step by one SNP and choosing the most likely phasing extensions Hem and Hef to continue with, as the base phasings of the next step, until all of the l SNPs within a haplotype block have been phased. Starting by the first two SNP positions in the block, the probabilities of the base and extended parental phasings, conditional on the reads and taking the observed offspring genotypes into account, are calculated using Bayes' formula. We use s = 2 to s = l to denote the current extension SNP (as the starting base phasing is just the SNP genotype at s = 1), and denote the phasing extensions and called SNP genotypes by ci  respectively. With these notations, the probability of each possible parental extension at s is related to its base phasing at s−1 according to:To implement this method, we follow a greedy SNP-by-SNP extension approach , extendiRset denotes the set of all of the reads in the population and ϵset stands for the set of base-calling error vectors, ϵj, associated with each rj ∈ Rset (1 ⩽ j ⩽ |Rset|). s, ϵset.where s we discard those extensions that have a posterior probability less than 0 < ρ ≤ 1, i.e., we apply branching with hard thresholding. We then prune further the remaining extensions using a soft threshold 0 ≤ κ ≤ 1 by discarding those with a posterior probability less than κPmax, where Pmax denotes the maximum posterior probability among the branched extensions , we first obtain the posterior probabilities of the parental genotypes, The details of calculating Equations (2) and (3) are given in s = l, a phasing is chosen for each offspring from each set of parental phasing estimates by looking into the possible transmissions of the parental l SNP haplotypes. Assuming each parent transmits half of its haplotypes to each offspring, which of course requires balanced meiosis and even ploidy levels, km and kf being the ploidy levels of the mother and the father, respectively. From this set of candidate phasings, we assign to each offspring the phasing that yields the smallest minimum error correction (MEC) score with respect to its individual sequence reads  and the sum of the MEC scores of the deduced offspring phasings. Thus, the output of the algorithm consists of sets of ranked phasing estimates for the whole population. In our simulations, we only kept the best set of population estimates for evaluation and comparison with other methods.dm, df and dci representing the sequencing depths of the mother, father and offspring ci, respectively), population size n + 2, and the number of SNPs l in the region of interest, we assume that the number of surviving extensions is effectively constant at each stage of the algorithm and denote it by η. Setting k = max, for each of the η base phasings at most (k!)2 extensions must be examined at each extension step. For each of these extensions, Equation (1) requires k in each parent , the total complexity is:complexity at each extension step. Multiplying the explained complexity by l and the number of offspring n and quadratically with the sequencing depth dmax.which increases linearly with the number of SNPs kb from the chromosome 5 sequence of the PGSC potato reference genome (release version 4.04)  according to the lognormal SNP density model and the dosage distributions described in Motazedi et al. , defined as the proportion of correctly estimated phasings for SNP-pairs  defined to measure the overall similarity between the original haplotypes and their estimates using the Hamming distance  and incorrect dosage rate (IDR) in the estimated haplotypes for each method.As the quality of haplotype estimation depends not only on the accuracy of the estimated haplotypes, but also on the ability of the haplotyping method to phase as many SNPs as possible and to efficiently handle missing SNPs and wrong dosages, we calculated the l, as number of gaps per SNP (NGPS). The number of haplotype blocks for a set of SNPs, SNP-connectivity graph, l) and an edge is drawn between two SNP nodes, , if s and s′ are covered together by at least one sequence fragment.Finally, to evaluate the continuity of phasing we measured the average number of phasing interruptions, i.e., the number of haplotype blocks minus one, in the estimates of each method and normalized it by the number of SNPs, S. tuberosum loci involved in tuber formation reported by Kloosterman et al. (S. tuberosum cultivars: Altus × Colomba (A × C). The nine investigated loci (StCDF) gene family, but also include other genes, such as CONSTANS (CO) genes CO1 and CO2, that are shown to be involved in StCDF regulation  using Illumina HiSeq X Ten technology. Paired-end sequences were obtained with an average insert size of 380 bp (single read length of 151 bp) and aligned to PGSC-DM-v4.03 reference genome  for the parents at the StFKF1 locus . To determine the genotypes, we used the R package fitPoly , which is a measure of overall phasing accuracy, obtained by PopPoly for the offspring was 0.96  across different population sizes, which was higher than the other methods . The secIt was also noted that the accuracy of PopPoly depends on the population size, especially for distant phasing evaluated by PAR, although this dependence gradually diminishes as the number of offspring grows. As seen in For the parents, the reported accuracy measures were very similar between the methods. However, H-PoP and PopPoly yielded the highest scores , S2, witWhile increasing the per homolog coverage from 5-5-2× (mother-father-offspring) to 30-30-30× yielded an average increase of 23-36% in PAR for TriPoly, H-PoP and SDhaP, the increase was only 14% for PopPoly –S5, as cAs seen in s = 2, if the only surviving parental extensions are s = 1 and s = 2 are s = 2 (and therefore is accepted by the point-wise dosage estimation of PopPoly), but no s = 2 is s = 2 from phasing.The 10% SMR of PopPoly can be explained by the algorithm's excluding a SNP position if the offspring genotypes at that position (either given as input or estimated anew) are incompatible with the surviving parental extensions. An example of this for a trio is the extension at Increasing the per homolog sequencing depth from 5-5-2× (mother-father-offspring) to 30-30-30× decreased the SMR by 16–17% for SDhaP, PopPoly and TriPoly, and by 26% for H-PoP –S11.As shown in k groups corresponding to the homologs of a k-ploid, so that the difference between the reads assigned to the same homolog is minimized and the difference between the reads assigned to different homologs is maximized. The haplotypes are determined by taking a consensus of the reads within each group, and the dosages are determined by the estimated haplotypes. SDhaP on the other hand employs a gradient descent scheme with Lagrangian relaxation to find the best phasing  according to the MEC criterion. Thus, its MEC solution determines the dosages of the SNP alleles.The differences in the IDR between the methods is due to the differences in each algorithm's approach to handle genotype dosages. Specifically, H-PoP attempts to obtain an optimal partitioning of the reads into s, TriPoly obtains the offspring extension and hence the offspring genotype at s by considering all of the possible allele transmissions from the parents at s and by choosing the most likely trio extensions. The dosage correction method of PopPoly is explained in In contrast to H-PoP and SDhaP, TriPoly and PopPoly use the input dosages as basis and make corrections to these based on parent-offspring relationships in the population. Specifically, if the genotype of an offspring in a trio is not compatible with the genotypes of the parents at position The simulation results show that the dosage correction scheme of PopPoly is the most successful approach if there are at least two offspring in the population . For a tConsidering the sequencing coverage, SDhaP profited the most from the higher depths with a 24% lower IDR at 30-30-30× compared to 5-5-2× (per homolog), while this decrease in IDR was 12% for TriPoly and H-PoP and only 7% for PopPoly –S14.As shown in A × C cross (SD = 0.06) showed high local similarity between the haplotypes, the numbers of distinct haplotypes were rather high, at 5 loci equal to the maximum of 8  assess the transmission of the eight haplotypes in the 181 offspring individuals according to genetic rules, i.e., the expected transmission ratio of each maternal and paternal haplotype.Based on the F1 locus . We chechtSNPs observed in the sequencing data and the KASP data , in the 10 offspring assessed with both technologies .Within the larger KASP genotyped offspring population, we could assess the transmission counts of the eight haplotypes for 2 goodness-of-fit tests were performed for the transmission of each haplotype from each parent, which showed no significant deviation at α = 0.05. This suggests that the PopPoly prediction for each of the eight StFKF1 haplotypes is correct. However, the obtained results also show that accurate SNP dosage calling is challenging in polyploids.Subsequently, we assessed the consistency of the uniquely estimated phasings with the assumptions of random polysomic segregation. For this purpose, χWe present a novel algorithm, PopPoly, to exploit parent-offspring relationships for the estimation of haplotypes in an outcrossing F1-population that consists of two heterozygous parents and their F1 offspring, using short DNA sequence reads and SNP genotypes called in the population. In this approach, we first estimate the phasings of the parents by combining the sequence reads of the whole population. If necessary, SNP genotypes are also (re)estimated for the parents from the reads considering parent-offspring relationships. Having the parental phasings, we determine the phasing of each offspring by choosing from the possible transmissions of the parental haplotypes, such that the phasing chosen for each offspring has maximal compatibility with its individual reads. A natural advantage of obtaining offspring phasings from the parents is that the SNP genotypes uncalled in an offspring are imputed in its haplotypes, provided that these SNPs are included in the parental phasings.The polyploid haplotyping problem is NP-hard and practical solutions thus by necessity depend on approximate optimization methods. PopPoly takes a greedy approach based on Bayesian probability, extending haplotype estimates one position at a time starting from the leftmost position. While PopPoly is similar in this respect to TriPoly  haplotypes, from which the haplotypes of each individual can be determined using a set of genotyped htSNPs. Such a strategy can be suitably adopted in QTL studies, with typical sizes of a few hundreds to a few thousands individuals, to increase the statistical power and to ease the interpretation of results.To demonstrate the utility of PopPoly, we used it to phase 579 SNPs segregating at 9 tuberization loci in an F1 population of tetraploid potato, the http://www.bif.wur.nl.PopPoly was developed in Python 2.7.0 and is freely available on the software page of the Bioinformatics group, Wageningen University and Research: EM developed the methods and wrote the manuscript. All authors contributed to the design of the study and to the manuscript, and approved its final 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.


Malus domestica and its supposed primary wild ancestor, M. sieversii, as well as between accessions of Danish origin and all others. Using the GBS approach allowed us to estimate ploidy levels, which were in accordance with flow cytometry results. Overall, we found strong concordance between analyses based on the genome-wide SNPs and the 15 SSR loci. However, we argue that GBS is superior to traditional SSR approaches because it allows detection of a much more detailed population structure and can be further exploited in genome-wide association studies (GWAS). Finally, we compare GBS with SSR for the purpose of identifying clones and pedigree relations in a diverse apple gene bank and discuss the advantages and constraints of the two approaches.In recent years, new genome-wide marker systems have provided highly informative alternatives to low density marker systems for evaluating plant populations. To date, most apple germplasm collections have been genotyped using low-density markers such as simple sequence repeats (SSRs), whereas only a few have been explored using high-density genome-wide marker information. We explored the genetic diversity of the Pometum gene bank collection  of 349 apple accessions using over 15,000 genome-wide single nucleotide polymorphisms (SNPs) and 15 SSR markers, in order to compare the strength of the two approaches for describing population structure. We found that 119 accessions shared a putative clonal relationship with at least one other accession in the collection, resulting in the identification of 272 (78%) unique accessions. Of these unique accessions, over half (52%) share a first-degree relationship with at least one other accession. There is therefore a high degree of clonal and family relatedness in the Danish apple gene bank. We find significant genetic differentiation between  Apple germplasm diversity has been explored and described for decades using low-density markers. Here, simple sequence repeat (SSR) markers have been the preferred approach to characterize and compare the germplasm kept in several European apple collections . The herHigh-density marker systems based on single nucleotide polymorphisms (SNPs) are useful because they allow genome-wide comparisons which can reveal small genetic differences between individuals that are otherwise quite similar. In apple, medium density Illumina Infinium arrays containing 8k and 20k SNPs were initially developed , followeImportant breeding material is often kept in gene bank collections where a lack of genomic information and scarce documentation of agronomic traits pose serious threats to the potential utilization of germplasm resources. Phenotyping of diverse gene bank material is essential for identifying accessions with superior traits for breeding purposes. Phenotype data collected from gene banks can be paired with genome-wide marker information to facilitate genomics-assisted breeding , 9. GenoMalus sieversii accessions, belonging to the Pometum gene bank collection . This is the most comprehensive collection of local Danish apple cultivars, which we recently studied using SSR markers and flow cytometry [Here, we used GBS to genotype a collection of 363 apple accessions, including 14 ytometry . We use Malus domestica and 14 M. sieversii accessions .We sampled 349 cessions  belonginwww.rosaceae.org) with bwa 0.6.1 [Raw sequence data was first parsed with a custom python program (see ) to spliwa 0.6.1  using dewa 0.6.1  for variwa 0.6.1  to allowwa 0.6.1  for downwww.rosaceae.org). We calculated identity-by-descent (IBD) for all pairs of samples using PLINK. We considered two accessions to be clones of each other when the IBD  >0.05, which resulted in 15,802 SNPs. Of these, 14,841 SNPs (93.9%) were mapped to the assembled portion of chromosomes 1–17 of the Golden Delicious genome version 1.0p , 10459 SNPs remained for analysis. Using fastSTRUCTURE [In order to examine the population structure, we initially used PLINK to filter for unique, diploid TRUCTURE  we testeSSR genotyping using 15 SSR markers was previously performed on 485 accessions, which included the 363 accessions genotyped using GBS in this study  with a LMalus sieversii were removed from the data set and accessions with >20% missing data were removed, resulting in 190 M. domestica accessions. Missing data was replaced by mean allele frequencies and PCA was performed again. M. domestica accessions were labelled according to origin and harvest time.A large number of the studied cultivars derive from few major ancestors, which resulted in distinct genetic clustering shown in previous SSR-based study . Therefok = 3, l = 18) and the resulting imputation accuracy was 93.7% [PCA was also performed using the SNP genotypes. First, the 204 accessions included in the SSR analysis were extracted from the genotype table using PLINK , 24. Misas 93.7% . SNPs wias 93.7% , 24, redas 93.7% , 28 in Ras 93.7% . We diviM. domestica accessions from the imputed SNP dataset and repeated the MAF filter of 0.01 and LD-pruning using PLINK [Next, we extracted the 190 ng PLINK , 24. Theng PLINK .GBS yielded on average 2.5 million sequence reads per sample for the 363 accessions, with a coefficient of variation of 17%. The accessions genotyped using GBS reflected a subset of accessions previously genotyped using SSR markers. Ploidy levels, determined using flow cytometry, were also available for accessions included in the previous work . This enUsing genome-wide SNP data, we calculated total heterozygosity by individual, which separated accessions into two groups with heterozygosity ≤ 0.335 or ≥ 0.345 . We compWe found 230 accessions without any clonal relationships and 119 accessions with at least one putative clonal relationship, resulting in a total of 272 unique genotypes. For some cultivars, somatic mutations have resulted in several clones, such as colour sports, that have been maintained through grafting. We identified 42 putative clonal groups, of which the majority (31) consists of two clonal accessions. The highest number of accessions within a clonal group was 15, which were identified for ‘Gravensteiner’ .Analysis of first-degree relationships revealed 142 (52%) accessions with at least one first-degree relative in the collection . 106 30.Malus domestica and M. sieversii was found for SSR-based analysis along PC1 (p = 5.19 × 10−10); and based on SNP data along PC1 (p = 2.83 × 10−8) and PC2 (p = 2.34 × 10−8)  (Differentiation between  × 10−8) . The gen= 0.002) .Next generation sequencing combines high-throughput SNP-discovery and genotyping, resulting in high-density SNP-marker data. It is currently replacing traditional genotyping techniques, like SSR-markers, primarily because of its ease and its suitability for GWAS and genomic selection. Genome re-sequencing provides higher resolution, but for many purposes fewer markers are sufficient. Thus, various approaches have been developed to reduce sequencing costs, either by focusing on expressed sequences through RNAseq or on sequences next to restriction enzyme sites. There are several variants of the former approach including GBS , RAD-seqHere we genotyped 349 apple accessions using the GBS protocol  and compPopulus tremuloides Michx.) was recently described [Triploid apples are relatively common among cultivars: they have a large fruit size and vigorous growth. We identified 19% of the accessions examined as triploids, which are assumed to be the result of fusions between unreduced diploid gametes and normal haploid gametes. Triploid individuals generally have a 50% higher level of heterozygosity than diploid individuals, assuming Hardy-Weinberg equilibrium conditions, independently of the allele frequency. We found a clear difference in the level of heterozygosity between diploid and triploid accessions . Howeverescribed  by applyescribed . HoweverThe number of SSRs and SNP-markers needed to achieve a sufficiently high probability of correct identification of first-degree relationships depends on the level of heterozygosity and the number and frequency of alleles. However, based on theoretical estimations , 41 abouFirstly, the use of SSRs is still the gold standard due to the large number of alleles, the high reliability of the resulting genotype calls, the transferability between studies, and the relatively small number of markers required to unambiguously reveal pedigree relatedness. The use of GBS markers for pedigree analysis is, however, still in its infancy and, due to the genotype uncertainty and the relatively low pedigree-relevant informativeness per SNP, there is still uncertainty about where to draw thresholds for pedigree relatedness and what metrics of relatedness (IBD or IBS or other) to use. In addition, pedigree relatedness in a highly heterozygous and clonally propagated organism like apple is severely complicated due to the nature of the pedigree relations: an individual can cross with its own grandparent, cousin etc. and popular cultivars end up being extensively crossed such that their alleles become highly overrepresented in the population overall. This was observed here, were ‘Cox’s Orange’ is a frequent parent to many of the studied cultivars.Nevertheless, from manually checking the SSR markers and from historical reports on parentages, we conclude that first-degree relationships revealed by both marker systems are parent-offspring relations. Relationships revealed by only one of the two marker systems are primarily first-degree relations with examples of second degree relations. The second degree relations are dominated by half siblings that have ‘Cox’s Orange’ as one parent. The studied accessions includes the most important cultivars that have been grown in Denmark during the past centuries –44, suchWe found differentiation between accessions of Danish origin and other geographical origins on the basis of the SNP data . This waMalus domestica accessions of Danish origin, which is in line with previous findings based on SSR data [M. domestica collection. At the species level, M. domestica and M. sieversii differentiate, which is in accordance with previous findings [No well-defined subpopulations were identified within the SSR data . Also faSSR data  that popfindings  from theAn accurate cost comparison between SSR-based marker analysis and GBS-based SNP-marker analysis is difficult because many laboratory-specific conditions will influence the cost, such as the price of labour and availability of instruments. In our study, sequencing costs for the genome-wide SNP data were at least 10 times more expensive than the laboratory costs of obtaining the SSR data. However, obtaining the final SSR genotype data is much more labour-intensive since it requires preparing a large number of PCR reactions, performing allele calling, and many more additional  steps.Prediction of ploidy levels based on the degree of heterozygosity of the SNPs, as observed here, may not account for all collections of apple and thus, needs confirmation by flow cytometry. However, it may well be the case that the degree of heterozygosity clusters in two clearly separated groups. We therefore hypothesize that the use of genome-wide SNP data using next-generation sequencing will be more desirable and efficient for future characterization of germplasm collections. Finally, even though both SSR- and SNP-markers are powerful tools for exploring genetic diversity, only GBS or high-density SNP-arrays provide enough SNP-markers for GWAS, which can then enable marker-assisted breeding. Therefore, despite uncovering similar results in our work using both SSRs and SNPs, most future work will benefit from using SNPs, which allow for both GWAS and detailed population studies to be performed.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S1 FigPCA plot made on basis of SSR-based analysis (A) and SNP-based analysis (B) which enabled to distinguish between accessions of Danish origin and other geographical origins (C).(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.


Capsicum L.) with abundant phenotypes that can benefit pepper growth. Using specific-locus amplified fragment sequencing (SLAF-seq), a genome-wide association study (GWAS) of 36 agronomic traits was carried out for 287 representative pepper accessions. To ensure the accuracy and reliability of the GWAS results, we analyzed the genetic diversity, distribution of labels (SLAF tags and single nucleotide polymorphisms (SNPs)) and population differentiation and determined the optimal statistical model. In our study, 1487 SNPs were highly significantly associated with 26 agronomic traits, and 2126 candidate genes were detected in the 100-kb region up- and down-stream near these SNPs. Furthermore, 13 major association peaks were identified for 11 key agronomic traits. Then we examined the correlations among the 36 agronomic traits and analyzed SNP distribution and found 37 SNP polymerization regions  that could be selected areas in pepper breeding. We found that the stronger the correlation between the two traits, the greater the possibility of them being in more than one polymerization region, suggesting that they may be linked or that one pleiotropic gene controls them. These results provide a theoretical foundation for future multi-trait pyramid breeding of pepper. Finally, we found that the GWAS signals were highly consistent with those from the nuclear restorer-of-fertility (Rf) gene for cytoplasmic male sterility (CMS), verifying their reliability. We further identified Capana06g002967 and Capana06g002969 as Rf candidate genes by functional annotation and expression analysis, which provided a reference for the study of cytoplasmic male sterility in Capsicum.There are many agronomic traits of pepper ( Capsicum L.) have many categories with abundant phenotypes, which affect the roots, stem, leaves, flowers, and fruits [The agronomic traits of pepper  markers [Previously, QTL mapping of agronomic traits in pepper has mainly focused on the agronomic traits closely associated with increasing pepper yield, such as fruit-related traits, disease resistance, and stress resistance . For inspulation . Blum et markers . There h markers ,16,17. HA gene (controlling purple color) was linked to the sw gene (controlling green color), and that both of them were linked to the O gene (controlling oblate fruit shape) in pepper [Fc and A) linked to a major quantitative trait locus, fs 10.1, for fruit-shape index [In addition, some QTLs showed a cluster distribution in rice and in pepper ,18, suggn pepper . Ben Chape index . These s1, F2, F3, recombinant inbred line, and double haploid) and then maps the genes based on mass molecular markers , cleaved amplified polymorphic sequence (CAPS), and insertion/deletion (INDEL)) ) . TherefoGWAS can result in a high frequency of false positive errors for screening SNPs associated with traits . TherefoPhytophthora root rot, and resistance to Cucumber mosaic virus [For species with larger genomes, GWAS is also expensive. Specific-locus amplified fragment sequencing (SLAF-seq) is a strategy for discovering SNPs facilitated by reduced-representation genome sequencing and next-generation sequencing technologies. SLAF-seq is not only cheaper, but also obtains markers with high coverage and can be typed among populations, providing an efficient method for the detection of SNP markers in species with larger genomes, such as wheat, hulless barley, sunflower, and pepper. Three agronomic traits of pepper have been analyzed by SLAF-seq: Ffn, resistance to ic virus ,44,45. TRf gene) traits. Our objective was to identify candidate genes controlling agronomic traits important for pepper growing and explore the correlations among the 36 traits. Our findings provide key information for breeding high-quality pepper varieties and will be useful for developing multi-trait pyramid breeding strategies and increasing pepper yield.In our study, 36 agronomic traits were investigated in 287 pepper accessions in a GWAS . The ratIn our study, 36 agronomic traits of pepper were surveyed and divided into 23 qualitative and 13 quantitative traits . For the30  and GC content were 95.72% (93.62–96.6%) and 39.30% (38.51–42.42%), respectively, indicating that our sequencing results for 287 pepper accessions were reliable  were used to investigate the extent of LD calculated within a 0–500-kb window. As shown in 2 was set at 0.1, which showed that there was a greater probability of linkage among those SNPs in the accessions. In addition, the LD decays were not evenly distributed among chromosomes within the 0–500-kb window. However, obvious LD differentiation was found among chromosome 1 and the other chromosomes.In the present study, linkage disequilibrium was calculated for every chromosome using 594,429 SNPs  > 0.05). Squared correlations of allele frequencies  in the 287 pepper accessions were used for GWAS analysis of 36 agronomic traits. To reduce the influence of population structure and increase the reliability of GWAS results, we used five statistical models: general linear model (GLM), mixed linear model (MLM), compressed mixed linear model (CMLM), efficient mixed-model association expedited (EMMAX), and factored spectrally transformed linear mixed model (FaST-LMM). Performance of the five models was compared based on Q-Q plots , and thep < 1.707 × 10−8 as the highly significant threshold from the optimal models, and 2126 candidate genes in the 100-kb region up- or down-stream near those SNPs were detected for 26 traits  mainly distributed on chromosomes 1–9, 11, and 12 were significantly associated with Pt . Chr11_3In total, 307 SNPs (548 genes) distributed on all chromosomes were significantly associated with Bh . Chr10_5In total, 22 SNPs (two genes) mainly distributed on chromosome 11 were significantly associated with main stem pubescence (Msp) . Chr11_2In total, 117 SNPs (247 genes) mainly distributed on chromosomes 1 and 3–12 were significantly associated with Lm . Chr09_2In total, 17 SNPs (51 genes) mainly distributed on chromosomes 2, 3, 9, 10, and 12 were significantly associated with Lp . Chr10_1In total, 125 SNPs (279 genes) distributed on all of chromosomes were significantly associated with Cc . Chr02_8In total, 291 SNPs (152 genes) distributed on all of chromosomes were significantly associated with Sc . Chr10_1In total, 126 SNPs (221 genes) distributed on all of chromosomes were significantly associated with Nfpa . Chr12_1In total, 16 SNPs (14 genes) distributed on chromosome 12 were significantly associated with flower pedicel growing state (Fpgs) . Chr12_3In total, 78 SNPs (17 genes) mainly distributed on chromosomes 1, 6, 10, and 11 were significantly associated with male sterility (Ms) . Chr06_2In total, 102 SNPs (96 genes), mainly distributed on chromosomes 1–5 and 7–10, were significantly associated with anthocyanin on immature fruit (Aif) . Chr07_1In total, 16 SNPs (49 genes) distributed on chromosome 6 were significantly associated with mature fruit color (Mfc) . Chr06_1In total, 44 SNPs (29 genes) mainly distributed on chromosomes 3, 4, 9, 10, and 12 were significantly associated with St . Chr12_3In total, seven SNPs (18 genes) distributed on chromosomes 1, 6, 8, and 12 were significantly associated with Pw . Chr12_2In total, 26 SNPs (65 genes) distributed on chromosomes 1, 4, 8, and 12 were significantly associated with Fw. Chr08_1There were few to no SNPs significantly associated with the remaining 21 traits: main stem color (Msc), Ls, Lc, leaf surface characteristics (Lsc), Ffn, anther color (Ac), fruit glossy (Fg), Fsf, fruit surface characteristics (Fsc), Fss, appendage at blossom-end (Ab), Fpl, placenta length (Pl), Fl, fruit apex shape (Fas), fruit shape index (Fsi), placenta size index (Psi), Wpf, Dmc, Tf, and number of locules (Nol). However, there were many peaks with relatively small effects observed distributed on multiple chromosomes , S20. ThWe explored the correlations among the 36 traits using correlation network analysis . The strWe compared their shared peak numbers in Manhattan plots based on descriptive classifications . For plaFor color-related traits, Cc and Msc, and Cc and Fg each had one shared peak distributed on chromosomes 9 and 8, respectively. Cc, Lc, and Fg had one shared peak on chromosome 12. Cc, Ac, and Aif had one shared peak on chromosome 2. Sc and Msc had one shared peak on chromosomes 8 and 10, respectively. Sc, Aif, and Mfc had one shared peak on chromosome 10. Ac and Aif had one shared peak on chromosomes 4 and 7, respectively. Msc and Mfc had one shared peaks on chromosomes 10. For shape-related-traits, Ls and Lsc had one shared peak on chromosome 6. Ls and Fas had four shared peaks distributed on chromosomes 1, 2, 10, and 12. Moreover, Fsc and Fsf, and Fsc and Fas each had one shared peak on chromosomes 10 and 12, respectively. For organ size-related traits, Fw and Pw had four shared peaks on chromosomes 1, 4, 8, and 12. Fl and Pl had two shared peaks distributed on chromosomes 6 and 8. Fsi and Psi had one shared peak on chromosomes 2 and 7, respectively. For pubescence-related traits, Msp and Lp had one shared peak on chromosome 10, and it was the main peak of Lp. However, the main peak of Msp was on chromosome 11, suggesting that the major genes controlling the two traits may be different. There were more peaks for Lp (eight) than Msp (two), implying that Lp was controlled by minor genes. For other traits, there were six shared peaks between Wpf and Tf on chromosomes 1, 3, 5, 8, 11, and 12. St and Tf had one shared peak on chromosomes 3 and 8, respectively. St and Dmc, and St and Ms each had one shared peak each on chromosomes 6 and 2, respectively. We compared the distribution of peaks of the traits between the different categories and observed one or two shared peaks for Sc and Fsf (chromosome 10), Sc and Fss (chromosomes 8 and 11), Fas and Fg (chromosome 12), and St and Fpgs (chromosome 12). It was worth noting that Sc, Fss, and Msp had one shared peak (chromosome 11), that Sc, Aif, and Lp had one shared peak (chromosome 10), and that Sc, Cc, Fw, and Pw had one shared peak (chromosome 08).Additionally, we identified the significant SNPs near peaks for pairwise traits and summarized the SNP and gene numbers. As shown in p < 1.707 × 10−7) were mapped independently on chromosomes 1–12. We found that some of the SNPs clustered in a small region on a chromosome, which was deemed to be an SNP polymerization region. Totally, 37 SNP polymerization regions  were found in the study, which could be selected areas in pepper breeding  was highly consistent with that of GWAS   b, which Rf gene localization, the 214,868,888–215,727,145-bp region of the ‘Zunla-1’ genome was annotated, and eight genes were found in this region  on chromosome 6 of the ‘Zunla-1′ genome, which was consistent with the two regions of 211.29–216.45 Mb and 215.11–215.68 Mb reported previously [However, GWAS does have the potential for false positive errors due to the population structure, relatedness, and inappropriate statistical models ,37,53,54eviously . AdditioPtl1) for stem pubescence on chromosome 10 [Pubescence is a key agronomic trait of pepper, which can improve drought resistance and protect plants from UV radiation, insects, and pathogenic bacteria ,60. Kim osome 10 . Chunthay gene controls the red or yellow mature fruit [c-2 gene controlling red or orange mature fruit was detected on chromosome 4 [cl gene, preventing chlorophyll degradation and controlling brown or olive-green mature fruit, was detected on chromosome 1 [Mfc is an important agronomic trait of pepper. Our results showed that the major peak for Mfc was on chromosome 6, where the re fruit . Howevermosome 4 . The cl mosome 1 . Aif is mosome 1 . Genes rmosome 1 ,65. In pmosome 1 ,66. In tCucumber mosaic virus resistance were also linked to QTL for fruit weight [Most agronomic traits of pepper are controlled by QTLs with complex correlations among them, causing difficulties for breeding programs and hindering the development of multi-trait pyramid breeding. Previous studies found that the genes controlling the color of different parts of pepper plants were aggregated on chromosomes and linked to the genes controlling fruit shape ,19,67. Bt weight . Our stut weight . Correlat weight . Second,t weight . FurtherIn summary, we obtained many SNPs associated with agronomic traits and genes near the SNPs, which provided a large amount of biological information for the study of agronomic traits in pepper. Furthermore, we comprehensively explored the correlations among different agronomic traits and found 37 SNP polymerization regions, which laid a theoretical foundation for future multi-trait pyramid breeding of pepper. In future research, we will analyze the genetic effect of SNPs in these regions and further clarify the association among different traits and the pleiotropism of candidate genes.C. baccatum (1), C. chinense (4), C. frutescens (4), and C. annuum (278). There were 13 F1 hybrids and 274 high-generation inbred lines  in their phenotypic data over 3 years  base on the phenotypic data of three years. As shown in  3 years . FurtheriP is the percentage of materials contained in class i for a quantitative trait type indicator. For qualitative trait type indices, Pi is the percentage of materials, of which, phenotype was assigned a value equal to i  was calculated in Microsoft Excel 2007 version (MSO: 12.0.4518.1014) as follows:Descriptive statistics  were calculated and graphs drawn in Microsoft Excel 2007. Results are presented as the mean of nine replications over 3 years.Genomic DNA was isolated from six fresh leaves from plants using the cetyltrimethylammonium bromide method ,72. A Nahttp://peppersequence.genomics.cn/page/species/index.jsp, version 2.0) was selected as the reference genome for an electronic digestion prediction experiment. The genome size of ‘Zunla-1′ was about 3.36 Gb, and GC content was 34.97%. The principles for selection of the enzyme digestion scheme were as follows: (1) the proportion of enzyme-digested fragments located in repeated sequences was as low as possible; (2) distribution of enzyme-digested fragments throughout the genome was as even as possible; (3) length of enzyme-digested fragments was in good agreement with the experimental system; (4) number of enzyme-digested fragments (SLAF tags) reached the expected number  of SLAF tags. The genome of ‘Zunla-1′ (http://rapdb.dna.affrc.go.jp/) to the same sequencing process, to verify whether the experimental process was reliable. The paired-end comparison efficiency of control data was 95.65%, and the enzyme digestion efficiency was 94.74%, showing that the construction of SLAF libraries was normal.According to the results of the electron prediction experiment, HaeIII  was used to separately digest the eligible genomic DNA. Single-nucleotide A overhangs from these DNA fragments were polished using the Klenow fragment (New England Biolabs), and fragments were then ligated to dual-index sequencing adaptors. Adaptor-ligated fragments were then amplified by PCR, purified, pooled, and screened to construct the SLAF library ,73. SLAF30 in the SLAF libraries and GC content were used to evaluate the quality of sequencing. The formula for calculating the Q30 value is listed below:Raw sequencing reads of SLAF were filtered for quality and trimmed to remove adaptors. The Q value was used to evaluate the single-base error rate of high-throughput sequencing. A Q value of 30  represents a 0.1% error rate or 99.9% sequence accuracy. Then, the proportion of sequencing quality scores at QFurthermore, we clustered all paired-end reads that had perfect index reads according to sequence similarity with the genome of ‘Zunla-1’. Reads with >90% identity were grouped into a single SLAF tag, and SLAF tags with a sequence that varied from sample to sample were defined as polymorphic SLAF tags .Sequencing reads were aligned to the pepper reference genome (‘Zunla-1’) using BWA software; local realignments were conducted, and SNPs were detected using GATK software . To ensuhttps://github.com/DReichLab/EIG; v.6.0.1) [K = 1–15 for all accessions to explore the population structure. The relatedness coefficient was counted using SPAGeDi (version 1.4C) software [2 using HAPLOVIEW software  [Based on the filtered SNPs , phylogeny analysis, PCA, and population analysis were performed in turn. All of the filtered SNPs  were genotyped by MEGA6 software and used for the construction of the phylogenetic tree by the neighbor-joining algorithm . PCA wasv.6.0.1) . Populatv.6.0.1) . The anasoftware . Linkageall.exe) .http://www.maizegenetics.net/tassel), FaST-LMM (https://www.microsoft.com/en-us/download/confirmation.aspx?id=52588), and EMMAX (http://csg.sph.umich.edu//kang/emmax/download/index.html) software with default settings [Total filtered SNPs  detected from 287 accessions were used for GWAS. GWAS for all traits  was conducted using TASSEL  = 8, blue line) and p < 1.707 × 10−7  = 7, red line) was defined as the suggestive (highly significant) threshold and genome-wide control (significant) threshold, respectively. The genes within 100 kb up- or down-stream of these significant SNPs were found by Jbrowser in SGN (Sol Genomics Network https://solgenomics.net/) and reported as potential candidate genes [The te genes ,47. The 2 populations were constructed separately in spring 2016  and spring 2017  for fine mapping, with parents (17C641 and 17C643) selected from the GWAS population. In these two populations, the 2016 population was used for initial mapping, and the 2017 population was subsequently used for fine mapping. All individuals were grown inside greenhouses with an inter-individual distance of 30 cm, and unified field management was used. Methodological details are provided in Two FC. annuum ‘Zunla-1’ genome scan of double-base repeats, and SSR loci that were repeated six times or more developed using misa.pl.300bp software were extended using Python scripts on each side of the SSR loci. The primers were designed using Primer 3 . The PCR solution was 10 μL in volume and contained 1.0 μL DNA template, 0.5 μL of both forward and reverse primers, 5.0 μL Taq polymerase mix , and 3 μL ddH2O. The PCR solution was 10 μL and contained 1.0 μL DNA template, 0.5 μL of both forward and reverse primers, 5.0 μL Taq polymerase mix, and 3 μL ddH2O. The PCR protocol was as follows: initial denaturation at 94 °C for 5 min; 35 cycles of denaturation at 94 °C for 30 s, annealing at 53 °C for 45 s, and extension at 72 °C for 30 s, before a final extension at 72 °C for 5 min. PCR products were stored at 4 °C and analyzed using 7% polyacrylamide gel electrophoresis. Joinmap 4.0 software  was used to analyze the linkage relationship between the markers and the Rf gene. RT-PCR, real-time PCR, and sequencing of PCR products were performed as described by Wang et al. (2019) [A genotypic assay was performed using SSR markers . The SSR. (2019) .p < 1.707 × 10−8) correlated with the 26 agronomic traits in pepper by GWAS. Thirty-seven SNP polymerization regions were obtained from the correlation analysis. The two traits with higher correlation index may locate in a same SNP polymerization region. These results is useful for multi-trait pyramid breeding of pepper. In addition, based on the gene annotation and expression patterns, Capana06g002967 and Capana06g002969 were identified as the candidates for the Rf gene, which provided a reference for the further study of CMS in Capsicum.Based on the SLAF-seq, the GWAS of 36 agronomic traits in 287 pepper accessions was performed in this study. A total of 1,824,874 SLAF tags, 1,025,395 polymorphism SLAF tags and 9,557,790 SNPs, were obtained. Those tags/labels were normally distributed in the population consisted of 287 peppers. Meanwhile, those tags/labels were uniformed distributed on the 12 pepper chromosomes, indicating those SLAF tags and SNP developed can be used for GWAS. In order to obtain a better result, Fast-LMM, EMMAX and GLM were selected as the optimal statistical models for certain traits. In our study, a total of 1487 SNP, 2126 candidate genes and 109 correlation peaks were significantly (


Bipolaris sorokiniana, is one of the most important diseases of wheat in the eastern part of south Asia causing considerable yield loss to the wheat crop. There is an urgent need to identify genetic loci closely associated with resistance to this pathogen for developing resistant cultivars. Hence, genomic regions responsible for SB resistance were searched using a wheat association mapping initiative (WAMI) panel involving 287 spring wheat genotypes of different origin. Genome-wide association mapping (GWAM) was performed using single nucleotide polymorphism (SNP) markers from a custom 90 K wheat SNP array. A mixed linear model (MLM) was used for assessing the association of SNP markers with spot blotch resistance in three consecutive years. Three traits were measured: incubation period, lesion number and area under the disease progress curve (AUDPC). Significant SNP markers were found linked to five, six and four quantitative trait loci (QTLs) for incubation period, lesion number and AUDPC respectively. They were detected on 11 different chromosomes: 1A, 1B, 1D, 4A, 5A, 5B, 6A, 6B, 6D, 7A, 7B with marker R2 range of 0.083 to 0.11. The greatest number of significant SNP-markers was found for lesion number and AUDPC on chromosome 6B and 5B, respectively, representing a better coverage of B-genome by SNPs. On the other hand, the most significant and largest SNP markers for incubation period were detected on 6A and 4A chromosomes indicating that this trait is associated with the A-genome of wheat. Although, QTLs for spot blotch resistance have been reported in wheat on these same chromosomes, the association of incubation period and lesion number with SB resistance has not been reported in previous studies. The panel exhibits considerable variation for SB resistance and also provides a good scope of marker-assisted selection using the identified SNP markers linked to resistant QTLs.Spot blotch (SB) caused by  Triticum aestivum L.), one of the most widely adapted cereal crops [Bipolaris sorokiniana, which is an anamorph (teleomorph Cochliobolus sativus).Wheat  population assembled by CIMMYT in 2009 was used .st to 10th December in the three years. Two replications were planted and each genotype was sown in two rows of two meters maintaining row-to-row and plant-to-plant distance of 20 cm and 5 cm respectively. The lines were allocated randomly to each replication using the Fisher and Yates Random Table [2O5: 40 kg K2O. The full amount of K2O and P2O5 were applied at sowing whereas nitrogen was applied in three split doses, half at sowing, one fourth at first irrigation (21 days after sowing-DAS) and the remaining one fourth at second irrigation (45 DAS). Data were recorded on three traits: incubation period (days), lesion number and spot blotch AUDPC.The WAMI population was evaluated for spot blotch in three consecutive crop seasons during 2012–13, 2013–14 and 2014–15 at the Agricultural Research Farm of Banaras Hindu University (BHU), Varanasi, India. The experiment was conducted using Alfa lattice design with sowing done between 1om Table . FertiliBipolaris sorokiniana  known to be highly aggressive was used [−4 spores per ml) in water was applied in the evening and irrigated immediately after inoculation [For creating artificial epiphytotic, a pure culture of was used . The isoculation . Frequen1/9× D2/9) × 100 using the double digit scale  [1) refers to vertical disease progress on the plant whereas the second digit (D2) was the disease severity score in the affected leaves. Thus, disease severity was used to estimate the AUDPC by following formula [Yi = disease severity at time ti, (ti+1−ti) = Time (days) between two disease scores, n = number of dates at which spot blotch was recorded.Incubation period (days) was recorded from the inoculation to first appearance of visible symptoms on five randomly tagged plants in each plot . For num, 00–99) . The fir formula .AUDPC=∑The DNA was extracted from fresh leaves of each line following the CTAB procedure  and genoK that ranged from 2 to 10 with additional parameters of 10,000 burn-in length and 10,000 Markov chain Monte Carlo (MCMC) iterations. Clusters with the greatest likelihood of occurrence were identified through the ΔK method [Q was also obtained for further analysis. TASSEL 5.0 was used to calculate population Kinship matrix, which is based on scaled IBS (identity by state) method that uses marker data which has passed quality filtering.Analysis of variance (ANOVA) was completed using SAS 9.4 to determine genotypic, year and genotypic × year variances among the traits measured over the years. Population structure (TRUCTURE . Ten iteK method . The webK method  was donehttp://www.maizegenetics.net/) [y = phenotype vector, β = vector of marker fixed effects; v = vector of fixed effects; u = vector of random effects (the kinship matrix); e = vector of residuals; X = genotype of a marker; Q = Kinship Matrix (obtained using STRUCTURE software) and Z = Identity matrix which considers the familial relatedness between accessions.Marker–trait associations (MTAs) were detected by Mixed Linear Model (MLM) in TASSEL 5.0 (cs.net/) . The genQ matrix was used as a covariate to improve the precision of results by avoiding false positives. The efficient mixed model analysis [Q + K model, takes into account the population structure as well as familial relatedness. Significant marker-trait associations were identified based on P-value and R2 used for estimating the magnitude of the QTL effects. Significant marker-trait associations were selected on basis of P-values ≤ 1e-06. In addition, pair-wise LD for pairs of markers was calculated using TASSEL 5.0 considering observed and expected allele frequencies of the markers.The analysis  was usedThe results of the analysis of variance (ANOVA) are presented in Distribution of phenotype under study has been generated which shows how performance of phenotype varies over the years . More ovOptimal number of cluster i.e. K was determined to be 3 based on delta K approach implemented in STRUCTURE . A bar g2) ranging between 0.083 and 0.11  SNP markers for lesion number were on chromosome 6B. The largest numbers of MTAs  were on chromosomes 1A, 1B, 5A, 6B, 7Bwhile no MTA was detected on the rest of the chromosomes(2 = 0.093). Two SNPs (RAC875_c68978_126 and BS00092845_51) significantly associated with lesion number on 6B chromosome were mapped at 0 cM, suggesting that one QTL for lesion number was detected by these SNPs . The greatest number (11) of SNPs were mapped on chromosome 5B for resistance to spot blotch; of these 11 SNPs, 10were mapped at 0 cM suggesting that one QTL for resistance to spot blotch may underly these SNPs Click here for additional data file.


With more than 10 million genotyped customers, the consumer genomics industry is maturing and becoming a mainstream phenomenon. At last, innovations and applications, some unforeseen, are being brought to the masses. Genome Biology on the state of consumer genomics [At the start of this year, the direct-to-consumer personal genomics industry surpassed 10 million genotyped consumers , a ten-fgenomics . Betweenif you have close relatives (at least in the United States), will converge upon total certainty [These enormous numbers of genotyped consumers will generate massive returns on scale, allowing for greater innovation and insight. If hundreds of millions of consumers contribute to genetic databases, then the power of genealogical algorithms to infer matches will increase, until the likelihood of matching a relative, ertainty . Public ertainty .The huge numbers of genotypes provided by consumers are valuable for genealogy, but as the numbers of genotypes increase into the millions, the data become even more valuable for trait prediction and medical applications. The large sample sizes allow for greater statistical power to detect genome-wide associations, which may be useful in linking genomic markers to functional traits and clinical phenotypes. 23andMe, for example, has amassed a database with sample numbers in the millions with which they are now working to obtain genotype–phenotype associations . The anaAll this brings us to emerging improvements in analysis technologies and platforms for consumer genomics. One of the new entrants is Helix, a consumer-focused genomics startup backed by Illumina. Utilizing exome sequencing, supplemented with single nucleotide polymorphism (SNP) positions that span the genome, Helix aims to go beyond the SNP-chip paradigm championed by current market leaders such as Ancestry and 23andMe. Meanwhile, Veritas Genetics, a well-funded Cambridge, MA, startup aims to take whole-genome sequencing (WGS) to the public. Fifteen years after the Human Genome Project, which cost about $3 billion dollars, Veritas is commercializing a retail product that provides a high-quality sequence for $1000. Gencove, a startup based in New York City, takes a middle ground between SNP arrays and full genome sequencing. Gencove has developed an affordable low-coverage WGS assay that represents an improvement over current SNP array technology, with coverage under 1×. The low-coverage sequencing assay is paired with imputation to deliver a product that is not only competitive but has advantages over SNP arrays in terms of cost and avoidance of ascertainment bias. We suspect that the SNP array era of the 2010s will be superseded by the sequencing decade of the 2020s.Although the number of genotyped and sequenced consumers has increased greatly, the services and products offered to make sense of their data remain curiously limited. This deficit might be a function of the late 2013 Food and Drug Administration (FDA) debacle with 23andMe, which cast a pall over the whole sector. Prior to the end of 2013, 23andMe had been providing a wide array of medically relevant analyses. In 2013, the FDA told the company that they could no longer do this, as these were unapproved ‘medical tests’. The firm could only provide information on ancestry and nonmedical traits to their future customers. Fortunately, over the past few years, 23andMe has established an avenue of communication with the FDA and, more importantly, a path for FDA approval for its tests . 23andMeSmaller companies such as Promethease, which leverage raw data generated by 23andMe, Ancestry, and others, continue to occupy a niche in analyzing genetic information. Using public data and stitching it together in a rough and basic user interface, Promethease and other such companies aim to eliminate the intermediaries between the consumer and the data. Instead of an institution or geneticist serving as a guide, these companies aim to educate the public in a raw and direct fashion, albeit with some controversy.Helix and Gencove take a third approach. Instead of providing the full stack of services or leveraging the raw data provided by third parties, these two companies want to be technology platforms for other independent players and foster an ecosystem of analysis applications for consumers. Helix seems to be modeling its ecosystems on the iTunes store, where Apple hosts and approves applications for consumers who already trust their brand and utilize their hardware platform. The difference here is that Helix is not an established brand and does not yet have a dedicated customer base. Remember that when the iTunes Store debuted in 2008, Apple was not a new company; it had sold more than 5 million computers and more than 50 million iPods.The need for a strong brand is probably one reason why Helix partnered with National Geographic, whose legacy reaches back over a century. In addition, it is partnering with blue-chip institutions such as the Mayo Clinic and with innovative companies such as Invitae. However, establishing a brand and a customer base are only part of the challenge of growing a successful app store. There are near infinite things you can do with a mobile computer, a camera, and a variety of sensors. There are far fewer apps that can be built on top of a genetic sequence, at least at present. Although the genomics space is in some ways less flexible than the applied engineering that drove innovation in Silicon Valley, it is also uncharted territory. There are still many variants to be discovered, structural features in the genome to be mapped, and parts of a genuinely complete human genome to be sequenced . As thesDNAGeeks.com is an example of genetics-inspired apparel. The shirt features your Y-DNA or MT-DNA haplogroup, along with a map of the related ‘Out of Africa’ migration pattern for the haplogroup. Furthermore, the Helix App Store has additional apparel items, including personalized scarves that allow customers to showcase their individuality through clothing and accessories [To date, the existing consumer genomics companies have apps that naturally segment into three classes: ancestry, entertainment, and medical. Medical applications often have the upside of being actionable, but they are also subject to regulatory oversight. Furthermore, there are limited medical insights to be gained today through genotyping or sequencing alone. Entertainment, ancestry, and genealogy are more loosely regulated, but they suffer from a perception that they are not an essential aspect of a person’s lifestyle. Entertainment apps often seem trivial or frivolous. But entertainment is important to allow us to flourish and for establishing connections. It is a something people are willing to spend money on because it is deeply rewarding. The key is not to undermine the credibility of the hard science while creating something fun and engaging. Not all apps have to provide a genome interpretation. Apps that leverage genetics can result in an efflorescence of cultural and entertainment possibilities previously not imaginable. For example, clothing and apparel that signals one’s genetic status or lineage is becoming more common. The ‘Haplotee’ offered by essories .Most recently, the arrest of the alleged Golden State serial killer in California using the methods of genetic genealogy shows how surprising and novel outcomes can be the product of disparate technological movements occurring in parallel . Now thaWe see the future through a dark mirror, but the current prediction for WGS is that 60 million Americans will be sequenced by 2025. Alongside the SNP array data, this sequencing will contribute to an enormous database that one can utilize to provide value to consumers. Until then, the next four years will see the SNP array and exome-based companies expand their markets at least geometrically, and perhaps even exponentially if there is more technological innovation. These certainties determine the parameters of the American and international market. But what sells and what does not; well, that is to be determined. The products and services that consumer genomics firms seeded in the SNP array era are likely to be the foundations of the WGS revolution that will come out into the open in the 2020s.