A GWAS (genome-wide association study) in an association panel of 211, including the 92 sequenced accessions, identified 22 major genomic loci showing significant association (explaining 23–47% phenotypic variation) with pod and seed number/plant and 100-seed weight. Eighteen trait-regulatory major genomic loci underlying 13 robust QTLs were validated and mapped on an intra-specific genetic linkage map by QTL mapping. A combinatorial approach of GWAS, QTL mapping and gene haplotype-specific LD mapping and transcript profiling uncovered one superior haplotype and favourable natural allelic variants in the upstream regulatory region of a CesA-type cellulose synthase (Ca_Kabuli_CesA3) gene regulating high pod and seed number/plant (explaining 47% phenotypic variation) in chickpea. The up-regulation of this superior gene haplotype correlated with increased transcript expression of Ca_Kabuli_CesA3 gene in the pollen and pod of high pod/seed number accession, resulting in higher cellulose accumulation for normal pollen and pollen tube growth. A rapid combinatorial genome-wide SNP genotyping-based approach has potential to dissect complex quantitative agronomic traits and delineate trait-regulatory genomic loci (candidate genes) for genetic enhancement in crop plants, including chickpea.We identified 44844 high-quality SNPs by sequencing 92 diverse chickpea accessions belonging to a seed and pod trait-specific association panel using reference genome- and  Cicer arietinum L.), a member of family Fabaceae is an annual, diploid, self-pollinated crop species with a very small genome size of ~740 Mbp. It is the most cultivated food legume/pulse crop world-wide that serves as an important dietary source of protein with essential amino acids for human. The seed and pod characters are major yield contributing traits of chickpea and vary widely across global germplasm collections, landraces and cultivated desi and kabuli accessions. Considering the agronomic importance of seed and pod traits, multiple efforts have been made for molecular mapping of quantitative trait loci (QTLs) governing major seed and pod yield contributing traits, including seed and pod number, double podding and seed weight in chickpea1234567891011121314151617181920212223242526272829302426Chickpea  approaches2433343536373839404142274445464848495051525354565758596061626364656667However, the well-established GBS approach is yet to be utilised for large-scale mining and validating of genome-wide SNPs in diverse natural and mapping populations of chickpea. The necessary information scanned from diverse germplasm lines (association panel) and mapping populations by GBS assay could facilitate construction of ultra-high-density genetic linkage maps, molecular mapping/fine-mapping of genes/QTLs and high-resolution genome-wide/gene-specific trait association analysis in chickpea. It would further expedite the identification of novel functionally relevant molecular tags  controlling qualitative and complex quantitative traits of agricultural importance  for genetic enhancement of chickpea.desi and kabuli chickpea accessions) at a genome-wide scale. The large-scale SNP genotyping, diversity and LD-based information of natural and mapping populations was further correlated with their robust field phenotyping data to identify functionally relevant potential genomic loci (gene-associated targets) regulating three seed and pod yield-contributing traits using a combinatorial approach of GWAS, QTL mapping, gene haplotype-specific LD mapping and transcript profiling in chickpea. These integrated analyses, coupled with our preliminary in vitro pollen tube growth and cellulose estimation assays, were able to identify one superior haplotype and favourable natural allelic variants in the upstream regulatory region of a candidate cellulose synthase (Ca_Kabuli_CesA3) gene. This candidate gene possibly governs high pod and seed number with its increased transcript expression and higher accumulation of cellulose, specifically in pollen and pod tissues, thereby promoting normal pollen and pollen tube growth in chickpea.In view of the aforesaid possibilities, the present study has utilized GBS approach to discover, validate and genotype SNPs in a constituted seed and pod trait-specific association panel  ( http://www.ncbi.nlm.nih.gov/SNP/snp_viewTable.cgi?handle=NIPGR)] showing polymorphism among 92 diverse desi and kabuli chickpea accessions were selected for GWAS. The neighbour-joining phylogenetic tree analysis, high-resolution population genetic structure and PCA among 92 cultivated desi and kabuli accessions (association panel) using 44844 genome-wide SNPs differentiated all these accessions from each other and clustered into three distinct populations  (desi and kabuli chromosomes (The sequencing of 96-plex POP III) ,2. The domosomes .desi and 24405 kabuli SNP genotyping information of 92 accessions (association panel) with their phenotyping (replicated multilocation/years) data of three seed and pod yield-contributing quantitative traits . A wider level of significant phenotypic variation  of these three quantitative traits was observed among 92 accessions belonging to an association panel (three population groups) based on two years of multi-location replicated field data  and kabuli (24405) SNP genotyping data were analysed and compared with each other. The GWAS by use of desi SNP genotyping information identified 37, 36 and 28 genomic loci showing significant association with PN, SN and SW, respectively at a P ≤ 10−4. Similarly, kabuli SNP genotyping data detected 31, 66 and 53 significant (P ≤ 10−4) genomic loci showing association with PN, SN and SW, respectively. The comparison of a quantile-quantile plot  and the remaining three loci were annotated in different non-coding intronic and URR sequence components of three desi genes  and GAPIT (EMMA and CMLM) using ile plot  of the eing data , Table 2ing data , Table 2kabuli SNP genotyping information identified nine, five and four genomic loci associated with PN, SN and SW, respectively , 5 (1), 6 (5) and 8 (1). The remaining six SNPs were represented from different scaffold regions (three SNPs) of the kabuli genome and were also derived from de novo GBS data (three SNPs)  , Table 2desi and kabuli genomes for phenotypic variation (R2) varied from 23 to 47% (mean: 33%), respectively (desi and kabuli genomes explained an average of 30 (ranging 23–47%), 32 (23–47%) and 37% (35–39%) of the PN, SN and SW phenotypic variations in chickpea accessions, respectively. Incidentally, two SNPs in the genes encoding cellulose synthase (P: 1.1 × 10−7 to 2.2 × 10−7 with R2: 40–47%) and AP2/ERF transcription factor (P: 4.5 × 10−5 to 7.7 × 10−5 with R2: 25–29%) significantly associated with PN and SN were identified commonly between desi and kabuli genomes (kabuli chromosome 6) showing missense non-synonymous substitutions had association (P: 2.7 × 10−5 to 2.8 × 10−5 with R2: 37–39%) with SW (kabuli chromosome 5) revealing strong association (P: 1.1 × 10−7 to 1.3 × 10−7 with R2: 45–47%) with PN and SN as compared to other identified SNPs was considered as a potential candidate for further characterisation. Large-scale validation of this potential regulatory SNP in 211 minicore accessions further inferred significant contributions of A and G-alleles with ~40–45% PN and SN phenotypic variation in 152 high and 55 low pod and seed number-containing accessions, respectively  and the entire association panel as a whole revealed no significant differences in terms of marker-trait association potential  between populations I and III (contained a greater percentage of 90.3% accessions) compared with that of whole populations. For large-scale validation of trait-associated genomic loci identified in our study, the GoldenGate assay genotyping information of 96 trait-associated SNPs  and fielectively . The sig genomes . Two SNP with SW . One SNPectively .2, SN; 19.8–225 with 70% H2 and SW; 6–45 g with 83% H2) among 283 segregating F4 mapping individuals and parental genotypes across two years was evident (2) varied from 13.6–28.7%. The PVE for all the 18 QTLs was 41.8%. All the identified QTLs showed positive additive gene effects (varied from 2.7–4.9), indicating the effective contribution of ICC 6013 alleles at these loci for increasing target traits (2: 17.9–28.7%) harbouring 18 genomic loci (six genes and six gene-associated targets) that were mapped on six chromosomes (spanned with 62 SNPs) had strong PN, SN and SW trait association potential based on our GWAS (2: 45–47%) and QTL mapping (R2: 28.7%) than that of other trait-associated genomic loci was evident (An intra-specific genetic linkage map (ICC 6013 x ICC 7346) was constructed by integrating 292 SNP markers (by MALDI-TOF SNP genotyping assay) on eight chickpea LGs (LG1 to LG8) . The gen evident . The nor evident  of these evident , Fig. 7.t traits . Thirteeour GWAS , Fig. 7. evident . Hencefo evident  were selin vitro grown pollen tubes and pods of these accessions compared with their respective vegetative and reproductive tissues, including leaf, root, flower bud, ovary and seed (−2) in two pod developmental stages (compared with vegetative tissues) of all 12 high and low pod and seed number-containing chickpea accessions (−2) as compared to the six low pod number accessions  were performed in seven different vegetative and reproductive tissues and two pod and seed developmental stages of high  and low  pod and seed number-containing chickpea accessions using semi-quantitative and quantitative RT-PCR assays . A stronand seed . This gecessions . Remarkacessions .in silico digital transcript profiling, our experimental expression profiling data of this target gene was compared with its available whole-genome microarray expression and global transcriptome sequencing information in different vegetative and reproductive tissues of Arabidopsis, soybean, Medicago and chickpea. Interestingly, the cellulose synthase gene had preferential expression in flower and young pod/silique than its vegetative tissues, including root and leaf  and cellulose synthase-like  genes from desi (22) and kabuli (16) genomes  genes were mapped on the scaffolds of desi genome. The genome-wide characterization of cellulose synthase superfamily in chickpea genome enabled to annotate a strong PN and SN trait-regulatory two functional domains (zinc-binding ring/U-box and cellulose synthase domains)-encoding cellulose synthase gene , mapped on kabuli chromosome 5 as CesA-type cellulose synthase (Ca_Kabuli_CesA3) , a comprehensive genome-wide survey of all cellulose synthase protein-coding genes composing cellulose synthase superfamily was performed individually in  genomes . Twenty-i_CesA3) .Ca_Kabuli_CesA3 gene among 102 cultivated and wild chickpea accessions (92 association panel and 10 wild accessions) identified 91 SNPs and seven SSRs  of wild accessions  of the gene with PN and SN. A significant higher degree of LD (r2 > 0.70 with P < 1.4 × 10−4) resolution was evident across the entire 10793 bp sequenced region of this strong PN trait-associated gene (6-T-A] and haplotype 2: [G-(CT)4-C-A] determined by three SNPs  and one SSR (−1245 bp) in URR of the gene showed strong association potential for high  and low pod number  differentiation, respectively (6-T-A] identified in the URR of a Ca_Kabuli_CesA3 gene was represented most commonly by high-pod/seed number-containing desi and kabuli accessions compared with the other three haplotypes. The pronounced up-regulated expression (~3.5-fold) of this high PN and SN trait-associated superior gene haplotype was observed in mature pollens and two pod developmental stages of six high -pod/seed number-containing chickpea accessions and homozygous mapping individuals compared with low -pod/seed number accessions and homozygous mapping individuals . In contrast, weak cross-hybridisation of this transcript was observed in the ovary tissue of ICC 6013 (desi and kabuli) and 10 wild accessions. However, the low pod/seed number-associated haplotype 2 was primarily represented by low pod/seed number (PN: 4–20)-containing wild accessions from primary and secondary gene pools (C. reticulatum and C. echinospermum), whereas high pod/seed number (PN: 35–63) accessions of the tertiary gene pool  had haplotypes 1, 3 and 4 (desi and kabuli accessions and low pod/seed number-regulatory haplotype 2 of wild accessions.The sequencing of a 10793 bp cloned amplicon covering the entire 2 kb URR (upstream regulatory region), 15 exons, 1 kb DRR (downstream regulatory region) and 14 intronic regions of ven SSRs . Eight Schickpea . About 6cessions , D. The ted gene . Markedlectively . The supividuals . The NorICC 6013 . The sha 3 and 4 . Maximumin vitro pollen germination and pollen tube growth assays were performed in two chickpea accessions representing high (ICC 6013) and low (ICC 7346) pod/seed number-regulating haplotypes. We observed low (87%) pollen germination and pollen tube growth in the low pod number-containing accession ICC 7346 compared to high pod number accession ICC 6013 (93%)  . To infe signals . In contccession . The estccession . Overalldesi and kabuli genomes, GWAS needs to be performed separately using desi and kabuli SNPs, which in turn would strengthen the possibility of identifying numerous trait-associated valid genomic loci with wider genome coverage in chickpea. This strategy is also essential to access the precise trait association potential of genomic loci by establishing their correlation with chromosomal LD estimates and LD decay separately for desi and kabuli genomes. The implications of such chromosomal LD patterns in GWAS for identification of potential trait-associated genomic loci have been demonstrated in many crop plants including chickpea70desi and 24405 kabuli SNPs in this study enabled to identify two common validated PN and SN-associated genomic loci between desi and kabuli along with six and twelve PN, SN and SW-specific unique genomic loci individually from desi and kabuli genomes, respectively. The trait association potential (P ≤ 10−4 with R2 23 to 47%) of these 22 genomic loci (gene-associated targets) for PN, SN and SW has been ascertained by combined use of four model-based algorithms of GLM, MLM, EMMA and CMLM and with a minimised confounding effect of population structure (FDR threshold <0.05). It is further supported by the large-scale validation, association study and strong marker-allele effects of 22 genomic loci on PN, SN and SW-specific phenotypic variation in 211 chickpea minicore accessions. Therefore, the phenotypic variation explained by SNP loci and trait association-related genomic information generated in our study is reliable and robust, thus having utility in establishing rapid marker-trait linkages and identifying genes/QTLs regulating important agronomic traits in chickpea. The significant association of multiple SNP loci in more than one genomic region (genes) with multiple quantitative traits  hints at pleiotropy and/or local LD (closely linked genes), which might account for the complex genetic inheritance pattern of trait regulation. This association was further evident from high chromosomal and population-specific LD estimates with desirable LD decay of trait-associated linked/unlinked multiple SNP loci and the observed genetic heterogeneity of three quantitative traits  across three populations. Despite diverse genetic architecture of PN, SN and SW in the three populations and along entire association panel, no significant differences concerning the marker-trait association potential of SNP loci in different populations was observed. The identification of strong PN, SN and SW trait-associated non-synonymous and regulatory SNP loci specifically in coding  and URRs of protein-coding genes  suggests their functional significance in rapid trait-regulatory gene identification and characterisation in chickpea. The non-synonymous substitution of SNP loci in the CDS  of genes encoding variable amino acid residues could create altered secondary structures of proteins and functional domain regions that may affect the DNA binding and transcriptional activity of target genes during growth and developmental processes. Such possible transcriptional mechanism of trait regulation due to non-synonymous SNP (amino acid) substitutions and regulatory SNPs has already been reported in high seed weight-associated chickpea transcription factor genes24GS3, GS5 and qGL3)727374Considering disproportionate draft genome assembly and size (Mb) of chromosomal pseudomolecules between qPN5.1 and qPN8.1) and one SW-associated QTL (qSW3.1) showing correspondence with QTLs reported in three different QTL mapping studies1318Eighteen PN, SN and SW-associated genomic loci (six genes and six gene-associated targets) underlying 13 robust QTLs were validated by both SNP marker-based GWAS and traditional genetic/QTL mapping, which could essentially help in characterising these complex quantitative agronomic traits in chickpea. Altogether, 18 robust PN, SN and SW-associated robust QTLs (PVE: 13.6–28.7% and LOD: 5.1–9.7) harbouring 15 major genomic regions mapped on eight chickpea chromosomes were identified. To ascertain the potential and novelty of trait-specific 22 genomic loci and 18 QTLs identified by GWAS and QTL mapping, respectively, the markers linked/flanking the PN, SN and SW-associated known QTLs/genes, already reported in chickpea association and QTL mapping studies1813182430327176Ca_Kabuli_CesA3) gene  in anther, mature pollen, in vitro grown pollen tube and pod, along with its higher differential up-regulation (~5.7-fold) in two pod developmental stages of primarily the high pod/seed number-containing chickpea accessions, reemphasises the functional relevance of this gene in possible regulation of pod/seed number in chickpea. The strong PN and SN trait association potential of this target gene was further ascertained by SNP-SSR marker-based haplotyping (four haplotypes), high-resolution gene-specific LD mapping in 102 cultivated and wild chickpea accessions and through haplotype-specific differential transcript profiling (semi-quantitative RT-PCR and Northern assays) in contrasting high and low pod/seed number-containing accessions and homozygous mapping individuals. These analyses confirm the strong association potential of a high pod/seed number-containing novel superior haplotype [A-(CT)6-T-A] identified in URR of a Ca_Kabuli_CesA3 gene for pod/seed number differentiation and its implication in deciphering the transcriptional regulatory gene function during pod development in chickpea. The SNP-SSR marker-based haplotypes in the URR of this gene have significant role in regulating gene expression and identifying genes/QTLs governing multiple agronomic traits, including starch biosynthesis, seed shattering and seed setting rate in rice and seed weight in chickpea243278Ca_Kabuli_CesA3 gene identified in chickpea by integrating GWAS and QTL mapping with marker-based haplotyping and superior haplotype-specific transcript profiling, can be considered a candidate for marker-assisted genetic enhancement of chickpea to increase pod numbers and yield. The sharing of haplotypes constituted in a Ca_Kabuli_CesA3 gene among 92 domesticated desi and kabuli and 10 wild chickpea accessions representing primary, secondary and tertiary gene pools was apparent. Out of these 102 accessions under analysis, 77 (75.5%) with high PN and SN-associated A-SNP allele of a Ca_Kabuli_CesA3 gene were represented by only two haplotypes suggesting high artificial selection pressure on the trait-specific locus/haplotypes of this gene during chickpea domestication. The influence of positive selection on Ca_Kabuli_CesA3 gene is more evident from its higher Ka/Ks (0.96) (higher than the average 0.84 Ka/Ks estimated in chickpea genes) and haplotype diversity (mean θπ: 0.61 and θω: 0.57) among cultivated and wild accessions. This clearly reflects the role of novel superior high pod/seed number-associated haplotypes and favourable natural allelic variants of the target cellulose synthase gene in regulating pod/seed number-specific traits and evolution during domestication of chickpea.The tissue-specific expression of a particular cellulose synthase (Ca_Kabuli_CesA3) gene homolog in Arabidopsis thaliana (AtCesA3), showing silique-specific expressionAtbHLH15 (basic helix-loop-helix) gene8183Arabidopsis80in vitro pollen tube growth and cellulose estimation assays, indicate that the high pod/seed number-specific novel superior haplotype [A-(CT)6-T-A] identified in URR of a Ca_Kabuli_CesA3 gene might regulate a higher accumulation of cellulose synthase transcripts specifically, in mature pollen and pod tissues/developmental stages of high pod/seed number-containing chickpea accession, leading to normal pollen germination and pollen tube growth due to increased cellulose synthesis. As per our observations in the low pod/seed number-containing accession, normal pollen and pollen tube growth was affected possibly due to low cellulose deposition and reduced expression of the superior haplotype in URR of the Ca_Kabuli_CesA3 gene in its pollens and pods . The higher expression of different isoforms of cellulose synthase gene in developing mature pollen and pollen tubes is known to increase cellulose content and enhance normal pollen tube growth in crop plants, including Arabidopsis and Nicotiana8788Ca_Kabuli_CesA3 gene in diverse chickpea accessions as well as its mutant complementation analysis in Arabidopsis is required to understand its definite role in pod and seed number regulation in chickpea. Interestingly, the cellulose synthase gene has an additional vital role in imparting enhanced abiotic (drought and osmotic) and biotic stress tolerance in plant species, including Arabidopsis by root growth and lignification9091The chickpea cellulose synthase (de novo-based GBS assays in a constituted seed and pod trait-specific association panel (92 desi and kabuli accessions) of chickpea. The use of a combinatorial approach of GWAS, QTL mapping, differential expression profiling, gene haplotype-based LD mapping and transcript profiling delineated one high pod/seed number-regulating superior haplotype and favourable allelic variants in the URR of a CesA-type cellulose synthase (Ca_Kabuli_CesA3) gene. The possible regulatory role of such superior gene haplotype with its increased transcript expression, cellulose accumulation in pollen and pod tissue and subsequently, normal pollen and pollen tube growth in high pod/seed number chickpea accession was evident. This novel integrative strategy for quick identification of functionally relevant trait-regulatory molecular tags can essentially dissect complex quantitative agronomic traits for marker-assisted genetic improvement of crop plants, including chickpea.Conclusively, our study identified 23798 high-quality genome-wide SNPs employing reference genome- and C. arietinum desi (39 accessions) and kabuli (53) accessions was used (4 mapping population derived from an intra-specific cross (ICC 6013 x ICC 7346) between two parental chickpea accessions (selected from the association panel) was developed. ICC 6013 (Indian origin) is a low 100-seed weight (10 g) and high pod (130) and seed number (210)-containing desi accession, whereas ICC 7346 (Mexican origin) is a high 100-seed weight (39 g) and low pod (26) and seed number (42)-containing kabuli accession. Genomic DNA was isolated from the leaves of 92 accessions and 283 mapping individuals using a QIAGEN DNeasy 96 Plant Kit  as per the manufacturer’s instructions.To compose a seed and pod trait-specific association panel, 92 phenotypically and genotypically diverse  accessions were selected from the available chickpea germplasm collections 9394was used  for largApeKI and ligating the digested DNA to adapters containing one of 96 unique barcodes. The pooling of libraries and their sequencing (100-bp single end) were performed using Illumina HiSeq2000 with respect to Elshire et al.et al.phred score >10) were de-multiplexed relying their unique barcodes and the individual sequence reads of 92 accessions were mapped to reference drafts of desi . The sequence reads aligned and unaligned with each of desi and kabuli genomes were processed using the reference-based GBS pipeline of STACKS and the de novo genotyping approach of STACKS to identify valid and high-quality SNPs  in 92 accessions. The structural and functional annotation of SNPs identified by reference-based GBS approach in various coding (synonymous and non-synonymous SNPs) and non-coding sequence components of genes and genomes (chromosomes/pseudomolecules and scaffolds) were performed using the available desi (CGAP v1.0kabuliThe 96-plex GBS libraries were made by digesting the genomic DNA of 92 chickpea accessions (association panel) with  ICC 4958 and kabuCGAP v1.0 and kabu4 mapping population (ICC 6013 x ICC 7346) were grown in the field as per a randomised complete block design (RCBD) for two consecutive years (2011 and 2012) at two diverse geographical locations  in India during the crop growing season. These natural and mapping populations were phenotyped for three yield-contributing quantitative traits; pod number/plant (PN), seed number/plant (SN) and 100-seed weight (SW). The PN (the count of average number of fully formed pods per plant at maturity), SN (the count of average number of fully formed seeds per plant at maturity) and SW (g) (the average weight of 100-matured seeds at 10% moisture content) from 10–12 representative plants of each accessions and mapping individuals was measured. Diverse statistical parameters, including coefficient of variation (CV), frequency distribution, broad-sense heritability (H2), Pearson’s correlation coefficient and analysis of variance (ANOVA) of the phenotyping data were evaluated using SPSSv17.0 ( http://www.spss.com/statistics) and the methods of Saxena et al.Ninety-two chickpea accessions representing an association panel along with two parental accessions and 283 segregating individuals of a F10 P-value for each SNP marker-trait association was compared individually with that of the expected distribution using quantile-quantile plot of GAPIT. The adjusted P-value threshold of significance in each trait was corrected for multiple comparisons basing upon false discovery rate (FDR cut-off ≤ 0.05). Combining the four model-based outputs of TASSEL and GAPIT, the SNP loci in the target genomic (gene) regions (significant LD regions) revealing significant contributions to phenotypic variation of three agronomic traits at highest R2 (magnitude of marker trait-association) and lowest FDR adjusted P-values (threshold P < 2 × 10−4) were identified. For large-scale validation and verification of the accuracy of identified SNP marker-trait associations, the high-throughput genotyping data (Illumina GoldenGate assayThe genome-wide SNP genotyping information (MAF ≥ 5%), robust field phenotyping data of three seed and pod yield-contributing traits , ancestry coefficient data  and relative kinship matrix (K) generated from 92 chickpea accessions (association panel) were analysed244 segregating mapping individuals using a MALDI-TOF SNP genotyping assay with respect to Saxena et al.26http://www.kyazma.nl/index.php/mc.JoinMap) at a higher LOD (logarithm of odds) threshold (>4.0) using Kosambi mapping function. The SNPs mapped on eight linkage groups (LGs) of an intra-specific chickpea genetic map were designated (LG1 to LG8) according to their corresponding physical positions (bp) on chromosomes, as determined in our study.For genetic linkage map construction, 384 SNPs  showing polymorphism between two parental accessions (ICC 6013 and ICC 7346) were genotyped in 283 F4 mapping individuals and parental genotypes were analysed using the composite interval mapping (CIM) function (LOD > 4.0 with 1000 permutations and p ≤ 0.05) of MapQTL 6.2) was estimated to identify and map the novel major genomic regions harbouring QTLs associated with three agronomic traits in chickpea.For QTL mapping, the genotyping data of parental polymorphic SNPs genetically mapped on eight LGs and field phenotyping information  of 283 Fin vitro grown pollen tube after flowering) tissues were collected from 40–60 days old healthy plants of high  and low  pod and seed number-containing accessions. Moreover, tissues from two pod (~5 mm beginning pod size at 5–10 days after anthesis/DAA and ~2 cm full pod at 15–20 DAA) and seed developmental stages (early cell division phase during 10–20 days after podding/DAP and late maturation phase during 21–30 DAP) of these 12 contrasting accessions were gathered -regulatory genes and gene-associated targets validated by both GWAS and QTL mapping was performed. Seven different vegetative (leaf and root) and reproductive  available for a strong PN and SN trait-associated gene  in different vegetative and reproductive tissues  of soybean , Medicago , Arabidopsis  and chickpea  were acquired and correlated with our experimental gene expression profiling results.For C. echinospermum, C. judaicum, C. bijugum, C. pinnatifidum and C. reticulatum)  amplified from 92 chickpea accessions (association panel) and 10 wild accessions (two accessions each from culatum)  were cloet al.32-dCTP using a random primer labelling kit . The blots were pre-soaked with pre-hybridisation buffer, hybridised with denatured radiolabelled probe (at 65 °C for 16–18 h) and washed using wash solution I (2x SSC + 0.1x SDS at 65 °C/20 min), wash solution II (1x SSC at 65 °C/20 min) and wash solution III (0.25x SSC at 65 °C/5 min). The blots were wrapped with plastic wrap; exposed to a phosphoimaging K-Screen  overnight and visualised using PharosFX of a Phosphoimager .For Northern analysis, RNA (10–20 μg) isolated from four different tissues  of one chickpea accession (ICC 6013) representing a high pod number-specific superior haplotype was transferred to a Nylon membrane and RNA blots were prepared following Doblin 2, 1 mM MgSO4, 0.01% boric acid and 18% sucrose)et al.Pollen grains were collected from only dehisced anthers of two chickpea accessions representing high (ICC 6013) and low (ICC 7346) pod and seed number-regulating haplotypes. The pollens were placed on glass slides coated with pollen tube growing medium .


The technical progress in the last decade has made it possible to sequence millions of DNA reads in a relatively short time frame. Several variant callers based on different algorithms have emerged and have made it possible to extract single nucleotide polymorphisms (SNPs) out of the whole-genome sequence. Often, only a few individuals of a population are sequenced completely and imputation is used to obtain genotypes for all sequence-based SNP loci for other individuals, which have been genotyped for a subset of SNPs using a genotyping array.First, we compared the sets of variants detected with different variant callers, namely GATK, freebayes and SAMtools, and checked the quality of genotypes of the called variants in a set of 50 fully sequenced white and brown layers. Second, we assessed the imputation accuracy  when imputing from high density SNP array data to whole-genome sequence using data from around 1000 individuals from six different generations. Three different imputation programs  were checked in different validation scenarios.There were 1,741,573 SNPs detected by all three callers on the studied chromosomes 3, 6, and 28, which was 71.6 %  of SNPs detected by GATK  in total. Genotype concordance (GC) defined as the proportion of individuals whose array-derived genotypes are the same as the sequence-derived genotypes over all non-missing SNPs on the array were 0.98 (GATK), 0.97 (freebayes) and 0.98 (SAMtools). Furthermore, the percentage of variants that had high values (>0.9) for another three measures  were 90  for GATK . With all imputation programs, correlation between original and imputed genotypes was >0.95 on average with randomly masked 1000 SNPs from the SNP array and >0.85 for a leave-one-out cross-validation within sequenced individuals.Performance of all variant callers studied was very good in general, particularly for GATK and SAMtools. FImpute performed slightly worse than Minimac and IMPUTE2 in terms of genotype correlation, especially for SNPs with low minor allele frequency, while it had lowest numbers in Mendelian conflicts in available father-progeny pairs. Correlations of real and imputed genotypes remained constantly high even if individuals to be imputed were several generations away from the sequenced individuals.The online version of this article (doi:10.1186/s12864-015-2059-2) contains supplementary material, which is available to authorized users. Thus, whole-genome sequencing has become available that allows us to gather more information on genetic variation, genes, gene function and other characterizations of genomes , 2 and tSeveral variant callers based on different algorithms have emerged using single or multiple samples simultaneously, e.g. SAMtools  or GATK Although over the past several years the cost of DNA sequencing has decreased by several orders of magnitude due to the rapid development of sequencing technology, it is still comparatively expensive . There aHeidaritabar et al.  showed tIn this study, we first compared the sets of variants detected with different variant callers, namely GATK , freebaySamples were collected by veterinarians in the Lohmann Company in the course of a routine health check for diagnostic reasons and a partition of retained samples was used to extract DNA. The authors collected no samples themselves.Blood samples and pedigree data of more than 5 generations backwards  were available for purebred individuals from different generations of a brown layer line. Number of individuals per generation is shown in Additional file http://broadinstitute.github.io/picard/) to remove potential PCR duplicates.Fifty individuals (25 brown layers and 25 white layers) chosen to be from one of the older generations and highly related to the set of already genotyped individuals were sequenced with the Illumina HiSeq2000 technology with a target coverage of 8X. Sequence reads were aligned to Build 4 of the chicken reference genome  using BWA (version 0.7.9a-r786)  with defVariants including SNPs and short insertion and deletion (INDELs) were called using different software programs: GATK  , freebayTo reduce the proportion of the false positive variants, different strategies to select the so-called high-quality variants have been suggested , 35, 36.Genotype concordance (GC), non-reference sensitivity (NRS), non-reference genotype concordance (NRC), and precision were calculated based on array genotypes and corresponding sequence-based genotypes obtained with different variant callers  were used.Since 24 out of the 25 sequenced brown layer chickens were also genotyped with the 580 k array, each of these individuals was excluded from the imputation reference data set once and imputation from SNP array data to whole-genome sequencing data was performed with the respective individual being one of the validation individuals in the resulting dataset. Genotype concordance and correlation between the imputed and sequenced genotypes from these run for all non-monomorphic SNPs being not on the array was calculated afterwards per the respective individual.Among the genotyped brown layer individuals there were 134 individuals that were progeny of one of the sequenced individuals. Thus, genotypes on imputed SNPs in the progeny could be compared to the father’s genotypic information at these SNP positions and genotype conflicts  were counted. Proportion of genotype conflicts were calculated per father-progeny-pair over all SNPs excluding the ones which were also genotyped using the 580 k array on chromosomes 3, 6 and 28, respectively.As imputation accuracy depends (amongst others) on the degree of relationship between sequenced individuals and individuals to be imputed, we also checked how imputing accuracy changes when different numbers of generations are between sequenced individuals and individuals to be imputed. For this analysis, we randomly masked (i.e. setting them to missing) 1000 SNPs  from the SNP array data in all genotyped individuals and imputed those SNPs as if they were SNPs from the sequence data. Afterwards, imputed genotypes on these 1000 SNPs and real array genotypes were compared and genotype correlation was calculated for each SNP and also for each individual. As Calus et al.  and MuldFor brown layer chickens, on average 88 million paired-end reads were obtained per individual. Among these reads, 1.72 % (ranging from 0.76–1.98 %) on average were marked as duplications and excluded and on average 96.7 % (ranging from 96.1–96.9 %) were mapped against the reference genome galGal4). Coverage per sample ranged from 5.0 to 16.6, with an average of 7.6. For white layer chickens, on average 94 million paired-end reads were obtained per individual. Among these reads, 1.69 % (range 1.45–1.92 %) were marked as duplications and excluded; and 96.7 % (range 96.3–96.9 %) were mapped against reference genome. Coverage per sample ranged from 7.9 to 15.6, with an average of 10.8. Based on this data set, the number of raw paired-end reads obtained was higher in white layer chickens than in brown layer chickens. However there was no difference in percentage of duplications and percentage of mapping. Details can be seen in Additional file . CoveragDepending on the variant caller, totally 13,442,923 (freebayes ), 13,642Figure https://github.com/ekg/freebayes). Although GATK and SAMtools have equivalent performances and both perform better than freebayes, the metrics used here relied on the accuracy of array-derived genotypes which were assumed to be the ‘true’ genotypes. Eventually existing genotyping errors may thus bias the results. Besides, SNPs on the array were selected to be almost evenly distributed across the genome and were preselected to match a certain MAF spectrum which differs from the MAF distribution present in the sequence [Except the number of SNPs shared by different variant callers, we also compared GC, NRS, NRC, and precision of different callers as suggested in DePristo et al. and Linderman et al. , 38. Difsequence , which asequence  discoverIn this study, the analyses were mainly focused on the comparison of specific versions  of different variant callers. Different versions of the same variant callers may result in a different set of variants for the same underlying sequencing data. We thus compared two different versions of freebayes. The older version of freebayes (version v9.9.2-22-gc283d6d) called 524,938 SNPs (29 %) more than the newer version (version v0.9.15-1-g076a2a2) based on the same data material on chromosomes 3, 6, and 28. Thus, our ranking of callers is only valid for the specific versions used here.It needs to be mentioned that the only alignment tool used in this study was BWA with different variant callers to make sure that variants are called based on the same basic data sets, to ensure a fair comparison of the callers. However, each step listed in the pipeline  to assess imputation accuracy from SNP array data to whole-genome sequence. For chromosomes 3, 6 and 28, the genotype correlation and concordance achieved by three different imputation packages  are shown in Fig. Over all three chromosomes, the average genotype correlation ± standard deviation between imputed and sequence-derived genotypes was 0.91 ± 0.028 for Minimac, 0.89 ± 0.028 for FImpute and 0.90 ± 0.027 for IMPUTE2. These results implied that also pedigree-free imputation software (Minimac and IMPUTE2) yielded accurate genotypes for whole sequence variants in this data set. Most of the sequenced individuals in this study were contemporaries in a commercial breeding program which controls for the level of relationship and inbreeding, thus pedigree relationship among these individuals was relatively low. This may explain why imputation programs based on pedigree algorithms, such as FImpute, have no advantage in this leave-one-out cross-validation strategy.Over all three imputation programs, the average genotype correlations for SNPs on chromosomes 3 and 6 were quite similar. However, for chromosome 28, which is much smaller than the other two chromosomes studied, the average genotype correlation was slightly lower and the standard deviation was larger compared to chromosomes 3 and 6. In the study of Hancock et al. , it was Leave-one-out cross-validation is the only strategy of assessment of imputation quality that allows taking all SNPs from sequenced data into account when calculating measures like genotype correlation. However, it should be mentioned that in most cases in practice individuals to be imputed are descendants of the sequenced individuals, which was not the case in this leave-one-out setup. Assessing whether the sequence of offspring is correctly imputed can only be done if a sample of such offspring is actually sequenced, and such data are presently not available in sufficient quantities. Thus these results of this analysis should only be extrapolated with caution to the practically most relevant case of imputing sequence of current selection candidates based on sequenced founder animals.Based on the available pedigree, it is possible to apply Mendelian rules to estimate the percentage of genotype conflicts for all SNPs in father-progeny pairs  for an imputed progeny compared to each sequenced individual. There were 134 father-progeny pairs in the available pedigree for which the father was sequenced and the progeny was imputed. The number of progenies per father varied from 1 to 44. Comparisons of the imputation performance based on the father-progeny pair conflict  for individuals from a commercial brown layer line cross and found a recent Ne of 70) and the results may differ in more open populations with higher effective population size, migration or variability in mating schemes.As imputation accuracy depends (amongst others) on the relationship between reference individuals and individuals to be imputed, the trend of imputation accuracy when there was a different number of generations between reference and validation individuals was investigated. The relationship between sequenced individuals and genotyped individuals, which was estimated as the percentage of genotyped individuals having a high relationship ≥ 0.25 (or 0.5) with at least one of sequenced individual is shown in Additional file Based on data from 50 sequenced individuals from two layer lines, we compared the performance of three variant callers for a subset of SNPs (~50 k) that were available from whole-genome sequencing and SNP array in 24 out of 1081 individuals that were both fully sequenced and genotyped with the 580 k array. Results showed that a high proportion of SNP calls had high values in different measures of quality (amongst others genotype concordance and non-reference sensitivity) with all variant callers. GATK showed a slightly better performance than SAMtools and freebayes. We further demonstrated that three commonly used imputation programs were capable of imputing from SNP array data up to whole-genome level in a brown layer line based on a small number of sequenced individuals with substantial imputation accuracy, even across several generations. FImpute performed slightly worse than Minimac and IMPUTE2 in terms of genotype correlation, especially for SNPs with low minor allele frequency, while it yielded the lowest numbers of Mendelian conflicts in available father-progeny pairs. Imputation accuracy was lower for rare SNPs than for common SNPs, which confirmed previous results in other species. Overall, sequence imputation from a very limited number of sequenced individuals appears to yield reasonably accurate results in closed breeding populations as available in many nucleus breeding programs.http://hgdownload.soe.ucsc.edu/downloads.html#chicken). PLINK binary files containing genotype and map information of all variants on chromosomes 3, 6 and 28 detected by GATK in the 50 sequenced individuals are available at doi: 10.6070/H47H1GKK.The reference genome used for alignment was taken from a public database and is available for download from UCSC genome browser (


WG-FAST) pipeline can identify unknown strains with much less read data than is needed for genome assembly. To test WG-FAST, we resampled SNPs from real samples to understand the relationship between low coverage metagenomic data and accurate phylogenetic placement. WG-FAST can be downloaded from https://github.com/jasonsahl/wgfast.We describe an approach for genotyping bacterial strains from low coverage genome datasets, including metagenomic data from complex samples. Sequence reads from unknown samples are aligned to a reference genome where the allele states of known SNPs are determined. The Whole Genome Focused Array SNP Typing (The online version of this article (doi:10.1186/s13073-015-0176-9) contains supplementary material, which is available to authorized users. Escherichia coli O104 [Vibrio cholerae [Klebsiella spp. [Staphylococcus aureus (MRSA) [Bacillus anthracis [Whole genome sequencing (WGS) is a powerful and increasingly available technology for understanding the evolutionary and epidemiological relationships among bacterial pathogens. For bacterial disease outbreaks, whole genome analysis has been used to identify and attribute the outbreak sources for many bacterial pathogens, including cholerae , Klebsiella spp. , methicinthracis . Adding nthracis ) but thenthracis .The advent of molecular diagnostics ) has led to improved pathogen identification, in part, because they are not dependent upon isolation and subsequent culturing of the pathogen. But the currently dominant disease-tracking methods ) only work with isolated pure cultures, leading to the possibility that disease tracking efforts will be diminished in this new age . MoleculWG-FAST) method, where only known SNPs with defined allele states are scored. These are derived from a reference population where high quality genomic data are available to generate a highly robust phylogenetic reconstruction. Sequencing reads are aligned to a reference genome annotated with the positions of known SNPs and their allelic states. The metagenomic SNP genotype of the unknown pathogen can then be placed into the most likely phylogenetic position. It is the reference population SNP database that defines the best possible model for population structure, which is then used as a reference for unknown SNP genotypes identified from less than ideal  datasets. We also present several approaches for establishing confidence in phylogenetic placement including hypothesis-testing methods that generate odds ratio probabilities. This is essential because the precision of phylogenetic placement will be unique for each application and is dependent upon a number of variables including: (1) the SNP/genome density in the reference population; (2) the depth of genome coverage from the unknown sample; and (3) the phylogenetic topology in the actual placement position of the reference population. Thus, placement confidence metrics must be established for each unknown sample. WG-FAST will allow the use of deep metagenomic sequencing data to identify strains from complex samples such as clinical specimens, food matrices, and the environment, alleviating the requirement for pure cultures to accomplish molecular epidemiological goals.WGS analysis to identify pathogen strains would seem possible through the metagenomic deep sequencing of clinical specimens, but genome coverage of a specific microbe is hard to predict and the pathogen may represent only a minor component in the microbiome of the infected tissue . Many paWG-FAST analysis pipeline. A pipeline to wrap methods discussed below, known as the Northern Arizona SNP Pipeline (NASP), is publically available (tgennorth.github.io/NASP/). Our strategy for reference SNP identification is to use only the non-redundant core genome sequences to avoid missing data and misuse of paralogous regions. To create a reference database, raw reads or assembled genomes are aligned to a reference genome with BWA-MEM [E. coli C227-11) with 12 million reads, 100 bases in length, took 4 h 25 min to place and perform 100 subsampling confidence tests using eight processors on a single node with 48 Gb of RAM.The robust characterization of SNPs in a reference set of isolates is a necessary first step in the  BWA-MEM  or NUCme BWA-MEM , respect BWA-MEM , 15, SAM BWA-MEM , VarScan BWA-MEM , and/or  BWA-MEM ). CalledWG-FAST is publically available at [WG-FAST analysis includes a NASP-formatted SNP matrix, a phylogeny inferred with RAxML [WG-FAST include BWA-MEM, GATK, Picard-tools  file is then filtered for minimum coverage and minimum allele proportion. If a position fails a filter, then the call is replaced with a gap (‘-’), indicating missing data. The VCF file is also filtered to only include genomic coordinates present in the input SNP matrix. The unknowns are then merged with the original SNP matrix, which is converted into a multi-FASTA file. All unknowns are then inserted into the phylogenetic tree using an evolutionary placement algorithm (EPA) method in RAxML [WG-FAST pipeline is shown in Fig. In the in RAxML ; this mein RAxML ), all unin RAxML , and theWG-FAST in order to test the robustness of a given placement on a phylogenetic tree. From the final phylogeny, the two closest genomes to each unknown, based on patristic distances, are identified. The SNPs from the two neighbors are then sampled at the same coverage level as each unknown and a new SNP matrix is created. Each matrix is then converted into a multi-FASTA and the samples are placed into the phylogeny with the EPA algorithm. The patristic distance to the reference isolate is then calculated for each subsample and is compared to the ‘true’ patristic distance using all SNPs; the reference is used because its position is fixed and the ‘Reference’ name is the same for each NASP-formatted SNP matrix, regardless of the target organism. The null hypothesis is that a random subsampling placement will differ significantly from the ‘correct’ placement based on a comparison of patristic distances. The number of times that the distance from the reference is different from the known placement is calculated for 100 replicates based on a user-defined threshold. From a set of 100 replicates, if the number of samples placed incorrectly is fewer than 5, then the P value is <0.05 and the placement can tentatively be trusted. For large datasets , this subsampling routine may be impractical, as 200 placements are required if 100 replicates is selected by the user.An optional subsampling routine is built into WG-FAST pipeline, approximately 700 E. coli genome assemblies were downloaded from GenBank [E. coli was used as the test case due to the large number of assembled genomes in public databases and due to the non-clonal nature of the species [E. coli K-12 W3110 (accession # NC_007779) [E. coli [To test the utility of the  GenBank ; E. coli species . SNPs fr_007779)  as the r[E. coli , were alWG-FAST pipeline using a low number of reads, sequence reads were randomly sampled at varied depths , from published E. coli datasets  between 0.99 and 1.01, was identified for each genome. Multiple isolates from different regions of the tree and sequence data from multiple sequencing platforms were analyzed.To test the robustness of the In addition to subsampling raw reads, positions present in the SNP matrix were subsampled for each genome in the phylogeny. SNPs were sampled at a lowest frequency of 50, then sampled every 100 SNPs subsequently, until the patristic distance of 95 % of 100 iterations, compared to the reference, was between 0.99 and 1.01, compared to the patristic distance of the placement using all available positions.WG-FAST (subsample_snps_pearson.py).To identify the fewest number of reference positions required in order to obtain a comparable matrix to a matrix using all available SNPs, a subsampling method was employed. A user-provided number of SNPs were randomly selected from the matrix, the reduced matrix was converted into a multi-FASTA, and a distance matrix was calculated with mothur . A distaWG-FAST method on metagenomic samples, 53 datasets from a recent metagenomic survey of stool samples from the 2011 E. coli O104:H4 outbreak [WG-FAST  with BWA-MEM, and a BAM file was generated. At each position in the reference chromosome, the most frequent base was removed and the counts of the alternate alleles, which represents error, were summed. Average error and associated standard deviation was calculated across the entire reference chromosome.WG-FAST was developed as a parallel, open source method to accurately genotype novel isolates from high read coverage  or from metagenomic data in the context of a known phylogenetic or population genetic structure  calling, and the placement of samples in a phylogenetic context.ure Fig. . This meE. coli chromosome in isolate TY-2482 [WG-FAST should reduce many of these errors, the common solution is to increase sequence coverage to verify a particular SNP. With high read coverage, the false SNP discovery is small, but this is difficult and expensive to achieve in a metagenomic analysis of complex specimens. Rather, the WG-FAST approach limits base calling to known SNP positions and therefore minimizes the impact of this error rate. In a 1,000 SNP genotype, fewer than 2 SNPs would be falsely identified at this rate. If metagenomic data are used to generate a genotype at known genomic positions and with known allele states, the sequencing error has little consequence on a multi-locus genotype determination.One potential pitfall to identifying SNPs from low coverage samples is mistaking sequencing errors for true variants. We estimated the single-read base call error rate across the  TY-2482  to under TY-2482  or Muske TY-2482 , prior tWG-FAST test case, approximately 700 E. coli genome assemblies were downloaded from GenBank; E. coli was used as a test case due to the large number of sequenced genomes. For this analysis, closely related genomes were manually removed based on phylogenetic redundancy, resulting in a dataset of 255 genomes  were generated during the investigation of the enteroaggregative/Shiga-toxin producing outbreak  and wereWG-FAST to help a user assess the robustness of a phylogenetic placement. The first piece of evidence is if an unknown meets the minimum number of SNP positions for accurate placement, which will ultimately depend on the dataset analyzed. Scripts are included with WG-FAST that can be used to identify the number of required positions across a phylogeny for robust phylogenetic placement. Additionally, the EPA algorithm in RAxML [WG-FAST that identifies the two closest genomes to the unknown, based on patristic distances, prunes them from the phylogeny, then subsamples the two neighbors at the same number of called positions as the unknown and re-inserts those genomes back into the phylogeny. The patristic distances between the two near neighbors and the reference are compared to the subsamples and the reference to see how SNP subsampling affects placement. A placement P value is then calculated by dividing all placements by the number of correct placements, based on a user-defined threshold, for each near neighbor sampling. The subsampling approach demonstrates that greater sequencing depth is correlated with P value significance  and focused primarily upon the 16S rRNA gene  but thesWG-FAST pipeline is able to phylogenetically genotype isolates from single isolate sequencing projects, low coverage sequencing projects, or from complex samples with variable coverage, such as metagenomics projects sequenced from human samples. Specifically, WG-FAST was designed to accurately genotype new isolates where the read coverage is below 1× and may not be able to be genotyped by methods that rely on higher query coverage. Although studies have been published that use a similar concept [WG-FAST represents the only publically-available pipeline that can perform these functions and provide statistical support for a given phylogenetic placement. Although WG-FAST currently only works on SNP data, other data, including indels, can also be used to discriminate between related isolates and may provide additional phylogenetic resolution between genomes.The availability of WGS data has led to the problem of how to analyze newly sequenced isolates in the context of existent data. This is especially a problem when trying to accurately genotype the causative agent of infections from WGS obtained directly from clinical specimens. Our  concept , WG-FASTWG-FAST towards real samples, a set of 53 metagenomic datasets from an analysis of diarrheal samples associated with a Escherichia coli O104:H4 outbreak [et al. as positive for O104:H4 and unmixed, based on a separate bioinformatics pipeline, were also correctly genotyped by WG-FAST. However, additional samples were identified as positive for O104:H4 by WG-FAST that were reported as negative by Loman et al. This demonstrates that the ability of WG-FAST to genotype based on partial genotypes may allow for lower level detection than read assembly or mapping methods that require higher reference coverage in order to classify a pathogen. The long branches on some samples demonstrate signs of mixtures of multiple isolates . There was a strong correlation with the number of positions required for accurate placement and the topology of the tree. In general, nodes that were filled with closely related isolates required only approximately 100 positions for accurate placement, while nodes containing isolates with long branch lengths required far more positions to be called for accurate placement. The sequence analysis of additional diverse isolates will help fill in blank regions in the tree and create a reference phylogeny that will be better able to place unknown isolates at very low read coverage.The placement of artificial mixtures demonstrated that at near equal proportions, Burkholderia pseudomallei, WG-FAST analysis may require additional positions to be called in order to separate the clonal signal from the recombinant signal. For highly clonal pathogens, the issue becomes the relative lack of polymorphisms in the dataset. For example, only 2,298 SNPs are able to describe the global phylogenetic diversity in Yersinia pestis [When compiling a reference database for a pathogen of interest, the clonality of an organism should be considered. For highly recombinant pathogens, such as a pestis , which wWG-FAST presents a method to characterize new isolates in the context of a reference population. The applications to this method include assigning isolates to known outbreaks, as described in this study, typing unknown isolates to specific phylogenetic lineages, and may provide the resolution to resolve transmission routes, although additional experimentation is required before this is verified. As sequence data, both single isolate and metagenomic, become more commonplace, methods that scale linearly with huge datasets, such as WG-FAST, will become critical for the analysis of clinical pathogens.The large sequence datasets that are now available to most researchers have presented new problems, both computationally and methodologically, for the analysis of new isolates. WG-FAST can be used to genotype isolates at the strain level from complex samples using low levels of sequence data obtained from metagenomics studies. While WG-FAST can also be used in conjunction with single isolate genomics datasets, it is especially powerful when analyzing low coverage datasets. In addition to genotyping, WG-FAST performs statistical analysis to help assess the quality of an unknown placement. We demonstrate that in E. coli, WG-FAST can be used to genotype from metagenomic datasets, place samples accurately at extremely low reference genome coverage, and provide a confidence landscape when assessing placement confidence. As reference databases and sequence datasets become more complex, methods such as WG-FAST are required for strain-level genotyping.In this study, we demonstrate how


This is the first study based on a genome-wide association approach that investigates the links between ovine footrot scores and molecular polymorphisms in Texel sheep using the ovine 50 K SNP array (42 883 SNPs (single nucleotide polymorphisms) after quality control). Our aim was to identify molecular predictors of footrot resistance.This study used data from animals selected from a footrot-phenotyped Texel sheep population of 2229 sheep with an average of 1.60 scoring records per animal. From these, a subset of 336 animals with extreme trait values for footrot was selected for genotyping based on their phenotypic records. De-regressed estimated breeding values (EBV) for footrot were used as pseudo-phenotypes in the genome-wide association analysis.2 at a distance of 50 kb between two SNPs) in the population analysed was estimated. LD declined from 0.15 to 0.07 and to 0.04 at distances between two SNPs of 100, 1000 and 2000 kb, respectively.Seven SNPs were significant on a chromosome-wise level but the association analysis did not reveal any genome-wise significant SNPs associated with footrot. Based on the current state of knowledge of the ovine genome, it is difficult to clearly link the function of the genes that contain these significant SNPs with a potential role in resistance/susceptibility to footrot. Linkage disequilibrium (LD) was analysed as one of the factors that influence the power of detecting QTL (quantitative trait loci). A mean LD of 0.20 (rBased on a relatively small number of genotyped animals, this study is a first step to search for genomic regions that are involved in resistance to footrot using the ovine 50 K SNP array. Seven SNPs were found to be significant on a chromosome-wise level. No major genome-wise significant QTL were identified. Dichelobacter nodosus, which is the main component of a mixed bacterial infection and is the major cause of lameness in sheep. The annual economic impacts of footrot are considerable; direct costs include the labour time needed to manage the disease as well as prophylactic and veterinary medicine treatment costs. Indirect costs of reduced fertility, milk supply and productivity are less obvious but lame sheep are less productive, have less milk and rear fewer lambs compared to unaffected ewes. The costs of footrot have been estimated to be $18.4 M [Footrot is an endemic disease of sheep that causes pain, reduced productivity and economic loss to the sheep industry in the United Kingdom (UK) as in other sheep-producing countries in the world. It is a highly contagious disease caused by  $18.4 M  and Rs15 $18.4 M , for AusReports indicate that at least three million sheep are lame in the UK at any one time and that six to nine million sheep become lame in the UK over the course of a year . PrevaleDQA2) in the region between 44 and 55 cM in Texel, Blackface and Welsh Mountain sheep and have shown that the DQA2 gene is very polymorphic but that the classification of animals into the same footrot sensitivity classes  was not informative for these breeds. However, a multiplexed fluorescent microsatellite marker set that covered chromosome 20 at 10 cM intervals was also developed during the project and revealed breed-specific relationships of footrot resistance with genotype. The advent of the genome-wide screening approach using single nucleotide polymorphism (SNP) technology facilitates the search for genes that are involved in resistance to footrot by using SNPs that are significantly associated with the trait. The ovine 50 K SNP chip has been available since 2008 and is used in New Zealand and Australia for key production traits to improve the accuracy of EBV [The potential for exploiting the host’s genetic resistance to footrot was reported by  and reviy of EBV ,26. HoweSheepbreeder [. All farms were requested not to treat their sheep for footrot in the 4-week period before the farms were visited.A 5-point scoring method for hoof lesions that follow the progression of footrot was described elsewhere , and morpbreeder . All farA total of 3573 records were obtained from 2229 animals i.e. 2875 ewes of mixed age and 698 lambs that were born between 1998 and 2006. The sheep were scored between one and three times within an 18 month period averaging 1.60 records per animal. On average, there were between 84 and 127 animals per farm for each scoring event. The average age of the lambs at the time of footrot scoring was 158 days. A total of 11 048 individuals were included in the pedigree file with 2723 sires, 6642 dams and 2165 founders. The maximum pedigree depth was 22 generations with a mean of nine generations. Heritability of footrot in the analysed population was 0.18 (unpublished results).th July and 12th September, 2006, the second scoring event took place between 1st and 24th May, 2007 when the ewes were in mid-lactation and the third scoring event was between 18th July and 10th September, 2007. The data were generated as part of a collaborative project with the Texel sheep society and could be made available for further analyses subject to the conditions of the original collaboration agreement.The first scoring event was carried out during late summer 2006, with scoring dates between 10Blood samples from 336 animals were collected in order to extract DNA. Animals were selected for extreme phenotypes (the highest scores) irrespective of the farm of origin and these were matched with animals with a 0 score from the same farms. The minimum number of genotyped animals per farm was 6 with the majority of farms with more than 10 genotyped animals. Animals with a 0 score were sampled from 15 farms with a minimum of 3 and a maximum of 21 animals per farm. Animals were genotyped commercially with the Illumina ovine SNP50 BeadChip at Ark Genomics . Animals with extreme phenotypes i.e. with no footrot detected on any of the scoring events  or with a score of 8 and above on at least one scoring event  were selected for genotyping. This made up the core of the genotyped population consisting of 204 animals which were subsequently combined with 132 animals with intermediate scores (between 1 and 7). The genotyped animals originated from 118 sires and 293 dams. They included 22 full-sib families with two offspring, and 117 half-sib families with an average of 3.2 animals per family.SNPs were removed from further analyses if they were not in Hardy-Weinberg equilibrium, had a minor allele frequency less than 0.05, were monomorphic, had a call rate less than 0.95 or if the GC score was less than 0.6. Missing genotypes were imputed as homozygous for the major allele.Basic statistics describing the data were estimated with R package . After ay is the vector of footrot scores, b is the vector of fixed effects consisting of birth year , type , farm (17 levels), and scoring event (3 levels), a is the vector of random animal effects, and e is the vector of random residuals.where Random effects were assumed to be normally distributed with zero means and the following covariance structure:A is the pedigree-based relationship matrix, where The software package MIX99 was also used for de-regression, using a full animal pedigree with effective offspring contributions (EOC) as weighting factors. EOC were calculated as:reli is the reliability of EBV for animal i and h2 is the heritability of footrot.where The de-regressing procedure was performed to eliminate bias in the EBV from animals with different numbers of offspring. De-regressed EBV were used as pseudo-phenotype in the subsequent GWAS analysis in order to maximise the use of available information since 100 of the genotyped animals had progeny with footrot records, and half of the genotyped animals had repeated measurements of footrot (scored two or three times).GWAS was performed using the Multi-Locus Mixed Model (MLMM) algorithm  implemeny is the vector of de-regressed EBV for footrot, β is a vector of coefficients for the SNP effects, a is the vector of random animal effects, e is the vector of random residual effects, and X and Z are incidence matrices relating observations to fixed and random animal effects, respectively. Random effects were assumed to be normally distributed with zero means and the following covariance structure:where G is the genomic relationship matrix [where p matrix  calculatS is a centred incidence matrix of SNP genotypes, N is the number of SNPs, and pi is allele frequency of marker i.where Ovis aries OAR 3.1 [Three different genetic models were used for this study: (1) an additive genetic model where the major homozygous genotype was recoded to 0, the heterozygous genotype to 1, and the minor homozygous genotype to 2; (2) a dominant model, where the minor homozygous and heterozygous genotypes were coded as 1 and the major homozygous genotype was coded as 0; and (3) a recessive model, where the minor homozygous genotype was coded as 1 and the heterozygous and major homozygous genotypes were coded as 0. SNP positions were determined using the map  OAR 3.1 .Quantile-quantile (Q-Q) plots were used to analyse the extent to which the observed distribution of the test statistic followed the expected (null) distribution. This was done to assess potential systematic bias due to population structure or analytical approach.Bonferroni correction was applied to obtain significance thresholds. A SNP was significant at the genome-wise level when the –log10 was greater than -log10 (0.05/N), where N is the total number of markers. Chromosome-wise significant SNP had an associated –log10 above –log10(0.05/n), where n is the number of markers on a given chromosome.Proportion of variance explained by SNP (pve) was calculated as:h0 is the Mahalonobis Root Sum of Squares (mrss) for the null hypothesis and mrssk is the same for marker k.where mrss2, which is the squared correlation of the alleles at two loci [Linkage disequilibrium (LD) was measured as rtwo loci :\documenf(AB), f(A), f(a), f(B), f(b) are observed frequencies of haplotype AB and of alleles A, a, B and b, respectively. LD was calculated for all syntenic SNP pairs (SNPs on the same chromosome). SNPs that could not be mapped to any chromosome were excluded from these analyses. Average LD was calculated as an arithmetic mean of r2 values for SNP pairs in 1 kb windows from all chromosomes. LD based on the marker data was compared with an approximate expectation of r2 [where on of r2 :\documene is the effective population size, c is the recombination distance in Morgans (we assumed 100 Mb = 1 Morgan) between SNPs. Assuming that LD at short distances depends on long-term population history [where N history ,37, the t is the effective population size t generations ago, with t = 1/(2c) [where N= 1/(2c) . The Ne Descriptive statistics for the footrot score of the animals used in this study are in Table 2. The average r2 among syntenic markers (within a 1 kb window) as a function of marker distance is in Figure 2 at 50 kb (distance between two SNPs) was 0.20. LD declined from 0.15 at 100 kb (distance between two SNPs) to 0.07 at 1000 kb, and 0.04 at 2000 kb. Compared with the expected LD for a population with Ne = 250, the r2 in the Texel sheep population was lower for small distances between SNPs and similar for large distances  were constructed but they did not reveal any population stratification that might have affected the current analysis Figure . The Q-QCPVL). Two other SNPs were located within uncharacterised genes on chromosomes 14 (s53098.1) and 24 (s34109.1). The remaining SNPs were 25 to 456 kb away from the nearest gene. The proportion of the total variance explained by the significant SNPs was between 5 and 7%.Profiles of the p-values ) for all SNPs from chromosomes 1 to 26 are in Figure Ovis aries [Our aim was to perform an association analysis for footrot using dense SNP information. To our knowledge, this is the first attempt of using SNPs for association analysis of footrot in sheep. The most recent genome map for is aries  was usedis aries . Moreoveis aries ,40. Theris aries .In the current study, we found no genome-wise significant SNPs but seven SNPs were significant on a chromosome-wise level. One of these SNPs was located in a known ovine gene, and three were located within uncharacterised genes. Unfortunately, the function of those genes has not been extensively studied and, currently, it is impossible to suggest a link between their functions and their potential role in the development of susceptibility/resistance to footrot. Therefore, based on the current analysis, it can be concluded that footrot resistance is not driven by single genes with a major effect. Most of the studies related to the sheep immune system are based on low-resolution genome screens, often resulting in very wide confidence intervals. This is the first time that a GWAS was performed for footrot in livestock. Therefore, considering the relatively small sample size, these results should be treated as a first step in unravelling the genetic background of footrot. Since no major genes responsible for footrot were identified, it can be hypothesised that this trait has a polygenic determinism. It is also worth noting that we found no significant SNPs in the MHC region, which could be due to the small sample size which inevitably reduced the power to detect significant associations. Similar problems have been reported in the case of QTL scans for nematode infections in sheep . The extAdditional analyses are required to increase our knowledge and understanding of the genetic determinism of footrot susceptibility. Future analyses will involve further GWA studies to refine the current results, as well as implementation of genomic selection for footrot. For this purpose, 3500 additional animals with footrot scores will be genotyped. These will serve as the reference population to estimate genomic breeding values. Given the low heritability and polygenic nature of footrot, it is expected that genomic selection will increase selection response in comparison to traditional best linear unbiased prediction (BLUP)-based selection . Potenti2 of 0.15 for an average distance between SNPs of 40 to 60 kb) [2 of 0.13 for an average distance between SNPs of less than 50 kb) [2 of 0.14 to 0.17) [2 of 0.18) dairy goats [2 of 0.20 to 0.23 for an average distance between SNPs of 40 kb) [2 of 0.47 to 0.49 for an average distance between SNPs of 30 kb) [Linkage disequilibrium in the investigated population of Texel sheep was analysed because it has a strong influence on the power to detect QTL and accuracy of genomic selection ,47. The o 60 kb)  and Frenn 50 kb) . Mean LDry goats . The avef 40 kb) -54 but if 30 kb) . The facThis study found seven SNPs significant on a chromosome-wise level. No major genome-wise significant QTL were identified. The current analysis did not identify any potential candidate genes for footrot susceptibility. Our results suggest there are no major genes responsible for this trait, which probably has a polygenic determinism. This study should be treated as an initial attempt to go some way towards identifying genomic regions that are associated with the development of footrot susceptibility/resistance in sheep.


In order to have a better understanding of unexplained heritability for complex diseases in conventional Genome-Wide Association Studies (GWAS), aggregated association analyses based on predefined functional regions, such as genes and pathways, become popular recently as they enable evaluating joint effect of multiple Single-Nucleotide Polymorphisms (SNPs), which helps increase the detection power, especially when investigating genetic variants with weak individual effects. In this paper, we focus on aggregated analysis methods based on the idea of Principal Component Analysis (PCA). The past approaches using PCA mostly make some inherent genotype data and/or risk effect model assumptions, which may hinder the accurate detection of potential disease SNPs that influence disease phenotypes. In this paper, we derive a general Supervised Categorical Principal Component Analysis (SCPCA), which explicitly models categorical SNP data without imposing any risk effect model assumption. We have evaluated the efficacy of SCPCA with the comparison to a traditional Supervised PCA (SPCA) and a previously developed Supervised Logistic Principal Component Analysis (SLPCA) based on both the simulated genotype data by HAPGEN2 and the genotype data of Crohn's Disease (CD) from Wellcome Trust Case Control Consortium (WTCCC). Our preliminary results have demonstrated the superiority of SCPCA over both SPCA and SLPCA due to its modeling explicitly designed for categorical SNP data as well as its flexibility on the risk effect model assumption.The online version of this article (doi:10.1186/1471-2164-15-S1-S10) contains supplementary material, which is available to authorized users. Genome-wide association studies (GWAS) aim to detect the association of genetic variants across the whole genome with traits of interest such as disease phenotypes. They have been successful in identification of susceptibility loci through association analysis of individual single nucleotide polymorphism (SNP) markers with common diseases . Limitedp-values of all SNPs or genes into a summary statistic determining the gene- or pathway-wise significance [Several popular methods have been proposed to generate a combined test statistic for multiple SNPs/genes for gene- or pathway-based association tests , 14–16. ificance . Howeverificance . Take paificance . AlthougThe second category of aggregated association analysis methods focus on deriving combined signals to aggregate information from multiple SNPs or genes whose joint effect can then be tested by analyzing the relationship between their corresponding combined signal and the trait of interest. The combined signal is mostly generated based on non-linear or linear transformation of individual SNP genotypes to extract the maximum relationship among them , 18, 19.Although both PCA based methods and kernel methods could explore the interaction among SNPs for high power in association studies, they share a potential bias since they always have the inherent assumptions that the risk of a SNP is proportional to the number of minor alleles. This bias arises from their data modeling based on the common SNP genotype representation, in which a SNP genotype is represented by numerical values in the domain {0, 1, 2} representing the number of minor alleles for either homozygous or heterozygous allele pairs. However, the induced risk by genotypic mutants may not be directly proportional to the number of minor alleles in a SNP. Instead of the data representation in {0, 1, 2}, it may be more appropriate to represent the SNP data with three different genotypes {00, 10/01, 11}, representing whether we see minor alleles at the corresponding allele pairs. It may not be appropriate to inherently introduce numerical information related to the genetic variation by taking numerical values of this genotype representation as typically done in the existing methods. Therefore, it is more reasonable to analyze SNP data on a premise that SNP data is categorical data except that we have high confidence on the underlying risk effect model, such as commonly adopted additive models. Another challenge with SNP data is the transformation from the quantitative intensity generated during genotyping, into the biologically known underlying number of SNP alleles at a locus. This transformation is performed using calling algorithms, which are specific to genotyping technologies. All subsequent analysis of data, such as GWAS, is dependent on the accuracy and ability of the calling algorithm, several of which were reviewed and improved on by Shah et al . The WTCWe have previously developed logistic PCA (LPCA) methods , 24 for In this paper, we develop a more general PCA denoted as categorical PCA (CPCA) that does not make any specific model assumptions of the effect of genetic mutants on the given trait. We first derive an optimization algorithm for CPCA suitable for categorical data analysis. Similar as conventional PCA, CPCA finds the optimal linear combinations that best explain the observed data but may not derive the principal components that are the most associated with a trait of interest. In order to derive the best principal components capturing the maximum combined effect from multiple SNPs with respect to a given trait of interest, we then apply it in a supervised framework. The best principal components are achieved with the highest correlation with a given trait and are further used in a logistic regression model for association analysis. The supervised framework is similar to the supervised PCA (SPCA) method first proposed for pathway-based gene expression analysis and GWAS based on traditional PCA , 25. By Interplaying among disruptions to multiple SNPs or genes has been conjectured to be systems impairments that cause complex diseases, such as cancer and diabetes. In this paper, we develop CPCA to extract optimal combined signals from multiple SNPs without any specific genotype-phenotype model assumptions, which allows more appropriate association analysis of categorical SNP data.n data points and consider that l-dimensional (l < d) linear manifold spanned by a basis μ'. PCA minimizes the following reconstruction error:PCA has been implemented in gene expression analysis and GWAS to alleviate the problems in analyzing small sample and high dimensional high-throughput profiling data which is often highly correlated, for example, due to LD. Specifically, PCA finds the orthogonal linear projection that minimizes the mean squared distances from the data points to their low-dimensional projections . Supposeμ'. The mean vector, bases, and the corresponding projections can be estimated by maximizing the data likelihood which is an optimization problem equivalent to minimizing (1). Based on the derivation of PCA, it is obvious that PCA is inherently only suitable for continuous variables by making the normal distribution assumption. Therefore, it is not appropriate to directly apply PCA on SNP data which is categorical and does not follow a normal distribution.subject to the constraint that It is desirable to develop variants of PCA based on respective modelings for different types of data such as integer, categorical, binary, and nonnegative data. PCA has been extended to the exponential family in previous work –29 by asith data point and θi∈ Rd is the "natural parameter" of the corresponding distribution. G(θi) is a function of the form p0 is a function depending only on G functions specified in [θi where it is assumed to be a linear combination of bases θi, the conditional log-likelihood function of the n data points with respect to their principal components can be written as:Here, ified in , which rp0 can be considered as a constant term and ignored here. The principal components resulted from a generalized PCA can then be estimated by maximizing (3). In a special case of the data following a normal distribution, it turns to be the tranditional PCA derived by maximizing this log-likelihood with G(θi) having a form of μ. As mentioned earlier, the SNP data in GWAS only has three different genotypes {00, 10/01, 11}. We focus on the derivation of exponential family PCA for categorical data denoted as CPCA in the equivalent categorical domain {0, 1, 2} instead of taking numerical values. For categorical SNP data which follows a multinomial distribution, each observation k ∈ {0, 1, 2} denotes the corresponding outcome equals to k or not. Each observation vector k. Each G function for θij is the j-th element of θi and c equals to 3 here denoting the number of categories. By substituting this G function into (3) and replacing μk, the log-likelihood function to be maximized for CPCA is rewritten as:where i-th row of j-th column of j-th element of μkrespectively.Where μ. Given the objective function (5) with respect to The principal component scores ℓ with respect to where where In each iteration, we also alternatively update and we have:μ can be estimated by the following Algorithm 1. As any non-convex optimization problem, our algorithm is not guaranteed to converge to a global maximum. To overcome the problem of being trapped by local optima, we randomly start the algorithm with different initialization values several times and find the best solution with the maximum likelihood value. The time complexity for this whole procedure is O(ks3) where s = min and k is number of iterations it takes to converge. Specifically, the calculation of the first derivatives and Hessian matrices takes O(dnl) and O(dnl2) respectively. The update of μ takes O(ql3) where q = max. The whole time complexity is mainly determined by QR decomposition procedure which takes O(s3) in each iteration. Our CPCA is in the same magnitude of time complexity as LPCA. Although PCA has a lower time complexity O(nd2 + d3) if n > d, one should be aware that our algorithms are designed for more general risk effect models and may achieve better performance with reasonable sacrifice on running time.The optimal solution of the corresponding parameters Algorithm 1 Categorical PCA (CPCA)Initialize with Compute QR decomposition Q for orthonormality constraints.Update Update μk by Update Repeat steps 2 through 5 until convergence.R columns of R principal component scores for combined signals from multiple SNPs and j-th element y, the statistical significance for the corresponding SNPs can be estimated by analyzing the association of these derived combined signals with the trait. Specifically, we learn a logistic regression model with the R principal components as predictors and y as the outcome:With the principal component scores jth subject exhibiting y given R combined signals and the coefficient βr reflects the joint effect size of a combined signal y. The statistical significance of multiple SNPs associated with y is estimated based on a test statistic where S1, ..., Sv} based on the model (18). We set v as 20 here. A subset is selected as the optimal subset if its derived principal component scores has the highest discriminating power, which corresponds to the maximum absolute value of the test statistic t from (18). For a given SNP set S, its test statistic M is set as the t statistic of its optimal subset, represented as:The principal components in CPCA are derived only based on the categorical data distribution with an aim to extract maximum information from the original data, which is not guaranteed to have the most significant association with the trait. Any individual SNP with no correlation with the trait may impair the joint effect estimate in the SNP set to which it belongs since CPCA, as traditional PCA, only focuses on approximating the underlying data distribution. Ideally, we desire a model for aggregated association analysis in which the principal components are derived only based on an optimal SNP subset significantly associated with the trait. However, prior to association analysis, we do not know which SNPs have risk effect on the outcome. In order to solve this problem, a heuristic search procedure is employed to search for an optimal subset of the most significantly associated SNPs with the trait by testing all candidate SNP subsets {S is estimated based on a permutation test of M as the distribution of M can not be approximated well by any known distribution due to the selection of SNPs.The statistical significance of a SNP set S:Generate candidate SNP subsets for a SNP set SS, its statistical significance reflected by the corresponding p-value can be computed by fitting a logistic regression model. Given all SNPs in S, we generate 20 incremental candidate subsets by setting 20 thresholds at each increment of 5 percentiles of p-values for those SNPs. Hence, 20 subsets of SNPs {S1, ..., S20} are formed by sequentially grouping SNPs with p-values less than each corresponding threshold.For each individual SNP in CPCA on candidate SNP subsetsCPCA can be implemented to compute the first PC scores for 20 candidate subsets respectively.Calculate M statistic for a SNP set SSv(1 ≤ v ≤ 20), we fit the logistic regression model (18) using the corresponding first PC scores and estimate t-statistic M = {tv: |tv| = maxv ≤ 201 ≤ |tv|}.For each candidate subset Estimate the null distribution of M statisticM statistic can be estimated by repeating steps (1) to (3) and pooled together as a random sample from the null distribution of M.We perform a permutation test by generating random trait status for each sample from a Bernoulli distribution with the success probability set to the disease prevalence. Based on randomly generated outcomes, the empirical null distribution of Calculate p-value for a SNP set SIn summary, based on the categorical data assumption of SNP data, our supervised CPCA takes the following steps to perform aggregated association analysis of a trait for a SNP set M statistic and the M value based on true trait, an empirical p-value for S can be calculated to estimate its significance.Given a null distribution of By deriving principal components based on the more general categorical model of the SNP genotype data, CPCA can eliminate the potential bias inherently introduced in PCA and LPCA, In addition, our CPCA is embedded in a supervised framework integrating the trait information for searching and aggregating those relevant SNPs so that we refine the principal components to best discriminate a given trait. With these two methodological contributions, we expect our supervised CPCA can aggregate information accurately from multiple SNPs and achieve higher power in subsequent association analysis by the supervised selection procedure.We evaluate our supervised CPCA method by a simulation study for gene-based association analysis as well as pathway-based GWAS for Crohn's disease using WTCCC case-control genome-wide data.To simulate SNP genotype data with real allele frequencies and linkage disequilibrium (LD) structure patterns, we use the HAPGEN2  simulatiπi = P(Yi = 1|Gi) that subject i has disease given SNP genotype Gi ∈ {0, 1, 2}, for which πi could take three values: f0, f1(= f0 × rr1), or f2(= f0 × rr2) corresponding to the genotype with different number of minor alleles . In this general disease model, f0, f1 and f2 are the corresponding penetrance of the disease and rr1, rr2 are the relative risk for heterozygous (Gi = 1) or homozygous ((Gi = 2)) pairs, respectivly. Under a null hypothesis SNP Gi has no effect on disease, rr1 = rr2 = 1. To test the power of our supervised CPCA method for detecting causal genes, we studied three different settings for risk effect sizes for disease SNPs in those causal genes. In order to model more general risk effect from different SNPs, we set the homozygote risk for a disease SNP slightly bigger than its corresponding heterozygote risk to avoid any proportional relationship assumptions between its genotype and risk effect size. For example, if we assume a commonly adopted additive model, the relative homozygote risk for a disease SNP is inherently assumed to be equal to the square of its relative heterozygote risk, which may not capture the actual genotype-phenotype relationships in real data. Therefore, we set the relative heterozygote risk and homozygote risk for all disease SNPs at three different levels at  = , , and . In our simulation study, 500 case and control samples are generated respectively in 100 replicates for each causal gene under different risk levels. The same number of cases and controls are also randomly generated in 100 replicates for 45 null genes. In summary, we simulate 500 (5 × 100) causal genes and 4500 (45 × 100) null genes for each scenario in total.HAPGEN2 models the probability The performance of our supervised CPCA (SCPCA) method on this set of simulated data is evaluated by comparing with the results obtained by SPCA and supervised LPCA (SLPCA) based on two criteria: statistical power and receiver operating characteristic (ROC) curves. The statistical power is computed as the proportion of detected causal genes that are significantly associated with the case-control outcome, for which we have the ground truth as we simulate the outcome based on selected "causal" SNPs. Table We further apply our SCPCA method for a pathway-based association analysis of Crohn's Disease (CD) based on the GWAS case-control data from Wellcome Trust Case Control Consortium (WTCCC) . In thishttp://www.broadinstitute.org/gsea/msigdb) and collect two categories of pathways as the prior biology knowledge: C2-CP and C5-BP, corresponding to annotated canonical pathways (CP) from online pathway databases such as KEGG, BioCarta and Reactome pathway databases and GO biological processes (BP), respectively. We further filter out those pathways with more than 250 genes to increase the specificity by avoiding overly broad pathways, which has been similarly done in literature [Homo sapiens Variation (dbSNP 130) and Homo sapiens genes (GRCh37.p7) datasets in the Ensembl database (Ensembl 67) using BiomaRt (http://www.biomart.org/). SNPs are first assigned to their neighboring genes and then mapped to their corresponding pathways according to the previously described pathway information. With the WTCCC CD data and mapped SNPs in all pathways, we implement SCPCA to each pathway and calculate nominal p-values from permutation tests. To correct for the multiple-testing issue, we estimate the adjusted p-value for each pathway based on the Benjamini-Hochberg method. Significant pathways are identified at false discovery rate level 0.05.To analyze the joint effect from multiple SNPs in functional regions that may be associated with Crohn's disease, we first map all the SNPs in the CD dataset into their corresponding pathways and thus implement SCPCA on each pathway to identify those pathways that are statistically significantly associated with the disease outcome. Specifically, we first download the pathway information from Molecular Signature Database Additional file 1: