The admixed South African Coloured population is ideally suited to the discovery of tuberculosis susceptibility genetic variants and their probable ethnic origins, but previous attempts at finding such variants using genome-wide admixture mapping were hampered by the inaccuracy of local ancestry inference. In this study, we infer local ancestry using the novel algorithm implemented in RFMix, with the emphasis on identifying regions of excess San or Bantu ancestry, which we hypothesize may harbour TB susceptibility genes.-5 for San ancestry and <2.00×10-16 for African ancestry), including chromosomes 15q15 and 17q22, which are close to genomic regions previously implicated in TB. Promising immune-related susceptibility genes such as the GADD45A, OSM and B7-H5 genes are also harboured in the identified regions.Using simulated data, we demonstrate reasonable accuracy of local ancestry inference by RFMix, with a tendency towards miss-calling San ancestry as Bantu. Regions with either excess San ancestry or excess African (San or Bantu) ancestry are less likely to be affected by this bias, and we therefore proceeded to identify such regions, found in cases but not in controls (642 cases and 91 controls). A number of promising regions were found  contains supplementary material, which is available to authorized users. The South African Coloured population (SAC) is a so-called admixed population that derived its origins from the diverse population groups that settled in the early Cape colony, including the indigenous San, early European settlers, slaves that were imported from Indonesia, India and other parts of Africa, and South African Bantu-speakers who later migrated to the area. Previous genetic research has shown that the SAC received ancestry contributions from click-speaking Africans (San), Bantu-speaking Africans, Europeans and South and East Asians, which is consistent with the historical records –5. A higOur study group of SAC individuals was recruited from metropolitan areas in Cape Town that have some of the highest reported incidences of tuberculosis (TB) worldwide, despite extensive BCG vaccination and low prevalence of HIV . As the When admixture occurs between two or more population groups that were previously isolated, recombination events result in chromosomes that are a mosaic of blocks of ancestry deriving from different source populations. Given genetic data of an admixed individual and their source populations, statistical techniques can be used to determine the bounds of these segments and to assign the most probable source ancestries to them. These techniques rely on the probability of recombination events to distinguish the bounds of segments, and differences in allele and haplotype frequencies between source populations for classification of the ancestry of segments. The process is known as local ancestry inference (LAI).Mycobacterium tuberculosis (M. tuberculosis), the most prevalent in our SAC study group [M. tuberculosis could possibly have resulted in decreased resistance to developing the disease, especially in densely populated areas with low socio-economic conditions. Supporting this argument, a significant positive association between San ancestry and TB susceptibility in the SAC was found by Chimusa et al. [In a previous study, Chimusa et al. concluded that accurate multi-way LAI was not feasible in the SAC using the LAI algorithms available at the time . In thisdy group , until tdy group . The rela et al. . The assa et al. . AlthougIn this study, we first explore the accuracy of LAI in the SAC using RFMix and compare its performance to other algorithms. After quantifying this using simulated data, we proceed to identify regions with excess San or Bantu ancestry found in TB cases but not in controls, and hypothesize that these regions may contain genes that affect TB susceptibility.Chimusa et al. previously evaluated the accuracy of LAI in the SAC using various software programs and found that LAMP-LD performed best . As RFMiHistograms of the difference between the mean ancestry called by RFMix per chromosome and the known mean ancestry in the simulated data set are shown in Additional file Methods - Delineating called ancestry segments), yielding 1077 segments. Deviation from the overall RFMix mean ancestry was calculated for each of the ancestries, for each of the segments. Table Could chromosomal segments with large deviations from the mean ancestry be the result of LAI errors? To answer this question, the simulated chromosome 1 data set was divided into segments by determining the positions where ancestry switches occur (see -16). Additional file -13), indicating that inferring local ancestry may be more error-prone for chromosomes with a large degree of admixture.The negative effect that short tracts of ancestry and a large degree of admixture could have on LAI accuracy was explored next. Additional file We proceeded to run LAI on our study group of 733 unrelated SAC individuals using RFMix (642 TB cases and 91 controls). Figure RFMix output was divided into 13 860 segments of local ancestry and local ancestry deviations were calculated. Histograms of the local ancestry deviations in cases and controls are depicted in Additional file -11 and p-value =3.00×10-2 respectively) [-5 (San) and <2.00×10-16 (Bantu). Regions that differ significantly between cases and controls are summarized in Tables Previous work has shown that San and Bantu ancestry is associated with increased susceptibility to TB in the SAC, and that the non-African ancestry components  are protective. The non-African ancestry components were however not associated with TB susceptibility after adjustment for the other ancestry components, whereas the San and Bantu components remained significant. The positive association between San ancestry and TB susceptibility was highly significant, whereas association with Bantu ancestry was relatively weak  . We werePreviously it was thought that admixture mapping in the SAC was not feasible using available LAI methods, due to the complex five-way admixture that occurred in this population . Using tGADD45A  is located on chromosome 1p31, a region with excess San ancestry in TB cases, and encodes a stress sensor protein that is involved in the regulation of myeloid cell innate immune function. Salerno et al. demonstrated that mice lacking Gadd45a are not able to recruit granulocytes and macrophages to the intraperitoneal cavity after administration of lipopolysaccharide (LPS) [OSM) is also located in a region with excess San ancestry in TB cases (chromosome 22q12). Friendland et al. found that M. tuberculosis infection of monocytes resulted in prostaglandin-dependent OSM secretion, which synergized with tumour necrosis factor- α to drive fibroblast matrix metalloproteinases-1/-3 secretion [Based on their putative role in TB pathogenesis, we identified a number of genes in regions of excess ancestry that are convincing candidates for future studies. de (LPS) . The rececretion .M. tuberculosis[B7-H5 (B7 homolog 5), located on chromosome 10q22. Its protein product plays a key regulatory role in T cell growth and cytokine production [Our analysis also found that chromosome 15q15 may contain an African TB susceptibility locus. Chromosome 15q was previously identified as containing TB susceptibility genes in a linkage study using African families  and fineerculosis. Anotheroduction  and is eGADD45A gene, and rs4745272 on 9q21). Considering that the GWAS used the same SAC data set, we note that a number of new putative susceptibility regions were identified by our admixture mapping study, which would not have been evident based solely on single SNP association statistics.Two of the 36 SNPs that were found to be moderately associated with having TB in a previous genome-wide association study (GWAS) are located in or proximal to regions identified in this study  . We therefore note that the reported p-values should not be interpreted as a strict quantification of the statistical significance of the results, but that the p-values rather indicate which regions are most likely to harbour ancestry related TB susceptibility variants.RFMix estimates of San ancestry were much lower than ADMIXTURE estimates. This may be explained by the small group size of our San source population as well as short tracts of San ancestry inherited via Southern African Bantu populations, and results in bias towards local ancestry deviations with lack of San ancestry. We speculate that roughly 50 San individuals would be required to alleviate the small group size problem . We have worked around these limitations by focusing our attention on finding regions of excess San or excess San or Bantu ancestry, which are less likely to be affected by spurious deviation in local ancestry, and this approach also supports our hypothesis regarding the directionality of these ancestries and TB susceptibility. The possibility of short tracts of San ancestry harbouring TB susceptibility genes are also less likely compared to longer tracts, as the former are less likely to overlap in a group of individuals. Miss-identification of these short tracts as Bantu ancestry has therefore probably not resulted in loss of information regarding TB susceptibility.The small size of the San source population also limited the number of ancestral chromosomes that were used in the generation of our simulated data set. As a result, the bias seen in the simulated local ancestry deviation distributions may have been exacerbated. Due to lack of accurate historical records, our simulation also did not take into account such complexities as the timing of admixture events, and potential inaccuracies of the source (reference) populations. Pasanuic et al.  recentlyOur SAC study group samples were genotyped with the aim of performing a case-only admixture mapping study, and as a result only a small number of controls were genotyped. As case-only admixture mapping has subsequently (and correctly) been described as inappropriate for multi-way admixed populations due to artefactual ancestry deviations arising from inaccuracies in LAI, we firsThe genetics of the South African Coloured population is arguably one of the most challenging and interesting examples found in present day multi-way admixed human populations. This is the first study to apply genome-wide admixture mapping to this highly complex group. We have demonstrated that admixture mapping is feasible in the South African Coloured population, a result which may be useful for other researchers that either study this population, or other populations with complex admixture. We have identified a number of novel candidate TB susceptibility genomic regions, as well as providing evidence to validate genetic loci previously implicated.M. tuberculosis[Individuals residing in the Cape Town suburbs of Ravensmead and Uitsig, and who self-identified as South African Coloured, were recruited to participate in this study. These suburbs have a low prevalence of HIV but a high incidence of TB, as well as a relatively homogenous socio-economic environment . Bacterierculosis. The majThis study was approved by the Ethics Committee of the Faculty of Health Sciences, Stellenbosch University . Blood samples for DNA were collected with written informed consent. The research was conducted according to the principles expressed in the Declaration of Helsinki.Web URLs, version information and parameter settings of the programs used in this study are summarized in Additional file ggplot2[hexbin[hierfstat package was used to estimate FST (fixation index) between the source populations of the SAC, using the same data set that was created for ADMIXTURE estimation [SHAPEIT  was usedggplot2 and hexbt2[hexbin R packagtimation .969 individuals from the SAC study group were genotyped on the Affymetrix GeneChip Human Mapping 500K Array Set. After SNP calling , qualityFST) between these source populations are summarized in Additional file Source (a.k.a. reference) populations used to infer the ancestry of SAC individuals are summarized in Table ftp://ftp.hapmap.org/hapmap/recombination/2008-03_rel22_B36/rates/). The genotypes of 733 SAC individuals were phased using 381 530 autosomal SNPs. The San data set of 21 individuals were phased using 866 382 autosomal SNPs.As local ancestry inference requires phased input data, the SAC and San data sets were phased using a Markov model to estimate haplotypes, implemented in the SHAPEIT software. The genetic map used by the HapMap project to phase the HapMap data sets was utilized for phasing the SAC and San data sets . The base pair positions of SNPs were obtained from the HapMap data, and in the case where an exact base pair position match was not found in the genomic map, the base pair position of the SNP was converted to cM by using a weighted average of the cM positions of the two SNPs closest to it.After quality control and phasing of the SAC and San source population data sets, the data sets were reduced to contain only those SNPs present in all the data sets . Where required, strand and reference alleles of the SAC and San data sets were flipped to match the HapMap data sets. The centimorgan (cM) genomic positions used by PCAdmix and RFMix to determine ancestry windows were calculated using the NCBI build 36 release 22 genomic map . 10 source population chromosomes were selected randomly from each of the source data sets. The remaining chromosomes were set aside to use as input source populations for LAI. Admixed chromosomes were constructed by randomly copying segments of ancestry from the selected source population chromosomes, corresponding to the ancestry assigned to the segment. As a simple example, consider an admixed chromosome with San ancestry for SNPs at positions 1 to 100 on the chromosome, and CEU ancestry for SNPs at positions 101 to 200. The admixed chromosome would be constructed by randomly selecting a San chromosome and copying the SNPs at positions 1 to 100, followed by randomly selecting a CEU chromosome and copying the SNPs at positions 101 to 200.Note that a limited number of source population chromosomes were used to simulate the admixed chromosomes. As a result, the data set does not contain independent observations. Unlike most LAI algorithms, the global ancestry proportion estimation algorithm implemented in the software program ADMIXTURE assumes independent observations. Taken together with the limited number of SNPs that are available compared to genome-wide data (only chromosome 1 SNPs are available), the simulated chromosome 1 data set is not suited to the estimation of chromosome-wide ancestry using ADMIXTURE, and RFMix chromosome-wide estimates were used instead.LAMP-LD, RFMix and ADMIXTURE were run using five source populations Table , but sinRFMix labels the called ancestry of each SNP along a chromosome and does not identify windows of ancestry across a chromosome in its output. Segments of ancestry were therefore identified by determining the SNP positions where a switch in ancestry occurs for at least one chromosome. Each ancestry segment starts at such a position, and ends one SNP before the next ancestry switch position.The genome-wide ancestry of each of the 733 SAC individuals was calculated using local ancestry called by RFMix, as follows: The number of SNPs labeled as a particular ancestry was counted per individual for each of the individual’s 22 × 2 chromosomes. This total count was divided by the total number of SNPs across the genome, yielding an estimate for the individual’s genome-wide ancestry.After delineating the called ancestry of the 1500 simulated chromosomes into 1077 segments, the mean San, Bantu and non-African ancestry of each segment was calculated. The local ancestry deviation of each segment was then calculated, seperately for each of the three ancestries, by subtracting the overall RFMix mean ancestry from the mean ancestry of the segment.In the same way, after delineating the called ancestry of 733×2×22 chromosomes in the SAC study group into 13 860 segments, the mean ancestry of each segment was calculated for each of the three source ancestries. The local ancestry deviation of each segment was calculated by subtracting the overall RFMix mean genome-wide ancestry from the mean ancestry of the segment, for each of the ancestries.After local ancestry deviations were calculated for each of the segments identified in the simulated data set, and segments with miss-called ancestry were identified by comparing ancestry called by RFMix to the known ancestry of each segment, the number of segments with miss-called ancestries were calculated per identified segment. This was done for each of the six possible pairs of miss-called ancestry . A large number of miss-calls occurred in the segment of ancestry that spans the centromere, with a standardized local ancestry deviation Z-value of -0.7109, 0.7052, and 0.1518 for San, Bantu and non-African ancestry respectively. After discarding this outlying segment, Pearson’s correlation coefficient was calculated between the number of miss-called segments and local ancestry deviation, for each of the six possible pairs of miss-called ancestry and San, Bantu and non-African local ancestry deviation combinations.Accuracy of LAI using the simulated data set was calculated per chromosome as follows. The ancestry assigned to each SNP in the data set by the simulation process was compared to the ancestry assigned to the SNP by LAI. The proportion of SNPs that the LAI correctly assigned to each chromosome was then calculated. For each of the three considered ancestries , the proportion of SNPs that were miss-called for each of the other two ancestries was also calculated. The miss-called proportions were calculated across all SNPs on the chromosome, as well as per number of SNPs of that particular ancestry.The lengths of tracts of ancestry in the simulated data set were calculated in terms of the number of SNPs that constitute a track. Each track’s corresponding proportion of miss-called SNPs were calculated by comparing the ancestry assigned by RFMix to each SNP with the known ancestry of the SNP, and then dividing the number of miss-called SNPs by the length of the tract. Pearson’s correlation coefficient was calculated to quantify the relationship between the length of a track and the proportion of errors on a track.The number of tracks of ancestry present in each simulated chromosome was counted, and was used to represent a chromosome’s degree of admixture. The number of miss-called SNPs was counted per chromosome, by comparing the ancestry assigned by RFMix to each SNP with the known ancestry of the SNP. Pearson’s correlation coefficient was calculated to quantify the relationship between the number of tracks of ancestry of a chromosome and the number of miss-called SNPs on a chromosome.A single linear mixed-effects model was used to compare the number of SNPs that were miss-called by LAMP-LD vs. RFMix in the simulated data set. The proportion of SNPs that were miss-called for a particular pair of ancestries  was log transformed for analysis, since the proportions were positively skewed. The interaction between software program (LAMP-LD or RFMix) and pair of ancestries was tested, while chromosome identifiers were specified as random effect, in order to adjust for the correlation between between different miss-call proportions on the same chromosome.Two joint logistic regression models were used to test whether San and African ancestry differs between TB cases and controls . Joint modelling has the advantage of providing estimates that are adjusted for all other predictors in the same model. Case-control status was specified as outcome variable and the interaction of segment identifier and presence or absence of the particular ancestry (San/not-San or African/not-African) was specified as predictor. This statistical interaction between a specific ancestry and a segment identifier can be described as a predictor combining an ancestry indicator with the segment identifier. That means that the odds ratio for each segment is the estimated odds of having TB versus not having TB for a San segment, compared to the odds for a non-San segment, and the interpretation is analogous for African ancestry. Since age, gender and genome-wide ancestry distributions differ between TB cases and controls, the models were also adjusted for these variables.Supplementary Figures and Tables. 10 figures and 4 tables are found in this file. (PDF 298 KB)Additional file 1:


Pinus monticola Douglas ex D. Don) is of high interest in forest breeding and conservation because of its high susceptibility to the invasive disease white pine blister rust . However, WWP lacks genomic resource development and is evolutionarily far away from plants with available draft genome sequences. Here we report a single nucleotide polymorphism (SNP) study by bulked segregation-based RNA-Seq analysis.Western white pine , differential expressed genes (DEGs) in plant defense response, and resistance gene analogs (RGAs). To estimate efficiency of in-silico SNP discovery, genotyping assay was developed by using Sequenom iPlex and it unveiled SNP success rates from 40.1% to 61.1%. SNP clustering analyses consistently revealed distinct populations, each composed of multiple full-sib seed families by parentage assignment in the WWP germplasm collection. Linkage disequilibrium (LD) analysis identified six genes in significant association with major gene (Cr2) resistance, including three RGAs (two NBS-LRR genes and one receptor-like protein kinase -RLK gene), two HCGs, and one DEG. At least one SNP locus provided an excellent marker for Cr2 selection across P. monticola populations.A collection of resistance germplasm was used for construction of cDNA libraries and SNP genotyping. Approximately 36–89 million 2 × 100-bp reads were obtained per library and C. ribicola. Our results demonstrate that integration of RNA-seq-based transcriptome analysis and high-throughput genotyping is an effective approach for discovery of a large number of nucleotide variations and for identification of functional gene variants associated with adaptive traits in a non-model species.The WWP shoot tip transcriptome and those validated SNP markers provide novel genomic resources for genetic, evolutionary and ecological studies. SNP loci of those candidate genes associated with resistant phenotypes can be used as positional and functional variation sites for further characterization of WWP major gene resistance against The online version of this article (doi:10.1186/s12870-014-0380-6) contains supplementary material, which is available to authorized users. Pinus monticola Douglas ex D. Don) is an economically and ecologically important forest tree species with wide distribution across western North America. WWP faces serious conservation challenges due to its susceptibility to white pine blister rust (WPBR), caused by the exotic fungus Cronartium ribicola J.C. Fisch., and its high vulnerability to other disturbance agents including the mountain pine beetle (Dendroctonus ponderosae) and fire, both of which are exacerbated by climate change [P. monticola exists in fragmented populations that occupy less than 10 percent of this species’ historical landscape [P. albicaulis Engelm) and limber pine (P. flexilis E.James), are subjected to similar conservation challenges [Western white pine  resistance against WPBR [In the past decade or so, molecular markers have been developed and used to facilitate conservation and WPBR resistance breeding programs . Analysinst WPBR . More renst WPBR . Progresnst WPBR ,13. Despnst WPBR ,15.P. monticola populations. To achieve this objective and realistically score individual genotypes using inexpensive high-throughput techniques, a large number of molecular markers that are easy to score on a large number of WWP populations are needed. While SNP markers are abundant in the genome and have the potential to be excellent tools for these research objectives, to date there is no SNP database or SNP arrays available for WWP.To develop effective, long term management strategies for WWP and WPBR, ongoing research is needed to improve understanding of the influence that climate and environmental factors have in changing and shaping Next generation sequencing (NGS) strategies for high-throughput SNP discovery and genotyping include restriction-site-associated DNA tags - RAD , genotypde-novo assembled from shoot-tip tissues based on bulked segregation of major gene resistance (Cr2/-) and susceptibility (cr2/cr2) to C. ribicola. The SNP assay was designed based on candidate genes related to disease resistance and Pinus highly conserved genes (HCGs). Those SNPs validated here by high-throughput genotyping in a collection of resistance germplasm improve the genomic tools available for WWP and other five-needle pines.The objective of this study was 1) to characterize the transcriptome of tree shoot tissues from resistance germplasm, 2) to develop SNP markers based on a candidate gene approach, and 3) to apply high-throughput SNP genotyping to the reconstruction of pedigrees and resistance screening in WWP conservation and breeding programs. We used RNA-seq for SNP discovery in the transcriptome P. monticola. A total of 348.6 million 100-bp paired-end reads were collected from the six cDNA libraries, which represents sequencing data of approximately 33.2 to 89.8 million paired-end (PE) reads per library. A total of 95,727 unique contigs with N50 of 920-bp and average length of 630-bp were produced by de-novo assembly with 123 million RNA-seq 100-bp PE reads from three cDNA libraries constructed from resistant tissues  database  genome database respectively (tBLASTn or BLASTp with E < 10e-6). tBLASTn search of P. taeda protein database  against WWP sequences revealed that 92.9% of them had significant homology hits (E < 10e-6) in WWP shoot-tip reference transcriptome  genome , suggesting rare fungal infections in the resistant tissues  encoding proteins with domains of nucleotide-binding site and leucine-rich repeats (NBS-LRR) by BLASTx search against 128 WWP RGAs cloned previously . A set oly stage , 740 of Using CLC Genomics Workbench 5.1 to map PE reads of the six cDNA libraries to the reference sequences, 2,043 indels, 2,857 multi-nucleotide variants (MNV), and 104,452 bi-allelic SNPs were mapped to 41,460 putative protein-coding regions, 57,139 SNPs (54.7%) resulted in an amino acid change (nonsynonymous SNP). We also detected 97,063 SNPs in the HCG group, 7,248 in DEG group, and 6,078 in the RGA group  of these HCGs were polymorphic in both resistant and susceptible samples while 10.9%  of HCGs were found to be polymorphic only in susceptible seedlings and 5.3% (716) of HCGs were found to be polymorphic only in resistant seedlings. The remaining 3.5% (478) of polymorphic HCGs were homozygous but their alleles were different between resistant and susceptible samples  SNP array, nine genomic DNA samples were removed from genotyping analysis due to too many missing data, resulting in a sample size of n = 179. Analysis of each SNP locus for the three genotypes (A/B/H) found that 301 SNPs (69.7%) were successfully genotyped while the remaining 131 SNPs (30.3%) were scored as ‘failed’ due to missing genotype data in more than 20% of all samples; poor PCR amplification and low signal intensities resulted in missing data.Two different genotyping assays tested a total of 432 P < 0.05 with Bonferroni-correction, probably due to breeding selection of the resistant germplasm from natural populations.As summarized in Table in-silico SNP loci into SNP markers (61.1%) while this rate was only 40.1% for the candidate group of RGAs. In total, 215 SNPs showed a MAF > 0.05 in the sets of tested seedlings. The twelve SNPs that had the highest Ho level of 100% were excluded for population genetics analysis. Thus, a final genotypic data set consisting of 203 SNP loci was used for pedigree reconstruction and LD analysis.The distributions of minor allele frequency (MAF) and Ho for the polymorphic loci were similar in two genotyping arrays  showed that the first three principal components explained approximately 60% of the total variation and clear ancestry clusters displayed within the collected samples  showed significant LD estimates for 962 SNP pairs , but this pair number was reduced to 183  with an average LD estimate at r2 = 0.2 after a highly conservative Bonferroni correction for multiple tests  were considered in the LD analysis, we detected 21 SNPs (each from one unique gene) in significant LDs with Cr2. After Bonferroni correction, six genes still showed significant LDs with Cr2, including three RGAs (two NBS-LRR genes and one RLK gene), two HCGs, and one DEG (Table Cr2) resistance . For this SNP marker, CC, GC, and GG genotypes accounted for 26.1%, 68.5%, and 5.4% of the total resistant seedlings; and 0%, 4.6%, and 95.4% of the total susceptible seedlings. This SNP locus thus is an excellent marker for Cr2-resistance selection across four populations in WWP germplasm.A total of 11,139 SNP pairs were compared for LD estimates. Chi-squared tests  in non-model species -23. By iin-silico SNPs to validated loci have been reported for maritime pine (P. pinaster) (42.5%), lodgepole pine (P. contorta var. latifolia) (30.0%), Aleppo pine  (76.6%), and Douglas fir (72.5%) [P. monticola. The HCG group showed a much higher conversion rate of 61.1%; this rate is comparable to those SNPs mined by genomic re-sequencing in other tree species [in-silico SNP selection and genotyping design, as well as types of genotyping platforms are important. For example, the in-silico SNP-mining process with stringent quality criteria can distinguish sequence variations from sequencing artefacts. It is possible that the rate of conversion of in-silico SNPs can be improved even more in WWP by optimizing primer design and PCR amplification conditions because we found that some iPlex failed SNPs could be genotyped properly by qPCR genotyping methods such TaqMan and HRM . Identification of exon-intron boundaries by exome sequencing will improve design of SNP genotyping arrays. Furthermore, as compared to sample-pooling strategy, SNP detection by NGS on individual samples, especially on haploid megagametophyte samples in conifer, has potential to increase overall confidence for in-silico SNP detection.The Sequenom iPlex has been reported as one of highly reliable high-throughput SNP genotyping platforms with wide applications ,26. We a (72.5%) -30. The  species ,32. Variin-silico SNPs. We believe that this cost-effective approach for detecting the most informative SNPs can be readily adapted and applied to other non-model conifers, including five-needle pine species ,Our work demonstrates how combining bulked segregation-based RNA-seq with high-throughput SNP arrays enables fast, cost-effective, and yet reliable identification of the most informative (population-specific) markers among hundreds of thousands of in-silico SNPs: first, RNA-seq-based transcriptome profiling identified WWP candidate genes  having potential biological functions in genetic resistance and host defense against attack by pests, pathogens, and environmental stresses; second, transcriptome profiling also revealed highly conserved genes, even orthologous genes, in conifer species [In the present study we demonstrated the utility of candidate-based approach for selection of a subset of available  species . Because species .Cr2 gene  resistance by LD analysis in the genotyped populations (Table Picea glauca protein ABF73316.1 (expect E = 0.0), and another two RGAs, F0_contig_48562 and F0_contig_9161, encoded NBS-LRR proteins. Additional SNPs, especially those non-synonymous SNPs in the above mentioned three RGAs, would provide both positional and functional variation sites for further characterization of major gene resistance against C. ribicola. The large amount of SNP markers, especially those SNPs in the candidate genes, may prove useful to study the evolution and adaptation of resistance mechanisms under selection pressure of climate change and WPBR in the native white pine populations across North America. In the future we will conduct sequence comparison and subsequent functional characterization of resistant and susceptible haplotypes of the related NBS-LRR and RLK genes to determine if any of these RGAs is responsible for the C. ribicola-resistance phenotype.Despite the disadvantages of relatively low read-mapping coverage and high polymorphism levels, we included  tissues . We prevulations . Here weDiscovery of a large number of SNPs along genome using NGS followed by genotyping of a set of samples with available phenotypes has become standard practice for fine genetic mapping of complex traits. In this study, we used a collection of WWP resistant germplasm to investigate genotype-phenotype relationships. LD, which is the non-random co-segregation of alleles at two loci, can result from many factors, including effective population size and structure, recombination rate, genetic drift, mating system, and selection . Recombir2 estimate of 0.3 within 600 ~ 700-bp in P. monticola DEGs [Cr2 with high LD in the tested germplasm across four populations with as many as 35 full-sib families  are two causes of population reduction and species extinction. Conifer seed orchards are commonly used to produce consistent, abundant, and genetically improved seeds with well-adapted environmental performance. These orchard seed lots are used for reforestation and restoration activities with species like WWP. Unfortunately, orchard seed lots are usually composed of undetermined proportions of seeds contributed by many parents through out-crossing and open pollination. Furthermore, it is critical that appropriate levels of genetic diversity are maintained to avoid inbreeding and loss of rare alleles by genetic drift in forest seed orchards or seed collections. While elite seed orchards can be developed by pyramiding favorable alleles, favourable alleles may be dispersed in different stands/ancestors. Complete pedigree information is thus an essential prerequisite for the selection and deployment of elite genotypes in modern conservation and breeding . MoleculP. monticola germplasm and could allow us to prioritize individuals on the basis of conservation value for minimizing loss of genetic variation in conservation program as well as to develop breeding recommendation with balance between maximizing gene diversity and minimizing inbreeding for tree improvement by identifying the main genitors. Genotypic data from our study may efficiently guide further application of this diversity in the long-term management and reforestation of this tree species across western North America.Sibship reconstruction in our study provided the clearest evidence for seed family structure in a collection of WWP germplasm. Accuracy of parentage analysis increases with the number and diversity of genetic loci. Popular parentage inference methods  can be applied with confidence in natural populations with highly polymorphic loci . SNPs arC. ribicola-resistant and -susceptible WWP germplasm that originated from a composite seed lot. A subset of 432 SNP loci were verified by high-throughput genotyping and 52.5% of them were polymorphic. Using genotypic data of these SNP markers, parentage relationship and genetic diversity were determined in WWP germplasm collection and SNP markers were identified for breeding screening of resistance to WPBR across WWP populations. These validated SNP resources may open up new avenues for ecological genomics and comparative genetic mapping in five-needle pine species.The present study represents the first research of candidate gene-based SNP discovery using pooled RNA-seq approach integrated with bulked segregation analysis in a five-needle pine. We generated novel transcriptome and SNP data from shoot-tip tissues of the P. monticola seed lot with ‘major gene (Cr2) for HR-like resistance’ to WPBR was used in the present study. The lot was sourced mostly from parent trees that originated from the Champion Mine area on the Cottage Grove Ranger District of the Umpqua National Forest in Oregon. These parent trees comprised the breeding crosses were in the early fields established at the Dorena Genetic Resource Center ; and the parents in those 1960’s grafts were heavily weighted toward Champion Mine parents (Cr2/-) and Bear Pass parents (many with Cr2/-) from the Bear Pass planting on the Willamette National Forest. There are also a few other clones in the breeding arboretum from other areas of Oregon and Washington.A composite C. ribicola, and phenotypic assessments were all performed at the DGRC, as described in Danchok et al. [C. ribicola in September 2010 using infected leaves of Ribes spp.  collected from locations outside of the geographical areas where the virulent isolate (vcr2) is known to occur. Inoculations were done using an average inoculum density of ~6,000 basidiospores/cm2 and a spore germination rate of 89%. Phenotypic traits were assessed at periodic intervals in 2011 when infection symptoms were evident on needles and stems. Each seedling was determined to be either a resistant (Cr2/-) or susceptible (cr2/cr2) genotype based both on their needle spot types  and their stem symptoms . Needle samples were collected in July 2011 and stored at −20°C for genomic DNA extraction. In Oct. 2011 (~13 months post C. ribicola-infection), branch and stem tissues were collected from a sub-set of seedlings for each genotype using liquid nitrogen and stored at −80°C until RNA extraction.Growing of seedlings, their artificial inoculation with k et al. . In brieCr2/-) phenotype and three with susceptible (cr2/cr2) phenotype. After DNase (RNase-free) treatment for 30 min at 37°C, mRNA was separated using an RNA-Seq sample preparation kit (Illumina) and used for construction of cDNA libraries as previously described [Shoot tips from each of 45 resistant and 45 susceptible seedlings were collected individually and used for total RNA extraction following a protocol described previously . RNA-seqescribed  except ede-novo transcript assembly with default settings at quality limit = 0.05, ambiguous limit = 2, and minimum number of nucleotides in reads = 15. Shoot-tips of resistant (Cr2/-) seedlings were considered free of C. ribicola mycelia, so trimmed reads from the three cDNA libraries of resistant (Cr2/-) seedlings were de-novo assembled for generation of WWP shoot tip transcriptome with graph parameters of automatic word size and automatic bubble size and the parameters for mapping reads back to the contigs at mismatch cost = 2, length fraction = 0.5, similarity fraction = 0.8, deletion or insertion cost = 3, and minimum contig length = 200.RNA-seq data analyses were performed using CLC Genomics Workbench 5.1 . Raw reads were trimmed before de novo assembly quality, putative open reading frames (ORFs) within transcript sequences was identified by TransDecoder (http://transdecoder.sourceforge.net/) at minimum protein length of 50. Putative WWP protein sequences were compared with the PGI database , and loblolly pine genome database . To estimate transcripts from infected C. ribicola, the WWP shoot-tip transcriptome was also search against the M. laricis-populina protein database (http://genome.jgi-psf.org/Mellp1/Mellp1.download.ftp.html).To verify http://pir.georgetown.edu/pirwww/), GO (http://www.geneontology.org/), UniProts (http://www.ebi.ac.uk/UniProt/), and KEGG (http://www.genome.jp/kegg/) using the BLAST2GO program . Annotation difference between WWP primary needle [C. ribicola infection [C. ribicola infection by BLAST search and sequence alignment analysis.As described in previous study , GO annoy needle  and shooy needle  and DEGsnfection  were useIn-silico SNP detection was performed by mapping RNA-seq PE reads of the six cDNA libraries back to the targeted sets of functional gene groups using CLC Genomics Workbench 5.1 with quality-based variation detection at the following parameters: window length = 11, maximum gap and mismatch count = 1, minimum average quality = 20, minimum central quality = 20, minimum coverage = 20, minimum variant frequency (MVF) = 30%, maximum expected variation (ploidy) = 2, and presence in both forward and reverse reads. Only reads that mapped to a single unique position on the reference sequences were used. To predict the effect of the mutation underlying each SNP at the amino acid level, the best ORFs predicted by TransDecoder were used for as reference sequences for SNP detection using CLC Genomics Workbench, and then each SNP was determined as a synonymous or non-synonymous mutation. SNPs in those WWP ORF sequences that showed best match to P. taeda and PGI databases at protein level by BLAST search were considered for SNP genotyping verification.in-silico SNP data were generated using HCGs, DEGs, and RGAs as separate mapping references.Due to unknown intron–exon boundaries and high proportion of paralogs in the pine gene families , additioFor SNP genotyping, genomic DNA was extracted from needle tissues of individual seedlings belonging to the same composite seed lot used in RNA-seq analysis. About 100 mg of needle tissues were cut into small fragments and homogenized in liquid nitrogen using a FastPrep®-24 Instrument . Genomic DNA was extracted using a DNeasy Plant Mini kit .High-throughput genotyping was conducted using the Sequenom iPlex MassARRAY platform   at the GThe quality of SNP genotyping was manually assessed for each SNP locus in the sample collection. Population characteristics of the SNPs such as MAF, Ho, He, and the deviation from HWE were calculated using GenAlex 6.41 . SNPs wiPCA and Bayesian phylogenetic methods were used to identify if there was any population structure/grouping in the composite seed lot. SNP data were converted into allele frequencies based on SNP genotype of each individual seedlings and PCA was performed using the variance-covariance matrix of SNP allele frequencies in TASSEL . SeedlinIndividual assignment by STRUCTURE analysis may group different seed families into one population. Sibship analysis and parentage reconstruction of the WWP gemplasm were conducted using the most accurate full-pedigree likelihood method of the COLONY program .Cr2) resistance in WWP was evaluated by looking at LD values (r2) among SNP sites and resistance phenotypes (Cr2/- vs. cr2/cr2) using TASSEL as described previously [r2 was calculated between each SNP pair [P-values for r2 were adjusted using a Bonferroni correction [Chi-squared test was used to evaluate LD between all pair-wise combinations of SNPs. Association of SNP loci with phenotypic traits of major gene (eviously . For mulSNP pair . P-valuerrection .The data sets supporting the results of this article are included within the article and its additional supporting information files. Illumina raw sequences were deposited in the NCBI GenBank SRA under accession number SRR1574690-1574692. The WWP shoot-tip reference transcriptome with 54,661 contigs was deposited at GenBank under accession GBQX01000000. SNP markers were deposited in GenBank under accession numbers ss#947846159 – ss#947846384.


Dominance effect may play an important role in genetic variation of complex traits. Full featured and easy-to-use computing tools for genomic prediction and variance component estimation of additive and dominance effects using genome-wide single nucleotide polymorphism (SNP) markers are necessary to understand dominance contribution to a complex trait and to utilize dominance for selecting individuals with favorable genetic potential.The GVCBLUP package is a shared memory parallel computing tool for genomic prediction and variance component estimation of additive and dominance effects using genome-wide SNP markers. This package currently has three main programs  and a graphical user interface (GUI) that integrates the three main programs with an existing program for the graphical viewing of SNP additive and dominance effects (GVCeasy). The GREML_CE and GREML_QM programs offer complementary computing advantages with identical results for genomic prediction of breeding values, dominance deviations and genotypic values, and for genomic estimation of additive and dominance variances and heritabilities using a combination of expectation-maximization (EM) algorithm and average information restricted maximum likelihood (AI-REML) algorithm. GREML_CE is designed for large numbers of SNP markers and GREML_QM for large numbers of individuals. Test results showed that GREML_CE could analyze 50,000 individuals with 400 K SNP markers and GREML_QM could analyze 100,000 individuals with 50K SNP markers. GCORRMX calculates genomic additive and dominance relationship matrices using SNP markers. GVCeasy is the GUI for GVCBLUP integrated with an existing software tool for the graphical viewing of SNP effects and a function for editing the parameter files for the three main programs.The GVCBLUP package is a powerful and versatile computing tool for assessing the type and magnitude of genetic effects affecting a phenotype by estimating whole-genome additive and dominance heritabilities, for genomic prediction of breeding values, dominance deviations and genotypic values, for calculating genomic relationships, and for research and education in genomic prediction and estimation.The online version of this article (doi:10.1186/1471-2105-15-270) contains supplementary material, which is available to authorized users. Genomic prediction using genome-wide single nucleotide polymorphism (SNP) has become a powerful approach to capture genetic effects dispersed over the genome for predicting an individual’s genetic potential of a phenotype –3. GenomGVCBLUP currently has three main programs and a graphical user interface (GUI) named GVCeasy that integrates the three main programs with an existing program for graphical viewing of SNP effects. The three main programs are GREML_CE, GREML_QM, and GCORRMX, which are developed using shared memory parallel computing technology. GVCeasy supplies users a user-friendly platform to run GVCBLUP.Two sets of formulations with complementary computing advantages and identical results based on two equivalent mixed models are implemented: the CE set for large numbers of SNP markers and the QM set for large numbers of individuals , 15. UsiX = N × c model matrix for fixed non-genetic effects, b = c × 1 column vector of fixed effects, Z = N × q model matrix allocating phenotypic observations to SNP marker genotypes of individuals, Tα = q × m normalized model matrix for gene substitution effects of SNP markers, α = m × 1 column vector of gene substitution effects of SNP markers, Tδ = q × m normalized model matrix for dominance effects of SNP markers, δ = m × 1 column vector of dominance effects of SNP markers, a = Tαα = q × 1 genomic breeding values, d = Tδδ = q × 1 genomic dominance deviations, Ag = q × q genomic additive relationship matrix = TαTα ', Dg = q × q genomic dominance relationship matrix = TδTδ ', and where Z1 = ZTα and Z2 = ZTδ. Computing difficulty is the V−1 and P = V−1 − V−1X(X’V−1X)−X’V−1 for the CE set of Equations C−1) for the QM set of Equations V−1 and P matrices is determined by the number of individuals  and does not change for different numbers of SNPs. Similarly, the QM set has the best potential for using large numbers of individuals because the size of the C−1 matrix is determined by the number of SNP markers and does not change for different numbers of individuals.where Two algorithms for restricted maximum likelihood (REML) estimation of variance components are implemented in both GREML_CE and GREML_QM: EM type algorithm (EM-REML) and AI-REML algorithm . AI-REMLV−1 and C−1 as well as dense matrix multiplications involving those two matrices in GVCBLUP.GVCBLUP is programmed in C++ language using Eigen  and InteBoth GREML_CE or GREML_QM can output additive and dominance marker effects as well as additive and dominance marker heritabilities for every SNP. SNP additive and dominance effects for GREML_CE are calculated at the last GREML iteration using the following formulations:P = V−1 − V−1X(X’V−1X)−X’V−1, and where V is defined by Equation th SNP, and th SNP. Then, for GREML_CE, additive and dominance variances of the ith SNP are calculated as:where and for GREML_QM,th SNP, th SNP, r = rank of the coefficient matrix of the mixed model equations, Cαα and Cδδ are defined by Equation 22 in [th SNP marker, additive heritability or heritability in the narrow sense (where on 22 in . For thewhere Two simulated datasets are supplied in GVCBLUP package for testing purpose. One data set (dataset_1) has 1000 genotyped individuals with 3000 SNP markers and the other (dataset_2) has 3000 genotyped individuals with 1000 SNP markers. The parameter files to run GVCBLUP programs for the simulated datasets are also included in the package. These simulated data are designed for GVCBLUP exercises and for showing the complementary advantages of the CE and QM sets of formulations. Users interested in GVCBLUP exercises using large datasets could use a publically available swine dataset with over 45,000 SNP markers on 3534 individuals  that wasThe structure of the GVCBLUP package with three main programs of GREML_CE, GREML_QM and GCORRMX is shown in Figure k) is Ek = Sk/k = 1, where Sk = the ratio of the execution time with one processor-core to the execution time of the parallel algorithm with k processor-cores [The GREML_CE and GREML_QM programs calculate GREML estimates of additive, dominance and residual variances, additive and dominance heritabilities, as well as heritability in the broad sense as the summation of the additive and dominance heritabilities. GBLUP and reliability of breeding value, dominance deviation and genotypic value  of each individual in the training or validation population are calculated at the end of variance component estimation. GREML_CE and GREML_QM offer complementary computing advantages with identical GREML and GBLUP results: GREML_CE for large numbers of SNP markers and GREML_QM for large numbers of individuals. Assuming one observation per individuals, GREML_CE is more efficient than GREML_QM if 2 m > q and is less efficient than GREML_QM if q > 2 m, where q = number of individuals and m = number of SNP markers. The example in Table or-cores .Table 110 scale  required more computing time than per-iteration of the estimation step. GREML_CE was able to use 50,000 individuals with 400 K SNP markers with total computing time about 23 hours for 13 iterations. For 20,000 individuals and one million SNP markers, GREML_CE only required 4.8 hours. GREML_QM was highly efficient for using low-density SNP markers, requiring only 2 hours for 200,000 individuals with 10 K SNP markers. For 100,000 individuals with 50 K SNP markers, GREML_QM required about 46 hours for 20 iterations : Windows, Linux and Mac OS XProgramming language: C++, JavaLicense: NoneSupplementary output files.(PDF 42 KB)Additional file 1:


LRP1) for MO. The number of loci associated with migraine increased to 11 at suggestive significance thresholds, including five additional selective associations for MO but none for MA. No two SNPs showed similar patterns of selective association with migraine characteristics. At one extreme, SNPs rs6790925 (near TGFBR2) and rs2274316 (MEF2D) were not associated with migraine overall, MA, or MO but were selective for migraine sub-classified by the presence of one or more of the additional migraine features. In contrast, SNP rs7577262 (TRPM8) was associated with migraine overall and showed little or no selectivity for any of the migraine characteristics. The results emphasize the multivalent nature of migraine pathophysiology and suggest that a complete understanding of the genetic influence on migraine may benefit from analyses that stratify migraine according to both aura status and the additional diagnostic features used for clinical characterization of migraine.Migraine can be sub-classified not only according to presence of migraine aura (MA) or absence of migraine aura (MO), but also by additional features accompanying migraine attacks, e.g. photophobia, phonophobia, nausea, etc. all of which are formally recognized by the International Classification of Headache Disorders. It remains unclear how aura status and the other migraine features may be related to underlying migraine pathophysiology. Recent genome-wide association studies (GWAS) have identified 12 independent loci at which single nucleotide polymorphisms (SNPs) are associated with migraine. Using a likelihood framework, we explored the selective association of these SNPs with migraine, sub-classified according to aura status and the other features in a large population-based cohort of women including 3,003 active migraineurs and 18,108 free of migraine. Five loci met stringent significance for association with migraine, among which four were selective for sub-classified migraine, including rs11172113 ( Migraine is among the most common and debilitating neurological disorders. Diagnostic criteria for migraine recognize a variety of symptoms including a primary dichotomous classification for the presence or absence of aura, typically a visual disturbance phenomenon, as well as others such as sensitivity to light or sound, and nausea, etc. We explored whether any of 12 recently discovered genetic variants associated with common migraine might have selective association for migraine sub-classified by aura status or nine additional migraine features in a population of middle-aged women including 3,003 migraineurs and 18,180 non-migraineurs. Five of the 12 genetic variants met the most stringent significance criterion for association with migraine, among which four had selective association with sub-classified migraine, including one that was selective for migraine without aura. At suggestive significance, all of the remaining genetic variants were selective for sub-classifications of migraine although no two variants showed the same pattern of selectivity. The selectivity patterns suggest very different contributions to migraine pathophysiology among the 12 loci and their implicated genes. Further, the results suggest that future discovery efforts for new migraine susceptibility loci would benefit by considering associations with sub-classified migraine toward the ultimate goals of more specific diagnosis and personalized treatment. Migraine is one of the most common and debilitating neurological disorders and its clinical presentation can be quite variable The heterogeneity of migraine characteristics raises both a challenge and opportunity for using genetics to understand migraine pathophysiology. While the power to detect genetic associations will be degraded by potential misclassification due to the heterogeneity of the clinical presentation, associations that are selective for migraine with certain characteristics may help reveal detailed biological causes of migraine, and anticipate the potential of gene-based migraine classification and treatment. Genetics is known to be an important determinant of migraine with heritability estimated in the range 30–60%; and the heritability for MA is estimated somewhat higher than for MO Here we apply a likelihood-based analytic framework Among the Women's Genome Health Study (WGHS) participants of European ancestry with available genetic data, there were 3,003 women who reported active migraine at baseline, defined as migraine experienced in the year prior to enrollment, compared with 18,108 who had never experienced migraine . An addiFHL5) were significant after correcting for testing across the 10 migraine sub-classifications  was significantly associated with migraine, correcting for testing across all 10 sub-classifications, but displayed no selectivity for any of the migraine-associated characteristics. The remaining four significant SNPs were selective for one or more migraine associated characteristics. For example, SNP rs1172113 (LRP1) was preferentially associated with the migraine without aura, i.e. MO (“inverse subset”), and also for migraine with duration 4–72 hours (“subset”). Models distinguishing status of the other characteristics except pulsatile pain were selected for at least one of the SNPs meeting significance thresholds for multiple testing. However, none of the characteristics showed selective association shared by all SNPs.Applied to 12 SNPs  recentlyTRPM8) (AJAP1) had at least one model with nominally significant empirical p-value for the LLR test (i.e. <0.05), and the same five SNPs that had empirically significant LLR tests in the BIC model selection were also significant in the AIC model selection, although in some cases different models were selected (MMP16), the AIC selected the “inverse subset” model for aura (i.e. MO) and a “basic” model for migraine characterized by aggravation by physical activity, inhibition of daily activities, or attack frequency ≥6/year compared with the BIC selected “null” model for these characteristics. Similarly, SNP rs13208321 (FHL5) was identified as “null” with BIC model selection but as “inverse subset” for aura by the AIC as well as “subset” for other features. Additional differences at the five SNPs included selection of “general” rather than “subset” models for phonophobia and migraine attack frequency ≥6/year at rs12134493 (TSPAN), and “general” rather than “basic” or “subset” models respectively for phonophobia and aggravation by physical activity at rs2651899 (PRDM16). Some of the remaining SNPs had AIC-selected models with nominally significant empirical LLR p-values (PHACTR1) and rs6478241 (ASTN2).Model selection with the Akiake information criterion (AIC) penalty was less stringent, identifying selective associations according to migraine characteristics for all SNPs except rs7577262 (TRPM8) . The greTRPM8) . Neverthp-values , althougp-values . NeverthMMP16), displayed preferential association suggesting stronger association with active migraine.Using the same BIC and AIC model selection methodology, there were few differences in the SNP associations between the 3,003 active migraineurs and the 2,119 former migraineurs who were excluded from the current analysis due to lack of information related to migraine sub-classification . With thTRPM8), rs11172113 (LRP1), rs6478241 (ASTN2), rs10915437 (near AJAP1), and rs9349379 (PHACTR1) form a cluster with relatively less pronounced differences in association by stratum status of the migraine-associated characteristics. In particular, associations with SNP rs7577262 (TRPM8) displayed associations essentially undifferentiated by stratum status, as reflected also by exclusively BIC-selected “basic” models (LRP1) in this group had mostly undifferentiated associations, except for stratum-specific associations according to aura status  and migraine duration 4–72 hours  but not duration under four hours , as reflected also by “inverse subset” and “subset” models with the BIC (boxes with heavy solid outline in MMP16), rs13208321 (FHL5), rs2651899 (PRDM16), and rs12134493 (near TSPAN2) all show significant associations for migraine sub-classified according to one or more of the migraine characteristics. These findings are consistent with corresponding “subset” and or “inverse subset” models from the BIC (PHACTR1) and rs6478241 (ASTN2) as above rs10915437 (near AJAP1), and rs13208321 (FHL5).To examine the model selection results in more detail, the association effects of each SNP for migraine sub-classified according to presence or absence of each characteristic were estimated by logistic regression  and depi the BIC . The rem the BIC , for exaTGFBR2) and rs2274316 (MEF2D), were found to have “null” models for stratification according to aura status, and inspection of the beta coefficients  and at lower stringency for five SNPs. Of these, only rs10504861 (near MMP16), had been discovered initially in an association analysis specifically targeting MO. Four additional SNPs had no evidence of selectivity for aura status in their associations with migraine. SNP rs7577262 (TRPM8) in particular was highly significant for association with active migraine but not selective for aura or any of the other characteristics. It is perhaps relevant that TRPM8, the candidate gene for this SNP, is thought to mediate the sensation of pain rather than specific neurological or vascular functions that might more directly differentiate the pathophysiology of the migraine sub-classes TRPM8, PRDM16 has roles in cardiac development MMP16 have been associated at genome-wide significance with psychiatric conditions LRP1, encoding the LDL receptor-related protein 1 with molecular functions in endocytosis in several settings, has been implicated by GWAS in lipid homeostasis TSPAN2, the final candidate gene with BIC-selective association in the WGHS, is largely unknown, but it belongs to the tetraspanin family that has been linked to signal transduction Examining the 12 SNPs recently discovered for association with migraine, we demonstrated significant preferential associations with MO compared to MA at high stringency for one SNP  in the previous analysis was identified exclusively in a previous sub-analysis restricted to MO and therefore including only the 1,826 WGHS MO cases rather than the 5,122 cases with history of any migraine. In contrast, rs10915437 (near AJAP1) and rs6790925 (near TGFBR2) were discovered exclusively among clinic-based samples, excluding the WGHS altogether. However, rs11172113 (LRP1) rather than rs10504861 was identified as an MO-specific in the analysis using the BIC penalized model selection. Thus, the design of the previous discovery meta-analysis is expected to confer only minimal bias at most in the selectivity of associations presented here, especially selective associations identified with the BIC penalty and those identified for sub-classifications other than the MA v. MO dichotomy.Instead, the patterns of selective association with migraine accompanied by the non-aura characteristics may be at least as important to understanding migraine pathophysiology as the selectivity toward aura. All of the SNPs (except rs7577262) displayed some selectivity according to the non-aura characteristics that commonly accompany migraine, at least with the AIC penalty, and there was at least one SNP with a selective association for each characteristic even with the BIC penalty, which enforced a very high stringency in model selection. No pair of SNPs shared an identical patter of subtype associations. The selective associations are likely not to reflect, trivially, the contribution of the WGHS population to discovery of the candidate SNPs since sub-classification of migraine was not used in the original discovery. Moreover, the WGHS contribution to the main discovery analysis in the previous study MMP16) that displayed selective associations using the stringent BIC . Finally, among SNPs where there is a selective association with migraine characterized by attack frequency ≥6/year, there are few similarities among the associations with sub-classification based on the other characteristics. For example, rs12134493 (near TSPAN2), which is highly selective for migraine with attack frequency ≥6/year, also shows selectivity for unilateral pain, phonophobia, and photophobia, but not for pulsation, duration of 4–72 hours, aggravation by physical activity, and inhibition of daily activities, all features that show selective association with other SNPs, including SNPs that are also selective for high frequency migraine.One potential, though ultimately not robustly supported, explanation of the subtype associations might be that they simply reflect associations among individuals suffering a greater severity of migraine rather than selectivity for specific features. If this were the case, then one would expect that selectivity patterns might be highly correlated, perhaps especially with associations according to strata for migraine attack frequency, one measure of migraine severity. However, these patterns were not observed. First, the patterns of selective associations are not shared by any of the SNPs. Second, some SNPs show no selectivity (i.e. “basic” model) for migraine attack frequency ≥6/year but selectivity (i.e. “subset” or “inverse subset” models) for other characteristics, for example aura status  in a given migraine patient and may allow for prioritization of treatment options. In this respect, discovery of more loci and therefore genes relevant to migraine in future genome-wide studies may provide further understanding of the complete set of biological interactions that underlie migraine in its various forms. Knowledge of these interactions may guide development of novel therapeutic strategies. The same knowledge may also be translated toward the ultimate clinical goal of delivering the most individually targeted therapy in treating migraine.The WGHS −6 or minor allele frequency <1%, leaving 339,596 SNPs in the final data set. Genotypes for additional SNPs in HapMap2  but not represented on the genotyping array were imputed with MaCH v. 1.0.16 Genotyping in the WGHS was performed with the Illumina Duo “+” platform as described AJAP1) and rs6790925 (near TGFBR2) had genome-wide significance exclusively in clinic-based samples and therefore excluding the WGHS. The final SNP, rs10504861 (near MMP16) was discovered exclusively in a sub-analysis including only the 6,550 MO cases, including the 1,826 from the WGHS included in this analysis. In the WGHS, SNPs rs2651899, rs6790925, rs4379368, and rs11172113 were genotyped directly. A small number of missing genotypes from these SNPs and the genotypes at the remaining eight candidate SNPs were imputed. The quality of imputation was adequate or excellent for all of these SNPs ranging from R2 values of 0.61 and 0.72 for rs9349379 and rs10915437, respectively, to >0.94 for the remaining SNPs  penalized likelihood for six different inheritance models. Each inheritance model was specified by SNP minor allele frequencies in three groups: a) migraineurs experiencing the characteristic, b) migraineurs not experiencing the characteristic, and c) non-migraineurs. Following previously published methodology Empirical significance estimates of the BIC and AIC model selection procedures were derived through a permutation approach. In order to simulate null distributions, the entire BIC and AIC model selection procedures were repeated after random reassignment of SNP genotypes to WGHS participants 10,000 times. For each combination of SNP and migraine characteristic, the probability of selecting a non-null model was estimated as the fraction of non-null models with the permuted data. Similarly, an empirical p-value for the LLR test of the selected model for each combination of SNP and migraine characteristic was estimated as the fraction of LLR test analytic p-values among models from the permutations less than or equal to the observed analytic p-value for a given combination of SNP and migraine characteristic. The permutations were also used to adjust the significance of model LLR tests for multiple hypotheses testing using rank statistics. Thus, for each SNP, a stage 1 correction was made by computing the fraction of permuted results with the smallest LLR test analytic p-value across all 10 migraine characteristics less than or equal to the observed smallest analytic LLR test p-value. Similarly, the second smallest LLR test p-value for that SNP was corrected by computing the fraction of permuted results with second smallest LLR test analytic p-value across all 10 migraine characteristics less than or equal to the observed second smallest LLR test analytic p-value for that SNP. The process was repeated for all 10 LLR test p-values for each SNP. These rank adjusted p-values were then corrected in a second stage using the Šidák procedure assuming 12 independent SNPs.www.genome.gov/gwastudies, accessed 3/13/2013). Hierarchical clustering was performed with the function “hclust” in R SNP effect estimates (beta coefficients) from logistic regression were computed for the association of each SNP with migraine either accompanied or not with each of the characteristics compared with non-migraineurs. The choice of coded allele in the logistic models was assigned as the allele associated with increased probability of any report of migraine in the WGHS population overall at baseline . SNPs weAll data collection and reported research complies with international guidelines and was approved by the Institutional Review Board (IRB) of Brigham and Women's Hospital.Figure S1Clustering of SNPs according to differential association for migraine characterized by aura or additional features. Scale indicates arbitrary units. See methods for details of clustering procedure.(DOCX)Click here for additional data file.Table S1SNP genotyping and imputation summary.(DOCX)Click here for additional data file.Table S2Empirical significance of BIC selected models derived from permutation analysis. Migraine characteristics designated as in (DOCX)Click here for additional data file.Table S3Empirical significance of AIC selected models derived from permutation analysis. Migraine characteristics designated as in (DOCX)Click here for additional data file.Table S4SNP association statistics (beta (SE), p-value) in the WGHS for migraine sub-classified by presence (+) or absence (−) of aura or additional characteristics. Migraine characteristics as in (DOCX)Click here for additional data file.


Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1% minor allele frequency in the British population. Here we demonstrate the value of this resource for improving imputation accuracy at rare and low-frequency variants in both a UK and an Italian population. We show that large increases in imputation accuracy can be achieved by re-phasing WGS reference panels after initial genotype calling. We also present a method for combining WGS panels to improve variant coverage and downstream imputation accuracy, which we illustrate by integrating 7,562 WGS haplotypes from the UK10K project with 2,184 haplotypes from the 1000 Genomes Project. Finally, we introduce a novel approximation that maintains speed without sacrificing imputation accuracy for rare variants. Imputation uses genotype information from SNP arrays to infer the genotypes of missing markers. Here, the authors show that an imputation reference panel derived from whole-genome sequencing of 3,781 samples from the UK10K project improves the imputation accuracy and coverage of low frequency variants compared to existing methods. Statistical inference of missing genotypes (imputation), where genotyped markers from SNP arrays are used to impute unobserved genotypes from haplotype panels such as the HapMap data, has been instrumental to the discovery of thousands of complex trait loci in meta-analyses of genome-wide association studies (GWAS)1Here we describe a novel WGS imputation panel comprising 3,781 samples from the UK10K Cohorts projecthttp://www.uk10k.org/studies/cohorts.html). The TwinsUK registry comprises unselected, mostly female volunteers ascertained from the general population through national media campaigns in the UKThe UK10K Cohorts Projectr2) between imputed genotype dosages in (0–2) and masked sequence genotypes in . The results were stratified into non-overlapping MAF bins for plotting.As a first assessment of the UK10K reference panel, we performed a leave-one-out cross-validation on a pseudo-GWAS of UK ancestry, corresponding to a sub-sample of 1,000 individuals from the UK10K WGS data set (500 from TwinsUK and 500 from ALSPAC). For this experiment, we removed each sample from the reference panel in turn, selected 13,413 sites on chromosome 20 from the Illumina 610 k bead chip (pseudo-GWAS panel), and imputed all other sites on this chromosome from a given reference panel. We conducted the imputation with three haplotype reference panels: the 1000GP Phase 1 panel, the ‘original' UK10K panel produced by initial genotype refinement and haplotyping with BEAGLE, and a ‘re-phased' UK10K panel that was generated by using SHAPEIT v2 ref.  to estimr2 at low frequencies increased by >0.1 (20%) after re-phasing, which implies a substantial boost in the power to detect associations. A large imputation panel is a resource that can inform a variety of association studies, so these results suggest that taking the time to improve a WGS panel's haplotype quality could have substantial downstream benefits.The results of this experiment are shown in It is becoming increasingly common for investigators to conduct their own WGS of particular study populations, and a natural goal is to combine these data sets with publicly available reference panels such as 1000GP) to increase sample size and variant coverage for imputation of GWAS cohorts. This is already a ubiquitous problem, and there are multiple ways to integrate WGS data sets that require different levels of data sharing and computing power. In this work, we suggest a simple approach that should be feasible for most groups that have sufficient computational resources for GWAS imputation. Our approach is to take two-phased reference panels and reciprocally impute them up to the union set of variants, then use this combined panel for GWAS imputation; we have implemented this functionality in IMPUTE2 . Unlike the true r2 metric, which is usually calculated by masking and imputing ‘truth' genotypes, the expected r2 metric allows direct comparisons of reference panel performance across study populations that have substantially different sets of genotyped truth variants. We have found that predicted r2 values tend to be larger than true r2 values for low-frequency variants , so the absolute numbers of high-confidence imputed variants reported in this section should be treated as upper bounds; the emphasis is on qualitative patterns between reference panels and between study populations.To evaluate this new functionality, we used a combined 1000GP+UK10K panel to perform imputation with pseudo-GWAS data sets drawn from the UK and Italy (details below). In each of these comparisons, we imputed all available reference variants and stratified them by expected r2>0.8) variants at MAFs of 0.5% and higher, implying that the combined panel is neither helpful nor harmful for imputing common and low-frequency variants when a large, population-specific panel is available. On chromosome 20, the combined panel added 2,356 high-confidence rare variants that were not captured by the UK10K panel , which could reflect mutations that have drifted to very low frequencies in the UK but persist on the same haplotype background elsewhere in Europe5khap approximation . This approximation reduces the computational cost of imputation by using a region-wide  Hamming distance metric to reduce the number of reference haplotypes used by a given GWAS haplotype  was less accurate for SNPs with MAF<5%. By contrast, our new approximation based on haplotype tract sharing (dashed orange line) was nearly as accurate as the full reference panel, at ∼10% of the computing time  when the 1000GP haplotypes were added to the UK10K panel, but their relative accuracies remained similar to those shown in In the course of our analyses, we noticed that some rare variants were imputed well when using the entire UK10K reference panel to drive imputation, yet poorly when using IMPUTE2's see also . Our invsee also . All of As WGS becomes a standard tool for population and disease genetics, there will be many questions about how to design sequencing studies, how to process the data, how to combine data across studies, and how to limit the computational costs of downstream analysis. With data from one of the most ambitious population sequencing studies to date, we have demonstrated the value of a large, UK-specific reference panel for imputation in British cohorts and in other European populations. Our results show that state-of-the-art phasing methods like SHAPEIT v2 are essential for creating high-quality haplotype panels. Combining WGS data across studies is a desirable goal, and we have implemented an approach in IMPUTE2 that can integrate sets of phased haplotypes to produce a unified reference panel; other strategies for combining WGS data may improve haplotype quality, but our approach has the advantage of being relatively simple and fast. Finally, we have proposed a new approximation that will help reduce the trade-off between imputation speed and accuracy as reference panels continue to grow. The novel strategies we have presented will inform other investigators who wish to use WGS reference panels for imputation, and they will spur additional methods development as population sequencing resources proliferate.https://www.ebi.ac.uk/ega/) under managed access conditions (http://www.uk10k.org/data_access). The functionality described in this work is available from the IMPUTE2 website (http://mathgen.stats.ox.ac.uk/impute/impute_v2.html) and the SHAPEIT v2 website (https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html).Future efforts to combine multiple large low-coverage sequencing datasets into a substantially larger haplotype resource will likely increase imputation performance, especially at variants with frequencies below 0.1%. We generated a combined reference panel with 42.4 million imputable sites, which is much larger than the 26.6 million imputable sites in the UK10K panel or 32.5 million imputable sites in the 1000GP panel. The UK10K WGS haplotypes for 3,781 samples are available for download from the European Genome-phenome Archive . DNA (1–3 μg) was sheared to 100–1,000 bp using a Covaris E210 or LE220 . Sheared DNA was size subjected to Illumina paired-end DNA library preparation. Following size selection (300–500 bp insert size), DNA libraries were sequenced using the Illumina HiSeq platform as paired-end 100 base reads according to manufacturer's protocol.Data generated at the Sanger Institute and BGI were aligned to the human reference separately by the respective centres. The BAM filesftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/human_g1k_v37.fasta.gz). Reads were aligned using BWA (v0.5.9-r16)  ref. . This in1. Index the reference fasta file:bwa index -a bwtsw <reference_fasta>2. For each fastq file:bwa aln -q 15 -f <sai_file> <reference_fasta> <fastq_file>3. Create SAM files [sam] using bwa sampe for paired-end reads:bwa sampe -f <sam_file> <reference_fasta> <sai_files> <fastq_files>http://picard.sourceforge.net/) SamFormatConverter and samtools (v0.1.11) sort. For alignments created at the BGI, this was done using samtools (v0.1.8) view and samtools sort.4. Create sorted BAM from SAM. For alignments created at the Sanger this was done using Picard SNP and INDEL calls were made using samtools/bcftools  was masked on chromosome Y in the reference fasta file. Male samples were called as diploid in the PAR on chromosome X, and haploid otherwise. No calls were made on chromosome Y for female samples. Diploid/haploid calls were made using the -s option in bcftools view. The PAR regions were: X-PAR1 ; X-PAR2 ; Y-PAR1 ; Y-PAR2 . The pipeline (run-mpileup) used to create the calls is available from The observation of spikes in the insertion/deletion ratio in sequencing cycles of a subset of the sequencing runs were linked to the appearance of bubbles in the flow cell during sequencing. To counteract this, the following post-calling filtering was applied. The bamcheck utility from the samtools package was used to create a distribution of INDELs per sequencing cycle. Lanes with INDELs predominantly clustered at certain read cycles were marked as problematic, specifically where the highest peak was 5x bigger than the median of the distribution. The list of problematic lanes included 159 samples. In the next step we checked mapped positions of the affected reads to see if they overlapped with called INDELs, which they did for 1,694,630 called sites. The genotypes and genotype likelihoods of affected samples were then set to the reference genotype unless there was a support for the indel also in a different, unaffected lane from the same sample. In total, 140,163 genotypes were set back to reference and 135,647 sites were excluded by this procedure. Note that this step was carried out on raw, unfiltered calls prior to Variant Quality Score Recalibration (VQSR) filtering.http://www.broadinstitute.org/gsa/wiki/index.php/Variant_quality_score_recalibration. SNPs and INDELs were modeled separately, with parameters given below:SNPs: QD, DP, FS, MQ, HaplotypeScore, MQRankSum, ReadPosRankSum, InbreedingCoeffINDELSs: MSD, MDV, MSQ, ICF, DP, SB, VDBAnnotationsSNPs: HapMap 3.3: hapmap_3.3.b37.sites.vcf, Omni 2.5M chip: 1000G_omni2.5.b37.sites.vcfINDELs: Mills-Devine, 1000 Genomes Phase ITraining setSNPs: HapMap 3.3: hapmap_3.3.b37.sites.vcfINDELS: Mills-DevineTruth SetSNPs: dbSNP build 132: dbsnp_132.b37.vcfINDELs: Mills-DevineKnown SetVQSRP<10−6 to remove sites that failed the Hardy–Weinberg equilibrium.The truth-set sites are defined as truly showing variation from the reference. VQSLOD scores are calibrated by how many of the truth sites are retained when sites with a VQSLOD score below a given threshold are filtered out. For single-nucleotide variants sites a truth sensitivity of 99.5%, which corresponded to a minimum VQSLOD score of −0.6804 was selected, that is, for this threshold 99.5% of truth sites were retained. For INDEL sites a truth sensitivity of 97%, which corresponded to a minimum VQSLOD score of 0.5939 was chosen. Finally, we also introduced the filter The VQSLOD score and other annotations from GATK  were copied back to the original samtools calls, excluding annotations which already existed in or did not apply to the samtools VCFs . Each VCF further contained the filters LowQual  and MinVQSLOD (Variant's VQSLOD score is less than the cutoff). All sites that did not fail these filters were marked as PASS and brought forward to the genotype refinement stage.Of the 4,030 samples  that were submitted for sequencing, 3,910 samples  were sequenced and went through the variant calling procedure. Low-quality samples were identified before the genotype refinement by comparing the samples to their GWAS genotypes using about 20,000 sites on chromosome 20. Comparing the raw genotype calls to existing GWAS data, we removed a total of 112 samples (64 TwinsUK and 48 ALSPAC) because of one or more of the following causes: (i) high overall discordance to SNP array data , (ii) heterozygosity rate>3SD from population mean (1 TwinsUK and 1 ALSPAC), suggesting contamination (iii) no SNP array data available for that sample (7 TwinsUK and 0 ALSPAC) and (iv) sample below 4x mean coverage (1 TwinsUK and 11 ALSPAC). Overall, 3,798 samples  were brought forward to the genotype refinement step.The missing- and low-confidence genotypes in the filtered VCFs were filled out through an imputation procedure with BEAGLE 4 rev909)  because of one or more of the following causes: (i) non-reference discordance (NRD) with GWAS SNP data>5% (12 TwinsUK and 1 ALSPAC), (ii) contamination identified by multiple relations to other samples (13 TwinsUK and 1 ALSPAC), (iii) failed sex check (3 TwinsUK and 0 ALSPAC). To identify contamination we pruned the WGS data to a set of independent SNPs and calculated genome-wide average identity by state between each pair of samples across the two cohorts. Samples were removed if they had >25 relations with IBS>0.125 (a high number of relationships may indicate contamination). The resulting set of contaminated samples corresponded almost completely to the set of samples with NRD>5%. This left a final set of 3,781 samples . These VCF files were submitted to the EGA.The UK10K final release WGS data of 3,781 samples and 45,492,035 sites was used for creation of haplotype reference WGS data sets. For each chromosome, a summary file was first generated and merged with that of the 1000GP WGS data to identify multi-allelic sites, sites with inconsistent alleles with that of the 1000GP data, and singletons not existing in 1000GP. These sites were excluded to create a new set of VCF files, leaving 26,032,603 sites. The VCF-QUERY tool was used to convert the new VCF files into phased haplotypes and legend files for IMPUTE2. VCF files were converted to binary ped (bed) format and multi-allelic sites excluded, and files were then split into 3MB chunks with ±250 kb flanking regions. SHAPEIT v2 was used to re-phrase the haplotypes. Phasing information from the SHAPEIT output was copied back to the original VCF files, with the phase removed for sites missing due to the MAF cutoff. The phased chunks were then recombined with vcf-phased-join from the vcftools packageftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20110521/, 23 November 2010 data freezes). This callset includes phased haplotypes for 1,092 individuals and 39,527,072 variants (22 autosome and chromosome X). The haplotypes were inferred from a combination of low-coverage genome sequence data, and they contain SNPs, short INDELs, and large deletions. For each chromosome, a summary file was first generated and merged with that of the UK10K WGS data to identify multi-allelic sites and singletons not polymorphic in UK10K. These sites were excluded to create a new set of VCF files. The final reference panel included all 1,092 samples and 32,449,428 sites. The VCF-QUERY tool was used to convert the new VCF files into phased haplotypes and legend files for IMPUTE2.The 1000GP Phase I integrated variant set release (v3) for low-coverage whole-genomes in NCBI build 37 (hg19) coordinates was downloaded from 1000GP FTP site  and ALSPAC  WGS data sets. Genotypes for a total of 13,413 sites (corresponding to the content of the Illumina HumanHap610 SNP-array) on chromosome 20 were extracted from the UK10K WGS data in these 1,000 samples.A random set of 500 samples passing QC filters was chosen from the TwinsUK  into panel 2 (UK10K).Impute the variants that are specific to panel 2 (UK10K) into panel 1 (1000GP).Treat the imputed haplotypes in both panels (with the union of variants from both) as known  and impute the GWAS cohort in the usual way.The following three steps were used to merge two WGS reference panels using IMPUTE2 (version 2.3 and later):The commands for combining haplotypes with the 1000GP are given in Imputation of genotypes from the three phased reference panels  into the two test panels was carried out on chromosome 20 split in 3MB chunks with 250 kb buffer regions. Imputation was performed using standard parameters with IMPUTE2, for example:./impute2 \-m genetic_map_chr20_combined_b37.txt \-h chr20.uk10k.hap.gz \-l chr20.uk10k.legend.gz \-known_haps_g chr20.incipe2gwas.known_haps.gz \-k_hap 10000 \-int 3e6 6e6 \-Ne 20000 \-buffer 250 \-use_prephased_g \-o_gz \-o chr20.01.incipe2gwas.uk10kRef.impute2r2) between imputed genotype dosages in (0–2) and masked sequence genotypes in . The results were stratified into non-overlapping MAF bins for plotting. In r2 (‘info') metric produced by IMPUTE2  and EGAD00001000776 (EGA dataset) under managed access conditions (http://www.uk10k.org/data_access).How to cite this article: Huang, J. et al. Improved imputation of low frequency and rare variants using the UK10K haplotype reference panel. Nat. Commun. 6:8111 doi: 10.1038/ncomms9111 (2015).Supplementary Figures 1-6, Supplementary Table 1, Supplementary Notes 1-3, and Supplementary References