Plasmodium falciparum clinical isolates to identify genetic determinants of malaria pathogenesis. However, genome-wide discovery methods, such as haplotype scans for signatures of natural selection, are hindered by missing genotypes in sequence data. Poor correlation between single nucleotide polymorphisms (SNPs) in the P. falciparum genome complicates efforts to apply established missing-genotype imputation methods that leverage off patterns of linkage disequilibrium (LD). The accuracy of state-of-the-art, LD-based imputation methods  was assessed by measuring allelic 2r for 459 P. falciparum samples from malaria patients in 4 countries: Thailand, Cambodia, Gambia, and Malawi. In restricting our analysis to 86k high-quality SNPs across the populations, we found that the complete-case analysis was restricted to 21k SNPs (24.5%), despite no single SNP having more than 10% missing genotypes. The accuracy of Beagle in filling in missing genotypes was consistently high across all populations , but the performance of IMPUTE was mixed  depending on reference haplotypes and population. Positive selection analysis using Beagle-imputed haplotypes identified loci involved in resistance to chloroquine (crt) in Thailand, Cambodia, and Gambia, sulfadoxine-pyrimethamine  in Cambodia, and artemisinin (kelch13) in Cambodia. Tajima’s D-based analysis identified genes under balancing selection that encode well-characterized vaccine candidates: apical merozoite antigen 1 (ama1) and merozoite surface protein 1 (msp1). In contrast, the complete-case analysis failed to identify any well-validated drug resistance or candidate vaccine loci, except kelch13. In a setting of low LD and modest levels of missing genotypes, using Beagle to impute P. falciparum genotypes is a viable strategy for conducting accurate large-scale population genetics and association analyses, and supporting global surveillance for drug resistance markers and candidate vaccine antigens.Whole-genome sequencing technologies are being increasingly applied to  Plasmodium falciparum, including identifying determinants of emerging drug resistance, is crucial to informing public health strategies to contain and eliminate this malaria parasite. The lack of a robust framework to handle missing P. falciparum genotypes arising from next-generation sequencing efforts, impedes genome-wide methods that depend on complete genotype information, and often leads to analysis that discards entire regions of the genome. This study is the first to evaluate the performance of missing data imputation or “filling in” in the P. falciparum genome, where the correlation between genetic markers is generally lower than in the human genome. We considered 86k markers in 459 clinical isolates from 4 malaria-endemic populations of Africa and Southeast Asia. Although low genotype missingness per SNP (<10%) results in complete datasets for only 25% of SNPs, imputation is accurate. This finding is corroborated by the ability of imputed haplotype analysis to recover several well-established vaccine candidates and drug resistance loci, including kelch13—a recently-validated gene involved in artemisinin resistance. Our work demonstrates that imputation can assist the application of genome-wide methods to identify the determinants of P. falciparum diversity, including those involved in drug resistance, immune evasion, and host virulence.Characterizing genetic diversity and function in  Plasmodium falciparum a major impediment to disease containment and eradication. A deeper understanding of the biology of this parasite and the epidemiology of the disease it causes is needed to inform public health responses, including the timely development of new therapeutics and vaccines [P. falciparum virulence has become a crucial aspect of malaria surveillance and control strategies [P. falciparum has been identified through whole-genome sequencing studies, enabling population genetics analysis to characterize global parasite diversity [P. falciparum clinical isolates [Malaria is a major global health burden, with drug resistance in vaccines ,2. Interrategies . A largeiversity . This adiversity ,5–7. Theisolates  may provP. falciparum genes are known to confer resistance to antimalarial drugs such as chloroquine , sulfadrt [dhps , dhfr [1ps [dhfr ), and arjima’s D  and is e D [ama1 .P. falciparum has a complex life cycle comprising a haploid phase in humans and a diploid phase in Anopheles mosquitoes, which involves gamete fertilization and sexual recombination [P. falciparum genome  were initially surveyed using Sanger sequencing on a limited set of laboratory-adapted clones [bination . Geneticd clones . Althougd clones , high ATP. falciparum lead to decay in inter-SNP correlation within 500 bp [P. falciparum samples and demonstrate its utility in population genetics analysis. Two state-of-the-art methods, IMPUTE [r2 statistic [Imputation methods that reconstruct haplotypes using linkage disequilibrium (LD) patterns have become routine ways of handling missing genotypes in human genome-wide studies –19. Howen 500 bp ,16. Vali, IMPUTE  and Beag, IMPUTE , were astatistic . Brieflytatistic , with eaWe took advantage of the structure of IMPUTE software to test LD-based recombination rates derived from 2 high-quality parasite genetic crosses  and pairwise population Fst [P. falciparum populations, with imputed genotypes reliably capturing known and potentially novel loci encoding drug resistance phenotypes and candidate vaccine antigens.By completing multiple SNP datasets using these methods, we performed genome-wide scans for signatures of natural selection using EHH-type  and Tajijima’s D  metrics,tion Fst . All resP. falciparum clinical isolates [P. falciparum 3D7 reference genome (version 3) [th percentile, 17) and was dominated by markers that are monomorphic in at least 1 population ; Cambodia, 34,049 (40%); Gambia, 53,539 (62%); Malawi, 50,287 (59%)). The Southeast Asian populations share 26,551 (31%) SNPs while the African populations share 24,089 (28%) SNPs. Only 7,855 (9.1%) SNPs are shared between all populations.Sequencing reads from 2 Southeast Asian  and 2 African  populations of th percentile 11%). Using common SNPs (MAF≥5%), there was evidence that LD decayed rapidly within a few hundred base pairs in the African populations, and reached a baseline within 500 bp. Compared to the Southeast Asian populations, LD decayed more rapidly in the African populations <5%: Thailand, 62%; Cambodia, 72%; Gambia, 45%; Malawi, 59%) , and theulations , consistulations . The estth percentile, 3.1%; maximum, 8.9%) and markers . The number of SNPs with complete genotypes varied by population ; Cambodia, 47,482 (55%); Gambia, 53,469 (62%); Malawi, 54,419 (63%); th percentile, 1.5%). This correlation could be an artefact of genotype calling from sequencing reads. In particular, because the genotypes were called using ratios of coverage, genotypes from SNPs with a higher minor allele frequency are less likely to pass the calling threshold due to more balanced coverage ratios. If the threshold was lowered, more of those genotypes are likely to be called erroneously.Isolates and SNPs had less than 10% of their genotypes missing, reflecting the current practice of analysing only high-quality samples , reaching levels seen in a human genome-wide setting [r2 values in Gambia (0.99) and high values in Cambodia (0.77–0.84), contrasting with lower values in Thailand (0.51–0.56) and Malawi (0.30–0.69). Using a cosmopolitan panel of reference haplotypes from all 4 populations (including 12 Vietnamese samples with complete genotypes), the accuracy of IMPUTE was improved in Thailand  and Cambodia (0.82–0.90) but degraded in Malawi (0.25–0.26). Using the same cosmopolitan panel, the accuracy of Beagle was substantially degraded in all 4 populations including Thailand . For IMPUTE, recombination rates inferred from LDhat were marginally more accurate than those inferred from analysing 2 genetic crosses. In Malawi, accuracy was substantially improved when using a reference panel specific to this population . Using a lower mutation rate parameter (θ = 0.001) or effective population size  led to only marginal improvements in accuracy in the Thai , but not Gambian  population (r2 closely (Pearson’s correlation = 0.75).SNPs in chromosome 13 were chosen to compare imputation strategies, since this is the second-to-longest chromosome containing correspondingly large numbers of SNPs, and it contains the sia [r2 . The imp setting . Howeverpulation . Rates or2~0. In contrast, the near-perfect accuracy of IMPUTE in Gambia is maintained across the allele-frequency spectrum. In Malawi, because of the limited range (3–23%) of MAF values and paucity of SNPs with higher frequencies, a trend for r2 could not be clearly discerned in any of the imputation designs. Across all designs there was no systematic change in MAF post-imputation  and between  and Rsb . IMPUTE-derived haplotypes systematically resulted in higher Rsb values compared to Beagle-derived haplotypes  between Beagle-imputed and complete-case haplotype analyses.Cross-validation experiments indicated that the optimal imputation accuracy of Beagle occurs when a population-specific reference panel is used, and that for IMPUTE occurs when LDhat-derived recombination rates and a cosmopolitan reference panel are used. For each imputation design, a set of 10 SNP datasets was completed by filling in missing data using sampling of posterior genotype probabilities . To assess the effect of imputation strategies, we focused on selection metrics that require complete SNP datasets, namely those that detect long-range haplotypes and extended-haplotype homozygosity, both within (|f XP-EHH ) populatplotypes  Figs. ThiHS| and Rsb metrics applied to Beagle-imputed haplotypes. Intra-population analysis revealed signals downstream of crt, including cg1 and cg2, in the African populations, but not in the Southeast Asian populations in which mutant alleles are already fixed  and other membrane and surface proteins . In contrast, analysis of complete-case haplotypes only identified ama1  genes with 3 or more SNPs. The vast majority of Tajima’s D values were negative , indicating a demographic history of recent population expansion [Tajima’s D [Tajima’s D values (>1) based on at least 10 SNPs  and msp1 . Other genes previously found to have high Tajima’s D values [eba175 , dblmsp , and clag2 . Both ama1 and msp1 have been previously identified to be under positive or balancing selection in certain parasite populations [Tajima’s D values were negative , reflecting the exclusion of common SNPs and inability to calculate pairwise nucleotide differences between haplotypes in a population with missing genotypes. The exclusion of common SNPs may also lead to an overestimate of population expansion.Using Beagle-imputed haplotypes, xpansion ,29 or pu 10 SNPs , severalFst differentiation metric. As expected, greater mean Fst values were attained between  than within  continents , further supporting the robustness of population structure analysis of datasets with low frequencies of missing genotypes.Principal components analysis using both unimputed or imputed data revealed similar well-defined population structure, namely clustering firstly at a continental level , and secntinents . As the P. falciparum is being revolutionised by whole-genome sequencing of large numbers of global clinical isolates. One unintended consequence of this effort, however, is that sequencing greater numbers of parasite genomes will increase the number of missing genotypes, presenting new challenges to haplotype-based population genetics analysis that requires complete SNP datasets. Imputation approaches have been successfully applied to human genomes, but high rates of recombination and rapid LD decay challenge their application to P. falciparum genomes. To help meet these challenges, our study shows that readily available software (Beagle and IMPUTE) produce accuracy levels comparable to those in human studies, and identify known P. falciparum loci involved in drug resistance and immune evasion. Application of Beagle with a population-specific panel of reference haplotypes provided consistently high levels of imputation accuracy across 4 parasite populations, suggesting that its estimation of local haplotype clusters reliably reproduces LD structure in each population. In contrast, accuracy with IMPUTE varied between populations using a similar population-specific referencing strategy.The ability to monitor for signatures of selection in Recombination maps were derived from genetic crosses of parental strains from Honduras, Indochina, Brazil, and Ghana. These are unlikely to represent haplotype configurations that accurately characterise LD structure in other populations, and therefore led to lower accuracy of samples from geographically-distant countries  in our study. Inferring recombination maps from population haplotypes via LDhat marginally improved the performance of IMPUTE. There were differences in recombination rates between inferred and experimentally derived maps . The neaP. falciparum. Newer methods of LD inference such as LDhelmet [While useful to test the validity of LD models via recombination maps derived from different sources, the design of the IMPUTE software is such that LD is modelled independently of missing genotypes using a complete reference panel , suggestLDhelmet  may imprLDhelmet  methods P. falciparum [crt signal using the inter-population metric Rsb. Signals from the intra-population metric |iHS| (with MAF≥5%) were likely to miss evidence of near or full selective sweeps, as in Southeast Asia where beneficial crt alleles and their associated long-range haplotypes have reached fixation. More-recent selective sweeps were observed in regions close to the sulfadoxine-pyrimethamine (SP) resistance genes dhfr and dhps in the Thai, Cambodian, and Gambian populations. Unlike chloroquine resistance that spread out of Southeast Asia, SP resistance arose independently across continents. The Rsb metric highlighted the known independent sweep in dhps in Malawi, leading to signal ‘shifting’ in other populations. This metric also identified the artemisinin resistance gene kelch13 within a ~600kb region in Cambodian parasites, with Thai parasites as the reference population. This study is the first to detect kelch13 using a phenotype-independent, whole-genome scan for signatures of positive selection. Since kelch13 mutations have evolved only recently, this finding suggests that global surveillance of whole-genome P. falciparum sequence data may successfully detect entirely new forms of resistance to future drugs and vaccines.The top gene hits using EHH-based metrics computed from imputed haplotypes suggest that imputation is successful in reproducing signatures of positive selection, even when large numbers of haplotypes with initially extensive missingness are used. As in previous haplotype scans for signatures of positive selection in lciparum ,33–36, wkelch13 signature of selection, likely because this gene had relatively high coverage (1.5-fold greater than the genome average). However, these haplotypes could not validate known drug resistance loci and, overall, their correlation with Beagle-imputed results was low. The absence of known loci was primarily due to exclusion of disproportionately more-common SNPs with missing genotypes. EHH-based methods depend on accurately measuring LD decay around a core SNP. There was a trade-off between inflation of EHH due to lower allele frequencies, and deflation of EHH due to a 75% reduced marker density . A shift in allelic-frequency spectra towards rare alleles inflates negative Tajima’s D values, indicating directional selection. By applying a Tajima’s D approach to the imputed data, genes under balancing selection as putative antigenic determinants were identified. For example, ama1, msp1, and msp7 encoding well-characterized parasite antigens were detected in multiple populations, although msp3.8 and trap previously detected in Malawi [Tajima’s D threshold of 1. Complete-case genotypes failed to produce robust hits, as ≥99% of Tajima’s D values were negative across all populations. The skew towards low-frequency SNPs in the complete-case dataset biases the nucleotide diversity and therefore the Tajima’s D statistic. Similarly, the effective sample size is smaller than the analysable sample size after imputation, and may bias variation estimation and Tajima’s D. Some loci thought to be under balancing selection were found to be under recent positive selection, highlighting that some loci may be under dual selective pressures depending on where and when parasite samples are collected. Although such loci have signatures similar to incomplete sweeps of positive selection [ama1 and trap corroborated similar findings in other populations [Analysis of complete-case haplotypes also detected the n Malawi  and Gambn Malawi , respectelection , evidenculations .P. falciparum isolates, taking into account differences in LD and population structure. Specifically, we recommend using Beagle as the preferred algorithm for imputing whole-genome sequences, and population-specific haplotypes as references. This approach may be useful for imputing genome data from other Plasmodium spp., with resultant datasets being more complete, better able to identify evidence of natural selection by antimalarial drugs and host immunity, and more likely to lead to new insights and applications for disease control.In summary, we have outlined a viable strategy for imputing missing genotypes in large numbers of global P. falciparum clinical isolates [P. falciparum 3D7 genome (version 3) using smalt  and 2 African populations  of samtools  and vcftvcftools  with defvcftools . SNPs th regions  were dis regions . Allowinr2) between the true and imputed allele probability for each marker [Performance of the imputation methods was evaluated with a leave-one-out cross-validation approach using markers on chromosome 13, where 215 (~2%) SNPs were masked in each haplotype and imputed using the others as references. This procedure produces an accuracy measure based on the squared Pearson correlation  were activated. Previously published recombination maps from 2 high-quality genetic crosses between laboratory clones GB4x7G8 [Ner) was inferred using the coalescent-based LDhat program, interval [θ) value of 0.01 were used. The effective population size  was derived by re-scaling the compound map distance for Gambian isolates inferred from interval, to match the empirical map distance estimated from the GB4x7G8 cross. The scaling factor was treated as a pseudo-Ne used to adjust compound rates uniformly on the other populations. To test the robustness of assuming the above parameter values for θ and Ne, we computed accuracy using different values for one African (Gambia) and one Southeast Asian (Thailand) population  and Rsb. iHS is the standardized log ratio of the integrated extended-haplotype homozygosity (EHH) between the ancestral and derived allele at a core SNP [iHS were used to capture long haplotypes centred around either type of allele, using a minimum MAF filter of 5%. Rsb is the standardized log ratio of the integrated site-specific EHH between populations at a core SNP, where site-specific EHH refers to the weighted average of EHH surrounding a core SNP according to squared-allele frequencies [iHS and Rsb were calculated using the scan_hh program in the R package rehh [Tajima's D approach [Tajima’s D. To negate any confounding effects of population expansion and difficulties in establishing significance levels, we report genes with Tajima’s D values in excess of one [neutrality.stats from the R package PopGenome was used [cmdscale in R. In addition, pairwise population Fst with 95% bootstrap confidence intervals was computed using the function stamppFst in the R package StAMPP [Multiple haplotype sets were sampled from the marginal posterior genotype distribution under the best-performing imputation designs. This procedure allowed the computation of population genetics metrics under a multiple imputation-like schema, accounting for uncertainty in the true identity of genotypes. Ten SNP datasets were completed for each imputation strategy through sampling from posterior genotype probabilities, and relevant population genetics metrics at each SNP or gene were computed and averaged across the sets. Two metrics were used to infer recent positive selection, the integrated haplotype score Click here for additional data file.S2 FigNo systematic increase or decrease was detected; (B) MAF of a sample of 5,000 SNPs in each population pre-and post-imputation, using Beagle. No systematic increase or decrease was detected.(PDF)Click here for additional data file.S3 FigDiagonal line indicates line of equality.(PDF)Click here for additional data file.S4 FigRsb values than Beagle-derived haplotypes.Malawi is used as the reference population. Diagonal line indicates line of equality. Mass of points above the line indicates that IMPUTE-derived haplotypes systematically produce larger (PDF)Click here for additional data file.S5 FigDiagonal line indicates line of equality.(PDF)Click here for additional data file.S6 FigMalawi (MLW) is used as the reference population. Diagonal line indicates line of equality.(PDF)Click here for additional data file.S7 FigRsb metrics indicate no signal for positive selection in dhfr but do indicate a signal for positive selection in neighbouring PF3D7_0417400. It appears that the beneficial allele is sweeping up in all 4 populations, causing attenuation of inter-population differences at dhfr. The longest haplotypes appear to occur in Thailand, producing the strongest signal in PF3D7_0417400.With Malawi as the reference population, (PDF)Click here for additional data file.S8 FigRsb metrics in Thai and Cambodian populations indicate no signal for positive selection in dhps but do indicate a strong signal for positive selection in neighbouring PF3D7_0809800. In contrast, dhps is detected by using Vietnam (VTN) as the reference population.With Malawi as the reference population, (PDF)Click here for additional data file.S9 FigPC1 and PC2 refer to the first and second principal components, respectively.(PDF)Click here for additional data file.S10 FigThe chromosomes are ordered from 1–14 by row. The highlighted inset shows putative recombination hotspot in the middle of chromosome 3, validated in Mu et al. , appeari(PDF)Click here for additional data file.S11 FigA 10% cut-off based on an approximate inflection point was used.(PDF)Click here for additional data file.S1 Table2Ner) to obtain the LDhat genetic map distance r . For comparison, the recombination rate between SNP intervals in the human genome is 1–2 cM/Mb (www.hapmap.org).Genetic maps from 2 experimental crosses were used as a benchmark to scale the compound population rate parameter ((DOCX)Click here for additional data file.S2 TableAccuracy improves slightly by assuming a lower mutation rate for the Southeast Asian (Thailand) population but does not change for the African (Gambia) population.(DOCX)Click here for additional data file.S3 TableOnly the 79 genes with 2 or more SNP hits across or within populations are shown.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 TableRsb values per gene are shown. Only the 99 genes with 2 or more SNP hits across or within populations are shown.Median (DOCX)Click here for additional data file.S6 TableRsb values per gene are shown. Only the 75 genes with 2 or more SNP hits across or within populations are shown.Median (DOCX)Click here for additional data file.S7 TableRsb values per gene are shown. Only the 103 genes with 2 or more SNP hits are shown.Median (DOCX)Click here for additional data file.S8 TableThe diagonal contains the number of SNPs that are polymorphic in complete-case and post-imputation datasets.(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.


Bos grunniens), Sahiwal (Bos indicus) cattle, malpura sheep (Ovis aries) and Gaddi goat (Capra hircus). Results of the study revealed presence of 11 SNP variants in all livestock species. Four SNPs were observed in Bos indicus; two SNPs in Bos grunniens; three SNPs in Ovis aries and three SNPs in Capra hircus. These variations are found to be synonymous in nature as these variations do not result in their corresponding amino acids. A total of five polymorphic sites have been described at the κ-casein (CSN3) locus in the Indian domestic Gaddi goat (Capra hircus) when compared with exotic goat (X60763) while sequence analysis of κ-casein gene in sheep showed three novel nucleotide changes in malpura sheep when compared with the exotic sheep (AY237637). These results highlight the importance of taking into consideration the CSN3 SNPs when performing selection for milk composition in dairy livestock breeds.The most polymorphic milk protein gene is β-casein; 13 protein variants are known in cattle. Milk protein genetic polymorphism has received considerable research interest in recent years because of possible associations between milk protein and economically important traits in livestock. The present study was undertaken to explore the genetic polymorphisms in exon 7 of β-casein and exon 4 of κ-casein genes in Arunachali yaks . Finding effective genetic markers for economically important traits in farm animals, especially those which allow for faster and more precise prediction of breeding values, has been a challenge in animal genetics for decades. Bovine chromosome 6 is an object of remarkable interest of many research groups searching for QTL for milk production traits.More than 95% of the proteins contained in ruminants' milk  are synthesized from 6 structural genes encoding proteins: α-lactalbumin and β-lactoglobulin, the two main whey proteins in ruminants, and the four caseins αs1, αs2, β and K which are encoded by four tightly linked and clustered genes . β-caseiFour casein genes  are located as a cluster on chromosome 6 in cattle, sheep, and goats . The disThe present study is focusing on the genetic polymorphism of casein protein genes, which are strongly related to economically important milk quantitative traits in Sahiwal cattle, Arunachali yak, malpura sheep and Gaddi goat breeds, as a tool for genetic improvement of these breeds. On the other hand, promoting selection programs that are depending on the use of genetic markers will help in improving breeding strategies. This study aimed to identify the genetic polymorphism of β-casein and κ-casein genes in Sahiwal cattle, Arunachali yak, malpura sheep and Gaddi goat.N = 12), Arunachali yak (N = 12), malpura sheep (N = 12) and Gaddi goat (N = 12). Approximately 8–10 ml blood was collected from each animal in EDTA coated vacutainer tubes and stored in deep freezer at − 20 °C until the isolation of DNA. Genomic DNA was isolated using the phenol-chloroform method . PCR amplification was performed using Eppendorf (Germany) with thermal cycling conditions of initial denaturation at 95 °C for 5 min followed by 30 cycles of 94 °C for 30 s, annealing temperature (60 °C) for 30 s and 72 °C for 30 s followed by final extension at 72 °C for 5 min. An amplified product was analyzed by electrophoresis on 1.5% agarose gel at 100 V for 20 min using ethidium bromide staining.The exon 7 (467 bp) of β-casein and exon 4 of κ-casein gene were successfully amplified using the primers reported by Amplified PCR products were purified by an enzymatic method using exonuclease 1 and Antarctic phosphatase. Purified PCR products were sequenced with both the forward and reverse primers using a BigDye Terminator cycle sequencing kit  on an automated Genetic Analyzer ABI 3100 .www.technelysium.com au > chromas.html). Multiple sequence alignments were performed using MegAlign tool of LASERGENE software . Genotype frequencies of single nucleotide polymorphism (SNPs) were determined by direct counting method. The coding DNA sequence of exon 7 of β-casein and exon 4 of κ-casein genes were conceptually translated to amino acid sequence using EDIT Seq tool of DNASTAR software. Nucleotide BLAST program at NCBI (http:www.ncbi.nlm.nih.gov.BLAST) was used for sequence homology searches in public databases.The Sequence data were edited manually using Chromas Ver.2.33,  was transitions and 28% (N = 3) was transversion. Sequence analysis of the 12 non-related Sahiwal cattle and Arunachali yak animals revealed a total of five SNPs across the exon 7 of the β-casein gene sequence analyzed. Overall 90% (N = 5) of the SNPs encountered were transitions and 10% (N = 1) were transversion changes in this gene. The sequence information obtained by directing sequencing of the PCR products was used to detect SNPs in the exon 7 of the β-casein gene in the Sahiwal cattle. A fragment of 465 bp of identified four nucleotide variations in exon 7 at the position g.8093 A > G, g.8101 A > C, g.8261 T > C (Heterozygous) and g.8461C > T (Heterozygous) in Sahiwal cattle. All the SNPs presented in Collectively, 467 bp fragment of β-casein gene was sequenced and compared to the equivalent reference. Overall 11 single nucleotide polymorphisms (SNPs) were identified, resulting in an average density of 1 SNP for every 42 bp sequence. Out of 11 SNPs identified, 72% (g.8261 T > C (transition) and g.8412C > T (transition) in exon 7 . The average density of polymorphisms in ovine β-casein gene was 1 SNP per 157 bp sequenced. All these SNPs are found to be synonymous in nature as these variations do not result in their corresponding amino acids. All SNPs are given in A fragment of 463 bp corresponding to Gaddi goat β-casein gene was investigated. A total of three SNPs were observed when compared with a template sequence (Acc. No. AF409096). All SNPs are given in Capra hircus CSN3 (CSN3 exon 4 sequences from Indian goats (gaddi) were analyzed for SNP identifications. Compared with cus CSN3 , nine pocus CSN3  in domescus CSN3 . The higcus CSN3  (Table 4Comparative sequence analysis of the fragment covering partial exon 4 of CSN3 gene in the malpura breed of domestic sheep was identified with three nucleotide variations at the position 308 C > T, 630 G > A and 650 C > T and all these SNPs transition in nature and all are synonymous in nature, these SNPs will not effect CSN3 protein. Bos taurus and Bos indicus, 97% with Bubalus bubaline, 99% with Bos grunniens, 95% with Ovis aries, 95% with Capra hircus and 81% with Camelus dromerius.The exon 7 of β-casein and exon 4 of κ-casein genes sequence of different breeds of livestock breeds were further subjected to basic local alignment search to know the sequence homology with the corresponding region of other species. BLAST analysis revealed homology of 99% with In conclusion, we demonstrate genetic variability in the β-casein and κ-casein genes of different livestock breeds. Although none of these SNPs caused an amino acid change, they could be in linkage disequilibrium with other functional allelic variants. These results will be useful in future works aimed at identifying possible associations with milk traits. This will help in determining if these polymorphisms are potential markers for milk production in Sahiwal cattle, Arunachali yak, malpura sheep and Gaddi goat for subsequent inclusion in selection programs.


Dairy cattle breeding objectives are in general similar across countries, but environment and management conditions may vary, giving rise to slightly different selection pressures applied to a given trait. This potentially leads to different selection pressures to loci across the genome that, if large enough, may give rise to differential regions with high levels of homozygosity. The objective of this study was to characterize differences and similarities in the location and frequency of homozygosity related measures of Jersey dairy cows and bulls from the United States (US), Australia (AU) and New Zealand (NZ).The populations consisted of a subset of genotyped Jersey cows born in US (n = 1047) and AU (n = 886) and Jersey bulls progeny tested from the US (n = 736), AU (n = 306) and NZ (n = 768). Differences and similarities across populations were characterized using a principal component analysis (PCA) and a run of homozygosity (ROH) statistic (ROH45), which counts the frequency of a single nucleotide polymorphism (SNP) being in a ROH of at least 45 SNP. Regions that exhibited high frequencies of ROH45 and those that had significantly different ROH45 frequencies between populations were investigated for their association with milk yield traits. Within sex, the PCA revealed slight differentiation between the populations, with the greatest occurring between the US and NZ bulls. Regions with high levels of ROH45 for all populations were detected on BTA3 and BTA7 while several other regions differed in ROH45 frequency across populations, the largest number occurring for the US and NZ bull contrast. In addition, multiple regions with different ROH45 frequencies across populations were found to be associated with milk yield traits.Multiple regions exhibited differential ROH45 across AU, NZ and US cow and bull populations, an interpretation is that locations of the genome are undergoing differential directional selection. Two regions on BTA3 and BTA7 had high ROH45 frequencies across all populations and will be investigated further to determine the gene(s) undergoing directional selection.The online version of this article (doi:10.1186/s12864-015-1352-4) contains supplementary material, which is available to authorized users. The widespread use of dense single-nucleotide polymorphism (SNP) assays for genomic prediction has led to the creation of large reference populations across multiple countries and breeds ,2. PreviA potential alternative to detect signatures of selection for complex traits may be to characterize regions of the genome that have a higher likelihood of occurring within a continuous run of homozygosity (ROH) ,14. A ROIn general dairy cattle breeding programs have similar breeding objectives, regardless of country, that are driven by traits of economic importance such as milk, fat and protein yield along with fertility, longevity and conformation. The environments and management conditions in which individual animals perform differ greatly across countries. This is confirmed by genetic correlations varying from 0.75 to 0.80 between US and New Zealand (NZ) and US and Australia (AU) for milk, fat and protein yield . FurtherA limited number of studies have investigated genetic differences across countries within the Jersey breed . Charactst statistic, was computed as outlined by Weir & Cockerham [st of a moving 8 SNP window. Measures used to characterize the homozygosity averaged across the genome included the proportion of the genome that is homozygous and the proportion of the genome that is contained within a ROH. Using a sliding window approach, a ROH was declared when a region of 45, 70 or 95 contiguous homozygous SNP with no heterozygotes was observed.The populations utilized to make comparisons across populations consisted of a subset of genotyped Jersey cows born in the US n = 1047) and AU (n = 886) and Jersey progeny tested bulls from the US (n = 736), AU (n = 306) and NZ (n = 768). The SNP in common across the 2 cow and 3 bull populations totaled 31,431 and 27,927, respectively. Each sex was analyzed separately as different selection pressures are likely to exist across sexes and a higher level of diversity is expected within the cow populations. The number of animals within a year on the complete set is outlined in Table 047 and Aockerham  and repoST across the genome for AU versus US cows was 0.008 (0.12) and the average (max) for US versus AU, US versus NZ, and AU versus NZ was 0.006 (0.08), 0.029 (0.21) and 0.009 (0.07), respectively. Although some differentiation based on Fst was seen, especially for US versus NZ bulls, the other populations appear to be similar. The average (±SD) homozygosity for each metric is outlined in Table Figure 0.008 0.1 and the In order to detect subtle differences across the genome in the location and length of long stretches of homozygosity, the ROH metric of SNP length 45, described by Kim et al. , was calST metric.Regions with high levels of autozygosity for all populations were detected on BTA3 (38.8 to 55.2 Mb) and BTA7 35.6 to 48.9 Mb), as shown in Figure 8.9 Mb, a∆ROH45) across time was modeled using logistic regression of autozygosity on year of birth, where there were at least 40 genotyped animals. Initially the analysis was conducted on both bulls and cows, but no regions were found to be significant for the cows, possibly due to the narrow range in birth year of cows  was calculated for each trait according to Benjamini and Hochberg [Regions that had differential ROH45 frequencies across population, high ROH45 frequencies in common across all population, and regions that have undergone significant autozygosity change across time were further investigated (N = 4849 SNP) to determine if SNP within these regions are associated with traits of economic importance. Yield deviations (YD) for cows that were derived from standardized lactation milk, fat and protein yield were weighted according to Garrick et al.  and a siHochberg .LCK), BTA7  and BTA18 (CEBPG) and the remaining 5 below the 0.01 threshold.Multiple regions contained SNP that were associated with milk, fat and protein yield and the FDR for milk, fat and protein yield was 0.30, 0.17, and 0.60, respectively. The region with the largest number of SNP was on BTA7 (38.6 – 58.0 Mb) and included 17 SNP associated with fat yield. Furthermore, a region on BTA17 (16.4 – 18.9 Mb) had 5 SNP associated with fat yield and a region on BTA3 had 6 SNP associated with milk yield. A complete list of the regions in addition to candidate genes are presented in Additional file st statistic across two populations. The use of this measure is advantageous when large differences in allele frequencies occur, such as across cattle breeds. Within a breed, small differences in allele frequencies are expected across populations and particularly when there is some degree of genetic exchange, as is the case of the Jersey population. Due to this the usefulness of Fst to determine regions that are different within a breed is reduced, therefore alternative methods were used.The current study characterized the frequency and distribution of ROH across cow and bull populations derived from US, AU and NZ. Previous reports across multiple dairy breeds have similarly found that the NZ population is genetically different from other dairy cattle populations -21. The One such alternative method to characterize the genomic differences across populations is to compute the average or a specific region’s ROH frequency. The ROH metric has previously been used to examine population history  and as aSLC35A3 gene at 43.4 Mb. A mutation in this gene is known to cause a lethal recessive mutation in Holstein dairy cattle known as complex vertebral malformations (CVM) [Furthermore, Kim et al.  found thns (CVM) . A lethans (CVM)  and Qanbns (CVM) , suggestMultiple regions of the genome displayed different autozygosity and interestingly, regions that were different across the US and NZ bull populations are similar to the results described by Kim et al.  where thFurthermore, SNP within regions that have undergone a significant autozygosity change have previously been reported to be associated with milk yield traits. The SNP with the largest significance within BTA9 (44.9-51.6) and BTA1  were within 1 Mb of SNP that have been previously shown to be associated with milk, fat and protein yield and fat and protein percentage, respectively . It is uNBEA), milk fat synthesis (FABP3), fatty acid metabolism (ACSL6), and metabolism (KCTD15). A previous study that investigated selection signatures across multiple beef and dairy breeds found a sweep region on BTA12 containing NBEA [FABP3 are one of the key intracellular FA transporters and is highly expressed in the mammary gland [Functional analysis of genes within 500 kb in both directions of the significant SNP revealed regions involved in behavior . The majority of the US cows (n = 7458) were genotyped with a low density chip, either GGP , BovineLD  or Bovine3K ) and imputed to medium density . The remaining cows (n = 777) were genotyped using the Illumina BovineSNP50 BeadChip . The AU cows (n = 4075) were part of the Australian genomic reference population and were genotyped by the Dairy Futures CRC  with the Illumina BovineSNP50 BeadChip . Bull genotypes from the Illumina BovineSNP50 BeadChip  were also obtained from the American Jersey Cattle Association (n = 2394) and the Australian Dairy Herd Improvement Scheme . The NZ population comprised of bulls genotyped by Livestock Improvement Corporation .http://bovinegenome.org/cgi-bin/gbrowse/bovine_UMD31/) and SNP on sex chromosomes were excluded from the analysis. Missing SNP within the USA population were imputed using Beagle [Genotype quality control, imputation and phasing were done within each population. For the US population genotype quality control included removing animals that had less than 90% of the SNP called, SNP with a minor allele frequency (MAF) below 0.01 or a p-value of a chi-square test for Hardy-Weinberg equilibrium less than 0.001. Full details of the quality control methods for the AU and NZ populations are described in detail in  and are g Beagle  and SNP In order to make comparisons across populations as equitable as possible a subset of the complete set of genotypes that met certain criteria were used to characterize difference across populations. To minimize the possible time trend effects and selective genotyping in a particular population cows and bulls included were selected that were born within a similar time frame. For the cow analysis, animals born within a three-year (2007–2009) period were used to make comparisons across and within populations. The AU cow resource population was created by selecting animals that had a large amount of individual phenotypic data and is tailored to represent the diversity across the AU Jersey population. To eliminate herds from the US that genotyped only a few of their elite cows, only herds that had greater than 20 genotyped individuals within a given year were used. The US animals n = 1047) selected for the comparison came from herds that had genotyped on average 31 animals per year while all AU cows (n = 886) were used within the given years. For the bull analysis, animals born within a six-year period (2001–2006) were used. No criterion was used for the bulls on the number of genotyped animals within a year and herd, as the bulls were representative of the progeny tested bulls in each population. The total number of bulls was 306, 736, 768 for AU, US and NZ bulls respectively. The use of the same year classes in the analysis across bulls and cows was not possible due to fewer number of genotyped animals within a given year for the bulls in comparison to the cows. The number of animals by year class is outlined in Table 047 selecThe SNP in common across the 2 cow  and 3 bull  populations were used to construct a GRM using the method outlined by Yang et al. . Only com is the allele frequency of SNPm and xm is the genotype at SNPm. A PCA was conducted on the GRM matrix using the R function eigen [where N is the number of SNP, pon eigen . The resCows and bulls born between 2007 to 2009 and 2001 to 2006, respectively, were used to characterize the homozygosity across and within populations. Homozygosity characteristics for each population were measured as the overall genomic homozygosity (proportion of SNP that were homozygous across the entire genome) as well as the proportion of genome contained within a ROH. Using a sliding window approach with a fixed SNP length, a ROH was declared when a set number of contiguous homozygous SNP with no heterozygotes was observed. The sliding window approach started with the first SNP on a chromosome and combined all SNP within a given SNP number into a window, then the window was shifted by one SNP to form a new window and this process was repeated until the end of a chromosome. The SNP lengths considered were 45 (average ± SD = 3.44 ± 0.92 Mb), 70 (average ± SD = 5.45 ± 1.25 Mb) and 95 (average ± SD =7.47 ± 1.54 Mb). These SNP lengths were chosen to provide a range of ROH lengths. A minimum heterozygous threshold was not utilized here as it has been shown that setting a threshold for the number of heterozygous SNP within a ROH region potentially leads to inaccurate ROH calling at the boundaries of a ROH region . The proDifferences across the genome in the location and length of stretches of homozygosity were investigated utilizing the method outlined by Kim et al. . Briefly∆ROH45) across time was modeled using logistic regression as described by Kim et al. [The change of locus autozygosity , YB is the year of birth of an individual, ion test   and included standardized lactation milk, fat and protein yield. Yield deviations were calculated separately for each population, by adjusting for the following effects using ASReml  in each ijklm refers to either standardized milk, fat or protein yield, μ is the intercept, HYSi is the fixed effect of herd-year-season of calving, parityj was the fixed effect of parity, monthk was the fixed effect of month of calving, and age was the regression of age at first calf. Random effects included the residual. For cows with multiple lactation records, mean adjusted records from the above model were used in the analyses as yield deviations. Yield deviation of cows were standardized to have a mean of 0 and a variance of 1 to make variances similar across populations. Then the following single marker regression model was used:where yijk refers to the yield deviation for milk, fat or protein yield, μ is the intercept, popi is the fixed effect of country of origin, SNPj is the additive effect of SNP. Random effects included uk the additive genetic effect of the kth individual assumed ~ N were calculated according to Benjamini and Hochberg [where Hochberg .Bos Taurus assembly  with regions declared significantly different and similar across the populations were obtained for functional characterization and the identification of gene ontology terms using DAVID [Cow positional candidate genes using ng DAVID ,61 and gng DAVID . Regionsng DAVID  and a tang DAVID  were use


P=0.046), bladder cancer (entropy P=0.039), and rheumatoid arthritis (PheWAS case–control P<10−4). Using ENCODE data sets, we further statistically validated that the biological mechanisms shared within prioritised SNP pairs are frequently governed by matching transcription factor binding sites and long-range chromatin interactions. These results provide a ‘roadmap’ of disease mechanisms emerging from GWAS and further identify candidate therapeutic targets among downstream effectors of intergenic SNPs.Functionally altered biological mechanisms arising from disease-associated polymorphisms, remain difficult to characterise when those variants are intergenic, or, fall between genes. We sought to identify shared downstream mechanisms by which inter- and intragenic single-nucleotide polymorphisms (SNPs) contribute to a specific physiopathology. Using computational modelling of 2 million pairs of disease-associated SNPs drawn from genome-wide association studies (GWAS), integrated with expression Quantitative Trait Loci (eQTL) and Gene Ontology functional annotations, we predicted 3,870 inter–intra and inter–intra SNP pairs with convergent biological mechanisms (FDR<0.05). These prioritised SNP pairs with overlapping messenger RNA targets or similar functional annotations were more likely to be associated with the same disease than unrelated pathologies (OR>12). We additionally confirmed synergistic and antagonistic genetic interactions for a subset of prioritised SNP pairs in independent studies of Alzheimer’s disease (entropy  Although downstream effects of missense and nonsense coding SNPs can be investigated straightforwardly in cellular and animal models, effects arising from intergenic SNPs remain largely uncharacterised and are often challenging to validate experimentally using in vitro and in vivo assays.The abundance of newly discovered disease-associated polymorphisms now enables inquiries about their summative and interactive effects.3–7 Other disease-mechanism-based methods have prioritised GWAS signals by leveraging prior biological knowledge inferred from the physical proximity of SNPs to gene loci8–11 or from expression quantitative loci (eQTL) associations.12–17 Recent high-throughput genomics projects such as The Encyclopedia of DNA Elements (ENCODE) have extended quantitative measures of biological activity into intergenic regions.19 These projects led to integrative genomic analyses and systemic mapping of disease-associated SNPs to regulatory elements, including enhancers, transcription factor (TF) binding sites or chromatin accessibility marks.20–25 Nonetheless, analysis of how downstream disease mechanisms emerge from intergenic SNPs located in biologically active regulatory genomic regions remains elusive.Computational biology can potentially bridge the mechanistic gap between detecting disease-associated SNPs and providing biological interpretations of how different risk loci contribute to disease incidence and prevalence. We and others have shown that systematically integrating studies of protein–protein interaction with experimentally verified disease-associated coding SNPs enables discovery of new disease-gene candidates and testable associations between biological pathways and disease.We hypothesised that the mechanisms by which polymorphisms contribute to disease risk can be unveiled by systematically analysing their downstream transcriptomic effects. The functional convergence of intergenic SNPs with intragenic ones may influence the course of disease via the same mechanisms. Building on eQTL and ENCODE data, we approached this hypothesis by identifying shared molecular and biological mechanisms by which two SNPs (irrespective of their genomic location but not in linkage disequilibrium) are associated with the same disease. We developed a computational method focused on ascertaining and quantifying disease mechanisms of SNPs with known disease relationships from the National Human Genome Research Institute (NHGRI) GWAS catalogue , which are also associated with altered messenger RNA (mRNA) expression levels via eQTL studies. We first devised a systematic method to identify overlap and similarity of biological activities shared between every two SNPs . Second, in support of the predicted shared mechanisms between SNPs associated with the same disease, we provided additional independent evidence by: (i) exploring non-additive synergetic and antagonistic SNP–SNP interactions in GWAS of bladder cancer, Alzheimer’s disease and rheumatoid arthritis (RA), and (ii) utilising ENCODE-derived data to identify Lead SNP pairs located in similar regulatory regions that might explain their shared downstream biological mechanisms. We focused our investigation on Lead SNP pairs comprised of at least one intergenic SNP.2 that had at least one eQTL association in the SCAN database,26 derived from lymphoblastoid cell lines. This yielded 2,358 Lead SNPs . Lead SNP pairs were categorised into three groups based on assertions by dbSNP (Build 138):27 intergenic–intergenic (inter–inter) pairs when both SNPs are at least 2,000 bp 5ʹ and 500 bp 3ʹ of protein-coding gene coordinates, intergenic–intragenic (inter–intra) pairs when one SNP is intergenic and the other is within gene coordinates, and intragenic–intragenic (intra-intra) pairs in cases where both SNPs were found within or near gene coordinates. This study focused on pairs of Lead SNPs comprised of at least one intergenic SNP (inter–inter or inter–intra), which left two million pairwise combinations (28). These annotations allowed us to prioritise SNP pairs (inter–inter and inter–intra) on the basis of having the same or similar functional biological mechanisms, even when the exact mRNA target is distinct . These data were then overlapped between each SNP comprising an inter–inter or inter–intra Lead SNP pair.29To determine intergenic SNPs’ contribution to disease risk, we computationally imputed biological mechanisms that are common to more than one intergenic Lead SNP associated with the same disease. We analysed Lead SNPs associated with any of the 467 diseases in the NHGRI GWAS catalogueinations . For eac30 and applied these systematically to the two million surveyed Lead SNP pairs. Pairs exhibiting sufficient overlap and/or similarity at FDR<0.05 were termed ‘prioritised Lead SNP pairs’  separately to the two million surveyed Lead SNP pairs  at False Discovery Rate (FDR)<0.05. This prioritised 5,011 total Lead SNP pairs, with 3,870 pairs containing at least one intergenic SNP  and multiple mRNAs associated with each Lead SNP (threshold ⩾3), we observed substantial disease-specific enrichment with respect to mRNA overlap . As an example of functional convergence in prioritised SNP pairs that come from the same disease, we have illustrated the mRNA transcript overlap, molecular function similarity and biological process similarity observed for all SNP pairs associated with RA , and all prioritised SNPs converged towards immune response (GO:0006955) and/or antigen processing and presentation via MHC class I (GO:0002474) or class II (GO:0002586) through at least one path—including SNPs that mapped outside of the MHC region. This is consistent with what is known about the biology of RA, and the importance of antigen responses in pathology.31Twenty percent of the pairs (16/80) were comprised of SNPs mapping to two different chromosomes, whereas 64 pairs of SNPs were mapped to the same chromosome, although not within the same linkage disequilibrium (LD) block . Involve with RA . Among er2<0.8 down to r2<0.01  to establish that our results were not the result of miscategorising SNPs within this region as intergenic . Overall, these controls demonstrated the approaches chosen for the pairwise comparisons and prioritisations were reproducible under multiple conditions. We additionally confirmed that the enrichment results were not driven by diseases that had few GWAS SNPs. On the contrary, more SNPs and more studies per disease increased the chance of yielding more SNP pairs with shared biological mechanisms  and Alzheimer’s disease (six pairs). The multifactor dimensionality reduction analysis revealed significant synergistic interactions for two Alzheimer’s disease pairs and one of the bladder cancer pairs , from which RA subjects were identified based on PheWAS (P<0.01). Using logistic regression incorporating the ratio of ORs for genetic interaction (RORi), we further identified both SNP–SNP synergy and antagonism among the RA-associated prioritised Lead SNP pairs  when we compared the diametric extreme ORs of their alleles  distinct SNP regions harbouring the same TFs  with each SNP within a Lead SNP pair. These Lead or LD SNPs may have a causative effect and/or contribute similarly to disease pathogenesis. By combining annotations, we showed Lead SNP pairs with shared biological mechanisms are more likely enriched in regions with common regulatory properties than non-prioritised SNP pairs (We further hypothesised that intergenic disease-SNPs located in genomic regions surveyed for DNA–protein interactions and P⩽10−4) or (ii) straightforward  overlap of mRNA(s) associated with each Lead SNP of a pair. Notably, the enrichment was generally more pronounced for prioritised SNP pairs associated with the same disease, as indicated when comparing the whiskers of each prioritisation method in Panel (I) to its counterpart in Panel (II) , rather than using a fixed eQTL cutoff of P⩽10−4 as performed in our initial enrichment analysis illustrated in P<0.05 . An even more stringent LD cutoff of r2<0.01  and knowledge base of gene annotations (GO) to impute biological effectors of SNPs derived from their shared biological downstream mechanisms. We showed that intergenic and intragenic SNPs predisposing an individual to the same disease most likely affect expression of the same mRNAs, mRNAs involved in similar biological pathways or governed by similar regulatory mechanisms. Among the 2 million surveyed SNPs, and at stringent cutoff of FDR<0.05, our prioritisation methods unveiled (i) 3,870 prioritised inter–inter and inter–intra Lead SNP pairs among 312 diseases that share at least one of the imputed biological mechanisms, (ii) about one third of the SNP pairs were selectively identified by at least two prioritisation methods, (iii) 80 disease-specific inter–inter and inter–intra Lead SNP pairs with shared mechanisms among 32 diseases and (iv) 473 prioritised inter–inter and inter–intra SNP pairs in regions with common regulatory properties, among which 26 inter–inter and inter–intra pairs are of the same disease. We further validated a subset of these predictions with non-additive genetic risk interactions in an independent association data set for three human diseases as well as with ENCODE-informed validations of regulatory elements. According to ENCODE regulatory data, prioritised Lead SNP pairs were also enriched for similar regulatory elements  and were involved in the same chromatin long-range interactions. These results showed that intergenic and intragenic SNPs share disease effects through shared functionality at different level of scale of biology.et al. recovered seven disease-specific unique SNP pairs (trans-eQTLs) at FDR<0.05 among four diseases that shared mRNAs with converged biological pathways.42 We showed that our prioritisation methods were able to recover substantially more predictions by GO–BP and GO–MF similarity to identify shared mechanisms for SNP pairs without mRNA overlap. This suggests that we have successfully enriched for those intergenic SNPs that reveal a functional impact on disease pathology, although identifying which GWAS SNPs are truly causal rather than associated or perhaps even spurious is a task beyond the scope of this study. If all GWAS SNP inputs could be refined to the causative variant, then we expect to see a significant increase in functional overlap across each disease. Another limitation of our approach is that it relies heavily on biased GO knowledge annotations that are not designed to uncover non-canonical and poorly characterised biological mechanisms. We also observed a high number of prioritised Lead SNP pairs related to immune related loci  and their downstream activities, which is consistent with the well-described role for HLA and inflammatory processes in many complex diseases, including those studied by GWAS. It is also possible that these are over-represented here due to the nature of the lymphoblastoid cell lines used for eQTL studies and their context-specific stimulations linked to particular diseases.42 Although many studies have reiterated such observations, neither consensus nor guidelines regarding the optimal cell lineage from which to derive eQTL associations that are most qualified for imputing disease-specific pathogenesis has been established. However, numerous eQTL and genomic annotation-based studies showed that analysing multiple cell types43–45 could uncover novel mechanisms and biomodules that explain organs or tissue system implications in overall disease pathology. Future directions for identifying biomodules from SNPs could involve the use of unbiased gene sets such as those obtained by co-expression networks47 or computational gene similarity measures.48 These prioritisation statistics can also be applied in a targeted manner to a given disease rather than the GWAS catalogue as a whole, where a specific disease-relevant eQTL dataset may be obtained and less stringent nominal P values can be used for biomodule discovery without as much need for multiple testing correction. Further investigation in this direction is supported by our independent prioritisation of SNP pairs associated with liver diseases (Primary biliary cirrhosis and Hepatitis B vaccine response) when using the liver eQTL data set. Finally, this current study was computationally intensive as the empirical resampling was conducted homogeneously across pairs. The algorithms can be optimised by conditioning the resampling according to SNP pairs and dynamically ending the resampling when P values observed are non-significant. These improvements should allow to investigate further the effect of eQTL derived from cell types more relevant to specific diseases, such as those available in Genotype-Tissue Expression data sets GTEx.49Using mRNA overlap, previous study of Fehrmann 50–52 In contrast, our approach leverages ENCODE data to determine whether prior SNP-associated disease mechanism predictions share regulatory elements that might explain their convergent effects. New genome-wide regulatory annotations and quantitative trait loci analyses are now increasingly available such as those derived from chromatin accessibility and DNA methylation patterns of non-coding regions. Approaches relying on similarity of biological mechanisms could be systematically applied to these growing genomic data sets and further inform how common polymorphisms are involved in transcriptional or post-transcriptional mechanisms underlying the regulatory and cellular networks of disease progression.Previous computational studies preferentially used ENCODE data sets as a seed to map SNPs to DNA regulatory elements with putative function and used the results to associate these SNPs qualitatively (literature curation) and quantitatively (gene set enrichment in knowledge bases or network models) to predict downstream biomolecular mechanisms.This study highlights the significance of mechanistic similarities for uncovering additional interacting downstream effectors of intergenic SNPs predisposing individuals to the same disease. Identifying and understanding mechanisms of disease can not only inform biology but also provide insight in identifying candidate therapeutic targets. These results can be pursued for generating a comprehensive ‘roadmap’ of disease mechanisms revealed by downstream effectors of intergenic SNPs.Data sets/database are described below and in detail in 26 which consisted of 4,189,682 associations between 833,004 distinct SNPs and 11,860 mRNAs at P⩽10−4. Each SNP included for further study was matched to at least one eQTL transcript with a median of 2 transcripts per SNP  GWAS catalogue) and mRNA expression (eQTL) were characterised as inter- or intragenic SNPs according to dbSNP (Build 138) definitions, which are based on RefSeq gene coordinates. Intragenic SNPs are located in regions whose boundaries extend 2 kb upstream of the transcription start site and 0.5 kb downstream of the terminator according to RefSeq.56 and used to associate mRNA (eQTL) with molecular function (GO–MF) and biological process (GO–BP) terms. The database consisted of GO–MF and GO–BP annotations for 11,774 and 9,717 distinct genes (mRNAs), respectively.GO annotations for human genes were retrieved from NCBI57 Only interactions between distinct TFs that scored ⩾0.9 were included in the enrichment analyses  combined data set of TF binding sites (TFBS-Clustered) comprising ChIP-seq of 148 TFs across 95 cell lines and (ii) three ChIA-PET data sets  with data collected from cell lines, K562, HeLa, MCF-7, HCT-116 and NB4.This data set provides DNA element annotations of the human genome based on various biochemical assays such as ChIP-seq, DNase-seq and RNA-seq.r2<0.8 or r2<0.01.58 SNP pairs in strong LD  were excluded from the study. Among the remaining pairs, we focused on inter–inter and inter–intra Lead SNP pairs  with at least one intergenic SNP. We then employed three methods based on a high-throughput computing system to prioritise biological mechanisms shared among SNP pairs: (i) mRNA overlap, (ii) molecular function similarity and (iii) biological process similarity. These prioritisations were controlled by permutation resampling of scale-free networks.30We included 2,358 SNPs ’.s1 was associated with a set of mRNAs G(s1), and |G(s1)| is the cardinality of the set G(s1), similarly for s2. The GENEITS is the biological similarity of two mRNAs60  and statistical (prioritised by permutation resampling) types of mRNA overlap. Prioritisation by biological similarity was based on GO annotations of mRNA molecular functions or biological processes associated with the SNPs within each pair. Briefly, as every SNP within a pair could be associated with multiple mRNAs, and every mRNA could be associated with multiple GO terms, we performed three steps to impute biological similarly between two SNPs. First, we calculated the information theoretic semantic similarity  among GO termsP value was calculated as the proportion of empirical permutations  with equal or greater strength of overlap or biological similarity than those observed. We then adjusted for multiplicity using the Benjamini–Hochberg FDR procedure independently for each of the three prioritisation methods using the p.adjust function in R software (http://www.r-project.org/). Prioritised SNP pairs were those yielding sufficient statistical significance using any of the prioritisation methods.The three prioritisation methods were subjected to stringent statistical measurements to filter the relationship between two SNPs that could be observed by chance . In contBeagle GLOBUS62 computing infrastructure housed in a Cray XE6 Supercomputer of the Computation Institute at the Argonne National Laboratory with peak performance of 151 teraflops generated by 17,424 compute cores (http://beagle.ci.uchicago.edu/).Approximately 20,000,000 core hours of high-throughput computations were conducted on the P values according to the following contingency table: (same disease versus across-disease SNP pairs)×(prioritised versus non-prioritised SNP pairs) using Fisher’s exact test in R. We also performed enrichment tests at different P value cutoffs of eQTL associations (⩽10−4 to ⩽10−6) from which the number of mRNAs associated with each SNP served as a threshold for calculations .We performed an enrichment analysis to assess whether shared mechanisms  were more likely found among SNP pairs related to the same disease than those across distinct diseases. Therefore, we dichotomised all SNP pairs into those associated with the same disease and those associated with distinct diseases based on the NHGRI GWAS catalogue. We then performed SNP pair enrichment by calculating ORs and r2⩾0.8) with each Lead SNP of a pair. RegulomeDB39 was used to determine Lead SNPs in strong LD  for which ENCODE-derived functional annotations were available. The first enrichment analysis assessed whether prioritised SNP pairs are more likely than non-prioritised pairs to be enriched in regions sharing common regulatory properties using the following contingency table: (same regulatory properties versus different regulatory property of Lead SNP pairs)×(prioritised versus non-prioritised Lead SNP pairs). We performed the second enrichment analysis to determine whether prioritised SNP pairs related to the same disease are more likely to share common regulatory properties than those associated with distinct diseases using the contingency table: (same disease and regulatory properties versus distinct diseases and/or different regulatory property Lead SNP pairs)×(prioritised versus non-prioritised Lead SNP pairs). We included a control in which SNP pairs were calculated from every possible combination of SNPs with an eQTL association. All Lead SNP pairs derived from the NHGRI GWAS catalogue were used as the background, and enrichment analyses were performed on SNP pairs derived from eQTL associations with P⩽10−4. Bar graphs were generated using Prism v.6 .Pairs were prioritised according to computed shared mechanisms as described above. For each mechanism, we determined whether prioritised SNP pairs were enriched in genomic regions with common regulatory properties: (i) same TF binding sites, (ii) interacting TFs and (iii) long-range chromatin interactions. Specifically, we leveraged ENCODE data sets to attribute DNA element annotation(s) to each SNP of the prioritised pairs, such as TF binding sites (ChIP-seq data) and/or anchored regions with long-range interactions (ChIA-PET) data. We extended the regulatory annotation of the Lead SNPs to SNPs in strong LD  or Alzheimer's disease (AD) were considered for genetic interactions in GWAS . We first applied the multifactor dimensionality reduction machine-learning method36 for modelling the joint effects of the Lead SNP pairs. The multifactor dimensionality reduction approach was implemented using 10-fold cross-validation for estimating generalisability, followed by a 1,000-fold permutation test to determine statistical significance and to address multiple testing issues. In addition, we applied the explicit test of epistasis, which uses permutation testing to determine statistical significance of interaction effects while holding the main effects constant.63 An entropy-based information gain approach65 was used as an additional method for interpreting the statistical pattern of epistasis. The BC GWAS included 3,532 cases and 5,119 controls from the Cancer Genetic Markers of Susceptibility for Bladder Cancer study,34 which is available from dbGaP (accession: phs000346.v1.p1). The AD GWAS included 529 cases with mild cognitive impairment or AD and 204 controls from Phase I of the Alzheimer’s Disease Neuroimaging Initiative,35 also available from dbGaP (accession phs000219.v1.p1).Per our 38 To identify RA case–controls cohort from the EHR, we utilised previously developed PheWAS case–control definitions for RA that can reproduce known genetic associations.67 From a population of approximately 36,000 individuals with extant Illumina Human Exome chip genotype data in the deidentified Vanderbilt University clinical data warehouse linked to BioVU,38 we identified 1,115 RA cases and 24,169 controls (http://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes.html) specific to RA  on different days. Controls were selected among patients with no RA or related diagnoses  reported in their billing history according to the PheWAS approach. Individuals with RA noted on a single day were excluded, as these cases often have poorer positive predictive value.Each RA-associated prioritised inter–inter and inter–intra Lead SNP pair was considered for SNP–SNP interactions using a data set selected from the Vanderbilt University EMR-linked DNA biobank (BioVU).controls . Cases hrs6457617-‘T/C’, rs9272219-‘T/G’ and rs9268853-‘C/T’) associated with RA were available for calculations. Only individuals identified from European ancestry by Structure68 were used in the analysis, resulting in 29,731 individuals before case and control selection. All association analyses were completed with PLINK v1.0769 using logistic regression adjusted for age and sex and assuming an additive genetic model. Interaction analyses were also performed on the second SNP of each pair and included an SNP–SNP interaction term (RORi). Interactions between specific alleles of Lead SNP pairs were analysed by Fisher’s exact test. ORs of allelic combination effects associated with RA and their 95% confidence intervals were reported using PLINK v1.07. Submission to dbGaP of RA genotypes and phenotypes of the present PheWAS study is in process.70For each patient, we had previously extracted DNA and genotype data for 233,605 SNPs with <5% missing data using the Illumina Human Exome 12v.1 array. Genotypes were quality controlled for call rate (>95%), minor-allele frequency (>1%) and identity by descent to remove related individuals. Among these genotyped SNPs, three prioritised Lead SNP pairs . Disease-specific networks curated to highlight overlap and similarity of mechanisms found among prioritised Lead prioritised SNP pairs associated with RA. Networks were visualised using Cytoscape.http://www.lussierlab.org/publicationsSource code used in this manuscript has been made freely available at


ZhongHuang13 (ZH) and ZhongPin03-5373 (ZP)] and discovered 794,876 SNPs with reference to the published Williams82 genome. SNPs were distributed unevenly across the chromosomes, with 87.1% of SNPs clustering in 4.9% of the soybean reference genome. Most of the regions with a high density of SNP polymorphisms were located in the chromosome arms. Moreover, seven large regions that were highly similar between parental lines were identified. A GoldenGate SNP genotyping array was designed using 384 SNPs and the 254 recombinant inbred lines (F8) derived from the cross of ZP × ZH were genotyped. We constructed a genetic linkage map using a total of 485 molecular markers, including 313 SNPs from the array, 167 simple sequence repeats (SSRs), 4 expressed sequence tag–derived SSRs, and 1 insertion/deletion marker. The total length of the genetic map was 2594.34 cM, with an average marker spacing of 5.58 cM. Comparing physical and genetic distances, we found 20 hotspot and 14 coldspot regions of recombination. Our results suggest that the technology of resequencing of parental lines coupled with high-throughput SNP genotyping could efficiently bridge the genotyping gap and provide deep insights into the landscape of recombination and fixed genomic regions in biparental segregating populations of soybean with implications for fine mapping of QTL.Identification of genes underlying agronomic traits is dependent on the segregation of quantitative trait loci (QTL). A popular hypothesis is that elite lines are becoming increasingly similar to each other, resulting in large genomic regions with fixed genes. Here, we resequenced two parental modern elite soybean lines [ Theoretical considerations suggest that an important prerequisite for a high power of quantitative trait loci (QTL) detection within biparental crosses is that the genetic distance between adjacent polymorphic markers is less than 10 cM . MoreoveGlycine max (L.) Merr.] and have been used to establish integrated genetic linkage maps. The first and second versions of the soybean integrated genetic linkage map were based on isozyme markers, restriction fragment length polymorphism, random amplified polymorphic DNA, amplified fragment length polymorphisms, and simple sequence repeats (SSR)  Merr.] accessions, including two parental lines [ZhongHuang13 (ZH) and ZhongPin03-5373 (ZP)] used in this study, have been resequenced, and several millions of SNPs have been identified . For resequencing analysis, total genomic DNA of ZH and ZP was extracted from fresh leaves of dark-grown plants at the first trifoliolate stage using a DNeasy Plant Mini Kit (QIAGEN).A cross was made between the modern cultivar ZH  as the male parent and elite line ZP (developed by ICS-CAAS) as the female parent. ZH is widely planted in China for elite agronomic traits such as high grain yield, large seed weight, low plant height, and wide adaptability. ZP is a line with resistance to soybean cyst nematode race 4   using the Illumina Solexa system. DNA library construction, short reads derivation, alignment onto the soybean reference genome (http://www.soybase.org/). In addition, 37 EST-SSR and 1 InDel marker, rhg1-I4, were included in the analysis , 37 EST-SSRs, and 1 insertion/deletion (InDel) marker, randomly distributed over the 20 genetic linkage groups of soybean were screened for polymorphisms between the parental elite inbred lines ZH and ZP. Among 880 SSRs, 789 SSRs were from the integrated linkage map of soybean . Each reaction volume of 20 μl consisted of 50–100 ng genomic DNA template, 2 μl 10×PCR buffer (containing Mg2+), 1.5 μl 2 mM of each dNTP, 1.5 μl 2 mM of forward and reverse primers, and 1 U Taq polymerase. The program for PCR was as follows: 5 min at 95° followed by 35 cycles of 30 sec at 94°; 30 sec at a primer-specific optimized annealing temperature; 30 sec at 72°; and a final extension at 72° for 8 min. The PCR products were separated on 6% denaturing polyacrylamide gels and visualized by silver staining.Polymerase chain reactions (PCRs) were conducted on a Model MG96www.illumina.com) was developed using these SNPs. The SNP array was used to genotype the complete set of 254 recombinant inbred lines and the two parental lines using the Illumina GoldenGate assay system following the manufacturer’s protocol. For each SNP, the lowest acceptable scores were set to 80% for GenCall and to 0.6 for GenTrain to separate homozygote and heterozygote clusters.The primary linkage genetic map constructed using 179 polymorphic SSRs, EST-SSRs, and InDel markers included 45 large gaps with distances more than 20 cM, which partitioned 20 normal soybean linkage groups into 45 linkage groups. To bridge these gaps, SNP markers were selected from the genomic region of these gaps. Of 384 SNPs selected, most  were nonsynonymous or nonsense. In addition, all of 384 SNPs were with a design ability rank score >0.6 and a pre-evaluation of 60 bp of upstream and downstream flanking regions. Then, an Illumina GoldenGate SNP array . For grouping, the threshold for limit of detection (LOD) scores was set to 3.0 and the threshold for marker distance was defined as 50 cM. The recombination frequency was converted to genetic map distances (in cM) using the Kosambi mapping function (A genetic linkage map was constructed using the software package IciMapping V3.1 (function . SERiatifunction  was usedfunction . We usedwww.phytozome.net) (http://www.ncbi.nlm.nih.gov/SNP/snp_viewTable.cgi?handle= NFCRI_ MOA_CAAS), with a distribution that differed significantly among the chromosomes. The highest number of SNPs  was on Gm18 and the lowest number  was on Gm04. Moreover, SNPs were unevenly distributed within chromosomes  showed stable amplification and clear polymorphic bands, including 173 SSRs, 5 EST-SSRs, and the InDel marker. When a soybean linkage map was constructed using these sparse markers, the 20 normal soybean linkage groups were broke into 45 linkage groups (data not shown) because of 25 large gaps with distances more than 50 cM. To increase the number of available polymorphic markers for bridging the gaps in the genetic map for the biparental cross of interest, the resequenced genomes of the two parental modern lines ZP and ZH were analyzed. A total of 2980 Mb and 5240 Mb raw sequence data consisting of sequence reads of 75 bp in length were obtained from ZP and ZH, respectively. After filtering out repetitive as well as unmapped sequences, the remaining 2700 Mb and 4470 Mb of low copy genomic sequences, respectively, were aligned to the soybean reference genome (variety Williams 82) pseudomolecules  were located in intergenic regions. Among the 126,910 SNPs in genic regions, only 22.9%  were identified in coding sequences (CDS), including 13,156 synonymous SNPs and 15,494 nonsynonymous SNPs. With the exception of Gm09, the number of nonsynonymous SNPs was slightly higher than that of synonymous SNPs  on 19 ofSupporting Information, Table S1). Of the 485 markers, a total of 464 could be mapped on the soybean reference genome (Glyma1.01) covering 880 Mb (92.6%) of the genome length. The average distance between two adjacent marker loci was 1.86 Mb.We selected 384 SNPs and designed an array using the Illumina GoldenGate assay system to fingerprint the segregating biparental population. From the 384 SNPs, 71 were excluded from analysis because of low data quality. The remaining 313 high-quality SNPs, including 195 nonsynonymous or nonsense, 44 synonymous, and 74 intergenic SNPs, were complemented by 167 SSRs, 4 EST-SSRs, and 1 InDel from the initial set mentioned above to construct a genetic linkage map  segregation distortions. These loci were located on Gm07(M), Gm09(K), Gm10(O), Gm18(G), and Gm20(I). The distorted segregation markers were unevenly distributed in the genome. For example, two hotspots of regions with significant segregation distortion were detected on Gm18(G) and Gm10(O) and included eight markers, from Map-3441 to satt570, and four markers, from Map-1806 to satt173, respectively.A total of 18 gaps of >20 cM in length were present in 14 of the linkage groups, with exceptions Gm07(M), Gm08(A2), Gm10(O), Gm16(J), Gm17(D2), and Gm18(G). Most of these gaps (83.3%) were ≤30 cM in length. The largest gap was located on Gm06(C2) at 33.45 cM in length. The markers in all of the 18 gaps were sparse. A total of 48,411 SNPs were detected within the length of 127.51 Mb of 18 gaps, with a frequency of 1 SNP every 2.6-kp genomic region. This frequency is much lower than that of the other SNPs (1 SNP every 1.1-kb genomic region). Segregation distortion of each locus was estimated by the goodness of fit test, and 21 marker loci showed significant  in 14 regions of the genome. All were located in the pericentromeric regions of chromosomes. In addition, 13 pairs of markers that were separated in the soybean reference genome  had no observable recombination events between them. All pairs were located in pericentromeric regions, with 10 of them being included in recombination coldspots, with the exception of Map-2639 in Gm14, Map-3033 in Gm16, and satt505 in Gm18.The traditional approach to select polymorphic PCR-based molecular markers in biparental populations is time-consuming. For instance, we tested 890 SSRs, mostly from the integrated linkage map of soybean (In addition, resequencing analysis provides us an opportunity to take a closer look at the landscape of polymorphisms among the two parental lines. Our analysis revealed that there are several regions with a low degree of polymorphisms between the two parents , which mUsing SNPs developed from resequencing of parental lines offers the potential to develop customized SNP arrays optimized for the targeted germplasm. We tested this option and developed 384 stable SNPs from the 794,876 SNPs to genotype the 254 RILs available. High-quality data were generated for 81.5% of the 384 SNP loci, which is comparable to that of previous studies (Variation of recombination rates plays a key role in shaping genomic features and influencing the strength of natural selection . We obse


Aedes aegypti, contributes significantly to global disease burden. Genetic study of Aedes aegypti is essential to understanding its evolutionary history, competence as a disease vector, and the effects and efficacy of vector control methods. The prevalence of repeats and transposable elements in the Aedes aegypti genome complicates marker development and makes genome-wide genetic study challenging. To overcome these challenges, we developed a high-throughput genotyping chip, Axiom_aegypti1. This chip screens for 50,000 single-nucleotide polymorphisms present in Aedes aegypti populations from around the world. The array currently used genotypes 96 samples simultaneously. To ensure that these markers satisfy assumptions commonly made in many genetic analyses, we tested for Mendelian inheritance and linkage disequilibrium in laboratory crosses and a wild population, respectively. We have validated more than 25,000 of these markers to date, and expect this number to increase with more sampling. We also present evidence of the chip’s efficacy in distinguishing populations throughout the world. The markers on this chip are ideal for applications ranging from population genetics to genome-wide association studies. This tool makes rapid, cost-effective, and comparable genotype data attainable to diverse sets of Aedes aegypti researchers, from those interested in potential range shifts due to climate change to those characterizing the genetic underpinnings of its competence to transmit disease.The dengue and yellow fever mosquito,  Arthropod-borne diseases affect millions of people world-wide every year . Many ofAedes aegypti, is a major vector of dengue, Chikungunya, and yellow fever viruses. It is found in tropical and subtropical climates worldwide and is estimated to transmit as many as 528 million dengue infections per year  There should be as many markers as possible; 2) they should be present as a single copy in the genome; 3) they should be inherited in a Mendelian fashion, and 4) they should be distributed across known positions in the genome so that physical linkage can be distinguished from linkage disequilibrium due to other processes.Ae. aegypti genome. Here, we validate a subset of the markers included on this chip, and show that these markers yield results consistent with previous studies of genetic variation in Ae. aegypti.To this end, we developed a genotyping chip that contains probes for 50,000 SNP loci, distributed throughout the Ae. aegypti were collected from 25 locations from around the world (he world  for SNP Ae. aegypti reference genome by TopHat (version 2.0.5). We removed reads generated from polymerase chain reaction duplicates with Picard v1.96, and reads with mapping quality lower than 30 were excluded for further analysis. Variants were called separately for each sample by GATK UnifiedGenotyper using default parameters only in regions from a list of genes compiled from literature associated with dengue competence and insecticide resistance . This decision was made partially due to computational restraints, but mostly because there was already an abundance of data to choose from in the analysis of the RAD-seq data. Variants were then filtered for depth of coverage (7×) and allele frequency . To avoid multiallelic sites, we calculated the percentage of reads with either reference allele or the called alternative allele in the total number of reads covering the corresponding site and those with less than 0.85 were left out for further analysis. These potential sites were further filtered for 35 bp of invariant sequence on each side to allow for probe design.Fastq data from Sequence Read Archive accession numbers SRP015697 and SRP035216 were aligned to the MluCI and NlaIII (New England BioLabs), barcoded with oligos described in Ae. aegypti reference genome  with default parameters, except with the call threshold set to 95%, to generate and post-process genotype calls. We genotyped a total of 160 wild and 101 lab-reared mosquitoes for this study. Full probe-by-probe results can be found in Table S4), which generated 27 cohorts of F1 offspring. All parents were genotyped using Axiom_aegypti1. We chose 32, 31, and 32 individual F1 offspring from crosses 5, 16, and 25, respectively. These crosses were chosen for genotyping so that they maximized the number of SNPs where the parental genotypes were heterozygous in one parent and homozygous in the other. This allowed for maximal power in testing of Mendelian inheritance across markers. We performed χ2 tests comparing expected and observed genotype frequencies in each of the three cohorts separately, combining P values where possible using Fisher’s method. We were able to test a total of 16,111 SNP loci in at least one cohort. These P values were then sequential Bonferroni-Holm corrected.We generated single pair matings from 5 laboratory colonies . The rest of the SNPs were randomly distributed throughout the genome. After quality filtering the SNP data, we used the highest scoring 50,000 SNP markers as ranked for expected performance on an Affymetrix Axiom MyDesign array (Ae. aegypti chromosomes in a recent florescence in situ hybridization study (Table S2 for full description of probes). There is no significant correlation between supercontig length and inter-SNP distance, which suggests that there is little if any bias in SNP position relative to others across the genome . We then tested the remaining 27,040 SNPs for a Mendelian pattern of inheritance by using the chip to genotype three F1 full-sibling cohorts and their parents. We found 1451 SNPs to be significantly deviant from what is expected under a Mendelian genetic model of inheritance (Table S4 and Table S5). This could either be due to true departure from a model of Mendelian inheritance, or due to a genotyping error in the parents. These positions were excluded, leaving 25,589 SNPs for further analysis.To develop the SNP markers for this chip, we mined previous RAD-seq generated SNP data . The global mean r2 value is 0.0348. The averages for SNP pairs on the same and different supercontigs are 0.1797 and 0.0345, respectively. When only SNP pairs with r2 values of 1 on the same supercontig are examined by themselves, 50% of these pairs are within a distance of ~148kb of one another , and less than 1% (204 SNP loci) that are out of Hardy Weinberg equilibrium after multiple test correction, α = 0.05 (Table S7).We then examined pair-wise linkage disequilibrium, as measured by r another . We measNext we performed principal components analysis on the wild individuals to examine variation in natural populations. Strong geographic signal is evident in the first two principal components when examining all populations together , as founAe. aegypti and test a set of SNP markers useful in genotypic analysis of natural populations. This robust set of SNPs do not suffer from the difficulties inherent to the development and analysis of microsatellites .The Axiom_aegypti1 chip is available from Affymetrix in 96 sample Axiom array format. Individual-level data on the samples included in this study are available in European Molecular Biology Laboratory−European-Bioinformatics Institute BioSamples group SAMEG188691, and their genotyping information is mirrored in VectorBase (


Zea mays L.) genome. SNPs have several advantages over simple sequence repeats, such as ease of data comparison and integration, high-throughput processing of loci, and identification of associated phenotypes. SNPs are thus ideal for DNA fingerprinting, genetic diversity analysis, and marker-assisted breeding. Here, we developed a high-throughput and compatible SNP array, maizeSNP3072, containing 3072 SNPs developed from the maizeSNP50 array. To improve genotyping efficiency, a high-quality cluster file, maizeSNP3072_GT.egt, was constructed. All 3072 SNP loci were localized within different genes, where they were distributed in exons (43 %), promoters (21 %), 3′ untranslated regions , 5′ UTRs (9 %), and introns (5 %). The average genotyping failure rate using these SNPs was only 6 %, or 3 % using the cluster file to call genotypes. The genotype consistency of repeat sample analysis on Illumina GoldenGate versus Infinium platforms exceeded 96.4 %. The minor allele frequency (MAF) of the SNPs averaged 0.37 based on data from 309 inbred lines. The 3072 SNPs were highly effective for distinguishing among 276 examined hybrids. Comparative analysis using Chinese varieties revealed that the 3072SNP array showed a better marker success rate and higher average MAF values, evaluation scores, and variety-distinguishing efficiency than the maizeSNP50K array. The maizeSNP3072 array thus can be successfully used in DNA fingerprinting identification of Chinese maize varieties and shows potential as a useful tool for germplasm resource evaluation and molecular marker-assisted breeding.Single nucleotide polymorphisms (SNPs) are abundant and evenly distributed throughout the maize (The online version of this article (doi:10.1007/s11032-015-0335-0) contains supplementary material, which is available to authorized users. Zea mays L.) is an important crop widely grown throughout the world for food, feed, and fuel production. In China, maize is the top-ranked crop in terms of cultivated area and total yield and plays a key role in the country’s agricultural economic structure. As a hybrid crop, maize is additionally a model plant species for genetic studies because of its high recombination rate and rich genetic diversity. Since the 1980s, the number of maize varieties in China has steadily increased. As of 2013, the total number of varieties approved by national and provincial governments is 6291, including 503 approved by the national government. Because thousands of maize samples are inspected annually in China , amplified fragment length polymorphisms, simple sequence repeats (SSRs), and single nucleotide polymorphisms (SNPs), have been widely applied in research areas such as DNA fingerprinting of varieties, genetic diversity analysis, association studies, and molecular marker-assisted breeding , four groups of similar lines, and a group of doubled haploid lines. Total genomic DNA was extracted from 50 pooled leaf samples using the CTAB procedure according to Wang et al. . The 40 hybrid samples included varieties with large planting areas, control varieties from different maize regional trial groups, and some specialized hybrids. The 56 inbred samples included the corresponding parents of the above hybrids and other elite inbred lines  SNPs with GenTrain scores between 0.70 and 1.00 were designated as candidate loci. Second, 20,212 SNPs were selected on the basis of reproducibility, missing data rate, signal strength, and their utility for defining the three genotypes. If a sample data point fell outside of a shaded call region on the GenomeStudio SNP graph, it was treated as missing data. SNPs with a missing data rate of more than 5 % of sites were removed. As the three genotypes of an ideal SNP should have obvious boundaries on the graph and be easy to differentiate, any SNP having a shifted cluster or a non-obvious boundary was deleted. Third, the candidate loci were further screened based on copy number, minor allele frequency (MAF), and even distribution. SNPs with copy numbers greater than or equal to 2 or MAF values under 0.2 were deleted. The remaining loci were screened according to the physical map. The best SNP in each genic region was chosen on the basis of coding region priority and good experimental quality principles.http://www.illumina.com) to assess their designability score values based on the GoldenGate assay. The key factors influencing these scores were the accuracy and conservation of the DNA sequences located 100 bp upstream and downstream of the SNP site. A score higher than 0.6 indicated that the SNP had a relatively higher probability of success, and SNPs with scores below 0.4 were deleted. A total of 3072 SNPs were obtained. The probe pool was developed according to the flanking sequences of the 3072 SNPs, and the maizeSNP3072 array chip was ordered based on GoldenGate technology.The 4050 candidate SNPs were submitted to Illumina , P, Improved Reid, Lancaster, Waxy, Landrace. In addition, 276 hybrid samples representing varieties approved by the Chinese Ministry of Agriculture were used to assess the effects of the 3072 SNPs on maize DNA fingerprinting and database construction.Cluster differentiation of the three possible genotypes  was performed for the 3072 SNPs based on genotype data from 22 triplets, 309 inbred lines, and 276 hybrids. Repeatability, miss rate, polymorphism, and variety-distinguishing efficiency of the 3072 SNPs were analyzed using the data from 96 selected samples, 309 inbred lines, and 276 hybrids. To assess the compatibility of the 3072 SNPs, we analyzed the genotype consistency of repetitive samples between Infinium and GoldenGate platforms. Polymorphism of each locus was analyzed based on the inbred-line data. The percentage of different loci was analyzed according to pairwise comparisons among 309 inbreds and 276 hybrids.1 triplets showed pedigree inconsistency , 27.5 % (0.61–0.80), and 71 % (0.81–1.00) . The parents had diverse genetic backgrounds that included Improved Reid, P, STPT, Luda Red Cob, SSS, NSSS, and landrace groups. Pedigree consistency values higher than 95 % were uncovered for 17 combinations, with a value of 100 % obtained for two combinations having B73 and/or Mo17 as parents. Consistency values below 95 % were observed in five combinations; this higher inconsistency was due to different seed sources for a hybrid and parents or the low purity rates of inbred lines such as Shen137 and Zong31.Analysis of several similar inbred groups uncovered no differences among T877-series maize samples . The genetic backgrounds of T877 lines were anticipated to be highly similar, as the series was produced by radiation-induced mutagenesis. Unlike T877 lines, Qi319, P25, and F349 lines were obtained through backcross breeding. Although some different loci were consequently uncovered in Qi319, P25, and F349 series , all genetic similarity values were greater than 97 %. To verify the ability of the 3072 SNPs to discriminate among maize varieties, we performed 37,950 and 47,586 pairwise comparisons among 276 hybrids and among 309 inbred lines. Differential locus percentages of between 5 and 70 % were observed in 99.9 % of these pairwise comparisons for both inbred and hybrid lines. The most frequent differential locus percentages uncovered were 50 % among inbred lines (in 4888 comparisons) and 60 % among hybrids (in 4216 comparisons) .The physical distribution of the 3072 loci on the 10 maize chromosomes was determined using their mapped positions on the B73 genome sequence. Each chromosome was divided into 1000-kbp-sized windows, and the number of SNPs per window was counted  and PZA loci in the Panzea database, of which only a small proportion were candidate loci in the current study. The 3072 SNP loci thus have few similarities with the 768 and 1536 loci. In conclusion, compared with the previously reported SNP arrays, the maizeSNP3072 set is more suitable for Chinese maize variety DNA fingerprinting and database construction.Although many types of SNP genotyping platforms exist, such as GoldenGate, Infinium, TaqMan, and KASPar, not all SNPs are transferable across different platforms. A success rate of approximately 89 % has been obtained on GoldenGate and Infinium platforms Supplementary material 2 (PDF 357 kb)Supplementary material 3 (DOCX 13 kb)Supplementary material 4 (XLSX 14 kb)Supplementary material 5 (XLSX 12 kb)Supplementary material 6 (XLSX 403 kb)Supplementary material 7 (XLSX 1243 kb)Supplementary material 8 (XLSX 24 kb)