Compared with the commercially available single nucleotide polymorphism (SNP) chip based on the Bead Chip technology, the solution hybrid selection (SHS)-based target enrichment SNP chip is not only design-flexible, but also cost-effective for genotype sequencing. In this study, we propose to design an animal SNP chip using the SHS-based target enrichment strategy for the first time. As an update to the international collaboration on goat research, a 66 K SNP chip for cashmere goat was created from the whole-genome sequencing data of 73 individuals. Verification of this 66 K SNP chip with the whole-genome sequencing data of 436 cashmere goats showed that the SNP call rates was between 95.3% and 99.8%. The average sequencing depth for target SNPs were 40X. The capture regions were shown to be 200 bp that flank target SNPs. This chip was further tested in a genome-wide association analysis of cashmere fineness (fiber diameter). Several top hit loci were found marginally associated with signaling pathways involved in hair growth. These results demonstrate that the 66 K SNP chip is a useful tool in the genomic analyses of cashmere goats. The successful chip design shows that the SHS-based target enrichment strategy could be applied to SNP chip design in other species. Because this process was expensive and time consuming2, breeding scientists tried to find more efficient methods to select desirable genetic traits. Nearly 35 years ago, Geldermann proposed the concept of quantitative trait loci (QTL) in animal breeding, which assumed that genes with related functions were often clustered in the genome to control biological traits3. Since then, the QTLs in association with genes or chromosome segments of interest have been widely studied to delineate complex animal traits6. In addition, marker-assisted selection (MAS)7 was devised to introduce desirable QTLs into an animal population or increase the proportion of desirable QTLs in the gene pool. Despite its usefulness, the MAS program used a small number of DNA markers to trace limited numbers of QTLs8. This disadvantage lead to the development of genomic selection (GS), which aimed to use simultaneously all available genome-wide dense SNP markers to predict breeding values9. Another strategy known as genome-wide association (GWA) analysis was also proposed, which believed that specific SNP markers could be in genetic linkage disequilibrium with a causative mutation affecting animal traits10. Therefore, identification of these significant genome-wide SNP markers is important for studying complex traits.The first-generation animal breeding strategies for important quantitative traits relied heavily on keeping meticulous documentation of animal phenotypes and breeding values over several generations11. Among all available products on the market, the GoldenGate and Infinium analyses are extensively used in animal genetic studies. Both assays are based on Illumina’s Bead Chip technology, which involves direct hybridization of whole-genome amplified genomic DNA to a bead array of 50-mer locus-specific primers14, an enzymatic-based extension assay, a sandwich-based immunohistochemistry assay, and the final imaging by a two-color confocal laser system  developed by the International Goat Genome Consortium included sequencing data from Saanen, Alpine, Creole, Boer, Katjang, and Savanna goat breeds28. This chip has been widely used to study the genetic diversity29, population structure30, effective population size31, QTL detection32, and GS in multiple goat populations33. No special SNP chip is designed for cashmere goat till now.Even though current commercial SNP chips based on the Bead Chip technology for different domestic animals have been successful35, and the downstream industries have increased international trade between Asia and the developed world37. The meat from free-ranging cashmere goat is also considered as delicacy, and has aroused the interest of many meat markets. Here, a moderate-density SNP chip for cashmere goat was designed using the SHS-based target enrichment strategy. This chip was subsequently tested in a population of Inner Mongolia cashmere goats, through which several potential loci related to cashmere goat traits were obtained through GWA analysis.Cashmere goat is a multipurpose breed that adapts well to the desert and semi-desert pastoral environments. This goat breed produces high-quality cashmere fiber, which is crucial to the world textile industry. It is estimated that cashmere goat herding has contributed substantial economic benefits to the local people in the remote regions of developing Asian countriesA total of 2,801,066 SNPs were called from the genome sequencing data of 73 cashmere goats Fig. . The secThese SNPs spread over 30 chromosomes and 27 scaffolds Fig. . The freThe average measurements of the diameters of cashmere fibers from 1,438 cashmere goats were ranked from the smallest value to the largest value with a range from 12.9 μm to 18.8 μm. At the population level, the diameter of cashmere fiber conformed to a normal distribution with 22 intervals . We selected 300 cashmere goats from each of the two tails of this distribution, so that the average diameters of cashmere fibers had the largest difference between the two subpopulations , 423 cashmere goats with 370,389 SNP were left. The MAF and Hardy-Weinberg equilibrium (HWE) screening resulted in 161,125 SNPs for the following analyses. Among these, 55,863 SNPs matched the target SNP loci on the 66 K SNP chip.We used PLINK software to conduct the population stratification analysis based on pairwise IBS distance, and found all the 423 samples were clumped into the same cluster. We ran STRUCTURE to determine the genetic ancestry constituents of samples and found that all samples almost have the same mixed ancestry when K = 2 or K = 3 Fig. . The res41. In this study, 423 goat individuals were subjected to quantitative trait association analysis. As shown in the Manhattan plot, no association were observed between SNPs and fiber fineness at the genome-wide level statistical significance , and combed cashmere fiber weight013 Fig. .Figure 8http://www.kegg.jp/)  in the cashmere goat populations. Another possible explanation might be that cashmere fineness is controlled by a combination of genetic and environmental factors.Lashmar et al. showed the impact of global climate change on native sheep rapid adaptations to extreme environments. Kijas et al. and Lv et al. used Ovine SNP50K Beadchip to study the genetic history of global sheep breeds47, the adaptation to climate-mediated selection48, and so on. These successful application cases showed the great potential of genome-wide SNPs and high-density SNP chip technology. Even though our chip is most suitable for analyzing cashmere fiber trait, it is worth mentioning that it can be modified to study other cashmere goat traits, and used in animal breeding. Our chip design method can also be exploited for other species in the future.It is now a common practice to use genome-wide SNPs generated by whole-genome sequencing or high-density SNP chip to carry out animal genetics research. It is not limited to traditional genomics research, but more and more used in studies on interesting animal traits. For example, Yang AKT1 gene belongs to the Notch and MAPK signal pathway  were used in the extraction of SNP variants and the subsequent design of a 66k SNP chip. A second group of 1,438 female goats from the Tonghetai Breeding Farm in Erlangshan were used in the testing of the designed SNP chip with GWA analysis  sodium citrate. The blood samples were snap frozen in liquid nitrogen, and stored at −80 °C until further processing. Genomic DNA was extracted from whole blood samples with the AXYGEN Blood and Tissue Extraction Kit  according to the manufacturer’s instructions. The extracted DNA was subjected to electrophoresis in 2% agarose gel and stained with ethidium bromide to assess overall quality. The DNA concentration was determined by Quant-iT™ PicoGreen ® dsDNA Reagent and Kits  according to the manufacturer’s instructions. All animal procedures were approved by the Inner Mongolia Agriculture University Animal Care and Use Committee in accordance with the National Animal Care Standard (GB 14925–2001). All experiments were performed in accordance with relevant guidelines and regulations. All efforts were made to minimize animal suffering.65 using the Burrows-Wheelser Aligner (version 0.7.10-r789)66 with default settings. The software SAMtools67 was used to convert file format from SAM to BAM. The package Picard (http://broadinstitute.github.io/picard/) was used to sort BAM files by coordinate and mark PCR duplications.For the group of 73 cashmere goats, paired-end libraries were constructed for each individual animal with an insert size of 300 bp from ~2 μg of sheared genomic DNA according to the procedures of NEB DNA Library Prep Kit for Illumina . These libraries were sequenced on an Illumina Hiseq. 4000 platform  using a PE-100 module. After data filtering, high quality reads were mapped to the goat reference genome (version 2.0)68 were used to obtain duplication-free reads with default settings. Next, ‘HaplotypeCaller’ in the GATK was used to generate a single call set from the sequencing data of all 73 individuals by joint calling with the parameter ‘-stand_call_conf 30 -stand_emit_conf 10’. The SNP data were then distinguished from the InDel data by ‘selectVariants’ in the GATK. The criteria68 used to exclude false positive SNP data were the following: (a) hard filtration with the parameter ‘QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum <−12.5 || ReadPosRankSum <−8.0′; (b) total depth does not range from 80 to 1000; (c) missing data rate >20% and >20% individual depth <2.After the BWA alignment process, ‘RealignerTargetCreator’ and ‘IndelRealigner’ in the Genome Analysis Toolkit In brief, the probe design process includes the selection of SNP-containing genomic regions and the optimization of the probe hybridization efficiency Fig. . The goaA SNP probe is a 120 bp oligonucleotide, which contains a 90 bp target-specific bait sequence and two 15 bp PCR primer ends (5′-GAA GCG AGG ATC AAC [N90] CAT TGC GTG AAC CGA-3′). The 90 bp target-specific bait sequence pairs with the genomic region containing the target SNP. In this study, a series of probes were designed to cover each specific SNP with the stipulation that the center of the probe should be less than 50 bp away from the target SNP. These probes were screened by the GC content criterion (between 30% and 70%). SNPs with no viable probe designs or with only one viable probe design were removed from the candidate pool. If multiple probe designs were available for a SNP, two or three best probes were selected on the condition that the center of the probe should be about 20 bp away from the target SNP Fig. . For som69:In order to cover the goat genome on a 60–70 K SNP chip, the average interval between SNPs is estimated to be about 40 kb. We further optimized the target SNPs density in consecutive 40 kb genomic windows. A ranking score was calculated for each of the remaining SNPs according to the following formulaMAF is the minor allele frequency. a is the coordinate of a SNP. S and E are the initial coordinate, and stop coordinate of the 40 kb genomic window, respectively. In SNP-dense genomic regions, only those with high ranking scores were kept for the chip design.All final SNP probes were obtained with a CustomArray B3™ Synthesizer  according to the manufacturer’s instructions. The probe libraries were aminolyzed, purified, and then dissolved in 10× TE buffer (pH = 8.0). The probe libraries were amplified by PCR with primers A (5′-GAA GCG AGG ATC AAC-3′) and B (5′-TCG GTT CAC GCA ATG-3′). With the addition of SP6 promoter sequences, the probe libraries were transcribed into RNA bait libraries with SP6 RNA polymerase. They were then labeled by biotin. After purification and quality control, biotinylated RNA bait libraries were prepared for hybridization and stored at −70 °C Fig. , which wFor the group of 1,000 cashmere goats for GWA analysis, about one gram of cashmere fiber sample was obtained from the left scapular region of each individual animal. The average diameter of the cashmere fibers from each individual was assessed by an OFDA-2000 optical-based fiber diameter analyzer  according to the manufacturer’s instructions.1 μg high-quality genomic DNA from each cashmere goat was subjected to sonication. The DNA fragments of 150–250 bp in lengths were selected for the targeted sequencing. These fragments were end-repaired before being ligated to the Ad153Ω_2B adapter . They were then amplified by a round of standard PCR. The biotinylated RNA bait libraries (see methods above) were used to capture and enrich SNP-containing DNA fragments. Captured DNA fragments were subjected to a round of standard PCR, and the PCR products were circularized and made ready for sequencing Fig.  on a BGIRaw sequencing reads were filtered according to the following criteria: (1) if a read has >10 percent of bases as N; (2) if a read has >40 percent of low-quality  bases; (3) if a read is contaminated by the adaptor sequence or produced by PCR duplication. The resultant clean reads were then mapped to the goat reference genome (v2.0) using BWA with parameters “-m 200000 -l 20 -k 2 -t 30”. The results were transformed into indexed BAM files with SAMtools (version 0.1.18). Picard package (version 1.105) was used to remove duplicate reads. Reads coverage and depth were calculated from BAM files with “samtools depth”. The variants were called using the Genome Analysis Toolkit’s HaplotypeCaller. After separating SNPs from Indel variants, SNPs was further filtered using the VariantFiltration package in GATK with parameters “–filterExpression “QD < 4.0 || MQ < 40.0 || ReadPosRankSum <−8.0 || FS > 60.0 || HaplotypeScore > 13.0 || MQRankSum <−12.5” –filterName LowQualFilter”.http://pngu.mgh.harvard.edu/~purcell/plink/download.shtml) unless stated otherwise. Chromosomal variant cell format files were transformed into Plink format by VCFtools v0.1.13, during which the non-biallelic SNPs were automatically filtered out in the PED/MAP files. Because human (n = 23) was set as the default chromosome handling type as in Plink, the parameter−dog (n = 39) was added in front of each command line to ensure that the system could process all goat chromosomes (n = 30). SNPs with call rates <0.90 and samples with genotyping call rates <0.90 were removed for the further statistical analyses. The SNPs from the final goat individuals were subjected to quality control according to the following two criteria: (1) Remove SNPs with a very low MAF filtration (MAF < 0.01); (2) Remove SNPs with significant deviations from HWE filtration (HWE < 0.001).The following quality control process for our data was conducted by Plink v1.07  distance with option–cluster–mc 2–ppc 0.05. Further STRUCTURE was used to infer genetic ancestry constituents and assign individuals to subpopulation. We also performed a PCA following the procedure as reported71. The eigenvector decomposition using the R function eigen, and the significance of the eigenvectors was determined with a Tracey-Widom test.Population substructure was investigated using Clustering, STRUCTURE72. The basic model underlying this software can be written asThe compressed mixed linear model(MLM) were used to identify association signals with the software EMMAXy1, …, yn} contain the phenotypes of the individuals, Y) was used to investigate the contribution of locus k to the phenotype which the effect of the genotype at locus k can be modeled as a main effect, whereas the relationships among all individuals are taken into account by means of variance components of random polygenic effects. We calculated an identity-by-state kinship matrix using the Affymetrix genotypes in EMMAX with command “emmax-kin -v -h -s -d 10”, pairwise relatedness matrix was used to represent the sample structure. Using a variance component model, we got an estimated covariance matrix that models the effect of genetic relatedness on the phenotypes. Animal pasture information was used as covariate matrix. For cashmere trait, the threshold P-value for declaring genome-wide significance (P < 4.2 × 10−7) which was set to control genome-wide type 1 error rate. Manhattan plot was drawn by qqman package of R (v3.2.0). A 500 kb region on each side of peak SNP was searched for gene annotation.In equation  the vectSupplemental FigureDataset 1Dataset 2Dataset 3Dataset 4Dataset 5


Peromyscus leucopus) to measure the impact of breeding regimes on genomic diversity. We bred mice in captivity for 20 generations using two replicates of three protocols: random mating (RAN), selection for docile behaviors (DOC), and minimizing mean kinship (MK). The MK protocol most effectively retained genomic diversity and reduced the effects of selection. Additionally, genomic diversity was significantly related to fitness, as assessed with pedigrees and SNPs supported with genomic sequence data. Because captive-born individuals are often less fit in wild settings compared to wild-born individuals, captive-estimated fitness correlations likely underestimate the effects in wild populations. Therefore, minimizing inbreeding and selection in captive populations is critical to increasing the probability of releasing fit individuals into the wild.Captive breeding programs are often initiated to prevent species extinction until reintroduction into the wild can occur. However, the evolution of captive populations via inbreeding, drift, and selection can impair fitness, compromising reintroduction programs. To better understand the evolutionary response of species bred in captivity, we used nearly 5500 single nucleotide polymorphisms (SNPs) in populations of white-footed mice ( The goal of many captive breeding programs is to retain the genetic/genomic diversity (GD) present in the captive founding population until such time that reintroduction of captive populations to wild habitats can occur , 2. RetaIn addition to the well-known impacts of inbreeding and genetic drift, selection can also rapidly deplete critical GD . RelaxedA reduction in GD, whether from inbreeding, drift, and/or selection, is often related to a reduction in fitness; this phenomenon is referred to as a heterozygosity-fitness correlation )14]). AlPeromyscus leucopus, all of which were started from the same 20 wild-caught founders, were bred for 20 generations using one of three commonly used breeding protocols: minimizing mean kinship to retain maximum GD, selection against particular stereotypic behaviors thought to be associated with docility, and random mating [Breeding species in captivity in ways that minimize GD loss and adaptation to the captive environment is costly and complicated. However, recent efforts to experimentally measure evolution in captive populations have provided critical insight into management –16. In om mating . Subsequm mating –16, whicm mating . Howeverm mating . ImportaOur current study had three primary objectives. First, we used a genome-wide SNP panel and six captive populations to evaluate how GD changes in light of three different breeding schemes that are often used in conservation efforts . We hypoThe breeding study and animal care protocols were approved by the Institutional Animal Care and Use Committee of the Chicago Zoological Society. Mice were collected from the wild under Scientific Permit W01.0845 from the Illinois Department of Natural Resources.The complete protocol and history of our captive populations is available elsewhere . In summPstI), uniquely barcoded, and sequenced at the Genomic Diversity Facility at Cornell (http://www.biotech.cornell.edu/brc/genomic-diversity-facility). We genotyped individuals from each of the six captive populations, including approximately 15 individuals from the sixth, twelfth, and nineteenth generations. We also typed 2–4 offspring from each of the ten founding pairs as well as 15 individuals collected from the original wild source population in 2012.We used a genotyping-by-sequencing approach in order to efficiently identify SNPs and genotype individuals . This meUsing the Uneak pipeline contained within the program Tassel –20 we siIn order to obtain unbiased estimates of population GD, we needed to ensure that our SNPs were independent. Using the ‘ld’ function in the snpStats package , we estiWe also sought to identify SNPs under selection, and did so using simulations in R. We modified a computer program designed to mimic the breeder-selection protocols  to use SNP data  and simuAt each simulated generation, we calculated allele frequencies. In order to insure that we compared the same allele over all generations, we calculated (at each generation) the allele frequency only for the allele identified as minor in the empirical founder genotypes. After 100 replicate runs, we compared the empirical allele frequency to the distribution of simulated frequencies, and calculated a p-value for each SNP as the proportion of simulated replicate frequencies that were more extreme (i.e. closer to either 0 or 1) than the empirical allele frequency. We adjusted these p-values to account for the false discovery rate using the Benjamini and Hochberg  correctiP. leucopus individual captured from the locale that served as the source for our captive populations. We generated sequence data using two lanes of paired-end sequencing (read lengths of 100 bp) using an Illumina HiSeq2000 and cleaned the resulting reads  using Trimmomatic [de novo assembly with a reference guided approach. We used ABySS to generate our de novo assembly [P. maniculatus assembly (NCBI assembly accession GCA_000500345.1) to improve our de novo assembly [ab initio predictions with SNAP [P. maniculatus EST libraries [Mus musculus protein sequences downloaded from the UniProtKB database (http://www.uniprot.org/uniprot/) to provide support for a subset of these predictions. We additionally used InterProScan to identify putative protein domains and provide support for ab initio predictions. Finally, we used Bowtie2 to map our SNP sequences onto the annotated scaffolds [As a method for assigning putative function to our SNPs, we performed genome sequencing and annotation. We sequenced a single wild mmomatic . In ordeassembly , and useassembly  and the assembly . We usedassembly  to annotith SNAP  and 2) uibraries  and Mus caffolds  and usedcaffolds , 32.We compared the impact of the three captive breeding protocols on GD using our SNP genotypes. We analyzed the SNPs using three data partitions: 1) all SNPs; 2) only SNPs identified as impacted by selection for a particular breeding protocol ; 3) only SNPs not identified as impacted by selection for a particular protocol . For each individual within each population at each generation, we calculated the average multilocus heterozygosity   as a meaWe evaluated the long-term impact of breeding protocol on GD using our estimates of genome-wide diversity. We compared the rate of change in average MLH by comparing the means ± SE, averaged across individuals at each generation for each population. We compared our estimates of GD calculated from SNPs (MLH) and the pedigrees (F) using a Spearman correlation, then permuted the SNP estimates to calculate a p-value for each comparison.We analyzed the effect of selection across protocols and generations using two approaches. In order to understand how the number of SNPs under selection differed between protocols, we first compared the total number of nonneutral SNPs across populations and generations using a 2-way ANOVA, and analyzed the direction of differences using a post-hoc Tukey test in R. We then sought to understand the effects of selection within an individuals’ genome. Therefore, we then analyzed the difference in MLH between neutral and nonneutral SNPs by calculating mean differences between the estimates for each individual. We compared the mean difference across individuals within each protocol using the standard error (SE) estimates around each.We examined the relationship between fitness and GD using a) the pedigree and b) the SNP data. We utilized our MLH estimates from the SNP genotypes and we used the inbreeding coefficient (F) from the pedigrees. We used an individual’s mass at weaning and the total number of offspring weaned as putative fitness estimates. Additionally, we compared three morphological measurements to GD: ear size, hind foot length, and tail length. We determined the relationship between our GD estimates for all individuals and for males and females separately across all of our replicate populations using a Spearman correlation. We used 1000 permutations over our GD estimates to estimate a p-value, and considered a fitness correlation significant only if it had a p-value < 0.05.10.5061/dryad.7pt2m). These SNPs included 705 singletons (unlinked to any other SNPs) as well as 4749 SNPs distributed among 531 putative linkage groups, which each contained from 2–35 SNPs (mean of 2.5). For all subsequent population GD estimates, we used all singleton SNPs and one linked SNP randomly pulled from each linkage group, averaged over 100 replicates.We attempted genotyping-by-sequencing  for a toWe identified nonneutral SNPs by comparing simulated allele frequency distributions under a null model of neutrality to our empirical allele frequencies . Across de novo assembly by leveraging a reference assembly. Our assembly contained 2,575,218 Kbp, including 64,3706 scaffolds with our longest scaffold being 79,125 bp and an N50 of 4087 bp. We also generated 30,269 unique ab initio gene predictions, 3905 of which were supported with EST, protein, or InterPro evidence.  In total, 68 of our SNPs mapped to within 10,000 bp of an annotated locus, 41 of which were located within a coding region .We examined the relationship between individual fitness and GD, again using MLH and F. Across all of the SNP-calculated fitness correlations, several correlations were positively significant; heterozygosity estimated from nonneutral SNPs was correlated to weight at weaning, as was heterozygosity estimated from all SNPs in males . In femaThe maintenance of genetic diversity (GD) in managed populations is an important goal of modern conservation , but capDrosophila [e), generated from our final estimates of heterozygosity. Assuming the founders represented the initial heterozygosity present in all populations [We hypothesized that the three breeding protocols would retain GD with varying efficiency, based on empirical data derived from populations of osophila –38 and posophila . Previouosophila . Howeverosophila . Similarosophila –16, SNP-ulations , we estiulations . In eithRandom genetic drift will nearly always be a problem for multi-generation captive programs due to small breeding populations, but reducing the effects of selection is an aspirational goal in captive breeding , 37, 42,In contrast to the MK populations, selection should have been the strongest in the DOC protocols due to the selection procedure employed. However, the number of nonneutral SNPs was not different between protocols , and theIn addition to the observed differences among breeding protocols, we also observed differences in GD within replicates. For example, the MK replicates had drastically different heterozygosity values in generation 12, but ended at similar values in generation 19 that were different from DOC and often RAN lines . AlthougSNPs on a genomic scale are expected to provide an estimate of genome-diversity commensurate with those estimated from a pedigree . HoweverTaeniopygia guttata; Typically, the correlation between GD and traits linked to fitness is positive, but lacks power , 45. We  fitness . Presuma fitness . TherefoF should be the best predictor of fitness because a true pedigree should offer a better measure of genome-wide diversity than any panel of markers short of full genome sequencing [With a sufficiently deep and accurate pedigree, the pedigree-calculated inbreeding coefficient quencing . Under tquencing –4, we exRegardless of the mechanisms behind the HFCs, minimizing the effects of reduced genetic diversity on fitness by limiting evolution in captive environments is important when reintroduction is the goal. As populations adapt to captivity over generations, it is likely that captive-born individuals become less fit in wild settings compared to wild-born individuals , meaningThe maintenance of genetic diversity in managed populations is critical in providing the best possible chance of successful reintroduction. However, captive populations are inherently small, have limited gene flow, and exist in artificial environments, and these characteristics often significantly alter the evolutionary trajectory, decreasing population fitness relative to wild progenitors. Our data indicate that minimizing mean kinship among breeders reduces inbreeding compared to random mating or selection on a particular behavior and helps temper adaptation to the artificial environment. Furthermore, we found that genome-wide heterozygosity was also related to fitness. Therefore using protocols that minimize mean kinship should result in the production of individuals better able to survive in wild environments. Beyond applied implications for conservation breeding programs, our results also elucidate how genetic variation is distributed and maintained across the genome in the face of drift and selective processes.S1 TablePfam descriptions were collected from the protein family database, and gene ontology terms and codes for biological processes, molecular functions, and cellular components were identified using InterPro.(DOCX)Click here for additional data file.S2 TableSNP counts are noted for each replicate protocol, at each generation and the number shared with all previous generations are noted in parentheses. The ‘Unique’ column indicates the number of unique SNPs within the row, whereas “Shared” indicates the number shared within the row. ANOVA indicated that there was no difference between the number of SNPs under selection between protocols , but there was a significant difference between generations . Our post-hoc Tukey test suggests that the effects of selection increased over time, as generation 19 had significantly more SNPs compared to generation 6 .(DOCX)Click here for additional data file.S3 TableWe used Spearman correlation coefficients (r) and p-values were estimated via 1000 permutations .(DOCX)Click here for additional data file.S1 FileDNA samples were taken across 6 populations  and 4 generations  and subsequently digested (using Pstl) and sequenced at Cornell according to . The raw(ZIP)Click here for additional data file.


VW), caused by infection by Verticillium dahliae, is considered one of the most yield‐limiting diseases in cotton. To examine the genetic architecture of cotton VW resistance, we performed a genome‐wide association study (GWAS) using a panel of 299 accessions and 85 630 single nucleotide polymorphisms (SNPs) detected using the specific‐locus amplified fragment sequencing (SLAF‐seq) approach. Trait–SNP association analysis detected a total of 17 significant SNPs at P < 1.17 × 10–5 ; the peaks of SNPs associated with VW resistance on A10 were continuous and common in three environments . Haplotype block structure analysis predicted 22 candidate genes for VW resistance based on A10_99672586 with a minimum P‐value (–log10P = 6.21). One of these genes (CG02) was near the significant SNP A10_99672586 (0.26 Mb), located in a 372‐kb haplotype block, and its Arabidopsis AT3G25510 homologues contain TIR‐NBS‐LRR domains that may be involved in disease resistance response. Real‐time quantitative PCR and virus‐induced gene silencing (VIGS) analysis showed that CG02 was specific to up‐regulation in the resistant (R) genotype Zhongzhimian2 (ZZM2) and that silenced plants were more susceptible to V. dahliae. These results indicate that CG02 is likely the candidate gene for resistance against V. dahliae in cotton. The identified locus or gene may serve as a promising target for genetic engineering and selection for improving resistance to VW in cotton.Verticillium wilt ( Verticillium dahliae, which has a broad host range of over 400 plant species, including economically important cotton, and can survive in soil for many years  is caused by the soil‐borne fungus et al., et al., et al., Gossypium hirsutum × G. barbadense , based on linkage disequilibrium (LD), has emerged as a powerful tool for gene mapping in plants, to take advantage of phenotypic variation and historical recombination in natural populations and overcome the limitations of biparental populations, resulting in higher QTL mapping resolution  loci and potential candidate genes to be verified by virus‐induced gene silencing (VIGS). Our objectives were to supply marker candidates for MAS, identify candidate genes involved in VW resistance and provide insight into the genetic mechanisms of resistance to VW in cotton.F = 8.26, P < 0.0001) , Yangtze River region (YZR), Northwest inland region (NW), Liaoning Province (LN) and Foreign group (FG) Table . We eval1) Table .To finely map the VW resistance genes and investigate beneficial haplotypes in the cotton germplasm, we constructed a haplotype map for the 299 cotton accessions using SLAF‐seq approach. Over 1297 million reads (125 bp each) were generated for the 299 genotypes, encompassing 324.19 Gb of the cotton genomic DNA sequence. Approximately 649 625 high‐quality tags (or SLAFs) were identified from 1297 million paired‐end reads after sequence alignment with the TM‐1 reference genome. The SLAFs used to call SNPs had an average depth of 5.97‐fold per individual among 299 accessions. In total, 884 799 SNPs were initially called for this set of lines; a proportion of these were found to have a minor allele frequency (MAF) of <5%, leaving 85 630 SNPs at MAF ≥ 0.05 and missing rate ≤50% to be used for analysis. The mean genomic distance between SNP tags was approximately 29.2 kb. The data set of 85 630 SNP markers covered all 26 chromosomes. The largest number of SNPs was found on chromosome A08 (8764 SNPs) followed by chromosome A06 (6295 SNPs), and the smallest number of SNPs was found on chromosomes D03 (970 SNPs) and D04 (879 SNPs) Table . The numA principal component analysis (PCA) was performed using 85 630 SNPs of the 299 accessions in the genome‐wide mapping panel to estimate the influence of population structure on the mapping results. Principal component 1 (PC1) explained 12.05% of the variation in the genotypic data, while PC2 and PC3 explained 8.12% and 3.01% of the variation, respectively. Based on the first three axes of the PCA, the testing accessions were divided into two groups designated Group I (mainly form YZR) and Group II (mainly from YR and NW)  decay rate for the entire genome to determine the mapping resolution of the GWAS. The r2 was approximately 0.47, where the mean LD decay of our population was 1.0 Mb. Due to variation among different populations, LD decay rates were estimated separately for each of the two groups and revealed that the LD decay distance in Group I was higher than that in Group II  in RDIG2015, RDIF2015 and RDIF2016, respectively , was identified in RDIG2015, then A10_98859056  in RDIF2015 and A10_99672586  in RDIF2016, which are located in a genomic region with a distance of 0.81 Mb  with the CMLM, and which accounts for both population structure and familial relatedness. The quantile–quantile plots showed that the model of the three environments could be used to identify association signals  had been previously reported  on A04 were mapped to regions where VW resistance QTL , and the R2 were 5.89%, 6.57% and 6.1%, respectively, explaining the most phenotypic variation among the significantly associated SNPs. The SNP alleles were C/T, A/G and A/C, respectively. For the first peak SNP (A10_99672586), the average RDI of accessions with favourable alleles (T) was 27.81, which was lower than the average RDI (48.68) of accessions with unfavourable alleles (C) investigated in RDIF2016. The RDI values of accessions with favourable alleles (A) at A10_98859056 (17.58) and (C) at A10_99071906 (26.26) were both lower than the average RDI values for accessions with unfavourable alleles (G) at A10_98859056 (34.57) and (A) at A10_99071906 (44.98) (P < 0.01)  on A10 as identified favourable alleles. These SNPs exhibited the minimum 98859056 4.57 and P < 0.01). These results indicate that favourable SNP alleles had significant pyramiding effects on VW resistance  in RDIF2016, and may be a major genetic locus responsible for VW resistance in cotton. Thus, we investigated the haplotype block structure based on A10_99672586, within 300 kb on either side, to determine candidate genes. Block structures showed that the first peak SNP (A10_99672586) was involved in a 372‐kb haplotype block encompassed by 45 SNPs  on A10 exhibited the minimum V. dahliae inoculation  analysis to determine differential gene expression in candidate genes identified by GWAS analysis. The results showed that five genes  were up‐regulated in the resistant (R) genotype Zhongzhimian2 (ZZM2) and susceptible (S) genotype Jimian11 (JM11) at different times after on Table . Two genn Figure . Arabido6 conidia/mL). After 3 weeks, the control plants seldom exhibited leaf wilting; approximately 15% of plants were diseased. Among the gene‐silenced plants, 27% of CG03‐silenced plants, 21% of CG12‐silenced plants, 20% of CG13‐silenced plants and 23% of CG19‐silenced plants exhibited the wilting phenotype when infected with V. dahliae, indicating no significant difference from the proportion of control plants. Furthermore, 67% of CG02‐silenced plants were severely infected by V. dahliae, similar to the proportion observed in susceptible control plants JM11, with 92% diseased plants;  Figure . We inoc Figures . The vasV. dahliae is one of the most destructive diseases in cotton. Understanding the mechanism of host resistance to VW through gene/QTL analysis and breeding resistant cultivars through MAS are thought to be the most practical and effective ways to manage this disease. However, it is difficult to reliably identify VW resistance QTLs for various reasons, including limited biparental allelic separation and the low genome coverage of molecular markers  and NW (Group II–B), which was consistent with the results of phylogenetic analysis Figure c. The acV. dahliae can serve as long‐term or permanent natural nurseries for screening and selecting cotton for VW resistance  markers were selected from 11 QTL mapping reports  was detected in the region of 0.97–3.87 Mb on A04   Figure b. The SNet al., et al., et al., et al., Trait–SNP association analysis showed that the peaks associated with VW resistance on A10 were continuous and common in three environments. We infer that it is a major genetic locus responsible for VW resistance in cotton. Comparative genomic analyses have indicated that plant genomes can encode several hundreds of NB‐LRR genes  domain facilitates the induction of the hypersensitive cell death response  have been cloned from diverse plant species, most of which encode NB‐LRR proteins  and 26 introduced from abroad .DI=∑(dc×nc)nt×4×100, where dc was the disease severity rating, nc the number of plants with the corresponding disease severity rating and nt the total number of plants assessed. The DI was further adjusted to create a relative disease index (RDI) to decrease error by a correction factor K, defined as: K=50.00DIck,where 50.00 was regarded as the standard DI of the susceptible control and DIck was the actual DI of the susceptible control. RDI was defined as: RDI = DI × K, where DI was the actual DI of the testing lines. When the DI of the susceptible control reached approximately 50, implying uniform and severe infection, disease severity ratings of each cotton line were recorded and DI and RDI were calculated, serving as an evaluation of disease resistance for the cotton line at adult‐plant stage  would reach an anticipated severity of infection. The field experiments followed a randomized complete block design with three replicates. Each accession was grown in a plot with 30–40 plants in each of two rows, with a distance of 10 cm between plants within each row and 45 cm between rows. The trial management followed standard breeding field protocols. A scale of 0–4 was used to rate disease severity based on the percentage of diseased leaves, where 0 = no symptoms, 1 = less than 25% symptomatic, 2 = 25%–50% symptomatic, 3 = 50%–75% symptomatic and 4 = more than 75% of leaves showing symptoms. These ratings were then converted to a disease index (DI) to estimate the severity of infection for a certain line. The DI was calculated as follows: V. dahliae strain Vd991 was cultured on liquid complete medium at 25 °C for 10 day, after which the concentration of conidia was adjusted to 5 × 106 conidia/mL. Symptom development was recorded from 22 to 25 day and categorized into five grades: 0 = healthy plant, no symptoms on leaves; 1 = one or two cotyledons showing symptoms but no symptoms on true leaves; 2 = both cotyledons and one true leaf showing symptoms; 3 = both cotyledons and two true leaves showing symptoms; and 4 = all leaves showing symptoms, symptomatic leaves dropped, apical meristem necrotic or plant death  with five seeds per pot and six pots per accession in each replication. The highly virulent et al., et al., HaeIII and Hpy166II  that digest cotton genomic DNA into DNA fragments of 364–464 bp. These were selected as SLAFs and prepared for pair‐end sequencing on an Illumina High‐seq 2500 sequencing platform  at Biomarker Technologies Corporation . The 125‐bp sequence read at both ends of the fragment in each library was generated using an Illumina Genome Analyzer II (Illumina Inc.) with a barcode approach, to identify each sample. The raw reads (125 bp) were filtered and trimmed according to predefined criteria, and then a 100‐bp paired length sequence was retained at each end. The BWA software was used to map raw paired‐end reads onto the reference genome (G. hirsutum v. 1.0)  of alleles using Haploview 4.2 . Those with P‐values lower than 1.17 × 10–5 were defined as significant trait‐associated SNPs; genes that were located within the region 300 kb upstream or downstream of trait‐associated SNPs were identified as candidate associated genes.A total of 85 630 SNPs were used for GWAS with a compressed mixed linear model (CMLM) implemented in the GAPIT software . cDNA was prepared using M‐MLV Reverse Transcriptase and qRT‐PCR analyses were conducted using the SYBR Premix Ex Taq kit (Takara) on a QuantStudio 6 Flex Real‐Time PCR System . The primers used for PCR amplification are listed in Table Agrobacterium tumefaciens GV3101. The primers used for the construction of the VIGS vectors are listed in Table V. dahliae inoculation with 200 mL of conidial suspension (5 × 106 conidia/mL). VW symptoms were investigated 3 weeks postinoculation. Seedling shoots were cut to investigate vascular wilt under a microscope postinoculation  cDNA were integrated into the vector pTRV2 to construct pTRV2:CG02/CG03/CG12/CG13/CG19 and introduced into Figure S1 Overlapping among the SNP loci identified through our genome‐wide association study (GWAS) and quantitative trait loci (QTLs) reported in previous studies.Figure S2 Resistance gene analogs (RGAs) in the vicinity of the significant SNP loci identified on A10.Figure S3 VIGS analysis of five candidate genes for V. dahliae resistance.Figure S4 Plant phenotypes for V. dahliae resistance as determined by VIGS at 21 day post‐inoculation with V. dahliae.Click here for additional data file.Table S1 Information of 299 upland cotton accessions.Table S2 Analysis of variance (ANOVA) results of VW resistance.Table S3 SNP distribution on each chromosome.Table S4 Five SNPs on A10 identified in all three environments.Table S5 Relative expression levels of candidate genes in qRT‐PCR.Table S6 The QTLs of VW resistance from 11 reports of QTL mapping.Table S7 Primers used in this study.Click here for additional data file.


In silico analysis of nucleotide polymorphisms in wild (Solanum habrochaites) and cultivated (Solanum lycopersicum) species of tomato to explore the consequence of substitutions both at sequence and structure level. A total of 8978 SNPs having Ts/Tv (Transition/Transversion) ratio 1.75 were identified from the Expressed Sequence Tag (EST) and Next Generation Sequence (NGS) data of both the species available in public databases. Out of these, 1838 SNPs were non-synonymous and distributed in 988 protein coding genes. Among these, 23 genes containing 96 SNPs were involved in traits markedly different between the two species. Furthermore, there were 28 deleterious SNPs distributed in 27 genes and a few of these genes were involved in plant pathogen interaction and plant hormone pathways. Molecular docking and simulations of several selected proteins showed the effect of SNPs in terms of compactness, conformation and interaction ability. Observed SNPs exhibited various types of motif binding effects due to nucleotide changes. SNPs that provide the evidence of differential motif binding and interaction behaviour could be effectively used for the crop improvement program.Single Nucleotide Polymorphisms (SNPs), an important source of genetic variations, are often used in crop improvement programme. The present study represented comprehensive  Solanum lycopersicum) and pepper, mainly to study the fruit developmentSolanum is composed of approximately 1500 tomato species, originated in Central and North America, and spread all over the world. Despite the availability of numerous wild tomato species, only Solanum habrochaites has been exploited extensively for domestication purposes. This species typically grows on the western slopes of central Ecuador to central Peru, making it extremely tailored to the particular soil type and climatic conditionsSolanum lycopersicum) speciesThe Solanaceae are the highly valuable plant taxon in terms of vegetable crops as well as agricultural utility.Various members of Solanaceae have been used as a model plants such as tomato (Solanum habrochaites species), available at http://www.ncbi.nlm.nih.gov/ and high throughput sequence dataset under Sequence Read Archive (SRA) have provided an oppertunity for the polymorphism detection. Generally, non-synonymous SNPs or substitutions have the ability to alter the amino acid sequence of a protein in contrast with synonymous SNPs which do not alter amino acid sequence. These non-synonymous substitutions could result in a biological change in any individual. Therefore, considerable attention has been given to non-synonymous SNPs  because sequence change leads to various functional impact in terms of protein stability, which hampers the interaction with other proteinsSingle Nucleotide Polymorphisms (SNPs) have gained the center stage in the era of molecular genetics, because of its high abundance, considerable information content and improvement in sequencing technologyDifferential motif binding has a direct correlation with the gene expression, suggesting the functional consequence of binding variation1416Protein coding genes have a tendency to develop either negative or positive evolutionary routes in natureSolanum habrochaites and Solanum lycopersicum) and high-throughput dataset under SRA (SRP041563) which enabled numerous SNPs mining among these two species. In addition, in-depth computational analysis of SNPs was performed at the sequence and structural level. Structural analysis enabled molecular dynamics simulations revealing many additional effects of SNPs in comparison to its native-type protein. The results may have the potential to improve our understanding of the consequence of SNPs in a novel way. Identified SNPs could be used as markers for traits of interest such as fruit ripening, color and texture under breeding program.In this study, we focused on analysis of functional consequences of variations, by the consolidated utilization of ESTs  and non-synonymous (1838) SNPs , and disThe non-synonymous and synonymous substitution rate (Ka/Ks) were assessed to deduce the direction of natural selection over studied species. This analysis revealed the list of 205 and 34 genes representing the purified (Ka/Ks < 1.0) and diversified (Ka/Ks > 1), groups, respectively . The Ka/In order to add another layer of refinement in SNP analysis, SIFT (Sorting Intolerant from Tolerant)Nucleotide change from cultivated to wild species resulted in preferential motif binding on either allele at sequence level, therefore called as allele-specific motif binding event. For better understanding, we created five possible combinations or classes as shown in To directly readout the effect of deleterious SNPs, we superimposed the native  and mutant  protein models after the energy minimization. Six SNPs significantly changed the structure alignment of native and mutant protein models . DetaileTo analyse interaction behaviour using docking simulations, one of the protein HMG1 consist of Tyr416Asp AA change, was selected. This SNP affected the active site of the HMG1 protein which could be clearly implied through docking simulations. HMG1 is known for its interaction with p53 ligand. Both the native HMG1-p53  and Tyr4For the detailed functionality of genes carrying the 28 deleterious SNPs, pathway analysis was performed using KOBAS web server. Deleterious SNPs were predicted to be the part of base excision repair pathways (affected the HMG1-high mobility group protein B gene), plant hormone signal transduction (affected the T1R1 and PR1 b- pathogen responsive 1b) and plant pathogen interaction pathway (affected the PR1-pathogen responsive 1) . Under tRadius of gyration (Rg) represents the measure of compactness of the protein conformation in biomolecular simulations. In one hand, the native and Cys284Arg mutant PR-1 protein showed a clear difference in gyration radius where native protein exhibited affinity towards convergence and maintains the steady flow between 2.4 to 2.2 nm (nanometer). Whereas the mutant protein represented its compactness point at 1100 ps between 2.6 to 2.4 nm . It indiIn plants, the majority of traits of interest are linked with SNPs and are thought to bring the individual variation, community diversity and the evolution of species. The link between single nucleotide change and gene function has been reported for a number of traitsEucalyptus grandisThe stringency of functional or structural constraints is thought to be a noteworthy key component behind the rate of amino acid substitutionsSNPs are known to be associated with many aspects of human development and diseases. In plants, SNPs are reported to be regulating various Quantitative Trait Loci (QTL) responsible for cold and disease resistance such as such as blight, bacterial canker and gray mold3537The pathways of biosynthesis of secondary metabolites and plant hormone signal transduction are known for their role in fruit ripening and defense responses4344http://www.ncbi.nlm.nih.gov/sra/) belonging to proximal and distal leaves of tomato of Solanum lycopersicum and Solanum habrochiates. We used SRA toolkit to get the single end reads (42 samples) in fastq format. In parallel, the EST sequence data of Solanum lycopersicum and Solanum habrochaites comprising a large amount of leaf, fruit, Trichome I and Trichome IV specific cDNA sequences, were downloaded from National Center for Biotechnology Information (NCBI) in fasta format. As ESTs were sequenced only once, sometimes the data has low-quality sequence and repeats generate problems during downstream analysis. To remove the interspersed repeats and low complexity regions, RepeatMaskerhttp://www.girinst.org/server/RepBase/index.php). This reduced the chance of biases in SNP analysis and the overall noise in EST sequences.Transcriptome sequencing data (SRP041563) was downloaded from the SRA (Solanum lycopersicum 2.5 version utilizing the Tophat v2.0Illumina short reads of wild and cultivated species were mapped to the reference genome of a), to the number of synonymous substitutions per synonymous site (Ks), in the given timeframe. In this way, cultivated and wild allele carrying gene sequences were created based on the SNP position and compared in Ka/Ks_calculatorhttp://string-db.org/).To annotate the effect of SNPs (synonymous and non-synonymous), SnpEffwww.sbg.bio.ic.ac.uk/phyre2/html/ under intensive mode. Simultaneously, active sites of protein model carrying deleterious SNP were predicted by using CASTP web server (http://sts.bioe.uic.edu/castp/). Furthermore, native and mutant structures were superimposed in UCSF-Chimera molecule viewer tool after energy minimization using Needleman-Wunch alignment method, applying gap extension penalty 1, gap opening penalty 12 and structure score > 30% with distance range of 2.0 A.To find out the potential functional effect of amino acid substitution on corresponding proteins, SIFThttp://cisbp.ccbr.utoronto.ca/TFTools.php), where sequences of each of 10 bp (in upstream and downstream) from SNPs site were extracted and used as input in CIS-BP web serverTo understand SNP impact at the sequence level, differential binding events were predicted by utilizing CIS-BP web server (http://autodock.scripps.edu). The grid size was set to cover acting domains present in selected protein with the grid spacing of 0.375 Å. Genetic algorithm (GA) was applied as searching parameter with 10 numbers of GA runs and setting population size 150, a maximum number of energy evaluations was set to 25,00,000 with considering the maximum number of generations to 27,000. The lowest binding energy conformation with H-Bonds in cluster was considered as the most favourable docking pose. Protein-ligand complexes obtained from AUTODOCK 4 were further viewed in UCSF-Chimera molecule viewer tool for better analysis of interaction. In each case, 10 different docking arrangements were produced. The conformations obtained as a result of rigid body docking were sorted by total binding energy, hydrogen bonds formed, bond lengths and close contacts between enzyme active sites.One of the deleterious SNP was randomly selected for protein-ligand analysis. Docking analysis was performed to predict the putative modification of binding modes of studied ligand (p53) with selected protein (HMG1) using AutoDock 4.0 (http://kobas.cbi.pku.edu.cn/home.do) was used for pathway analysis. Furthermore, molecular simulations were performed on the selective 2 non-synonymous SNPs to gain insight into the impact of deleterious SNPs in terms of protein functionality. All the molecular simulations were carried out using Gromacs programTo elucidate key role of deleterious SNPs at a biological level, Kobas web server .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Malus×domestica) cultivar Honeycrisp has become important economically and as a breeding parent. An earlier study with SSR markers indicated the original recorded pedigree of ‘Honeycrisp’ was incorrect and ‘Keepsake’ was identified as one putative parent, the other being unknown. The objective of this study was to verify ‘Keepsake’ as a parent and identify and genetically describe the unknown parent and its grandparents. A multi-family based dense and high-quality integrated SNP map was created using the apple 8 K Illumina Infinium SNP array. This map was used alongside a large pedigree-connected data set from the RosBREED project to build extended SNP haplotypes and to identify pedigree relationships. ‘Keepsake’ was verified as one parent of ‘Honeycrisp’ and ‘Duchess of Oldenburg’ and ‘Golden Delicious’ were identified as grandparents through the unknown parent. Following this finding, siblings of ‘Honeycrisp’ were identified using the SNP data. Breeding records from several of these siblings suggested that the previously unreported parent is a University of Minnesota selection, MN1627. This selection is no longer available, but now is genetically described through imputed SNP haplotypes. We also present the mosaic grandparental composition of ‘Honeycrisp’ for each of its 17 chromosome pairs. This new pedigree and genetic information will be useful in future pedigree-based genetic studies to connect ‘Honeycrisp’ with other cultivars used widely in apple breeding programs. The created SNP linkage map will benefit future research using the data from the Illumina apple 8 and 20 K and Affymetrix 480 K SNP arrays.The apple ( Malus×domestica) cultivar in North America that has steadily gained market share since its introduction by the University of Minnesota (UMN) apple breeding program in 1991. It has also been an increasingly important parent due to its reportedly ultracrisp texture, its ability to retain this high level of crispness in storage and its resistance to apple scab. ‘Honeycrisp’ is a parent of multiple commercially released cultivars including ‘Minneiska’, ‘New York 1’, ‘CN B60’, ‘CN 121’, ‘DS 22’, ‘WA 38’, ‘MAIA1’, ‘MN55’ and new Chinese cultivars, with many additional offspring under testing around the world as advanced breeding selections. The importance of ‘Honeycrisp’ in production and breeding has led to many studies of the physiology and genetics of the variety. It was quickly discovered that the originally recorded parentage, ‘Honeygold’×‘Macoun’, did not complement phenotype information for several key traits. As DNA markers became increasingly available for apple, analysis of simple sequence repeat (SSR) markers confirmed that the recorded parentage was incorrect. Breeding records of the UMN apple breeding program indicated that the cross that begat ‘Honeycrisp’ was likely made around 1960, but a lack of records from this period rendered direct validation of parentage inconclusive.‘Honeycrisp’ has emerged as an economically lucrative apple  arrays have become available for apple, making possible the generation of a large amount of standardized genetic data that are useful for relationship analyses. SNP array-based parental testing has been successfully used to identify ‘Common Antonovka’ as the parent of a series of selections that had been widely used in the breeding for scab resistance. In sweet cherry (Prunus avium), SNP array data have been successfully used for the identification of the previously unknown paternal parent of the important U.S. cultivar Bing. The SNP arrays and wealth of genetic data generated through the RosBREED project, funded in part by the USDA-Specialty Crop Research Initiative, has provided an excellent opportunity for this type of relationship testing.Past studies evaluating pedigree relationships in apple have been conducted with a range of genetic marker types including SNPs in chloroplast DNA, and to identify and genetically describe the unknown parent and grandparents of ‘Honeycrisp’ using a much larger set of DNA markers than was previously available with this germplasm. This work relied on the adequate phasing of thousands of SNP markers, which was made possible through the development and use of a high-quality integrated SNP linkage map, also described in this study, that was developed by closely following many methods used by Di Pierro et al.The objective of this study was to test an earlier hypothesis that ‘Keepsake’ is one parent of ‘Honeycrisp’et al. and planted as clonal replicates on ‘Budagovsky 9’ rootstocks at the UMN’s Horticultural Research Center in Chanhassen, Minnesota, USA in 2010. These families were ‘Honeycrisp’×MN1764 (n=156), ‘Honeycrisp’×‘Monark’ (n=91), ‘Honeycrisp’×‘Pitmaston Pineapple’ (n=60), ‘Honeycrisp’×‘Jonafree’ (n=57) and ‘Honeycrisp’×MN1702 (n=49). Leaf samples were collected from these individuals for extraction of DNA that was hybridized onto the International RosBREED SNP Consortium 8 K Illumina Infinium array v1 following the methods outlined in Clark et al. The first two of these five populations were previously used in the creation of an earlier ‘Honeycrisp’ consensus genetic map. The raw iScan data output from the apple 8 K SNP array from all individuals was imported into the genotyping module of GenomeStudio software version 1.9.4 for SNP scoring . SNP scores were coded as A and B in GenomeStudio. SNP probe sequences and targeted polymorphic nucleotides for the A/B scoring for each SNP in the 8 K SNP array can be found in the et al. and at the Genome Database for Rosaceae, as well as in our et al. with the exception that all SNPs were visually inspected and manually clustered when necessary regardless of GenTrain scores and heterozygote frequencies. SNP scores from each individual in each population were compared to both its parents’ SNP scores to ensure that the listed parentage was correct. Seedling individuals that had >200 inheritance errors out of the total 8788 SNPs (2.3% error rate) on the apple 8 K SNP array were removed from analysis. These types of inheritance errors, referred to as Mendelian-inconsistent errors , were recorded as instances where an individual had a SNP score that could not have arisen given the SNP scores of its parents or offspring. For example, if an individual’s SNP score at an individual locus was AA and one or both of its parents’ SNP scores were BB this would count as an inheritance error as either the individual and/or its parent(s) must have incorrect SNP scores. Another type of observed Mendelian-inconsistent error was where an individual’s SNP score was AB and its parents’ SNP scores were both either AA or BB.Five families with ‘Honeycrisp’ as a common parent were used in the creation of the integrated genetic map used in this study. These families were described by McKay SNPs with >15% missing data and SNPs that had apparent null alleles in one or more parents were excluded from analysis. Marker segregation classes were then generated with the remaining SNPs by evaluating segregation ratios within each family. Genotypic score classes that comprised at least 2% of the individuals within a family were taken into account when defining the segregation classifications in order to account for possible segregation distortion. Individual SNP scores that did not follow the overall Mendelian segregation classifications for <2% of the individuals within the family were recoded as missing data. After applying these rules, the marker segregation classes of seedlings from each family were compared to parental SNP scores. Parental SNP scores that still did not match the segregation classes of seedlings were altered to fit the segregation classes if a change in ‘Honeycrisp’ resulted in agreement in segregation classification across all five populations consistent with the SNP score of the other parent in each family or if a change in the other parent could resolve the inconsistency between it and the segregation of its progeny. These steps were conducted in this order because the original GenomeStudio project was not available for re-examination of the SNP clustering at the time of map construction. SNPs were organized into linkage groups using a combination of independence tests using logarithm of odds scores from JoinMap output for individual families, consensus for these groupings between families and physical data for SNPs from the version 1.0 of the ‘Golden Delicious’ genome. SNPs that were observed to be in concordance with grouping with other SNPs based on logarithm of odds scores but were located on different chromosomes according to their physical position in the apple genome were kept in the groupings based on logarithm of odds scores.The remaining SNPs were used to generate individual parental maps for each family in JoinMap 4.1. Linkage maps were oriented based on general SNP physical positions. SNPs were removed from these initial maps if they created gaps >50 centimorgans (cM). These initial single parent genetic maps were evaluated using graphical genotyping. This process is outlined in the et al. Data were scanned for singletons, defined as double recombinant single points, that is, individual SNPs that displayed apparent recombination on both sides despite close linkage to adjacent markers. These singletons were recoded as missing values to achieve single-family SNP orders that minimized instances of probably spurious double recombination. Several SNPs that had many apparent singletons and poor-quality SNP score clustering were removed from analysis. The resolution of each individual parent’s map order was iteratively improved by increasing the levels of stringency until single parental maps were produced with fixed SNP orders containing no instances of double recombinations due to singletons or likely incorrect SNP order as evidenced through graphical genotyping.Initial single parent genetic maps were created in JoinMap 4.1 through the software’s Monte Carlo maximum likelihood mapping algorithm approach. in the statistical software R version 3.2.4. This initial marker order was refined iteratively, starting first with markers that were heterozygous in ‘Honeycrisp’. Following this, SNPs that were heterozygous in gradually fewer of the other parents were built into the order. Graphical genotyping was used at each step to order the SNPs in a way that approximated the consensus map created using LPmerge, but without instances of false double recombinations caused by incorrect map order. This ordering process was also aided by a draft of the linkage map created by Di Pierro et al. SNPs within areas of no recombination were ordered based on physical position information where possible from version 1.0 of the ‘Golden Delicious’ genome or SNP sequences blasted against version 3.0.a1 available on the Genome Database for Rosaceae .An integrated SNP map for all SNPs from all families was built from the resulting 10 individual parental maps in four stages. In the first stage, a fixed marker order was created for all of the markers across all of the mapping families. Hereto initial marker orders were determined through the construction of a consensus map created using the LPmerge packagehttp://www.wageningenur.nl/en/show/HaploblockAggregator.htm) was then used in the third stage to aggregate the segregation information of individual SNPs into these haploblocks and to convert this haploblock-aggregated data from a ‘cross pollinated’ JoinMap formatted data set into a ‘back cross’ JoinMap formatted data set for simplified integration of all separate individual family maps, as described by Di Pierro et al. In the final stage, the resulting data were incorporated back into JoinMap and Monte Carlo maximum likelihood mapping was again used along with fixed SNP orders for the haploblocks to calculate mapping distances in the integrated genetic map. Each individual marker within a haploblock was given that haploblock’s cM position.For the second stage of the integrated map construction process, SNPs were organized into haplotype blocks (haploblocks). These haploblocks were designed to include sets of adjacent SNPs with no recombination among all mapping individuals. The physical positions of SNPs were used to help define the boundaries of haploblocks to ensure the resulting genetic position of any SNP within a region of no recombination was associated with the haploblock it was more closely linked to physically. Haploblock Aggregator . SNP order was altered if the reordering could resolve double recombination events observed in the pedigree-connected data set and not introduce double recombination events within the mapping set. Following this process, Haploblock Aggregator and JoinMap were again used to create a revised integrated map. Following publication of the Di Pierro et al. linkage map, 20 SNPs that were polymorphic in only one of the five families in this study that had insufficient physical position information available and were within large blocks of SNPs that were inherited together were reassigned to haploblocks to make this linkage map more consistent with the Di Pierro et al. linkage map. These changes were only made if they did not introduce additional double recombinations within the mapping populations and the pedigree-connected data set.Following the construction of this integrated genetic map, a highly curated SNP-genotyped and pedigree-connected apple data set comprising 540 individuals from the RosBREED projectThe pedigree-connected genetic data set used in the map construction described above was also used to elucidate the pedigree of ‘Honeycrisp’ by offering a wide germplasm set that might hold the unknown parent of ‘Honeycrisp’ or earlier ancestors of this unknown parent, and by allowing the phasing of each individual’s SNPs through the comparison to directly related individuals. In the phasing process, the parameter MSegDelta was set 1, which instructed FlexQTL to not adjust parental calls in case of highly distorted segregation patterns. that were probably due to incorrect SNP scores. This latter type of marker score error was observed when short  double recombinations, usually involving only single SNPs, were observed in phased SNP output from FlexQTL. An example of this type of error would be if an individual’s offspring all have AB scores for a single SNP and one parent’s SNP score is AA and the other is AB but closely linked SNPs do not show any evidence of segregation distortion. This type of error would not show as a typical inheritance error as the offspring could have inherited the B allele from the AB parent, but for each progeny to have a B-allele, half of the progenies would have a recombination on either side of this SNP allele (as long as closely linked SNPs did not have any type of segregation distortion in this region). In cases like this, the changing of the parental AB score into BB would resolve all issues. SNPs that had many inheritance errors or many cases of likely false double recombination were re-evaluated in GenomeStudio, which had become available at this stage, and SNP scores were revised or the SNP was removed from the data set. SNP scores that were suspected of being incorrect due to null alleles were recoded as missing data. Phased SNP data of this data set from FlexQTL output was used to validate the parenthood of ‘Keepsake’ and to further elucidate the ‘Honeycrisp’ pedigree.SNP scores for individuals were changed in this data set if the change resolved the types of Mendelian-inconsistent SNP inheritance errors described above in map construction or if the change resolved cases of likely false double recombination, also known as Mendelian-consistent errors,A simple, manual unphased genotype-matching analysis compared SNP data of ‘Honeycrisp’, with its parent ‘Keepsake’ and likely candidate grandparents of ‘Honeycrisp’ using data from the initial noncurated pedigree-connected genetic data set. Likely candidate grandparents included individuals that were known to be used as breeding parents prior to the 1970s in the UMN apple breeding program and that were still available. In this analysis, unphased SNP data for a subset of SNPs deemed to have good clustering quality in GenomeStudio and organized by physical position were compared between ‘Honeycrisp’, its validated parent ‘Keepsake’ and the candidate grandparents to see whether SNP scores in ‘Honeycrisp’ could have arisen from ‘Keepsake’ and each candidate grandparent pair through manually conducting possible phasing of markers. Two likely grandparent candidates were identified. They were confirmed to be grandparents in further analyses using SNP data from the curated pedigree-connected genetic data set. Phased SNP data from these candidate grandparents and the parents of ‘Keepsake’, ‘Frostbite’ and ‘Northern Spy’ were evaluated to determine whether the SNP data from all of them could constitute the entirety of the phased SNP data from ‘Honeycrisp’ across a 3435 SNP subset of the integrated map.a priori as parents if Mendelian-inconsistent and Mendelian-consistent errors composed >1% (35 SNPs) of their curated SNP data.Once these candidate grandparents were confirmed, the pedigree-connected genetic data set was searched for UMN selections that had these newly identified ‘Honeycrisp’ grandparents as recorded parents either in breeding program records or by analysis of the SNP data. These UMN selections were tested as being the unknown parent of ‘Honeycrisp’. Individuals were excluded SNP data were evaluated from several UMN selections to determine if they share the unknown parent of ‘Honeycrisp’. This process was conducted in several steps. First, existing pedigree records for all UMN selections genotyped on the 8 K SNP array were evaluated for identity by descent consistency across the pedigree-connected data set by evaluating inheritance errors between them and their recorded parent(s). If at least one parent of a UMN selection was not identified, the SNP data for that selection were queried against the SNP data for all founding UMN selections and cultivars that could be possible parents. A parent was assigned to a selection if the parent-offspring relationship was historically concordant and exhibited <1% of SNPs with Mendelian-inconsistent errors and if those Mendelian-inconsistent errors could be resolved through steps outlined in the description of the RosBREED data set curation. UMN selections having one parent confirmed using SNP data and one remaining unknown parent were evaluated to determine whether the newly identified grandparents of ‘Honeycrisp’ were also probable grandparents of these selections. A selection was deemed to also share the newly identified ‘Honeycrisp’ grandparents if phased SNP data from the selection indicated that the unknown parent was composed entirely of haplotypes from phased SNP data of the newly identified ‘Honeycrisp’ grandparents with <1% of marker calls being Mendelian-inconsistent errors.The UMN selections that matched the criteria for sharing the newly identified grandparents of ‘Honeycrisp’ through an unknown parent were then evaluated to determine whether ‘Honeycrisp’ and these selections shared this common unknown parent. The unknown parent of ‘Honeycrisp’ was determined to be shared with a UMN selection if the UMN selection and ‘Honeycrisp’ had between them, at maximum, only one of the two haplotypes from each of the newly identified grandparents of ‘Honeycrisp’ at any given locus, with a SNP mismatch rate of <1%. This determination was made through the evaluation of the unknown parent portion of the phased SNP data for ‘Honeycrisp’ and the UMN selection under consideration and the phased SNP data for the newly identified ‘Honeycrisp’ grandparents.The haplotype composition and SNP scores of the unknown parent of ‘Honeycrisp’ were then imputed using phased SNP scores from the newly identified ‘Honeycrisp’ grandparents, ‘Honeycrisp’ and the UMN selections identified to also be offspring of the unknown parent of ‘Honeycrisp’. SNP scores for the unknown parent were coded as missing data when there was insufficient haplotype information from the offspring of the unknown parent, when there was missing data in the parents of the unknown parent, and when >50% of the offspring of the unknown parent had markers scores that were discordant with the marker scores of the parents of the unknown parent.Finally, the original UMN selection records were evaluated to determine the likely identity of this unknown parent. Pedigree records of the UMN selections determined to share the unknown parent of ‘Honeycrisp’ were first evaluated and a likely parent was identified. Additional offspring of this likely parent were then identified and one was found to have been genotyped but not included in previous steps because both of its parents were not present in the pedigree-connected data set. The SNP calls for this individual and the imputed SNP calls for the unknown parent were evaluated for a parent-offspring relationship to validate the identity of the individual and its imputed SNP calls. and spans a total length of 1 172 cM across 17 linkage groups , ‘Golden Delicious’ , ‘Keepsake’ , ‘Northern Spy’  and ‘Frostbite’   were observed to have one identified parent for which SNP data was available and one unknown parent that was consistent with itself being the offspring of a cross between ‘Duchess of Oldenburg’ and ‘Golden Delicious’. The phased SNP data strongly suggested that these four selections and ‘Honeycrisp’ indeed share a common parent . Mendeliet al. suggested MN1708 was derived from ‘Keepsake’×unknown parent, which was confirmed in this study. MN1837 and MN1888 were recorded as offspring from MN1691 (‘Goodland’×‘Fireside’)×MN1627. MN1691 was confirmed to be a parent of MN1837 and MN1888. MN1627 was recorded as an offspring from ‘Duchess of Oldenburg’ (as the sport ‘Daniel’s Red Duchess’)×‘Golden Delicious’ and was selected in 1951. Unfortunately, trees of this selection no longer exist in the UMN apple breeding program. The last recorded distribution of MN1627 was in 1965. We identified current contacts for recipients, where possible, and queried them about the presence of MN1627. All responding recipients indicated it was no longer present in their orchards or collections.MN1789 was recorded as an offspring from MN1736×‘Beacon’, but was determined to be an offspring from MN1691×(‘Duchess of Oldenburg’×‘Golden Delicious’). MN1708, like ‘Honeycrisp’, was originally recorded as ‘Honeygold’×‘Macoun’ but SSR markers examined by Cabe The confirmed breeding records for MN1837 and MN1888, coupled with the finding that these individuals are half-sibs of ‘Honeycrisp’  suggestsThe availability of SNP data for five siblings and both identified parents of MN1627 allowed for the imputation of 97.8% of its SNP scores across the 3 435 SNPs under consideration , ‘Jonagold’ (‘Golden Delicious’×‘Jonathan’) and ‘Cripps Pink’ (‘Golden Delicious’×‘Lady Williams’) and is famous in the scientific community for being the first sequenced apple genome.The pedigree of ‘Honeycrisp’  was dedu released in 1970. Many of these selections would have been available for breeding at the time of the cross that begat ‘Honeycrisp’.The previously unknown parent of ‘Honeycrisp’, MN1627, was indirectly identified through the analysis of phased SNP data of five UMN selections identified to be siblings of ‘Honeycrisp’, three of which had MN1627 listed as parents in the original selection records. MN1627 was recorded as being a cross between ‘Duchess of Oldenburg’ and ‘Golden Delicious’. Historical breeding records indicated that crosses between ‘Duchess of Oldenburg’ and ‘Golden Delicious’ were made numerous times in the UMN apple breeding program from the 1920s through the 1930s and had resulted in several selections and the cultivar Red Baron, and the unknown parent of ‘Golden Delicious’. ‘Northern Spy’ originated in New York in the early 1800s, ‘Grimes Golden’ originated in West Virginia in the 1790s, and ‘Golden Delicious’ originated in West Virginia in the 1890s, making it chronologically and geographically possible that ‘Northern Spy’, ‘Grimes Golden’ and the unknown parent of ‘Golden Delicious’ share one or more recent common ancestors. The relatively large extended regions of shared SNP haplotypes between ‘Frostbite’ and ‘Duchess of Oldenburg’ suggest that ‘Frostbite’ is a grandchild of ‘Duchess of Oldenburg’. ‘Frostbite’ was released in 2008 but was originally selected in 1922. It is thought that the tree originated from open-pollinated seeds of ‘Malinda’ that were described by Dorsey, however ‘Malinda’ was determined to not be the parent of ‘Frostbite’. The time of the origin of ‘Frostbite’ coincides with the presence and use of ‘Duchess of Oldenburg’ in the UMN breeding program. That timing, in combination with the proportion of haplotype sharing, indicates it is a likely possibility that ‘Frostbite’ is a grandchild of ‘Duchess of Oldenburg’.This study also revealed that many large regions of homozygosity in the ‘Honeycrisp’ genome  to create an integrated genetic map that reduced cases of false marker order. The high quality of our current map and the 20 K iGL map is underlined by the low number of SNPs that are in discordant order , the small size of the genetic segments in which these discordant orders occurred , and the similar small size of both genetic maps. The map created in this study is 76 cM smaller than that of Di Pierro et al., which is likely a function of a lower representation of the chromosomal ends and the smaller numbers of families used here. The similarity of these maps in size and map order will be useful for the transferability of genetic data across studies using apple SNP array data.The quality of the map was achieved because of intense data curation and the approach in map construction, which was similar to that proposed by Di Pierro This study provides a revised pedigree for ‘Honeycrisp’ that is consistent across a pedigree-connected data set using a new dense and high-quality integrated SNP map. This pedigree and the identification of relatedness between two pairs of ‘Honeycrisp’s grandparents will be useful in future genetic studies involving ‘Honeycrisp’. The haplotype and SNP data for the newly identified parent of ‘Honeycrisp’, MN1627, has been imputed and made available in this study . Though


Helicobacter pullorum (H.pullorum) commonly colonizes the gastrointestinal tract of poultry causing gastroenteritis. The bacterium may be transmitted to humans through contaminated meat where it has been associated with colitis and hepatitis. Despite the high prevalence of H. pullorum observed in poultry, little is known about the mechanisms by which this bacterium establishes infection in host and its virulence determinants. In this article we aim to provide an overview of this emerging zoonotic pathogen; its general characteristics, hosts, prevalence, and transmission as well as its pathogenic potential. We also discuss possible control strategies and risk of disease emergence. Helicobacter pullorum (H. pullorum) was first discovered by Stanley in 1994. He reported Campylobacter-like organisms in the liver, duodenum and caecum of chickens, as well as humans suffering from gastroenteritis. Due to its unique DNA homology and total protein electrophoretic patterns, it was classified as a novel species belonging to the Helicobacter genus  which predominantly colonize the intestine and the hepatobiliary system of the host  isolated from a patient suffering from gastroenteritis and four poultry isolates  are available at the NCBI database. The database also includes plasmid sequences from 2 strains. The genomic DNA has 33% GC content with a 1,919 kb circular chromosome coding for 2,044 genes of which 2008 are protein coding  using electrophoretic, serological, and chemical methods reveals O-polysaccharide chain bearing lipopolysaccharides. 3-hydroxytetradecanoic acid and 3-hydroxyhexadecanoic acid are important components of H. pullorum LPS with low variability between chicken and human isolates. The bacterium exhibits high hydrophilicity, therefore water based extraction instead of acid glycine is considered to be more effective. H. pullorum LPS has the highest relative Limulus amoebocyte lysate activity of all Helicobacter species lipopolysaccharides, indicating high endotoxin activity , neither of which is located within a larger locus like C. jejuni. PglB1 protein of H. pullorum displays oligosaccharyltransferase activity in complementation experiments. On the other hand pglB2 lacks oligosaccharyltransferase activity in vitro. Moreover, insertional knockout mutagenesis of pglB2 gene proved lethal for the bacterium suggesting that it is essential for its survival  was shown to be involved in the control of nitrogen metabolism by regulating the expression of glutamate dehydrogenase. H. pullorum TCS is composed of an AmtB ammonium transporter and a PII protein consisting of the HPMG439 and its cognate histidine kinase (HK) HPMG440 , Shigella species, 11 (7.0%), and Salmonella species, 4 (2.5%) indicating that diarrheagenic pathogens other than H. pullorum are the main etiologic agents of diarrhea in children  has been speculated  aid in degradation of extracellular matrix, allowing bacteria to interact with host cells , a type VI secretion protein and a protease were identified as important virulence and colonization factors in H. pullorum  was first reported by Jhonson and Lior in Hcp and VgrG proteins, respectively  is also resistant to ciprofloxacin. This is consistent with the finding that individual missense mutations were detected in gyrA which is responsible for conferring ciprofloxacin resistance  shows susceptibility to aminoglycosides and third-generation cephalosporins, β-lactams, and doxycycline (Tee et al., On the other hand H. pullorum in various geographical zones as well as its wide range of hosts may pose a potential health risk. This is further confounded by the H. pullorum outbreak reported in mice raised in a barrier facility (Boutin et al., Zoonotic pathogens are twice as likely to be associated with emerging zoonosis including approximately 12% of human pathogens (Taylor et al., H. pullorum in humans is acquired by eating uncooked poultry, as is the case with C. jejuni acquired zoonotic infection. However, human transmission from poultry seems likely, considering the high prevalence rates reported from various regions. Routine surveillance of the pathogen in poultry as well as clinical samples is necessary. Future studies determining common sequence types in isolates of human and poultry origin as well as description of specific source markers will enable source tracking and infection risk in the population.Recent epidemiological data shows Salmonellosis and Campylobacteriosis to be the most frequent food-borne bacterial zoonoses in Europe team Ee, . It is uFG, KJ, and SJ contributed to the literature review and writing of the manuscript. SJ edited the manuscript and approved final draft. HB contributed to the final edit and critical review of 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.


SNP) analysis identified a large number of genetic variants with small effects, yet not easily replicable in different populations. New insights may come from the combined analysis of different SNPs, especially when grouped by metabolic pathway. We applied this approach to study the joint effect on longevity of SNPs belonging to three candidate pathways, the insulin/insulin‐like growth factor signalling (IIS), DNA repair and pro/antioxidant. We analysed data from 1,058 tagging SNPs in 140 genes, collected in 1825 subjects  for evaluating synergic interactions by SNPsyn. Synergies were further tested by the multidimensional reduction (MDR) approach, both intra‐ and interpathways. The best combinations (FDR<0.0001) resulted those encompassing IGF1R‐rs12437963 and PTPN1‐rs6067484, TP53‐rs2078486 and ERCC2‐rs50871, TXNRD1‐rs17202060 and TP53‐rs2078486, the latter two supporting a central role of TP53 in mediating the concerted activation of the DNA repair and pro‐antioxidant pathways in human longevity. Results were consistently replicated with both approaches, as well as a significant effect on longevity was found for the GHSR gene, which also interacts with partners belonging to both IIS and DNA repair pathways . The combination GHSR‐MREA11, positively associated with longevity by MDR, was further found influencing longitudinal survival in nonagenarian females (p = .026). Results here presented highlight the validity of SNP‐SNP interactions analyses for investigating the genetics of human longevity, confirming previously identified markers but also pointing to novel genes as central nodes of additional networks involved in human longevity.In human longevity studies, single nucleotide polymorphism ( Nevertheless, such analysis is unable to explain the biological complexity of several diseases and complex phenotypes such as human aging and longevity.Recent advances in DNA technology and association studies have uncovered a very large number of common susceptibility variants for complex traits; however, the translation into practice of their role is complicated by the evidence that such variants operate together to influence the final phenotype. The major challenge is represented by the relatively small genetic contribution to the variation in phenotype, which in human lifespan was estimated to be approximately 25% , we found a set of 73 best‐interacting SNP pairs (Table r2 < 0.8), as indicated by the positive values of synergy (Syn) among variants . In any case, the single contributions (I1 and I2) of SNP1 and SNP2 to the synergic interaction can give information about the more influencing SNP of the pairs and thus help in prioritizing the SNP in future analysis, so that no one SNP was a priori removed ‐PON2 (rs2299267) and TP53 (rs2078486)‐RECQL (rs2284392) ; although not holding an FDR<0.005, they suggest a central role of TP53 in mediating the concerted activation of DNA repair and pro‐antioxidant pathways, as for example in stressful conditions.Less significant interactions were found between 2.2SNPsyn gave us a whole picture of the significantly interacting SNPs in the whole data set available for our study; however, that approach does not include covariates in the analysis, such as sex or age, simply dividing the data in cases and controls. Thus, to improve the case–control analysis of SNP‐SNP interactions, a MDR approach was applied. Table 2.2.1GHSR significantly interact with rs4837525 in the PAPPA gene inside the IIS pathway. rs572169‐GHSR also showed an interesting interaction with rs606748‐PTPN1. As indicated by the interaction graph (a), interaction dendrogram (b) and histograms (c) in Figure ERCC2 gene) and rs2078486 (TP53), and another between rs170548 (ATM) and rs2436514 (LONP1). Although the two indicated SNP‐SNP interactions reported low CV (Table p < .002). Interestingly, the ERCC2‐TP53 interaction was highly significant also in SNPsyn analyses (FDR <.001 and p < .001), thus indicating a consistent association across methods of this SNP‐SNP combination with longevity. The largest univariate effect on longevity for the DNA repair pathway was recognized to rs13251813 (WRN gene), with 1.44% of explained entropy in the network ‐ rs162557 (CYP1B1 gene)‐rs3756704‐GLRX (p < .001)  belonging to the IIS pathway, which still has the largest univariate effect on the longevity phenotype (1.53% explained entropy), and rs533984 (MRE11A gene), from the DNA repair pathway. rs533984 positively interacts with rs225119 (PARK7), which belongs to the pro‐antioxidant pathway. Both the two‐locus interaction GHSR‐MRE11A and the three‐locus combination with PARK7 were found to be significant  and rs533984 (MREA11) was found significantly associated with extreme survival in females . In males, no effect on survival was observed for any of the candidate risk combinations of different SNPs.Based on the information about postsurvey mortality available for the oldest cohort, the Danish 1905, we tested two‐ and three‐locus interactions significantly associated with longevity status in the previous analyses. With this analysis, we aimed to test whether those combinations, associated with an advantage in older individuals compared to younger subjects, were also favourable for survival at very old ages. Thus, we classified the subjects from the 1905 Danish cohort as carriers or noncarriers of a candidate risk combination for longevity and tested their survival as number of months survived from the recruitment. We performed survival analysis separately in male and female cohorts, considering the great importance of sex in survival differences inside a human population . Furthermore, while the interaction analysis confirmed the association of rs572169 of GHSR reported in a previous study using single‐SNP‐analysis  has a positive effect on female survival (p = .026), with carriers of the combination rs572169‐GG /rs533984‐G showing a less mortality. Interestingly, MRE11A, according to SNPsyn analysis, is part of a network with IGFIR, PTPN1 and MSH3. This multiple level interaction highlights the interaction between the IIS pathway (IGF1R and PTPN1) and DNA repair (MRE11A and MSH3). The link between the IGF1R‐PTPN1 proteins is known: literature data indicate a crucial role of PTPN1 as a tumour suppressor, able to negatively regulate multiple pathways of cell growth directly through IGF1R and IR, or indirectly through leptin receptor GHR , but found novel interaction partners of such gene . Furthermore, this methodology identifies novel genes as central nodes of additional networks  involved in human longevity. Obviously, these interesting findings will need further validation, possibly by the confirmation of top SNP‐SNP interactions found in this work independently replicated in additional study populations. Moreover, functional analyses (expression studies and protein interaction testing) should be implemented, to determine the biological relevance of the statistical interactions.In conclusion, the results here presented illustrate the validity of interaction approach and network analyses for investigating the genetic contribution in human longevity. The analyses not only confirm a gene previously identified by single‐SNP‐analysis in the same data set  was reported in Table The genotype data set and genotyping methodology by the Illumina GoldenGate platform has been described in previous papers . As reported in Soerensen et al., r2 = .8, LOD = 3, minor allele frequency (MAF)>5% and a minimum distance between SNPs = 60 bp criteria. The selection of common variants (MAF ≥ 5) and a LD r2 < .8 is widely used in genomewide association studies . Because FDR is a measure of the per cent of false‐positive results, and it is inversely proportional to the number of SNPs tested, we chose a stringent level to minimize the false‐positive combinations in our analysis.SNP‐SNP interactions significantly differing in frequency between cases and controls were computed by SNPsyn, according to the method described in Curk et al., http://snpsyn.biolab.si/.Interaction analyses were computed online, at the website 4.4Gene set enrichment analysis was performed by SNPsyn on the whole data set to find biological processes in which represented genes were involved in human longevity (based on the known information about their gene products (data driven by Gene Ontology)). Which gene sets to test were determined by SNPsyn based on significant association with the phenotype (as previously explained). Subsequently, the gene sets were used as a cluster (query) set in this gene enrichment analysis, with the goal to determine whether members of a gene set tend to occur towards the top (or bottom) of the list. In such case, the gene set was correlated with the phenotypic class distinction. Fold enrichment score reflects the degree to which a set is overrepresented at the extremes (top or bottom) of the entire ranked list.4.5For testing the epistatic interaction between pairs of SNPs, multifactor dimensionality reduction (MDR) was applied Moore, . This mehttp://www.multifactordimensionalityreduction.org/).MDR analyses were implemented in the open‐source MDR software package version 3.0.2 (available on 4.6Kaplan–Meier survival curves with the Mantel–Cox log‐rank test were used to compare survival of oldest‐old subjects with different SNP‐SNP combinations (Therneau & Grambsch, Serena Dato drafted the manuscript and generated the conception of the study, and participated in the data acquisition and interpretation of data. Mette Soerensen generated the study design and participated in the data acquisition, data management and interpretation of data. Francesco De Rango carried out the statistical analyses and participated in the interpretation of data. Giuseppina Rose participated in the interpretation of data and revision of the manuscript. Lene Christiansen participated in generating the study design, acquisition of data and interpretation of data. Giuseppe Passarino participated in the interpretation of data and revision of the manuscript. Kaare Christensen participated in the acquisition of data and interpretation of data. All authors have revised the manuscript and given their final approval. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.