Solanum lycopersicum) being intertwined with introgressions from their wild relatives. Comparative analysis of genomes among cultivated tomatoes and wild species that have contributed genetic variation can help identify desirable genes, such as those conferring disease resistance. The ability to identify introgression position, borders, and contents can reveal ancestral origins and facilitate harnessing of wild variation in crop breeding.Decades of intensive tomato breeding using wild-species germplasm have resulted in the genomes of domesticated germplasm  contains supplementary material, which is available to authorized users. Solanum lycopersicum) is an important food crop and a model species for studying processes such as fleshy fruit ripening, fruit development [A priority in modern plant breeding is the introduction of novel variation for desirable traits; Biotic and abiotic stresses are the most crucial to increase yield and provide reliable food production. Tomato  in early 2012 has enabled a multitude of new genetic and genomic approaches , such asSUN has been duplicated in some varieties, but its functional copy is not present in Heinz 1706 (H1706) [Pto, which was introgressed from the wild tomato species, S. pimpinellifolium, in the 1930’s and later positionally cloned [Since virtually all tomato disease resistance genes originate from wild relatives, further knowledge of these genomes will facilitate introgression of multiple disease resistances into elite cultivars. Also, while all tomato species share largely syntenic genomes and can outcross, the genome content of the reference genome is not completely identical even to other commercial tomato cultivars. For example, the fruit shape gene  (H1706) . Anothery cloned ,27. A fuIntrogression of wild-species genomic regions into domesticated species is a widely used practice for increasing diversity in tomato as well as other crop species . After sOther crops, such as maize, rice, barley , bean 33, and meS. habrochaites [S. chilense accession LA1932 [Ty-3 resistance allele, as well as several other resistance genes from several wild accession sources.For this study, two begomovirus-resistant inbreds were chosen, Gh13  and BTI-ochaites . BTI-87 n LA1932 . Both inWe used whole-genome sequencing (WGS) to detect introgressions from wild species in two begomovirus-resistant inbreds. The boundaries of the introgressions were established and the source of several introgressions was determined Figure . The finPaired-end libraries of the Gh13 and BTI-87 genomes were each sequenced in one Illumina HiSeq lane. Mapping the Gh13 genome to the reference tomato H1706 genome yielded 14.7× coverage of the H1706 genome, after removing low quality reads and duplicates, with 97.6% coverage of the reference genome. Gaps in the Gh13 genome were estimated to span 9.2 Mb, and the total number of SNPs was 288,640  was attributed to the quality of the genomic DNA. The DNA library of BTI-87 was of higher quality than the one of Gh13, in that it contained fewer exact-duplicate reads. The difference in coverage did not affect the ability to map the reads to the reference genome and to call SNPs with high confidence using the same criteria. These genomes yielded similar genome coverage levels (97.6% and 96.5%), but the coverage in Gh13 is slightly higher since it has fewer SNPs and gaps than BTI-87, mainly due to fewer regions of introgressions from wild species.http://solgenomics.net). Positions of SNPs in both genomes can be found in the Genome Browser track, and can be used for designing new markers.Both Gh13 and BTI-87 genome sequences are available on the Sol Genomics Network  and 4.49 Mb (43.18–47.67 Mb) .The total number of SNP-peak regions identified using these criteria was 144, spanning 49.42 Mb with a total of 171,711 SNPs, of which 94 regions were 100 Kb or larger (Table http://solgenomics.net/organism/Solanum_lycopersicum/genome). These regions could indicate introgressions in either the analyzed genome or in the reference genome. By plotting the number of SNPs in the Gh13 and BTI-87 genomes in windows of 10 Kb, a number of regions across the genome that could be potential introgressions from wild species were identified  but different in H1706 (H1706 X Gh13 and H1706 X YP) most likely represent wild introgressions in the H1706 genome.To test the hypothesis that regions with high SNP density correspond to introgressions from wild species, the SNPs between each of the inbred lines, Gh13 and BTI-87, and the reference tomato genome were compared to SNPs in the genomes of m LA1589 , and thepersicum , and theckground ,44. Ther species . Thus anhis line ,44. The S. pimpinellifolium genome, can be designated as introgressions in Gh13 originating from a different wild species . The second largest SNP peak in chromosome 4 shows similarity to S. pimpinellifolium from 57.53 Mb to 57.91 Mb, immediately followed by 1.73-Mb region (57.91 Mb to 59.64 Mb) that most likely corresponds to an introgression in H1706 due to the high SNP density shared between Gh13 and YP , which is closest to S. pimpinellifolium, S. galapagense, S. chilense, and S. habrochaites.To investigate the origin of each detected SNP peak region on chromosomes 6 and 11 of Gh13, PCR primers were designed for amplifying fragments outside and inside the selected SNP peak regions Figures A, 3A. PCS. lycopersicum genomes, H1706, and YP, and very different from the wild species S. chilense and S. galapagense. Non-peak sequences of Gh13 were also nearly identical to S. pimpinellifolium sequences   [Solanum chilense accessions (LA2779 and LA1969) known to be sources of alleles of the Ty-3 locus [S. chilense accessions  , and two-3 locus . PhylogeS. pimpinellifolium, and the H1706 genome showed the Gh13 introgression regions overlap mostly with S. pimpinellifolium SNPs  that have different genome contents.The SolCAP SNP chip array containing 7,720 SNP markers  was usedTy-3 were used to demonstrate that a known introgression for the Ty-3 locus on chromosome 6 could be detected and boundaries determined , SNP calling, PCR sequencing, and phylogenetics. Two tomato inbreds (Gh13 and BTI-87) with alleles at the begomovirus resistance locus S. chilense, identifying this wild species as the source for the Ty-3 locus. Other notable introgressions were detected on chromosomes 4 and 11, where their origin is most likely S. pimpinellifolium. SNP peak regions that show high similarity between Gh13 and YP indicate introgressed region in H1706 from an unknown source, or from a different S. pimpinellifolium accession. The more than double the number of BTI-87 SNPs compared to Gh13 ; however, only 29 SNPs were polymorphic, compared to more than 35,000 SNPs detected with WGS, thereby providing a greater breadth of data related to the introgression content.An important property of introgression detection is the ability to determine its boundaries accurately. The ability to detect the starting and ending nucleotide of the Ty-1 and Ty-3 loci were recently mapped to the same region of chromosome 6 [Ty-1 and Ty-3 loci was time-consuming and required large mapping populations over many generations of selection [The mosome 6 , which ielection . With reS. pimpinellifolium, S. chilense, and YP, to detect introgressions in two begomovirus-resistant inbreds and identify the origin of some of these introgressions. The discovered introgressions vary greatly in size, location, and content, and our analysis with the heirloom line YP shows many of the introgressions are in the H1706 genome, which is known to have S. pimpinellifolium in its pedigree. These findings emphasize the need for additional genomic sequences of tomato wild species, which can be used to identify the origin of tomato introgressions, and study genome sequences that may not exist in the H1706 genome [We utilized the H1706 reference genome and other genome sequences from 6 genome . In addiSolanum lycopersicum inbred Gh13 was derived from the TYLCV-resistant germplasm FAVI 9 [Ty-3 on chromosome 6; homozygous for Ve on chromosome 9; heterozygous for I2 on chromosome 11, susceptible for Mi, Sw5, Ty2, Ph3, Tm2a, and Pto. Molecular scanning by sequencing PCR fragments showed that Gh13 had an introgression on chromosome 6 from 20 to 32 cM , which corresponds to the location of the Ty-3 locus [Ty-3 locus in conferring resistance to begomoviruses [m FAVI 9  by multim FAVI 9 ,46. Dise-3 locus ,48. Gh13oviruses ,49.S. lycopersicum inbred, BTI-87, was obtained from the commercial seed company Semillas Tropicales, S.A. The source of begomovirus resistance in BTI-87 was from the inbred line Gc171, which is known to have the Ty-3a and Ty-4 resistance loci on chromosome 6 and chromosome 3, respectively [S. chilense LA1932 [Ty-3 or Ty-3a on chromosome 6; heterozygous for Mi on chromosome 6; homozygous for the gene Tm2a on chromosome 9; and susceptible for I2 and Sw5.The proprietary begomovirus-resistant ectively ,50. These LA1932 . DiseaseS. habrochaites LA0386 and LA1777, S. chilense LA1932, LA1969, and LA2779, and S. galapagense LA0436 were obtained from the Tomato Genetics Resource Center at UC Davis (http://tgrc.ucdavis.edu).Seeds of accessions S. lycopersicum H1706 (LA4345) and YP were provided by Gregory Martin, Boyce Thompson Institute for Plant Research (BTI). S. lycopersicum Purple Russian seeds were available from the laboratory of Douglas Maxwell, University of Wisconsin-Madison. The SNP assay for resistance loci by AgBiotech, Inc. showed that the S. lycopersicum lines, H1706, YP, and Purple Russian, had susceptible loci for Ty-3, Mi, I2, Sw5, and Tm2a.Seeds of Gh13 seedlings were grown at the University of Wisconsin-Madison. DNA was extracted using CTAB method , yieldinAbout 20 seedlings of tomato line BTI-87 were grown in a greenhouse under standard conditions  at Boyce Thompson Institute for Plant Research. Young leaves of 4- week-old seedlings were collected for DNA extraction using CsCl gradient as described previously . Plants S. chilense LA1932 were generated and sequenced on Illumina HiSeq 2000 machine at the Weill-Cornell Genomics Core Facility, New York, NY. Each PE library had an insert size of 300 bp. The reference genome for S. lycopersicum H1706 used is from the international tomato genome project, version SL2.40 (http://solgenomics.net/organism/Solanum_lycopersicum/genome). Dr. Zach Lippman, at the Cold Spring Harbor Laboratory, sequenced the S. pimpinellifolium accession, LA1589, [S. galapagense accession LA0436 and the S. lycopersicum heirloom line YP sequences were obtained from a previous study at BTI [Paired-end (PE) libraries of Gh13, BTI-87, and  LA1589, . S. galay at BTI .http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads were mapped to the S. lycopersicum H1706 reference assembly version 2.40 using BWA [http://picard.sourceforge.net) and Samtools (http://samtools.sourceforge.net/) [http://samtools.sourceforge.net/mpileup.shtml).Illumina reads were inspected for quality using FastQC and rechecked after cleaning. Cleaning was performed with fastq-mcf (sing BWA  with defge.net/) , respectde novo assemblies of Gh13 and BTI-87 were created using SOAPdenovo version 1.05 (http://soap.genomics.org.cn/) [Whole genome org.cn/) . AssemblS. chilense introgression breakpoints in Gh13, variants of accession LA1932 were called using VarScan2 [https://github.com/nmenda/GenomeTools).For determining exact VarScan2  and uniqS. pimpinellifolium, Gh13, and BTI87 that were called in reference to H1706 were compared to each other, and labeled ‘unique’ or ‘common’. SNPs for each group were then aggregated into bins of 10 Kb using a custom Perl script (https://github.com/nmenda/GenomeTools). SNP density for each comparison was plotted along every S. lycopersicum ‘Heinz’ chromosome using R statistics (http://www.R-project.org).SNPs of Introgressions were defined as SNP-peaks having at least 10 SNPs per 10 Kb window, with minimum size of 50 Kb, and up to 40 Kb of continuous gaps. Minimum size was chosen for capturing small introgressions, and the gaps were introduced to offset the significant decrease in genome coverage in introgressed regions due to the difficulty to map those regions to the reference H1706 genome. The minimum number of SNPs per window was selected based on the hypothesis that having no introgressions means the average number of SNPs per 10 Kb window in the entire genome will be similar to this number in non-peak regions. If introgressions can be defined as having significantly higher number of SNPs in peak-regions and lower number of SNPs in non-peak regions, then the average number of SNPs per window in the entire genome should be higher than the number of SNPs in the non-peak regions. We tested introgressions using minimum number of 3, 5, 10, 15, or 20 SNPs per 10 Kb, extracting for each condition the SNP-peak and non-peak regions, and comparing the average number of SNPs in 10 Kb windows in the non-peak regions to that number in the entire genome of Gh13, and comparing each pair using Student’s t-test ,58.S. chilense, S. habrochaites, and S. lycopersicum . PCR was performed at 55 degrees Celsius, 32 amplification cycles, 60 seconds extension step. All designed primers are listed in Table S. lycopersicum H1706 and YP, the inbred BTI-87, S. pimpinellifolium, and S. galapagense were extracted from their genome assemblies by best BLAST match of primer pairs.PCR primers were developed for regions of interest based on previous markers and genic regions. PCR products were generated from S. lycopersicum H1706 sequence selecting the top BLAST hit followed by reciprocal BLAST back to S. lycopersicum H1706. Sequences from Gh13, BTI-87, S. lycopersicum H1706, YP and Purple Russian, S. pimpinellifolium, S. galapagense, S. chilense, and S. habrochaites when available, were aligned using ClustalW [http://tree.bio.ed.ac.uk/software/figtree/) was used for drawing the gene tree figures. All trees were submitted to TreeBase http://purl.org/phylo/treebase/phylows/study/TB2:S16453.Putative orthologous sequences for regions of interest were obtained from draft genome assemblies by using ClustalW  with defClustalW . FigTreeTy-3 locus, were genotyped using a tomato array with 7,720 SNPs as implemented in the Infinium assay . HUJ-VF, a processing type tomato, was provided by Dr. Favi Vidavsky, Hebrew University of Jerusalem. For each accession, genomic DNA was isolated from fresh, young leaf tissue using a Qiagen DNeasy kit  at the University of Wisconsin-Madison. Double-stranded DNA concentrations were quantified using the PicoGreen assay  and normalized to 50 ng/ul with 10 mM Tris–HCl pH 8.0, 1 mM EDTA. Genotyping was conducted with 250 ng of DNA per accession following the manufacturer’s protocol for the Infinium assay. For SNP calls, the resulting intensity data was loaded in GenomeStudio version 1.7.4 . In order to determine SNP genotype, the automated cluster algorithm was first used to generate initial SNP calls. Clustering for every SNP was determined using the SolCAP cluster file [Lines Gh13 and a begomovirus-susceptible inbred, HUJ-VF that lacked the ter file .http://solgenomics.net/organism/Solanum_lycopersicum/inbred_genomes). Sequences of PCR products and primers designed and sequences in this work are available from the NCBI GenBank nucleotide database, accession numbers KF887310–KF887341.The genomes of lines Gh13 and BTI-87 are available to browse, BLAST, and download at the Sol Genomics Network website (https://github.com/nmenda/GenomeTools.Custom perl scripts are available from GitHub


These are coupled with the ForenSeq™ DNA Signature Prep kit (Illumina) and the HID-Ion AmpliSeq™ Identity Panel (Thermo Fisher Scientific), respectively. In this study, we compared the genotyping performance of the two MPS systems based on 83 SNP markers that are present in both MPS marker panels. Results show that MiSeq® FGx™ has greater sample-to-sample variation than the HID-Ion PGM™ in terms of read counts for all the 83 SNP markers. Allele coverage ratio (ACR) values show generally balanced heterozygous reads for both platforms. Two and four SNP markers from the MiSeq® FGx™ and HID-Ion PGM™, respectively, have average ACR values lower than the recommended value of 0.67. Comparison of genotype calls showed 99.7% concordance between the two platforms.The potential of Massively Parallel Sequencing (MPS) technology to vastly expand the capabilities of human identification led to the emergence of different MPS platforms that use forensically relevant genetic markers. Two of the MPS platforms that are currently available are the MiSeq Several commercial kits that use the MPS platforms for forensic STR5 and mtDNA7 analyses are now available. MPS has also boosted the power of SNP markers for human identification by increasing the number of SNP loci that are analyzed in one reaction. Before MPS, SNP typing was performed using single base extension (SBE) reaction. This produced SNP typing procedures that are capable of multiplexing around 50 SNP loci. On its own, human identification via SNP markers wasn’t able to gain as much popularity as STRs and mtDNA markers in the forensic community10. Since the inception of MPS in forensic science, Illumina®11 and Life Technologies™14 developed marker panels with more than 100 SNP loci. The MPS platforms dedicated for forensic genetics are the MiSeq® FGx™ Forensic Genomics System (Illumina) and the HID-Ion Personal Genome Machine (PGM)™ (Thermo Fisher Scientific). These platforms are coupled with the commercially available panels for human identification, the ForenSeq™ DNA Signature Prep kit (Illumina) and the HID-Ion AmpliSeq™ Identity Panel (Thermo Fisher Scientific). These MPS systems sequence in one reaction 124 (HID-Ion PGM™) and 173  SNP markers, respectively. 83 of these SNP markers are common for both platforms. These 83 SNP markers, which are spread across the 22 autosomes, have high heterozygosity and low Fixation Index (Fst) giving them a high combined discrimination power16. In addition, these 83 SNPs have relatively small amplicon sizes ranging from 40 to 135 bp increasing the likelihood of successful DNA profiling of degraded DNA18.Massively parallel sequencing (MPS) has undoubtedly taken DNA-based analysis to a higher level of exploration and discovery. In forensic sciences, MPS surpassed conventional DNA profiling technologies, e.g. capillary electrophoresis-based sequencing, because 1) MPS is capable of obtaining detailed sequence information on conventional genetic markers; 2) MPS has increased multiplexing capabilities; 3) MPS reports massive amounts of data simultaneously from one or many individuals; and 4) MPS provides a higher throughput DNA sequencing procedure® FGx™ systems utilize different approaches to sequencing, it is necessary to assess the reliability and consistency of their genotyping results. Concordance of the shared 83 SNP markers will allow merging of data that were generated by the two systems and enable expansion of existing databases. Using the overlapping 83 SNPs, we performed concordance analysis and parallel evaluation in terms of coverage per SNP locus and heterozygote balance of genotype calls on 143 blood samples that were blotted on FTA™ paper (Whatman). FTA™ paper is used in forensics genetics research and biobanking because it allows for easier and longer storage of DNA from samples such as blood19. Recently, Kampmann and co-workers demonstrated the utility of FTA samples for MPS20 thus opening the doors for laboratories with archived samples to adopt the MPS technology.Since HID-Ion PGM™ and MiSeq® FGx™ marker panel with the corresponding genotypes in the HID-Ion PGM™ marker panel. Overall concordance analysis of the 143 samples showed an average of 99.70% concordance and a non-concordance range of 0 to 9% across all the 83 SNPs  values of 0.186 and 0.097, respectively, for the samples listed in Table 1.The two MPS systems initially showed more than 43% non-concordance at 28 out of the 83 SNPs analyzed  with monoclonally amplified SNP target that were successfully read21. On the other hand, SNP coverage for the MiSeq® FGx™ is affected by the PCR amplification efficiency, purification, and bead-based library normalization11. Increased number of markers multiplexed in one sequencing reaction could also increase variation in coverage of the SNP markers11. Comparison of the markers’ SNP coverage between the two MPS systems  gave 11 extreme outliers with SNP coverage values that are at most 8,740 reads away from the average SNP coverage (618 reads) of the 143 samples  was the parameter used for evaluationems Figs  showed t® FGx™ platforms  of heterozygous SNPs is 0.89 and 0.88 for HID-Ion PGM™ and MiSeqrms Fig. , respectGx™ Fig. , gave av® FGx™ Forensic Genomics System were highly concordant and SNP data may be pooled to provide a more comprehensive database of forensically relevant SNPs. Further work is needed to address the quality of MPS data from the SNPs rs10776839, rs1031825 and rs1736442 – with greater than 4.8% non-concordance between platforms– and the SNP rs338882 in MiSeq® FGx™ marker panel– with observed imbalance in heterozygous SNPs and with large sample-to-sample coverage read variation.The study puts forward the need to include the information on sequence nomenclature when reporting MPS data. Genotyping data generated using HID-Ion PGM™ and MiSeq® FGx™ ForenSeq™ Genomics System were performed following the manufacturers’ protocols24.The study was implemented at the DNA Analysis Laboratory, Natural Sciences Research Institute, University of the Philippines Diliman. Laboratory work involving the use of MPS machines was conducted at Illumina Headquarters in Singapore and at the Philippine Genome Center, University of the Philippines Diliman. Steps in processing the samples using the HID-Ion PGM™ and MiSeq® FGx™ Forensic Genomic System and the HID-Ion PGM™ following manufacturers’ protocols. This study was approved by the University of the Philippines Manila, Research Ethics Board (UPMREB No. 2014-499-01). All procedures were performed in accordance with the approved guidelines of UPMREB. Volunteer’s informed consent was obtained before sample collection was performed.Archived blood DNA samples on FTA™ paper from 143 unrelated Filipino male individuals were processed using the MiSeq15 and 47 SNPS used by the SNPforID Consortium16, with one common SNP (dbSNP ID rs2046361). Primers targeting these SNPs are included in the HID-Ion AmpliSeq™ Identity Panel (Thermo Fisher) and ForenSeq™ DNA Signature Prep Kit Primer Mix B (Illumina). HID-Ion AmpliSeq™ Identity Panel is composed of the 34 Y-clade SNPs and 90 autosomal SNPs. ForenSeq™ DNA Signature Prep Kit Primer Mix B (Illumina) is composed of 95 Identity Informative SNPs, 22 Phenotypic Informative SNPs, and 56 Ancestry Informative SNPs22.The 83 overlapping autosomal Individual Identification SNPs are composed of 37 SNPs reported by Ken Kidd® Thermal Cycler (Applied Biosystems) using reagents from the HID Ion AmpliSeq™ Identity Panel kit24. The amplification reaction contained 5X Ion AmpliSeq™ HiFi Master Mix (4 ul) and 2X HID-Ion AmpliSeq™ Identity SNP-124 Panel (10 ul). PCR cycling was reduced to 18 cycles for FTA™ discs. Partial digestion of primers was performed by treating the amplicons with FuPa Reagent (2 ul) (Thermo Fisher Scientific). This was followed by the ligation of barcodes to the amplicons using Switch Solution (4 ul), DNA ligase (2 ul), and Ion Xpress Barcode (2 ul) (Thermo Fisher Scientific). Barcoded libraries were purified with Agencourt AMPure XP Reagents  and quantified using the Ion Library TaqMan PCR Mix (Thermo Fisher Scientific) and the 20X Quantitation Assay (Thermo Fisher Scientific). Pooling of the library was performed by mixing equal volumes of barcoded samples with a concentration of 20 pM. Clonal amplification via emulsion PCR and library enrichment was performed using the Ion OneTouch™ 2 (OT2) System with the Ion PGM™ Template OT2 200 Kit and the Ion OneTouch™ ES (Thermo Fisher Scientific). Sequencing was performed in the Ion Torrent PGM™ (Thermo Fisher Scientific) with Ion PGM™ Sequencing 200 Kit and the Ion 318 Chip Kit v2 (Thermo Fisher Scientific) following manufacturer’s protocol.Library amplification was performed in the Veriti22. The libraries underwent a bead- based purification and normalization using the ForenSeq™ DNA Signature Prep Kit. The normalized libraries were pooled using equal volumes of preparations with 0.2 ng/ul concentration. Pooled libraries were diluted in hybridization buffer and denatured. Sequencing was performed on the MiSeq® FGx™ desktop sequencer with the MiSeq® ForenSeq™ Sequencing Kit (Illumina) following manufacturer’s protocol.Library amplification, amplicon indexing and barcoding were performed using the ForenSeq™ DNA Signature Prep Kit following recommended manufacturer’s protocol for FTA samples® FGx™ Forensic Genomics System. Genotype calls and coverage reads were exported as Microsoft® Office Excel® (2007) files from the HID SNP Genotyper Plugin and ForenSeq™ UAS software. Data analysis and presentation were performed in Matlab v.2  and R software v.3.3.1 using the ggplot2 package.For the HID-Ion PGM™, raw sequencing data were processed in the Ion Torrent Suite™ Software with the HID SNP Genotyper Plugin adapted for data analysis. ForenSeq™ Universal Analysis Software (UAS) (Illumina) was used for the MiSeq


Populus tomentosa gene PtoNERD with its putative regulator, the lncRNA NERDL (NERD-related lncRNA), which partially overlaps with the promoter region of this gene. Expression analysis in eight tissues showed a positive correlation between NERDL and PtoNERD (r = 0.62), suggesting that the interaction of NERDL with its putative target might be involved in wood formation. We conducted association mapping in a natural population of P. tomentosa  to evaluate genetic variation and the interaction of the lncRNA NERDL with PtoNERD. Using additive and dominant models, we identified 30 SNPs (P < 0.01) associated with five tree growth and wood property traits. Each SNP explained 3.90–8.57% of phenotypic variance, suggesting that NERDL and its putative target play a common role in wood formation. Epistasis analysis uncovered nine SNP-SNP association pairs between NERDL and PtoNERD, with an information gain of -7.55 to 2.16%, reflecting the strong interactions between NERDL and its putative target. This analysis provides a powerful method for deciphering the genetic interactions of lncRNAs with mRNA and dissecting the complex genetic network of quantitative traits in trees.Long non-coding RNAs (lncRNAs) are important regulatory factors for plant growth and development, but little is known about the allelic interactions of lncRNAs with mRNA in perennial plants. Here, we analyzed the interaction of the NERD (Needed for RDR2-independent DNA methylation)  NERD, ated loci . In ricensposons . Naturalnsposons . Howeversearches , but theArabidopsis (Populus (Arabidopsis (LncRNAs (long non-coding RNAs), which are longer than 200 nucleotides and have no apparent coding ability, are emerging as important participants in numerous biological processes . In animbidopsis  and drou  . A clona3). The correlations of these phenotypic data in the association population were calculated as previously described  following the manufacturer’s instructions. DNA contamination was removed with three additional on-column DNase digestions during RNA purification with an RNase-Free DNase Set (Qiagen). The three RNA samples were used to construct RNA-seq libraries, and transcript sequencing was performed on an Illumina HiSeq 2500 by Shanghai Biotechnology Corporation . High-quality reads were mapped to the Populus reference genome (SRP073689). Expression analysis of seven genes  involved in the NERD pathway showed that PtoNERD was highly expressed in vascular tissue, suggesting that it might function in wood formation . Therefore, PtoNERD (Accession number: KY344444–KY344487) was selected as a candidate gene for further analysis. RNA sequencing was performed to identify the mRNA sequence of PtoNERD, which was compared with available Arabidopsis or P. trichocarpa NERD sequences, showing that PtoNERD mRNA shares high sequence similarity with AtNERD AT2G16485 (45%) and Potri.009G1238000 (95%).Cambium, developing xylem, and mature xylem tissues were obtained from 1-year-old e genome  using Toe genome . The trae genome , and thePtoNERD with similar genes in other species, the amino acid sequences of NERD from A. thaliana, P. trichocarpa, P. euphratica, Picea abies, Pinus taeda, Zea mays, and Oryza sativa ssp. japonica were identified by searching public databases at NCBI1 using BLAST, and sequence alignment and phylogenetic tree construction were performed with MEGA 5.0 as previously described   as described  . To idenescribed .P. tomentosa clone LM50, including juvenile leaf, mature leaf, apical shoot meristem, phloem, cambium, developing xylem, mature xylem, and root tissue as described (PtoNERD and NERDL through reverse-transcription quantitative PCR (RT-qPCR). The qPCR was performed on the 7500 Fast Real-Time PCR System with SYBR Premix EX Taq  with gene-specific primers . Data analysis was performed with Opticon Monitor Software 3.1 .Total RNA was extracted from fresh tissue samples from 1-year-old escribed . The RNAescribed . All cDNPtoNERD. The 16,119 bp genomic DNA sequence of PtoNERD, including the flanking region (2000 bp), was obtained by direct sequencing of the P. tomentosa (LM50 clone) using conserved primers and the BigDye Terminator Cycle Sequencing kit version 3.1  on a Li-Cor 4300 genetic analyzer . The Prosite database2 was used to identify protein domains, families, and functional sites, and SMART3 was used to analyze the protein sequence. To identify SNPs, NERDL, and PtoNERD were sequenced and analyzed in 44 unrelated individuals from the association population with MEGA 4.0 and DnaSP v4.90.1 (NERDL and KY344488-KY344531 for PtoNERD). Sequence alignment was performed with BioEdit, and manual editing was performed to remove primer sequences and to confirm sequence quality. All common SNPs  detected in NERDL and PtoNERD were genotyped in the association population using the Beckman Coulter sequencing system .Total genomic DNA was extracted from fresh young leaves with a Plant DNeasy Mini kit  according to the manufacturer’s instructions. The primer sets used for amplification and sequencing were specifically designed based on the sequence of the lncRNA NERDL and the cDNA sequence of  v4.90.1 . Insertiw (The summary statistics for SNP polymorphisms were obtained using DnaSP version 4.90.1 . The avew  were usew  statistiNERDL and PtoNERD were assessed using TASSEL V.5.0 (r2), which can be affected by both recombination and differences in allele frequencies between sites  between common SNP sites within both candidate genes was estimated by non-linear regression analysis with 105 permutations of the data  with 105 permutations with the software package TASSEL v5.04 (Q) and pairwise kinship (K) were used to evaluate the effects of population structure and relatedness among individuals for marker–trait associations. In this Q + K model, Q matrix was obtained based on the significant subpopulations (k = 3)   using ST = 3)  (5  using th = 3) , improving the reliability of probability approximation. The five most-significant SNPs were calculated for each trait. The genetic effects of significant SNP–SNP pairs were assessed by IG (information gain) analysis, which was calculated via an entropy-based measurement  algorithm  was devesurement .PtoNERD. For each SNP genotypic class, 10 individuals were selected and used to measure the expression of the lncRNA NERDL and PtoNERD via RT-qPCR. The differential expression across different genotypes was calculated with ANOVA. The effects of different genotypes of the shared locus on phenotypic variation were also investigated.The overlapping significant SNPs were used to investigate the roles of different genotypes on the expression of NERDL and PtoNERD, we cloned a cDNA from PtoNERD. This cDNA is 9527 bp long, including a 5163 bp open reading frame and 364 bp of 3′UTR. Based on the alignment of the full-length cDNA sequence to the genomic sequence, PtoNERD (16119 bp) has 10 introns and 11 exons flanked by 2000 bp of promoter region, 364 bp of 3′UTR, with a 2000 bp flanking region ; the length of each region is listed in Table 1. PtoNERD encodes a putative polypeptide of 1720 amino acids, with an estimated molecular mass of 188.4 kD and a pI of 4.82. Characterization of the amino acid sequence of PtoNERD revealed that it contains the essential domain PHD, SWIB, Plus-3, and GYF . Phylogenetic analysis indicated that PtoNERD is an ortholog of AtNERD and PtrNERD . Phylogenetic analysis using the neighbor-joining method showed that PtoNERD in P. tomentosa shares a close relationship with the homologous sequences in P. trichocarpa and P. euphratica, indicating that PtoNERD is highly conserved in Populus, whereas the conservation of PtoNERD in distantly related species is weak.To identify SNPs within PtoNERD in plant development, using genome annotation and a genome browser, we identified NERDL, an lncRNA located in the antisense strand and overlapping with the promoter region of PtoNERD, which acts as a cis-regulator of PtoNERD. Sequencing analysis revealed the 2286-nt sequence of NERDL, which shares an overlapping region of 961 bp with PtoNERD . Therefore, PtoNERD was regarded as the candidate cis target gene of the overlapping lncRNA NERDL (lncRNA related to PtoNERD). Several functional motifs are found in the overlapping region, such as light responsive element 3-AF1 binding site, Box I, and GT1-motif, as well as as-2-box involved in shoot-specific expression and light responsiveness.To further explore the roles of PtoNERD in eight different plant tissues via RT-qPCR . NERDL was highly abundant in phloem (1.82 ± 0.09) , followed by mature xylem (1.33 ± 0.07), with the lowest abundance detected in cambium (0.56 ± 0.03), suggesting that NERDL plays a regulatory role in wood formation. PtoNERD mRNA was enriched in juvenile leaves (4.23 ± 0.20), followed by mature xylem (3.10 ± 0.14), with the lowest abundance in cambium (0.41 ± 0.19). Overall, PtoNERD was more highly expressed than NERDL. The Pearson’s product moment correlation was 0.62 (P < 0.05), indicating a strong correlation in transcript levels between NERDL and PtoNERD. The reads numbers of NERDL and PtoNERD obtained by RNA-seq showed a similar trend in cambium, developing xylem, and mature xylem tissue .We explored the tissue-specific expression patterns of the lncRNA NERDL and NERDL and PtoNERD from 44 unrelated individuals of P. tomentosa. In total, 26 SNPs were detected in NERDL at a density of 1/88, and 142 SNPs in PtoNERD were detected at a frequency of approximately one SNP every 114 bp. The nucleotide diversity (excluding InDels) was higher in NERDL (π = 0.00257 and 𝜃w = 0.00261) than in PtoNERD (π = 0.00174 and 𝜃w = 0.00203). As expected, the average nucleotide diversity in PtoNERD was higher in introns (π = 0.00096) than in exons (π = 0.00030), since under selection pressure, the coding region of a gene shows more conservation than the non-coding regions (Table 1). Within the coding region of PtoNERD, the average nucleotide diversity of synonymous polymorphisms  was higher than that of non-synonymous polymorphisms  at a ratio of dN/dS (0.788 < 1), indicating that non-synonymous sites in the exons are under purifying selection.To characterize the intraspecific polymorphisms for use in association mapping, we obtained the full-length genomic sequences of NERDL and PtoNERD, respectively, were successfully genotyped across 435 individuals in the association population. Four common SNPs were located in the overlapping region and were shared by NERDL and PtoNERD (SNP21–24 in NERDL and SNP1–4 in PtoNERD). A plot of the r2 combined with physical distance between SNPs within NERDL and the PtoNERD genes is shown in Supplementary Figure S2. In this population, the rate of LD declined rapidly, and the r2 values decreased to 0.1 within 1200 and 8000 bp in NERDL  and PtoNERD , respectively, indicating that LD does not extend across the entire gene region.To perform LD analysis, 24 and 135 common SNPs  from PtoNERD on growth and wood formation in P. tomentosa, we performed an association study among 159 common SNPs from NERDL and PtoNERD and 10 quantitative traits using MLM. In all, we detected 35 significant associations  between 30 unique SNPs and 5 quantitative traits (Supplementary Table r2) explained by each association ranging from 3.90 to 8.57%. Four SNPs in NERDL are associated with three growth and wood property traits in P. tomentosa , with an average r2 of 5.78%. For PtoNERD, 26 significant SNPs from the promoter, exon, intron, and 3′UTR regions are associated with five traits , with r2 ranging from 3.90 to 8.57%. Of the seven SNPs in the coding region, three non-synonymous SNPs  were detected that are significantly associated with FW . For example, SNP33, a missense mutation in exon 1, results in an encoded amino acid change from Thr to Ile, which is significantly associated with FW.To thoroughly investigate the effect of NERDL and its putative target NERDL. In addition, three traits  formed 1–14 associations with SNPs from both NERDL and PtoNERD simultaneously, with various contributions to phenotypic variation, suggesting that NERDL and its target gene PtoNERD play common roles in phenotypic variation  in PtoNERD were associated with more than one trait. Interestingly, an overlapping significant SNP locus, L23/N3, was detected in NERDL  and its putative target gene PtoNERD  that associated with D (r2 = 4.70%). In general, the single SNP-based associations of lncRNA NERDL and its potential target suggest that they play a role in tree growth and wood properties.Among the identified associations, the most-significant SNP–trait associations affected wood property traits, including 14 for FW and 9 for wood chemical composition ; of these associations, 11.54% involve SNPs within PtoNERD in tree growth and wood formation, we investigated the additive and dominant effects of the 35 SNP–trait associations, with an average r2 of 5.48% . The additive effect of these associations varied from 0.17 to 14.50% . In particular, SNP12 in PtoNERD, associated with AC, had the largest additive effect (14.50%); this additive effect was lower when associated with FW (2.80%) . The highest PtoNERD mRNA levels were detected in the AA genotype (0.46 ± 0.02), followed by the AG genotype (0.05 ± 0.002). For NERDL, the expression levels were higher for the AA genotype (0.57 ± 0.02) than for the AG genotype (0.08 ± 0.004), indicating that the same locus can influence the expression of different transcripts. In addition, the average D depended on the genotype of L23/N3, with an average D of 22.74 cm in this association population for the AA genotype and 17.28 cm for AG .To further explore whether the allelic variation in the overlapping SNP L23/N3 influences the expression of lncRNA and its putative target, we used RT-qPCR to measure the transcript levels of NERDL and NERDL (3) and PtoNERD (14) associated with four traits , explaining 1.06–13.16% of the phenotypic variance (Table 3). Two haplotypes (G-A-C and T-G-A from NERDL_SNP22-24/PtoNERD_SNP2-4) associated with D were shared by NERDL and PtoNERD since they share an overlapping region. Each of these four traits is associated with one to nine haplotypes. For example, FW is associated with nine haplotypes from PtoNERD  (Table 3). In addition, one haplotype (T-A-G-A) from PtoNERD_SNP6-9 is associated with both HEC and FW, with different r2 , implying the pleiotropy effects and conspicuousness of the gene. In total, the results of haplotype association analysis were validated by single-SNP-based association analysis and explained more phenotypic variation. In addition, the association of L23/N3 with D supports the notion that the two overlapping haplotypes (G-A-C and T-G-A) from NERDL and PtoNERD are associated with the same trait, D, indicating that different SNPs located in different regions might have a common effect on the same trait .We detected 15 common haplotypes (frequency ≥ 0.05) within P < 0.01, Q < 0.1) representing 32 SNPs from NERDL (2 SNPs) and PtoNERD (30 SNPs) (Supplementary Table Table 4). The effects of these nine lncRNA–mRNA interactions ranged from -7.55 to 2.16%, and nearly half showed negative IGs, indicating the redundant information for the same traits.We uncovered strong interactions of different genes with phenotypic variation by investigating the epistatic effects between the lncRNA NERDL and its target. In total, we detected 90 significant SNP pairwise associations (Tables 4). The epistasis network consisting of SNPs from NERDL and its putative target offered strong genetic evidence for the lncRNA–mRNA interactions and their potential function in wood formation and tree growth . For example, L3 and N11 showed an epistatic interaction for FW, and the epistasis value for the combination of the AA genotype of L3 and the AC genotype of N11 was higher than those of the other combinations. In addition, the epistasis values of the AA-AC combination (2.829%) in SNP pair L3-N11 were significantly higher than the values for single genotypes (-0.201% for AA and 2.229% for AC) . L3 formed five SNP pairs with SNPs from the target gene, showing different pairwise effects (0.97–2.96%) on FW, indicating the pleiotropy of L3 and the important roles of these interactions in tree growth and development.Of these associated SNPs detected in the epistasis model, 23.08% of unique SNPs also showed significant associations under the additive and dominant models , suggesting a potential correlation between this lncRNA and PtoNERD. The overlap of lncRNAs with the promoter regions of protein-coding genes can affect the expression of these genes (P < 0.05), providing evidence for a strong correlation between NERDL and its putative target gene, PtoNERD . LncRNAs are usually shorter and are expressed at lower levels than protein-coding transcripts in P. tomentosa . NERDL was expressed at the highest level in phloem, suggesting that it may play a regulatory role in secondary wood formation. Furthermore, NERDL and PtoNERD were highly expressed in xylem , suggesting that they play important roles in wood formation.Unlike microRNAs, whose roles in post-transcriptional gene regulation are well-characterized , the biose genes . Transcromentosa  and in momentosa . Here, RTable 1), implying that the coding regions are conserved relative to the other regions under natural pressure, with a dN/dS ratio (0.7879) less than 1 for PtoNERD (NERDL (π = 0.00257) is slightly higher than that of PtoNERD (π = 0.00174), further confirming the conservative nature of the protein-coding gene. SNP diversity in genes has primarily been explored in exons, introns, and UTRs, while less attention has been paid to promoter regions and lncRNAs. It would be quite useful to analyze the levels of nucleotide diversity within the promoters of numerous genes compared with other gene regions. In the current study, we found that the promoter region possessed a significantly higher frequency of SNPs than the other regions in PtoNERD . This finding suggests that this region may be an unstable regulatory region that plays important roles during the evolutionary process , leading to the suggestion that NERDL and its potential target might play a role in tree growth and wood formation. We identified 35 associations with both additive and dominant effects associated with five traits , representing 30 significant SNPs from NERDL and PtoNERD , suggesting that the lncRNA–mRNA pair might have a common effect on certain traits. Eight SNPs from NERDL and PtoNERD were associated with HEC, with additive and dominant effects , an association considered to indicate generalized pleiotropy . Considering the associations based on additive and dominant effects, our findings suggest that NERDL and PtoNERD co-regulate HEC and D and that the role of NERDL might be similar to that of its target, PtoNERD. Furthermore, under the additive and dominant models, several SNPs are associated with multiple traits but with different contributions, such as SNP9 in NERDL, which is associated with two traits, with r2 varying from 11.28 to 13.08%, revealing the pleiotropy of specific SNPs, as observed in a previous study  overlapping in NERDL and PtoNERD was significantly associated with D (r2 = 4.7%). Notably, genotypes of the overlapping SNP L23/N3 led to significant differences in the transcript levels of NERDL and PtoNERD . The transcript levels of the AA and AG genotypes showed similar trends in both NERDL and its putative target PtoNERD, indicating that this overlapping locus can influence the expression of different transcripts. Overall, the average D was higher in the AA genotype (22.74 cm) than in the AG genotype (17.28 cm) , indicating that variations in genotype may lead to changes in phenotype. Moreover, two shared common haplotypes from NERDL_SNP22-24/PtoNERD_SNP2-4 associated with D surround L23/N3. The locus might serve as a functional marker involved in the regulation of D, which should be explored in the future.Three non-synonymous markers , one synonymous marker (SNP105), and 10 non-coding markers in promoter and introns were associated with FW simultaneously , reflecting the possible genetic interaction of the lncRNA NERDL with PtoNERD. Furthermore, 55.56% of these SNP–SNP pairs had negative IG values, implying that redundant information is carried in the pairs for each trait and that their similar functional modes contribute to phenotypic variation. In associations with positive IG values, the contribution of pairwise interactions to phenotypic variation is more than the sum of the contributions of two SNP individuals. The genotypic combinations with the highest and lowest IG values for FW were found for SNP pair L3-N11 , where the value for the genotypic combinations was significantly higher than that of the single-locus genotype. The identification of positive and negative IGs contributes to our understanding of the close interactions of NERDL with its target.Epistasis analysis is a crucial method for deciphering the effects of genetic variation and interactions . EpistasNERDL formed five SNP pairs with N11, N12, N20, N98, and N135 in PtoNERD under the epistasis model associated with FW with diverse IGs, revealing the complexity of the interactions between NERDL and PtoNERD . L20 also showed similar associations with four SNPs associating with D from PtoNERD  . These observations further demonstrate the important functions of L3 and L20 in NERDL and the importance of the SNP pairs under the epistasis model for phenotypic variation, and they help confirm the notion that the interaction of NERDL with PtoNERD contributes to wood formation. In addition, 3 SNPs  out of 11 also showed significant effects in the additive and dominant models, while the remaining epistatic effects involved pairs of SNPs that showed deficiency for both additive and dominant effects. These findings are consistent with the suggestion by PtoNERD via similar pathways and that their complex interactions play a role in regulating tree growth and determining wood properties.Of the SNPs showing lncRNA–mRNA interactions identified under the epistasis model, L3 in r = 0.62; Figure 1C). This finding is consistent with the results of association studies of additive and dominance effects, strongly suggesting that NERDL and its potential target affect phenotypic variation in a similar manner. NERDL and PtoNERD were more highly expressed in secondary tissues (xylem) than in primary tissues (cambium), indicating that they may be involved in secondary wood formation. The high abundance of the two transcripts in phloem and xylem and the 35 associations with growth and wood formation traits suggest that NERDL and PtoNERD together perform a regulatory function in secondary xylem formation in trees. The overlapping SNP L23/N3 also had significant effects on the transcript levels of NERDL and PtoNERD . Analysis of genotype data indicated that the 15 haplotypes explained 1.06–13.16% of phenotypic variation, which was confirmed by the finding that significant SNPs are located in blocks of haplotypes associated with the same traits.Our RT-qPCR analysis uncovered a positive correlation in expression of NERDL and its putative target  studies and expression profiling to examine the common roles of NERDL and its putative target PtoNERD in tree growth and wood formation. Epistasis modeling helped reveal interactions between NERDL and PtoNERD for the traits D and FW, suggesting that the genetic interactions of NERDL and PtoNERD affect wood formation. Taken together, these results show that our method was successfully used to help decipher the allelic variation and genetic interactions of lncRNA and its target in trees through population genetics studies. The phenotype-related SNPs detected in this study could be applied to marker-assisted breeding in the near future.In the present study, we identified NERDL and PtoNERD in this article have been deposited in the GenBank Data Library (accession numbers KX344444-KX344531), and the transcriptome sequencing data are available in the SRA database (accession number SRP073689).Sequence data for DZ conceived and designed experiment; WS and MQ performed the experiments and data analysis; WS wrote the manuscript; MQ and QD provided valuable advice on the manuscript; QD and DZ revised the manuscript; DZ obtained funding and is responsible for this article. All authors read and approved the manuscript.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.


Haplotype assembly, reconstructing haplotypes from sequence data, is one of the major computational problems in bioinformatics. Most of the current methodologies for haplotype assembly are designed for diploid individuals. In recent years, genomes having more than two sets of homologous chromosomes have attracted many research groups that are interested in the genomics of disease, phylogenetics, botany and evolution. However, there is still a lack of methods for reconstructing polyploid haplotypes.In this work, the minimum error correction with genotype information (MEC/GI) model, an important combinatorial model for haplotyping a single individual, is used to study the triploid individual haplotype reconstruction problem. A fast and accurate enumeration-based algorithm enumeration haplotyping triploid with least difference (EHTLD) is proposed for solving the MEC/GI model. The EHTLD algorithm tries to reconstruct the three haplotypes according to the order of single nucleotide polymorphism (SNP) loci along them. When reconstructing a given SNP site, the EHTLD algorithm enumerates three kinds of SNP values in terms of the corresponding site’s genotype value, and chooses the one, which leads to the minimum difference between the reconstructed haplotypes and the sequenced fragments covering that SNP site, to fill the SNP loci being reconstructed.Extensive experimental comparisons were performed between the EHTLD algorithm and the well known HapCompass and HapTree. Compared with algorithms HapCompass and HapTree, the EHTLD algorithm can reconstruct more accurate haplotypes, which were proven by a number of experiments. Increasing evidence shows that haplotypes play a crucial role in studying the variations relating to diseases prediction and gene expression  denotes the SNP allele of the ith fragment at the jth SNP site. Let M, where jth SNP site. Figure Triploid somatic cells contain three sets of chromosomes, i.e., a triploid organism has three copies of each chromosome. Since haplotype consists of the sequence of all SNPs along a chromosome, a triploid individual owns three haplotypes. It is commonly regarded that a SNP locus shows merely two possible alleles, hence the major allele can be represented as mentclass2pt{minimmentclass2pt{minimM, define r(j) as the set of fragments that cover the jth column. Given a row M, let l(i) indicate the index of the leftmost SNP HD is defined as Formula  = 0. The larger HD is, the greater the probability of fragments X and Y coming from different chromosome copies or having sequencing errors is. If there are no errors in the data, the rows of M can be divided into three classes of compatible fragments. Three haplotypes can be reconstructed by assembling the fragments in the three classes. In this situation, the SNP matrix M is called feasible or error-free. Given haplotype G are regarded as compatible.Given a column mentclass2pt{minimmentclass2pt{minimBased on the above mentioned concepts for the haplotype reconstruction problem, the MEC/GI model can be described as follows :M and a genotype matrix G, correct the minimum number of entries in M (0 into 1 and vice versa) so that the resulting matrix is feasible, and the three reconstructed haplotypes are compatible with the genotype G.MEC/GI: Given a SNP matrix M and a genotype matrix G. The output is three assembled haplotypes h =  SNP site in terms of its genotype, and chooses the one leading to the minimum difference between the reconstructed haplotypes and the fragments covering the jth site. After this iteration process is completed, three haplotypes h is obtained. Some key steps of the EHTLD algorithm will be introduced in detail as follows.In this section, the EHTLD algorithm is described. The input consists of a SNP matrix M and G to improve the efficiency of assembly. Drop column G where M. The deleted column j  values in ascending order. For each column j  which contains the rows covering the jth column.Since homozygous sites do not contribute to haplotype reconstruction, they are deleted from matrices j − 1 sites of the three haplotypes k = 1, 2, 3, j = 2, 3,…, n) has been assembled, and the jth site is under consideration. The calculating method comprises the following two steps.if if Enumerating three kinds of possible values according to jth site value  value. Given the In the following, we give an example for enumerating and computing by using the matrices in Fig. D, D and D are computed respectively according to the fragments in Fig. D = 3, D = 2, D = 1. Because D is the smallest, . In summary, the time complexity of the algorithm is Now the time complexity of the EHTLD algorithm is discussed. In preprocessing, dropping redundant information and calculating set http://groups.csail.mit.edu/cb/haptree/, was implemented on a Linux system. All the tests below are conducted on a 64 bit PC with Intel Core i5 2.50GHz CPU and 6GB RAM. One hundred data sets were generated for each parameter setting. The average over 100 runs at each parameter setting was calculated and presented.In this section, the EHTLD algorithm is compared with two state-of-the-art algorithms, i.e., the HapCompass  and the The vector error (VE) , the rech = (The reconstruction rate (RR), which measures the similarity degree between the pair of true haplotypes and the pair of reconstructed ones, is a widely adopted index to evaluate diploid phasing , 9, 15.  Formula .4\documeM, as shown in Formula (h = (n were created by using the following method. d. The minimum error correction (MEC) score measures the minimum number of mismatches between the reconstructed haplotypes  Formula .5\documementclasspt{minimar et al.  used com, CELSIM  and Meta, CELSIM , were adm1 single SNP fragments and m2 mate-pair SNP fragments were generated. A single fragment had a length ranging from n/10. The coverage was c/2 for both kinds of fragments, and the total coverage was c. Reading errors were planted into the fragments with probability c ranges from 5 to 10, and In this section, the evaluation of the EHTLD, the HapCompass and the HapTree algorithms is described by using CELSIM instances. CELSIM was invoked to simulate shotgun sequencing platform. 2 and 5% . In the c = 10, n = 100 and d = 0.3. It can be seen from this table that algorithm EHTLD can achieve much higher reconstruction rates, smaller vector errors and MEC scores than the HapCompass and the HapTree algorithms in every RRs of algorithms EHTLD, HapCompass and HapTree are 0.92, 0.89 and 0.88, the vector errors of them are 14, 31 and 26, and the MEC scores of them are 335, 407 and 364, respectively. The three algorithms all execute very efficiently when In Table c, where n = 100, d = 0.3. From Table RRs of algorithms EHTLD, HapCompass and HapTree are 0.94, 0.89 and 0.86, the vector errors of them are 10, 30 and 29, and the MEC scores of them are 16, 40 and 19. When the coverage increases, the RR of algorithm EHTLD increases gradually, while that of algorithm HapCompass fluctuates between 0.89 and 0.90, and that of algorithm HapTree varies between 0.85 and 0.91. Generally, the increase of coverage plays a positive role in the improvement of algorithm performance, for much more original fragment information can be utilized. However, it is not apparent for algorithms HapCompass and HapTree.In Table entclass1pt{miniman, where c = 10, d = 0.3. As can be seen from this table, algorithm EHTLD still obtains superior results to the other two algorithms under each parameter setting. With the increase of haplotype length, the three algorithms experience a gradual degradation in the performance. When n is 100, the RR of algorithm EHTLD is 0.97, which is higher than both HapCompass and HapTree algorithms by about 7.8%, the vector error of algorithm EHTLD is 4, which is less than algorithms HapCompass and HapTree by about 86 and 85%, the MEC score of algorithm EHTLD is 85, which is less than algorithms HapCompass and HapTree by about 56 and 21%, respectively. When n is 1000, the RRs of them decrease to 0.92, 0.88 and 0.86, the vector errors of them increase to 136, 322 and 314, and the MEC scores of them go up to 479, 1029 and 595, respectively. The running time of the three algorithms increases significantly with the increase of n. When n = 100, the running time of algorithms EHTLD, HapCompass and HapTree is 0.01, 0.03 and 0.01 s, respectively, while n = 1000, it increases to 4.36, 4.67 and 4.51 s, respectively.Table c = 10, n = 100 and d = 0.3. As shown in Table RRs of algorithms EHTLD, HapCompass and HapTree are 0.97, 0.90 and 0.89, the vector errors of them are 4, 29 and 26, and the MEC scores of them are 85, 194 and 117, respectively. With the decrease of the length of single fragment, the decline of fragments overlap might be disadvantageous for haplotype reconstruction. When RRs decrease to 0.94, 0.90 and 0.88, the vector errors increase to 14, 30 and 28, and the MEC scores drop to 65, 86 and 76, respectively. The decrease of the MEC scores explain the shorter the fragments are, the more probability the fragments agree with the reconstructed haplotypes. The change of single fragment length plays little effect on the running time of the three algorithms.In Table d, where c = 10, n = 100. It can be seen from this table that the performance of algorithm EHTLD remains relatively stable under different d, while that of algorithms HapCompass and HapTree suffers strong negative influence with the increase of hamming distance. For example, when d varies from 0.1 to 1.0, the RR of the EHTLD algorithm fluctuates between 0.97 and 1.0, while those of the HapCompass and the HapTree algorithms achieve decrease rate up to 35 and 20%, respectively.Table m SNP fragments, including c equals to MetaSim was used to simulate 454 sequencing platform. n = 100, d = 0.3. In Table c = 20, d = 0.3. In Table n = 100, c = 20, and d = 0.3. In Table d are shown, where n = 100, c = 20, and d settings.Table The minimum error correction with genotype information (MEC/GI) model is one of the important computational models for solving single individual SNP haplotyping problem. In this paper, an enumeration-based algorithm EHTLD is presented for haplotyping a triploid single individual by using this model. Algorithm EHTLD reconstructs the three haplotypes according to the order of SNP loci along them. For a SNP site being reconstructed, the EHTLD algorithm enumerates three possible values in terms of the site’s genotype, and chooses the one leading to the minimum difference between the reconstructed haplotypes and the fragments covering that SNP site. The reconstructed alleles of a SNP site mainly depend on the fragments which cover the site, and are little affected by other former reconstructed alleles. Therefore, the former wrongly reconstructed SNP alleles would not affect the latter reconstructed SNP alleles, i.e., reconstructed errors on the former SNP alleles would not spread to the latter ones. The kind of enumeration strategy can also be apply to reconstruct haplotypes of other ploidy, which will be studied in the future.Compared with algorithms HapCompass and HapTree by using two kinds of simulated sequencing data, the EHTLD algorithm can get the highest reconstruction rates, the smallest vector errors and MEC scores, which was tested by a number of experiments. In addition, algorithm EHTLD still achieves satisfying performance even with high error rate, low fragment coverage, or long haplotype length. All of these advantages may contribute to the practical application of the EHTLD algorithm when haplotyping a triploid single individual.


FOXJ3 gene, a transcription factor which regulates sperm function in mice. This study represents the first comprehensive characterisation of genetic variation in bovine β-defensin genes and functional analysis supports a role for β-defensins in regulating bull sperm function. This first application of WES in AI bulls with divergent fertility phenotypes has identified a novel role for the transcription factor FOXJ3 in the regulation of bull fertility. Validated genetic variants associated with bull fertility could prove useful for improving reproductive outcomes in cattle.Bovine fertility remains a critical issue underpinning the sustainability of the agricultural sector. Phenotypic records collected on >7,000 bulls used in artificial insemination (AI) were used to identify 160 reliable and divergently fertile bulls for a dual strategy of targeted sequencing (TS) of fertility-related β-defensin genes and whole exome sequencing (WES). A haplotype spanning multiple β-defensin genes and containing 94 SNPs was significantly associated with fertility and functional analysis confirmed that sperm from bulls possessing the haplotype showed significantly enhanced binding to oviductal epithelium. WES of all exons in the genome in 24 bulls of high and low fertility identified 484 additional SNPs significantly associated with fertility. After validation, the most significantly associated SNP was located in the  Selection for increased male fertility is difficult as the heritability of fertility traits is much lower (2–4%) than that of production traits5 in cattle. Importantly however, genetic correlation between milk production and fertility is not in unity, and therefore, genetic improvement in both traits is possible with improved breeding selection3. Bull fertility and semen-related traits are important factors in maintaining overall herd fertility as semen from a single bull is generally used to breed large numbers of cows and heifers6 when used in artificial insemination (AI). While much research attention has focused on cow fertility traits, understanding of bull fertility traits is not as advanced7.While selection for production traits such as milk yield has been successful in dairy cattle there has been a simultaneous decrease in fertility8 and a significant proportion of reproductive failures in dairy cows has been attributed to bull sub-fertility9. However accurate measurement of bull fertility is complicated by different definitions of the phenotype and limited numbers of repeated measures per sire10. Sufficient numbers of repeatable bull fertility phenotypes are only currently available on bulls used in AI, where large numbers of inseminations can be used to reliably predict the fertility of an individual bull. Often these evaluations are undertaken by breeding companies which do not account for potentially confounding effects , may not be publicly available8 or are complicated by the issue of compensable sperm traits11, where sperm numbers per insemination dose can be adjusted to improve fertility of sub-fertile bulls, thereby affecting fertility outcomes. In vitro assays  provide some quantitative data on sperm function but they explain only approximately 40% of the variation in bull field fertility12. Therefore, no single current diagnostic test can consistently and accurately predict fertility in bulls which produce morphologically normal semen13.A successful outcome to a given service  is a combination of both male and female fertility7). High levels of conservation of genes regulating some specific reproductive processes14 has enabled comparative genomic approaches to uncover novel candidate single nucleotide polymorphisms (SNPs) for bull fertility in a hypothesis driven manner15. Our earlier work uncovered a suite of β-defensin genes16, which are extensively expressed in the reproductive tract of the bull17 and on sperm18. While their functions have not been explored broadly in livestock species, knockout of a β-defensin gene cluster resulted in completely sterile male mice19. In humans, sequence variation in an orthologous gene to BBD126 has been shown to contribute to significant subfertility in men20 and the peptide has been shown to mediate sperm binding to oviductal epithelium21. Therefore, we hypothesised that genetic variation in the bovine β-defensin genes may be associated with bull fertility.To overcome some of these limitations, multiple alternative approaches have been employed to identify genes regulating bull fertility; incorporating these markers into national selection indexes would lead to cumulative and permanent improvements in this critical trait. Both candidate gene and genome-wide approaches have been successfully used to understand the genomic architecture of bull fertility  of 57 β-defensin genes and whole exome sequencing (WES) to identify, validate and functionally characterise genes associated with an adjusted animal model (AAM) of bull fertility. Use of TS allows the capture of sequence diversity in this newly discovered (and therefore largely unannotated) β-defensin gene family. WES is a novel method to capture and sequence all protein coding exons in the genome. Identified associated variants were then validated in an independent population of AI bulls and using sperm-binding functional tests.The adjusted animal model (AAM) of fertility is a multiple regression mixed model of pregnancy rate adjusted for factors such as cow parity, herd, breed and time of insemination. AAM data for 7,000 bulls were filtered to the 602 bulls used in >1,000 matings. The phenotype ranged from −0.21 to + 0.12 with a mean of 0.0174. Extremes of fertility were identified as greater than one standard deviation from the mean, high- and low-fertility groups corresponded to an AAM range of <−0.017 or >0.052 , removing the second cysteine and is heterozygous in two bulls. rs437613002 causes a Cys55Arg change in BBD131, removing the fifth cysteines, it was heterozygous in six bulls and one animal was homozygous for the alternate allele (data not shown).A total of 92,829 variants were identified from all TS reads. Of these, 3,134 remained after filtration and removal of all those with <80% call rate. This dataset consisted of 2,892 SNPs, 105 insertions and 137 deletions. A total of 45% of these variants were intronic, 18.7% were located upstream and 15.9% located downstream of the coding region and 16.9% were intergenic . The 98 SNPs were located between positions 61,329,700 and 61,467,209, a region of 137.5 kb containing the genes BBD128, BBD127, BBD126, BBD125, BBD115, BBD142 and BBD116. Of the 98 SNPs in the haplotype, 3 were located within coding regions, a nonsynonymous SNP in BBD115 (Ser52Asn) and two synonymous SNPs in BBD126 and BBD125. 76 SNPs were located within introns, 5 were located downstream and 13 located upstream of genes and 18 were intergenic.Quality control (QC) removed 1,578 TS SNPs of very low minor allele frequency MAF < 2.13%), 907 SNPs with <95% call rate, 913 SNPs not in Hardy-Weinberg equilibrium and 4 bulls with <95% call rate. The final TS data set comprised 1,399 SNPs in 149 bulls, with 25 on BTA8, 626 on BTA13, 356 on BTA23 and 392 on BTA27. The differential SNP frequencies in high and low fertility bulls are shown in Fig. %, 907 SN02 Table . Upon ex22. Full details of sequencing statistics are given in Table In the 24 bulls in which all exons were sequenced, coverage of on-target regions (regions of the exome targeted by our sequence capture probes) was 98.6%. A mean of 39 million reads per sample were retained following read filtering, ranging from 23,716,853 to 78,048,261. The proportion of reads uniquely aligned to the genome averaged 94%, with 3.45% PCR duplication of reads. Percentage exome coverage was 95.5% at a depth of 2X, and 69% at 10X depth, ranging from 95% to 98% and 49% to 88% respectively. At higher depth, the percentage of the targeted genome sequence with reads mapping was low with a mean of 33% at 20X and 12% at 30X, ranging from 11% to 69% and 2% to 50% respectively. The 69% of the genome sequenced at 10X coverage was deemed sufficient for SNP discovery, as shown previouslyP < 0.01. A list of all significantly associated variants is shown in Table P = 0.00014. It is predicted to be in the U6 novel snRNA in bulls, a pseudogene of the small non-coding RNA class.A total of 3,437,419 variants were identified in all 24 divergent fertility bulls. Removal of variants outside the targeted regions with a read coverage threshold of <5X, reduced this number to 289,210. Of these 12,124 were insertions and 13,048 were deletions, leaving 264,038 SNPs. Further quality control using GenABEL removed SNPs with an overall call rate of <80% or minor allele frequency of <5% and samples with a call rate of <90% of SNPs, leaving 144,178 variants. Following association analysis, 484 variants had an association with the bull fertility at an unadjusted significance level of P = 0.00044). The third most significantly associated SNP, rs109065788, is synonymous and located on BTA3 in the exonic region of the forkhead box J3 (FOXJ3) gene.A SNP in the signal regulatory protein alpha, SIRPA, gene located on BTA13 at position 53691410 was the second most significantly associated variant (P = 0.0056) and ‘glycosylation site: N-linked’ (P = 0.00024). There are 27 genes involved in ‘glycosylation site: N-linked’ and 28 genes involved in ‘glycoprotein’. Of the 12 over-represented terms and keywords identified, 8 contained innate immunity terms, although ‘disulphide bond’ and ‘glycoprotein’ have also been linked to the innate immune system, specifically to β-defensins. A full list of GO terms over-represented in the whole-exome sequencing dataset is shown in Table A list of genes which contain or are nearest to the 484 SNPs identified via WES and associated with fertility was used for Gene Ontology (GO) analysis. Approximately 220 genes were included in gene ontology over-representation analysis, as the DAVID database did not contain all submitted Ensembl gene IDs. Two functional categories were significantly over-represented in this dataset after Benjamini-Hochberg correction for multiple testing; ‘glycoprotein’   on BTA30 which is orthologous to A-kinase anchor protein 17B in mice and humans.A SNP genotyping assay for 58 SNPs identified as being associated with bull fertility from TS and WES datasets, of which 42 passed all QC filters and were validated in an independent population of 123 bulls. Allele frequencies of validation SNPs along the line of regression, with an RP < 0.05) but there was no bull x haplotype interaction detected (P > 0.05).The haplotype spanning several β defensin genes identified through TS was shown to have a significant effect on sperm binding density to bovine oviductal epithelial cell (BOEC) explants . SNPs in this region may explain previously published genome-wide associations between this region and bull fertility7. At least 2 of the 4 β-defensin clusters are copy number variable27 and therefore these regions were difficult to sequence and assemble unambiguously. It is possible therefore that these genes, and the novel SNPs identified herein explain a higher proportion of phenotypic variation in bull fertility that can currently be explained using current genome-wide association approaches.Association analysis identified a β-defensin haplotype spanning seven genes on BTA13 associated with bull fertility . After validation in an independent population, a SNP in U6, a snRNA gene/pseudogene remained significantly associated. Although known to be highly-expressed in the testes of bulls29, little functional information on this gene exists, and therefore future studies are required to verify its function, especially in relation to fertility. In addition, another associated SNP variant in the signal-regulatory protein alpha (SIRPA) gene was identified. SIRPA is a promising candidate gene for spermatogenic impairment and male fertility30. Another associated synonymous variant was in the exonic region of the forkhead box J3 gene (FOXJ3). Furthermore, gene ontology analysis identified the terms “glycoprotein’ (P = 0.0056) and ‘glycosylation site: N-linked’ (P = 0.00024) as significantly over-represented terms in the SNP dataset, reaffirming the emerging important relationship between the immune system and male fertility31.As a new technological approach, WES has been employed in a few previous studies, with one recent application to successfully uncover causative genetic variants underlying defective embryo development in Holstein and Brown Swiss cattle breedsFOXJ3 gene and confirmed the finding from the WES analysis, with a SNP frequency differential between low and high-fertility bulls of 21% (48–69%), Fig. FOXJ3 is a transcription factor, and a recent publication showed that this gene is required for the survival of spermatogonia in mice32. This bovine SNP may be linked to another causal variant or alternatively, it has been shown in humans that synonymous mutations can affect mRNA abundance and protein concentrations, and can alter substrate specificity via altered protein conformations33. The second most associated SNP in the validation dataset was located in an unannotated gene (LOC536148) (adj. P = 0.203) on BTA30 which is orthologous to A-kinase anchor protein 17B in mice and humans, and is thought to play a structural role in the cytoskeletal scaffold of sperm34. Further studies are now warranted to determine the function of these genes in bovine and identify potential causal variants linked to bull fertility.To validate the genetic variants identified via both TS and WES approaches, validation was performed in an independent dataset of AI bulls and one SNP validated as significantly associated with bull fertility (adj. P < 0.1). The SNP most associated with the AAM fertility phenotype was in the BBD123, and an upstream variant in BBD124 were identified as significantly associated with bull fertility through WES but they did not survive correction for multiple testing in the validation population . These genes are both located in the 19 β-defensin genes on BTA13 reported by our group to be expressed in the reproductive tract of the bull35. As putative fertility related genes in cattle were previously successfully identified on the basis of cross-species conservation15, in conjunction with a documented role for β-defensin in mediatinjg sperm-binding21, a sperm-binding assay was used to investigate the functional importance of the significantly associated β-defensin haplotype uncovered in this study. The β-defensin haplotype was only found in high fertility bulls and sperm from these bulls showed significantly higher binding to oviductal epithelium than similar bulls without the associated haplotype. Thus, sperm from these bulls appear to have an enhanced capacity to establish an oviductal sperm reservoir in preparation for ovulation and this may improve the chances of successful fertilisation. Currently, the identity of binding molecule(s) for BBD126 on epithelial cells is unknown36; however, the expression pattern of BBD126 on the bull sperm head37 appears to be consistent with the location and pattern of sperm attachment to the oviductal epithelium. Fertility is a complex multifactorial phenotype and these results confirm a contributory role for β-defensins in mediating sperm function in cattle. Moreover, this haplotype spans the region containing BBD126, which we have recently shown to promote sperm motility in cattle38.A missense SNP in the β-defensin gene 39, and the highly selected nature of AI bulls, our results show that novel fertility-associated SNPs are polymorphic and therefore amenable to selection. This study represents the first comprehensive catalogue of genetic variation in bovine β-defensin genes and the first whole-exome sequencing of divergent fertility AI bulls. This dual approach has successfully identified novel variants in both β-defensin and FOXJ3 genes potentially regulating reproductive function, and these biomarkers may contribute to future strategies to breed for improved bull fertility.Given the important role of the male in determining herd fertility in cattle and the pace of genetic gain now possible with the advent of genomic selection, multiple approaches are required to unravel the complexity of bull fertility. Despite the small effective population size in most cattle breeds8. Of these, only bulls which had >1000 inseminations were selected, and furthermore as bulls were selected in 2013, inseminations from that year had not yet produced live calves and therefore bulls with >25% of their straws used in 2013 were removed, reducing the total number of bulls suitable to 602. Bulls used in AI have already undergone extensive pre-screening and have passed all the pre- and post-freezing semen quality control checks in the AI centre prior to release into the field. Infertile bulls which got no/few females pregnant were either screened out as part of the quality control checks or were not used extensively in AI as the bulls were retracted from use once extremely low fertility was captured by AI companies; therefore, these bulls were not part of the dataset as they did not reach the threshold of >1000 inseminations. Extremes of high and low fertility were defined as greater than one standard deviation from the mean, 160 bulls were chosen for final TS, consisting of 82 Holstein-Friesian, 21 Belgian Blue, 14 Limousin, 11 Aberdeen Angus, 10 Charolais, 6 Simmental, 5 Hereford, 5 Jersey, 2 Montbeliarde, 2 Norwegian Red and 2 Shorthorn. For WES, a subset of 24 bulls which had been used for TS were selected. High-fertility Holstein-Friesian ranged in between 0.05–0.07 for AAM, and the low fertility bulls ranged between −0.12 and −0.03 (n = 6/group). High-fertility Limousin ranged between 0.06–0.07, and low-fertility bulls ranged between −0.09 and −0.06 (n = 3/group). Belgian blue high-fertility bulls ranged between 0.05 and 0.08 with low fertility between −0.04 and −0.02 AAM (n = 3/group). Pedigree analysis was performed to minimise the selection of related individuals (pedigree score of relatedness <0.2).The Irish Cattle Breeding Federation (ICBF) collated male fertility data for 7,000 bulls used in AI in Ireland over four years (2010–2013 inclusive). Data from all recorded artificial inseminations were provided by ICBF and used to calculate bull fertility based on an adjusted animal model (AAM). AAM is a multiple regression mixed model of pregnancy rate, where a cow/heifer was confirmed to be pregnant to a given service either by a calving event and/or whereby a repeat service (or a pregnancy scan) deemed the animal not to be pregnant. Females which were culled or died on farm were omitted. The model was then adjusted for various factors, including semen type , cow genotype, parity of cow, month of service, day of the week when inseminated, service number, herd, AI technician, bull breed. Finally, the estimate from the model was weighted for number of insemination records. This gave the AMM which was expressed relative to the mean of the population for all bullswww.diagenode.com) and the fragment length of approximately 600 bp confirmed using a Bioanalyzer . End-repair, A-tail and adapter ligation were carried out as specified in the TruSeq Nano DNA LT Sample Prep protocol (Illumina Ltd.). Twenty-four samples were chosen at random to barcode and pool for sequence capture.DNA was extracted using the Maxwell® 16, per manufacturer’s instructions. Genomic DNA (gDNA) was purified using Zymo Research’s DNA Clean and concentrator ™ kit  and heated to 52 °C for 2 min prior to concentration estimation. gDNA concentrations were estimated using the Qubit® dsDNA BR Assay Kit for use with the Qubit® 2.0 Fluorometer. A total of 200 ng DNA was sheared with a Bioruptor Plus sonicator  were used for Roche Nimblegen SeqCap EZ Developer bait design, per standard protocols. Where possible, baits which align to only one genomic location were used. This criterion was relaxed to allow baits which align up to five locations for important exonic regions. In total, it was possible to design baits to target 235 kb; the unsuccessfully targeted regions consisted primarily of intronic and intergenic repetitive sequences. The suggested dual-capture protocol was used. Following pre-capture amplification, 24 libraries were pooled to form 1 μg of DNA which was hybridised with oligos and baits overnight, captured, amplified, hybridised with another aliquot of the baits overnight, amplified and cleaned. Libraries were quantified using Qubit Hi Res (Life Technologies) and run on an Illumina MiSeq at 10pM with 1% PhiX (300 bp paired-end protocol).Bos taurus genome UMD3.1. Picard tools were used to sort and order the files. Enrichment, insert size and alignment metrics were calculated using the Genome Analysis Toolkit (GATK). Variant discovery was also performed using GATK, following the Best Practice Pipeline (https://www.broadinstitute.org/gatk/); variants were called individually using the Haplotype Caller and joint genotyping performed on all samples simultaneously using GenotypeGVCFs. The results were filtered using GATK Variant Filtration with the following parameters: filter out variant calls if located within a cluster where three or more calls were made in a 10 bp window [clusterWindowSize 10]; filter out variant if there were at least four alignments with a mapping quality of zero (MQ0) and if the proportion of alignments mapping ambiguously corresponds to 1/10th of all alignments [MQ0 > = 4 && ((MQ0/(1.0 * DP)) > 0.1)], DP: total (unfiltered) depth over all samples; filter out variants which were covered by less than 5 reads [DP < 5]; filter out variants having a low quality score [Q < 50]; filter out variants with low variant confidence over unfiltered depth of non-reference samples (QD) [QD < 1.5]; filter out variants based on strand bias using Fisher’s exact test: FS > 60.0 for SNP calling, FS > 200.0 for indel calling. In house Perl scripts were also written to identify any individual genotypes identified as heterozygous with an allele ratio of >80:20 and any SNP which had a read depth of less than 8 in a given individual was coded as missing data for that individual.Reads were assigned to appropriate samples using the barcode sequence. Paired end reads were quality filtered and adaptor trimmed. Reads were aligned to the 40. Quality control was performed with the check.marker function and SNPs were examined for association to fertility using breed and the number of inseminations performed as fixed effects.Association analysis was performed using the R package GenABELGenomic DNA for 24 selected bulls was extracted using the Maxwell® 16, per manufacturer’s instructions. Genomic DNA (gDNA) was purified using Zymo Research’s DNA Clean and Concentrator ™ kit and heated to 52 °C for 2 min prior to concentration estimation. gDNA concentrations were estimated using the Qubit® dsDNA BR Assay Kit for use with the Qubit® 2.0 Fluorometer.Bos taurus UMD3.1 genome, plus 100 base pairs of 5′UTR, per standard protocols. An in-house Perl script was used to identify target regions. Probes were manufactured by Roche (Roche NimbleGen). Magnetically labelled, oligonucleotide probes capture the target regions of DNA during library preparation. One hundred base pairs of 5′ untranslated region (UTR) were also included for all genes. In total, there were 227,647 exons in the genome transfer file. Of these, 202,899 were targeted in this design, covering 56,671,697 bp. Less than five total matches and five mismatches were allowed. Of these probes, 92.5% were unique to a single genomic position, and 4.5% had only 2 possible matches within the genome. The length of probes was 200 bp with approximately 2.1 million probes made to cover the exome. Probes were also designed to target mitochondrial DNA and were 1/5th of the concentration of nuclear DNA. Using probes unique within the genome, that is probes that map exactly once in the genome, covers only 80% of the target regions; whereas using probes that map up to 5 times in the genome covers approximately 98.4% of the target regions.Roche Nimblegen, SeqCap EZ Developer Library was purchased from Roche  to perform a custom-designed sequencing of the bovine exome. Liquid capture probes were designed to target all exons annotated in the Library preparation for WES was commercially performed  following the Roche Nimblegen SeqCap EZ Developer Library protocol, as per the manufacturer’s instructions. Briefly, 100 ng gDNA of each sample were prepared using the TruSeq Nano DNA Library Prep Kit. This step involved shearing of gDNA using Bioruptor DNA shearing and Agencourt AMPure XP beads, end-repair of fragments, adapter ligation, LM-PCR and quality control. The strategy to hybridise fragments of gDNA to the exome baits was to make one equimolar pool of all 24 gDNA libraries. This pool was then split into four separate pools and hybridisation captures were performed on each pool with oligonucleotide probe capture reagents, resulting in 1X capture for every 6 samples. All captures were then re-pooled and split into four groups for sequencing on four HiSeq. 2500 lanes. Pooling ensures that samples were mixed prior to capture, and to give an even distribution of sample to each sequencing lane.Bos taurus UMD3.1.70 genome using the Burrows-Wheeler Aligner (BWA) ‘sampe’ algorithm with default parameters41. Picard Tools was used to convert the resulting SAM file to BAM format, sort and index BAM files, and to remove PCR duplicates from all BAM files. Alignment summary metrics, insert size metrics, and PCR duplicate metrics were all collected. GATK’s Depth Of Coverage walker determined coverage levels per interval.Sequencing files (FASTQ) were assessed for quality using FastQC. FASTQ files were trimmed using Trim Galore! in order to remove contaminant adapter sequences. A Phred quality score threshold of 25 was applied, to discard poor quality base calls, and to reduce the effect of incorrect base calling. All remaining reads were aligned to the 42. Local re-alignment around indels was performed using the GATK tools: Realigner Target Creator, Indel Realigner and Fix Mate Information. Base quality score re-calibration via GATK Base Recalibrator was then applied, which recalibrates scores around known variants. Haplotype Caller walker was used to call mutations on BAM files with Phred-scaled emit and call confidences of 30, in ‘GVCF’ mode and with a BED file of the exome targets. This BED target file is used by the walker to identify regions in the genome which were active/variable, which were marked for local de-novo assembly of reads aligning to such regions.Variant calling and genotyping across all 24 animals was performed using Genome Analysis Toolkit (GATK), following the GATK best practice guidelines for WES43. In house Perl scripts were also written to identify any individual genotypes identified as heterozygous with an allele ratio of >80:20 and any SNP which had a read depth of less than 8 in an individual and code them as missing data.Strict hard-filtering of variants was performed to remove variants of low quality and which fall outside certain parameters, to reduce the number of false positives. The parameters for hard filtering of variants were as follows: Filter out variant calls if located within a cluster where three or more calls were made in a 10 bp window [clusterWindowSize 10]; filter out variant if there were at least four alignments with a mapping quality of zero (MQ0) and if the proportion of alignments mapping ambiguously corresponds to 1/10th of all alignments [MQ0 > = 4 && ((MQ0/(1.0 * DP)) > 0.1)], DP: total (unfiltered) depth over all samples; filter out variants which were covered by less than 5 reads [DP < 5]; filter out variants having a low quality score [Q < 50]; filter out variants with low variant confidence over unfiltered depth of non-reference samples (QD) [QD < 1.5]; filter out variants based on strand bias using Fisher’s exact test: FS > 60.0 for SNP calling, FS > 200.0 for InDel calling, similar to other SNP filtering protocols40 was used to perform association analysis between fertility and SNP genotypes. A custom R file converted a Variant Call Format file containing all variants from GATK Haplotype Caller to GenABEL format. A linear mixed model approach analysed each SNP separately for association with a phenotype, which allows for fixed and random effects. SNPs identified were analysed as continuous variables, in which case an allelic effect will be estimated. The null hypothesis is that there was no association between the SNP and the fertility trait. This model assumed a linear relationship between the trait and genotype as well as a common variance at each genotype. SNPs associated with the AAM fertility phenotype (p < 0.01) were sorted based on their unadj. P-value.The R package GenABELThis data set was submitted to NCBI Gene Expression Omnibus under accession number GSE41637 and is available in ArrayExpress as E-GEOD-41637.An independent population of bulls was used to validate variant calls from whole-exome sequencing in bulls divergent for fertility. Fertility phenotypes of bulls from ICBF fertility records were used, as previously described. However, updated fertility data for this dataset from years 2013, 2014 and 2015 were available, which were not used for initial sample selection for TS and WES. In total, phenotypic information was available for 1,414 bulls over the three years. Of these, 123 bulls from the three breeds as used in WES (69 Holstein-Freisian (HO), 29 Limousin (LM) and 22 Belgian blue (BB) all with >100 insemination records per bull were selected for the validation analysis .® System was used for assay design and SNP validation. SNP genotyping on the MassARRAY System combines the multiplexed primer extension chemistry of the iPLEX® assay with sensitive matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF)44. In total, 58 SNPs were targeted for validation in 4 multiplex reactions . DNA of each selected bull was obtained, and (10 ng) was used for each multiplex reaction. gDNA concentrations were estimated using a Nanodrop ND-1000 spectrophotometer. gDNA was dried down overnight in a PCR-free environment and shipped to Agena Bioscience GmbH, Germany.Agena Bioscience MassARRAYOf 58 SNPs targeted for validation in the MassARRAY assay design suite, 42 variants and 123 cattle passed filters and QC. SNPs which had a call rate <80%, or minor allele frequency (MAF < 0.04) were filtered out of the dataset. Statistical analysis of association was performed using PLINK v1.90b3l. Association analysis was performed with phenotypes as a quantitative trait using the Wald test statistic. A pedigree file was used to store animal pedigree and phenotypic data and a map file was used to store genetic marker data. This test statistic compares asymptotic allele frequencies between high-fertility and low-fertility (cases and controls).in vitro46. At the laboratory, oviducts were trimmed free of connective tissue, and ligated using single use sterile umbilical cord clamps . Oviducts were then washed twice with PBS following which the isthmic section of the oviduct was isolated. The epithelial cells were extruded in sheets by squeezing the oviduct with a sterile glass slide, fragmented by pipetting, centrifuged for 1 min (200 g), transferred to M199 culture media supplemented with fetal calf serum (10%) and gentamicin sulphate , and incubated for 1 h at 37 °C in 5% CO2 to form everted vesicles with apical ciliated surfaces oriented outwards47. Explants were used for binding assessments within 5 h of slaughter. Three reproductive tracts were flushed on each day and explants from each tract were processed separately.Reproductive tracts from non-pregnant crossbred heifers of beef breeds, were collected at a commercial abattoir immediately post-mortem and transported to the laboratory within 1 h in phosphate buffered saline (PBS) supplemented with Gentamicin sulphate  at 4 °C. Reproductive tracts from heifers at various stages of the oestrous cycle were used, as stage has been shown to have no effect on sperm binding g for 5 min. Post-centrifugation, the supernatant was removed and explants (20 µL) from each reproductive tract were added to sperm aliquots (140 uL) from high fertility bulls with  and without  the β-defensin haplotype as well low fertility bulls without the β-defensin haplotype . Sperm were pre-stained for 30 min with 1% (w/v) Hoechst 33342 at 37 °C prior to addition to the explants for enhanced binding visualisation. The final sperm concentration in the presence of the explants was 5 × 106 per mL. After 30 min incubation at 37 °C in 5% CO2, loosely bound sperm were removed from explants by gently pipetting through two droplets of M199 (75 µL) on a pre-warmed 24 well culture plate (37 °C). A droplet of each treatment (10 μL) was placed on a slide, a coverslip added and viewed using a microscope at 400X fitted with a heated stage at 37 °C  under half-light and half-fluorescence. The number of sperm bound was recorded and relative surface area of each explant was determined using a micrometre. Ten explants of each treatment were assessed at random for sperm binding density, calculated by determining the number of sperm bound per 0.1 mm2 of explant surface. The evaluator was blinded to treatment for all sperm binding assessments. Three reproductive tracts were used per replicate (replicate = day) and three semen straws per bull were assessed giving a total of twelve straws assessed per haplotype.M199 medium (5 mL) was added to explants from each tract and centrifuged at 200 Supplementary Info 1Supplementary Dataset 1