Recent studies have demonstrated the power of deep re-sequencing of the whole genome or exome in understanding cancer genomes. However, targeted capture of selected genomic whole gene-body regions, rather than the whole exome, have several advantages: 1) the genes can be selected based on biology or a hypothesis; 2) mutations in promoter and intronic regions, which have important regulatory roles, can be investigated; and 3) less expensive than whole genome or whole exome sequencing. Therefore, we designed custom high-density oligonucleotide microarrays (NimbleGen Inc.) to capture approximately 1.7 Mb target regions comprising the genomic regions of 28 genes related to colorectal cancer including genes belonging to the WNT signaling pathway, as well as important transcription factors or colon-specific genes that are over expressed in colorectal cancer (CRC). The 1.7 Mb targeted regions were sequenced with a coverage ranged from 32× to 45× for the 28 genes. We identified a total of 2342 sequence variations in the CRC and corresponding adjacent normal tissues. Among them, 738 were novel sequence variations based on comparisons with the SNP database (dbSNP135). We validated 56 of 66 SNPs in a separate cohort of 30 CRC tissues using Sequenom MassARRAY iPLEX Platform, suggesting a validation rate of at least 85% (56/66). We found 15 missense mutations among the exonic variations, 21 synonymous SNPs that were predicted to change the exonic splicing motifs, 31 UTR SNPs that were predicted to occur at the transcription factor binding sites, 20 intronic SNPs located near the splicing sites, 43 SNPs in conserved transcription factor binding sites and 32 in CpG islands. Finally, we determined that rs3106189, localized to the 5′ UTR of antigen presenting tapasin binding protein (TAPBP), and rs1052918, localized to the 3′ UTR of transcription factor 3 (TCF3), were associated with overall survival of CRC patients. Therefore, we designed custom high-density oligonucleotide microarrays (NimbleGen Inc.) to capture a total of approximately 1.7 Mb target regions comprising the genomic regions of 28 genes related to colorectal cancer including the exonic, intronic, 10 kb upstream and 5 kb downstream sequences followed by analysis using the Illumina Genome Analyzer. The selected genes include those belonging to the WNT signaling pathway, as well as important transcription factors or colon-specific genes that are over expressed in CRC.http://www.genome.jp/kegg/pathway/hsa/hsa04310.html) to begin our investigation. In addition, we selected 22 important transcription factors (transcription regulator activity GO:0030528) and four colon-specific or enriched genes As the WNT signaling pathway is a critical pathway implicated in CRC To reduce expenses, we first sequenced a pool of 30 CRC tissues (the CRC pool) and a pool of 30 adjacent normal tissues (the CRN pool) and then validated the SNPs identified using PCR or Sequenom’s technologies. We created a custom oligo array using NimbleGen technology to capture the target sequences. The total length of the target genomic regions designed was 1.7 Mbp. The captured DNAs were subjected to sequencing using the Illumina Genome Analyzer. After removing PCR duplicates from the raw sequences, the average coverage ranged from 32x to 45x, and the coverage by sequence length for the targeted regions of each gene ranged from 83.5 to 100%. The coverage for the different regions of the target genes differed, which might be due to the property of NimbleGen sequence capture technology, sequence complexity or other uncharacterized factors. The raw sequencing data was deposited in the NCBI sequence read archive (SRA) under accession number SRX277359.We tabulated the coverages of all 28 genes by comparing to regions covered by the designed probes or to the total targeted regions including promoters and 3′ distal regions  to calcuwww.r-project.org). Here, the coverage was counted after removing sequence duplicates. The correlation coefficients were −0.51 and −0.38 for CRC and CRN samples, respectively, suggesting little correlation between SNP detection and read coverage. We further computed whether the SNP percentage accounted for the total SNPs with different coverages  for genotyping single nucleotide polymorphisms Due to the low efficiency and sensitivity of SNP validation by PCR, we decided to use the Sequenom MassARRAY iPLEX Platform for the validation studies. We chose 66 SNPs for validation in a separate cohort of 30 CRC tissues because the DNA used for sequencing was depleted. In the end, we were able to confirm the existence of 56 SNPS in the 30 CRC tissues , suggestWe found 15 SNPs that would change protein sequences among the exonic variations in the CRC and normal colon tissues, including 14 missense mutations and 1 nonsense mutation . These mFour of the mutations failed to be validated by Sequenom’s MassARRAY iPLEX  and wereThere were 29 synonymous SNPs detected in the coding region in the CRC and CRN samples and 73 SNPs in the 5′ or 3′ UTR regions. FastSNP was used to predict the regulatory roles of these SNPs including exonic splicing enhancer (ESE), exonic splicing silencer (ESS), motif changes for synonymous SNPs , and TF To understand the functional consequences of the intronic SNPs, the online tool SNPnexus was used to annotate the SNPs. The distances to the splicing sites were computed by SNPnexus. There were 20 intronic SNPs located near the splicing sites with a distance less than 30 bp, and only one was novel. The mutations at these regions may affect splicing and transcription. C6orf1, ETV4, KAT5 and VAV1 each had two variations located near splicing sites, and TNKS2 had 3 variations located near splicing sites . The SNPThe public ChIP-seq datasets, especially the ENCODE project, provide vast TF binding or DNAase hypersensitivity sites in various cell lines. Here, we used RegulomeDB to annotate the SNPs with regulatory regions. Each SNP was given a score that represented different regulatory regions by RegulomeDB , Table 6We chose nine SNPs  that werwww.stata.com) statistical analysis program. We found that two SNPs (rs3106189 and rs1052918) were associated with overall survival of CRC patients  variants and then tested their disease association in over 30,000 participants In this manuscript, we describe our analysis pipeline that consists of (1) initially sequencing pooled DNA samples followed by validation and further analysis in larger cohorts of samples for cost reduction and (2) a hypothesis-driven targeted capturing and analysis of SNPs and their associations with the cancer phenotypes. Pooling genomic DNAs for sequencing has the advantage of reducing sample preparation and sequencing costs. For example, capturing 30 individual samples would require using 30 capture arrays to perform hybridization and sample recoveries, which are tedious and may potentially introduce sample-to-sample variations during the sample preparation stage. Sequencing 30 individual samples would also be substantially more costly than sequencing one pool. Although it is possible to use barcoding and multiplexing reactions and sequencing to achieve similar sequence coverage at a similar cost to pooling samples, the sample preparation complexity would be substantially higher. In a recent GWAS analysis of type 1 diabetes (T1D) published in Science, Nejentsev et al. showed that a single nucleotide polymorphism in the MDM2 promoter could attenuate the p53 tumor suppressor pathway and accelerate tumor formation in humans et al. showed that SNPs in the estrogen receptor beta promoter are associated with survival of postmenopausal women with CRC et al. found that a polymorphism in the 3′UTR region of insulin-like growth factor I (IGF1) gene predicts survival of non-small cell lung cancer in a Chinese population et al. found that a SNP  in the XRCC1 gene 5′ UTR contributes to diminished promoter activity and increased risk of non-small-cell lung cancer Another distinct feature of our analysis pipeline is that we sequenced the genomic regions that included exonic and intronic regions, i.e., the 10-kb promoter and the 5-kb downstream genomic regions of the selected genes. This method was in contrast with most studies that only analyzed the exonic sequences (exome capture) We have chosen genes related to the WNT pathway, as the Cancer Genome Atlas Network found mutations in 16 different genes in the WNT pathways including APC, CTNNB1, FAM123B and TCF7L2 We identified a total of 2342 sequence variations in CRC and corresponding adjacent normal tissues. Among them, 738 were novel sequence variations based on comparison with the current SNP database  that alters a potential binding site for an exonic splicing enhancer could affect the risk of non-Hodgkin lymphoma We also identified SNPs that might affect exon splicing because they localize to the ESE (exonic splicing enhancer) and ESS (exonic splicing silencer), which are critical in exon splicing. For example, we identified SNPs in the far upstream element (FUSE) binding protein 1 (FUBP1), peroxisome proliferator-activated receptor alpha (PPARA) and transcription factor Dp-1 (TFDP1) that might affect exon splicing for these genes, and these SNPs were found only in the CRC tissues . Zhang eWe determined that rs3106189, localized at the 5′ UTR of TAP binding protein , and rs1052918, localized at the 3′ UTR of the TCF3, were associated with overall survival of CRC patients  with hazTAPBP encodes a transmembrane glycoprotein that mediates the interaction between newly assembled major histocompatibility complex (MHC) class I molecules and the transporter associated with antigen processing (TAP) Transcription factor 3  is a member of the TCF/LEF transcription factor family that is central in regulating epidermal and embryonic stem cell identity and is involved in the WNT signaling pathway Informed written consent was obtained from each patient, and the ethics committee of the Second Affiliated Hospital, College of Medicine and Zhejiang University approved all aspects of the study.Tissue samples were obtained from four control subjects  and 30 colorectal cancer patients for the first cohort and 117 CRC patients for the validation study. All subjects were Chinese, and the samples were collected at the Second Affiliated Hospital, Zhejiang University. The DNA samples were isolated using the DNeasy Blood & Tissue Kit  according to the manufacturer’s protocol.Genomic DNAs from 30 colon cancer patients were mixed in equal ratios and the DNA pools were captured on a custom NimbleGen 385K array following the manufacturer’s protocol  with modifications at the W. M. Keck Facility at Yale University. A custom tiling 385K sequence capture array targeting genomic sequence for all the 28 genes (including 10-kb upstream and 5-kb downstream regions) was designed and manufactured by Roche NimbleGen. Briefly, the genomic DNA sample was fragmented and hybridized to the custom NimbleGen Sequence Capture array. Unbound fragments were removed, and the target-enriched DNA was eluted and amplified.The captured DNA was sheared through sonication, and adapters were ligated to the resulting fragments. The adapter-ligated templates were fractionated by agarose gel electrophoresis, and the fragments of the desired size were excised. The extracted DNA was amplifed using ligation-mediated PCR, purifed, and hybridized to the capture array at 42.0°C using the manufacturer’s buffer. The array was washed twice at 47.5°C and three times at room temperature using the manufacturer’s buffers. Bound genomic DNA was eluted using 125 mM NaOH for 10 min at room temperature, purifed, and amplifed through ligation-mediated PCR. The resulting fragments were purifed and subjected to DNA sequencing on the Illumina platform. Captured and non-captured amplifed samples were subjected to quantitative PCR to measure the relative fold enrichment of the targeted sequence.Amplified DNA samples were sonicated for 10 minutes  to generate DNA fragments with an average size of 500 base pairs (bp). The fragments were further purified and concentrated with Qiaquick PCR purification spin columns . Genomic fragments were end-repaired with a mixture of T4 DNA polymerase, Klenow DNA polymerase and T4 PNK , and a 3′ overhang A was added using Klenow exo-enzyme . The resultant fragments were ligated with the Illumina classical adapters by DNA T4 ligase  according to Illumina’s protocol. Adapter-linked DNA fragments were separated by agarose gel electrophoresis, and the band between 150 and 200 bp was excised from the gel. The DNA fragments were extracted from the agarose slices using the Qiaquick Gel Extraction Kit (Qiagen Inc.). Extracted DNA was enriched by an 18-cycle amplification using Illumina’s universal adapters. The DNA fragments were purified and quantified and then sequenced for 40 cycles using Illumina’s protocol.Short-sequence reads were extracted from the image files with the Illumina GAPipeline 1.4.0 and mapped to the genome sequence using BWA (version 0.5.3) with default parameters. BWA is an efficient program for aligning relatively short nucleotide sequences against a long reference sequence allowing mismatches, thus detecting SNPs SNPs were identified by SAMtools (version 0.1.6), which migrated and improved various downstream data processing implemented in Maq/Maqview such as indexing, pileup, viewer and consensus caller. SAMtools generated the consensus sequence with the statistical model implemented in MAQ Regions of no or low read coverage can be caused by either ineffective sequence capture or inaccuracy in short reads mapping. GC content can affect Solexa base calling and PCR amplification. In addition, low complexity regions can affect short reads mapping. GC content and read coverage were computed in an 81-bp window.The final filtered SNPs were annotated using ANNOVAR SNP Genotyping Using the Sequenom MassARRAY iPLEX Platform was conducted by the Beijing Genomic Institute  according to the manufacturer’s protocol, as previously described Overall survival analysis was conducted using Stata 12. The overall survival time was defined as the month from initial surgery to death from any cause. The relationship between the survival time and an allele was assessed using the Kaplan-Meier method and plotted using Stata 12. All patients alive or lost were censored for analysis. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated from a multivariate Cox proportional hazards model, adjusting for age, gender, tumor position, differentiation, tumor stage, positive lymph node status, and infiltration depth, adopting three genetic models . The Hardy-Weinberg equilibrium (HWE) for each SNP was determined using the hwsnp function in Stata 12.et al.http://cedar.genetics.soton.ac.uk/public_html/primer1.html) 2+, 250 µM of each dNTP,0.2 µM of each primer and 5 U/µl TaKaRa Ex Taq polymerase. The results were analyzed using 2% agarose gel electrophoresis.To confirm SNPs identified by the next-generation sequencing techniques, we performed allele-specific PCR (AS-PCR) Figure S1The landscape of the two SNPs viewed by the UCSC genome browser. (A) rs3106189 at the 5′ UTR of TAPBP. (B) rs1052918 at the 3′ UTR of TCF3. The red boxes represent the target SNP sites. Each row represents a regulatory element in UCSC genome browser such as the surrounding SNPs, binding region from ChIP-seq by ENCODE project and conservation scores.(TIF)Click here for additional data file.Table S1List of SNPs identified in CRC patients.(XLSX)Click here for additional data file.Table S2List of SNPs locating in conserved transcription factor binding sites.(XLSX)Click here for additional data file.Table S3List of SNPs locating in CpG islands.(XLSX)Click here for additional data file.Table S4List of primers used for SNP validation.(XLSX)Click here for additional data file.Table S5SNP validation of 8 randomly selected SNPs.(XLSX)Click here for additional data file.Table S6SNP Genotyping using the Sequenom MassARRAY iPLEX platform in 30 CRC patients.(XLSX)Click here for additional data file.Table S7SNP genotyping using the Sequenom MassARRAY iPLEX platform in 117 CRC patients.(XLSX)Click here for additional data file.


Malus×domestica Borkh.) was published more than a year ago, which helped develop an 8K SNP chip to assist in implementing genomic selection (GS). In apple breeding programmes, GS can be used to obtain genomic breeding values (GEBV) for choosing next-generation parents or selections for further testing as potential commercial cultivars at a very early stage. Thus GS has the potential to accelerate breeding efficiency significantly because of decreased generation interval or increased selection intensity. We evaluated the accuracy of GS in a population of 1120 seedlings generated from a factorial mating design of four females and two male parents. All seedlings were genotyped using an Illumina Infinium chip comprising 8,000 single nucleotide polymorphisms (SNPs), and were phenotyped for various fruit quality traits. Random-regression best liner unbiased prediction (RR-BLUP) and the Bayesian LASSO method were used to obtain GEBV, and compared using a cross-validation approach for their accuracy to predict unobserved BLUP-BV. Accuracies were very similar for both methods, varying from 0.70 to 0.90 for various fruit quality traits. The selection response per unit time using GS compared with the traditional BLUP-based selection were very high (>100%) especially for low-heritability traits. Genome-wide average estimated linkage disequilibrium (LD) between adjacent SNPs was 0.32, with a relatively slow decay of LD in the long range , contributing to the higher accuracy of GS. Distribution of estimated SNP effects revealed involvement of large effect genes with likely pleiotropic effects. These results demonstrated that genomic selection is a credible alternative to conventional selection for fruit quality traits.The genome sequence of apple ( Malus×domestica Borkh.) genome sequence using DNA from a popular apple variety ‘Golden Delicious’ During the last 10 years, genome sequences of about 20 plant species including some from the Rosaceae family were made publicly available Standard apple cultivar breeding follows three stages. The first stage (stage-1) is to identify parents from the pool of available candidates, cross them and select the best offspring from large families. In stage-2, multiple copies of the selections are propagated onto clonal rootstock for trial across different environments, while in stage-3, larger-scale testing of the best selections is conducted, often on commercial orchards. Phenotypic data from stage-1 can be analysed using individual-tree mixed models to obtain best liner unbiased prediction (BLUP) of breeding values (BVs) of seedlings. Various apple breeding programmes now routinely use BLUP-BVs for making selections e.g. . It geneMethods for deriving GEBV differ in terms of the prior assumptions about the distribution of the effects of the single nucleotide polymorphisms (SNPs). Ridge-regression BLUP (RR-BLUP), where the effect of each marker is assumed to come from a normal distribution with equal variance across all markers, is simple to understand and implement DNA markers located close to major causal loci controlling disease resistance have been used for selection by apple breeders in the last 10 years GenCall score lower than 0.15, 10% were monomorphic, and a further 30% were discarded due to thresholds used for ClusterSep and 50%GC score. After further screening for frequency of missing calls, allele frequencies, and segregation discrepancies, a high quality set of 2,500 SNPs with an average call rate of 98% was retained for developing GS prediction models. The number of SNPs dropped varied from 58% to 70% for various linkage groups (LG)  . The retups (LG) . The aveups (LG) .r2) between adjacent SNPs pairs in the training population was 0.32. Three percent of the adjacent marker pairs had r2 = 0 and 17% had r2 values ranging between 0.90 and 1.00 , and 1000 kb was 0.33, 0.25, and 0.19, respectively ) to 0.89 (for soluble solids (SSC)), whereas for all other traits, the prediction accuracies were similar and ranged between 0.81 and 0.83 . The stah2 varied from 0.16 (for TA) to 0.60 (for russet), and contributed to the observed accuracies of conventional- and genomic selection methods for various traits. The accuracy of BLUP-based selection varied from 0.73 to 0.84 for various traits, while accuracies of GEBV-based selection were >0.90. Some of the estimated correlations between GEBV and TBV were outside the parameter space (>1.0), so these were constrained to a maximum theoretical value of 1.0. The selection response per year was higher for GS for all traits considered, with the efficiency of GS being 100% to 141% higher than that of BLUP-based conventional selection for various traits (Accuracy of one of the GS models (RR-BLUP) was also compared with the conventional BLUP-based selection . Estimats traits .Ranking of SNPs in terms of the size of their individual effect on a trait was very similar for RR-BLUP and Bayesian LASSO, except that the latter induced stronger shrinkage of estimates for SNPs with relatively small effect and less shrinkage of estimates for SNPs with sizable effect . Estimatpolygalacturonase (PG) gene (MDP0000232611), 20.833 kb from the top of LG10. In addition, there were few SNPs with moderate effect on FF located on LGs 3, 11 and 16 (MdMYB10 gene (MDP0000259616), 32.840 kb from the bottom of LG9. A chromosome segment on LG16 also appeared to have some influence on the expression of WCI (Leucoanthocyanidin Reductase (LAR1) gene (MDP0000376284) that is located between 1.496 kb and 1.669 kb on the top of LG16. A summary of the largest SNP effects for each trait is presented in The SNP with the largest (more than 10 times the average) effect on FF was located on LG10, and this SNP is a T/G variant located within the first exon of the 1 and 16 . The fiv1 and 16 . The SNP1 and 16 . A SNP w1 and 16 . This SNn of WCI . A clustn of WCI . The SNPn of WCI . The samThe BLUP-BVs obtained from equation 1 were used as such for developing GS models, but some previous studies . However, the LD decay can vary significantly between different types of genetic materials, so efforts are now being made to better understand LD patterns in apple germplasm, breeding populations and commercial cultivars. As long as the mean r2 between adjacent SNPs was >0.2, GEBV could be predicted accurately at least in the one or two successive generations before needing recalibrating of SNP effects r2 of <0.2 between such markers  origin has not been explicitly modeled to avoid spurious signals When the training data for GS consist of individuals from reproductively isolated ancestral populations, estimates of marker effects may be biased due to population stratification and admixture We aim to apply the model derived from the training population to a selection population consisting of 2,000 young seedlings generated from 10 full-sib families. The pollen parents of these 10 families were selected from our training population and the seed parents (some of which have genetic relatedness with some of the six parents of our training population) were identified from previous progeny trials, so providing a strong genetic link between training and selection candidates, which will also provide higher GEBV accuracy When a training population is established in one environment but the aim is to select individuals for multiple environments, the realized accuracy of GS could be lower at the sites that are not represented in the training set h2 = 0.60) and TA (h2 = 0.16) respectively; supporting results from earlier studies  ethylene-related gene, which depolymerizes cell wall pectin and the involvement of this gene in fruit softening process was recently confirmed in a cross between ‘Fuji’ and ‘Mondial Gala’ Estimated SNP effects capture at least partly the underlying QTL effects, especially when LD estimates between adjacent markers are reasonable. Consequently, the distribution of estimated SNP effects should resemble the distribution of the underlying QTL effects. The SNP array used in this study was designed to encompass SNPs in the coding region of predicted gene models and some candidate genes such as, MdMYB10 gene on anthocyanin biosynthesis in Type 1 red flesh apple was demonstrated using transient approach in tobacco, stable transformation in apple, and by mRNA transcript profiling in red flesh apple fruit MdMYB10 marker is unlinked to Type 2 red flesh phenotype MdMYB10, which is physically close to the R6 motif h2 (0.26) of WCI in this study.Red colour in apple flesh results from high concentration of anthocyanins. Seedlings of two red flesh phenotypes, putatively named Type 1 and Type 2 MdLAR1 candidate gene. LAR1 is a key enzyme in the flavonoid biosynthetic pathway, reducing leucoanthocyanidin into the flavanol compound catechin, a monomer of condensed tannins . This reaction branches off from the cyanidin biosynthetic pathway. It is likely that condensed tannins (CTs) act as a co-pigment of cyanidin to create more intense red coloration in the fruit and hence the effect on WCI. Furthermore, CTs are known for their role in imparting astringency to fresh fruits, juices and wine. MdLAR1 was linked to QTLs controlling fruit skin and cortex concentrations of CTs in a ‘Royal Gala’×‘Braeburn’ population A cluster of SNPs at the top of LG16 is associated with AST and WCI. This cluster of SNPs resides in the h2 (0.16) compared to some other studies (e.g. Ma), which was mapped to LG16 Mal-DDNA), isolated by Distribution of SNP effects for TA  suggesteies e.g. . To our ies e.g.  is locatDGAT1. Interestingly the highest accuracy of GS was achieved for SSC which is characterized largely by QTLs of small effects. Perhaps in the presence of few QTLs of large effects, it becomes essential to have SNPs in high LD with these large effects in order to obtain higher accuracy of GS. Even in the presence of large effect QTLs, there might be a large number of small loci for which no clear evidence is found for a direct association, but together these loci still may explain a substantial part of the BV and thus make different GS models less separable. Bayesian shrinkage regression methods primarily capture LD, while the RR-BLUP method captures genetic relationships and polygenic resemblance The RR-BLUP and Bayesian method provided similar accuracy of GEBV , and theIntrogression of monogenic disease resistances from an inferior donor into high-quality recipient apple cultivars is generally performed by backcrossing. Although GS can be used to achieve this goal, the introgression of favourable alleles from the donor parent cannot be guaranteed unless the SNP density is such that at least one SNP allele is in high LD with the functional mutation. Introgression of monogenic traits could be fast-forwarded using a two-step approach, i.e. gene-assisted selection (GAS) for monogenic traits followed by GS for oligogenic or polygenic traits. This strategy is currently being implemented in our apple breeding programme.Distribution of estimated SNP effects suggested genes of major effect especially for traits such as WCI, TA, and AST, while large effect QTLs appeared to be involved in the expression of FF and russet. Various factors including training population size, number of SNPs, and magnitude of LD led to similar accuracies of the RR-BLUP and Bayesian LASSO methods. Thus, either of these two models could be used in practical applications of GS in apple breeding programmes. Relative gain per unit time from GS compared with the traditional BLUP-based selection were very high (>100%) especially for low-heritability traits. Because of the high degree of genetic relatedness among the commonly used parents, the model developed in this study could be applicable to other apple cultivar breeding populations. Based on the high accuracy of GS in our study, we conclude that if the objective of any apple breeding programme is to accelerate the breeding cycle by making selections prior to extensive fruit-quality phenotyping, GS shows strong potential as a means of achieving this goal.A set of four white-fleshed female parents  and two red-fleshed pollen parents (NZSelectionT31 and NZSelectionT51) were crossed in a factorial (4×2) mating design, except that the cross between NZSelectionT179 and NZSelectionT31 was unsuccessful leaving seven full-sib families. The pollen parents NZSelectionT31 and NZSelectionT51 represent Type 1 and Type 2 red flesh phenotype, respectively Harvesting and fruit assessment began in the second season (February–May 2010) after orchard planting, and was repeated for a second consecutive year. Fruiting trees were harvested twice at 7- to 10-days intervals beginning when fruit were judged mature, based on a change in skin background colour from green to yellow, and when the starch pattern index was between 3 and 4. Samples of six fruit per harvest were stored for 70 days at 0.5°C, then a further 7 days at 20°C and evaluated. Six traits were evaluated on the fruit samples using instrumental, sensory, or visual assessment methods. Fruit flesh firmness (FF) was determined on opposite sides of each fruit after peel removal using a Fruit Texture Analyzer (GÜSS) fitted with an 11-mm diameter probe tip. Soluble solids concentration (SSC) for each fruit was measured with the juice from the probe using a digital refractometer (Atago PR-32). Average russet coverage (russet) and flesh astringency (AST) were scored for each sample on a scale from 0 ( = none) to 9 ( = highest) by two trained assessors. Fruit from each seedling were then cut in half across the equator and the proportion of the cortex area that was red (PRA) and the intensity of the red (RI) ( = 0 (none) to 9 (highest)) were scored. A weighted cortical intensity (WCI) was then calculated (PRA×RI) as an estimation of the amount of red pigment in the fruit. A cortical wedge (10 g) was then removed immediately from each half of each apple, combined for each seedling, juiced in a blender (Magimex Le Duo), and frozen. Titratable acidity (TA) was measured on the thawed juice using an automatic acid titrator (Metrohm 716 DMS) and the percentage of malic acid in fruit juice was recorded.y is the vector of observations, b is the vector of fixed effects , a is a vector of additive genetic effects of seedlings, p is a vector of permanent environmental effects and e is a vector of residual effects. The matrix X is the incidence matrix for the fixed effects and Z is the incidence matrix relating observations to seedlings. Each seedling has an additive genetic as well as a permanent environmental effect, so both effects have the same design matrix (Z). The associated variances with the random effects, a, p and e were h2) of each trait were obtained as the ratio of additive variance  were first averaged for each seedling, and then averaged over the two harvests in a given year. As repeated records for each seedling occurred over two years, there was an element of ‘permanent environmental effect’ associated with a seedling's performance. In other words, when a seedling has multi-year records, its breeding value and part of the environmental effects are repeated. We used the following individual-tree mixed linear model accounting for repeated records for each trait:www.illumina.com; GenCall score set at a minimum of 0.15, which is a lower bound for calling genotypes relative to its associated cluster. SNPs were subsequently discarded using a sequence of criteria in the following order: GenCall score at the 50% rank (50% GC)<0.40; cluster separation (ClusterSep)<0.25; more than 5% missing calls; segregation discrepancy. The BEAGLE 3.1 software The training population was genotyped using the International RosBREED SNP Consortium (IRSC) apple 8K SNP array v1  design matrix allocating records to the m SNP effects, with element ijX = 0, 1, or 2 if the genotype of seedling i at SNPj is AA, AB, or BB, respectively; g is a (m×1) vector of SNP effects. For RR-BLUP, g is assumed to be normally distributed, ig∼N, As the aim of our project was to predict BV, we developed models by fitting only the additive effects at each SNP. The two methods used for prediction of BV in this study were RR-BLUP and Bayesian LASSO  and the predicted  breeding values was used as a measure of the accuracy of the GEBV prediction. The observed BLUP-BVs were linearly regressed on the predicted GEBV, where the regression coefficient reflected the degree of bias of the GEBV prediction and a regression coefficient of one indicates no bias.r). Ideally one would also be interested in correlation between GEBV and true breeding values (TBV), i.e. r, which could directly be used to compare the relative efficiency of GS with that of conventional BLUP-based selection as:As described in the preceding paragraph, the accuracy of GS in empirical studies is calculated as correlation between GEBV and BLUP-BV  can be obtained as [r/r] as suggested by r) in outbred full-sib families was calculated following YCS) in New Zealand conditions is seven years, including three years for the assessment of phenotype. Since GS would obviate the need of phenotyping, the breeding cycle length for GS (YGS) was assumed four years.An estimate of


The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used.Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content.In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip.Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available. In genomic selection programs, the breeding value (GEBV) of an individual is assessed by combining both genomic and traditional pedigree-based predictions. High-density marker platforms (HDP) of different SNP (single nucleotide polymorphism) densities (50K and 777K) are currently used to genotype bulls under selection  and elitGenotyping costs are among the major constraints for large-scale implementation of genomic selection in many breeds. However, the commercial availability of low density SNP panels (LDP), such as the Illumina Bovine3K Genotyping BeadChip or the Illumina BovineLD BeadChip, which contains around 7K markers , has offin silico reconstruction of missing genotypes. Several techniques have been proposed to routinely impute SNP genotypes. The following three steps are common to all procedures: (1) a training population (TP) genotyped with an HDP is created; (2) a prediction population (PP) is generated by using an LDP; and (3) a suitable algorithm is used to impute missing SNPs in the PP.Genotype imputation refers to On the basis of the information considered to infer missing marker genotypes, imputation methods can be classified into three groups. The first relies on linkage and family information ,7, the sThe impact of imputed genotypes on GEBV accuracies has been investigated. Results are sometimes discordant or expressed in different ways. For example, Chen et al.  comparedSeveral imputation algorithms have been proposed and implemented in freely available software such as Beagle , DAGPHASA method that uses the Partial Least Squares Regression (PLSR) technique to impute SNP genotypes was proposed recently . It was The aim of the present work was to test the PLSR imputation method on real data. In particular, a scenario with a 50K genotyped TP and a PP genotyped using either the 3K or 7K panel was simulated. Moreover, the ability of the PLSR method to predict SNP genotypes for different bovine breeds and in a multi-breed approach was tested.Data consisted of SNP genotypes belonging to 2179 Italian Holstein bulls genotyped with the Illumina 50K Beadchip (single-breed dataset). Only markers located on the 29 autosomes were considered. Monomorphic SNPs and SNPs with more than 2.5% missing values were discarded. No editing for minor allele frequency (MAF) was applied. A total of 43 427 SNPs were retained and any missing genotypes for these SNPs were replaced by the most frequent genotype at that locus. Data on a total of 86 bulls were discarded, of which 48 were replicates or had inconsistent Mendelian inheritance information, and 38 had a low overall call rate (lower than 95%).To study the performance in a multi-breed sample, 749 Brown Swiss and 470 Simmental bulls were also available. For the multi-breed data set, data from the three breeds were edited together to obtain the same SNPs in all data sets. At the end of the editing procedure, 30 055 markers were retained.Genotypes were coded according to the number of copies of a given SNP allele they carried, i.e. 0 , 1 (heterozygous) or 2 . The phenotypes available for all animals were polygenic estimated breeding values for milk yield, protein and fat content. Animals were ranked according to their age: the oldest were designed as TP with all genotypes considered known, whereas the youngest represented the PP. For both the single and multi-breed approach, SNPs belonging to 3K and 7K LDP were identified in the PP animals and all other genotypes were masked, thus mimicking the two Illumina LDP.Yn × p)( from a predictor matrix Xn × m) which are linear combinations of the explanatory variables X, that best model Y. Dimauro et al. [Y). The maximum number of latent factors depends on the size of X, which has a lower number of columns than Y. For this reason, in each run, the number of extracted latent factors was fixed to be equal to the number of predictors (the number of columns of X). PLSR is a multivariate statistical technique particularly useful in genomic studies in which a great number of variables are involved. It can overcome the strong collinearity between SNP variables in X or Y and, at the same time, maximize correlations between Y and X variables [PLSR is a multivariate statistical covariance-based technique that is able to predict a response matrix matrices . In botho et al.  demonstrariables ,19. A moariables .Y) and SNPs used as predictors (X). In particular, X contained only SNPs belonging to the 3K or 7K LDP. For animals in the PP, genotypes in Y were masked and constituted the SNPs to be predicted.In the present work, each chromosome was processed independently and data were analyzed by using the PLS procedure of SAS® software . Datasets were organized in a multivariate manner, having SNPs as columns and animals as rows. The 50K SNPs were divided into SNPs that have to be imputed  and PP = 100 (the younger) and both 3K and 7K scenarios were investigated. The Beagle software was applied to the same data. No pedigree information was used for either PLSR or Beagle.X increased. The reason is that when many variables have to be predicted (the columns of the Y matrix), the number of extracted latent factors should be large. The maximum number of possible latent factors is, however, less or equal to the number of variables in X. So, for chromosomes with a relatively low number of markers in X, a lower PLSR predictive ability is expected. This hypothesis can be easily tested by comparing the imputation accuracies obtained in the 3K and 7K scenarios. Moreover, a PLSR run using an X matrix obtained by combining SNPs belonging to chromosomes 26, 27 and 28, was carried out to test for possible improvement in genotype imputation accuracy when X is artificially enlarged.On simulated data, Dimauro et al.  demonstrThe availability of a sufficiently large TP is a crucial factor for genotype imputation. Therefore, it is interesting to investigate if a multi-breed TP could enhance the accuracy of genotype predictions. Some authors ,22 reporThe ability of PLSR to impute SNP genotypes was quantified by considering the allele imputation error rate. This index represents the number of falsely imputed alleles divided by the total number of imputed alleles . In pracThe effect of SNP imputation on accuracy of DGV was also evaluated. DGV for milk yield, fat content and protein content were calculated using both the actual 50K markers (DGV) and the imputed genotypes (DGV_IMP). Briefly, effects of SNP genotypes on phenotypes in the TP population were estimated using a BLUP model :y is the vector of polygenic breeding values, 1 is a vector of ones, μ is the overall mean, Z is the matrix of SNP scores, g is the vector of SNP regression coefficients assumed identically and normally distributed with gi~ N where σgi2 = σa2/k , and e is the vector of random residuals. The overall mean (ĝ) of the marker effects estimated in the TP were used to calculate the DGV for PP as:where ŷ is the vector of estimated DGV and Z* is the matrix of SNP scores in PP. For each phenotype, both DGV and DGV_IMP were obtained and correlations between DGV and DGV_IMP were calculated (r).where Results obtained by analyzing Daetwyler’s data are reported in Table Table Imputation accuracies obtained by including the Brown Swiss and Simmental breeds, both for imputation within breed and in the multiple breed scenario, are reported in Table EBV,DGV) and correlations between EBV and DGV_IMP  were quite similar with actual and imputed data. This result is in agreement with the relatively high correlations between DGV and DGV_IMP , which were on average 0.96 across the three considered traits with the 7K LDP. However, rDGV,DGV_IMP was lower when using the 3K LDP, for which rDGV,DGV_IMP was on average 0.89.Accuracies of DGV predictions were moderate Table , in accoResults of PLSR applied to Daetwyler’s data Table  showed tPLSR imputation accuracies, from 3K and 7K LDP to the 50K panel, were higher than accuracies obtained with Beagle and ChromoPhase. These results indicate that, if no pedigree information is available, the PLSR method should be preferred over the other methods studied here when imputation is from 3K or 7K to 50K.PLSR was further used to impute SNP genotypes both in single and multi-breed scenarios based on Holstein, Simmental and Brown Swiss data sets. No MAF threshold was applied in the editing procedure. To investigate whether differences in imputation accuracies between PLSR and the Beagle algorithms could arise with edits based on MAF, the impact of several MAF thresholds  was evaluated. However, no differences in imputation accuracies were observed between the PLSR and Beagle results.n = 2179 Holstein bulls) involved in the study and the mean number of markers (m = 1497) on each chromosome, Rn/m, was 1.45. Dimauro et al. [m = 1000 markers on a chromosome and n = 5865 individuals. The resulting Rn/m was 5.9. In ordinary statistics and, even more, in multivariate statistics, the availability of a larger number of observations guarantees more accurate results. Thus, Dimauro et al. [Rn/m ratios suggests that PLSR also works properly with actual data. This is an important result because, if a particular technique gives good results when applied to simulated data, it is not obvious that similar performances are obtained with actual data.Mean R values obtained with PLSR in the single-breed scenario were 89,6% and 94,2% for the 3K and 7K LDP, respectively. It is worth mentioning that, in the present study, the ratio between the number of animals  was more accurate than imputation from 3K to 50K (R = 0.90). This is an expected result and it is comparable to that obtained by Mulder et al. , who fouo et al.  found thResults for imputation based on information from multiple breeds obtained in this study, basically confirm previous reports. Values of R using multi-breed information Table  were conThe impact of the SNP genotype imputation on the accuracy of DGV was small. Correlations between DGV and DGV_IMP were, on average, 0.96 for all traits for imputation from 7K to 50K, and 0.89 for imputation from 3K to 50K. Similar results were obtained by Berry and Kearney , who repX matrix is high. However, combining markers from several chromosomes did not increase the accuracy of imputation, which confirms that chromosomes are independent genetic and statistical units. The 7K LDP gave good results both in terms of R and DGV prediction. Similar to the 3K LDP, the multi-breed approach applied to the 7K scenario, did not yield better results than the single-breed approach.This study demonstrates that the PLSR imputation method can efficiently impute missing genotypes from LDP to HDP. With this method, the same good results are obtained whether animals in the PP have parents in the TP or not. Moreover, the computing time was markedly lower than with Beagle. The PLSR method was applied chromosome-wise and the results indicate that imputation accuracies are higher when the number of SNPs in the The authors declare that they have no competing interests.CD conceived the original ideas and wrote, under the supervision of NPPM and PAM, the first version of the SAS code. MC, RS and GG performed the analysis. GM contributed to the development of the ideas and algorithms. CD, MC and NPPM wrote the draft of the paper and all authors contributed in refining the manuscript. All authors read and approved the final manuscript.


Good genetic progress for pig reproduction traits has been achieved using a quantitative genetics-based multi-trait BLUP evaluation system. At present, whole-genome single nucleotide polymorphisms (SNP) panels provide a new tool for pig selection. The purpose of this study was to identify SNP associated with reproduction traits in the Finnish Landrace pig breed using the Illumina PorcineSNP60 BeadChip.P-values.Association of each SNP with different traits was tested with a weighted linear model, using SNP genotype as a covariate and animal as a random variable. Deregressed estimated breeding values of the progeny tested boars were used as the dependent variable and weights were based on their reliabilities. Statistical significance of the associations was based on Bonferroni-corrected P-value < 2.0E-06) were obtained for total number of piglets born in first and later parities and piglet mortality between birth and weaning in later parity, and suggestive associations  for piglet mortality between birth and weaning in first parity, number of stillborn piglets in later parity, first farrowing interval and second farrowing interval. Two of the statistically significant regions for total number of piglets born in first and later parities are located on chromosome 9 around 95 and 79 Mb. The estimated SNP effect in these regions was approximately one piglet between the two homozygote classes. By combining the two most significant SNP in these regions, favourable double homozygote animals are expected to have 1.3 piglets  more than unfavourable double homozygote animals. A region on chromosome 9 (66 Mb) was statistically significant for piglet mortality between birth and weaning in later parity .Deregressed estimated breeding values were available for 328 genotyped boars. Of the 62 163 SNP in the chip, 57 868 SNP had a call rate > 0.9 and 7 632 SNP were monomorphic. Statistically significant results (Three separate regions on chromosome 9 gave significant results for litter size and pig mortality. The frequencies of favourable alleles of the significant SNP are moderate in the Finnish Landrace population and these SNP are thus valuable candidates for possible marker-assisted selection. Estimated breeding values (EBV) based on best linear unbiased prediction (BLUP) for total numbers of piglets born and farrowing intervals have been available to Finnish pig breeders since 1991 . During http://www.genome.iastate.edu/cgi-bin/QTLdb/SS/index) [Over the past twenty years, several microsatellite-based linkage studies have been conducted to locate quantitative trait loci (QTL) that affect pig reproduction traits. The results are listed in the Pig Quantitative Trait Locus database  ,6. ThrouS/index) .The objective of this study was to identify SNP associated with female reproduction traits in the Finnish Landrace pig breed. Significant SNP can then be incorporated into the national evaluation and selection scheme. The analysis is based on 328 progeny-tested artificial insemination (AI) boars genotyped with the Illumina PorcineSNP60 BeadChip.The study included 328 Finnish Landrace AI boars born between 1996 and 2009. The average and maximum numbers of daughters per sire were 141 and 782, respectively. All boars were related to each other. The data included 114 sires with genotyped sons, for which the average and maximum numbers of sons per sire were 2.2 and 10, respectively.DNA was extracted either from hair follicles or from semen using a DNeasy Blood & Tissue kit . For more information on extraction methods see Sironen et al. . ExpecteNine female reproductive traits were studied: total number of piglets born in first (TNB1) and later parities (TNB2), number of stillborn piglets in first (NSB1) and later parities (NSB2), piglet mortality between birth and weaning in first (PM1) and later parities (PM2), age at first farrowing (AFF), first farrowing interval (FFI), and second farrowing interval (SFI). EBV for all nine traits were obtained for each genotyped AI boar from the national breeding value evaluation (multi-trait BLUP). The linear model for TNB1, TNB2, NSB1, NSB2, PM1, and PM2 used in the national evaluation includes herd-year, year-month, type of insemination, litter breed, and age at farrowing as fixed effects, and litter sire, permanent environmental effects, and additive genetic  effects as random effects. Additionally, parity number was included as a fixed effect for TNB2, NSB2, and PM2. The linear model for AFF, FFI, and SFI included herd-year and herd-month as fixed effects and animal as a random effect. The models for FFI and SFI also included the effect of dam breed.Prior to SNP association analysis, unstandardized EBV were deregressed and corresponding weights were calculated based on individual and parental EBV and reliabilities . The proThe association of SNP with deregressed EBV was studied using a mixed linear model, for each SNP separately. The model included a fixed SNP effect and a random polygenic effect to account for residual genetic variance not explained by the SNP in the model and the relationship between animals in the data. The model used was:i is the deregressed EBV; xi is the number of minor alleles ; b is the corresponding regression coefficient; ai is a random polygenic effect with covariance structure ai ~N, where A is the additive relationship matrix and σ2a is the polygenic variance; and ei is a random residual effect with ei ~N, where I is an identity matrix, σ2e is the residual variance, and wi is the weight. The analyses were performed using the AI-REML method in the DMU program package [2 = 0.07 vs. 0.10 for TNB1) but were quite constant across SNP.where y package . VariancP-values. This method treats individual tests as independent and thus is very conservative for data for which the correlation (linkage disequilibrium) between tests (SNP) is high; the linkage disequilibrium (r2) between adjacent SNP in the Finnish Landrace population is 0.43 [P-value should be between 1.0E-06 and 2.0E-06. In this article, individual SNP with a P-value of 2.0E-06 or less were considered statistically significant, and SNP with a P-value of 4.0E-06 or less as suggestive. More precise estimates of multiple-test-corrected P-values can be obtained by a permutation procedure but this was not possible for this research because of its high computation demand.Statistical significance of the associations was based on Bonferroni-corrected  is 0.43 . Aiming A total of 390 animals were originally genotyped; 366 animals had a call rate above the commonly used limit of 90% and five samples (DNA extracted from hair follicles) had a call rate of 0. Only the samples with a call rate equal or above to 0.90 and with available national EBV were used in the association analysis .http://www.illumina.com. One quarter of these had a call rate of 1.0, and 57 868 SNP had a call rate equal to or above 0.9 and were used in the association analyses. A large proportion of the SNP (7 632) were monomorphic  and 7 642 SNP had a minor allele frequency below 0.05. Most of the SNP with a low frequency were evenly distributed across the genome, but regions with low polymorphism longer than 1 Mb were also detected on different chromosomes. These low-polymorphism regions could be the result of selection, random genetic drift, or a bottleneck effect. Otherwise, the distribution of minor allele frequency was uniform across SNP. The SNP were mapped to pig genome build 9 .The Illumina PorcineSNP60 BeadChip contains 62 163 SNP For each trait, animals with a weight of the deregressed EBV less than 1.0 were removed from the analysis. The choice of this limit was arbitrary and subsequent analyses showed that inclusion or exclusion of these animals in the data had no real effect on the association results because of their large residual variance  in the linear mixed model equations. Figure 2 = 0.36) with DRGA0009645 (95 Mb)  more piglets than animals that are double homozygotes GG. To test the sensitivity of the analysis, observations with a weight less than 5 were discarded. For this analysis, the estimated SNP effect was also 0.32, with a P-value of 1.08E-08. Figure When the two regions were combined in the analysis and the regression variable corresponded to the number of favourable alleles (A) in H3GA0027863 and DRGA0009645 , the estimated SNP effect was 0.32, with a 2 ranging from 0.85 to 0.94) but nearly in linkage equilibrium with the SNP in region 79 Mb on the same chromosome that were significant for litter size (r2 < 0.01)  with a SNP in the same proximal area (DRGA0009645) that was significant for litter size . The frequencies of favourable alleles for pig mortality have increased during the last 15 years. This could be due either to random drift or, given the fact that the SNP are associated with the trait under selection, to selection force  was statistically significant for piglet mortality before weaning  Figure . Moreovee Figure . P-valueSuggestive evidence for associations between SNP and female reproduction traits was also observed for number of stillborn piglets in later parities NSB2) on chromosome 1, for pig mortality in later parities (PM2) on chromosome 7, for first farrowing interval (FFI) on chromosome 4, and for later farrowing interval (SFI) on chromosome 1  as 130 genotyped AI boars [P-values. The reported effects of these SNP are most likely overestimated due to the "winner's curse" effect [Marker-assisted selection on known marker-QTL associations and genomic selection on an overall sum of marker effects across the genome both rely on strong linkage disequilibrium between markers and QTL. Finnish pig breeds are very suitable for SNP association studies because of their high linkage disequilibrium over the typical distances between SNP in the PorcineSNP60 BeadChip  and the AI boars ). Althou" effect  that is http://www.ensembl.org). It is well known that build 9 contains errors in the actual position of the SNP and even in the order of the SNP, as suggested by the linkage disequilibrium pattern of the significant SNP on chromosome 9 in this study. However, single SNP association tests are not sensitive to mapping errors. Thus, results  presented here hold even with an updated pig genome build but the candidate genes that are in the proximity of the significant SNP may change from the ones presented here.Annotation of the SNP was based on the pig genome build 9  that were significant for female reproduction traits in this study are within the same region as that reported for ovulation rate by Rohrer et al.  (57 to 1estrogen receptor 1 (ESR1) gene on chromosome 1 (15186680 to 15450209 bp) is a good candidate gene for all reproduction traits. However, none of the SNP in this region gave significant or suggestive P-values for any of the studied traits here. The best SNP (ALGA0000673 for SFI) is located 7 Mb from the ESR1 gene. A QTL for number of stillborn has been reported on chromosome 7 [The suggestive region for NSB2 at the end of chromosome 1 that was found in this study has been reported to carry a QTL for age at puberty  but not mosome 7  and a QTIt is interesting that the genome regions that reached statistical significance in our study are all on chromosome 9. Without further studies, it is difficult to say whether this is by chance or whether there is some biological or population-based mechanism that explains this result. No linkage disequilibrium was observed between the significant regions on chromosome 9 at the population level, which implies that the regions have segregated independently in the Finnish Landrace population and thus rules out the hitchhiking effect as the most likely explanation.aryl hydrocarbon receptor (AhR) gene between positions 81024216 and 81081616 bp is involved in folliculogenesis, gonadotrophin receptor expression, proliferation of granulosa cells, and intraovarian estrogen signalling [AhR knockout mice have reduced fertility due to disturbed follicle development, with significantly fewer pre-antral and antral follicles and less ovulations compared with wild-type mice [interleukin 6 (IL6), which is located on chromosome 9 between positions 85801970 and 85806347 bp. IL6 is a multifunctional cytokine that regulates various aspects of the immune response and is also expressed during the ovulation process [protein tyrosine phosphatase non-receptor type 12  gene between positions 95427072 and 95467282 bp has been shown to play an essential role in early murine embryogenesis. PTPN12 functions in embryonic vascularization, mesenchyme formation, neurogenesis, and early liver development [The region on chromosome 9 that is associated with TNB1 and TNB2 contains several potential candidate genes that may contribute to the physiology of variation in sow fertility. The gnalling . Its wel process . IL6 has process . FurtherP-values were also observed on chromosomes 1, 4, and 7 for second and first farrowing intervals and for piglet mortality in later parities, respectively. The frequencies of favourable alleles of the significant SNP are still moderate in the Finnish Landrace population. Thus, if these initial findings are confirmed, the specified SNP will be valuable in the national breeding program through their use in marker-assisted selection.To conclude, this whole-genome SNP association study using 328 Finnish Landrace AI boars revealed three highly significant regions on chromosome 9 with effects on litter size and piglet mortality. Suggestive The authors declare that they have no competing interests.PU undertook the statistical analysis and drafted the paper. AS participated in the discussion of the results and genotyping the animals. MLSA prepared the data and participated in the statistical analysis. All authors read and approved the final manuscript.


Numerous efforts have been made to elucidate the etiology and improve the treatment of lung cancer, but the overall five-year survival rate is still only 15%. Although cigarette smoking is the primary risk factor for lung cancer, only 7% of female lung cancer patients in Taiwan have a history of smoking. Since cancer results from progressive accumulation of genetic aberrations, genomic rearrangements may be early events in carcinogenesis.HDAC9 in chromosome 7p21.1 was identified from concurrent analyses of CNVs, SNPs, and LOH.In order to identify biomarkers of early-stage adenocarcinoma, the genome-wide DNA aberrations of 60 pairs of lung adenocarcinoma and adjacent normal lung tissue in non-smoking women were examined using Affymetrix Genome-Wide Human SNP 6.0 arrays. Common copy number variation (CNV) regions were identified by ≥30% of patients with copy number beyond 2 ± 0.5 of copy numbers for each single nucleotide polymorphism (SNP) and at least 100 continuous SNP variant loci. SNPs associated with lung adenocarcinoma were identified by McNemar’s test. Loss of heterozygosity (LOH) SNPs were identified in ≥18% of patients with LOH in the locus. Aberration of SNP rs10248565 at The results elucidate the genetic etiology of lung adenocarcinoma by demonstrating that SNP rs10248565 may be a potential biomarker of cancer susceptibility. One of most commonly diagnosed cancers is lung cancer, which accounts for nearly 18% of all cancer-related deaths worldwide ,3. Even Smoking is the primary risk factor for lung cancer . In WestMYC and ERBB2[RB1, CDKN2A, and PTEN[EGFR) and the under-expression of dual specificity phosphates 4 (DUSP4) served as effective prognostic biomarkers in lung cancer [Cancer appears to result from the progressive accumulation of genetic aberrations ranging from large, visible chromosome events to single nucleotide polymorphisms (SNPs). Genomic rearrangements that affect DNA sequences are called structural variants and include such things as insertions, deletions, duplications and inversions . When thand ERBB2-11), whe and PTEN-15). The and PTEN. DNA CNV and PTEN. Concord and PTEN,19. Furt and PTEN,21. For g cancer . TherefoIn this study, the genomic aberrations of lung adenocarcinoma in non-smoking women were examined using genome-wide human SNP arrays. The primary advantage of SNP arrays for our purposes was that the probe intensity of both alleles at each SNP allows the detection of CNV breakpoints and the estimation of the associated number of copies. In addition, loss of heterozygosity (LOH), used for surveying segments of allelic losses, can be examined by analyzing the genotype of both normal and lung tissues ,24.HDAC9, the gene encoding histone deacetylase, was related to lung carcinogenesis. In this study, we demonstrated that SNP rs10248565 in HDAC9 may be a potential biomarker for identifying important genetic determinants of cancer susceptibility and elucidating the genetic etiology of lung cancer in non-smoking females.Although SNP genotyping is often used for examining the associations between cancer and normal tissues, the main focus of this study was not to identify the association of SNPs with lung adenocarcinoma in non-smoking women. Instead, we took advantage of the ability of whole-genome SNP arrays to concurrently analyze CNVs, SNPs, and LOH in order to identify the novel focal loci of lung adenocarcinoma. All results indicated that the SNP rs10248565 in 2 and stored at -80°C until being further processed for DNA extraction. Only those paired samples passing quality checks (n = 61 pairs) were processed for SNP arrays.The study protocol was approved by the institutional review boards of National Taiwan University Hospital and Taichung Veterans General Hospital. The written consent form was approved by ethics committees, and all participants agreed with their written consents to participate in this study. In total, 120 pairs of cancer and normal lung tissue specimens were collected from non-smoking females. The selection criteria of clinical specimens depended on the pathology report, physical examination and cigarette smoking history. Surgical lung tissue specimens were immediately snap-frozen in liquid NNspI or StyI) and ligated to adaptors that recognize the cohesive four bp overhangs. All fragments resulting from restriction enzyme digestion were substrates for adaptor ligation. A generic primer that recognizes the adaptor sequence was used to amplify adaptor-ligated DNA fragments. PCR conditions had been optimized to preferentially amplify fragments in the 200 to 1,100 bp size range. The amplified DNA was then fragmented, labeled, and hybridized to Genome-Wide Human SNP 6.0 arrays . After 16 hours of hybridization at 49°C, the arrays were washed by Fluidics Station 450 and scanned by GeneChip Scanner 3000. Microarray data of this study are MIAME compliant, and have been submitted to the MIAME compliant Gene Expression Omnibus (GEO) database (accession number GSE33355).Genomic DNA was isolated by phenol/chloroform extraction following standard protocols with 0.5% SDS and 200 μg/ml proteinase K. Total genomic DNA (250 ng) was digested with a restriction enzyme . Since both tumor and adjacent normal tissues were from the same individual, the reference baseline for each tumor tissue was its corresponding normal tissue. The criteria for searching for CNV regions in the whole genome were as follows: 1) copy number intensity ratio of tumor to normal tissue for each SNP was >2.5 or <1.5; 2) each individual had ≥100 continuous SNP variant loci; 3) the CNV regions existed in ≥30% of the study population. The overlapping genes located within the detected CNV regions were annotated using the documentation file version 30 provided by Affymetrix.TaqMan® assays were used to validate the total copy number of CNV regions. Total genomic DNA  was used for TaqMan® Copy Number assays . All reactions were performed in duplicate, including the FAM™ dye label-based assay for the target of interest and the VIC® dye label-based TaqMan® Copy Number Reference Assay. The TaqMan® probes for the target of interest were labeled with FAM at the 5′ end and linked by a non-fluorescent quencher at the 3′ end. RNase P labeled with VIC dye (Life Technologies) was utilized as the reference gene, which is known to exist in two copies in a diploid genome. All TaqMan® assays were performed following manufacturer’s instructions and copy number calculation was conducted by the delta-delta threshold cycle (∆∆Ct) method. PCR was performed with an Applied Biosystems 7900HT Fast Real-Time PCR System . Results were analyzed by CopyCaller™ version 1.0. Tumor samples with a delta Ct value between target and reference sequences were measured, and then compared to their paired normal samples.For SNP analysis, SNPs were obtained using Affymetrix® SNP Array 6.0 . After excluding SNPs with allele frequency <1%  or call rate <90% (123 SNPs), 748,774 SNPs were further analyzed by McNemar-Bowker’s test to examine the difference of genotypes between normal and tumor tissues from the same subject. SNPs were coded according to the number of minor alleles, i.e., AA, Aa and aa, denoted as 0, 1, 2, respectively. The nonparametric McNemar-Bowker’s test was applied to examine the association between SNPs and tissues. The analyses were done in R version 2.9.0.Loss of heterozygosity (LOH) was defined as heterozygosity in normal tissue and homozygosity in tumor tissue. The genotypes between tumor tissue and its normal counterpart from the same subject were compared using Genome-Wide Human SNP 6.0 arrays . LOH SNPs were identified in ≥18% of patients with LOH in the locus.In this study, pairs of adenocarcinoma and adjacent normal lung tissue specimens were collected from 61 non-smoking women for the purpose of examining genome-wide DNA aberrations. The majority  of women were in early stages (I + II) and the mean (SD) age was 59.4 (11). Their clinical characteristics are listed in Table We first identified common CNV regions among these lung adenocarcinoma samples. The criteria for searching the CNV regions in whole genome were stated in the Methods section. In total, there were 424 CNV regions. Figure In order to validate the common amplification regions, four CNV regions in 7p21.1 were chosen for TaqMan® copy number assays. In the upper panel of Figure EGFR  and HDAC9 were previously reported to have an association with lung tumorigenesis.In order to understand the function of genes in common amplification regions, functional analysis was done using Ingenuity Pathway Analysis (IPA). The results revealed that the common amplification regions contain 29 genes. Network analysis showed that these genes were mainly involved in cellular development, cellular growth and proliferation, and cancer. Among these 29 genes, P-value <0.0001; 2,816 SNPs), the remaining 684,877 SNPs on autosomal chromosomes were further analyzed by McNemar-Bowker’s test to examine the differences in genotypes.Next, genotyping of SNPs was analyzed in normal and adenocarcinoma tissues from the same subject. SNPs with minor allele frequency <0.01 were excluded. After excluding SNPs with low minor allele frequency  or SNPs with departing Hardy-Weinberg Equilibrium  of each SNP was plotted across chromosomes. Information on these four SNPs  is listed in Table TGFB2 and LYPLAL1); the rest of the SNPs were located in introns. Among them, the SNP with the lowest P-value, rs10248565, is located in an intron of HDAC9.Since this study adopted a paired design, which provides less variation than general case–control studies and can achieve a higher statistical power, a strict criterion of Lastly, we examined the distribution of LOH in each chromosome Figure . BecauseAmong these LOH SNPs, we noticed that SNP rs10248565 was associated with lung adenocarcinoma and was located in the CNV region. Combining the results of CNV, SNP, and LOH analyses  as well as expression of baculoviral IAP repeat-containing 2/3 (BIRC2/3) can facilitate lung cancer cell proliferation and contribute to lung tumor development [Further investigations of the 29 genes located within these CNV regions identified several key players involved in the tumorigenic process. For instance, loss of docking protein 2 . This may be due to the location of the SNP in an intron of HDAC9, and further investigation of the mechanism of genomic aberration in HDAC9 is warranted.The gene encoding HDAC in chromosome 7p21.1 was identified in all CNV, SNP and LOH analyses and is worthy of mention here. HDAC is involved in deacetylation of lysine residues in the N-terminal tails of nucleosomal core histones , and it g cancer . UnfortuP <0.01) associated with lung cancer. SNP rs1700874 is located at an intergenic region in 1q41 between TGFB2 and LYPLAL1. The transforming growth factor beta family plays an important role in cell cycling, cell growth, apoptosis, and protein synthesis, and is therefore involved in many pathological processes [TGFB2 may correlate with heart disease and pulmonary function in mice [LYPLAL1 is still unclear. SNP rs10248565 is located in 7p21.1 within HDAC9, the significance of which was discussed above. SNP rs11761619 is in 7p14.3 within BBS9. BBS9 is associated with kidney and ovarian diseases [GTF2F2, which is known to affect the progression and survival of epithelial ovarian cancer [The hypothesis underlying our SNP analysis was that if SNPs were associated with cancer, the proportions of different alleles would be different in cancer and normal groups. Previously, rs7086803 at 10q25.2, rs9387478 at 6q22.2 and rs2395185 at 6p21.32 were identified as lung cancer susceptibility loci in never-smoking women in Asia . In thisrocesses . A previ in mice . The fundiseases ,45, and diseases . SNP rs9RASSF1A, a member of the Ras association domain family, and FUS1), 3p14.2 , 9p21 (p16), and 17p13 (p53) was identified as an early event in the development of non-small cell lung cancer [In this study, we identified 30 SNPs with LOH. Most of these LOH SNPs were located in chromosome 7 .The authors declare that they have no competing interests.LCL, MHT, CKH, and EYC provided conception and design. LCL, CKH, and EYC provided financial support. JML and CPH provided study materials and patients. PCC and SKC collected and assembled data. LCL, MHT, PCC, LHC, JHH, TPL, and EYC analyzed and interpreted data. LCL, MHT, PCC, and EYC wrote manuscript. All authors read and approved the final manuscript.


Multi-category classification methods were used to detect SNP-mortality associations in broilers. The objective was to select a subset of whole genome SNPs associated with chick mortality. This was done by categorizing mortality rates and using a filter-wrapper feature selection procedure in each of the classification methods evaluated. Different numbers of categories  and three classification algorithms  were compared, using early and late chick mortality rates in low and high hygiene environments. Evaluation of SNPs selected by each classification method was done by predicted residual sum of squares and a significance test-related metric. A naïve Bayes classifier, coupled with discretization into two or three categories generated the SNP subset with greatest predictive ability. Further, an alternative categorization scheme, which used only two extreme portions of the empirical distribution of mortality rates, was considered. This scheme selected SNPs with greater predictive ability than those chosen by the methods described previously. Use of extreme samples seems to enhance the ability of feature selection procedures to select influential SNPs in genetic association studies. In genetic association studies of complex traits, assessing many loci jointly may be more informative than testing associations at individual markers. Firstly, the complexity of biological processes underlying a complex trait makes it probable that many loci residing on different chromosomes are involved ,2. SeconStandard regression models have problems when fitting effects of a much larger number of SNPs  than the number of observations available. To address this difficulty, a reasonable solution could be pre-selection of a small number of SNPs, followed by modeling of associations between these SNPs and the phenotype . Other se.g., disease classification. For example, Sebastiani et al. [et al. [et al. [Machine learning methods are alternatives to traditional statistical approaches. Machine learning is a branch of artificial intelligence that "learns" from past examples, and then uses the learned rules to classify new data . Their ti et al.  applied  [et al.  and Li e [et al. . It is mIn a previous study of SNP-mortality association in broilers , the proe.g., "A" or "G" alleles) and genotypes were arbitrarily coded as 0 (AA), 1 (AG) or 2 (GG). A detailed description of these SNPs is given in Long et al. [Genotypes and phenotypes came from the Genomics Initiative Project at Aviagen Ltd. . Phenotypes consisted of early (0–14d) and late (14–42d) age mortality status  of 333,483 chicks. Birds were raised in either high (H) or low (L) hygiene conditions: 251,539 birds in the H environment and 81,944 in the L environment. The H and L environments were representative of those in selection nucleus and commercial levels, respectively, in broiler breeding. Information included sire, dam, dam's age, hatch and sex of each bird. There were 5,523 SNPs genotyped on 253 sires. Each SNP was bi-allelic . Similarly, the other three strata were EH (early age-high hygiene), LL (late age-low hygiene) and LH (late age-high hygiene). Adjusted sire mortality means were constructed by fitting a generalized linear mixed model (with fixed effect of dam's age and random effect of hatch) to data  from individual birds, to get a residual for each bird, and then averaging progeny residuals for each sire (see Appendix). After removing SNPs with missing values, the numbers of sires and SNPs genotyped per sire were: EL and LL: 222 sires and 5,119 SNPs; EH and LH: 232 sires and 5,166 SNPs. Means and standard deviations (in parentheses) of adjusted sire means were 0.0021 (0.051), -0.00021 (0.033), -0.0058 (0.058) and 0.00027 (0.049) for EL, EH, LL and LH, respectively. Subsequently, SNP selection and evaluation were carried out in each of the four strata in the same way.K classes . The adjusted sire means were ordered, and each was assigned to one of K equal-sized classes in order to keep a balance between sizes of training samples falling into each category. For example, with K = 3, the thresholds determining categories were the 1/3 and 2/3 quantiles of the empirical distribution of sire means. This is just one of the many possible forms of categorization, and it does not make assumptions about the form of the distribution.Sire mortality means were categorized into et al. [A two-step feature selection method, "filter-wrapper", described in Long et al.  was usedA collection of 50 "informative" SNPs was chosen in this step. It was based on information gain , a measuThis procedure is an iterative search-and-evaluate process, using a specific classification algorithm to evaluate a subset of SNPs (relative to the full set of 50 SNPs) searched . Three cX1,..., Xp) be features (SNPs) with discrete values  used to predict class C . A schematic is in Figure x1,..., xp), the best prediction of the mortality class to which it belongs is that given by class c which maximizes Pr. By Bayes' theorem,Let X,..., Xp C = c) can be estimated from training data and Pr is irrelevant for class allocation; the predicted value is the class that maximizes Pr. NB assumes that X1,..., Xp are conditionally independent given C, so that Pr can be decomposed as Pr(X1 = x1 | C = c) × ⋯ × Pr(Xp = xp | C = c). Although the strong assumption of feature independence given class is often violated, NB often exhibits good performance when applied to data sets from various domains, including those with dependent features [e.g., Pr(X1 = x1 | C = c), are estimated using the ratio between the number of sires with genotype x1 that are in class c, and the total number of sires in class c.Pr, and the other nodes represent SNPs or covariates, as illustrated in Figure C) given its "parent" nodes (Pa(C)) , one chooses c which maximizes Pr(C = c | Pa(C)) [M) given data (D): Pr(M|D)∝ Pr(M)Pr(D|M) Given a network M, if Dirichlet distributions are used as conjugate priors for parameters θM (a vector of probabilities) in M, then, Pr(D | M) = ∫ Pr(D|θM)Pr(θM)dθM, has a closed form. The search method used for learning M was a hill-climbing one, which considered arrow addition, deletion and reversal during the learning process [Bayesian networks are directed acyclic graphs for representing probabilistic relationships between random variables . Most ape.g., ). A node| Pa(C)) . To lear| Pa(C)) . In this| Pa(C)) , which i process . This sexj is SNPj and wjh is the weight applied to connection from SNPj to hidden node h . Similarly, a node in the output layer takes a weighted sum of all hidden nodes and, again, applies an activation function, and takes its value as the output of that node. The sigmoid function ranges from 0 to 1, and has the advantage of being differentiable, which is required for use in the back-propagation algorithm adopted in this study for learning the weights from inputs to hidden nodes, and from these to the output nodes [K-category classification problem with continuous outputs (as per the sigmoid function), K output nodes were used, with each node being specific to one mortality category. Classification was assigned to the category with the largest output value. The back-propagation algorithm (a non-linear least-squares minimization) processes observation by observation, and it was iterated 300 times. The number of parameters in the network is equal to (K + M) +M (K + N), where N, M and K denote the number of nodes in the input, hidden and output layers, respectively. For example, in a binary classification (K = 2) with 50 input nodes representing 50 SNPs, and two hidden nodes, the number of parameters is 108.A neural network is composed of a set of highly interconnected nodes, and is a type of non-parametric regression approach for modeling complex functions ,24. The ut nodes . For a KComparison of the three classification algorithms  yielded a best algorithm in terms of classification accuracy. Using the best classification algorithm, there were five optimum SNP subsets selected in the wrapper step in each stratum, corresponding to the 2, 3, 4, 5 or 10-category classification situation, respectively. The SNP subset evaluation refers to comparing the five best SNP subsets in a certain stratum . Two measures were used as criteria; one was the cross-validation predicted residual sum of squares (PRESS), and the other was the proportion of significant SNPs. In what follows, the two measures are denoted as A and B. Briefly, for measure A, a smaller value indicates a better subset; for measure B, a larger value indicates a better subset.et al. [PRESS is described in Ruppert et al. . It is cMi was sire i's adjusted mortality mean ; SNPij denotes the fixed effect of genotype of SNPj in sire i; and ng is the number of SNPs in the subset under consideration. Although the wrapper selected a "team" of SNPs that act jointly, only their main effects were fitted for PRESS evaluation (to avoid running out of degrees of freedom). The model was fitted by weighted least squares, with the weight for a sire family equal to the proportion of progeny contributed by this sire. The errors were assumed to have a Student-t distribution with 8 degrees of freedom (t-8) distribution, after examining Q-Q plots with normal, t-4, t-6 and t-8 distributions. Given this model, PRESS was computed bywhere Mi is predicted using all sire means except the ith  sire, and this predicted mean is denoted by ® macro was written to generate PRESS statistics and it was embedded in SAS® PROC GLIMMIX .Here, F-statistic (in the ANOVA sense) was computed for each SNP. Subsequently, given an individual SNP's F-statistic, its p-value was approximated by shuffling phenotypes across all sires 200 times, while keeping the sires' genotypes for this SNP fixed. Then, the proportion of the 200 replicate samples in which a particular F-statistic exceeded that of the original sample was calculated. This proportion was taken as the SNP's p-value. After obtaining p-values for all SNPs in the subset, significant SNPs were chosen by controlling the false discovery rate at level 0.05 [This procedure involved calculating how many SNPs in a subset were significantly associated with the mortality phenotype. Given a subset of SNPs, an vel 0.05 . The proet al. [This comparison addressed whether or not the loss of information from using only two extreme tails of the sample, as in Long et al. , affectevs. extreme sires only), the best subset was chosen by the PRESS criterion, as well as by its significance level. That is, the smallest PRESS was selected as long as it was significant at a predefined level ; otherwise, the second smallest PRESS was examined. This guaranteed that PRESS values of the best SNP subsets were not obtained by chance. Significance level of an observed PRESS statistic was assessed by shuffling phenotypes across all sires 1000 times, while keeping unchanged sires' genotypes at the set of SNPs under consideration. This procedure broke the association between SNPs and phenotype, if any, and produced a distribution of PRESS values under the hypothesis of no association. The proportion of the 1000 permutation samples with smaller PRESS than the observed one was taken as its p-value.In each situation  of the final SNP subsets selected by the "wrapper" with the three classification algorithms are in Table K = 2, 3, 4, 5 and 10) using measures A and B are shown in Table K = 2 as best. For EH, K = 2 and K = 3 were similar when using measure A; K = 3 was much better than the others when using method B. For LL, K = 2 was best for measure A whereas K = 3 or 4 was chosen by B. For LH, K = 3 and K = 2 were best for measures A and B, respectively. Overall, classification with 2 or 3 categories was better than classification with more than 3 categories. This implies that measures A and B were not improved by using a finer grading of mortality rates.Results of the comparison of the five categorization schemes . This was possibly due to the very small size of the corresponding SNP subset; there were only four SNPs with 34 = 81 genotypes, so the observed PRESS would appear often in the null distribution. Therefore, the second smallest PRESS value (0.510) was used to compare against using all sires. Figures p ≤ 0.007), indicating that the PRESS of each SNP subset was probably not due to chance.As shown in Table i.e., independence of SNPs given class, is often violated. However, it greatly simplifies the learning process, since the probabilities of each SNP genotype, given class, can be estimated separately. Here, NB clearly outperformed the two more elaborate methods (BN and NN). One reason could be that, although simple decomposition using the independence assumption results in poor estimates of Pr, the correct class still has the highest estimated probability, leading to high classification accuracy of NB [e.g., 5 times as many training cases as parameters). If sample size is fixed, approaches for reducing model complexity have to be used. In the case of NN, one can reduce the number of hidden nodes or use regularization (weight decay), to control magnitude of weights [et al. [Arguably, the conditional independence assumption of NB, cy of NB . Another weights . For BN, weights  and the  weights . These m weights . Guyon e [et al.  presenteet al. [An alternative to the filter-wrapper approach for handling a large number of genetic markers is the random forests methodology , which uet al.  pointed et al. [The number of top scoring SNPs (50) was set based on a previous study , where iet al. . RedundaK) increases. This is because the baseline predictive power decreases with K, and average sample size for each category also decreases with K, which makes the trained model less reliable. To make a fair comparison among SNP subsets found with different K, the same evaluation procedure, neutral with respect to filter-wrapper and K, was uniformly applied to each setting. By using mortality means in their original form , two measures were used. Measure A (PRESS) evaluated a subset of SNPs from the perspective of predictive ability, while measure B estimated the proportion of SNPs in a subset that had a statistically significant association with mortality. Although the best SNP subset was measure-dependent, it was either with K = 2 or K = 3. Thus, it appears that classification into two or three categories is sufficient.As illustrated by the error rates found in the present study, multi-category classification gets harder as the number of categories  times, generating B permutation samples. For each of the B samples, apply "extreme" and "all" to get PRESS, and then take their difference. This would produce a null distribution, against which the observed difference in PRESS can be referred to. This was not done in this study, due to extra computational intensiveness.The comparison between SNP subsets selected using sires with extreme phenotypic values and those selected using all sire means indicated better performance of the former strategy of SNP detection. This is so, at least in part, because concern is about classification accuracy, and obtaining more informative samples for each class is more important than avoiding loss of information resulting from discarding some sire means. Perhaps including all sire samples brings noise, leading to a poorer predictive ability of the selected SNPs. In order to assess significance of the observed difference in PRESS between using "extreme" and "all" strategies, one can shuffle sire means over genotypes B  samples, even at the cost of losing some information, to achieve better predictive ability on the selected SNPs.et al. [In summary, and in the spirit of the studies of Long et al. ,35, a fiet al. ,8,36-38.The authors declare that they have no competing interests.NL conceived, carried out the study and wrote the manuscript; DG conceived, supervised the study and wrote the manuscript; GR, KW and SA helped to coordinate the study and provided critical insights.To create a response variable for each of the sires genotyped, effects of factors (dam's age and hatch) potentially affecting individual bird survival were removed via a generalized linear mixed model (GLMM) without sire and hygiene effects. For each bird, a residual derived from the GLMM was calculated. Birds were classified into two groups, corresponding to the hygiene environments (L and H) in which they had been raised. In each hygiene group, residuals of progeny of a sire were averaged, producing an adjusted progeny mortality mean as the response variable for each sire.The individual record on each bird was binary , and the GLMM fitted was:pijk is the death probability of bird k, progeny of a dam of age i and born in hatch j. Here, DAi stands for the fixed effect of the ith level of dam's age ; Hj denotes the random effect of hatch j , which was assumed normal, independent and identically distributed as Hj~NIID and rijk = yijk - ® PROC GLIMMIX .where Model (1) was fitted to both early and late mortality data. Subsequently, birds were divided into the two hygiene groups, and progeny residuals were averaged for each sire, as described above. Thus, four strata of age-hygiene combinations were formed, with each stratum containing the adjusted progeny mortality means calculated for: 1) birds of early age raised in low hygiene (EL); 2) birds of early age raised in high hygiene (EH); 3) birds of late age raised in low hygiene (LL); and 4) birds of late age raised in high hygiene (LH).K = 3 categories, computing time and predictive (classification) performance were monitored simultaneously for BE and FS (Fig Choosing a proper search method was relevant primarily to a BN-wrapper, because of computational issues. Using data from the EL stratum and for d FS Fig . Computa