We have accumulated considerable data on the genetic makeup of the Icelandic population by sequencing the whole genomes of 2,636 Icelanders to depth of at least 10X and by chip genotyping 101,584 more. The sequencing was done with Illumina technology. The median sequencing depth was 20X and 909 individuals were sequenced to a depth of at least 30X. We found 20 million single nucleotide polymorphisms (SNPs) and 1.5 million insertions/deletions (indels) that passed stringent quality control. Almost all the common SNPs  over 2%) that we identified in Iceland have been observed by either dbSNP (build 137) or the Exome Sequencing Project (ESP) while only 60 and 20% of rare (DAF<0.5%) SNPs and indels in coding regions, the most heavily studied parts of the genome, have been observed in the public databases. Features of our variant data, such as the transition/transversion ratio and the length distribution of indels, are similar to published reports. The HapMap project focused primarily on common variants (MAF>5%) and methods were subsequently developed to accurately impute 2.5 million HapMap phase 2 SNPs into such chip data2. This has led to the discovery of a plethora of associations between common sequence variants and human diseases and traits3.Genome-wide association scans were initially based on 300–600 k SNP chip genotyping arrays designed based on the HapMap dataset4, the Exome sequencing project (ESP)6 and the GoNL project7.Large scale whole genomic sequencing has allowed the detection of rare sequence variants that range in effect from causing diseases to modifying complex disease risk—variants that would recently either not have been observed or could not be tested for association with disease on a sufficiently large scale. Several large sequencing projects are ongoing such as the 1000 Genomes project8. We used information about haplotype sharing, taking advantage of the fact that all the sequenced individuals had also been chip-typed and long range phased to improve variant genotyping9.We have sequenced the whole genomes of 2,636 Icelanders using Illumina technology. The individuals were selected for sequencing based on having a wide range of phenotypes  and 2. T10 was annotated using the Variant Effect Predictor (VEP)11. The VEP predicts the consequence of each sequence variant on all neighboring RefSeq genes based on a set of 35 consequence terms defined by the Sequence Ontology (SO)  LoF, loss of function including stop gained/lost variants, frameshift indels, donor/acceptor splice and initiator codon variants , ii) MODERATE, including missense variants, inframe indels and splice region variants , iii) LOW, including synonymous variants 3′ and 5′ UTR variants ; and iv) OTHER, including deep intronic and inter-genic variants . We find that the fraction of variants with a minor allele frequency (MAF) below 0.1% , is 61.6, 46.4, 37.5 and 36.0% in the LoF, MODERATE, LOW and OTHER categories, respectively  12. We grectively .et al.13These methods are an expanded version of the descriptions contained in Gudbjartsson This study is based on whole-genome sequence data from the whole blood of 2,636 Icelanders participating in various disease projects at deCODE genetics  and 2. IAll participating individuals, or their guardians, gave their informed consent before blood samples were drawn. The family history of participants donating blood was incorporated into the study by including the phenotypes of first and second degree relatives and integrating over their possible genotypes. This integration is performed without the genotypes being kept in storage.All sample identifiers were encrypted in accordance with the regulations of the Icelandic Data Protection Authority. Approval for these studies was provided by the National Bioethics Committee and the Icelandic Data Protection Authority.The Icelandic genealogical database contains 819,410 individuals back to 740 AD. Of the 471,284 Icelanders recorded to have been born in the 20th century, 91.1% had a recorded father and 93.7% had a recorded mother in the database. Similarly, of the 183,896 Icelanders recorded to have been born in the 19th century, 97.5% had a recorded father and 97.8% had a recorded mother.http://skra.is), local records of inhabitants and other official documents, but also from other sources such as old manuscripts, letters, annals, books of Althingi, books of judgments, books of family pedigrees, registers of farmers, registers of professional and lists of descendants.The Icelandic genealogy was extract from many sources. Primarily from church books, censuses, the Registers Iceland , (iv) if they produced an excessive inheritance error rate (over 0.001), (v) if there was substantial difference in allele frequency between chip types . All samples with a call rate below 97% were excluded from the analysis. The final set used for long-range phasing comprised 676,913 autosomal SNPs.The chip-typed samples were assayed with the Illumina HumanHap300, HumanCNV370, HumanHap610, HumanHap1M, HumanHap660, Omni-1, Omni 2.5 or Omni Express bead chips at deCODE genetics. Chip SNPs were excluded if they had (i) yield less than 95%, (ii) minor allele frequency (MAF) less than 1% in the population or (iii) significant deviation from Hardy-Weinberg equilibrium . In short, approximately 1 μg of genomic DNA, isolated from frozen blood samples, was fragmented to a mean target size of 300 bp using a Covaris E210 instrument. The resulting fragmented DNA was end repaired using T4 and Klenow polymerases and T4 polynucleotide kinase with 10 mM dNTP followed by addition of an 'A' base at the ends using Klenow exo fragment (3′ to 5′-exo minus) and dATP (1 mM). Sequencing adaptors containing 'T' overhangs were ligated to the DNA products followed by agarose (2%) gel electrophoresis. Fragments of about 400–500 bp were isolated from the gels (QIAGEN Gel Extraction Kit), and the adaptor-modified DNA fragments were PCR enriched for ten cycles using Phusion DNA polymerase (Finnzymes Oy) and a PCR primer cocktail (Illumina). Enriched libraries were further purified using AMPure XP beads (Beckman-Coulter). The quality and concentration of the libraries were assessed with the Agilent 2100 Bioanalyzer using the DNA 1000 LabChip (Agilent). Barcoded libraries were stored at −20 °C. All steps in the workflow were monitored using an in-house laboratory information management system with barcode tracking of all samples and reagents.x and/or HiSeq 2000 instruments. Paired-end libraries were sequenced at 2×101 (HiSeq) or 2×120 (GAIIx) cycles of incorporation and imaging using the appropriate TruSeq™ SBS kits. Each library or sample was initially run on a single GAIIx lane for QC validation followed by further sequencing on either GAIIx (≥4 lanes) or HiSeq (≥1 lane) with targeted raw cluster densities of 500–800 k mm−2, depending on the version of the data imaging and analysis packages . Real-time analysis involved conversion of image data to base-calling in real-time.Template DNA fragments were hybridized to the surface of flow cells (GA PE cluster kit (v2) or HiSeq PE cluster kits (v2.5 or v3)) and amplified to form clusters using the Illumina cBot. In brief, DNA (2.5–12 pM) was denatured, followed by hybridization to grafted adaptors on the flow cell. Isothermal bridge amplification using Phusion polymerase was then followed by linearization of the bridged DNA, denaturation, blocking of 3′ ends and hybridization of the sequencing primer. Sequencing-by-synthesis (SBS) was performed on Illumina GAIIhttp://picard.sourceforge.net/). Only non-duplicate reads were used for the downstream analyses. Resulting BAM files were realigned and recalibrated using GATK version 1.2–29-g0acaf2d17.Reads were aligned to NCBI Build 36 (hg18) of the human reference sequence using Burrows-Wheeler Aligner (BWA) 0.5.7–0.5.9 ref. . AlignmeMulti-sample calling was performed with GATK version 2.3.9 using all the 2,636 BAM files together.9. Extending the previous example, if the individual shares a haplotype with another who is heterozygous given his sequence reads, then the ambiguous individual would be called as heterozygous. Conversely, if the individual shares both his haplotypes with others who are homozygous for the major allele his genotype would be called homozygous. In order to improve genotype quality and to phase the sequencing genotypes, an iterative algorithm based on the IMPUTE HMM model2 which uses the LRP haplotypes was employed. Assume a SNP with alleles 0 and 1 is being phased. We let H be the long range phased haplotypes of the sequenced individuals and applied the following hidden Markov model (HMM) based algorithm.Genotype calls made solely on the basis of next generation sequence data yield errors at a rate that decreases as a function of sequencing depth. Thus, for example, if sequence reads at a heterozygous SNP position carry one copy of the alternative allele and seven copies of the reference allele, then without further information the genotype would be called homozygous for the reference allele. To minimize the number of such errors, we used information about haplotype sharing, taking advantage of the fact that all the sequenced individuals had also been chip-typed and long range phased 9. Extendi the haplotype h has a common ancestor with a haplotype in H\{h} and denote the variable indicating this with the latent variable zi∈H\{h}, the hidden variable in the HMM.Assuming that at each marker k∈H\{h}. Given a haplotype h in H, γh,k are calculated simultaneously for all k∈H\{h} using the same HMM model as IMPUTE 2. Given the Markov assumptions of the HMM, the model is fully specified by emission and transition probabilities.Then i as:λ=10−7 in our implementation. We define the transmission probabilities of the HMM model as:We define the emission probabilities of the HMM at each marker N is the number of haplotypes in k∈H\{h}, which for autosomal chromosomes is 2 here and ρi=4Neri, where ri is the genetic distance between markers i−1 and i according to the most recent version of the deCODE genetic map18 and Ne was originally meant to be an estimate of the effective number of haplotypes in the population that our sample comes from, we used Ne=7.000. These definitions fully specify the probability distribution P. Calculating γh,k for a single haplotype requires O(MN) operations, where N is the number of haplotypes and M is the number of markers. Since these calculations can be performed for one haplotype at a time, the calculations can be parallelized across a computer cluster for efficiency. In practice most of the γh,k will be close to zero and can be safely ignored  greatly reducing storage requirements.Where h in H, initialize the parameter θh, which specifies how likely the one allele of the SNP is to occur on the background of h from the genotype likelihoods obtained from sequencing. The genotype likelihood Lg is the probability of the observed sequencing data at the SNP for a given individual assuming g is the true genotype at the SNP. If L0, L1 and L2 are the likelihoods of the genotypes 0, 1 and 2 in the individual that carries h, then set θh:Now we are set to describe an iterative algorithm for the actual phasing. For every h and k in H that are carried by the same individual, use the other haplotypes in H to predict the genotype of the SNP on the backgrounds of h and k:For every pair of haplotypes Combining these predictions with the genotype likelihoods from sequencing gives un-normalized updated phased genotype probabilities:θh and θk to:Now use these values to update ε. We used ε=10−7.Iterate until the maximum difference between iterations is less than a convergence threshold −7, SI >1.4, or if SI<0.6 for SNPs and SI<0.9 for indels. The GATK call annotation AN corresponds to the total number of chromosomes in called genotypes which equals 2×2,636=5,272 when all chromosomes can be called. The additional filtering removes 2% of the remaining SNPs but 50% of the remaining indels. It is primarily the SI condition that removes indels, which usually indicates that the failing indel is not called with a high degree of certainty and that its calling cannot be resolved in a coherent manner based on haplotype sharing.The variants identified by GATK were filtered using thresholds on GATK variant call annotations . SNPs we19. Variants called in these regions were ignored in the analysis.Simple repeat regions were defined by combining the entire Simple Tandem Repeats by TRF track in UCSC hg18 with all homopolymer regions in hg18 of length 6 bp or more20.Coordinates of variants and regions were converted between hg18 and hg19 using the liftOver tool from UCSC12 annotation from VEP. Sequence variants that could be assigned to more than one category (primarily due to their impact on more than one gene transcript) were assigned to the most severe of the applicable categories.Variants were annotated with information from Ensembl release 72 using Variant Effect Predictor (VEP) version 2.8 refs ,21. Only22. These sequences are created using the Enredo-Pecan-Ortheus (EPO) pipeline24 for doing multiple sequence alignment and inference of ancestor alignments using sequences from six primates: human , chimp , gorilla , orangutan , macaque , and common marmoset . The emf2maf parser available with the Ensembl Compara API was used to convert the Compara EMF files storing the multiple alignments to MAF format. For polarization of indels, ancestral sequences were retrieved from the MAF files using bx-python.The inference of ancestral states for SNPs and indels was based on the Ensembl Compara ancestral sequences for Homo sapiens (GRCh37) corresponding to release 65 of EnsemblAncestral states of SNPs were inferred by comparison with the FASTA sequence for the Ensembl Compara human ancestor sequence for Homo sapiens (GRCh37).When infering the ancestral state of an indel based on the Ensembl Compara multiple alignments and inferred ancestor alignments for the six primates, we considered the ancestral state closest to Homo sapiens in the phylogenetic tree in a window spanning 5 bp to the left and right of the indel and assignment position plus the indel length; the polarization state was left undetermined for indels in regions where an ancestral state was not available from the Ensembl Compara data. We compared the difference in the number of bases in the aligned sequences in the window to find out whether there was an evidence of an indel in the ancestral sequence. In cases where there was no difference in the number of bases between ancestral sequence and the sequence for Homo sapiens, the ancestral state was said to be ‘derived’  if there was at most one mismatch in the sequences in the window; if there were more than one mismatch in the sequence, the state was left undetermined. When the difference in the number of bases was equal to the indel length the most likely ancestral state was only designated as ‘ancestral’  if at most one mismatch was in the sequence in the window to the left and right of the indel, where, in the case of insertions, we also required the insertion bases to match the insert sequence exactly. In all other cases the ancestral state was left undetermined.Transition/transversion (Ts/Tv) ratios were calculated for various sets of identified SNPs. SNPs with alleles is A and G, or C and T, were caused by transition mutations and all other SNPs by transversions. The Ts/Tv ratio within a set of SNPs was estimated as the number of SNPs caused by transitions divided by the number of SNPs caused by transversions.25 and with those reported by the ESP15. The Icelandic variants were counted as existing in the ESP or dbSNP if a variant at the same position and with the same allele was present in the database.We assessed the overlap between the variants we discovered in Iceland with those in dbSNP (build 137)List of 21 million sequence variants identified in 2,636 whole genome sequenced Icelanders is accessible at the European Variant Archive (EVA) (Data Citation 1). The data for each sequence variant consists of the chromosome and position (in NCBI builds 36 (hg18), 37 (hg19) and 38 (hg38)), reference allele, alternative allele, non-reference allele frequency, the maximal consequence VEP annotation, the affected gene, the HGVSc coding sequence names, the HGVSp protein sequence names, the impact class and the dbSNP rsIDs from build 142.5 and those reported by the Huvariome resource based on sequencing by Complete Genomics26. Similarly, we examined the distribution of indels in coding regions and observed a clear deficit of indels that shift the reading frame (frameshift indels)  to previously reported ones . Interge indels) .25 and the ESP15 revealed that essentially all common SNPs found in Iceland have been recorded in dbSNP (99.8% of SNPs with DAF>2%) (A comparison of our variants to those in dbSNP DAF>2%) . Indels  DAF>2%) . Few of 2>0.9)).The 104,220 long range phased of Icelanders genotyped for 676,913 autosomal SNPs using Illumina chips  provide To further assess the quality of the variant calling we Sanger sequenced individuals predicted to be homozygous for a set of rare LoF variants, enriched for indels over SNPs. Genotypes were observed for 47 out of 49 LoF variants (96%). Out of the 140 individuals imputed to be homozygous for one of these 47 variants yielding genotypes, genotypes were obtained for 134 (96%), 128 of which were homozygotes (96%).P-values for meta-analysis, or the collaborators travelling to our Icelandic facilities for local data access.Icelandic law and the regulations of the Icelandic Data Protection Authority prohibit the release of individual level and personally identifying data. We are actively participating in multiple meta-analysis based on our data and are in collaboration with groups at over 100 international universities and institutions. Therefore, collaborations based on our sequencing data are based on the release of summary level statistics, such as effect sizes and How to cite this article: Gudbjartsson, D. F. et al. Sequence variants from whole genome sequencing a large group of Icelanders. Sci. Data 2:150011 doi: 10.1038/sdata.2015.11 (2015).


Collectively, our data suggests that 8 SNP and 5 genes might be promising candidate markers or targets for marker-assisted selection of egg numbers in geese.Laying performance is an important economical trait of goose production. As laying performance is of low heritability, it is of significance to develop a marker-assisted selection (MAS) strategy for this trait. Definition of sequence variation related to the target trait is a prerequisite of quantitating MAS, but little is presently known about the goose genome, which greatly hinders the identification of genetic markers for the laying traits of geese. Recently developed restriction site-associated DNA (RAD) sequencing is a possible approach for discerning large-scale single nucleotide polymorphism (SNP) and reducing the complexity of a genome without having reference genomic information available. In the present study, we developed a pooled RAD sequencing strategy for detecting geese laying-related SNP. Two DNA pools were constructed, each consisting of equal amounts of genomic DNA from 10 individuals with either high estimated breeding value (HEBV) or low estimated breeding value (LEBV). A total of 139,013 SNP were obtained from 42,291,356 sequences, of which 18,771,943 were for LEBV and 23,519,413 were for HEBV cohorts. Fifty-five SNP which had different allelic frequencies in the two DNA pools were further validated by individual-based AS-PCR genotyping in the LEBV and HEBV cohorts. Ten out of 55 SNP exhibited distinct allele distributions in these two cohorts. These 10 SNP were further genotyped in a goose population of 492 geese to verify the association with egg numbers. The result showed that 8 of 10 SNP were associated with egg numbers. Additionally, liner regression analysis revealed that SNP Record-111407, 106975 and 112359 were involved in a multiplegene network affecting laying performance. We used IPCR to extend the unknown regions flanking the candidate RAD tags. The obtained sequences were subjected to BLAST to retrieve the orthologous genes in either ducks or chickens. Five novel genes were cloned for geese which harbored the candidate laying-related SNP, including  Geese possess strong/variable broodiness and poor egging performances, which are impacted by many factors, such as genetics, nutrition, environment and disease. Asthe heritability of reproduction is low,it is hard to improve reproductive traits using traditional selection methods. Marker-assisted selection (MAS) is an effective way to improve such traits with low heritabilities. However, mining trait-linked sequence variationor functional genesis needed for developing MAS strategies. Single nucleotide polymorphism(SNP) is the most abundant type of genetic marker, and itshigh genetic stability makes itideal for studying the inheritance of genomic regions ,2. HowevProlactin (PRL) intron 2, and 5 SNPs in Prolactin Receptor (PRLR) exon 10 in Wanjiang white geese. These polymorphisms were significantly related to the egg productivity FSHβ  FSHβ[PRL  FSHβ[PRyrhynchos).TherefoMAGI-1gene, Kranjec et al. (2014) demonstrated itcan promote the cell-cell contact in HPV-positive cells, thereby has the function of represseing cell proliferation and promoting apoptosis [ARHGAP21functions preferentially as a GTPase-activating protein (GAP) for CDC42 and regulates the ARP2/3 complex. It is localized in the nucleus, cytoplasm, or perinuclear region and participates in cell-cell adhesion formation and cellular migration[KIAA1462 is a protein-coding gene which is localized in the nucleus, cytosol and plasma membrane. Diseases associated with KIAA1462 include artery disease and coronary artery disease [KIAA1462 as a novel protein localized at cell–cell junctions, and concluded that the accumulation of KIAA1462into endothelial cell–cell junctions depends on VE-cadherin-mediated cell–cell adhesion [MAGI-1, ARHGAP21 and KIAA1462) have a high possibility to affect the granulosa cell proliferation and apoptosis, then interfere with oocyte growth. In addition, KIAA1462 plays a very important role in meiotic recombination. Chowdhury et al. (2009) foundKIAA1462 was one of six loci associated with variation in human recombination rates [For the poptosis .ARHGAP21migration,55. KIAA disease –58. Akasadhesion . Oocyte adhesion ,61. A weadhesion ,62,63. Ton rates . Failureon rates ,66.ACSF2 and ASTN2.ACSF2is the member of Acyl-CoA synthetases (ACS) family, which is involved in fatty acid synthesis and the tricarboxylic acid cycle [ACSF2 is a mitochondrial matrix enzyme and located inthe mitochondrial matrix [ACSF2 found in the engery metabolism processes, and be related with mitochondrial function, suggested this gene may play a role in the reproduction. ASTN2 is expressed at high levels in migrating, cerebellar granule neurons [ASTN2 gene participates in cell adhesion and neuronal cell–cell communication [ASTN2 gene, and was preferentially expressed in the gonads [Theother two cloned genesincludeid cycle . ACSF2 il matrix . The cha neurons . It play neurons ,71. Lescnication . Ahn et e gonads .We applied the pool-based RAD sequencing strategy for SNP discovery in geese. Eight laying-related SNP were verified by individual-based association analysis. Five novel genes for geese were cloned based on the laying-ralated SNP. Our data suggested that these SNP or genes might be promising candidate markers or targets for marker-assisted selection of animals prolific in production of egg numbers in geese.These methods could be performed in other production animals to help identify more efficient, greater performing animals for human consumption/use. Our studies also demonstrate that molecular methods can serve useful purposes for reasons (other than) simply determine molecular mechanisms underlying some physiological cascade. Indeed, more research in this vein will help in the production of all sorts of animals.S1 FigM1 indicates 1kb DNA maker. M2 indicates 250bp-I DNA ladder maker.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


Bemisia tabaci between species are partially attributed to the B. tabaci-associated bacteria, the factors that affect the diversity of B. tabaci-associated bacteria are not well-understood. We used the metagenomic approach to characterize the B. tabaci-associated bacterial community because the approach is an effective tool to identify the bacteria.Although discrepancy in the specific traits and ecological characteristics of tomato yellow leaf curl virus (TYLCV), on the bacterial communities of B. tabaci sibling species B and Q, we analyzed the bacterial communities associated with whitefly B and Q collected from healthy cotton, healthy tomato, and TYLCV-infected tomato. The analysis used miseq-based sequencing of a variable region of the bacterial 16S rDNA gene. For the bacteria associated with B. tabaci, we found that the influence of the host plant species was greater than that of the whitefly cryptic species. With further analysis of host plants infected with the TYLCV, the virus had no significant effects on the B. tabaci-associated bacterial community.To investigate the effects of the host plant and a virus, B. tabaci-associated bacterial communities were successfully analyzed in this study. To explain why B. tabaci sibling species with different host ranges differ in performance, the analysis of the bacterial community may be essential to the explanation.The effects of different plant hosts and TYLCV-infection on the diversity of  Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae), is a complex of species that contains at least 36 morphologically indistinguishable species [tomato yellow leaf curl virus (TYLCV) [B. tabaci-associated bacteria, in part. The symbioses between B. tabaci and endosymbionts are well-documented [B. tabaci are not well-understood.The sweet potato whitefly,  species –4 that c (TYLCV) , 5. The cumented , but theB. tabaci is a mix of mutualistic, pathogenic, and commensal bacteria. The primary symbionts compensate for the insufficient nutrients that B. tabaci obtains from a restricted diet of plant phloem [The bacteria community associated with t phloem . In addit phloem , 11, impt phloem , 13, inct phloem , and inct phloem . Other bt phloem –18.Lymantria dispar [Helicoverpa armigera [Drosophila melanogaster, and D. simulan [Anopheles gambiae [B. tabaci on different host plants have clearly different levels of performance [B. tabaci were indirectly affected by the TYLCV obtained from the host tomato [B. tabaci-associated bacteria.Based on recent metagenomic studies, the variation in gut-associated bacterial communities was dependent on the host plants in a dispar , Helicovarmigera , 21, Dro simulan , 23, and gambiae . The B. formance , 26. Addt tomato , 28. TheB. tabaci-associated bacteria did not completely characterize the bacterial community, including isolation of the bacteria from B. tabaci that could be cultured [However, until recently, all approaches to identify the cultured , 29, 30,cultured , 31, 32,cultured . By contcultured , 35.B. tabaci-associated bacteria, we analyzed the composition of the bacterial community of B. tabaci sibling species B  and Q  on different host treatments  with a metagenomic approach that used miseq-based sequencing of a variable region of the bacterial 16S rDNA gene.To determine the effects of the biotic factors on the diversity of The research complied with all laws of the country (China) in which it was performed, and the research was approved by the Department of Science and Technology of the Qingdao Agricultural University, China (permit number: 20110712).B. tabaci sibling species B and Q used in this study were obtained from laboratory populations established from prior field collections. The details of the methods to maintain the populations are described in Fang et al. [Gossypium hirsutum  cv. Lu-Mian 21, a host plant suitable to both B. tabaci B and Q. Using the Vsp I-based mtCOI PCR-RFLP method [The samples of g et al. . BrieflyP method , 38, theLycopersicon esculentum (Solanaceae) cv. Zhe-Fen 212, which included healthy plants and those infected with the TYLCV. Three types of treatments were used in the study. In the first type of treatment, the populations were maintained in separate climatic cubicles on cotton, a host plant suitable to both B. tabaci B and Q , for one generation. In the second type of treatment, the populations of B and Q were transferred to healthy tomato plants from cotton  and were maintained for one generation. In the third type of treatment, the populations of B and Q were transferred from cotton to tomato infected with the TYLCV  and were maintained for one generation. All experiments used plants at the 5–7 fully expanded true leaf stage and were conducted in climate chambers . All samples were collected in the 2nd generation and stored at -20°C.Two species of crop plants were used in this study: (i) cotton, cv. Lu-Mian 21, and (ii) tomato, Each insect sample  and the cotton leaf sample were rinsed at least three times in 75% ethanol. The insect and cotton leaf genomic DNA were extracted from the samples using the TIANamp Genomic DNA kit and Plant Genomic DNA kit , respectively.Amplicon liberates were constructed for miseq-sequencing using bacterial fused primers 341F (5’-CCTACACGACGCTCTTCCGATCTN-barcode-CCTACGGGNGGCWGCAG-3’) and 805R (5’-GACTGGAGTTCCTTGGCACCCGAGAATTCCA-barcode- GACTACHVGGGTATCTAATCC-3’) for the V3-V4 region of the 16S rDNA . The barThe products of the amplicon of the 16S rDNA from different samples were pooled in equimolar ratio, and then added the library barcodes on the Illumina PE adapters to construct the PCR amplicon libraries, and finally carried out on an Illumina Miseq for sequencing. The raw data have been deposited in the Sequence Read Archive (SRA) database under accession number SRS1022467.B. tabaci was constructed with SPSS v.19.0 software. The statistical significance of differences in abundance in the bacterial community associated with B. tabaci among treatments was determined with student t-tests. The principal coordinates analysis (PCoA) was conducted using the unifrac metric [The sequences were grouped into OTUs using uclust software (uclust v1.1.579) with the 97% identity thresholds. The richness rarefaction curves, Shannon index, ACE, Chao1, and coverage were calculated with Mothur analyses . The RDPc metric .B. tabaci-associated bacterial community, rarefaction curves, Good’s coverage, ACE, Chao1, and Shannon parameters were applied to estimate these qualities , Plesiomonas , Fabibacter , and Delftia  , Plesiomonas , Delftia , and Enterobacter   . The pre0–0.96%) .B. tabaci, 22 phyla were identified from both B. tabaci B and Q from healthy tomato , Plesiomonas , Pseudomonas , and Streptophyta  , Rickettsia , Pseudomonas , and Streptophyta   . The pre4–1.46%) .B. tabaci, 21 and 23 phyla were identified associated with B. tabaci B and Q from TYLCV-infected tomatoes, respectively , Plesiomonas , Pseudomonas , and Streptophyta . The predominant genera in the community associated with QTV were Plesiomonas , Rickettsia , Pseudomonas , Streptophyta   .B. tabaci-associated bacterial diversity between cotton and tomato populations was obvious  can be found in cotton leaf, respectively.The similarity analysis between the bacterial genera in cotton leaf and that in B. tabaci, more than 300 genera including unculturable symbionts, culturable bacteria and unculturable bacteria, showing that many more bacteria are associated with B. tabaci than previously described [Our protocol of using a metagenomic approach that used miseq-based sequencing of a variable region of the bacterial 16S rDNA gene provide the complete picture of bacteria associated with escribed .Proteobacteria, which composed over 90% of the community. Oesi-Poku et al. [Proteobacteria was typically the predominant bacterial taxon in the gut of mosquitoes, which was consistent with the reports of Wang et al. [Morever, the predominant phylum of the communities in the six groups was u et al.  found thg et al.  and Joneg et al. .B. tabaci. Our results can also be supported by Pan et al. [Portiera, Cardinium, Rickettsia, and Hamiltonella in B. tabaci. We have further analyzed the similarity between the bacterial genera in cotton leaf and those in B. tabaci Q, which confirmed the important role that the host plant played in shaping the composition of the bacterial community in insects.This study showed that the host plant played an important role in shaping the composition of the bacterial community associated with n et al.  that hosB. tabaci than the cryptic species. This result was consistent with Chandler et al. [Drosophila, than Drosophila species. Anderson et al. [Our result also revealed that the host plant played a more important role in shaping the composition of the bacterial community associated with r et al.  that hosn et al.  also fouB. tabaci while the gut-associated bacteria were mainly obtained from the host plant [Pseudomonas, Bradyrhizobium, Azorhizobium, Azospirillum, and Bacillus [Pseudomonas and Bacillus were detected in B. tabaci in this study, which may be obtained from host plant. Alternatively, the same plant could provide a suitable living environment for the same bacteria in the guts in different whitefly species, and thus different cryptic species of B. tabaci feeding on the same host plant may have high similarity of bacterial composition.These results can be explained by two possibilities. One possibility was that the gut-associated bacteria have relatively high ratio in the bacterial community associated with st plant . For exaBacillus . Of thesB. tabaci. In previous work that compared the feeding behaviors of B. tabaci B and Q on TYLCV-infected tomatoes, the B and Q also responded similarly to infected plants, and no differences were found [B. tabaci-associated bacterial community.The TYLCV had almost no effect on the bacterial community associated with re found , which ire found , 50, whiHamiltonella was not detected in B. tabaci B, which is inconsistent with the previous studies [Hamiltonella collected from field populations of B. tabaci B ranged from 46.70% to 100%. 454 pyrosequencing of 16S rRNA gene sequences showed that the relative abundance of Hamiltonella range from 1% to 50% in seven field populations of B. tabaci B from Israel [Hamiltonella in whitefly. Another possibility is that the different databases were used to identify the bacterium. Ribosomal Database Project was used to assign sequences to phylogenetic taxonomy in this study, while the NCBI StandAlone BLAST (megablast program) was used to identity bacterial species in Jing et al. [In this study,  studies –53 that B. tabaci-associated bacteria, such as Rickettsia. This result was consistent with Pan et al. [B. tabaci B and Q. And the Rickettsia in B. tabaci had some involvement with the resistance against insecticides [B. tabaci-associated bacteria, which thereby affect the performance of B. tabaci, for example, in the susceptibility to insecticides. It requires further research.In this study, we found that host plants had significant effects on the relative amounts of n et al. , 55, whocticides , 11. Howcticides –58. TherS1 FigB. tabaci-associated bacterial communities.Scatter plot of PCA scores depicting variance of fingerprints derived from different (TIF)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.


Bubalus bubalis) have an important socio-economic role. The majority of the population is situated in developing countries. Due to the scarce resources in these countries, very few species-specific biotechnology tools exist and a lot of cattle-derived technologies are applied to buffaloes. However, the application of cattle genomic tools to buffaloes is not straightforward and, as results suggested, despite genome sequences similarity the genetic polymorphisms are different.Asian buffaloes  and gene network analysis carried out in buffaloes is presented. Target phenotypes were six milk production and four reproductive traits. GWAS identified SNP with significant associations and suggested candidate genes that were specific to each trait and also genes with pleiotropic effect, associated to multiple traits.Network predictions of interactions between these candidate genes may guide further molecular analyses in search of disruptive mutations, help select genes for functional experiments and evidence metabolism differences in comparison to cattle. The cattle SNP chip does not offer an optimal coverage of buffalo genome, thereafter the development of new buffalo-specific genetic technologies is warranted. An annotated reference genome would greatly facilitate genetic research, with potential impact to buffalo-based dairy production.The online version of this article (doi:10.1186/s12864-015-1986-2) contains supplementary material, which is available to authorized users. Asian buffaloes are a livestock species with a high socio-economic importance and promising characteristics for production. The species is mostly found in developing countries integrating production system by providing meat and milk to local communities. Asian buffaloes are also used as draught animals. In developed countries, such as Italy, the buffalo population is selected for dairy production, specially the production of mozzarella cheese, the most famous trademark product. Buffalo milk has high fat content and solids concentration and these intrinsic characteristics are favourable for cheese manufacturing. and swamp buffalo  [There are two species of domesticated buffaloes: river or water buffalo 2n = 48)  . The ge2n = 48) .http://www.ncbi.nlm.nih.gov/assembly/GCF_000471725.1/). In an effort to generate a reference set to aid polymorphism discovery and gene annotation of the buffalo genome, RNA from 30 different tissues was extracted and sequenced [There are two water buffalo genomes sequenced , 5. Bothequenced . The lacCattle and buffalo species are in a close evolutionary relationship and the cattle genome is far better characterised than buffalo. An initial genome maps for buffaloes using cattle-derived markers and possible rearrangements were identified between species . Positio2 equal to 0.28 indicating that these SNP could be used for genomic selection and SNP association analyses. Studies that used cattle SNP chips did identify SNP associated with production and reproductive traits in buffaloes; using the 54 k cattle SNP chip [More recently, a cattle SNP chip was applied to characterize buffalo genome. Genotyping 10 buffaloes with a 54 k cattle SNP chip (Illumina) and found that ~80 % of the SNPs were successfully genotyped, but only ~2 % 1,159) were segregating in the population [ were segSNP chip . HoweverThe aim of the study was to identify SNPs, genomic regions and genes that affect production and reproductive traits. To this aim genome-wide association analyses and gene network predictions were carried. Gene network analyses aid the identification of genes that have pleiotropic effects and/or regulatory roles . The genAnimal ethics committee approval was not required for the present study. The data and samples used here were obtained from an existent databank of the Animal Science Department from São Paulo State University (Unesp), Jaboticabal-SP, Brazil. The department is responsible for the Milk-Recording Buffalo Program. The farmers gently contribute with phenotypes, pedigree information and samples of the animals.Six production traits and four reproductive traits were targeted. The production traits were: milk production (MP), fat production (FP), protein production (PP), fat percentage (%F), protein percentage (%P) and somatic cell score (SCS). The reproductive traits were: age at first calving (AFC), calving interval (CI), open days (OD) and number of services per conception (NSC). The data analyzed was based on 11,530 lactations of 3,431 buffaloes, monthly recorded from 1995 to 2013. Murrah buffaloes were from 12 farms with 186 sires with registered daughters. The final pedigree archive had 14,346 animals. The structure of the data is presented in Table Records were obtained apart from the fifth production day in milk. First five day of colostrum production were not considered. Only lactations longer than 90 days were used in the analyses. The cumulative milk production over 305 days (MP), fat production (FP) and protein production (PP) were calculated apart from the production in the milk-recording day. The %F, %P and SCS were the monthly record means per lactation. The age at first calving (AFC) was defined as the difference, in months, between the first calving and the birth of the buffalo. The calving interval (CI) was defined as the difference, in months, between consecutive calving events. The number of services per conception (NSC) is the number of artificial inseminations per conception for each buffalo. The open day (OD) is the difference, in days, between the calving and the subsequent conception. The contemporary group (CG) was formed by herd, year and calving season (October-March and April-September) for all the traits, except CI and by herd, year and birth season for CI. Each CG had at least four animals and trait records between ±3.5 standard-deviations of the group mean.A repeatability animal model was used for the genetic analyses of all traits except AFC. For AFC, an animal model without repetition was used, because this trait can only be measured once. Variance components were estimated by Restricted Maximum Likelihood method (REML) using the Wombat software . The modorentclass1pt{minimad Tables . The gend Tables , de-regrP-value test less than 10−6), and correlation between markers (if higher than 0.998 one SNP of the correlated pair was excluded). Also, coincident SNPs were eliminated. Minor allelic frequency was not used to discard markers. Some SNPs were genotyped twice when there was a probe in each strand for the same SNP. In the case of coincident SNP, the probe with the most animals genotyped was used. Markers present in Y chromosome and mitochondrial DNA were discarded. Markers in X chromosome were considered. The males have only one X chromosome, so they are always homozygous for the markers (0 or 2), females have two, so they were codified like the autosomes . After the quality control, the number of SNPs retained for association analysis was 61,145.A total of 452 buffaloes (57 sires and 395 dams) were genotyped using the 90 K Axiom ® Buffalo Genotyping (Affymetrix). The animals genotyped were the ones with the best accuracies. The sires have at least 40 progenies and the dams at least three calvings and many of the dams are mothers of sires used in the herds. Initially, the SNP chip contained 92,826 markers. Sample quality control observed the call rate of 0.95 and above, and a heterozygozity of ± 3 standard-deviation of the mean. For SNPs quality control, thresholds were set for call rate (superior to 0.98), Hardy-Weinberg equilibrium  and ε is the vector of the residual error with normal distribution ε ~ N.In order to associate SNPs with the de-regressed breeding values (DEBV) of the studied traits, a mixed linear model was implemented using R software and GenABEL package . The DEBσu2 and σe2 were estimated using Restricted Maximum Likelihood (REML) method for each SNP. The generalized least square (GLS) was used to estimate the β effects using the F test for the null hypothesis H0 : β = 0.y is the vector of the DEBVs, x is the design matrix, β is the vector of coefficients of the regression on recoded SNP genotypes; z is the incidence matrix for animal effects; μ ~ N is a vector of the polygenic animal effects and e ~  is the vector of residuals, in which A is an additive genetic relationship matrix of animals and I is an identity matrix, and σa2 and σe2 are the animal’s additive polygenic variance and residual error variance, respectively. SNP allele substitution fixed effects (β) and random background polygenic effects were evaluated in this model. Values in the design matrix, x, were coded as 0, 1, 2 for the SNP genotypes, representing the number of copies of the minor allele carried by the individual. The parameters and were estimated using Restricted Maximum Likelihood (REML) method for all SNP. The generalized least square (GLS) was used to estimate the β effects using the F test for the null hypothesis H0 : β = 0.The pedigree relationship matrix based on pedigree, G, describes the relation of the whole genome among the individuals, since it is estimated based on alleles identical by state (IBS) of the markers. The parameters Subsequently, a Wald chi-square statistics was used to determine if the SNP was associated with the traits studied .α = allelic substitution effectth observed SNP in the populationp = allelic frequency for iσp2 = Phenotypic variance estimate of the traitThe percentage of the phenotypic variance (Vp) explained by each SNP was estimated according to the equation:P-values, t-values calculated served to ground gene network predictions, (t ≥ 2.80 ≈ p ≤ 0.05). These statistics determine the importance of the SNPs across the traits and are interpreted as a measure of pleiotropic effect. All the SNPs were used in the analysis regardless of their location. Normally in AWM, SNP-to-gene distances are considered prior to construction of gene networks. However, in this study, inclusion of all SNPs was preferred since genotyped SNPs were buffalo variants with locations annotated in the cattle genome .The association weight matrix (AWM) methodology  was adapPCIT) algorithm [To identify significant SNP-SNP interactions we used the partial correlation and information theory (lgorithm . Pairwislgorithm . The SNPlgorithm  and MCodlgorithm . In the Variant Effect Predictor (VEP) from Ensembl website was used to verify if the significant SNP was near a gene and determine the distance. Analyses were done using the cattle genome.http://cbl-gorilla.cs.technion.ac.il/) to aid interpretation of GWAS results. The top genes associated with the traits were compared to a genome-wide background gene list. Top genes were defined as genes with a SNP whose P < 0.001 (distance of the SNP to gene determined by VEP default). These GO enrichment analysis were carried for each trait separately.Gene ontology (GO) enrichment analyses were carried using Gorilla tool  because there is no public complete genome reference available for buffalos. The numbers of significant SNP were similar between traits, within significance thresholds Table . They weWe described the data structure Table  and haveThe SNPs that explained most of the phenotypic variance indicated regions of the genome that have an influence in the traits studied and indicate new candidate genes. Phenotypic variance percentage, positions and nearby genes were provided for these significant SNPs Table . SignifiIFN-TAU, and other interferon genes were identified. Embryonic production of IFN-TAU is the primary signal for maternal recognition of pregnancy in buffaloes [SELP), a gene with up-regulated expression during inflammatory processes related to follicular atresia in cattle [SELP gene, mapped to chromosome X, had the most significant SNP associated to AFC. The sexual chromosomes influence reproductive and andrological traits in cattle [Association analyses for the reproductive traits resulted in candidate genes for buffaloes that have known roles in reproductive physiology. For age at first calving (AFC), the gene coding for interferon-Tau, uffaloes . Anothern cattle . It is cn cattle , 26. SELn cattle –29, amonn cattle –33. The TPCN1 [TPCNI with CI suggests an interesting thought: a gene related to male fertility might be more relevant to herd performance (in terms of CI) than genes related to female fertility. Increased conception rates after calving, and, as consequence, decreased CI may also reflect fertilization ability of bulls in the studied population. As a complex trait, CI may be influenced by several component traits linked to both male and female fertility, including spermatozoa quality andrological parameters [For calving interval (CI), a gene involved in spermatozoa acrosome reaction in humans was identified: TPCN1 . Spermatrameters .ABCC4). This gene has its expression increased in the endometrium of pregnant cows [ABCC4 was identified as the gene with the biggest number of non-synonymous SNPs, splice-site variants, and coding indels [ABCC4 may be a useful source of variation to be studied in buffaloes and cattle. In Angus cattle, the ABCC4 expression was significantly correlated with residual feed intake (RFI) [ABCC4 was correlated with marbling score [ABCC4 acts in basic metabolic pathways and is highly polymorphic with potential effect in a variety of phenotypes .For number of services per conception (NSC), the top gene found was LOC100336232 (ant cows  and seemg indels . ABCC4 mke (RFI) , being ung score . The emeP < 0.001) with a genome-wide background gene list. For AFC and CI, many processes involving neural development and activity were listed . There were other genes expressed in the central nervous system that were associated to puberty in female cattle [Gene ontology enrichment analyses were also performed using GOrilla to compare the top genes associated with the traits  and one gene related to lipid metabolism (ESRRG) were identified. The KCTD8 gene interacts with genes that act in the insulin secretion and glucagon liberation pathways, participating in the glucose absorption [FOXO4 gene down-regulates gluconeogenesis and up-regulates glycolysis [SSTR3 inhibits the activity of Glucose-dependent insulinotropic polypeptide’s function in intestine, promoting the accumulation of glucose and fat [LOC782855 (RPS26) was related to diabetes in humans [ESRRG to fat production in the present GWAS could be expected, since this gene regulates other adipogenic genes [ESRRG was also considered a key regulator of puberty in a multi-trait analyses that included fat deposition traits [Regarding %F and FP, four genes related to the carbohydrate metabolism , the biological process of regulation of lymphocyte migration was significant (GO:2000401). This immunological metabolic process is correlated with SCS because this trait is used as an indirect measure of mastitis which severely diminishes milk production. Buffaloes with a more efficient immune system (better variants for genes that regulate lymphocyte migration) might do better in avoiding the disease.Buffalo and cattle chromosomes have an extensive similarity and 84 % of cattle markers were successfully used in buffaloes . HoweverPLAG1 in cattle [The identification of genes with pleiotropic effects could contribute to the genetic evaluation of many traits. An example of a gene with important pleiotropic effects is n cattle . To idenC14H8orf34 gene was a central node of the network, having 23 predicted interactions. Information about this gene is scarce; however some indications of its function and pleiotropic effect might be discussed. SNPs in this gene were associated, in humans, with “fasting serum aspartate aminotransferase” and “urinary free epinephrine excretion per day” [C14H8orf34 with epinephrine production may also support a pleiotropy claim. Epinephrine is a hormone and a neurotransmitter synthesized in the adrenal glands that acts via many pathways to accelerate metabolism under stress situations [A SNP located in per day” . Some coper day” . The livtuations .CA10 as its seed . The first cluster had eed Fig. . The fun tissues . In humass index , 67. TheRARB, a retinoic acid receptor important for cell growth and differentiation [RARB is expressed in many tissues from liver and intestine [RARB in its development is expressive. Considering the traits herein analysed, six out of ten traits analysed are related to the udder and its physiology, so the finding of a TF that has a crucial role in its development as a central regulator in the network is plausible  genes, a total of 28 in the network, also had their clusters mined for regulatory information. Genes that work as TF are important in terms of pleiotropy effect since they can guide transcription and interact with many other genes. The TF with highest number of predicted interactions was ntiation . RARB isntestine  to spermntestine  in cattlntestine . Studiesntestine  suggestsble Fig. .ATF1. This gene regulates other genes involved in growth and survival and was associated with angiogenesis in the mammary gland [The TF gene with the second largest number of connections in the network was ry gland . Milk isry gland   and ATF1 gene has six interactions, four of them are with known genes . It is curious to verify that some of these genes also have a variety of roles in the metabolism and are also candidates to have pleiotropic effects.The A genome wide association study with milk production was done with the buffalo SNP chip in Mediterranean buffaloes  with 78,GWAS studies were done for reproductive and production traits in China , and usiDairy cattle GWAS of varying breeds and traits were further evidence for species differences when compared to results herein , 74–79. Comparing different breeds of the same species already results in differences regarding associated genes and the proportion of phenotypic variance that they explain , 81. TheSpecies-specific technologies are important and needs to be further developed. Particularly, for the buffalo species, the lack of a publicly available complete and annotated genome complicates the advance and development of new methodologies for genetic evaluation for the specie.The genes identified in this study are candidates for fine-mapping with the aim to find putative causative mutations. The incorporation of this information in a low density SNP chip is informative and auxiliary to genetic evaluation, with cost-benefit for producers. The identification of causative mutations reduces the need for tag SNP (in linkage disequilibrium with causative mutations), promotes higher accuracy for genomic breeding values, which can persist over generations and permits a higher transferability across breeds . The resThe present article is a genome wide association and gene network analyses in buffaloes using a SNP chip specifically developed for the species. Putative genes for production and reproductive traits were found and these are candidates for searching causative mutations. Comparative analyses between cattle and buffaloes support that although the genome sequence is similar, the variants between them are different. Evidence that species-specific technology should be developed for buffaloes was presented discussed herein.


NRG1 - NRG3, GRIK1 - GRIK3 and IL23R - ATG4C gene pairs may modify susceptibility to TB. Gene pairs involved in the NF-κB pathway were also identified in the discovery data set , but could not be tested in the Gambian study group due to lack of overlapping data.Tuberculosis (TB) is the second leading cause of mortality from infectious disease worldwide. One of the factors involved in developing disease is the genetics of the host, yet the field of TB susceptibility genetics has not yielded the answers that were expected. A commonly posited explanation for the missing heritability of complex disease is gene-gene interactions, also referred to as epistasis. In this study we investigate the role of gene-gene interactions in genetic susceptibility to TB using a cohort recruited from a high TB incidence community from Cape Town, South Africa. Our discovery data set incorporates genotypes from a large a number of candidate gene studies as well as genome-wide data. After limiting our search space to pairs of putative TB susceptibility genes, as well as pairs of genes that have been curated in online databases as potential interactors, we use statistical modelling to identify pairs of interacting SNPs. We attempt to validate the top models identified in our discovery data set using an independent genome-wide TB case-control data set from The Gambia. A number of models were successfully validated, indicating that interplay between the  Tuberculosis (TB) is a serious global health problem, with 8.6 million new infections and 1.2 million deaths reported in 2012 .Although up to a third of the world’s population has latent TB infection , only abOne of the common explanations for the missing heritability of complex disease is gene-gene interactions, a.k.a. epistasis , 10–12. The immune system is complex and comprises many intricate elements, thus progression to active TB may be elucidated by identifying the interplay of gene products in the host defence against TB infection. Only a small number of TB susceptibility gene-gene interaction studies have been published to date –35 and tA large variety of software packages have been developed to detect gene-gene interactions –52. ApprWith the advent of new genome editing technologies such as CRISPR, detecting pairs of SNPs rather than pathways or networks lends itself to experimental validation. Given the size of our study group, limiting our search to pairs of SNPs only is also appropriate. Our SAC case-control data set comprises sample sets that were genotyped in a number of different studies which did not always overlap well, resulting in a relatively sparse data set. Age, gender and ancestry also differ between cases and controls . We therMycobacterium tuberculosis (M. tuberculosis) [Individuals from the Ravensmead and Uitsig suburbs in Cape Town, who self-identified as South African Coloured, were recruited to participate in this study.  These suburbs have a homogenous socio-economic environment, low prevalence of HIV and high incidence of TB . TB paticulosis) . The majThis study was approved by the Ethics Committee of the Faculty of Health Sciences, Stellenbosch University . Blood samples for DNA were collected with written informed consent, and written informed consent was obtained from the next of kin, caretakers, or guardians on behalf of the minors enrolled in the study. The research was conducted according to the principles expressed in the Declaration of Helsinki.Sample collection and ethical approval of a Gambian tuberculosis study group, obtained from the Welcome Trust Case Control Consortium (WTCCC), are described by Thye et al.  .A total of 955 case and 521 control SAC samples were collected between 1994 and 2007. The samples were used to perform a number of candidate gene studies, using unrelated individuals, summarized in The Gambian tuberculosis data set was used to validate the top interaction models found in the SAC data set. A total of 1 498 cases and 1 496 controls was genotyped on the Affymetrix 500K chip set . After http://genome.igib.res.in/cgi-bin/hgvtb/inter.cgi), with at least one SNP genotyped in the SAC chip data, was used to generate 5 886 candidate gene-gene pairs, composed of 1 278 unique candidate gene SNPs. Seventeen of these SNPs were genotyped on both the Affymetrix chip and another platform, and the strand orientation of 10 of these SNPs were flipped when combining the data sets. Duplicate genotypes were available for 4 686 SNPs, of which 280 genotypes mismatched (error rate of 0.06). The mismatched genotypes were discarded. Another 2 438 interacting gene-gene pairs were identified, comprised of 28 936 unique SNPs. After discarding SNP pairs with less than 60 genotypes available for either cases or controls, 854 703 candidate gene SNP pairs and 1 040 161 biofilter SNP pairs were identified for testing.We limited tests for interaction to SNP pairs of genes that have been identified as TB susceptibility candidate genes (which we refer to as candidate gene SNP pairs), and pairs of genes that are known to interact based on experimental evidence, or that are found in the same biological pathway, ontological category or protein family . A total of 76 candidate genes previously genotyped by our group, as well as 33 additional tuberculosis and pulmonary tuberculosis candidate genes curated in the HGV&TB database based on literature reviews .The p-value of the interaction term was used to detect and report the significance of interactions (4 degrees of freedom test). Reported p-values were not corrected for multiple testing. To aid interpretation of the results, the nature of the association is illustrated by graphs of the observed genotype combination proportions in the data, as interaction effects such as odds ratios are difficult to describe and interpret. Furthermore, reliable estimates of odds ratios could often not be calculated, as some of the genotype combinations include zero counts. Graphs of allele combination frequencies in cases and controls are also provided. An expectation-maximisation (EM) algorithm was used to infer allele combinations per subject. The particular algorithm was originally designed to infer haplotypes, but does not assume the physical coupling of SNPs, and is therefore also appropriate for estimating allele combination frequencies. We note that the only uncertainty in inferring these allele pairs is double heterozygotes. The logits of the possible genotype combinations are also illustrated. This demonstrates the differing direction or magnitude that a SNP has on the odds of having disease, depending on the genotype of the second SNP; non-parallel lines being indicative of an interaction effect. The effects were estimated by absorbing the marginal effects of the SNPs into the SNP × SNP interaction term, and adjusting for the covariates included in the model by averaging over them .After the top interacting pairs of SNPs were identified, the individual effects of each of the identified SNPs were tested separately in the SAC and Gambian cohorts using logistic regression. SNPs were encoded as factor variables and covariates were included in each of the models (Case/Control ∼ Covariates + SNP).Allelic interaction of the identified top SNP pairs was also tested in the SAC cohort. SNPs were encoded as numeric variables, according to the number of copies of the rare variant, as follows: 0, 1 or 2 copies of the rare variant for additive encoding, 0 or 1 for dominant encoding, with 1 representing heterozygotes and rare homozygotes, and 0 or 1 for recessive encoding, with 1 representing rare homozygotes. Each of the nine possible allelic encoding combinations were then tested for each of the identified top SNP pairs.Age and gender are differentially distributed in the SAC TB cases and controls and gender is differentially distributed between the Gambian TB cases and controls , none of the interaction associations would be statistically significant if adjusted for multiple testing. In addition, many of the multiple testing methods that have been suggested in the literature have severe shortcomings. The straightforward Bonferroni adjustment is too stringent when several genetic associations are tested in the same study group due to correlation (LD) between markers , 68. Altr2 value of at least 0.3 with the SNP in SAC controls. Although some of the SNPs genotyped in candidate gene studies were selected for their putative functional effects, we note that all the variants in the top twenty models that were genotyped in candidate gene studies were originally selected as they were variants in a gene of interest, and not for their functional effects per se.As patterns of linkage disequilibrium (LD) differ between populations, tag SNPs of causal variants may vary between the SAC and Gambian populations. A SNP associated in the SAC study group points to a region of LD, and any SNP within this region may be the causal SNP . The 20 After selecting SNP pairs to test using this strategy, a resulting total of 245 regression models were fitted to the Gambian study group. P-values smaller than 0.05 were described as statistically significant.Version information, web URLs and important parameter settings of the software packages used in this study are summarized in http://www.well.ox.ac.uk/~wrayner/strand/.PLINK was used for quality control of the SAC chip data set and Gambian chip data set . The SACADMIXTURE was used to estimate ancestry proportions of the SAC study group . For theBiofilter was used to generate SNP pair combinations of genes that are known to interact based on experimental evidence, or that are found in the same biological pathway, ontological category or protein family. Only those combinations having three or more sources were used for testing interaction in the SAC chip data set. Biofilter was also used to find SNPs within gene regions that are available in the Gambian data set for validation of the top SAC gene-gene models.genetics package was used to test for HWE in the SAC candidate genes and was also used to calculate pairwise LD r2 and D′ values [haplo.stats package was used to estimate allele combination frequencies in cases and controls [effects package [ggplot2 package [The freely available R programming environment was used for statistical analyses, quality control of the SAC candidate genes and graphing . The R g′ values . The R hcontrols . The adj package . Figures package .−2 and 1.64 × 10−2, interaction p-values of 6.23 × 10−6 and 1.37 × 10−5, respectively). Only one of the genes reported in The top 20 unique gene pair models discovered in the SAC cohort are summarized in Interaction effects observed in the SAC study group are illustrated in Figs NRG1—NRG3 (Neuregulin 1 and 3) interaction effect observed in the SAC study group  was also detected in the Gambian study group . The SAC and WTCCC NRG1 SNP is the same, and the Gambian NRG3 SNP is located 66 235 base pairs upstream from the SAC NRG3 SNP. In both the SAC and Gambian study groups, compared to the GG-AC/GG-AG combination, the GG-AA combination decreases the odds of having TB and the AG-AA combination increases the odds of having TB. The same pattern is thus observed, albeit using a different SNP in the second gene in the Gambian study group. Studies investigating the link between NRG1 and schizophrenia have demonstrated that NRG1 has a functional effect on the immune system by influencing immune cell adhesion [NRG1 and NRG3 has also been observed in a schizophrenia study [The adhesion  and the adhesion . Gene-geia study , and accGRIK1 and GRIK3 (glutamate receptor 1 and 2) genes was also detected in both study groups . The TT-GG genotype combination was observed only in cases (5%) and the T-G allelic combination is more frequent in cases compared to controls (21% vs. 7%). The Gambian GRIK1 SNP is located 3 478 base pairs downstream from the SAC GRIK1 SNP and the Gambian GRIK3 SNP is located 1 286 base pairs downstream from the SAC GRIK3 SNP. GRIK1 has been associated with susceptibility to diabetes [http://www.t1dbase.org), GRIK3 is also a putative diabetes susceptibility gene. Having diabetes increases susceptibility to TB [GRIK1—GRIK3 interaction association we observed in the data.An interaction between the diabetes , and accty to TB , and thiIL23R (interleukin-23 receptor) and ATG4C  . T helper 17 (Th17) cells are subsets of activated CD4+ (cluster of differentiation 4 plus) T cells that mediates the recruitment of macrophages to infected tissues. The Th17 response to M. tuberculosis infection is largely dependent on interleukin-23 [IL23R and ATG4C gene products may therefore both be involved in the Th17 response to M. tuberculosis. The same SNPs are used in both models, with the CC-CC genotype combination decreasing the odds of having TB in both cohorts, compared to the CC-CT and CC-TT combinations. Both of the SNPs are located on chromosome 1p31 and are 6 centimorgans (4 451 385 base pairs) apart. Linkage disequilibrium between the SNPs is high in SAC controls (D′ = 0.5451) but not in SAC cases (D′ = 0.0136), and low in both Gambian controls and cases (D′ = 0.0011 and D′ = 0.0386 respectively).Another model that was observed in both study groups is the interaction between eukin-23 . ATG4C ieukin-23  and is ueukin-23 , 87. TheNELL1  and CADM2 (cell adhesion molecule 2) genes is also evident in both the SAC and WTCCC study groups , as well as interaction between the NELL1 and CADM3 (cell adhesion molecule 3) genes, although the latter was not validated in the Gambian study group . The same trend between the effects of the heterozygote genotype combinations is observed in both study groups for model 13, compared to the pairing with the wildtype homozygote genotype (CC) of the second SNP in the pair. A large degree of homology exists between the CADM1, CADM2 and CADM3 genes [NELL1 is expressed in pre-B cell development [Interaction between the M3 genes , 89 and M3 genes . NELL1 ielopment  and has elopment . The linCHST11—CHSY3 (carbohydrate (chondroitin 4) sulfotransferase 11—chondroitin sulfate synthase 3) gene pair interaction was also detected in both study groups . Uhlin et al. [CSPG (chondroitin sulfate proteoglycan) decreased when monocyte-derived macrophages are treated with interferon-gamma. CSPG is composed of a protein core and a chondroitin sulfate side chain. According to the NCBI BioSystems database, both the CHST11 and CHSY3 genes are involved in the chondroitin sulfate pathway. The CT-CG genotype combination has a higher frequency in SAC controls compared to cases (10% vs. 3%). The CHST11 SNP is the same in the SAC and Gambian models, and the CHSY3 Gambian SNP is 8 683 base pairs upstream from the SAC CHSY3 SNP. The CT-TT combination was observed in 7 Gambian controls and in 1 case.The n et al.  showed tκB (nuclear factor kappa-light-chain-enhancer of activated B cells) signalling plays in an important role in the host defense against M. tuberculosis infection [SFTPD (surfactant protein D) and NOD2 (nucleotide-binding oligomerization domain containing 2) genes are involved in this pathway [NF-nfection . Both th pathway , 96. IntISG15 (ISG15 ubiquitin-like modifier) and TLR8 (Toll-like receptor 8) gene products may also affect NF-κB signalling. ISG15 stimulates interferon-gamma production [κB signalling [κB signalling [The oduction  which ingnalling . It has gnalling . This geNLRC5  and IL12RB1  (model 14). The interferon-gamma/interleukin-12 pathway is an important component of the immune defense against mycobacterial infections [NLRC5 promoter region is responsive to interferon-gamma, which implies that NLRC5 may function as a molecular switch of interferon-gamma activation [Another model of interest that could not be validated in the Gambian cohort due to lack of suitable SNPs is the interaction between fections . IL12RB1tivation . The TT-Finally, we explored whether allelic encoding of the SNPs may better explain the interactions detected in the SAC cohort. The South African Coloured population is an ideal cohort for the discovery of TB susceptibility genetic variants, since they received genetic contributions from diverse source populations that may differ in their susceptibility to TB. Seldin et al.  has arguGenotypes available for testing the gene pair models were limited to SNPs that were genotyped on the Affymetrix 500K SNP chip as well as candidate gene studies performed in our group. The Affymetrix chip was originally designed based on LD patterns in European populations, and as a result the proportion of variants that are tagged in African populations may be much reduced .Minor allele frequencies of the SNPs representing genes in the top models are in general quite different between the SAC and Gambian cohorts . Of the −7, p-value of model 1 was 8.33 × 10−7). Given that roughly 2 million models were tested, and that Emily et al. found that the number of effective independent tests was approximately six times less than the actual number of tests, it is unlikely that we would be able to demonstrate this (a 33 times reduction would be required). The SAC genome-wide data set was originally genotyped with a view to perform a case-only admixture mapping study, and for this reason, a limited number of controls was available for many of the two SNP interaction tests. Whilst the available group sizes are sufficient to detect two-SNP interactions, it is unlikely that any of the results would achieve statistical significance. For validation models fitted to the Gambian data set, we describe tests with p-values < 0.05 as statistically significant. As 20 gene pair tests were done Click here for additional data file.S2 FigThe frequencies of the four possible SNP pair allele combinations from models 5, 6, 8, 9, 10, 11, 12, 15, 16, 17, 18 and 20 are depicted in this figure, per cases and controls. The frequencies were estimated using an EM-algorithm.(PDF)Click here for additional data file.S3 FigThe logits of genotype combinations from models 5, 6, 8, 9, 10, 11, 12, 15, 16, 17, 18 and 20 are depicted in this figure. Genotypes are ordered according to minor allele frequency, with the wildtype homozygote appearing first, and the rare homozygote appearing last. Non-parallel lines are indicative of interaction effects. The effects were estimated by absorbing the marginal effects of the SNPs into the SNP × SNP interaction term, and adjusting for the covariates included in the model by averaging over them.(PDF)Click here for additional data file.S4 FigThe observed proportions of SNP pair genotype combinations from models 3, 5, 7, 8, 9, 16, 17 and 18 are depicted in this figure, per cases and controls. Recessive/dominant effects in these models may better explain the interactions observed in the cohort Click here for additional data file.S1 TableThe table summarizes the total number of samples that were successfully genotyped in each candidate gene study and how many samples have complete confounder information .(PDF)Click here for additional data file.S2 TableP-values were calculated using logistic regression.(PDF)Click here for additional data file.S3 TableA summary listing web URLs, version information and important parameter settings of software used in this study.(PDF)Click here for additional data file.S4 TableA spreadsheet with two worksheets, showing the results of the top 250 SAC models and the 245 Gambian models that were used for validation.(XLS)Click here for additional data file.S5 TableThis table provides a summary of each SNP’s individual minor allele frequency (MAF) and association with having TB.(PDF)Click here for additional data file.S6 TableThe genotypic model p-values, which were used to select the top 20 models, are presented in this table. The p-values of the corresponding allelic interaction models that achieved the smallest p-values are also shown.(PDF)Click here for additional data file.


Phoenix dactylifera L.) is one of the oldest cultivated trees and is intimately tied to the history of human civilization. There are hundreds of commercial cultivars with distinct fruit shapes, colors, and sizes growing mainly in arid lands from the west of North Africa to India. The origin of date palm domestication is still uncertain, and few studies have attempted to document genetic diversity across multiple regions. We conducted genotyping-by-sequencing on 70 female cultivar samples from across the date palm–growing regions, including four Phoenix species as the outgroup. Here, for the first time, we generate genome-wide genotyping data for 13,000–65,000 SNPs in a diverse set of date palm fruit and leaf samples. Our analysis provides the first genome-wide evidence confirming recent findings that the date palm cultivars segregate into two main regions of shared genetic background from North Africa and the Arabian Gulf. We identify genomic regions with high densities of geographically segregating SNPs and also observe higher levels of allele fixation on the recently described X-chromosome than on the autosomes. Our results fit a model with two centers of earliest cultivation including date palms autochthonous to North Africa. These results adjust our understanding of human agriculture history and will provide the foundation for more directed functional studies and a better understanding of genetic diversity in date palm.The date palm ( Collecting fruit samples ensures that the collection is based on the commercially important phenotype (fruit type) and allowed documentation of phenotypic information such as fruit size, color, and weight for future analysis. Because fruit is one of the clearest phenotypes in date palm, we chose to exclude the use of males in this study because proving their geographical origin is difficult. Due to the fact that various countries use similar names for different fruit or different fruit may have the same name . Although packaging of the dates reveals the country of production, the genetic origin of these date palm fruit cannot be confidently pinpointed to a country. For example, Medjoul dates were collected from Saudi Arabia and from California, although the genetic origin is likely Morocco/Algeria (Table S1). Multiple dates from each cultivar were stored for future phenotypic analysis. Date fruit samples were washed multiple times in distilled water and cut open, the seeds were removed, and 200 mg of mesocarp was crushed with the TissueLyser II (Qiagen). DNA was extracted with the DNeasy 96 Plant Kit (Qiagen) according to the manufacturer’s protocol. As an internal control for the process, DNA extraction and genotype analysis were performed twice for four samples. DNA was quantified and normalized using a Nanodrop (ThermoScientific). For four fruit samples, two DNA extractions were repeated from the same fruit sample and passed through the entire genotyping and analysis process to provide an internal quality control.Date fruit were collected from commercial outlets across the region. In addition to eight date palm samples from leaves already sequenced (/Algeria . Given tGenotyping-by-sequencing (GBS) libraries were constructed as described  using BOWTIE2 . We then re-ran STRUCTURE 12 times with K set to 2. STRUCTURE HARVESTER  set from 2 to 7, with 4 replicates for each K (ARVESTER  was usedARVESTER  was usedFile S1). The tree topology was determined based on homozygous SNPs by applying FastTree  and two Phoenix sylvestris trees in the USDA collection in California.Using genotyping-by-sequencing we genotyped 74 P. hanceana as the most distant outgroup, resulting in a set of 26,846 SNPs passing cutoffs (see File S1). For date palm population STRUCTURE analysis, we included both P. hanceana and P. sylvestris and allowed a maximum of eight missing genotypes for a given SNP position. This resulted in 12,995 SNPs passing cutoffs (see File S2). For allelic differences between populations, we included only the 70 date palm cultivars and allowed a maximum of 15 missing genotypes at a given SNP position. This resulted in a total of 67,496 SNPs passing cutoffs (see File S3).We used various cutoffs for allowed numbers of missing genotypes depending on the analysis. For phylogenetic tree analysis, we allowed a maximum of 10 missing genotypes at a given SNP position and included only Analysis of the duplicated samples and a comparison of both Medjoul and Khalas date fruit to their respective whole genomes  group  group . Increasern Asia  and therFor the analysis of population structure among the date palm cultivars, we used the STRUCTURE program  showed tPhoenix species: two independent samples of P. hanceana (loureiroi) and two independent samples of P. sylvestris. P. hanceana is believed to be among the most distant Phoenix species from P. dactylifera, whereas P. sylvestris is believed to be among the closest species    of the PPhoenix species  on the same data set including the 70 date palm cultivars and four samples from outgroup  species . The fir species .Fst (Table S1). When samples from the Western and Central (Mixed) regions were considered, the mean and weighted stF were 0.159 and 0.214, respectively. When Western and Eastern populations alone were compared, the mean and weighted stF were 0.208 and 0.267, respectively. This suggests that the more distant Western and Eastern populations show significant separation with low levels of breeding between them, as expected based on their geographical separation. These results agree favorably with those found by We calculated Fst  on the 6We screened for population-specific alleles using 67,496 SNPs to better understand the effects of population division and regional selective pressures on date palm genetics. Multiple forms of allele differences between the subpopulations including alleles that were private to one subpopulation, alleles that were overrepresented in either subpopulation, and alleles that appeared to have reached fixation differently between the subpopulations were analyzed .vs. the Eastern cultivars were investigated. As discussed above, date palm cultivars were assigned to the Western or Eastern cluster if they showed at least 75% purity for the STRUCTURE results, with the remainder assigned to a mixed category (Table S1). The mixed group included cultivars from Egypt, Libya, and Pakistan that we combined with North Africa in this analysis. Of 67,496 variant positions, 23,988 (36%) qualify as nonreference private alleles, with 15,302 originating from the Western or Mixed population and 8686 originating from the Eastern population. This imbalance between the two is expected given that the reference sequence is derived from the Eastern cultivar "Khalas." The majority of private alleles that segregate the populations have not reached fixation.Alleles occurring exclusively in one subpopulation between the combined Western plus Mixed group P values, and a corrected P < 0.01 was considered significant. Using this cutoff we obtained 3219 SNPs (4.8%) considered to be significantly segregating between the Western and Eastern date palm populations. This large number of alleles at different frequencies between the two populations reinforces the observation of significant Western to Eastern population structure observed in stF, PCA, and STRUCTURE analysis.Although private SNPs are only present in one subpopulation, we also identified alleles that were overrepresented in one subpopulation. Alleles at each position were counted and a simple Fishers exact test was applied to detect allele frequency differences between the two populations. We applied the Bonferroni correction to vs. the total number of SNPs for each LG . This revealed that LG 12 has approximately three-fold more SNPs with different allele frequencies between the Western and Eastern Date Palm cultivars than expected by the genome average. We previously determined LG 12 to harbor the putative sex determination region in a nonrecombining region spanning a large section of this linkage group . The major North Africa/Arabian Gulf divide still represented the major subpopulation fractions; however, the Arabian Gulf showed a second minor population, with the highest fraction individuals being from Iraq. This may suggest a second genetic influence on the Arabian Gulf date palms.To detect whether some Linkage Groups (LGs) of the genome displayed higher levels of population-specific allele frequencies, we plotted the number of SNPs with significantly deviated frequencies between Western and Eastern clusters Whereas LG 12 showed elevated fixation, LG 8 showed approximately half the number of expected significant SNPs given the genome average. A possible reason for this may be selective sweeps for the preservation of domestication alleles on this chromosome that are equally critical to both geographical regions of cultivation. Further work will be necessary to better understand the reason and possible functional implications of decreased or increased allele fixation on a specific linkage group.Figure S3), which contains two predicted genes and that showed elevated numbers of SNPs segregating between Western and Eastern cultivars. Of these 32 SNPs showing geographical segregation, 25 SNPs (78%) showed the Eastern cultivars had deviated from the ancestral allele in Oil Palm while the Western varieties had maintained the Oil Palm allele. Two SNPs (6%) showed the Western cultivars had deviated from the ancestral allele. One SNP (3%) showed both Western and Eastern alleles had deviated from the oil palm sequence. This last category likely represents some of the most ancient Phoenix or date palm–specific polymorphism and will be of interest for future studies. For the remaining four SNPs (13%), the surrounding sequence could not be unambiguously aligned to the oil palm genome sequence and were not scored. Regions showing elevated segregation of SNPs by geographical region may offer a starting point for understanding differences in cultivation and environmental pressures between North Africa and Arabian Gulf date palms.Scaffolds from the genome that showed elevated levels of private SNPs between Western and Eastern populations were further investigated. Using previously sequenced genomes  will be critical to our understanding of date palm domestication origins.We have shown that approximately 36% of SNP locations contain alleles private to either date palm subpopulation. This highlights the strong genetic separation of the subpopulations. Whether the genetic differences in subpopulations of date palms in North Africa and the Arabian Gulf are due to differentiation during extended isolation or different domestication events remains a question. It is also possible that a single domestication occurred with various hybridizations to endemic species for each region, as may have happened in the domestication of rice  between the date palm subpopulations in scaffolds linked to the X chromosome. The differences in variability we observed in the putative date palm sex chromosomes could be associated with the differences in divergence of sex chromosomes compared to autosomes and would make the date palm a candidate for the “faster-X hypothesis.” This hypothesis explores the idea that sex chromosomes diverge at a different rate compared to autosomes (Drosophila, chimpanzee, and humans (Linkage Group 12, harboring the putative sex determination region (ulations . Our finBased on our observations that the date palm putative X chromosome has more rapidly fixed differences between the subpopulations of date palm cultivars, we propose that X-linked markers, combined with Y-linked markers, may be of interest for finer-scale geographical studies of date palm (Understanding the genetic structure among date palm cultivars will assist in selecting cultivars that span the major subpopulations for functional studies. Studies, such as those investigating date fruit nutritional values, have not typically taken into account the importance of regional variation in the fruit. Our findings here suggest that future studies should sample, at minimum, from the two major regions to better cover the natural date palm fruit differences, including fruit length, color, sugar content, and others. Work presented here will form an important foundation for genetic conservation and further functional analysis of the date palm genome.