Lhcb1, a LHCP gene in barley, were investigated across 292 barley accessions collected from 35 different countries using EcoTILLING technology, a variation of the Targeting Induced Local Lesions In Genomes (TILLING). A total of 23 nucleotide variations were detected including three insert/deletions (indels) and 20 single nucleotide polymorphisms (SNPs). Among them, 17 SNPs were in the coding region with nine missense changes. Two SNPs with missense changes are predicted to be deleterious to protein function. Seventeen SNP formed 31 distinguishable haplotypes in the barley collection. The levels of nucleotide diversity in the Lhcb1 locus differed markedly with geographic origins and species of accessions. The accessions from Middle East Asia exhibited the highest nucleotide and haplotype diversity. H. spontaneum showed greater nucleotide diversity than H. vulgare. Five SNPs in Lhcb1 were significantly associated with at least one of the six agronomic traits evaluated, namely plant height, spike length, number of grains per spike, thousand grain weight, flag leaf area and leaf color, and these SNPs may be used as potential markers for improvement of these barley traits.Light-harvesting chlorophyll a/b-binding protein (LHCP) is one of the most abundant chloroplast proteins in plants. Its main function is to collect and transfer light energy to photosynthetic reaction centers. However, the roles of different LHCPs in light-harvesting antenna systems remain obscure. Exploration of nucleotide variation in the genes encoding LHCP can facilitate a better understanding of the functions of LHCP. In this study, nucleotide variations in  Lhca1, Lhca2, Lhca3 and Lhca4Lhcb1, Lhcb2 and Lhcb3, are highly homologous and probably form homo- or heterotrimers a-binding complexes CP29, CP26 and CP24 that are encoded by the Lhcb4, Lhcb5 and Lhcb6 genes, respectively Light-harvesting chlorophyll a/b-binding protein (LHCP) is one of the most abundant proteins of the chloroplast in plants. It roughly accounts for half amount of the chlorophyll involved in photosynthesis. The main function of LHCPs is collecting and transferring light energy to photosynthetic reaction centers Puccinia triticinaLHCP transcripts was detected in high osmotic adjustment (OA) plant of Oryza sativa spp. japonica, IR62266, than that in low OA CT9993 at a moderate level of dehydration stress LHCP was observed in the drought-tolerant genotypes, Martin, than in the drought-sensitive genotype, Moroc9-75 under drought stress LHCP have not been systematically examined. The exploration of genetic variation in genes encoding LHCPs may facilitate a better understanding of functions of LHCPs and provide useful information and selection tools for plant breeders to improve plant with high photosynthesis efficiency.Several studies have postulated that the LHCP genes were down-regulated in stress conditions such as cold LHCP region using EcoTILLING technology in 19 elite inbred lines mlo and Mla resistance genes Many techniques can be used for analysis of nucleotide variation within a gene. Sequencing is the most accurate approach, but is relatively expensive when applied in large numbers of individuals Lhcb1 gene. A total of 23 nucleotide changes were detected with 31 distinguishable haplotypes in the germplasm collection. The potential association of SNPs with protein function changes was evaluated. Distribution of SNPs in accessions from different geographic origins  and genotypes  was investigated. In addition, association analysis between SNPs in the Lhcb1 and six agronomic traits of barley has been performed.In this study, a natural population of 292 barley accessions with diverse geographical origins was analyzed using EcoTILLING technology to examine allelic variation of an Hordeum vulgare L.) accessions was obtained from the International Center for Agricultural Research in the Dry Areas (ICARDA) (H. vulgare landraces (VUL-LA), 82 H. vulgare cultivars or improved genotypes (VUL-IG) and 39 wild relatives H. spontaneum (SPON), which were collected from 35 countries in six geographic regions including Africa, Middle East Asia, North East Asia, Arabian Peninsula, Australia and Europe.A set of 292 barley ((ICARDA) . These aGenomic DNA of barley accessions was extracted from 200 mg young leaf tissue using a modified CTAB method 2), spike length (SL in cm), number of grains per spike (NGS), leaf color , plant height (PH in cm) and 1000-kernel weight (TKW in g) in field at the Experimental Station of Guangzhou University Guangzhou, Guangdong Province, China . The experiments were repeated twice (2009/2010 and 2010/2011) with three replications. Eleven plants per genotype were planted in a single-row plot at 1.5 m long and 30 cm apart. Three randomly selected plants per genotype from each replication were characterized for six traits  with melting temperatures around 60°C using Primer 5.0 software  (5′-cacgacgttgtaaaacgac) in 5′-end of forward primers or an M13R sequence (5′- ggataacaatttcacacagg) in 5′-end of reverse primers  . The pri primers  for secoLhcb1 was amplified by nested PCR as described by Wienholds 2, 0.4 mM dNTPs, and 0.4 U Taq DNA Polymerase  under the following conditions: 5 min denaturation at 94°C followed by 35 cycles of 30 s at 94°C, 45 s at 58°C and 1 min at 72°C, and a final step of 3 min at 72°C for additional PCR extension. The PCR product was diluted in 90 µl of distilled water as template for second round nested PCR.For EcoTILLING assay, the mRNA sequence of the 2, 0.4 µM dNTPs, and 0.04 U Taq DNA polymerase . Thermocycling conditions consisted of an initial step of 94°C for 1 min followed by 38 cycles of 20 s at 94°C, 30 s at 58°C and 1 min at 72°C, and a final step of 3 min at 72°C. After the nested PCR, heteroduplexes formation was performed by incubating the reaction mix at 99°C for 10 min, followed by 70 cycles starting at 70°C for 20 sec with a decrement of 0.3°C in subsequent cycles and then holding at 4°C.The second round of PCR was carried out in a 10 µl solution containing 1 µl of initial PCR product, 1.0 µl 10×PCR buffer, 0.02 µM M13F-tailed gene-specific forward primer, 0.04 µM M13R-tailed gene-specific reverse primer, 0.08 µM IRD800-labeled M13 forward primers, 0.06 µM IRD700-labeled M13 reverse primers, 2.5 mM MgCl4, 0.002% (w/v) Triton X-100, 0.2 µg/ml of bovine serum albumin, and 0.4 µl CEL I enzyme. CEL I enzyme was prepared following Guo and Li Heteroduplex DNA was cleaved at 45°C for 15 min in a 20 µl of reaction solution containing 10 µl PCR product, 10 mM HEPES (pH 7.5), 10 mM MgSODuring electrophoresis, the LI-COR DNA analyzer captured two images in IRD700 and IRD800 channels, respectively. Tiff images were manually scored using the GelBuddy program http://www.ebi.ac.uk/tools). The potential effect of SNPs on protein function was predicted using SIFT (Sorting Intolerant from Tolerant) Once a polymorphism was identified, the corresponding DNA sample was amplified using gene-specific primers. The resulted PCR fragment was directly sequenced. Each polymorphic site was sequenced from more than one accession to confirm that only two alleles segregated at any specific site. Multiple sequence alignment was conducted using ClustalW software . To evaluate population structure, all barley accessions were genotyped with 21 genome-wide SSR molecular markers (3 SSRs for each chromosome)  , and thrLhcb1 across 292 accessions. The frequency of polymorphic sites ranged from 0.003 to 0.264, with an average of 0.06 per polymorphic site in 292 samples (Lhcb1 has a frequency of one SNP per 49.3 bp in 292 barley accessions. The ratio of transitions (C-T and A-G) to transversions  of SNPs was 15 to 5 in the targeted region of Lhcb1. In 20 sequence validated SNPs, nine sites were missense changes, eight were silent synonymous changes, and three were indels in the 3′ downstream of non-coding region. Two of nine missense changes were predicted to be deleterious to the function of Lhcb1 protein  to 0.00212 for Middle East Asian accessions (56 accessions). Similarly, π for SPON was the highest among the three groups, SPON, VUL-LR and VUL-IG  although a high negative value of −1.12884 was estimated. Thus, the Lhcb1 in the population did not significantly deviate from neutral selection.The nucleotide diversity (π) of the d VUL-IG . Tajima'For the 23 sequence-validated nucleotide variations including 20 SNPs and 3 indels, 292 accessions demonstrated 31 distinguishable haplotypes  with varLhcb1 haplotypes also differed significantly among the geographical regions of tested accessions (Lhcb1 haplotype diversity for each geographic region ranged from 0.556 (Europe) to 0.903 (Middle East Asia) with a mean of 0.768 . However, the accessions from North East Asia had a very low haplotype diversity value, but the Lhcb1 haplotypes (n = 14) second to Middle East Asia due to majority of low-frequency haplotypes in this region.The frequencies of the cessions . This waof 0.768 . These vLhcb1 haplotype diversity was observed among three barley groups, i. e. SPON, VUL-LR and VUL-IG  within subgroups or rare alleles (frequency <3%), only nine distinct SNPs were used for association analysis. Among them, five SNPs were significantly associated (P<0.01) with one or two phenotypic traits, with one SNP that were highly significantly associated (P<0.001) with two phenotypic traits (Lhcb1 was highly associated with SL and NGS (P<0.001), and explained 8.0% or 5.3% and 5.0% or 5.6% of the variation for SL and NGS in both seasons, respectively. Another SNP at position 1006 bp exhibited significant association (P<0.01) with SL, explaining 2.7% and 2.6% phenotypic variation for the SL in both seasons. The SNP at position 463 bp was significantly associated (P<0.01) with FLA and LC, explaining 3.0% and 2.2% phenotypic variation for the FLA in season one and LC in season two. Two SNPs (positions 589 bp and 961 bp) were significantly associated (P<0.01) with TGW, both explaining approximately 2.4% phenotypic variation in 2009 and 2010 experiments.Association analysis was performed to find tentative association between nucleotide variations in c traits . The perPopulus trichocarpaBrassicaLhcb1 in the germplasm collection.EcoTILLING was initially used to characterize the variability of genes within a collection of Arabidopsis ecotypes LHCP from various plant species have been identified by transcriptome analysis. However, the allelic variation in LHCP has not been systematically characterized. Fusari LHCP after screening 19 elite inbred lines using EcoTILLING. Our primary goal was to characterize genetic variation of an Lhcb1 in barley. To this end, a set of barley accessions originated from several geographic regions was selected for allele mining. EcoTILLING revealed 23 nucleotide changes including 20 SNPs and 3 indels in the Lhcb1, which formed 31 haplotypes in 292 accessions. Compared to previous report on an Lhcb2 in 24 unrelated black poplar Lhcb1 was lower. The average frequency of SNPs was 1 per 49.3 bp, which was higher than reported on an Lhcb2 (1SNP/73.9 bp) LHCP (1SNP/76.7 bp) Lhcb1 in this study, which supports the earlier observations of high genetic diversity in SPON Many LHCP family in plants encodes many LHCPs that play essential roles in light capture and photoprotection in the photosystem. A strong relationship between the photosynthetic capacity and grain yield was observed in cereals such as wheat and maize Lhcb1 were significantly associated with at least one agronomic trait. Of these five SNPs, two at positions 463 bp and 589 bp of Lhcb1 were missense mutations, but they did not severely affect protein function according to SIFT, and other three SNPs at positions 907 bp, 961 bp and 1006 bp were in a non-coding region. Due to low minor allele frequency, association data for three of these five SNPs at positions 463 bp, 589 bp and 961 bp should be interpreted with caution and need to be validated for individual cultivars involved in crosses before they can be applied to marker-assisted selection Lhcb1 and other important agronomic traits may provide useful markers as selection tools to improve barley yield under stress conditions.Association analysis emerged as a powerful approach to search for the role of genetic polymorphisms in phenotype variations in responses to environmental stresses Lhcb1 among 292 barley accessions from 35 countries. The results indicated that the accessions from Middle East Asia had the highest nucleotide diversity in the Lhcb1, and H. spontaneum exhibited greater genetic diversity than H. vulgare. Thus introgression of genes from Middle East Asian accessions or H. spontaneum in to cultivated barley may enhance genetic diversity. Association analysis showed that five SNPs in the Lhcb1 were significantly associated with at least one agronomic trait and these SNPs can be used in future studies to assess their usefulness as selection criteria for improving these agronomic traits.In conclusion, we have demonstrated EcoTILLING as an efficient approach for allele mining of barley candidate genes. Haplotype sequencing confirmed 23 nucleotide mutantions including 20 SNPs and 3 indels with 31 unique haplotypes in the Table S1General information of barley accessions used in this study.(DOC)Click here for additional data file.Table S2The information of SSR markers used in evaluation of population structure.(DOC)Click here for additional data file.Table S3Distribution of polymorphic SNPs across hyplotypes. SNPs relative to the most common sequence (haplotype H31) are indicated in boldface. The number of SNP positions is relative to the sequence on GenBank accession number AK359563.1. A horizontal dash indicates the absence of the indicated bases.(DOC)Click here for additional data file.


The mineral concentrations in cereals are important for human health, especially for individuals who consume a cereal subsistence diet. A number of elements, such as zinc, are required within the diet, while some elements are toxic to humans, for example arsenic. In this study we carry out genome-wide association (GWA) mapping of grain concentrations of arsenic, copper, molybdenum and zinc in brown rice using an established rice diversity panel of ∼300 accessions and 36.9 k single nucleotide polymorphisms (SNPs). The study was performed across five environments: one field site in Bangladesh, one in China and two in the US, with one of the US sites repeated over two years. GWA mapping on the whole dataset and on separate subpopulations of rice revealed a large number of loci significantly associated with variation in grain arsenic, copper, molybdenum and zinc. Seventeen of these loci were detected in data obtained from grain cultivated in more than one field location, and six co-localise with previously identified quantitative trait loci. Additionally, a number of candidate genes for the uptake or transport of these elements were located near significantly associated SNPs . This analysis highlights a number of genomic regions and candidate genes for further analysis as well as the challenges faced when mapping environmentally-variable traits in a highly genetically structured diversity panel. Oryza sativa L.) is perhaps the most important crop plant given that it is estimated to be the staple food of half the world’s human population. As this population rises over the coming decades the demand for staple crops, like rice, will grow further and increasing crop yield while maintaining grain quality is, therefore, essential. For people who are reliant on cereals as a dietary staple, a significant proportion of their minerals come from these grains. Improved concentrations of essential micro nutrients such as zinc, and reductions in the potentially toxic trace elements such as arsenic would therefore hold significant potential for improvements in human health Rice  mapping, allowing the unravelling of complex physiological traits Improvement in the content of the micro nutrient zinc within rice grains could have a positive impact on human health Unlike zinc, there is not a widespread global issue with copper deficiency in humans Elevated inorganic arsenic concentration in rice grains has been identified as a risk to human health where rice is a dietary staple MOT1 in Arabidopsis thaliana where natural variation in this gene is responsible for variations in the accumulation of molybdenum in shoots and roots Molybdenum is an essential element required for plants and animals as a component of the molybdopterin cofactor in a number of enzymes including nitrate reductase, sulfite oxidase, xanthine dehydrogenase, aldehyde oxidase and mitochondrial amidoxime reductase To date GWA mapping has not been used for the exploration of loci controlling accumulation of essential minerals and potentially toxic trace elements in rice grain. Here, we report such a study, using the RDP1 to identify a large number of SNPs associated with grain accumulation of zinc, copper, molybdenum and arsenic in rice grains for plants grown in four different field locations. The stability of these QTLs is explored across years and between different environmental conditions, as well as their co-localisation with previously identified QTLs and candidate genes.aus tend to be highest in grain arsenic while either tropical or temperate japonicas were the lowest, the opposite is true for grain copper, indicas were the lowest in zinc and no clear pattern across sites exists for grain molybdenum.At the Faridpur field site, over 50% of the variation for each element in grain  is explained by differences between cultivars . For thetemperate japonicas, that have low grain arsenic across four of the five field sites Previously using the grain arsenic data for each of the cultivars it was possible to identify cultivars that have low grain arsenic across multiple field sites temperate japonica), Jamir (aus), Khao Tot Long 227 (aus) and DZ 193 (aus). These cultivars have the potential to be exploited in rice breeding programs to increase rice grain zinc, therefore addressing human zinc deficiency.An increase in rice grain zinc could have an impact on human health aus, indica, temperate japonica and tropical japonica) as defined by Zhao et al. Due to the complex population structure within rice, there is no single design or analysis method which will sufficiently disentangle the genetics underlying complex polygenic traits tropical japonica subpopulation at the Qiyang field site, and in the same region in “all” accessions at the Arkansas field site in 2006 SNPs. Of these 17 locations, six of them were located with a previously detected QTL As the population was grown across multiple sites, the stability of the QTLs across multiple environments was explored. As described in the methods section, a SNP was called significant if the P-value was <0.0001 and with a minor allele frequency (MAF) >5%. The significant SNPs had to be within 200 kb of each other to be considered close enough to be the same genomic region. A value of 200 kb was selected as this was the estimated global linkage disequilibrium (LD) previously employed in this rice population tropical japonica cultivar from the USA and TeQing is an indica cultivar from China). In the current study, the significant arsenic association was detected in the aus subpopulation and “all” accessions in the Arkansas 2007 field site, and the aus subpopulation at the Qiyang field site. One of the copper associations is on chromosome 1 where significant SNPs from the tropical japonica subpopulation GWA mapping from the Arkansas 2006 field site and the indica subpopulation GWA mapping from the Texas field site co-localise with a QTL detected in the ‘Bala’ (derived from a cross between an indica and aus cultivar) × ‘Azucena’  mapping population tropical japonica subpopulation at the Qiyang field site and the “all” accessions analysis at the Arkansas 2006 field site. This QTL for copper was previously detected in the population of TeQing-into-Lemont introgression lines  mapping population . This would indicate that either LD is greater for the aus subpopulation within this region or that this region contains multiple QTLs for grain zinc. Previously, it was demonstrated that LD varied for the different rice subpopulations aus. In A. thaliana it was demonstrated that LD is 10 times greater around the FLOWERING LOCUS C (FLC) compared to the whole genome LD For zinc, significant SNPs on chromosome 9 were associated with grain zinc in the Also of note is the observation, clearly seen in aus subpopulation. The lack of year-to-year stability for QTLs is unexpected, as it could be predicted that the largest driving forces for the accumulation of different concentrations of elements within grains would be soil chemistry which should vary relatively little as the plants were grown in nearby field areas  using essentially identical field management practices in two consecutive years. However, if other factors like grain yield and flower time have an effect on the accumulation of the elements in the grain then this year-year variation in QTLs may not be unexpected. It has been shown that QTLs for both yield and flowering time can be either stable or unstable across years As well as addressing environmental stability of QTLs, the year-to-year stability of QTLs was explored. In 2006 and 2007 the panel was grown at the same site in Arkansas, using a nearby field area  and identical field management practices. The correlations of the traits across both years are presented in A second method of determining if the same genomic regions have a significant effect on the accumulation of elements across different field experiments was performed. For this method the 1% most highly significant SNPs (∼369 SNPs) for each trait separately , based on p-value, were compared across all the GWA studies analysed per individual location. This analysis was performed on the GWA studies only across “all” accessions analysis and the 1% most significant SNPs were selected regardless of the minor allele frequency (MAF) value. Where a SNP was detected in four of the five sites it is reported in For the elements measured in the rice grains in this study, there is varying knowledge on the molecular mechanisms of their uptake, transport and accumulation in plants. To test if candidate genes involved in the uptake, transportation or accumulation of these mineral elements were located near associated SNPs, two approaches were taken. For arsenic, copper and zinc, a small subset of candidate genes were tested, with all these genes having a known function for transporting these elements in rice. The transporters of molybdenum are unknown in rice, but transporters of molybdenum have been identified in other plant species Lsi1 (a NIP type aquaporin) and Lsi2 (a silicon/arsenite efflux carrier) have been identified as inter and extra cellular transporters of arsenic LSI1 is located on chromosome 2 at 31266245–31269960 bp (LOC Os02g51110) (Rice Genome Annotation Project (RGAP) (http://rice.plantbiology.msu.edu)), whereas the gene encoding LSI2 is located on chromosome 3 at 432878-430478 bp (LOC_Os03g01700). There were no significant SNPs associated with grain arsenic within 200 kb of either of these two genes.For arsenic transport in rice, MOT1 and its homologue MOT2 in A. thaliana, plus SHST1 in Stylosanthes hamata and MOT2  from Chlamydomonas reinhardtiiMOT1 orthologue on chromosome 8 (LOC_Os08g01120) there were significant SNPs for grain molybdenum detected at the Texas field site within the “all” accessions analysis. Also of note, a QTL in this region was identified in a Bala × Azucena mapping population SHST1 gene on rice chromosome 3 (LOC_Os03g09970), there were significant SNPs associated with grain molybdenum concentration within 200 kb of the orthologue. The SNPs that were significant at this location were detected within the “all” accessions analysis at the Faridpur field site and within the “all” accessions analysis and temperate japonicas subpopulation analysis at the Arkansas field site in 2006. The other two SHST1 orthologues in rice were not near significant SNPs. For the A. thaliana MOT2 orthologue on rice chromosome 1 (LOC_Os01g45830) there were significant SNPs detected within 200 kb for the Texas “all” accessions analysis, while for MOT2 from C. reinhardtii there were no SNPs significantly associated with grain molybdenum for either of the two rice orthologues. The location of significant SNPs associated with grain molybdenum at three of the four known plant molybdenum transporters suggests these genes are likely rice molybdenum transporters.For molybdenum there are four known transporter genes in plants ZIP (Zinc-regulated transporter (ZRT), Iron-regulated transporter (IRT)-like protein) family, YSL (yellow stripe-like) proteins, HMA  family, MTPs , COPT (COPper Transporter)/Ctr (Copper transporter) family, metallotheioneins, copper chaperone proteins, and NRAMPs . Some of these gene families have been implicated in the transport of copper and zinc as well as other elements such as iron, magnesium and cadmium. As there are such a large number of genes implicated in the transport of copper and zinc in plants, we limited our analysis to only those that have been directly identified as having a role in the uptake, transport, and accumulation of these elements in rice. For zinc, six ZIP genes  have all been identified as being directly involved in rice zinc transport There are a number of genes (and gene families) that have been identified as being involved in the uptake, transport, and accumulation of copper and zinc in plants  transport copper in rice COPT1 (LOC_Os01g56420) and COPT2 (LOC_Os01g56430) are neighbouring genes on rice chromosome 1 at 32523328–32524183 bp and 32526290–32526942 bp, respectively. For COPT1 and COPT2 there were a large number of SNPs significantly associated with grain copper concentration in the analysis conducted for the tropical japonica subpopulation at the Arkansas field site in 2006  and soil chemistry. Mean monthly maximum and minimum temperatures for the four field sites during the experimental period are given in Another factor that could also affect the detection of QTLs across multiple years is the different experimental setups that were used. The field management was performed differently for each site, generally reflecting the common practises (except the Texas field site) of rice cultivation in the region. The Texas field site was kept flooded until harvest, a practise not carried out at the other field sites. These differences in field management may have contributed to the lack of consistency of QTLs across field sites.temperate japonica subpopulation analysis only uses 13,295 SNPs which equates to 1 SNP every ∼32.2 kb. Therefore, the addition of more markers to this population in the near future with a high density rice array  should improve the resolution at which mapping could be done. The population used in this study is an excellent germplasm collection representing the wide geographical and ecological diversity of rice indica cultivars. Using populations with high levels of genetic diversity and those with less population structure has advantages and disadvantages. Zhao et al. temperate japonica) the mixed model approach will miss these associations, however if the alleles segregate across multiple subpopulations the mixed model has the best power to identify them. The approach of using a single subpopulation aus population where the accessions have been collected from Bangladesh and India A. thaliana a regional population (RegMap) has been developed for GWA mapping A. thaliana, GWA mapping within both global and regional populations can be performed, which allows for the comparison of the genetic basis of adaptation of traits within ecologically different subsets A potential limitation with the analysis conducted here is the small number of genetic markers at present, a total of 36,901 across the genome, which equates to 1 marker every ∼11.7 kb. This becomes more of an issue when looking at the number of markers within the subpopulations as not all markers are informative (polymorphic) in all subpopulations. For example, the The data presented here have been analysed using the standard methods for this population GWA mapping of grain composition for four elements in rice plants grown in five experiments has revealed associations with a complex pattern of QTL × environment interaction. Despite this complexity a number of loci and candidate genes have been highlighted by the GWA mapping for future research. These include loci where QTLs have previously been detected for arsenic, copper, molybdenum and zinc –4, and lO. sativa cultivars collected from 79 countries, and has representatives from each of the major rice subpopulations ) The cultivars used in this study were from an established rice diversity panel O. sativa cultivars were grown at the Faridpur field site (in a farmer’s field where permission had been granted) and at the Qiyang field site  295 were grown. At both these sites the plants were transplanted in a randomised complete block design (RCBD) with four replicates. Plants were hand transplanted and each replicate consisted of 10 hills (one plant per hill) 20 cm apart. Each row was sown 20 cm apart from the previous, and every other row was a check variety. All the grains from all of the panicles from the six central plants from each row were harvested by hand, and a subsample of the grain was dehusked for elemental analysis.The experimental design and rice growth conditions have previously been described in Zhao et al. For the field site in Arkansas (University of Arkansas Rice Research and Extension Center), the field layout in both years was a RCBD with two replications. In 2006 336 cultivars were sown in to the field and in 2007 348 cultivars were sown. Seeds of each cultivar were planted with a drill seeder about 2 cm deep in a single row 5 m long with spacing of 25 cm between each plant and 50 cm between the rows. Three representative plants were harvested by hand for the grain elemental determination.For the field site in Texas (Texas A&M AgriLife Research Center), 370 cultivars were grown using a RCBD with three replications. Plots were planted using the same methods as those used in Arkansas. Five seeds per cultivar were drill-seeded ∼2 cm deep into 13 cm length lines, hereafter called hills. Five hills were planted per row with 61 cm between hills within each field-row, and 25 cm between rows. Genotypes were represented by one hill per replication. Twenty fully mature seeds per hill were dehusked, from which three seeds were randomly selected for elemental analysis. The fields were fertilized in accordance with standard production systems in each growing area  was run on an external line as the internal standard. Analysis was performed as described in Sun et al. The following method was used for the analysis of the Faridpur and Qiyang samples. Trace-element grade reagents were used for all digests, and for quality control replicates of certified reference material (CRM) (Rice flour (NIST 1568a)) were used; spikes and blanks were included. Rice grain samples were dehusked, oven dried (80°C), and 0.2 g weighed into 50 ml polyethylene centrifuge tubes. Samples were microwave digested with concentrated HNO3 in 16×100 mm Pyrex tubes, at temperatures stepped from ambient to 110°C over a period of 12 h. Indium (EM Science) was added to the acid to a final concentration of 20 µg L−1 as an internal standard. Samples were diluted to 10 ml and analyzed on a PerkinElmer Elan DRCe ICP-MS for total element analysis. Portions of the samples were combined and used as a matrix-matched standard for drift correction, measured after every nine samples. Samples were normalized to the averaged signals of the best-measured elements and weights of seven samples per run.The following method was used for the analysis of the grain from the Texas and Arkansas sites. Three whole grains of fully mature dehusked rice (∼0.05 g) were digested with 1 ml of concentrated HNOGWA mapping was performed using a mixed model on all the cultivars (including the admix and aromatic cultivars), and also for each of the four subpopulations separately according to Zhao et al maximum-effect locus fell within a 200 kb region containing the previously identified candidate genes Two approaches were used to identify if there were any genomic regions significantly associated with a trait and present in more than one GWA mapping analysis. The first method looked at the regions with significant SNPs . In previous studies The second method used a SNP enrichment approach. For this the top 1% most significant SNPs only were analysed (regardless of MAF) from the GWA mapping that used all the data per element  for each field experiment. These SNPs were then analysed to determine if they were present in multiple experiments.Oryza sativa.Protein sequences of known transporters of molybdenum from different species were compared to rice protein sequences using BLASTp. The default parameters were used for BLASTp with the species specified as A. thaliana MOT1 gene (NP_180139) −142). There are at least three rice orthologues for SHST1 from S. hamata (CAA57710) −239), another on chromosome 8 at 19427423–19432708 bp (LOC_Os08g31410) , and the third is located on chromosome 3 at 4996773–5002177 bp (LOC_Os03g09980) . For MOT2 from A. thaliana (NP_178147) −146). For MOT2 from C. reinhardtii (AEY68285) −128) and the other on chromosome 3 at 842386–846445 bp (LOC_Os03g02380) .The protein sequence of the four known transporter genes of molybdenum in plants were obtained from the National Center for Biotechnology Information (NCBI) database. The closest rice orthologue(s) for these four known transporter genes were identified using BLASTp analysis against the RGAP. The closest rice orthologue to the Figure S1Correlation for elements at the Arkansas field site across both years. (A) copper P<0.001, r = 0.407; (B) zinc P<0.001, r = 0.630; (C) arsenic P<0.001, r = 0.407; (D) molybdenum P<0.001, r = 0.555.(TIF)Click here for additional data file.Table S1Elemental concentrations within each subpopulation at the five field experiments.(DOCX)Click here for additional data file.Table S2Mean monthly temperatures (°C) and total rainfall for the regions where field experiments were conducted (from www.worldweatheronline.com).(DOCX)Click here for additional data file.Table S3List of significant SNPs associated with the accumulation of arsenic within rice grains.(XLSX)Click here for additional data file.Table S4List of significant SNPs associated with the accumulation of copper within rice grains.(XLSX)Click here for additional data file.Table S5List of significant SNPs associated with the accumulation of molybdenum within rice grains.(XLSX)Click here for additional data file.Table S6List of significant SNPs associated with the accumulation of zinc within rice grains.(XLSX)Click here for additional data file.


Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV1 and FEV1/FVC across 19 studies . We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER , HLA-DQB1 and HLA-DQA2 , and KCNJ2 and SOX9  were associated with FEV1/FVC or FEV1 in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.Genome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second  HLA-DQB1 and HLA-DQA2, and (3) KCNJ2 and SOX9. Expression analyses in human lung tissue from ours or prior studies indicate that these regions contain genes that are plausibly involved in pulmonary function. This work highlights the utility of employing novel methods for incorporating environmental interaction in genome-wide association studies to identify novel genetic regions.Measures of pulmonary function provide important clinical tools for evaluating lung disease and its progression. Genome-wide association studies have identified numerous genetic risk factors for pulmonary function but have not considered interaction with cigarette smoking, which has consistently been shown to adversely impact pulmonary function. In over 50,000 study participants of European descent, we applied a recently developed joint meta-analysis method to simultaneously test associations of gene and gene-by-smoking interactions in relation to two major clinical measures of pulmonary function. Using this joint method to incorporate genetic main effects plus gene-by-smoking interaction, we identified three novel gene regions not previously related to pulmonary function: (1)  These measures also predict other morbidities and mortality in the general population SERPINA1 gene, is a well-established genetic risk factor for accelerated decline in pulmonary function, but it accounts for little of the population variability in pulmonary function.Spirometric measures of pulmonary function, particularly forced expiratory volume in one second (FEVHHIP for FEV1/FVC 1/FVC and FEV1 from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) and SpiroMeta Consortia have together identified 26 additional novel loci in or near the following genes: ADAM19, AGER-PPT2, ARMC2, C10orf11, CCDC38, CDC123, CFDP1, FAM13A, GPR126, HDAC4, HTR4, INTS12-GSTCD-NPNT, KCNE2, LRP1, MECOM (EVI1), MFAP2, MMP15, NCR3, PID1, PTCH1, RARB, SPATA9, TGFB2, THSD4, TNS1, and ZKSCAN3Genome-wide association studies (GWAS) have identified many common genetic variants underlying pulmonary function. The first GWAS of pulmonary function implicated 1/FVC and FEV1) in 50,047 study participants of European ancestry.Inhaled pollutants, especially cigarette smoking, can have important adverse effects on pulmonary function. Candidate gene studies have not consistently identified interactions with cigarette smoking in relation to pulmonary function. Despite the importance of smoking and other environmental factors in the etiology of many complex human diseases and traits, few GWAS have incorporated gene-by-environment interactions 1 and FVC values were lower in studies with the oldest participants. Standardized residuals of FEV1 and FEV1/FVC  SNP-by-pack-years for standardized FEV1/FVC residuals, (3) SNP-by-ever-smoking for standardized FEV1 residuals, and (4) SNP-by-pack-years for standardized FEV1 residuals. Study-specific genomic inflation factors (λgc) were calculated for the 1 degree-of-freedom (d.f.) SNP-by-smoking interaction term, to ensure that there was no substantial inflation due to the main effect of smoking being misspecified gc≤1.09  SNP-by-ever-smoking for standardized FEV gc≤1.09 , which igc values from the 2 d.f. JMA, calculated across all SNPs, ranged from 1.056 to 1.064. The quantile-quantile plots . The JMA results corresponding to the top SNP from each previously implicated locus P values for high-signal SNPs suggesting the presence of novel signals.The study-specific regression coefficients from each of the four models were then combined in JMA, and the resulting λle plots  show sub1/FVC, we observed two novel loci containing several significant SNP associations at the standard genome-wide Bonferroni-corrected threshold of PJMA<5×10−8, when considering interaction with ever-smoking  on chromosome 2, which gave PJMA = 2.64×10−9 (corresponding PINT = 0.27) in the ever-smoking model and PJMA = 5.00×10−11 (corresponding PINT = 0.0069) in the pack-years model (PID1 gene (PID1. There is no linkage disequilibrium (LD) between rs7594321 and rs1435867 .The most statistically significant result was for rs7594321, an intronic SNP located in rs model . For theID1 gene , but it HLA-DQB1 and HLA-DQA2, on chromosome 6  but only low to moderate LD with all other genotyped and imputed SNPs. A GWAS meta-analysis of asthma implicating the HLA-DQ region similarly found highly significant associations with only a few SNPs PJMA = 4.39×10−9 in the ever-smoking model and PJMA = 4.35×10−9 in the pack-years model for FEV1/FVC , there is some LD between the two SNPs . Conserved binding sites for two transcription factors, HTF and Lmo2, are located within 100 kb of rs7764819.Our next most statistically significant SNP (rs7764819) is intergenic between two human leukocyte antigen (HLA) genes, mosome 6 . The HLAFEV1/FVC . The cor0.05 see . The rs7DNER and HLA-DQB1/HLA-DQA2 loci, SNPs from 12 other chromosomal regions having PJMA values between 5×10−8 and 1×10−6 from either smoking model in relation to FEV1/FVC are presented in 1/FVC. SNPs from two chromosomal regions had PINT values between 5×10−8 and 1×10−6 in relation to FEV1/FVC, as shown in Besides the 1, the JMA of SNP and SNP-by-smoking gave genome-wide significant associations (PJMA<5×10−8) in the ever-smoking model for four SNPs on chromosome 17  and SOX9 (sex determining region Y-box 9) , and an absolute β value of 0.046 would be equivalent to 31 mL per copy of the risk allele  The most significant SNP (rs11654749) from both smoking models is intergenic between Y-box 9) . Conservrs model . The cor0.05 see . The rs1KCNJ2/SOX9 locus, SNPs from five other chromosomal regions have PJMA values between 5×10−8 and 1×10−6 from either smoking model in relation to FEV1 as shown in 1. SNPs from four chromosomal regions had PINT values between 5×10−8 and 1×10−6 in relation to FEV1, as shown in Besides this 1/FVC or FEV1 at or near genome-wide significance in our previous GWAS meta-analysis of 48,201 participants from the CHARGE and SpiroMeta Consortia. In fact, the lowest P value observed for these SNPs was 1.04×10−5  Consortium  P<0.05 , adding DNER, KCNJ2, and SOX9) harboring or flanking novel genome-wide significant SNPs were selected for follow-up mRNA expression profiling in human lung tissue and a series of primary cells. Transcripts of all three genes were found in lung tissue, airway smooth muscle, and bronchial epithelial cells; DNER and KCNJ2 transcripts were also found in peripheral blood cells , according to an eQTL analysis database of lymphoblastoid cell lines In a separate line of investigation, using the publically available Gene Expression Omnibus repository -smokers  [29]. Exg status  [29]. WeDNER and HLA-DQB1/HLA-DQA2) having highly significant evidence for association with FEV1/FVC. A third novel locus (KCNJ2/SOX9) was associated with FEV1. For the most significant SNPs at each of these three loci, there was no evidence for heterogeneity across the studies , indicating that the associations were not driven by one or a few studies and thus reflect accumulation of evidence across the studies. None of these three loci had previously been associated with pulmonary function. The comparison of results with our prior GWAS meta-analysis of SNP main effects 1/FVC and FEV1, as similarly observed in our previous GWAS meta-analyses of SNP main effects Few GWAS have accounted for potential interaction with environmental risk factors. To identify novel genetic risk factors that are missed when considering only genetic main effects per se. The lack of strong interactive effects does not negate the well-established harmful effects of cigarette smoking nor the need for broad public health campaigns to curb smoking. Instead, our findings demonstrate the value of applying the newly developed joint methods to uncover novel genetic risk factors that might shed light on the mechanisms leading to reduced pulmonary function.Given that pulmonary function is a phenotype for which numerous genetic loci have been identified in GWAS and smoking is clearly associated with pulmonary function, it might seem surprising that none of the genome-wide significant SNPs implicated by the JMA demonstrated a substantial interaction P value of 1.6×10−4P value for our top JMA finding was 6.9×10−3. In both our GWAS of smoking and pulmonary function and the recent GWAS of BMI and diabetes traits per se.Our pattern of SNP main and interactive results resemble the patterns seen in another recent application of the same JMA method to incorporate the interaction with body mass index (BMI) into GWAS of type 2 diabetes traits (fasting insulin and blood glucose) The current analysis of 50,047 participants included only 1,846 more participants than our previous GWAS meta-analysis of SNP main effects DNER gene were significantly associated with FEV1/FVC, even at the more conservative P value threshold of 1.25×10−8. The JMA results for DNER SNPs were driven by both smoking-adjusted main effects and interaction with quantitative smoking history. The DNER protein product is a ligand of the Notch signaling pathway that has been implicated in neuronal differentiation and maturation DNER is expressed in bronchial epithelial cells of non-smoking adults and, importantly, its expression is significantly higher in smokers  associations with pulmonary function in the JMA, but some were associated with pulmonary function at Nineteen independent studies contributed to our analyses. All study protocols were approved by the respective local Institutional Review Boards, and written informed consent for genetic studies was obtained from all participants included in our analyses.Of the 19 studies contributing to our analyses, 18 studies came from the CHARGE 1/FVC and FEV1 measured at that visit. For studies with spirometry at more than one visit, we analyzed measurements from the baseline visit  or the most recent examination with spirometry data (FHS and TwinsUK).All studies were included in our previous GWAS meta-analysis of pulmonary function or the follow-up replication analyses, wherein their pulmonary function testing protocols were described Smoking history  was ascertained by questionnaire at the time of pulmonary function testing. Pack-years of smoking were calculated for current and past smokers by multiplying smoking amount (packs/day) and duration (years smoked). Study participants were genotyped on various genotyping platforms, and standard quality control filters for call rate, Hardy-Weinberg equilibrium p-value, MAF, and other measures were applied to the genotyped SNPs . To gene1/FVC (%) and FEV1 (mL) on age, age2, sex, and standing height as predictors. Principal component eigenvectors and recruitment site were also included as covariates to adjust for population stratification (if applicable). The residuals were converted to z scores . We confirmed that smoking was inversely associated with the FEV1/FVC and FEV1 standardized residuals in all 19 studies .Our analyses included 50,047 participants from 19 studies who passed their study-specific quality control and had complete data on pulmonary function and smoking. Each study transformed the pulmonary function measures to residuals using linear regression of FEV1/FVC and FEV1 standardized residuals were used as the phenotypes for genome-wide association testing with linear regression models, which included the following predictor variables: imputed SNP genotype dosages, smoking history , smoking status , pack-years of smoking (continuous variable), and a SNP-by-smoking interaction product term. Two of the 19 studies (FHS and TwinsUK) had much relatedness among participants, and we took appropriate account of relatedness in the association testing  Our analyses were aimed at finding novel loci associated with pulmonary function when considering an interaction with cigarette smoking, so we chose to implement JMA of SNP main and interactive SNP-by-smoking effects (two d.f. test of the null hypothesis βgc as the ratio of the observed and expected (2 d.f.) median chi-square statistics and dividing the observed chi-square statistics by λgc. SNPs having PJMA<5×10−8  were considered statistically significant PJMA threshold of 1.25×10−8. In addition to reporting the PJMA for the most significant SNP from each novel locus, we used the β and standard error (SE) estimates from the JMA results to calculate the P values corresponding to the SNP main association (PSNP) and the SNP-by-ever-smoking interaction (PINT) The JMA was conducted with fixed effects on approximately 2.5 million SNPs using METAL software (version 2010-02-08) http://genome.ucsc.edu). The “sno/miRNA” track from the USCS browser was used to search for any microRNA within 100 kb upstream or downstream of each SNP, and the “TFBS Conserved” track was used to search for conserved transcription factor binding sites (TFBSs) at or near the most significant SNPs. The SNAP program Gene annotation was performed using the gene prediction tracks “UCSC Genes” and “RefSeq Genes” in the UCSC browser . RNA was sourced from lung (Ambion/ABI), human bronchial epithelial cells (Clonetics) http://www.ncbi.nlm.nih.gov/geo/) P value for the difference in means between smokers and nonsmokers was calculated using the nonparametric Mann-Whitney test.Second, we used another publically available data repository to investigate whether any of the implicated genes showed evidence for differential expression depending on smoking history. The gene expression profiles of human airway epithelium from healthy smokers (N = 10) and nonsmokers (N = 12) were obtained from the Gene Expression Omnibus site  data repository based on lymphoblastoid cell lines Figure S1P values for JMA testing of SNPs by (A) ever-smoking in relation to FEV1/FVC, (B) pack-years of smoking in relation to FEV1/FVC, (C) ever-smoking in relation to FEV1, and (D) pack-years of smoking in relation to FEV1. The corresponding two degree-of-freedom genomic inflation factors (λgc) are shown, as calculated across all SNPs before the exclusion of previously implicated SNPs. The JMA results of all SNPs were plotted (in blue), along with the SNPs remaining after exclusion of the 27 previously implicated loci (in black).Quantile–quantile plots for the genome-wide joint meta-analysis (JMA) of SNP and SNP-by-smoking interaction in relation to pulmonary function. The plots compare the observed vs. expected (DOCX)Click here for additional data file.Figure S2DNER, (B) SOX9, and (C) KCNJ2, using microarray data from the Gene Expression Omnibus site (http://www.ncbi.nlm.nih.gov/geo/) (GSE4498). The y-axes reflect the probe intensities of each gene transcript from Affymetrix HG-U133 Plus 2.0 microarrays SOX9 was represented by two different probes on the microarray; therefore, the intensities were averaged for each sample. The P value was calculated using the nonparametric Mann-Whitney test.mRNA expression profiling in human airway epithelium from healthy smokers versus nonsmokers. Expression profiles of 10 smokers (indicated in blue) and 12 nonsmokers (indicated in red) were obtained for (A) (DOCX)Click here for additional data file.Table S1Characteristics of study participants  at the time of pulmonary function testing.(DOCX)Click here for additional data file.Table S2gc) for study-specific results (corresponding to the 1 degree of freedom SNP-by-smoking product term) in each of the four regression models.Genomic inflation factors (λ(DOCX)Click here for additional data file.Table S3PJMA) is shown, along with the corresponding JMA model from which the result was obtained.Regions surrounding the most significant SNP from each of 27 previously implicated loci (500 kb upstream to 500 kb downstream of each SNP). These loci were excluded when identifying novel loci from the joint meta-analysis (JMA) of SNP and SNP-by-smoking interaction. The smallest P value from the JMA ((DOCX)Click here for additional data file.Table S4DNER gene. β estimates and P values are shown for the SNP main association (βSNP and PSNP) and interactive association (βINT and PINT) by smoking (ever-smoking and pack-years) in relation to FEV1/FVC. The P values corresponding to the joint test of SNP main and interactive associations are also shown.Study-specific results for the genome-wide significant SNP rs7594321 , located in the (DOCX)Click here for additional data file.Table S5HLA-DQB1 and HLA-DQA2 genes. β estimates and P values are shown for the SNP main association (βSNP and PSNP) and interactive association (βINT and PINT) by smoking (ever-smoking and pack-years) in relation to FEV1/FVC. The P values corresponding to the joint test of SNP main and interactive associations are also shown.Study-specific results for the genome-wide significant SNP rs7764819 , located between the (DOCX)Click here for additional data file.Table S6P values between 5×10−8 and 1×10−6 for the joint meta-analysis of SNP and SNP-by-smoking (ever-smoking or pack-years) in relation to pulmonary function (FEV1/FVC or FEV1). A hyphen (“−”) indicates P>1×10−6. For each regression model, the SNP having the smallest PJMA from each locus is shown.SNPs from each of 16 chromosomal regions with (DOCX)Click here for additional data file.Table S7P<1×10−6 from the 1 degree-of-freedom meta-analysis of regression coefficients corresponding to the SNP-by-smoking (ever-smoking or pack-years) interaction term in relation to FEV1/FVC. No SNPs exceeded the standard genome-wide significance threshold (P<5×10−8). A hyphen (“−”) indicates P>1×10−6. For each regression model, the SNP having the smallest PINT from each locus is shown.SNPs with (DOCX)Click here for additional data file.Table S8KCNJ2 and SOX9 genes. β estimates and P values are shown for the SNP main association (βSNP and PSNP) and interactive association (βINT and PINT) by ever-smoking in relation to FEV1. The P values corresponding to the joint test of SNP main and interactive associations are also shown.Study-specific results for the genome-wide significant SNP rs11654749 , located between the (DOCX)Click here for additional data file.Table S9Look-up evaluation of main SNP associations with cigarette smoking phenotypes using data generated by the Oxford-GlaxoSmithKline Consortium , for the most significant SNP from each of the three novel loci implicated at genome-wide significance in the joint meta-analysis.(DOCX)Click here for additional data file.Table S10mRNA expression profiling of three candidate genes in the human lung and periphery. Primer sequences are provided in (DOCX)Click here for additional data file.Table S11Questionnaire data used to ascertain cigarette smoking history (ever-smoking), amount, and duration across the 19 studies. Smoking amount and duration were used together to calculate pack-years.(DOCX)Click here for additional data file.Table S12Details of single nucleotide polymorphism (SNP) genotyping, quality control (QC), imputation, and statistical analysis across the 19 studies.(DOCX)Click here for additional data file.Table S13Study participants of European descent and quality control (QC) across the 19 studies. Participants passing QC filters and having acceptable spirometry data and complete covariate data were included in the meta-analyses.(DOCX)Click here for additional data file.Table S14Primers for mRNA expression profiling.(DOCX)Click here for additional data file.Text S1Detailed explanation of joint meta-analysis significance levels, in relation to main and interactive significance.(DOCX)Click here for additional data file.


ORAI1 channels play an important role for breast cancer progression and metastasis. Previous studies indicated the strong correlation between breast cancer and individual single nucleotide polymorphisms (SNPs) of ORAI1 gene. However, the possible SNP-SNP interaction of ORAI1 gene was not investigated.ORAI1 gene. For individual SNPs, the differences between case and control groups in five SNPs of ORAI1 gene were not significant. In contrast, GA-generated SNP models show that 2-SNP (rs12320939-GT/rs6486795-CT), 3-SNP (rs12320939-GT/rs12313273-TT/rs6486795-TC), 5-SNP (rs12320939-GG/rs12313273-TC/rs7135617-TT/rs6486795-TT/rs712853-TT) have higher risks for breast cancer in terms of odds ratio analysis .To develop the complex analyses of SNP-SNP interaction, we propose a genetic algorithm (GA) to detect the model of breast cancer association between five SNPs  of ORAI1 gene in breast cancer association study were well demonstrated in terms of GA-generated SNP models.Taken together, the cumulative effects of SNPs of  Single nucleotide polymorphisms (SNPs) are one of the most common variants in human genome . CurrentORAI1) gene have reported to be involved in breast cancer susceptibility , i = |C|}; a binary value at b is randomly generated. The one value of bj indicates that jth elements of two parents are must be exchanged; the zero value represents the no exchange. Two offspring are generated by exchanging the elements of two parents according the binary mask set, and the offspring are represented C’. For example, let a generated binary mask B = {1, 0, 1, 0} and two parents C1 = {, , , } and C2 = {, , , }. The generated two offspring are C’1 = {, , , } and C’2 = {, , , }, respectively.Crossover operation is used to generate the offspring from the parents, and the operation use a uniform crossover scheme. Uniform crossover firstly generate a binary mask set M = {m1, …, mi| m∈, i = |C|} is used to indicate the mutation points in the offspring C’. Each binary value in M is randomly generated according to the mutation probability. The one value of mj represents that jth element of C do the mutation; the zero value represents the no mutation. The mutation randomly generates a possible c element, where c∈A, to replace the original element at a mutation point. For example, let a generated mutation point M = {0, 0, 1, 0} and offspring C’1 = {, , , }. Suppose the number of SNPs is five, a possible set is E = {, , , , , }. After mutation the offspring could be C’1 = {, , , }.Mutation operation can facilitate the diversity of chromosomes in population, and avoid population for trapping the local optimal. The operation uses a one-point mutation operation. A mutation point set C’1 and C’2 are evaluated by fitness function, and are used to compare the value to all chromosomes. When an offspring is higher value than a chromosome of population, it replaces the chromosome; otherwise, the offspring is deleted.Replacement operation aims to gradually improve value of population. The generated two offspring In the GA parameters, both of the exchange probabilities in the tournament selection and uniform crossover are 1.0. The exchange probability of a one-point mutation is 0.1. The population size is 50, and the number of generations is 100.OR) with 95% confidence interval (CI) is used for measuring a single SNP and the model of association between SNPs; a P value of < 0.05 is considered statistically significant difference between the cases and controls.All statistical value is computed using SPSS version 19.0 . Odds ratio  is regarded as the reference for analyzing breast cancer risks in terms of single SNPs. Minor allele is selected according the dbSNP database of NCBI . No significant differences between the breast cancer patients and controls in all genotypes for each single SNP were found.Table During GA processing, the best ten models of two SNP combinations with genotypes (2-SNP models) were demonstrated in Table OR, 95% CI, and P value) of five types of the best models (2- to 5-SNP models) addressing the breast cancer association. The 2-SNP model, i.e., SNPs  in genotype 2-2, indicates the borderline significance with breast cancer . The 3-SNP model, i.e., SNPs  in genotype 2-1-2, indicates three SNPs  have a significant association when their genotypes are GT, TT, and TC, respectively . The 5-SNP model, SNPs  in genotype 1-2-3-1-1, indicates all SNPs  have a strongly association when their genotypes are GG, TC, TT, TT, and TT, respectively .Table GA is a robust non-parametric method that detects nonlinear interactions amongst multiple discrete genetic factors. The advantage of GA is that the method can directly search the good models from the huge number of possible combinations without the training data set. In this study, the fitness function is designed based on the unbalanced data set to compute the difference between case data set and control data set. The function can effectively measure high-risk to search the good model in real data set.OR values of 2- to 3-SNP models are larger than 1 but small, suggesting that the cumulative effect of these four SNPs  are weak. When five SNPs included, the OR value is 13.148, indicating that the cumulative effect of 5-SNP model becomes strong. This unstable cumulative effect of SNP combinations in SNP models may be partly explained by the experiment design that these five SNPs were only derived from a single gene ORAI1. Because breast cancer is a kind of multigene disease [In current study, the  disease -30, ther disease ,12,31-33n iterations is implemented in a test, the computational complexity of GA is O(n) which represents the big-O in complexity analysis. GA in search of good association model has the below advantages: (1) GA effectively identify the high-risk models in high-order interaction, (2) the best model with statistical significant can be fast identified, and (3) it only has two parameters to need setting and is easily to fulfil for searching the good model. Further, GA is able to analyze high order SNP interactions amongst the huge number of SNPs from GWAS and pharmacogenomics studies in our experiences.The computational complexity of GA is calculated by a fitness function of computation. Suppose ORAI1 gene have been reported to associate with inflammatory diseases [ORAI1 gene. The resulting SNP models can predict the breast cancer susceptibility more effective than the individual SNPs. This methodology can also apply to any kinds of SNP association studies, such as GWAS, pharmacogenomics and others. Therefore, the possible cumulative effect of SNP combination will be uncovered by this methodology.Although the polymorphisms of diseases -36, effeThe authors have no competing interests to declare.W-CC and M-FH carried out the molecular genetic studies. W-CC and Y-YF drafted the manuscript. C-HY instructed Y-DL to perform SNP interaction analysis by algorithm. Y-YF, H-WC, and L-YC performed statistics analyses. H-WC, C-HY, and M-FH were involved in discussion and editing the manuscript. All authors read and approved the final manuscript.


High-throughput sequencing has opened up exciting possibilities in population and conservation genetics by enabling the assessment of genetic variation at genome-wide scales. One approach to reduce genome complexity, i.e. investigating only parts of the genome, is reduced-representation library (RRL) sequencing. Like similar approaches, RRL sequencing reduces ascertainment bias due to simultaneous discovery and genotyping of single-nucleotide polymorphisms (SNPs) and does not require reference genomes. Yet, generating such datasets remains challenging due to laboratory and bioinformatical issues. In the laboratory, current protocols require improvements with regards to sequencing homologous fragments to reduce the number of missing genotypes. From the bioinformatical perspective, the reliance of most studies on a single SNP caller disregards the possibility that different algorithms may produce disparate SNP datasets.Unified Genotyper of the Genome Analysis Toolkit and SAMtools performed significantly better than a caller from CLC Genomics Workbench (CLC). Of all conflicting genotype calls, CLC was only correct in 17% of the cases. Furthermore, conflicting genotypes between two algorithms showed a systematic bias in that one caller almost exclusively assigned heterozygotes, while the other one almost exclusively assigned homozygotes.We present an improved RRL (iRRL) protocol that maximizes the generation of homologous DNA sequences, thus achieving improved genotyping-by-sequencing efficiency. Our modifications facilitate generation of single-sample libraries, enabling individual genotype assignments instead of pooled-sample analysis. We sequenced ~1% of the orangutan genome with 41-fold median coverage in 31 wild-born individuals from two populations. SNPs and genotypes were called using three different algorithms. We obtained substantially different SNP datasets depending on the SNP caller. Genotype validations revealed that the Our enhanced iRRL approach greatly facilitates genotyping-by-sequencing and thus direct estimates of allele frequencies. Our direct comparison of three commonly used SNP callers emphasizes the need to question the accuracy of SNP and genotype calling, as we obtained considerably different SNP datasets depending on caller algorithms, sequencing depths and filtering criteria. These differences affected scans for signatures of natural selection, but will also exert undue influences on demographic inferences. This study presents the first effort to generate a population genomic dataset for wild-born orangutans with known population provenance. The availability of high-throughput sequencing has revolutionized the fields of population genetics and molecular ecology . Early gTo date, population-genomics studies have mainly focused on humans ,11, someIn the laboratory, several strategies have recently been developed enabling so-called ‘reduced genome complexity sequencing’, i.e. sampling only a small fraction of the genome in several individuals. These strategies include sequencing of reduced-representation libraries (RRLs) , restricOne of the key characteristics of the aforementioned methods is that, at least in theory, read mapping can be carried out regardless of the availability of a reference genome by constructing a reference sequence from overlapping sequence stacks ,23-26. Met al.. Median numbers of mapped reads for each individual were 32,345,177 for the West Alas population and 43,451,986 for the South Kinabatangan population . iRRL efficiency varied among individuals but was very high with a median of 97% for West Alas individuals and 86% for the South Kinabatangan individuals . In addition, we performed identical population-based filtering for all three algorithms. We only accepted SNPs with a maximum of two alleles and genotypes meeting all quality filter criteria in at least eight individuals per population (n ≥ 16 chromosomes), allowing accurate allele frequency estimations. Applying all filters we retrieved 57,396 SNPs in the GATK dataset, 75,364 SNPs in the CLC dataset, and 24,103 SNPs in the SAMtools dataset . For the CLC and SAMtools dataset, genotype calls often failed the minimum coverage requirement of 10 reads. For the GATK dataset, many genotype calls did not have a sufficiently high genotype quality score.For all overlapping SNPs, we evaluated the concordance of genotype assignments by comparing for each individual whether two callers produced identical genotypes. The percentage of identical genotype calls varied among individuals with median values of 97.51% for GATK-CLC, 98.32% for SAMtools-GATK, and 97.24% for SAMtools-CLC . This procedure has been suggested to reduce caller-specific errors and increase specificity ,58. We ret Table .Over all six datasets, there were more sites segregating in the Sumatran West Alas population compared to the Bornean South Kinabatangan population Table . The vasp < <0.001, Figure To investigate the potential impact of the different SNP datasets on biological downstream analyses, we calculated three important statistics. (i) Kernel-density distributions for site heterozygosity and (ii) minor allele frequency were not identical among the SNP datasets  to measure population differentiation. We arbitrarily defined outlier regions as windows with an average population differentiation D > 0.95 (covered by at least 2 SNPs). The overlap of outlier windows among datasets was low. Only 3.8% of all detected outlier windows were identical among all three single-caller datasets  sliding-window analyses  to identify signals of putative selective sweeps based on population differentiation. We used the allele-frequency differential  and hard-filtering (CLC) callers.Our results show that GATK/SAMtools clearly outperformed CLC, with a correct genotype assignment in 83% of the conflicting calls Table . GATK/SAGATK seemed to be conservative in calling singletons and low frequency alleles in our dataset, as it exhibited the lowest proportion of singletons among all SNP datasets. Yet, among all datasets, GATK had the highest medium site heterozygosity. It appears that GATK slightly overestimates mid-frequency alleles, because our genotype validations revealed that in 30% of the cases where GATK called a heterozygous and CLC a homozygous genotype, CLC was correct. Thus, our results suggest that with increasing minor allele frequency, GATK starts calling alternative alleles more aggressively due to the population prior in multi-sample analysis.It appears that CLC generally underestimates heterozygosity. The CLC dataset consisted of an excess of singletons, suggesting that CLC called sequence errors as a genetic variants to a greater extent. Thus, the CLC dataset contained the lowest overall site heterozygosities among all datasets. Detailed investigation of discordantly called genotypes revealed that almost all of these genotypes were homozygous with CLC, but heterozygous with the other callers. To our surprise, CLC largely miscalled genotypes as heterozygous which were correctly assigned as homozygous by GATK (89% correct by GATK).Our results indicate that SAMtools is more restrictive in SNP calling than GATK and CLC. The SAMtools dataset consisted of considerably fewer SNPs than GATK and CLC, but the degree of overlap with the other datasets was much higher than for the other datasets. SAMtools showed the highest tendency to assign heterozygous genotypes in cases of discordantly called genotypes among callers. For example, the few discordantly called genotypes were strongly biased in that 84% were heterozygous with SAMtools, but homozygous with GATK.CLC Genomics Workbench, GATK Unified Genotyper and SAMtools). These inconsistencies among the SNP datasets led to strong disagreement in outliers detected in scans for signatures of natural selection. This shows the potential impact on downstream biological analyses and emphasizes the need to critically evaluate the accuracy of SNP and genotype calling in population genomic studies.Our study provides a framework for the generation of genome-wide SNP datasets for population genomic studies, from laboratory procedures to bioinformatics, which is widely applicable in non-model species. We present an improved protocol for highly efficient and more precise reduced genome complexity sequencing that simultaneously allows discovery of novel SNPs and genotyping. Using data generated from 31 wild-born orangutans from two populations, we observed significant inconsistencies among three commonly used SNP callers  compared to Bornean South Kinabatangan individuals (median 86%) were most likely caused by carrying out the initial ars ago, ,53,56, BWe also improved previous RRL approaches by minimizing the loss of DNA during purification steps, thus facilitating single-sample library construction. Economical handling of DNA is particularly relevant when studying species for which sample quantity is a limiting factor, which is the case for most wild animal populations. A high DNA recovery rate during purification steps is especially important when dealing with low template amounts (<100 ng), where DNA loss will be disproportionately higher for technical reasons, and/or targeting only a small fraction of the genome. So far, these problems have been circumvented by pooling samples. Our DNA recovery rate of >95% in the purification steps is considerably higher than obtained through conventional methods using extractions from gels and/or silica columns .From a bioinformatical perspective, we demonstrate that different SNP callers lead to substantially different SNP datasets, in spite of applying rather conservative quality filters. For example, we applied a phred-scaled mapping and base quality threshold of ≥ Q30, corresponding to an error probability of ≤0.1%. In contrast, other studies only apply Q20 (1.0% error probability) ,26,59,60There are three main reasons for the conspicuous differences among the SNP datasets. First, the SNPs dropping out because of our population-based filtering were different among the GATK, SAMtools and CLC datasets. Second, although we used identical mapped short reads and filtering criteria on the raw data to call SNPs and genotypes, we cannot exclude a potential influence of the poor F5 sequence read performance due to specific internal filters of SNP callers. Third and most importantly, some differences will arguably be related to the conceptually very different methods of SNP identification and genotype assignment ,36-38.Intersect strategies have been proposed to reduce caller-specific errors ,58. The The intersect strategy seems to be appealing because false-positive assignments should be minimized. However, it is inevitably less sensitive towards SNP discovery . The appAmong all datasets, the general patterns tend to agree with previous detailed studies on orangutan population genetics and demographic history. For instance, the higher number of singletons and low-frequency alleles we observe in the Sumatran West Alas population is in agreement with previous studies using conventional genetic markers  -54. FurtMany downstream analyses in population genomics, such as selection tests or demographic inferences rely on the allele-frequency spectra . Thus, bApart from reliable SNP analysis, the accurate characterization of the allele-frequency spectra is mainly influenced by three sources of bias. First, allele frequencies will not be representative of the population if there is a sampling bias ,66,67. TSecond, the discovery of SNPs in a subset of individuals for subsequent genotype calling in an extended sample set will lead to ascertainment bias ,17,27. TThird, it is biologically relevant to also capture rare alleles, which is the reason why we established individual libraries (i.e. no pooling of samples). Low-frequency alleles are important in estimates of demographic parameters  and studThe framework provided in this study will be valuable to generate genome-wide SNP datasets in the emerging fields of population, conservation and landscape genomics. Our iRRL protocol is part of a growing suite of sequencing methods, which have completely changed study designs and hold great promise for studies of ecology and evolution in diverse species. The strength of reduced-genome-complexity RRL methods is that they can be applied to any DNA-based life form, opening up the field of population genomics to smaller research groups studying organisms for which large-scale genetic data is not yet available. Until high-throughput sequencing becomes more affordable and bioinformatical advances allow routine whole-genome re-sequencing of populations, we expect that reduced-genome-complexity approaches will remain essential for population genomic studies particularly in non-model organisms with large genomes.Unified Genotyper of the GATK showed a higher sensitivity in discovering SNPs than the framework of SAMtools with similar genotype calling accuracy.We generated SNP datasets for 31 wild-born orangutans from two populations representing the first effort of large-scale SNP discovery and genotyping of orangutans with known population provenance. In the field of population genomics, researchers need to exert caution when generating genome-wide SNP datasets. We show that accurate generation of homologues fragments in reduced-genome-complexity sequencing is paramount, especially for pooled samples with no control for missing genotypes in the estimation of allele frequencies. We present an improved RRL protocol (iRRLs), which allows sampling only a fraction of the genome with maximized sequence overlap among individuals. The scale and efficiency achieved with our iRRL protocol demonstrates its suitability to generate genome-wide SNP datasets. Our direct comparison of three popular SNP callers demonstrated that depending on the calling algorithm, sequence depths and filtering criteria, substantially different SNP datasets are obtained that will affect downstream analyses and thus might have a substantial effect on biological conclusions. When only applying a single SNP caller, we advise to use a probabilistic algorithm and call genotypes in a multi-sample mode. In our study, the Bayesian framework of the Pongo abelii, northwestern Sumatra) at the Batu Mbelin Quarantine Center of the Sumatran Orangutan Conservation Programme, and 16 individuals from the South Kinabatangan population  at the Sepilok Orangutan Rehabilitation Centre, Shangri-La's Rasa Ria Resort Sanctuary and Lok Kawi Wildlife Park in Sabah. Whole blood samples were taken during routine veterinary examinations and stored in EDTA blood collection tubes at −20°C. The collection and transport of samples were conducted in strict accordance with Malaysian, Indonesian and international regulations. Samples were exported from Malaysia and Indonesia to Switzerland under the Convention on International Trade of Endangered Species in Fauna and Flora (CITES) permit numbers 4872/2010  and 06968/IV/SATS-LN/2005 (Indonesia), respectively. Detailed information on the sampled individuals is provided in Additional file We sampled two orangutan populations, one from Borneo and one from Sumatra Figure . To obta2O instead of DNA Hydration Solution (Qiagen) to facilitate DNA concentration using a SpeedVac vacuum centrifuge (Savant).To minimize DNA shearing, we avoided repeated thawing and freezing of samples and used only wide-bore tips and avoided vortexing during DNA extraction. Genomic DNA was extracted using the Gentra Puregene Kit (Qiagen) according to the manufacturer’s instructions, including RNase treatment, but with the following modifications for clotted blood: we added twice the amount of Cell Lysis Solution as well as 7 μl of Proteinase K  per 100 mg blood clot to the samples, followed by incubation for 3 hours at 55°C in a slowly revolving overhead rotator. If the solution still appeared to be viscous after this treatment, we increased incubation time and added more Proteinase K as required until complete liquefaction. We also used twice the recommended amount of Protein Precipitation Solution and incubated samples on ice for 10 minutes after addition of the solution to promote protein precipitation. DNA pellets were eluted in ddHin-silico digests of the orangutan reference genome  to eval to evali studies ,31,60, aIn cases where there is no reference genome available, the evaluation for a suitable enzyme could also be carried out in the laboratory, for example by analyzing the fragment distribution of digested genomic DNA using high resolution electrophoresis . These instruments offer tools to estimate the represented genome proportion of fragments within a given size range.HaeIII  in a total volume of 32 μl. Digests were run on high-resolution Spreadex EL400 Wide Mini S-2 × 13 gels with M3 size marker in a SEA 2000 electrophoresis chamber  in 1× TAE buffer at 120 Volt for 147 min, keeping temperature constant at 55°C to ensure reproducibility of fragment migration. The running time was the evaluated optimum for the target size range using the ELQuant Software (http://www.elchrom.com). Each digest was equally distributed in two separate wells to avoid DNA overloading. We stained gels with GelRed (Biotium) and excised fragments between 104 bp and 123 bp on a UV-transilluminator using a long-bladed sharp knife, keeping UV exposure as short as possible.We established iRRLs for each individual by digesting 20 μg of genomic DNA with 200 units of DNA fragments were recovered by electro elution to achieve high DNA recovery rate (>95%). For this, we prepared dialysis membranes  of approximately 5 cm width, which we sealed on one side with a plastic clip . We filled each dialysis membrane with 1 ml of 1× TAE buffer and placed gel slices in the membrane in the same running orientation as in the electrophoresis run  as ‘N’. We only accepted sites with a maximum of two alleles and covered by minimal eight individuals per study population, as we had done for the GATK and SAMtools datasets.As an alternative non-probabilistic approach, we discovered SNPs with the quality-based variant detection tool of the CLC Genomics Workbench v.5.0.1 (CLC bio) following the same quality requirements as applied in the GATK/SAMtools calls. Since the CLC version we used did not offer multi-sample calling  at the time of this study, we detected SNPs for each individual separately and merged the SNP data subsequently using R scripts. In this merged dataset, a missing call for an individual for a certain SNP position could arise either because this individual is homozygous for the reference allele or because this site was not sequenced. To obtain this information for all missing genotypes, we used SAMtools v.0.1.12a . We callet. al./2, where p and q denote the frequencies of the two alleles for each SNP. We scanned each chromosome ('chrXY_random' excluded) and calculated for each window  the average D of all SNPs. We arbitrarily defined outlier regions as windows with an average population differentiation D > 0.95 (covered by at least 2 SNPs).In addition, we performed sliding-window analyses for each dataset to detect selective sweeps in the genome based on population differentiation using custom R-scripts. For all SNPs we estimated population differentiation using allele-frequency differentials, defined as:  The raw sequence data to this article is available in the NCBI Sequence Read Archive, BioProject: PRJNA230877; BioSamples: SAMN02439270-SAMN02439300.Unified Genotyper of the Genome Analysis Toolkit (Broad Institute); CLC: SNP caller of the CLC Genomics Workbench (CLC bio); bp: Base pair; Gb: Gigabases; D: Allele frequency differential; MAF: Minor allele frequency.SNP: Single-nucleotide polymorphism; RRL: Reduced representation library; iRRL: Improved reduced-representation library; WA: West Alas population (Sumatra); SK: South Kinabatangan population (Borneo); bphiqual: Sequenced bases with mapping and base quality phred scores of ≥30 and a minimal sequence depth of 10; GATK: The author(s) declare that they have no competing interests.in-silico analyzes. AP contributed reagents and performed sequencing. RB carried out short read mapping. MPG performed bioinformatical analyses. KNS supported the bioinformatical analyses. MPG, MK, and NA wrote the manuscript. KNS, AN, BN, RHSK, LC, RB, RS, BG, and CPvS critically revised the manuscript. All authors read and approved the final manuscript.MPG, MK conceived and coordinated the study with input from CPvS. MPG designed and performed experiments. KNS, AN, and RHSK contributed to the experimental design. BG, MPG, MK, RS, IS, LNA, LC, and CPvS provided genetic samples. AN and NA supported the population genetic assessment of study individuals. BN contributed ideas and reagents. RB and MPG conducted Describes the population genetic assessment of study individuals and the genotype validations through Sanger-sequencing in detail. Contains Figure S1. which shows the STRUCTURE analysis results, Figure S2. which shows an example image of the high precision excision of target fragments from Spreadex gels, and Figure S3. which illustrates the low overlap of outlier regions among SNP datasets in genomic screens for positive selection.Click here for fileList of study individuals.Click here for fileBasic sequencing and mapping statistics for all study individuals.Click here for fileComparison of the SNP and genotype calling of GATK and CLC for each individual.Click here for fileComparison of the SNP and genotype calling of GATK and SAMtools for each individual.Click here for fileComparison of the SNP and genotype calling of SAMtools and CLC for each individual.Click here for fileList of tested restriction enzymes.Click here for filePCR primer sequences used for the genotype validations.Click here for file


Bos taurus autosome 15. The haplotype, denoted JH1, was associated with reduced fertility, and further investigation identified one highly-influential Jersey bull as the putative source ancestor. By combining SNP analysis of whole-genome sequences aligned to the JH1 interval and subsequent SNP validation a nonsense mutation in CWC15 was identified as the likely causative mutation underlying the fertility phenotype. No homozygous recessive individuals were found in 749 genotyped animals, whereas all known carriers and carrier haplotypes possessed one copy of the mutant allele. This newly identified lethal has been responsible for a substantial number of spontaneous abortions in Jersey dairy cattle throughout the past half-century. With the mutation identified, selection against the deleterious allele in breeding schemes will aid in reducing the incidence of this defect in the population. These results also show that carrier status can be imputed with high accuracy. Whole-genome resequencing proved to be a powerful strategy to rapidly identify a previously mapped deleterious mutation in a known carrier of a recessive lethal allele.With the recent advent of genomic tools for cattle, several recessive conditions affecting fertility have been identified and selected against, such as deficiency of uridine monophosphate synthase, complex vertebral malformation, and brachyspina. The current report refines the location of a recessive haplotype affecting fertility in Jersey cattle using crossover haplotypes, discovers the causative mutation using whole genome sequencing, and examines the gene’s role in embryo loss. In an attempt to identify unknown recessive lethal alleles in the current dairy population, a search using deep Mendelian sampling of 5,288 Jersey cattle was conducted for high-frequency haplotypes that have a deficit of homozygotes at the population level. This search led to the discovery of a putative recessive lethal in Jersey cattle on  Approximately 400 Mendelian disorders have been identified in cattle species Several studies have demonstrated the utility of dense SNP genotypes for identifying chromosomal regions associated with autosomal recessive conditions in livestock . Mutations underlying recessive disorders in cattle have successfully been identified using DNA sequencing Bos taurus (BTA) autosome 15, and was determined to have a significant, negative effect on fertility. Despite the relatively high carrier frequency (23.4%) in the population, no homozygous animals have been identified in the population, leading to the conclusion that JH1 is associated with early embryonic loss. The 5-Mb region identified using SNP genotypes was therefore, an ideal candidate for resequencing to identify the causal variant associated with this phenotype.VanRaden et al. The objective of this study was to identify the DNA sequence variation associated with lethal haplotype JH1 using targeted resequencing of heterozygous animals and to identify the putative biological mechanisms underlying the JH1 phenotype.360 or 10−45.The initial search for lethal recessives The inheritance of JH1 was traced to just one source ancestor, Observer Chocolate Soldier (US registration number 596832) born in 1962 Examination of 52,449 fertility records of JH1 heterozygous bulls mated to daughters of JH1 heterozygous bulls revealed a conception success rate of 33.3% vs. 37.0% for 290,373 contemporary matings of normal bulls to daughters of normal bulls Embryonic losses from JH1 appear to occur very early in gestation (<60 d). Analysis of 60-, 100-, and 140-day non-return rates, a measure of fertility defined as the proportion of cows that do not return to estrus at a fixed time after insemination, shows that pregnancy losses did not increase after 60 days post-insemination Crossover haplotypes contain part of the source haplotype and part of a non-source haplotype, and a descendant’s phenotype status may be unknown when crossovers occur. Crossovers were detected from genotypes by directly comparing progeny to parent haplotypes within the pedigree using findhap.f90 Fine mapping was accomplished by checking for animals with both the original JH1 haplotype and a crossover haplotype Whole genome re-sequencing of animals was done on the Illumina HiSeq 2000 according to the manufacturer’s protocols . Paired-end libraries were prepared from 5 µg of genomic DNA purified from semen and sheared to about 300 bp using a Covaris S1 ultra-sonicator . Bulls were selected for sequencing to maximize diversity of the alternative (non-JH1 carrier) haplotype and minimize relationships among animals with the JH1 haplotype, and semen was provided by the Cooperative Dairy DNA Repository . Each library was loaded to a single channel on a flow cell (no pooling), and sequence data was generated as 2×100 bp reads. Initial sequence processing and base-calling was performed using Illumina CASAVA v.1.8.2 pipeline.Bos taurus genome assembly using BWA Sequence reads for each animal were aligned to the UMD 3.1 Bos taurus annotations from Ensembl release 65 for the UMD 3.1 assembly.The aligned reads were used to identify sequence variations within the suspect region of BTA15. The SAMtools utility mpileup All SNP and indels within the JH1 candidate interval 15,162,470 to 15,949,175 were subject to repeat mask analysis. Sequences (100 bp flanking) containing the SNP and no repeat sequences were sent to NeoGen-GeneSeek  for sequenome assay design. SNP assay designs were attempted for the flank of each SNP, and bi-directional assays were designed for 14 SNP and only a single assay could be designed for SNP 15438546 . AnimalsThe initial JH1 interval spanning about 5 Mb on BTA15 from 11,439,502 to 16,147,383 on the UMD 3.1 map Considering the refined area JH1 interval defined by 15 SNP50 markers, 17 crossover haplotypes were detected. The carrier status of animals with these crossover haplotypes was unknown. Only crossover haplotypes that included all of the 15 markers were labeled as carriers, and the remaining haplotypes labeled as non-carriers. Thus, reported JH1 status is conservative, and some heterozygous JH1 animals may have been reported as non-carriers. The true status of these animals could be discovered by breeding trials or by identification of the causative variation through re-sequencing.Based on this mapping analysis, all heterozygous JH1 animals contained with the CDDR repository were considered as candidates for whole genome re-sequencing. Criteria for selection considered both pedigree relationships between JH1 carriers and non-carrier allele haplotype. The animals chosen for whole genome sequencing included an Observer Chocolate Soldier son  and 10 more recent carriers with differing non-carrier haplotype . SequencCWC15, the bovine protein CWC15 homolog of a spliceosome-associated protein CWC15 protein product from 231 amino acids in length to only 54 amino acids. A NCBI conserved domains search on the bovine CWC15 protein product reveals that this truncated protein would not have the conserved Cwf_Cwc_15 (pfam04889) domain present in the wildtype. None of the other 14 SNP or the INDEL fell within the coding regions of the three genes in the refined JH1 interval, but there was one SNP within the 3′ UTR of A7YY77. A Sequenom panel of 29 SNP assays was designed for the validation test Within the original JH1 interval from 11,439,502 to 16,147,383, 20,805 variants were identified that included 17,585 SNPs and 3,220 INDELs. After filtering to retain only heterozygous loci a total of 262 variants remained: 244 SNPs and 18 INDELs. Within the refined JH1 interval, there were only 36 SNP and 2 INDELs. Repeat masking removed 22 SNP and 1 INDEL from the candidate list leaving 15 SNP and one INDEL as potential candidates for the JH1 mutation. Functional annotation identified a single high-impact stop-gain SNP located at position 15,707,169 on BTA15. This C-to-T transition SNP results in an Arginine to a stop codon in exon 3 of The diagnostic validation of SNP15707169 as the causative mutation was tested against 749 samples. After correcting for one incorrect JH1 assignment due to a crossover in the region, SNP15707169 was 100% concordant with JH1 status based on haplotype . CompariCWC15 stop gain SNP as the causative SNP was bovine expression data. Although quantitative expression data for this gene is limited, Harhay and colleagues CWC15 was expressed in all 87 bovine tissues supporting its role as an essential gene for cell function. Interestingly, comparison of the expression levels between tissues revealed there was a 7-fold difference normalized tag abundance. Some of the tissues with the highest relative expression of this gene included portions of the placenta and the uterine attachment to the placenta  has shown that having >1,000 animals genotyped for a polymorphism are sufficient to impute genotypes with close to 99% accuracy for >100,000 other animals For JH1, the causative mutation in Three previously identified genetic defects in Holstein cattle  Figure S1The expected decrease in conception rate for carrier × carrier matings as a function of misidentification rate and carrler frequency(EPS)Click here for additional data file.Figure S2The expected decrease in conception rate for noncarrier × noncarrier matings as a function of misidentification rate and carrler frequency(EPS)Click here for additional data file.Table S1The 15 SNP in the refined JH1 interval region used to design a Sequenom validation test.(PDF)Click here for additional data file.Text S1The effect of pedigree error rates and carrier allele frequencies on the observed reduction in conception rate.(DOCX)Click here for additional data file.