Gene variants within regulatory regions are thought to be major contributors of the variation of complex traits/diseases. Genome wide association studies (GWAS), have identified scores of genetic variants that appear to contribute to human disease risk. However, most of these variants do not appear to be functional. Thus, the significance of the association may be brought up by still unknown mechanisms or by linkage disequilibrium (LD) with functional polymorphisms. In the present study, focused on functional variants related with the binding of microRNAs (miR), we utilized SNP data, including newly released 1000 Genomes Project data to perform a genome-wide scan of SNPs that abrogate or create miR recognition element (MRE) seed sites (MRESS).ST statistics (a measure of population subdivision) for predicted MRESS SNPs against non MRESS SNPs revealed a significantly higher (P = 0.0004) degree of subdivision among MRESS SNPs, suggesting a role for these SNPs in environmentally driven selection.We identified 2723 SNPs disrupting, and 22295 SNPs creating MRESSs. We estimated the percent of SNPs falling within both validated (5%) and predicted conserved MRESSs (3%). We determined 87 of these MRESS SNPs were listed in GWAS association studies, or in strong LD with a GWAS SNP, and may represent the functional variants of identified GWAS SNPs. Furthermore, 39 of these have evidence of co-expression of target mRNA and the predicted miR. We also gathered previously published eQTL data supporting a functional role for four of these SNPs shown to associate with disease phenotypes. Comparison of FWe have demonstrated the potential of publicly available resources to identify high priority candidate SNPs for functional studies and for disease risk prediction. The mosTP53 expression in prostate [HLA-C 3'UTR revealed a SNP modulating an MRE for the binding of miR-148. Furthermore, this SNP was shown to be in LD with rs9264942 which is found 35 kb upstream of HLA-C and associates with control of HIV [It has long been thought that disease causing variants act through alteration of exon sequence resulting in altered protein function. However, SNPs may also act to modulate gene expression, and this has been demonstrated for many promoter SNPs in which the risk allele alters the affinity of a transcription factor to its binding motif ,6. Furthprostate . Alternal of HIV . These dCurrently over 1000 human miR sequences are reported in the miRbase catalog . EstimatWith such information in mind, one aim of the 1000 Genomes Project is to catalog over 95% of human variation in order to inform association studies of all potential causal SNPs . FurtherHere we have performed a genome-wide survey for SNPs falling within both experimentally validated and computationally predicted conserved MRESSs, by utilizing these data (dbSNP build132) . In addiTo assess the frequency of SNPs falling in validated MRESSs, we first determined the genomic DNA (gDNA) coordinates of 606 validated mRNA target seed-sites for all mRNA-miR interactions, from the miRecords database . For a sNo population frequency data are available for 29% of the MRE SNPs 9 of 31), a value that will change as whole genome sequence data from more individuals surface and with completion of more encompassing GWAS studies. Nine SNPs have minor allele frequencies (MAF) < = 2%, and may be considered rare in the general population and therefore unlikely as common factors in complex disease. The 13 remaining SNPs have allele frequencies above 2% in at least one population listed in dbSNP  in LD with those reported in the GWAS. We limited our search to an r2 of > 0.8 for the CEU population. The results of this query were searched against both the MRESS SNP and the CNM SNP predictions. This query identified 35 instances of an MRESS SNP (some MRESS SNPs are in LD with more than one reported GWAS SNP associating with multiple phenotypes), in LD (r2 > 0.8) with a least one reported GWAS SNP or an original GWAS SNP, associating with disease phenotypes  with CNM SNPs  in very strong LD with SNPs reported as associating with disease related phenotypes. These 87 SNPs represent 2.22% of the total unique SNPs reported in the GWAS data. Using the SNAP pairwise LD tool we determined that 6 of the 3943 GWAS SNPs are in LD with each other, giving us 3940 SNPs or regions associating with disease traits. To determine the possibility of this number occurring by chance we first filtered dbSNP 132 for SNPs having a minor allele frequency (MAF) > = 1%, which was the lowest MAF reported in the GWAS data. We next selected randomly 3940 SNPs and, as we did with the GWAS data, ran them through SNAP to determine the SNPs in LD. From this list we determined the number of SNPs found in our MRESS and CNM SNP data, and this analysis was repeated 1000 times. From these 1000 simulations the mean number of SNPs found in the MRESS and CNM SNP data was 24.99 and the standard deviation 6.19. The probability of finding 87 SNPs by chance based on this distribution was calculated to be 1.08 × 10Support for a prediction of a miR regulating an mRNA target is strongly lent by co-expression of both RNAs. Therefore, to further refine this list of 87 cases where a SNP is predicted to create or abrogate a MRESS and be in LD with a GWAS SNP, we searched for evidence of co-expression of the miR and mRNA using the mimiR web tool . In addiVariation in gene transcript levels is thought to be an important modulator of disease risk in humans and SNPs that may mediate this variation are thought to be of great functional significance . To inveTo determine if the 39 miR predictions with co-expression data identified in the previous section are supported by eQTL data we utilized the Genevar eQTL database web tool . GenevarSYS1 transcript levels were shown to be significantly different among rs2245717 genotypes from LCL in one of the two twin study groups  statistic measures population differentiation and provides a test for the influence of selective pressures, where higher FST values indicate local positive adaptation and lower values negative or neutral selection [ST values may be considered high priority candidates for association studies for gene by environment studies. Such variants also may play a role in the observed variation and potentially influence disease prevalence across populations [ST calculations for HapMap Phase 3 data [ST values of combined MRESS and CNM SNPs (n = 2448) and the remaining (i.e. non-MRESS or non-CNM) 3'UTR SNPs (n = 19906) for which FST data were available. Figure ST values between MRESS and CNM SNPs and non-MRE SNPs across 10 FST bins. As FST values increase there is a clear increase in MRESS and CNM SNPs compared to the remainder. This observation further supports that MRESS and CNM SNPs are likely functional variants.Genetic variants that have been subject to selection are most likely the functional variants ,35. The election . As adapulations ,38. To de 3 data . We founST outliers we selected all SNPs falling 2 standard deviations (SDs) or more from the mean  for which we show evidence of co-expression and eQTL effect.To identify Fan Table . In totaIn the work presented here, we utilized the latest release of dbSNP, including the 1000 Genomes Project data, to perform a genome-wide scan of human variation within validated and predicted miR binding sites, our hypothesis being that genetic variants at miR binding sites are functional, and important contributors to phenotypic variation and disease susceptibility. We have taken careful measures to assign SNPs as creating or altering miR-mRNA interactions. We identified 5797 instances of a SNP falling within a conserved predicted MRESS based on stringent filtering of conservation and interaction scores predicted by Betel et al . InteresRecently, it has been demonstrated that SNPs previously identified in GWAS are in LD with SNPs found in enhancer motifs regulating gene expression . FurtherIZKF3 transcript and is in LD (r2 > 0.90) with eight SNPs associating with increased risk for a variety of autoimmune diseases. IZKF3 is a transcription factor important for B-cell activation, and mice lacking this gene develop a lupus like syndrome, suggesting a role for IZKF3 in autoimmunity [IZKF3 in human B-lymphocytes. Interestingly, miR-326 is important for T-cell differentiation and has been implicated in the pathogenesis of autoimmune multiple sclerosis [IZKF3 in subjects carrying the T allele [Our analyses have identified four SNPs predicted to modulate allele-specific miR-mRNA interactions which are supported by co-expression and eQTL data. The rs907091 SNP falls in the immunity . The rs9clerosis . A studyT allele . These dZC3H4 transcript and associates with BMI. ZC3H4 is a poorly characterized zinc finger protein. There is eQTL evidence supporting this prediction where minor allele carriers have reduced ZC3H4 expression compared to non-carriers, in adipose tissue [ZC3H4 are expressed in adipose [SYS1 transcript, is in perfect LD with rs1008953 a SNP associating with psoriasis [SYS1 is also associated with lower SYS1 transcript levels in LCL cells [GFOD2) transcript showing association with HDL-C [GFOD2 levels. Interestingly, both RNAs are expressed in adipose tissue [ST outlier suggesting this SNP may be undergoing population specific selection.In addition the minor allele of rs3810291 is predicted to create an MRE for mir-502-3p within the e tissue . Both mi adipose . The rs2soriasis . The MRECL cells . The rs1th HDL-C . Our anae tissue . FurtherHOXB2 loci has shown eQTL peaks identified from lymphoblastoid cell lines [In addition to these four SNPs, we identified 39 others with data indicating co-expression with the predicted target mRNA and these should be considered as candidates for functional studies. Of these 39 candidates, a SNP within the ll lines ,46. Whilll lines . TherefoST than do those SNPs which do not create or disrupt a predicted MRESS. We identify those MRESS and CNM SNPs showing the highest degree of population subdivision and suggest these SNPs and the interactions they are predicted to modulate, as candidates for functional studies.As new variants arise in a population and are exposed to different environmental conditions, those variants may be subject to forces of selection. Moreover, if these SNPs alter gene expression they may modulate the individual's response to the environment and potentially the risk for particular disease state. Based on this, we hypothesized that allele-specific miR-mRNA interactions would show a greater level of selection than SNPs not classified as MRESS SNPs. We show that, as a group, predicted MRESS and CNM SNPs have a significantly higher mean FWe show that the frequency of MRESS SNPs in validated MREs (5.5 SNPs/kb) is less than in surrounding regions and this supports prior work showing a higher degree of negative selection on MRESSs ,10. AlthThere are several web based MRE SNP prediction databases available to query a SNP for creation or disruption of a MRESS, however these tools incorporate a relatively limited amount of functional annotation  for identification of the most promising MRESS SNPs -50. SNPiIn Summary, we have surveyed the most current human SNP data and identified variants that provide functional hypotheses for observed GWAS associations. Our work also suggests that a considerable number of SNPs create or abrogate MREs in the human genome. Our results further suggest MRE SNPs that modulate gene expression are likely to be under selective pressure. With relevance to human disease we show that publicly available resources can be used to identify high priority candidate SNPs for functional studies.We retrieved all dbSNP build 132 SNP (as of 11-31-10) information by downloading the vcf file available through the 1000 genomes home page . To ensumiRecords hosts a collection of validated miR-mRNA interactions built from an exhaustive literature search and the database of records was download in a tab-delimited format . We nextTo calculate SNP density, a Perl script was written to perform a sliding window search (W = 6 bases as this corresponds in size to bases 2-7 of the MRESS) of the 606 validated MREs for SNPs, starting 18 bases upstream of the first base of the MRESS and ending 18 bases downstream of the last base of the MRESS. We report the number of SNPs for each position of the window across this sequence. Values are reported as SNPs/kb.http://microrna.org website. This file contains all predicted mRNA target motifs for targeting microRNAs which belong to conserved microRNA families. Conservation signal is used to predict functional MREs, however it has been determined that a conservation signal above background for MREs of the most recent mammalian specific miRNA families (non-conserved) was unlikely due to the relatively short time between the emergence of these miRs and the occurrence of new MREs within 3 'UTRs [http://microRNA.org. Additionally, predictions generated by the http://microRNA.org tool provide a ranking score (mirSVR score) which is calibrated to correlate linearly with the extent of down regulation of a miR on its target. Importantly, these scores may be interpreted as an empirical probability of down regulation. From these data we selected a mirsvr score cutoff < = -0.6, representing the top 12% of all predictions. A Perl script was then used to compare the gDNA coordinates of each predicted MRESS (n = 197287) against the gDNA coordinates of each 3'UTR SNP in dbSNP132.To identify SNPs falling in conserved MRESSs, we downloaded the "good mirsrv_score conserved miRNA" datafile from the  3 'UTRs . Therefo 3 'UTRs ,52,53. TTo identify CNM SNPs, we utilized the Ensembl variation Perl API tools (Build 61) to retrieve the 22 bases flanking the 5' and 3' regions of every 3'UTR SNP in dbSNP 132. We generated the reverse complement for those mRNAs transcribed from the negative strand. These data were then run locally through the miRanda target prediction algorithm. To limit identification of potential false positives we implemented an arbitrary paring score cutoff of > = 150 and an energy cutoff of < = -20. We identified all predicted MRESS created by CNM SNPs by filtering hits on the position of their target prediction on the mRNA, where each SNP is located at position 23 of 45.http://www.broadinstitute.org/mpg/snap/ldsearch.php using r2 of > 0.8 for the CEU population. The resulting list was then searched against each of the CNM and MRESS SNP lists to identify 87 MRESS and CNM SNPs. To determine the probability of observing this number by chance, 3940 SNPs were randomly selected from the dbSNP build 132 data set filtered for SNPs with a MAF > = 1% and run through the SNAP tool to identify all SNPS in LD, using r2 of > 0.8 for the CEU population. This procedure was repeated 1000 times. The probability of observing 87 MRESS or CNM SNPs randomly from the genome was determined based on the normal distribution generated from the 1000 simulations.We first downloaded a catalog of human variation associating with disease phenotypes (1-25-11) . The lisWe utilized the mimiRNA web tool to identify miR-mRNA predictions with co-expression evidence . The mimTo identify association of transcript levels with MRESS and CNM SNPs we searched eQTL data from the MuTHER study using the Genevar web tool ,32. eQTLGenevar provides Spearman's rank correlation coefficient estimates for the strength of relationships between alleles and gene expression intensities for each study group. Furthermore, to test the significance of the relationship, Genevar generates a t-statistic for correlation analysis. Adjusted non-parametric permutation P-values are also provided .ST values where calculated for each HapMap Phase 3 SNP between these 4 groups and reported in a downloadable file [ST values from this dataset. We used the statistical analysis software (SAS) boxcoxar macro to transform the FST data to fit a normal distribution. We then performed an unpaired Student's t-test using the transformed values for these two groups to determine if they were significantly different. To identify MRESS and CNM FST outliers we selected those SNPs with FST > 2 SDs from the mean.Genotype characteristics of 11 HapMap Phase 3 populations were split into 4 groups of similar ancestry; Asian, African, European and American. Fble file . Using aAuthors have no competing interests to declare.ST analysis. KR, CQL, and JMO conceived the work and drafted the manuscript. All authors read and approved the manuscript.KR wrote all Perl scripts for comparisons and analysis. KR and YCL performed all genome SNP scans. KR and LDP performed sliding window SNP density analysis. KR and CQL performed FSNPs identified to abrogate MRESSs in LD with GWAS SNPs. All MRESS SNP minor allele frequencies (MAF) reported are for the CEU pilot panel of the 1000 Genomes Project, except where indicted. *indicates MAF in low coverage 1000genomes CEU panel. Abbreviations: GWAS SNP = SNP reported in GWAS, Proxy = MRESS SNP in LD with GWAS SNP, SNP coord = genomic coordinate of MRESS SNP, PhastCon = conservation score, PID = PubMed accession, SVR = miRSVR score, S-T = seed type.Click here for fileCNM SNPs in LD with GWAS SNPs. CNM SNPs in LD with variants association with disease traits. All CNM SNP minor allele frequencies (MAF) reported are for the CEU pilot panel of the 1000 Genomes Project, except where indicted. * indicates MAF in low coverage 1000genomes CEU panel. Abbreviations: GWAS SNP = SNP reported in GWAS, Proxy = CNM SNP in LD with GWAS SNP, SNP coord = genomic coordinate of CNM SNP, PMID = PubMed accession, PS = miRanda Pairing Score, ES = miRanda energy score, S-T = seed type.Click here for fileCNM SNPs in LD with GWAS variants and showing co-expression of miR and mRNA. CNM SNPs in LD with variants association with disease traits. All minor allele frequencies (MAF) reported are for the CEU pilot panel of the 1000 Genomes Project, except where indicted. * indicates MAF in low coverage 1000genomes CEU panel. Abbreviations: PMID = PubMed accession, PS = miRanda Pairing Score, ES = miRanda energy score, S-T = seed type, miRlit = evidence of miR and mRNA expression collected from the literature, where numbers indicate pubmed ids, except those beginning with GDS, which indicate the Geoprofile dataset ID for which expression was demonstrated. Co = The number of cell and tissue samples in the mimiRNA database for which co-expression of miR and mRNA were found. eQTL = Reports available eQTL data in the mUTHER study, where F = -Fat cell biopsy (n = 160), L = LCL cells (n = 166), and S = skin cell biopsy (n = 160).Click here for file


HINT1) and acyl-CoA synthetase long-chain family member 6 (ACSL6). Other overlapping ROHs may contain novel rare recessive variants that affect SCZ specifically in our samples, given the highly heterozygous nature of SCZ. Analysis of patients whose parents are first cousins may provide new insights for the genetic analysis of psychiatric diseases.The recent development of high-resolution DNA microarrays, in which hundreds of thousands of single nucleotide polymorphisms (SNPs) are genotyped, enables the rapid identification of susceptibility genes for complex diseases. Clusters of these SNPs may show runs of homozygosity (ROHs) that can be analyzed for association with disease. An analysis of patients whose parents were first cousins enables the search for autozygous segments in their offspring. Here, using the Affymetrix® Genome-Wide Human SNP Array 5.0 to determine ROHs, we genotyped 9 individuals with schizophrenia (SCZ) whose parents were first cousins. We identified overlapping ROHs on chromosomes 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 19, 20, and 21 in at least 3 individuals. Only the locus on chromosome 5 has been reported previously. The ROHs on chromosome 5q23.3–q31.1 include the candidate genes histidine triad nucleotide binding protein 1 ( Schizophrenia (SCZ) is categorized as a severe chronic debilitating psychosis that affects approximately 1% of the global population. Although genetic factors are reported to contribute to the disease and multiple responsible loci have been identified from linkage analysis and case-control association studies, there have been few reproducible results to date Morrow et al. (2008) We hypothesized that runs of homozygosity (ROHs) could contribute to SCZ by a recessive effect. We use the term “ROH” Here, we describe a homozygosity mapping strategy that consisted of 2 stages . The firDiagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) and the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID). The study received ethics approval from the Committee for Ethical Issues on Human Genome and Gene Analysis at Nagasaki University, Japan. All of the patients were from the main islands of Japan, excluding Okinawa. We obtained written informed consent from all participants. The consanguineous patients were from 8 first-cousin marriages. Seven individuals (patients a to g) were unrelated and 2 were siblings (patients h-1 and h-2). We also recruited 92 healthy individuals from non-consanguineous marriages  from the main islands of Japan, excluding Okinawa. We confirmed consanguinity by interview. We did not match for gender in the SCZ and non-CJ individuals because we only intended to analyze autosomal chromosomes.A total of 9 subjects with SCZ   were recruited to this study after being diagnosed as having typical paranoid schizophrenia by a certified psychiatrist (N.K.) using the After obtaining written informed consent, genomic DNA was isolated from peripheral blood. We did not collect blood samples from the patients' parents, except for 1 patient, or siblings; however, we confirmed that they had no history of psychiatric illness, with the exception of the older brother of patient g, by direct interview or from the medical records of the other related individuals.http://www.hapmap.org/.Furthermore, we also assessed the International HapMap Project We performed genome-wide SNP genotyping of 9 SCZ samples and 92 non-CJ samples using the Affymetrix Genome-Wide Human SNP Array 5.0 (Affy5.0) according to the manufacturer's instructions. Our microarray data is MIAME compliant and the raw data has been deposited in the CIBEX database (CIBEX accession number: CBX141).We generated the CHP genotype files from the CEL signal intensity files using the BRLMM-P genotype calling program Detected ROHs were statistically analyzed and visualized  and 2 byFurthermore, to validate the data quality of our non-CJ samples, we also compared our data to HapMap3 JPT. Affy6.0 raw signal intensity data in CEL files were subjected to allele calling using Birdseed software version 2 To detect the overlapping ROHs among the SCZ dataset, the identified ROHs were filtered by a size threshold on Partek GS, analyzed using an in-house Ruby script (available on request) to generate a table of overlapping ROHs, and visualized with Partek GS. Then, we extracted the loci shared among more than 3 unrelated individuals (Model I) . FurtherWe genotyped 440 794 SNPs in each individual. Genotype calling rates for each sample ranged from 97.23–98.83% and their call rates were high and accurate enough for their subsequent evaluation. We utilized the data from 92 non-CJ and 91 HapMap3 JPT samples in addition to the data from 9 SCZ individuals.Our homozygosity mapping strategy utilized differences in the length distribution of ROHs between offspring from consanguineous and non-consanguineous marriages. Individuals from consanguineous families are expected to have an increased number of longer ROHs containing autozygous segments. These segments were also expected to be discriminated by their length from ROHs containing homozygous segments by chance or by linkage disequilibrium (LD). To demonstrate the strategy, we performed detailed comparisons of the length distribution of ROHs between the non-CJ, HapMap3 JPT, and SCZ datasets.We initially plotted the total number and size of ROHs in the non-CJ, HapMap3 JPT, and SCZ datasets . The non10 scale on the x-axis to indicate the length of each ROH; however, the SCZ dataset showed a secondary peak in the larger ROH region. We expected that the autozygous region from the founders of the third ancestral generation (great-grandparents) would be larger in the SCZ dataset than in the non-CJ dataset, in whom LD may encompass ROHs by chance. The proportion of larger ROHs in the SCZ dataset was clearly higher than in the non-CJ dataset. As we can expect that 1/16 of the whole genome in the offspring of first-cousin marriages would be autozygous regions from their great-grandparents, we highlighted the graphs in We then analyzed the length distribution of ROHs in the non-CJ and SCZ datasets. Bar plot histograms of the length of ROHs were obtained and the probability density curves were estimated by the “KernSmooth” package in R . DescripTo show further differences in the probability density distribution of the SCZ and non-CJ individuals, we also plotted an SCZ/non-CJ odds ratio (OR) plot , which iThe sum lengths of the overlapping regions among 0–7 independent family patients are shown in HINT1) and acyl-CoA synthetase long-chain family member 6 (ACSL6) genes. Our results suggest that recessive variants of these candidate genes could be involved in the pathogenesis of SCZ in our patients.Overlapping ROHs found in 3 or more SCZ individuals on chromosomes 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 19, 20, and 21  suggesteIn the analysis of 2 siblings (h-1 and h-2) from a first-cousin marriage, we searched for the ROH regions shared by the siblings as a single gene defect. The detection of loci shared by the siblings and 1 or more unrelated individuals demonstrated ROHs on chromosomes 1, 5, 7, 8, 10, 12, 13, 16, 17, 19, and 21 that might be causative for SCZ . Those lWe recruited 9 offspring from first-cousin marriages (SCZ) and 92 from non-consanguineous marriages (non-CJ). As shown in Most of the previous homozygosity mapping studies were based on genotypes derived from microsatellites or simple tandem-repeat polymorphisms (STRP). The highly polymorphic nature of multiallelic STRP markers is suitable to cover the whole genome with a fewer numbers of markers. However, recent DNA microarray technologies have enabled massive genome-wide SNP genotyping to be performed in a short time. The problem with homozygosity mapping based on SNPs is the accurate detection of regions with ROHs. As SNPs have a less informative biallelic nature, using the naïve definition of an ROH as just a contiguous homozygous region may skew the detection of ROHs because of the frequency of low minor allele SNPs, genotyping errors, and “no-call” SNPs.a priori default frequency. This method is expected to more accurately detect ROH regions that reflect actual recombination.The solution to this problem using the Affymetrix Human Genotyping 500K arrays and Illumina Infinium HumanHap300v2 arrays was the application of ROH detection bins sliding through each chromosome to filter out low SNP density bins and to allow the small number of heterozygous SNPs and no-call SNPs to be placed in a bin Selection of the reference population for the baseline data is crucial for the HMM-based detection of ROHs. If the reference population is carefully selected to match the background of the case population, the baseline generated from the observation of actual SNPs in the reference population can omit ROHs resulting from LD and regions with low SNP density, such as centromeres. However, if strict matching of the used population background is difficult, use of the fixed default heterozygous frequency, whose default value is 0.3, still has the advantage of minimizing false-negatives in the detection of ROHs.To determine the optimal length threshold of ROHs to extract autozygous segments from whole ROHs, we adopted OR = 3 for the analysis. This approach may work well when a large enough reference sample is available. When a reference population is not available, a threshold where the sum of the ROH length in the upper tail of its distribution is equal to the theoretical autozygous length of a genome, that is, 1/16 of a genome in the offspring of a first-cousin marriage, could be another option. In our SCZ dataset, the threshold using this approach was approximately 10.6 Mb.Our results demonstrated obvious differences in the proportion of the length distribution of ROHs between the non-CJ and SCZ datasets. A recent report on European populations, including endogamy subpopulations, has shown that a higher proportion of individuals in endogamy subpopulations have ROHs longer than 1.5 Mb compared with other subpopulations The size distribution of ROHs for a given population is affected by its inbreeding coefficient (F). Studies of consanguineous marriages in subpopulations from Japan during the 1980s compared the F values for Japan (F = 0.00134) to those in Kuwait (F = 0.0219), India (F = 0.02313), England (F = 0.00017), and the United States (F = 0.00003) We presented here some threshold lengths of ROHs to detect IBD regions from great-grandparents. As recombination will, of course, occur everywhere by chance, small autozygous regions could be overlooked with the threshold shown here. However, no systematic analyses have so far identified IBD regions in consanguineous marriages by whole-genome SNP typing. Our method shown here, which 1) detects longer ROHs in each individual and 2) aligns ROHs and identifies overlapping regions, will be helpful for autosomal recessive disorders and also for complex disorders resulting from rare variants. If collecting patients in geographically and historically isolated areas is possible, this homozygosity mapping approach is likely to be successful. Nonetheless, the effectiveness of homozygosity mapping for complex disorders remains controversial We identified several putative SCZ loci that are presented in HINT1ACSL6For Model I , from thOn the basis of the rare variant-common disease hypothesis, it is appropriate that the genetic etiology between sibling cases and other unrelated cases may be various. In addition, our results suggested that multiple loci influenced the susceptibility to SCZ, as other reports have suggested We presented here the systematic analyses of the homozygosity mapping method using whole genome SNP typing, and we identified ROHs that potentially contain SCZ causative recessive regions that are shared among our samples. When we explain SCZ as a result of the homozygous state of rare variant mutations, the number of overlapping individuals may be challenging, as it is possible that each individual has a different variation. The heterogeneity of SCZ may explain the lack of overlap for our results with previously reported regions Table S1Novel loci identified in this study.(DOC)Click here for additional data file.


Genomic structure in a global collection of domesticated sheep reveals a history of artificial selection for horn loss and traits relating to pigmentation, reproduction, and body size. Through their domestication and subsequent selection, sheep have been adapted to thrive in a diverse range of environments. To characterise the genetic consequence of both domestication and selection, we genotyped 49,034 SNP in 2,819 animals from a diverse collection of 74 sheep breeds. We find the majority of sheep populations contain high SNP diversity and have retained an effective population size much higher than most cattle or dog breeds, suggesting domestication occurred from a broad genetic base. Extensive haplotype sharing and generally low divergence time between breeds reveal frequent genetic exchange has occurred during the development of modern breeds. A scan of the genome for selection signals revealed 31 regions containing genes for coat pigmentation, skeletal morphology, body size, growth, and reproduction. We demonstrate the strongest selection signal has occurred in response to breeding for the absence of horns. The high density map of genetic variability provides an in-depth view of the genetic history for this important livestock species. During the process of domestication, mankind recruited animals from the wild into a captive environment, changing their morphology, behaviour, and genetics. In the case of sheep, domestication and subsequent selection by their animal handlers over thousands of years has produced a spectrum of breeds specialised for the production of wool, milk, and meat. We sought to use this population history to search for the genes that directly underpin phenotypic variation. We collected DNA from 2,819 sheep, belonging to 74 breeds sampled from around the world, and assessed the genotype of each animal at nearly 50,000 locations across the genome. Our results show that sheep breeds have maintained high levels of genetic diversity, in contrast to other domestic animals such as dogs. We also show that particular regions of the genome contain strong evidence for accelerated change in response to artificial selection. The most prominent example was identified in response to breeding for the absence of horns, a trait now common across many modern breeds. Furthermore, we demonstrate that other genomic regions under selection in sheep contain genes controlling pigmentation, reproduction, and body size. Man's earliest agricultural systems were based on the captive management of sheep and goats. The transition from hunting to animal husbandry involved human control over the reproduction, diet, and protection of animals. The process of domestication was initiated approximately 11,000 years ago in the Fertile Crescent ovine SNP50 Beadchip, an array consisting of approximately 50,000 evenly spaced SNP. We present the relationship between breeds in terms of divergence time, estimated from the extent of haplotype sharing. Secondly, we sought to characterise the genetic legacy that selection and adaptation have imparted on the sheep genome. By performing a genome-wide scan for the signatures of selection, 31 genomic regions were identified that contain genes for coat pigmentation, skeletal morphology, body size, growth, and reproduction. By combining the collection of a global sample of ovine breeds with the ability to interrogate 50,000 genetic loci, the results provide unprecedented insight into the phylogeographic structure of sheep populations and the results of centuries of breeding practices.Patterns of genetic variation have long proven insightful for the study of domestication, breed formation, population structure, and the consequences of selection. Variation within the mitochondrial genome has documented the global dispersal of two major haplogroups in modern sheep ovine SNP50 Beadchip, an array consisting of SNP derived from three separate sequencing experiments  within breeds and between regions . This appears much less pronounced in sheep compared with human migration out of Africa Ne, Ne of 150 or less Ne exceeding 500 and only two sheep populations showed evidence of a comparatively narrow genetic base (Ne<150).Analysis of genetic variation was performed for 2,819 animals in the global sheep diversity panel. Breeds were sampled from each continent across the species range , includi regions . Allele  methods . Followi methods . This liK) K = 2), a clear division was observed between Northern European and Asian breeds  was located at Mb position 29.54 near the Relaxin/insulin-like family peptide receptor 2 (RXFP2), which was recently linked with the absence of horns (poll) in sheep FST between breeds defined as either being horned or polled. This recapitulated a single strong and striking selection signal at RXFP2  and their neighbouring SNP. Global osome 10 . The higat RXFP2 . ImportaFST  and bone resorption (p = 4.0×10−5). Given it is unlikely all 181 genes have undergone selection but each contributed to the GO analysis, caution is required during interpretation. Nonetheless, the content of the differentiated regions strongly suggests enrichment for genes under selection given their roles in pigmentation, body size, reproduction, animal production, and domestication. Selection for specialised coat pigmentation represents breed-defining characteristics across domestic animals including sheep. Selection signals were detected spanning KIT, ASIP, and MITF  is a key regulator of mammalian reproduction that is critical for the onset of lactation and is associated with milk traits in dairy cattle TSHR) in chicken, which given its pivotal role in metabolic regulation and the control of reproduction, was postulated to be a domestication gene FST peak on Chromosome 6 spanned the FGF5 gene, recently shown to contain mutations in dog responsible for variation in hair type KIT, FGF5, MITF, and RXFP2 are targets for selection across multiple mammalian lineages . Conversely, sheep from the Americas (Brazil and the Caribbean) had high Ne and clustered separately from European, African, or Asian populations. Decomposing the genome into two or more components  gives the fraction of total SNP that displayed both alleles within each population. Expected heterozygosity (He) and the inbreeding coefficient (F) were estimated using PLINK Ar) and private allele richness (pAr) were estimated by ADZE Five metrics were used to estimate levels of within-breed genetic diversity . The proAnalysis of allele frequency distributions, plotted separately for SNP identified by Roche 454 and Illumina GA sequencing, indicated the presence of ascertainment bias . To deteK (number of subpopulations). For supervised clustering, prior population information was introduced from six meta-populations consisting of regional pool of breeds considered to represent ancestral populations. The same meta-populations were used for updating the allele frequencies during the simulations. NeighborNet graphs were constructed from a matrix of Reynolds' distances using Splitstree In order to understand the relationship within and between breeds across each major geographic group, Principal Components Analysis (PCA) was performed using EigenStrat 2r between all SNP pairs where MAF for each SNP in the pair was >0.10. 2r values were grouped into bins based on the distance between SNP from the physical map. tN was then calculated as (1−2r)/(42cr), where c is the distance between the SNPs in Morgans (we assumed 100 Mb = 1 Morgan) and tN is the effective population size t generations ago, where t = 1/2c. The most recent estimate of effective size was taken as tN when c = 1 Mb. We performed simulations to assess the sensitivity of the estimates of effective population size over generations based on LD, in populations with and without admixture events (2) was calculated between all marker papers and Ne estimated at different times in the past as described for the real data. Five replicate simulations were performed for each scenario. The extent of haplotype sharing among populations was characterised with the r statistic, where r is a signed 2rr values for all locus pairs separated by the same physical distance among two breeds requires that the same haplotypes are found within both breeds. This means the sign of the r statistic is preserved across breeds only if the phase relationship among alleles is the same in both populations . The correlation of r between breeds was calculated for SNP separated by <10 kb, 10–25 kb, 25–50 kb, 50–100 kb, and 100–250 kb . To determine the extent of this error, we calculated the correlation of the r values at these different lengths of haplotypes for the Merino and Industry Merino samples, which are samples from the same breed. This gave a correlation between the r values for each bin size of 0.6. All correlations of r values for all breed comparisons were then divided by 0.6 to correct for sampling. Only corrected values are presented. As detailed in r between two breeds with increasing marker distance can be used to estimate generations since divergence from a common ancestral population. From r after T generations of divergence is E(rT) = ecT−2. The natural logarithm of the expected correlation of r then follows a linear decrease as a function of distance with slope −2T, and this was used to calculate divergence time between all breeds  was calculated as e events . A mutat0–250 kb , S8, S9.l breeds .FST was calculated as described by FST values were smoothed using a local variable bandwidth estimator as described in FST within each population was smoothed into 500 genomic divisions (98 SNP per region). The number of breeds with smoothed FST in excess of one standard deviation of the mean was plotted for values at each tail of the distribution. Analysis was performed to identify gene ontology (GO) terms that were significantly overrepresented in 181 genes residing within the 31 regions under selection  based on SNP type. MAF was estimated for each of five geographically defined breed groupings separately using either 33,115 SNP derived using Roche 454 (top panel) or 15,427 SNP derived using Illumina GAII (bottom panel). The breed membership of each group is given in (TIF)Click here for additional data file.Figure S2Diversity between breeds compared using different SNP panels. Expected heterozygosity was calculated using four SNP panels: “49034” contains all SNP passing quality control ; “20279”(TIF)Click here for additional data file.Figure S3Principal components analysis (PCA) of European-derived sheep. To visualise the complex relationships between European-derived populations, PCA was performed separately using northern European breeds  and central and southern European breeds . The inbred Soay, Boreray, and Macarthur Merino have been omitted. In the left panel, PC1 and PC2 resolve all British and most central European breeds and show the intermediate position of the Swiss crossbred Swiss Alpine, Swiss Mirror, and Swiss Brown-Black Mountain sheep. Individuals from three geographically distinct populations of Texel formed a single cluster Click here for additional data file.Figure S4K components, which may correspond to the genomes of ancestral populations. Representative results are shown for unsupervised clustering using K = 2, 5, and 8. At K = 2, a marked division is observed between European and Asian breeds, which corresponded to the first principal component in PCA , South-West Asian (Middle Eastern), Asian, and African breeds. Separate clusters were assigned to the Soay and Boreray (at K = 5), Dorset (at K = 6), Friesian (K = 7), and Wiltshire breeds (K = 9). The admixed nature of Brazilian and Caribbean animals can be seen at higher values of K. Essentially the same patterns were obtained with the Admixture program K (unpublished data). Supervised clustering  was performed using six predefined regional genomic components reconstructed by pooling breeds as indicated by the coloured horizontal line.Model-based clustering of sheep. The diverse origin of the sheep genome was examined by model-based clustering using STRUCTURE t in PCA . For inc(TIF)Click here for additional data file.Figure S5Proportion of variance explained in principal component analysis. Genetic distance between each pair of animals in the global sheep diversity panel was analysed using PCA. The proportion of variance explained by each of the first 50 PCs is given at left (blue diamonds) and the cumulative proportion is shown at right (red triangles). The first PC explained approximately 3% of the variance, while the first 20 PCs together explain 16.3% of the total variation.(TIF)Click here for additional data file.Figure S6Ne was estimated at different times in the past as described for the real data. This shows the admixture event did affect the inferred Ne, with a higher estimate for the generations in which the admixture event is occurring. However, impact on estimates of Ne for generations that were not within 100 generations of the admixture event were minimal. Further, the pattern of inferred Ne for the admixed population suggests that there is information in the pattern of LD to pick up such events.Effective population size inferred from linkage disequilibrium (r2) with and without an admixture event in a simulated sheep population. The decline in effective population size was simulated to be similar to that observed in the real HapMap data for many breeds. The admixture event occurred 220 to 200 generations ago and lasted 20 generations before the breeds diverged for a further 200 generations without further admixture. LD (r2) was calculated between SNP and (TIF)Click here for additional data file.Figure S7r statistic r was calculated using SNP pairs separately by short physical distances (0–10 Kb). The values are given as heat map, where each cell represents haplotype sharing between a breed pair. This revealed a high degree of conservation in LD phase between sheep populations.Extensive sharing of short-length haplotypes between breeds. The persistence of haplotype phase between breeds was evaluated using the signed (TIF)Click here for additional data file.Figure S8r statistic r was calculated using SNP pairs separately by short physical distances (10–25 Kb). The values are given as heat map, where each cell represents haplotype sharing between a breed pair. The values were used to plot haplotype sharing in Haplotypes sharing between breeds at intermediate physical distances. The persistence of haplotype phase between breeds was evaluated using the signed (TIF)Click here for additional data file.Figure S9Long-distance haplotype sharing between breeds. The persistence of haplotype phase between breeds was calculated in the same way as in (TIF)Click here for additional data file.Figure S10Divergence time between breeds. Divergence time (in generations) between breeds was calculated from the extent of haplotype sharing that persists at increasing physical distance between SNP pairs (TIF)Click here for additional data file.Figure S11The effect of SNP ascertainment on breed relationships. To evaluate the effect of SNP ascertainment on visualised relationships between sheep breeds, NeighborNet graphs were constructed and compared using five different SNP sets. Graphs were constructed using all 49,034 SNP (A); 33,115 SNP identified using Roche 454 (B); 15,427 SNP identified using Illumina GA (C); 22,678 SNP identified by application of LD pruning of the full SNP set (D); and 20,279 SNP polymorphic in non-domestic sheep that displayed LD<0.05 (E). The topology of each graph is very similar, indicating SNP ascertainment doe not have a strong impact on interpretation of genetic relationships.(TIF)Click here for additional data file.Table S1Global Sheep Diversity Panel.(DOC)Click here for additional data file.Table S2ovine SNP50 BeadChip.SNP discovery for the (DOC)Click here for additional data file.Table S3Quality control filters used to remove SNP.(DOC)Click here for additional data file.Table S4Genetic diversity and recent effective population size.(DOC)Click here for additional data file.Table S5Enrichment analysis of gene ontology (GO) terms for genes in regions under selection.(DOC)Click here for additional data file.Table S6Selection signals identified in both sheep and cattle.(DOC)Click here for additional data file.


The most distinguishing features of this technology are the ability to genotype any population structure, regardless whether parental data is included, and the ability to co-dominantly score SNP markers segregating in populations. To demonstrate the capabilities of SBG, we performed marker discovery and genotyping in Arabidopsis thaliana and lettuce, two plant species of diverse genetic complexity and backgrounds. Initially we obtained 1,409 SNPs for arabidopsis, and 5,583 SNPs for lettuce. Further filtering of the SNP dataset produced over 1,000 high quality SNP markers for each species. We obtained a genotyping rate of 201.2 genotypes/SNP and 58.3 genotypes/SNP for arabidopsis (n = 222 samples) and lettuce (n = 87 samples), respectively. Linkage mapping using these SNPs resulted in stable map configurations. We have therefore shown that the SBG approach presented provides users with the utmost flexibility in garnering high quality markers that can be directly used for genotyping and downstream applications. Until advances and costs will allow for routine whole-genome sequencing of populations, we expect that sequence-based genotyping technologies such as SBG will be essential for genotyping of model and non-model genomes alike.Conventional marker-based genotyping platforms are widely available, but not without their limitations. In this context, we developed Sequence-Based Genotyping (SBG), a technology for simultaneous marker discovery and co-dominant scoring, using next-generation sequencing. SBG offers users several advantages including a generic sample preparation method, a highly robust genome complexity reduction strategy to facilitate  Marker assisted selection (MAS) is used to significantly accelerate the plant breeding process. In MAS, molecular markers such as single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs) are used to indirectly select for genetic determinant(s) of a trait of interest. Commercially important crop traits include abiotic stress tolerance, disease resistance, high yields, and improved nutritional qualities Although the number of molecular markers has rapidly expanded with the development of high-throughput marker discovery and genotyping technologies, in some crop plants the number of markers remains surprisingly low Next-generation sequencing (NGS) technologies are providing researchers with the unprecedented means to unravel the underlying sequence variation associated with heritable traits. The costs associated with sequencing complete plant genomes is one factor that still limits its' routine use in SNP discovery, especially when a reference genome is unavailable. Despite this, massively parallel sequencing, with a sequencing capacity from millions, to billions of bases per run, will significantly revolutionize the way in which SNP discovery and genotyping is achieved in the future ® technique Coupling genomic reduction strategies to NGS may further reduce the costs of detecting a large number of novel SNPs in a high-throughput manner. This requires however that genome complexity reduction is performed in a reproducible manner, in all samples to be sequenced in a specific experiment, but also over multiple experiments. Traditionally, methods such as the AFLPThere are currently several approaches that combine marker discovery and genotyping with the express aim to provide high quality markers in a single, synchronous step. These include sequencing of reduced representation libraries In the present work, we describe Sequence-Based Genotyping (SBG). SBG incorporates the high-throughput capacity of NGS platforms, and the proven, reproducible and robust genome complexity reduction capabilities of AFLP, to score random SNP markers across an entire genome. Using SBG, genome-wide SNP discovery and genotyping of large populations can be attained in a single experiment, without the need for prior knowledge of a reference genome sequence. Depending upon the user's needs, this method allows for the customization of the type of complexity reduction required, the optimal number of samples to be analyzed, as well as the desired number of SNPs. Additionally, SBG markers and genotypes can be directly used for downstream applications, which in turn can bring added value to the user. Here we present applications of SBG in arabidopsis and lettuce populations, two plant species of diverse genetic complexity and backgrounds. We demonstrate that SBG is applicable to a wide range of species using a generic sample preparation process, and standardized bioinformatics analysis workflows for germplasm and parent-based genotyping.Total genomic DNA was isolated from leaf material using a modified CTAB procedure Arabidopsis thaliana ecotype Columbia and the homozygous insertional mutant WiscDsLox353E12  were crossed to A. thaliana ecotype Landsberg. F1 plants were backcrossed to arabidopsis ecotype Landsberg resulting in two backcross (BC1) populations, one wild-type population (Col×Ler)×Ler and one mutant population (Col mutant×Ler)×Ler. The sampled population consisted of a total of 220 offspring plants resulting from the two aforementioned BC1 populations and the parental lines (n = 222 samples).The Lactuva sativa) cv. Salinas 88× cv. La Brillante, generation eight recombinant inbred lines (RILs) and the parental lines (n = 87 samples) The sampled population consisted of 85 lettuce  and a sample-specific tagged EcoRI P5 adapter  for 3 hours at 37°C. Sample-specific EcoRI P5 adapters contained a unique 5-nt sample identification tag adjacent to the EcoRI restriction site overhang for identification of individual samples, and were designed such that each sample identification tag differed by at least two bases from all other tags. A complete list of all sample identification tags used is shown in the supplementary materials (5′-AATGATACGGCGACCACCG-3′), 30 ng Illumina P7 primer (5′-CAAGCAGAAGACGGCATACGA-3′), 0.2 mM dNTPs, 0.4 U AmpliTaq® (Applied Biosystems) and 1× AmpliTaq® buffer. PCR was performed with a cycle profile that consisted of 2 minutes at 72°C, followed by 50 cycles of 30 seconds at 94°C, 60 seconds at 58°C, and 2 minutes at 72°C. Reactions were held at 4°C until ready for use. Next, sets of 32 PCR amplified samples were pooled (5 µl each) to make 7 libraries and these were purified using the MinElute PCR Purification Kit (Qiagen). Single-end sequencing (76 nt) was performed using 7 lanes of the Illumina Genome Analyzer II (1 library per lane). Clusters for each library were generated on a GAIIx flow cell v2 using a Cluster Kit v5, according to manufacturer's instructions. Following the completion of the run, image analyses, error estimation and base calling were performed using the Illumina Pipeline (SCS 2.5/RTA 1.5.35.0) to generate primary data.In brief, 100–500 ng total genomic DNA was digested using 5 units aterials . PCR wasTaqI for 1 hour at 65°C. This was immediately followed by digestion with 5 units PstI and 5 units MseI for 1 hour at 37°C. The use of a third enzyme, in this case MseI, allowed for additional genomic complexity reduction. Following digestion, the mixture was heated at 85°C for 10 minutes. Adapter ligation was then performed using a modified Illumina Paired-end TaqI P7 adapter , modified Illumina Paired-end PstI P5 adapter  and an AFLP MseI adapter , for 3 hours at 37°C. Paired-end PstI P5 adapters contained a unique 5-nt sample identification tag adjacent to the PstI restriction site overhang for identification of individual samples. As with arabidopsis, the sample identification tags differed by at least two bases. The full list of sample identification tags are shown in the supplementary materials (5′-AATGATACGGCGACCACCG-3′), 30 ng Illumina P7 primer (5′-CAAGCAGAAGACGGCATACGA-3′), 0.2 mM dNTPs, 0.4 U AmpliTaq® (Applied Biosystems), and 1× AmpliTaq® buffer. PCR was performed with a cycle profile consisting of 2 minutes at 72°C, followed by 50 cycles of 30 seconds at 94°C, 60 seconds at 58°C, and 2 minutes at 72°C. Reactions were held at 4°C until ready for use. Next, sets of 32 PCR amplified samples were pooled (5 µl each) to make 3 libraries. For each library, fragments were separated, sized and quantified using the Agilent High Sensitivity DNA Kit and loaded onto the Agilent 2100 Bioanalyser (Agilent Technologies) for evaluation. Paired-end sequencing (100 nt) was performed using 3 lanes of the Illumina HiSeq2000 (1 library per lane). Clusters for each library were generated on a HiSeq flow cell v3 using a TruSeq Paired-End Cluster Kit v3, according to manufacturer's instructions. Following the completion of the run, image analyses, error estimation and base calling were performed using the Illumina Pipeline (HCS 1.4.8/RTA v1.12.4.2) to generate primary data.A two-step digestion was performed whereby 100–500 ng total genomic DNA was first digested with 5 units aterials . Excess The Illumina short read sequences were pre-processed by applying several filtering criteria . We remoSEED The short reads were aligned to the reference sequences using BWA and the mapping results were processed with Samtools In order to determine the impact of each of the thresholds on SNP and genotype validation rates, a set of 312 genotypes was selected from 8 SNPs. These SNPs were chosen on the basis of mapping the arabidopsis reads to the publicly available arabidopsis reference genome. The genotypes were selected at different coverage values, varying from 4× to 92×, and included approximately an equal number of homozygote and heterozygote genotypes as expected from the BC1 population that we used. Primers were designed for each of the 8 SNPs, and fragments amplified by PCR. Subsequently, Sanger sequencing was used to sequence the fragments and evaluate the genotypes. We considered that a Unified Genotyper call was correct when the genotype called was the same as the genotype determined by Sanger sequencing. Genotypes based on Sanger sequencing were independently called by two persons, and the results were matched afterwards. We discarded all Sanger genotypes that could not be called conclusively.Upon completion of the SNP and genotype validation step, a new dataset was generated by applying thresholds at which the SNP and genotype validation rates were maximized. The same thresholds were applied to both the arabidopsis and lettuce datasets. Moreover, we performed parent-based genotyping by determining the SNP positions where the parental samples were both present and fixed for alternate alleles, and genotyping the offspring samples that were detected at those positions. For each given SNP position, the genotypes were labeled as A or B, for the two types of homozygous genotypes, in accordance with the parental genotypes, and also H, for the heterozygous genotypes.De novo grouping/mapping of markers as well as anchor-grouping/mapping is supported. Recombination frequencies between markers were estimated using a likelihood-based approach. Recombination frequencies of 0.5 indicate that markers are unlinked. Thus, recombination frequencies greater than 0.4, are re-estimated through a shortest path algorithm using intermediate markers.All genetic maps for arabidopsis and lettuce were calculated using the CarteBlanche software package The first step in the mapping process was the assignment of markers to linkage groups, the genetic equivalent of chromosomes, based on observed recombination fractions. After formation of the initial linkage groups, the group contents were optimized by merging splitting groups as required, and by placing additional markers from the ungrouped set that can still be placed in one of the groups unambiguously.The next step involved the estimation of pair-wise recombination frequencies, corresponding LOD scores, and genetic distances for all marker pairs in each linkage group. For each linkage group a genetic map was constructed using five different mapping algorithms. After completion, the best map was selected out of these preliminary results, based on minimal sum-of-adjacent-recombination-frequencies, and maximal sum-of-adjacent-LOD-scores. Finally, the genetic distances in this map were optimized.The quality of the best map found was judged by plotting its marker order amongst those of the other maps, which shows the stability of the selected map. Frequent positioning of markers in one of the alternative maps in orders deviating from the best map, indicates that either insufficient information was present to obtain a definite solution, or a part of the locus segregation data conflicts. The quality of the map was also judged by evaluating genome configurations of the individuals of the population.II in arabidopsis; HiSeq2000 in lettuce). After applying filtering criteria, the percentage of reads used in analyses varied from 57.7% (lettuce read 1) to 69.3% (arabidopsis), which is similar to what was observed in other studies The total number of reads generated was approximately 177 M and 383 M in the arabidopsis and lettuce datasets, respectively. The average number of reads obtained per lane was 25.3 M in arabidopsis and 127.6 M in lettuce, which is in agreement with the throughput of the sequencing platform used to sequence each crop  Short Read Archive (submission SRA052230).The results of the strategy we used to generate sets of reference sequences are shown in The accuracy of the clustering strategy adopted was evaluated in arabidopsis. The clusters generated were mapped to the publicly available arabidopsis reference genome, and evaluated for their mapping position . We defiA set of 312 genotypes, were selected from a subset of 8 SNPs that were identified after mapping the arabidopsis reads to the reference genome. SNP and genotype data were generated using the Unified Genotyper as the tool to call variation. We then used Sanger sequencing to validate these SNPs and genotypes. A genotype was considered to be correct when the call by the Unified Genotyper and Sanger sequencing matched. A detailed description of the genotypes selected for validation is included in The validation rates obtained for the set of 312 genotypes subject to Sanger sequencing were further evaluated for the Unified Genotyper genotype quality parameter, which is the Phred-scaled confidence that the true genotype was called. A minimum validation rate of 96.5% was observed when no threshold was placed on genotype quality, a parameter generated by the Unified Genotyper. The validation rate was highest at a minimum genotype quality of 20. This was the threshold at which 98.9% of the called genotypes were confirmed by Sanger sequencing. The results obtained for the validation of genotypes at different coverage and genotype quality thresholds, were the basis for establishing a default stringency level to consider a genotype to be valid or discarded. These thresholds were set at 7× coverage and 20 for genotype quality. An additional threshold for SNP quality, another parameter generated by the Unified Genotyper, was also included and set at 30. Hence, all genotypes we have presented in this study passed all these thresholds.The initial variant output generated for each sequence dataset is summarized in The results of the parent-based genotyping performed in each sequence dataset are included in The SNP dataset generated with parent-based genotyping was subjected to additional filtering steps to remove false-positives. For each identified SNP, thresholds were placed on the percentage of missing data and the frequency of each of the genotypes. SNPs that displayed an amount of missing genotypes above the threshold, or genotypic frequencies exceeding the thresholds, were removed from the dataset. We applied this additional filtering step to the SBG datasets generated with parent-based genotyping for both arabidopsis and lettuce . For araAlthough in arabidposis the additional filtering steps removed only a small percentage of the SNPs identified with parent-based genotyping, a much more pronounced effect was observed in lettuce. Similar rules were applied to filter the initial SNP output generated in lettuce parent-based genotyping. These rules included the removal of SNPs that displayed i) more than 60% missing genotypes; ii) a frequency of more than 75% or less than 25% for the homozygote genotypes; and iii) a frequency for the heterozygote genotype of more than 15%.For lettuce, a higher percentage of SNPs were removed after application of these filtering criteria. In fact, 78% of the SNPs identified initially did not pass the filtering criteria applied, when data from both sequence datasets was considered. The number of SNPs removed was larger in the lettuce read 2 dataset, when compared with read 1. As a consequence, the differences observed between the two lettuce sequence datasets were also much smaller in the filtered SNP set. Finally, the average number of genotypes per SNP increased significantly (from 38.5 to 58.3) and the frequency of the heterozygote genotype decreased (from 9.4 to 2.0).These results illustrate the gains that can be achieved in the quality of a SNP set after using filtering criteria based on expected genotypic frequencies, and desired percentage of missing genotypes. It should be emphasized that these results were obtained using only the data derived from parent-based genotyping. However, this strategy can be adopted for every SNP detected, regardless of the availability of parental information.Both the arabidopsis and lettuce SNP datasets were subjected to linkage mapping using the CarteBlanche software package . For araThe lettuce SNP dataset proved to be more challenging, as the amount of missing data was relatively high for a mapping dataset. When we tried to map the complete dataset in a single pass, most markers were placed in a single group. This was likely due to the large amount of missing data. To circumvent this problem, a two-step approach was used. First a subset of 493 markers with the fewest missing data (up to 30%) was used to create the groups. This resulted in the formation of 27 groups of variable size, in which 481 of the 493 markers were present. A stable, high quality map was generated using the aforementioned subset of markers, based on 150 map orders. Using these groups as anchors, the remaining markers were then assigned to the group with the best fit, based on the recombination fraction. Using this type of approach, 632 of 733 markers with missing data in the range of 30–60%, could be unambiguously assigned to one of the groups.For the 27 lettuce marker groups, 150 map orders were once again determined, and the best obtained order was preserved. Stability analysis of the 150 map orders showed that in most cases a stable marker order could be obtained. Some unstable regions were also observed but in the majority of the cases, these corresponded to regions with many markers within a small cM interval. When markers are very similar or co-segregating, the order amongst these markers becomes less defined. The final lettuce linkage map consisted of 1113 markers mapped out of a total of 1226 markers, and spanned 947.7 cM.Conventional marker-based genotyping technologies have several disadvantages that can readily be improved through the use of a sequence-based method. Namely, genotyping platforms like SNP arrays are large-scale operations that require a substantial investment to initially discover SNPs, and subsequently genotype a large number of individuals. Moreover these systems tend to be limiting in flexibility and scalability of fixed ordering volumes, fixed number of SNPs per assay, and/or a relatively long lag time in ordering and receiving. Others are not well suited as high-throughput assays for a large number of SNPs, often requiring the design of allele specific primers Sequence-based genotyping methods such as SBG, combine SNP discovery and genotyping in one single step. This makes SBG considerably time and cost-effective in comparison to conventional genotyping technologies. SBG is also a generic technology, with minimal amount of pre-experimental setup since there is no additional primer, adapter or assay design required. This ensures that the project turnaround time is low, and the user has tangible results in just a few days. Multiplexing of samples using tagged adapters further contributes to the high-throughput nature of this technology, allowing a large number of samples to be screened in a given experiment. Lastly, the user gains invaluable information about the type, the location, and the sequence context of each SNP marker since SBG is sequence-based. This information can immediately be incorporated into existing sequence-based frameworks such as Whole Genome Profiling (WGP™) SBG offers several competitive advantages over other sequence-based SNP genotyping technologies. In comparison to RAD-seq for example, we have refined our library preparation protocol such that we have limited the amount of sample handling steps involved, reduced the number of PCR and purification steps, and we do not utilize DNA size fractionation. These measures help to increase the efficiency and ease of library preparation. Furthermore, the genome complexity reduction strategy that we apply is anchored within the highly robust and reproducible complexity reduction capabilities of AFLP. Unlike other sequence-based technologies, we utilize a combination of at least two restriction enzymes, one rare cutting and another frequent cutting. This strategy not only allows us to effectively reduce genome complexity, but in doing so we create an even distribution of genomic fragments covering the length of a given genome. Depending on individual genome specificities, we are able to tailor complexity reduction using the specific properties of certain restriction enzymes. This feature of SBG is especially important in the case of complex genomes such as lettuce whereby a methylation sensitive enzyme was used to avoid highly repetitive portions of the genome. Our method is also suited for further complexity reduction using selective amplification, which we have recently demonstrated EcoRI/MseI combination was chosen with the expectation that it would provide the maximal output of genome-wide SNP markers for a genome of its size Enzyme combinations for use in SBG were selected based upon the expected complexity of the genomes under study, and from our past AFLP experience from which the genome complexity reduction power of our current technology is derived. For arabidopsis, the PstI. From previous studies, approximately 50% of PstI sites in maize were expected to be methylated PstI, TaqI and MseI) to further reduce the complexity in lettuce. We chose MseI, a frequent cutting enzyme, to remove any fragments containing an MseI restriction site . Effectively, this is equivalent to using selective nucleotides in AFLP for complexity reduction, without the need for additional amplification steps.Although lettuce has yet to be sequenced in its entirety, it is believed that the lettuce genome is very repetitive. This adds to the need for an effective complexity reduction method prior to sequencing. The highly methylated, repetitive fraction of the lettuce genome could potentially be avoided with the use of a methylation sensitive enzyme such as The choice of complexity reduction is highly dependent on the nature and size of the genome under study. The core AFLP-based complexity reduction that we have used is highly robust and reproducible in all genomes to be sequenced. This gives the user the freedom to choose the best combination of restriction enzymes to achieve their genotyping goals, in terms of SNP numbers and distribution of markers across the genome.Adapter dimers can potentially pose a problem, as these small fragments tend to be preferentially sequenced. Adapter dimers were also noted in the original GBS method, and were resolved through a series of adapter titrations to empirically determine the correct ratio of adapters to sample DNA ends. In the course of development, we noticed the effect of adapter dimers when we used paired-end adapters to prepare the lettuce samples for sequencing. We were able to resolve the adapter dimer issue, and simultaneously size-select our library fragments, by employing a strategy that included a step in which all fragments below 200 bp were removed immediately following adapter ligation. Libraries were considered suitable only if adapter dimers were absent and all remaining fragments were greater than 200 bp.We have shown that SBG is capable of genotyping populations regardless of whether a reference genome is available. Through the uniform and streamlined bioinformatics analysis workflows we have developed, we have given the user the flexibility to screen germplasm populations or to perform parent-based genotyping, as well as performing co-dominant scoring of segregating SNP markers in a given population. We emphasize that genotyping of any population structure is possible with SBG, whereas most sequence-based genotyping studies have relied upon the inclusion of parental genotypes for the purposes of SNP discovery de novo linkage maps for arabidopsis and lettuce. This is unlike the approach used recently by Poland et al. 2012 in which a wheat linkage map was developed such that existing genetic markers were used to anchor the GBS SNP markers to the respective wheat chromosome. Our SBG approach therefore gives the user the possibility to construct linkage maps without the need for pre-existing genetic map frameworks, which may not always be available for a given genome.Lastly, we have demonstrated that we could use the SBG SNP markers to construct high quality In the future, we expect that advances in NGS will increase the propensity for sequence-based genotyping. With SBG, we have targeted users that require genotyping information for populations that may or may not have a reference sequence available, and population structures that do not necessarily include parental data. The latter is one important distinguishing feature that sets SBG apart from all other sequence-based genotyping technologies. Therefore we conclude that SBG offers users the greatest flexibility in achieving their genotyping goals. Any future improvements to SBG can only positively contribute to this.Figure S1Distribution of the number of reads per sample in the arabidopsis sequence dataset.(EPS)Click here for additional data file.Figure S2Distribution of the number of reads per sample in the lettuce sequence dataset.(EPS)Click here for additional data file.Figure S3Example of a typical linkage group for arabidopsis (A) and lettuce (B).(EPS)Click here for additional data file.Table S1Set of 96 sample identification tags used for SBG library construction.(DOC)Click here for additional data file.Table S2Results of mapping the clusters formed with SEED to the arabidopsis genome.(DOC)Click here for additional data file.Table S3Validation of arabidopsis SNP genotypes at different coverage thresholds.(DOC)Click here for additional data file.Table S4Impact of GATK Unified Genotyper thresholds on genotype validation rate.(DOC)Click here for additional data file.


Solanum paniculatum; pequi, Caryocar brasiliense; pitaya, Hylocereus undatus; siriguela, Spondias purpurea; umbu, Spondias tuberosa) in order to (i) verify the correlations between results obtained by the different assays, with the final purpose to obtain more reliable results avoiding possible measuring-method linked mistakes and (ii) individuate the more active fruit species. As expected, the different methods give different responses, depending on the specific assay reaction. Anyhow all results indicate high antioxidant properties for siriguela and jurubeba and poor values for pitaya, umbu, and pequi. Considering that no marked difference of ascorbic acid content has been detected among the different fruits, experimental data suggest that antioxidant activities of the investigated Brazilian fruits are poorly correlated with this molecule, principally depending on their total polyphenolic content.Four different assays  based on radically different physicochemical principles and normally used to determine the antioxidant activity of food have been confronted and utilized to investigate the antioxidant activity of fruits originated from Brazil, with particular attention to more exotic and less-studied species (jurubeba,  It is known that the consumption of fruit and vegetable reduces the incidence of cardiovascular and cerebrovascular diseases, stroke, cancer, and ageing related disorders –3. This Substantial damages have been observed when ROS interact with DNA, membrane lipids, and proteins –10. ROS Antioxidant substances represent one of the most important defense mechanisms against free radicals, but the only endogenous antioxidant molecules cannot be effective enough to counteract the injuries caused by ROS, particularly in the current times, where lifestyles based on smoke, drugs, alcohol, unbalanced diet, pollution, incorrect exposure to solar radiation, and so forth can facilitate free radicals formation. For this reason increasing the intake of dietary antioxidant is of great importance to enjoy good health, as evidenced by studies on food characterized by high antioxidants content .Unfortunately, no reliable biomarker of antioxidant activity is available up to now , 15 becaThe aim of this work is to compare of the results obtained by four different methods usually employed to measure antioxidant properties, that is, reducing capacity by the Folin-Ciocalteu assay, radical scavenging ability towards 2,2′-diphenyl-1-picrylhydrazyl (DPPH method), inhibitory ability on peroxidation of linoleic acid (LA), and total phenolic determination by the enzymatic method  in orderThese assays were applied to a series of Brazil fruits, with particular interest in the more exotic and less studied species. In fact, information on the nutritional values of the most exotic species of tropical fruits are limited: some studies –22 proviThe results obtained by these measurements were compared with each other and with those obtained by Italian soft fruits known for their antioxidant activity , 26. FurSimilitude and differences were discussed on the light of the chemical characteristics of the assay reactions. All chemicals, of the highest available quality, were obtained from Sigma Chemical Co. ; ABIP propane] dihydrochloride) was obtained from Wako Chemicals (Germany). The aqueous solutions were prepared with quality milli-Q water. Each experiment was in triplicate. Spectrophotometric measurements were recorded on a UV-VIS Shimadzu UV-1800 instrument equipped with a temperature controlled quartz cell. The measures of oxygen consumption were performed with a potentiostat Amel 559, equipped with an oxygen microelectrode .50. The antioxidant activity of fruits to prevent linoleic acid (LA) peroxidation was determined in sodium dodecyl sulfate (SDS) micelles. As previously reported , the fru This method is based on the capacity of an antioxidant to scavenge the stable free radical DPPH . The pro The Folin-Ciocalteu assay and the Total Phenolic Content were determined spectrophotometrically, according to the procedures previously reported , and the Hydroxycinnamic acid content was determined according to Zaporozhets et al. . The com The TAC was determined according the pH-differential method . Absorba The TAA is assayed as previously described  with min50 values  in the range of those found for the more active Italian soft fruits . For the same fruits, DPPH, TPC, and Folin assays give very high values of CE, if compared with the average of other fruits, indicating an univocal high antioxidant activity of these two fruits.In 50 values, the investigated Brazilian tropical fruits can be roughly divided into three groups characterized approximately by good, medium, and poor antioxidant properties, respectively (50) ≤ 2 (IC50 ≤ 100 ppm): graviola, jurubeba, siriguela, carambola, and tamarind; (2) fruits with 2 < log(IC50) ≤ 2.5 (IC50 ranging from 100 to 316 ppm): avocado and mango; (3) fruits with log(IC50) > 2.5 (IC50 > 316 ppm): pequi, umbu, and pitaya. On the basis of their ICectively : (1) fru50 and other adopted methods expressed as catechin equivalent amount (CE) are reported.In R = 0.79); in fact only few points referred to that strawberry, blueberry, jurubeba, and, in less amount, siriguela scatter from the linear relationship. The comparison of the data obtained by ILP versus DPPH scavenging methods  points oR = 0.60 and 0.30, resp.), but also in this case strawberry, blueberry, jurubeba, and, in part, siriguela appear to worsen the correlation coefficient. Analogous graph created for comparison of ILP with enzymatic or the Folin methods Figures  shows le eq for jurubeba.TAC measurements showed the absence of anthocyanins in analyzed Brazilian fruits, while as regards the hydroxycinnamic acid content, the values of HCA equivalents obtained for the studied fruits and reported in  No correlation appears comparing TAA values with the data obtained by the other analytical methods (data are not showed).Several methods are proposed to evaluate the antioxidant activity of molecules or food , 32–35. The Folin-Ciocalteu is a very aged and largely used assay, based on the absorbance changes due to the oxidation of any reduced compounds by a phosphomolybdate and phosphotungstate solution. It is a nonspecific method of measuring the reducing capacity of all the components of the sample other than polyphenols, such as ascorbate , 36. In TPC enzymatic method, on the contrary, being a measure of the total phenolic content of fruit due to the specificity of peroxidase-catalyzed reaction towards phenolic structures, is an indirect evaluation of the antioxidant power, which actually depends not only on the measured total phenolic content, but also on the chemical structure of each phenolic component . it may react slowly or be inert to many antioxidants ;reaction kinetic with antioxidants appears not linear to DPPH concentrations ;reaction of DPPH with some phenolic structures could not go to completion, reaching an equilibrium state, as found for eugenol .DPPH method is a measure of the electronic transfer from the phenolic structure to the stable free radical DPPH, but this reaction presents the following disadvantages which can underestimate the antioxidant capacity:in vivo action of antioxidant substances against radical-induced lipid peroxidation of unsaturated fatty acids residues of biological membranes, measuring in vitro the slowdown, due to an antioxidant, of the oxygen consumption in linoleic acid containing SDS micelles. In this case, the influence due to the different lipophilicity of the antioxidant molecules is taken in account too. Moreover, in this work, only clear juices have been analyzed, and, as a consequence, only water soluble antioxidants have been assessed.By a physicochemical point of view, ILP technique appears to better reproduce the Anyway, in order to be certain of the data reliability and to give a wider outlook of the problems related to the definition of the antioxidant activity of foods, the same samples were studied by the above cited four analytical assays, and the results were compared to put in light possible correlations. In fact, good correlations between results obtained by different assays can guarantee the best evaluation of the antioxidant properties of a sample.Siriguela, jurubeba, carambola, graviola, and tamarind show high antioxidant activity, similar to that of soft fruits , 26. ThiWith reference to the scattering from the linear correlation of the data referred to strawberry, blueberry, jurubeba, and siriguela, as it results in all three graphs of Jurubeba and siriguela are two striking examples of how different assays may assign different rankings to antioxidant molecules: as it appears in 50 and DPPH experiments are particularly efficient for separating poor antioxidants from good ones: IC50 values that are lower than 100 ppm and/or CE values that are higher than 2 mM could be assumed as a reasonable rule for discriminating very good antioxidants. The better correlation results from the comparison of the ILP and DPPH data . In factEven if there is a bad correlation between DPPH and enzymatic data , most ofAnthocyanins are not contained in examined Brazilian fruits, while hydroxycinnamic acids are detected; their correlation with ILP is practically absent, as shown in No evident relationship between the antioxidant activity of fruit and the content of ascorbic acid is observed: siriguela and jurubeba have the highest antioxidant activity, but they exhibit lower values of vitamin C than mango, which is not a good antioxidant instead . It follBrazilian fruits were used as arbitrary alimentary products to compare four different assays normally utilised to determine antioxidant activity of food.The better correlation was found between the inhibition of lipid peroxidation and DPPH method. Both these assays are based on monoelectronic transfer, and, in our opinion, they mime, more than others, the efficacy of an antioxidant compound to prevent oxidative damage on cell membrane, despite all the limitations of the DPPH assay above reported and taking into account the laboriousness of the ILP method. From data obtained by these two methods, siriguela and jurubeba show the higher antioxidant activity. The antioxidant activity of the majority of the studied fruit is due to compounds different from vitamin C, like flavonoids, because no evident relationship between the antioxidant activity of fruit and the content of ascorbic acid was observed.


Long-term environmental variables are widely understood to play important roles in DNA variation. Previously, clinical studies examining the impacts of these variables on the human genome were localized to a single country, and used preselected DNA variants. Furthermore, clinical studies or surveys are either not available or difficult to carry out for developing countries. A systematic approach utilizing bioinformatics to identify associations among environmental variables, genetic variation, and diseases across various geographical locations is needed but has been lacking.Using a novel Geographic-Wide Association Study (GeoWAS) methodology, we identified Single Nucleotide Polymorphisms (SNPs) in the Human Genome Diversity Project (HGDP) with population allele frequencies associated geographical ultraviolet radiation exposure, and then assessed the diseases known to be assigned with these SNPs.SLC45A2 gene with melanoma was used as a case study for analysis of disease risk, and the results were consistent with the incidence and mortality rates of melanoma in published scientific literature. Finally, by analyzing the ontology of genes in which the radiation SNPs were significantly enriched, potential associations between SNPs and neurological disorders such as Alzheimer’s disease were hypothesized.2,857 radiation SNPs were identified from over 650,000 SNPs in 52 indigenous populations across the world. Using a quantitative disease-SNP database curated from 5,065 human genetic papers, we identified disease associations with those radiation SNPs. The correlation of the rs16891982 SNP in the A systematic approach using GeoWAS has enabled us to identify DNA variation associated with ultraviolet radiation and their connections to diseases such as skin cancers. Our analyses have led to a better understating at the genetic level of why certain diseases are more predominant in specific geographical locations, due to the interactions between environmental variables such as ultraviolet radiation and the population types in those regions. The hypotheses proposed in GeoWAS can lead to future testing and interdisciplinary research. Over the past decade, high-throughput technologies and advancements in computational biology have made it possible to determine genotypes and complete human DNA sequences at a continued reduction of cost, and these technologies have been used to determine numerous variants associated with various diseases and traits. Why and when variants leading to disease susceptibility entered the human genome remains unclear, but could be elucidated through the study of genetics in indigenous populations, more closely representing original human subpopulations across the world . How theTo study early human migrations, in 2005, Li et al. analyzed 1064 individuals from indigenous populations at over 650,000 Single Nucleotide Polymorphisms (SNPs) in the Human Genome Diversity Project (HGDP) . The HGDOne important environmental factor is ultraviolet (UV) radiation, which has been linked to a wide range of human diseases. Interactions between skin pigmentation genes and UV radiation have been previously studied, but only on a country-wide scale ,6. UsingIn another recent study , evidencIn this paper, we investigate UV radiation and its effects at the genome-wide level across a wider range of human diseases in different countries. Human genomes from 52 native populations in the HGDP panel were mapped to 193 different countries. We then designed a bioinformatics-based Geographic-Wide Association Study (GeoWAS) to systematically identify DNA variants that were the most strongly correlated with UV exposure levels across the world, which we called radiation SNPs. To identify diseases that are strongly associated with radiation SNPs, we queried these SNPs against a curated disease-SNP database called VARIMED (Variants Informing Medicine) ,9. With A Geographic-Wide Association Study (GeoWAS), outlined in Figure 2), are population-weighted averages, and are provided for each of 193 countries in the database. The radiation data was obtained from 1997 to 2003, and was assumed to remain at a constant level since its collection. We also assumed the UV radiation exposure was constant going back to the original establishment of the populations sampled in the HGDP. Using an interface between R and the Google Visualization API, we generated a map illustrating UV radiation exposure as a function of color for all of the countries across the world  for individuals from each of the 52 native populations. Each SNP has a corresponding ancestral allele and a derived allele produced by mutation events over time. The derived allele may become more common in human loci due to random genetic drift or selective pressures from the environment. For a given locus, the allele frequency represents the fraction of the chromosomes that carry the specified allele. For every SNP, the ancestral allele frequency (AAF) in each country was correlated with the radiation levels measured for those countries. Both Pearson’s and Spearman’s correlation coefficients and p-values were calculated to assess the relative strength of the associations. Each SNP was then annotated with its corresponding chromosome number, function type, gene symbol, and NCBI Gene ID .r > 0.8), a strict cutoff value determined from our preliminary permutation studies. These SNPs were classified as radiation SNPs.These initial correlations served as a means of filtering the SNPs that we targeted for further analysis. We identified SNPs that had ancestral allele frequencies significantly associated with UV radiation exposure by focusing only on the SNPs with a Pearson’s absolute rho of greater than 0.8 , taken from Reactome [To ascertain the effectiveness of our proposed methodology, we first examined biological processes known to be linked to radiation, and analyzed the degree to which these processes are supported by our approach. It is widely understood that variations in UV exposure influence the synthesis and metabolism of Vitamin D . Six difReactome , were idAfter running our initial correlations, the radiation SNPs were queried against the VARIMED database to identify diseases and other phenotypes that the SNPs may be linked to. VARIMED is a quantitative human disease-SNP association database, curating from approximately 5500 different publications, as previously described .n number of associated SNPs, then n samples were taken from the entire list of SNPs for a population. In this case, our null hypothesis for each population would be that it has not experienced selection for radiation SNPs. Those populations in which the empirical p-value is below a scalar alpha (α < 0.01) are considered to be under significant positive selection for the set of SNPs currently in question. We chose to implement this procedure using the HGDP data set, since we may view selection trends across a gamut of populations.The Integrated Haplotype Score (iHS) is a statistic for identifying recent positive selection at a particular locus, and is dependent upon differences in linkage disequilibrium (LD) around a positively selected allele compared to the background allele ,15,17. IFrom the complete pool of 650,000 SNPs, those SNPs identified to be associated with UV radiation (radiation SNPs) were joined with their corresponding NCBI Entrez Gene IDs to create a gene list. From the list, we identified the genes in which the radiation SNPs were significantly enriched by joining the list with a table containing the Gene Ontology (GO) terms for these genes. We focused on the GO terms that had a p-value of under 0.05 after performing a Bonferroni correction. These genes and their potential connections to diseases were surveyed in published literature. Based on our findings, we formulated new hypotheses on how DNA variation in the genes of interest may be potentially related to UV radiation and diseases. These proposed mechanisms of disease formation can lead to future research and follow-up testing.For each SNP measured in the Human Genome Diversity Project (HGDP), the ancestral allele frequency (AAF) in each of 193 countries was correlated with the radiation levels measured for those countries. After running these initial correlations, 2,857 of the 650,000 SNPs were identified to have AAFs significantly associated with UV radiation exposure, using a Pearson’s absolute rho of 0.8 as a strict cutoff value. These SNPs were designated as radiation SNPs, and each was annotated with its corresponding chromosome number, function type, gene symbol, and Entrez database Gene ID.r = -0.831). A fairly strong negative relationship exists, with countries receiving greater levels of UV radiation having a lower SNP AAF. The rs16891982 SNP, which plays a critical role in pigmentation and in certain forms of skin cancer, will be emphasized in a case study discussed below.The association between AAFs and radiation for each UV SNP can be represented as a scatterplot. As a specific example, the plot for the SNP rs16891982 is shown in Figure -4) than the set of background SNPs in other genes, indicating that the SNPs found using our methodology are relevant to pathways known to be associated with radiation.To validate the efficacy of our methodology, we first evaluated genes involved in Vitamin D synthesis, known to be influenced by UV exposure. We curated six genes known to be involved in Vitamin D synthesis pathways, and conducted a Mann–Whitney U test to compare the Pearson p-values of the SNPs within these genes against those of background SNPs in other genes. In total, 155 different SNPs were contained in these six genes. We found that SNPs in genes for Vitamin D biosynthesis had a significant p-value in association with UV radiation  and a 13.6-fold enrichment in genes for glutamate receptor activity , as shown in Table After a Bonferroni correction was carried out, the radiation SNPs were found to be significantly enriched in two gene ontology categories, with a 2-fold enrichment in genes for calcium ion binding  to incorporate countries into the analysis. The algorithm designed in this study integrates worldwide SNP and UV data to simultaneously compare the risks of different diseases across the world. Using again the case study of the radiation SNP rs16891982 as an example, we were able to expand earlier approaches that only accounted for single countries ,6 by sysin vitro methods based on the expression of the SLC45A2 gene [The GeoWAS methodology presented in this paper can also be applied to other long-term environmental variables such as temperature, precipitation, and humidity. The algorithm can be similarly used to carry out analyses of the genomes of other species. Furthermore, the UV radiation SNPs identified in this work may potentially be used in aiding skin cancer diagnostics, through 5A2 gene .With our developed methodology, we were able to investigate the effects and implications of UV radiation on human phenotypes and diseases at both the genomic and geographic levels. The significance of GeoWAS and its promises are discussed from both biomedical and methodological perspectives. Ultimately, the GeoWAS approach can benefit epidemiologists, and physicians, and policy-makers in regions of the world where conventional clinical studies are either costly or unavailable. Future work will involve refining the current approach by performing the country-population mappings based on updated HGDP coordinate systems.Atul Butte is a founder of Personalis, Inc., which has acquired a license to VARIMED from Stanford University. Rong Chen is now an employee of Personalis, Inc.IH and RC contributed equally to this work and carried out the analysis. IH performed the research, designed the case study, and wrote the manuscript. RC first proposed the problem to be addressed. AJB and RC conceived the study and provided input on methodology throughout. AR and EC provided expertise in algorithm development for positive selection. PK contributed to analysis of SNPs in Vitamin D synthesis. DR supplied statistical expertise and input on research. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/14/62/prepub