Puccinia recondita f. sp. secalis) and pre-harvest sprouting are still not satisfactory. High α-amylase activity is also among the main disadvantages of this species. Therefore, effective tools, e.g. molecular markers, allowing precise and environmentally independent selection of favourable alleles are desirable. In the present study, two kinds of association mapping—genome-wide association mapping (GWAM) based on sequences of DArTSeq markers and candidate gene association mapping (CGAM) based on sequences of ScBx genes—were chosen for development of molecular markers fulfilling these criteria. The analysed population consisted of 149 diverse inbred lines (DILs). Altogether, 67 and 11 single nucleotide polymorphisms (SNPs) identified in, respectively, GWAM and CGAM, were significantly associated with the investigated traits: 2 SNPs with resistance to brown rust, 71 SNPs with resistance to pre-harvest sprouting and 5 SNPs with α-amylase activity in the grain. Fifteen SNPs were stable across all environments. The highest number (13) of environmentally stable SNPs was associated with pre-harvest sprouting resistance. The test employing the Kompetitive Allele Specific PCR method proved the versatility of four markers identified in both GWAM and CGAM.Rye is a crop with relatively high resistance to biotic and abiotic stresses. However, the resistance to brown rust (The online version of this article (doi:10.1007/s11105-017-1030-6) contains supplementary material, which is available to authorized users. Secale cereale L.) is one of the most important cereals in Eastern, Central and Northern European countries. It was grown on approximately 5.8 million ha worldwide in 2013 [http://faostat.fao.org]. Its importance results both from the unique properties such as low water and soil fertility requirements and good tolerance for biotic and abiotic stresses, good overwintering, high nutritional value connected with the content of dietary fibre and the multipurpose usage—for bread making, animal feed and alcohol production. Recently, it is also gaining attention as a biomass crop , pre-harvest sprouting (PHS) and high α-amylase activity.Rye  mapped on each of the seven rye chromosomes  or SNP haplotypes)—phenotype correlations in unrelated individuals using dedicated statistical methods  to characterize a broad collection of rye diverse inbred lines representing the variability among Polish breeding materials with respect to brown rust resistance, pre-harvest sprouting and α-amylase activity, (2) to identify SNPs in The plant material used in the experiments consisted of 153 diverse inbred lines (DILs) bred in two Polish breeding companies—Danko Plant Breeders Ltd.  and Poznań Plant Breeders Ltd. ; Botanical Garden of Polish Academy of Science ; Warsaw University of Life Sciences, Department of Plant Genetics, Breeding and Biotechnology (5 lines designated as W1–W5); West Pomeranian University of Technology, Department of Plant Genetics, Breeding and Biotechnology (20 lines designated as S1–S20); and Wrocław University of Environmental and Life Sciences, Department of Genetics, Plant Breeding and Seed Production (50 lines designated as WR1–WR50).In 2013, 103 lines were tested, and the set was increased to 153 lines in 2014.The name W1 is a synonym of L318. Additional plant material, not related (but belonging to the same Petkus germplasm pool) to DILs used in the association analysis, consisting of 60 recombinant inbred lines (RIL) from the mapping population 541 × Ot1-3 . The experiments were performed in two seasons, 2013 and 2014, in two locations: an experimental field of the West Pomeranian University of Technology in Szczecin  and Danko Plant Breeders Ltd. in Choryń . Together, observations from four experiments designated as WPUT 2013, WPUT 2014, DANKO 2013 and DANKO 2014 were scored.The following traits of DILs were phenotyped: brown rust resistance (R-R), pre-harvest sprouting resistance (PHS-R) and α-amylase activity in grain (AMY).P. recondita was examined in field conditions where the source of inoculum was uredospores of a local population of the fungus, naturally occurring in the environment and spontaneously infecting the plants. No special treatments were applied for enhancing/increasing the infection potential of the fungus. For comparison purposes, the set of tested lines was supplemented with two rye cultivars, Dańkowskie Złote as a susceptibility reference and Bosmo as a tolerance reference.The reaction of DILs to Rating was performed at maximum brown rust epidemic intensity and based on average evaluation of all plants in the plot.0—resistant, no symptoms on plants1—resistant, chlorosis/necrosis and/or single uredinia sporadically visible on leaves2—moderate resistant; single small uredinia regularly present on majority of plants3—moderately susceptible; up to 40% of leaf surface covered with uredinia4—susceptible; up to 70% of leaf surface covered with uredinia5—very susceptible; over 70% of leaf surface covered with uredinia.Plant infection level was evaluated according to a six-degree scale:Three to six spikes from each DIL and each replicate were harvested at full ripeness and kept at room temperature for 3 days. Then, the spikes were sprayed with water for 5 min each day and kept in a moisture chamber at 20 °C for a period of 7 days. Resistance to PHS was determined as a percentage of germinated kernels .1.5 g of kernels with no visible signs of sprouting from each DIL and each replicate was milled into flour. Grain used for the α-amylase assay was stored at room temperature for the first 2 weeks after harvest and then kept at 4 °C for the few months before analysis. For α-amylase extraction, 1 g of flour was mixed with 4 ml of distilled water, equilibrated for 10 min at room temperature and finally centrifuged at 8000 rpm at 4 °C for 20 min. Alpha-amylase activity in mature grain was detected using a simple gel diffusion method  were mapped to the reference sequence using Bowtie 2 , locations (L), and of Y × L interaction, and random effects of DILs and DIL ×  interactions. Association mapping, based on SNP data and on mean values of traits obtained in individual experiments, was done using the method allowing for interaction of genetic effects with the environment developed by van Eeuwijk et al.  and MaloP < 0.001 was analysed based on a genetic map of population S  mapping algorithm was applied.Selected SNPs found to be significantly and stably associated with R-R and PHS-R were evaluated using the KASP assays . An additional selection criterion was the ability to design good quality primers for the analysed sequence. The 50 bp sequences, each upstream and downstream around the SNP, were the templates for design of primer pairs. KASP reactions were run in 10 μl reactions including 5 μl of KASP master mix, 0.14 μl of primer mix , and 5 μl of 17–20 ng/μl genomic DNA. PCR and fluorescent endpoint readings were carried out using the CFX96 Touch Real-Time PCR Detection System (Bio-Rad). Altogether, 120 lines were genotyped: 40 DILs included in the association analysis —20 DILs in the experiment on SNPs associated with PHS-R and 20 DILs in the experiment on SNPs associated with R-R—and 80 lines not included in the association analysis , comprising 60 recombinant inbred lines (40 taken for the experiment on SNPs associated with PHS-R and 20 for those associated with R-R) and 20 inbred lines (taken for the experiment on SNPs associated with R-R) phenotyped previously by Masojć et al.  and by bThe comparison of R-R, PHS-R and AMY levels in all experiments is shown in Table Correlations of observed trait levels were not significant Table  except fThe groups of lines provided by different breeders differed with respect to mean R-R and AMY, whereas for PHS-R, the differences were not significant Table . Lines DOut of 36,894 polymorphisms obtained, 2330 SNPs were selected for GWAM using the criteria specified above. Population structure estimated by eigenanalysis Fig. a shows lScBx genes and their promoters, 70 SNPs were identified in a set of 143 DILs—26, 22, 1, 7 and 14 in ScBx1, ScBx2, ScBx3, ScBx4 and ScBx5 genes, respectively. All 70 polymorphisms were included in CGAM. Homozygosity of DIL with respect to these SNPs ranged from 0.28 to 1.00. There were six SNPs with the fraction of homozygous observations less than 0.65  to 2R, 110.7 cM in the map of Martis et al.  (Table 5P < 0.01 was 11 (Table SScBx1 gene. The polymorphism associated with R-R was found in the gene ScBx4 (in the first intron). Polymorphisms associated with PHS-R were detected in ScBx1 , ScBx2 (three SNPs: two in the first exon and one in the second exon) and ScBx4 (one SNP in the first intron).The number of SNPs associated with investigated traits significant at ScBx polymorphism was found to be associated with AMY activity.The SNP associated with R-R had an effect stable over all experiments. For PHS-R, 6 out of 10 of the associations were stable across all environments. All associations exhibited a positive effect of the ALT allele (resulting from a given SNP) in comparison to the REF allele (the same as present in line L318). No ScBx1 gene, predominantly. No SNP associated with the analysed traits was detected in the ScBx3 gene.SNPs significantly and stably associated with the traits of interest Table  were locP < 0.05, but it was included in the analysis as stable, with a large favourable effect.To verify whether the SNPs associated with the investigated traits of DILs are also associated with a given phenotype in the unrelated rye inbreds, KASP analysis was performed. Out of 16 SNPs significantly and stably associated with the investigated traits, four SNP markers were chosen for KASP analysis: ScBx5_1593 associated with R-R, 3602388, ScBx1_1367 and ScBx1_4515 associated with PHS-R. The marker ScBx5_1593 was associated with R-R only at All markers, both those associated with R-R and those related to PHS-R, identified the phenotype with correctness higher than 86%. Usually, a slightly higher percentage of mismatches  was noted in the case of the experimental set  on chromosome 2A is significantly associated with late maturity α-amylase (LMA). It can be assumed that the gibberellins (GA), phytohormones that regulate many common processes throughout the plant life cycle, are the common factor linking these two traits. De Vleesschauwer et al.  from the repetitive fraction of the genome and use the next-generation sequencing platforms , were notably different. For example, in June 2013, the temperatures were extremely high and relatively stable, whereas in June 2014, significant temperature fluctuations were observed. In Szczecin, in July 2014, there was drought and in Choryń, the rainfall in this month was much higher than average. June and July are critical months for the expression of the analysed traits. Nevertheless, the trait relationship with eight SNPs (12%)—seven for PHS-R and one for AMY—turned out to be stable across all environmental conditions represented by two locations and two consecutive seasons.Pr3, Pr4 and Pr5  or PHS-R (10 SNPs). Three SNPs—ScBx1_4515, ScBx4_1583 and ScBx4_1627—were previously shown to be associated with BX content in the above ground parts of plants (the first of the listed markers) and roots (the remaining two markers) , and ScBx2 is responsible for the synthesis of the first hydroxamic acid: indolin-2-on  and 3′ regulatory sequences (promoters of ScBx1). These polymorphisms can modify protein properties, at least, or impair enzyme synthesis. Bakera et al. . All these polymorphisms may probably serve as a template for developing effective molecular markers for MAS. As stated above, the weather in summer, in two consecutive seasons, was considerably different. These circumstances show the potential of the detected SNPs. To test the reliability, we performed a validation experiment by applying the SNPs in an independent set of accessions not included in the association analysis  using the KASP method. This analytical approach is agreed to be a cost-effective and accurate marker assay, especially for genotyping large segregating populations and in the case of numerous samples with a few SNPs  associations were not affected by the environment. Altogether, we identified 15 environmentally stable associations with SNPs  were observed. They might be caused by insufficiently accurate phenotyping in single cases rather than by the genetic determinants. Nevertheless, the accuracy of genotype-based selection at a level higher than 86% may be of interest to breeders, especially as the evaluation set consisted of lines which were not included in the association mapping and were not related by pedigree with the experimental set of DILs, even when they were bred in the same institution or company. Of course, lines in EVS were not entirely unrelated to DILs, as all plant material used in our study belongs to the same Petkus germplasm pool.Moreover, the obtained results, especially KASP-evaluated markers for dominantly inherited R-R, can be almost directly implemented in practice, e.g. in marker-assisted selection, because of the structure of the analysed population consisting of inbred lines. Nearly 50% of DILs come from the breeding programmes of two Polish breeding companies. As pointed out by Begum et al. , performESM 1(DOCX 549 kb)


Pinus albicaulis Engelm.) is an endangered conifer species due to heavy mortality from white pine blister rust  and mountain pine beetle (Dendroctonus ponderosae). Information about genetic diversity and population structure is of fundamental importance for its conservation and restoration. However, current knowledge on the genetic constitution and genomic variation is still limited for WBP. In this study, an integrated genomics approach was applied to characterize seed collections from WBP breeding programs in western North America. RNA-seq analysis was used for de novo assembly of the WBP needle transcriptome, which contains 97,447 protein-coding transcripts. Within the transcriptome, single nucleotide polymorphisms (SNPs) were discovered, and more than 22,000 of them were non-synonymous SNPs (ns-SNPs). Following the annotation of genes with ns-SNPs, 216 ns-SNPs within candidate genes with putative functions in disease resistance and plant defense were selected to design SNP arrays for high-throughput genotyping. Among these SNP loci, 71 were highly polymorphic, with sufficient variation to identify a unique genotype for each of the 371 individuals originating from British Columbia (Canada), Oregon and Washington (USA). A clear genetic differentiation was evident among seed families. Analyses of genetic spatial patterns revealed varying degrees of diversity and the existence of several genetic subgroups in the WBP breeding populations. Genetic components were associated with geographic variables and phenotypic rating of WPBR disease severity across landscapes, which may facilitate further identification of WBP genotypes and gene alleles contributing to local adaptation and quantitative resistance to WPBR. The WBP genomic resources developed here provide an invaluable tool for further studies and for exploitation and utilization of the genetic diversity preserved within this endangered conifer and other five-needle pines.Whitebark pine (WBP,  Pinus albicaulis Engelm.) is a native keystone conifer species in subalpine ecosystems of western North America. WBP forests provide a food source for animals, reduce soil erosion, and help retain snow in dry and cold mountain regions with steep slopes at high elevations. The ecological roles played by WBP populations are not replaceable by other tree species. Due to threats from white pine blister rust (WPBR) caused by the introduced invasive fungus Cronartium ribicola (J.C.Fisch.), mountain pine beetle , altered fire regimes, and climate change [Whitebark pine , and grizzly bears , a decline in biodiversity, and loss of soils and snowpack across subalpine landscapes [C. ribicola has now almost spread across the entire distribution of WBP [Loss of WBP populations has been occurring at an increasing rate ,6. The cndscapes ,7–9. C. n of WBP , and blin of WBP . As a ken of WBP , 13. MajKnowledge about genetic diversity and population structure is important for the restoration of wild WBP populations and the sustainable maintenance of biodiversity and bioprocesses in WBP ecosystems. Efforts to address this need involved characterization of adaptive traits for understanding WBP genetic variation and population differentiation –16. GenePlant breeding programs usually aim to develop new varieties that have higher productivity and quality, as well as better fitness in habitats undergoing constant changes due to evolving pests/pathogens and shifting climates. In forest tree programs this typically includes maintaining high levels of genetic diversity and adaptation to local environments by capturing a wide range of the adaptive genetic variation of the parents collected for the breeding programs. However, the potential to reach breeding goals depends on the sustainability of the germplasm collected in breeding programs, which is in turn determined by the variability of distinct genotypes and their phylogenetic relationships inside the germplasm. Although efforts on WBP conservation, breeding, and restoration have increased in recent years , the WBPde novo assembly of the transcriptome, bioinformatic SNP mining, development of SNP arrays, and application of high-throughput genotyping technology. WBP breeding seed families in western North America were characterized using SNP markers, which may provide a comprehensive insight for efficient management of genetic resources in their ecological restoration. The genomic information and tools will help us understand the underlying patterns of genetic variation in WBP populations across western North America and facilitate WBP genetic improvement for better adaptation to environmental stressors, including enhanced resistance to WPBR and MPB through identification of elite genotypes underlying desirable traits. Maintaining a high level of genetic diversity in WBP restoration populations will help ensure WBP has the potential to continue to evolve in the face of future abiotic and biotic threats.This study was undertaken to characterize seed families collected in WBP breeding programs using an integrated genomics approach. Here we report generation of WBP genomic resources by de novo assembly, and in silico SNP discovery, needles were collected in July, 2013 from one year-old healthy seedlings of 11 open-pollinated seed families growing in a growth chamber at the Pacific Forestry Centre, Victoria, British Columbia (BC), Canada. The seeds for growing these seedlings originated from wild mother cone trees in BC. Needle samples were collected individually for each seedling. Ten seedlings per seed family were pooled when total RNAs were extracted.For RNA-seq analysis, transcriptome C. ribicola at Dorena Genetic Resource Center (DGRC), USDA Forest Service.For verification and application of the SNP genotyping arrays in the population study, we sampled needle tissues for genomic DNA extraction from a total of 372 seedlings from 124 open-pollinated seed families , with three seedlings per seed family. The seeds for these 124 seed families were previously collected from wild mother trees represented in breeding programs. The present work did not include any field studies although WBP is considered as an endangered or protected species. The locations and other related information (geographic coordinates and seed zone assignment) of the seed families are shown in Assessment of phenotypic traits relating to quantitative resistance to WPBR was performed at DGRC, with special attention to the number and types of stem infections and overall seedling disease severity . The numRNA extractions, cDNA synthesis, and RNA-seq analysis were performed as described previously . Messenghttp://www.usadellab.org/cms/?page=trimmomatic) was used to trim RNA-seq raw reads with default settings at ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 [de novo assembly using Trinity, version: trinityrnaseq_r2013-02-25 [Trimmomatic  was predicted by TransDecoder in the Trinity software package at a minimum protein length of 50. To check the WBP proteome predicted from the P. taeda  and P. monticola ,30. Geneonticola .in silico DNA variations in the WBP transcriptome, the CLC Genomics Workbench  was used to align RNA-seq reads back to the de novo assembled transcriptome with parameters: masking mode = no masking; mismatch cost = 2; insertion cost = 3; deletion cost = 3; length fraction = 0.95; similarity fraction = 0.95; auto-detect paired distances = yes; global alignment = yes; non-specific match handling = ignore.To find DNA variations  were then called with parameters: minimum coverage = 10; maximum expected variants = 2; ignore quality scores = no; ignore non-specific matches = yes; ignore broken pairs = yes; variant probability = 90.0; require presence in both forward and reverse reads = yes.vs. coding regions, synonymous vs. non-synonymous), gene groups where they were localized, and gene expression patterns. The ns-SNPs were selected from candidate groups with putative gene functions in plant disease resistance, defence, or adaptation. Candidate gene groups were determined based on above GO analysis and BLAST analysis against local databases of the P. taeda proteome derived from a genome sequence draft [P. monticola resistant gene analogs (RGA) of the NBS-LRR and RLK gene families [P. monticola defence-related genes in response to C. ribicola infection [Highly differentiated genetic variants are more informative per locus than randomly chosen markers. To design genotyping arrays, SNPs within the WBP transcriptome were evaluated as previously reported ,32. We sce draft , P. montfamilies , as wellnfection . The ns-A total of 216 ns-SNPs, each per unigene with putative function, were selected for design of Sequenom iPLEX arrays . MultiplGenAlex v6.5  was usedAt the population level, the genetic differentiation index (Fst) was estimated with a confidence interval of 95% for 999 permutations. The pattern of allelic differentiation among populations was explored through principal coordinate analysis (PCoA) based on the genetic distance matrix with data standardization using GenAlex v6.5. The software POPTREE2  was usedThe Bayesian approach was used to infer the genotype structure without introducing any a priori classification using the program STRUCTURE v2.3.4 . The admde novo assembled with N50 of 1,692-bp and an average length of 895-bp using Trinity, and estimated to be expressed from 80,323 unigene sequences in a total length of 55-Mb. Following de novo assembly, 97,447 coding DNA sequences (CDS) were detected using TransDecoder, and 53.7% of them were predicated as complete for the open reading frames (ORFs). Other characteristics of the assembled transcriptome were shown in Using the Illumina Hiseq-2500 platform, three RNA-seq runs generated a total of 160 million 2 x 100-bp PE reads. After trimming, 129,522 transcripts were P. taeda proteome [P. taeda sequences. When the putative P. taeda proteome was used as a query to search the WBP transcriptome, 94% of the putative P. taeda proteome was found to have significantly homologous hits in the WBP transcriptome  had significant homologous hits  with the putative proteome . Among tcriptome . These rata sets ,30, 6,88In silico SNPs were predicted within the WBP needle transcriptome. A total of 100,320 DNA variant sites were identified, 91% of them were SNPs  were involved in metabolic processes , cellular processes , single-organism processes , biological regulation (656), response to stimuli (638), regulation of biological processes (569), localization (534), and cellular component organization or biogenesis (387) .P. monticola defense genes in response to C. ribicola infection, 216 in silico ns-SNPs (one per unigene) were selected to design Sequenom iPLEX high-throughput SNP genotyping arrays. After initially screening six panels of SNP arrays (36 SNPs per panel) on a set of 96 samples, 117 ns-SNP loci  passed quality control and their genotypes scored successfully across the sample set. Forty-three SNP loci were revealed as homozygous in the set of genotyped samples and three showed low levels of minor allele frequency (MAF ≤ 5%). The remaining 71 ns-SNP loci  zones in three regions  . Only onThe comparison of mean expected heterozygosity (He) of alleles across 16 (sub) zones revealed that BC-EK seed families were highly heterozygous (0.40 ± 0.02). In contrast, heterozygosity of the seed families inside SZ-1 (0.26 ± 0.02) was scored at the lowest level . CorrespAs a measurement of excess homozygosity, fixation index (F) for all trees within a (sub) zone varied from -0.06 (± 0.05) (SZ-1) to 0.28 (± 0.03) (SZ-4_1) with a mean value of 0.09 (± 0.01), suggesting a general pattern of random mating. In a consistent trend, low mean values for Fis (0.102 ± 0.025), Fit (0.180 ± 0.024), Fst (0.088 ± 0.004), and a relatively high value for Nm (3.092 ± 0.192) were calculated based on all genotyped SNP loci . These rPair-wise genetic distances were measured and used for two-dimensional (2-D) PCoA using GenAlEx . PCoA plThis 2-D clustering pattern was further supported by a phylogenetic analysis using matrix of Nei’s standard genetic distances with sample size correction (Dst) . A phyloAMOVA was further used to evaluate genetic differentiation . Of the The possible WBP population structure was explored among all genotyped samples without introducing any a priori classification using the Bayesian clustering approach implemented in the program STRUCTURE. An admixture model was used here because of the potential that individuals may have mixed ancestry and the presence of gene-flow between different geographical areas. This genotype-based classification provided data for a biological interpretation of the sub-population structure in addition to the geographical origins and classification of the seed zones. There was clear evidence of sub-structuring within all genotyped individual seedlings. The highest peak of ΔK was detected at K = 2 by the Bayesian clustering , supportVisual inspection of the STRUCTURE barplots indicated that plots for K = 9 were informative with respect to population substructure . Obvious2 = 0.4742, p < 1e-5), followed by the correlation between PC-2 and longitude   of genetic variations among seed families and eco-geographical parameters at the site of origin. PC-1 and PC-2 explained 7.82% and 5.50% of the variation at the seed family level, respectively. The most significant correlation was found between PC-1 and latitude (R < 0.05) .Structure inferred genetic subgroups at K = 9 were assigned to each seed (sub) zone. The spatial distribution pattern of the genetic subgroups across the landscape of three regions  in western North America is shown in C. ribicola inoculation, relative levels of WPBR disease severity were assessed and compared among genetic subgroups. The mean rust disease severity levels of seedling groups with each of nine genetic subgroups (GG1 to GG9) are shown in P < 0.05, or P < 0.01). The other four subgroups  exhibited medium disease severity levels without significant differences from the others  [The development of modern genomic resources can provide baseline knowledge for breeding, conservation, and restoration of endangered organisms. Recent advances in technologies for NGS and automatic SNP high-throughput genotyping have accelerated genomic studies for the characterization of molecular variations in a few five-needle pines. Recently, other investigators identified a large set of quencing . In addi& Balf.) . RNA-seq& Balf.) ,30,32,41& Balf.) ,29,42.De novo assembly of RNA-seq reads generated a WBP needle transcriptome with > 80,000 expressed unigene sequences  inside the WBP needle transcriptome. Due to changes in primary protein sequence, ns-SNPs may be more informative per site than SNPs picked up randomly. Several studies revealed that ns-SNPs are more likely to be related to specific biological functions and phenotypes ,45. AminP. monticola resistance to C. ribicola in the WPBR pathosystem [The candidate gene approach is suited for population genetic studies to detect genes underlying complex traits, i.e. traits for which single candidate genes make a small contribution. A number of candidate genes have been identified as potential targets for selection of adaptive traits in conifers ,47. A sehosystem ,50.With these considerations, this study documented over 22,291ns-SNPs  throughout the WBP transcriptome and annotated genes containing ns-SNPs , S1 Fig.Information about the genetic diversity and population structure in the seed families collected for breeding programs is of fundamental importance for WBP improvement and subsequent restoration efforts. We evaluated genetic diversity using ns-SNPs of 71 candidate genes within and among 124 seed families, which were selected as representative of the WBP breeding materials in regions of BC, WA, and OR. As estimated by expected heterozygosity (He) and percentage of polymorphic SNP loci (P), genetic diversity levels were similar across seed (sub) zones in western North America . The meaA mean number of migrants (Nm = 3.092±0.192) calculated in our study was much lower than previous report on WBP populations in the Inland West (Nm = 9.354) . Most coBased on genetic distance, PCoA and UPGMA clustering clearly separated WBP populations from sampled regions into two major groups. One included northern WA and BC, and the other included southern WA and OR , suggestThe presence of nine genetically distinct subgroups allowed us to estimate the composition of genetic subgroups in populations. Co-occurrence of all nine genetic subgroups in SZ-2_E, SZ-4, and SZ-7_3  suggestsHigh genotypic diversity in three seed (sub) zones  suggests that these regions may be candidate areas for selection of elite seed families with adaptation to pathogens/pests or other environmental stressors. However, admixtures have been detected in all the sampled seed zones, demonstrating that the trees examined in this study are heterogeneous. Genetically heterogeneous, admixed stands may have better fitness, providing candidate parent trees for breeding selection and restoration efforts.C. ribicola [Principal components of WBP genetic variations were recently detected with links to heterozygosity, latitude, and longitude in WBP stands . In the ribicola . These rThis study reports on the development and application of WBP ns-SNP markers. Our SNP markers were developed by transcriptome comparison using RNA-seq technology in a core collection of seed families. These markers cover a wide range of expressed genes, and a large proportion of them produce amino acid changes in the putative proteins encoded by the genes, and thus a potential role in contributing to phenotypic variation in association studies. Using ns-SNP markers, genetic diversity of WBP seed families currently used in breeding and conservation programs were assessed. This study provides novel insights into the population structure of this endangered species. Experimental verification of a subset of ns-SNPs in high-throughput suggests that WBP genomic resources developed here may be invaluable in the future for functional genomics studies, population genetic study, germplasm resource assessment, and genome-wide association study in WBP and related five-needle pines.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S1 Figde novo assembled using RNA-seq reads. Gene annotation with GO terms was presented at the 2nd level for the biological processes.CDS were derived from the whitebark pine transcriptome (TIF)Click here for additional data file.S2 FigData in the phylogenetic dendrogram were drawn to scale with the branch length proportional to the genetic dissimilarity.(TIF)Click here for additional data file.S3 FigGenetic variations of 124 seed families were calculated by Principal Component Analysis based on genotypic data of 71 SNP loci. Above: PC-1 vs. latitude; Bottom: PC-2 vs. longitude.(TIF)Click here for additional data file.


From this overlap we identified 21 lead SNPs with 100% validation rate containing two clusters: one consisted of DGAT1 (chr14:1.8 M+), MGST1 (chr5:93 M+), PAEP (chr11:103 M+) and GPAT4 (chr27:36 M+) affecting protein, milk and fat yield and the other included CSN2 (chr6:87 M+), MUC1 (chr3:15.6 M), GHR (chr20:31.2 M+) and SDC2 (chr14:70 M+) affecting protein and milk yield. Combining beef cattle data identified correlated SNPs representing CAPN1 (chr29:44 M+) and CAST (chr 7:96 M+) loci affecting beef tenderness, showing pleiotropic effects in dairy cattle. Our findings show that SNPs with a large effect on one trait are likely to have small effects on other uncorrelated traits.While single nucleotide polymorphisms (SNPs) associated with multiple phenotype have been reported, the knowledge of pleiotropy of uncorrelated phenotype is minimal. Principal components (PCs) and uncorrelated Cholesky transformed traits (CT) were constructed using 25 raw traits (RTs) of 2841 dairy bulls. Multi-trait meta-analyses of single-trait genome-wide association studies for RT, PC and CT in bulls were validated in 6821 cows. Most PCs and CTs had substantial estimates of heritability, suggesting that genes affect phenotype via diverse pathways. Phenotypic orthogonalizations did not eliminate pleiotropy: the meta-analysis achieved an agreement of significant pleiotropic SNPs ( Most mammalian phenotypes are quantitative or complex traits, whose variation is controlled by many genomic mutations with small effects and by environmental effects. While thousands of single nucleotide polymorphisms (SNPs) have been found associated with individual complex traits by genome-wide association studies (GWAS), an important question is the extent to which the same causal variants affect multiple traits, i.e. the extent of pleiotropy4.Understanding genetic control of mammalian phenotype, including body growth, health outcomes and metabolic pathways can improve patient treatment5 and livestock7. However, it is also possible that uncorrelated traits share some causal variants. This possibility can be tested by transforming a set of correlated traits into uncorrelated traits, for instance, by principal components (PCs) analysis9. If genes influence a set of traits through a limited number of physiological pathways, it may result in that only the first few PCs showed strong genetic effects leading to a simple picture of pleiotropy. A previous study analysing simulated and a small amount of real human data showed that large genetic variances can exist in PCs explaining small amount of total phenotypic variances10.It is expected that correlated traits share some causal variants and this has been observed in humans6, an equivalent analysis of a large number of dairy traits has not been conducted, although some pleiotropic patterns were reported9. The 25 dairy traits included measures of milk production, fertility11, conformation and management traits12 which contribute to the profitability of dairy farming13. Secondly, we examined the effects in dairy cattle of SNPs significantly associated with quantitative traits in beef cattle7.To further describe the extent and nature of pleiotropy in large mammals, we used data on 2841 progeny-tested dairy bulls with phenotypes on 25 traits and high density genotypes of 632,002 SNPs. Whilst many pleiotropic SNPs have been found in beef cattle7 to analyse 25 raw traits (RTs), 25 PCs and a novel set of phenotypic orthogonalisation, Cholesky transformed traits (CTs)14 in the dairy cattle discovery population. This was followed by the selection of lead SNPs representing major dairy QTL, confirmed by conditional and joint analysis. Finally, SNPs associated with both dairy traits alone and those SNPs shared by dairy and beef traits were validated at both multi-trait (linear index approach) and single-trait (GWAS) level in a separate population of 6821 dairy cows.We used genome-wide meta-analysis modelling Chi-square distributions of SNP effect sizehttp://www.datagene.com.au/). Daughter trait deviations were the average trait deviations of a bull’s daughters and all phenotypes were pre-corrected for known fixed effects. Only those bulls’ phenotype which were based on records from more than 15 progenies were included. Complete phenotype data were from 9,662 dairy cattle from the breeds Holstein, Jersey, MIX (crosses between Holstein and Jersey) and Australian Red, a genetically distinct breed15  were discarded. 2,841 bulls were used as the discovery population due to the high accuracy of the phenotype which was the average of >15 daughter records. The 6,821 cows, who had individual phenotypic records and weak genetic relationships with the bulls  = D, a diagonal matrix of eigenvalues; gn was an k × 1 vector of RT for animal n. The 25 eigenvectors were shown in Supplementary Table n × k CT matrix was calculated based on Cholesky decomposition:cn was a k × 1 vector of Cholesky scores for the animal n; L was the k × k matrix of the Cholesky factor which satisfied LLt = COV, the k × k covariance matrix of raw scores after standardisation as z-scores14; gn was an k × 1 vector of RT for animal n. The kth CT can be interpreted as the kth raw trait corrected for the preceding k-1 traits. Consequently, 1st CT equals the 1st RT.The principal components (PCs) and Cholesky transformed traits (CTs) were calculated based on centered and z-score scaled raw traits (RTs) of the discovery population in R (v3.3.1)18:breedi = three breeds, Holstein, Jersey and Australian Red; bx = regression coefficient b on SNP genotypes x; a = polygenic random effects ~N where G = genomic relatedness matrix based on all SNPs19. The same model was applied to GWAS and, without including SNP in the model, calculations of SNP heritability for all RTs, PCs and CTs.25 RTs with the derived sets of 25 PCs and 25 CTs were analysed one trait at a time with linear mixed models using GEMMAps < 0.05) for both of a pair of traits were compared with the expected number using the Fisher’s exact test (pf) implemented in GeneOverlap20 in R.The count of SNPs that were significant  × 1 vector of the signed t-values of SNPi effects, i.e., beta/se, for the k traits; ti′ was a transpose of vector ti (1 × k); and V−1 was an inverse of the k × k correlation matrix where the correlation was calculated over the all estimated SNP effects  of the two traits. The χ2 value of each SNP was examined for significance based on a χ2 distribution with k degrees of freedom to test against the null hypothesis that the SNP had no significant effects on any one of the k traits. The false discovery rates of χ2 tests were calculated following Storey’s method21 by ‘qvalue’ package in R. An additional test of pleiotropy was performed by carrying out the multi-trait test separately for odd and even numbered PCs. This was to confirm the existence of pleiotropy using PCs’ orthogonality: if a SNP had strong pleiotropic effects on all PCs, the variances of which were randomly attributable to RTs, this SNP was then expected to have strong effects on any subsets of PCs. Following this logic, this pleiotropic SNP was expected to have consistently strong effects on randomly selected odd and even PCs. t values of single-trait GWA of odd PCs and of even PCs were combined to calculated odd PC χ2 and even PC χ2. We then tested whether or not the same SNPs were significant in both analyses.Multi-trait meta-analysis of 25 RTs or 25 PCs or 25 CTs followed previous procedurespm of RT, PC and CT at least <1 × 10−5) within non-overlapping 1-Mb intervals of a chromosome of the discovery population. For chromosomes with multiple significant SNPs, the 1-Mb interval started from the most significant SNP and approached to the distal ends. Such selected lead SNPs were refined by step-wise analyses . The most significant ones of these few SNPs one per 1 Mb interval were again selected, added to the list of existing lead SNPs and the process repeated until there were no additional SNPs significant after fitting the lead SNPs. Then, joint analyses22 fitting all lead SNPs in a regression was used to estimate their effects on traits. Those SNPs without significant effects (joint P < 0.05) on at least one trait were removed from the lead SNP list.The selection of lead SNPs representing major QTLs was based on the most significant SNP  that had the same direction of effect in both the discovery and validation (see below) were used to calculate the effect correlation matrix within RTs, PCs and CTs. The correlation matrices were used to perform hierarchical clustering.23 and previous publications including27. Published results overrode ensemble predicted annotation if both existed. If no genes from Ensembl or published results could be identified for the SNP, the closest gene within 1 Mb was assigned.Genes associated with SNPs were annotated by variant effect predictorpm < 1 × 10−5 in the meta-analysis were selected and compared with dairy cattle SNPs with pm < 0.05 in the meta-analysis of 25 dairy RTs. The significance of the overlap between SNPs significant in the dairy and beef analysis was tested with the GeneOverlap.Beef cattle traits with animal numbers >1900 from a previous study (Table 1 in ref. b′ was the transpose of a vector of the size effects (beta) of the SNP  on the 24 RT, PC and CTs; C−1 was an inverse of the 24 × 24 (co)variance matrix among the k traits calculated from the beta of all tested SNPs for RT, PC and CTs only in the discovery population. yRT was a 24 × 1 vector of the phenotype values for the 24 traits (matching 24 RTs in bulls) for each animal in the validation population. yPC and yCT in the validation population were calculated using the same formula for calculating 24 PCs (PC eigenvectors) and 24 CTs (Cholesky L matrix) as in the discovery population according to ref. yi) for each cow in the validation population was analysed as a new response variable for an association with only the lead SNPi used to define this linear index. Such linear index analysis was also applied to validate in dairy cows the SNPs that were significant in the analysis of beef cattle and the dairy bulls. Dairy lead SNPs and dairy-beef shared SNPs were also validated for individual RT by comparing SNP effect directions between GWAS of the discovery and validation populations.The phenotypes on the validation population (i.e. the cows) did not include one trait (‘Mamm’), so the multi-trait analysis on the bulls was repeated with 24 traits so that the results could be compared directly with results on the cows. Based on these results the linear index of 24 traits on which each lead SNP had the most significant effect was calculated by:15 and ref. The genotype data used in this study were included in published articlesAmong the RTs and CTs there was a tendency for the traits with the highest heritability to have the highest number of significant SNPs Table . Milk prThe single trait analysis of the PCs’ showed little relationship between the phenotypic variance explained by each PC, the number of significant SNPs and the heritability Table . PC1 wh accounteps < 0.05) effects on both traits than expected by chance   Fig. . Althoug05) Fig.  and CTs 05) Fig.  still ha05) Fig. .Figure 1DGAT1 locus which is strongly associated with milk and fat traits28, also had the largest number of significant SNPs segregating across single-trait GWAS for RTs, PCs and CTs  SNPs were 416 for RTs, 466 for PCs and 425 for CTs, each with a FDR < 0.01. These numbers are greater than the number of significant SNPs detected in any single trait analysis. Each of the three meta-analysis was an approximation to a full multi-trait analysis because they used summary statistics. However, they largely agreed with 368 SNPs being significant in all 3 meta-analyses 28, SDC2 15, GHR 15, CSN2 15, MGST1 25, PAEP 30 and GPAT4 29. The significance peak on chr18, 57 M+, detected in our study overlapped with reported CTU1 locus associated with calving difficulty27. The small peak detected on chr3 (15.6 M+) was associated with previously identified MUC1 locus26.Three multi-trait analyses were performed. The number of significant  or only on the even 2, 4, 6…) PCs again detected pleiotropic QTL on chromosome 14 and 18  from each chromosome of the dairy bulls , MGST1 , PAEP  and GPAT4  loci. These SNPs had an allele which increased fat yield but decreased protein and especially milk yield , MUC1 , GHR  and SDC2 , the clustering was stronger across PCs  of pleiotropic SNPs were detected between the 25 dairy cattle traits and 15 beef cattle traits which included body height, muscle and fat mass. Although these dairy-beef shared SNPs had small effects in the dairy cattle population, more than 86% had effects in the same direction in bulls and cows  and CAST , both associated with beef tenderness31 showed correlated effect patterns across dairy cattle RTs, PCs and CTs. Both of these two loci contained an allele which decreased RT udder texture (11.UdTex), rear attachment width (11.RearAw) and stature, but increased likeability (farmer’s preferences score)  Fig. . The likre) Fig. . In addi9, we identified the mid-to-high range of heritability estimates of bull’s RTs  identified many significant pleiotropy SNPs with 100% validation rates of 21 lead SNPs 26, MGST1 25, PAEP 26 and GPAT4 29, each of which has an allele that increases fat yield but decreases protein and milk yields. DGAT1, MGST1 and GPAT4 have an effect on fat synthesis but PAEP is the gene for the milk protein beta lactoglobulin. The simplest explanation for this clustering is competition for substrate within the mammary gland. That is, a mutation in DGAT1 that decreases fat synthesis causes more substrate to be available for lactose and protein synthesis. In human cell lines, DGAT1 and MGST1 were reported to show co-expression in a gene group regulating adipogenesis35.The lead SNPs representing the dairy cattle major pleiotropic loci in Holstein, Jersey and Australian Red, largely overlap with reported dairy production-traits related loci Fig. . Using cnd cluster contained CSN2 , MUC1 , GHR  and SDC2 . Each member has an allele that increases protein yield but decreases milk yield  and CAST . Previously, these two SNPs were reported for both independent7 and epistatic31 effects on beef tenderness and the CAST protein binds to CAPN1 to inhibit its activities38. The effects on tenderness are partially post-mortem but in live cattle these proteins probably affect protein turnover. However, we did not observe significant effects of these two loci on protein yield in dairy cattle. Instead, the majority of observed effects of these two SNPs were on conformation traits  on a wide range of body type traits appear to be consistent with its role in regulating muscle development and growth.Some SNPs with large effects on beef cattle traits were shown to have small effects on dairy traits. Fig. . The twoCAST 7, 9.1 M+. Prits Fig.  which maKIZ  and within PGBD5  and close to SASH1  with widespread pleiotropic effects on dairy RTs, PCs and CTs  had effects on somatic cell count but not on milk production traits42. However, we also identified its significant effects on milking speed (in both RT and CT). RT and CT milking speed are also associated with the SNP close to SASH1 which regulates cell proliferation and apoptosis in human (RefSeq). There is too limited knowledge of the function of the piggyBac transposable element derived gene PGBD5 for us to explain its pleiotropic effects, especially these effects on RT, PC and CT protein yield, in the dairy cattle. Interestingly, PGBD5 gene is highly conserved across invertebrates and its encoded protein affected many other genes’ actions by inducing DNA transposition and may contribute to complex traits by genome remodelling43. It may also have an important regulatory role in the ruminant species.We identified less characterised loci, including SNPs within gene CTs Fig. . ConsistWe conclude that SNPs with a large effect on one trait are likely to have much smaller effects on other traits. Transforming the correlated traits to uncorrelated PCs or CTs reduces but does not eliminate this pleiotropy at the single-trait GWAS level. We hypothesise that this is because each causal variant has a unique pattern of effects across traits and hence generates a set of correlated effects that is different to the average correlations caused by all the causal variants together. Consequently, when the average correlations are used to construct PCs or CTs, some causal variants still have effects on multiple uncorrelated traits. A previously less characterised locus CTU1  showed strong pleiotropic effects on production and non-production traits. We also identified SNPs with strong effects in beef cattle showing small but validated effects in dairy populations. Our findings are not only useful for the dairy industry to use multi-functional genetic markers to achieve efficient selection, also provide important information for other researchers to consider when conducting multi-trait genome-wide analysis.Supplementary TableSupplementary Figure


Eighty-five passed the genome-wide significance threshold. Cross-validation of cojo-GCTA by CLUMP was 76%, i.e., 26 of the loci identified by the PGC using CLUMP were found to be dependent on another locus by cojo-GCTA. The overlap between cojo-GCTA and multi-SNP was better (up to 92%). Three markers reached genome-wide significance (5 × 10−8) in a joint effect model. In addition, two loci showed possible allelic heterogeneity within 1-Mb genomic regions, while CLUMP analysis had identified 16 such regions. Cojo-GCTA identified fewer independent loci than CLUMP and seems to be more conservative, probably because it accounts for long-range LD and interaction effects between markers. These findings also explain why fewer loci with possible allelic heterogeneity remained significant after cojo-GCTA analysis. With multi-SNP, 86 markers were selected at the threshold 10−7. Multi-SNP identifies fewer independent signals, due to splitting of the data and use of smaller samples. We recommend that cojo-GCTA and multi-SNP are used for post-GWAS analysis of all traits to call independent loci. We conclude that only a few loci in SCZ show joint effects or allelic heterogeneity, but this could be due to lack of power for that data set.We have tested published methods for capturing allelic heterogeneity and identifying loci of joint effects to uncover more of the “hidden heritability” of schizophrenia (SCZ). We used two tools, cojo-GCTA and multi-SNP, to analyze meta-statistics from the latest genome-wide association study (GWAS) on SCZ by the Psychiatric Genomics Consortium (PGC). Stepwise regression on markers with  Genome-wide association studies (GWAS) have contributed important information about genetic markers of the disorder; the most recent Psychiatric Genomics Consortium (PGC) meta-analysis identified 108 genomic loci containing common genetic variants associated with SCZ3. However, each of these common genetic factors has only a small effect on the disease susceptibility. The relatively low proportion of variance explained by genome-wide-significant hits from GWAS data is a typical observation for complex traits with polygenic architecture.Schizophrenia (SCZ) places a heavy burden on patients and on society. Its heritability is estimated by recent twin or family studies to be between 64p value below the genome-wide threshold (5 × 10−8) constitutes the set of associated loci. Although this cutoff is necessary to avoid type 1 errors, it has been shown since such a conservative approach probably creates many type 2 errors, leaving numerous associations of smaller effect undetected. Purcell et al.4 first showed that with increasingly liberal significance thresholds, more variability in complex disorders, including SCZ, could be explained. The proportion of variance in case–control status that can be explained by the genotyped single-nucleotide polymorphisms (SNPs) significantly increases when the threshold is lowered from the genome-wide significance level to 0.05 for each of 40 target subgroups of the primary GWAS 3. Across all the samples, the estimated variance rises from 0.026 to 0.184. The polygenic risk score calculated using the significance threshold of 0.05 explains nearly 7% of variance on the liability scale across all the samples, whereas the genome-wide significant hits only explain about 3.4% of variance. This difference between the phenotypic variance explained by genome-wide significant SNPs and the phenotypic variance explained by genotyped variation is well-documented and known as “hidden heritability.” It will probably become smaller as larger samples are analyzed, but alternative statistical methods may also help to capture some of this association signal. For instance, joint effects tools focus on allelic heterogeneity and imperfect tagging data6. In the case of imperfect tagging, a single genotyped (or imputed) variant might not entirely explain the variation at a locus that occurs due to a single unknown causal variant. In the case of allelic heterogeneity, a single hit is unlikely to capture all the linkage disequilibrium (LD) between the several unknown causal SNPs and the genotyped variants at the locus. For example, the locus may contain two causal variants, the first increasing the risk for a trait, and the second one being protective. If these markers are correlated, marginal effect methods cannot detect the associations, because individuals who carry both variants have very little or no increased risk for a disorder. For those situations, a model where observed phenotype is influenced at each locus by variants that could be approximated by a linear combination of several independent observed markers is more reasonable and would explain more phenotypic variation. Using joint and conditional (cojo-GCTA) analysis of GWAS data for height, Yang et al.5 identified loci with multiple independent SNPs, and found 49 additional associated SNPs that explained around 1.3% of the phenotypic variation. Using the multi-SNP approach, Ehret et al.6 showed that 3, 2, and 1% of additional phenotypic variance could be explained for height, body mass index, and waist-to-hip ratio, respectively.In classical GWAS analysis, the collection of signals with an association 3) to test whether they can identify loci with joint effects.Our goal is to better understand the complex genetic architecture of SCZ using post-GWAS analysis tools. One of the most common approaches is to apply conditional regression within the locus of interest. However, this requires genotype data that is not available for meta-statistics. Cojo-GCTA and multi-SNP are the only methods with robust approximations for conditional regression analysis of summary data. We therefore applied these two statistical methods to the meta-statistics from the latest PGC SCZ GWAS  protocol 7 and the Norwegian Thematically Organized Psychosis sample 8. QC for each of these cohorts was performed with the PLINK tool9. Samples were excluded based on heterogeneity, relatedness, and call rate. Parameters for QC were: HWE p value of <1 × 10−3; minor allele frequency of <0.01; missingness 0.05. SNP data for the NCNG and TOP cohorts were imputed to the 1000 Genomes panel by MACH10 separately and merged after QC.The Norwegian reference sample comprised individual genotypes from the Norwegian Cognitive NeuroGenetics sample ; subject missingness <0.05; HWE p value >10−4. In addition, only individuals with concordant sex information were retained, and only one subject was kept for each pair of individuals with 13.The German reference samples comprised individual genotypes from the Heinz Nixdorf RECALL studyp values, imputation score, missingness, and outliers. After stringent QC  show significant association in the discovery subset  and (2) are not in pairwise LD (r2 > 0.1) with any other markers with the lowest p value. The replication subset is used for unbiased estimation of the effect sizes for the selected multi-SNP list. In this step, the tool performs estimation of the joint effect of the multi-SNP list.Since we wanted to further validate the results from cojo-GCTA, we also compared the list of independent SNPs identified by cojo-GCTA with the list of loci of joint effect identified by the multi-SNP method14, we performed meta-analysis on the summary statistics from 26 individual subsamples as the discovery set and on the summary statistics from 26 individual subsamples as the validation set. We kept the same 3,485,365 SNPs in both sets that were analyzed by cojo-GCTA. The European cohort from the HapMap project release II + III15 was used to estimate the LD structure6.We applied the multi-SNP association method to the summary statistics from the PGC SCZ subsets. We obtained access to summary statistics for 52 individual subsamples and randomly split the subsamples into discovery and validation sets. Using the METAL toolp values after cojo-GCTA, and which had not been reported previously by the PGC-SCZ, was used to define new genomic loci associated with SCZ. The genomic loci were defined by the associated SNP and all SNPs in LD with the associated SNP (r2 > 0.2). Gene annotations were performed with the aid of the LDsnpR package16 and the RefSeq gene list17.The final list of SNPs that showed joint effects and genome-wide significant 5; multi-SNP6; PLINK9; MACH10; METAL14; and LDSnpR16. All are publicly available.The following programs were used in this study: GCTA (cojo-GCTA option)5 and multi-SNP6 to the PGC-SCZ GWAS summary data. In these two methods, each locus is analyzed with a combination of several independent markers, corrected for LD between the markers.We applied conditional regression-GCTA (cojo-GCTA)p value, and allele frequency) and genotypes from a reference population for LD estimation. We used about 3.5 million SNPs from the publicly available PGC-SCZ summary statistics3. Since the LD structure of the major histocompatibility complex (MHC) region on chromosome 6 is challenging for “multi-SNP” analysis and requires specific analyses based on genotypes, we chose to exclude this region from our study. In the PGC SCZ paper3, the MHC is represented only by the single most significant SNP. We used a Norwegian sample (N = 2200 after QC) for LD reference, and an independent reference sample of German origin (N = 2478) for validation.Cojo-GCTA requires summary statistics  based on the LD between the SNPs. This stepwise procedure ensures that the SNP with the lowest p value is selected first for conditioning the effect on neighboring loci based on the LD between the neighboring SNPs and the selected SNP. Following LD-based correction of effect, all SNPs that remained significant under a fixed threshold are run through the same process in a stepwise manner. This process identifies (1) the number of independent signals in a region, and (2) association signals due to the joint effect of several SNPs. By lowering the threshold for cojo-GCTA, we can include more SNPs in the analysis and potentially identify more loci with joint effects that failed to be identified by single-marker analysis. We still keep the genome-wide significance threshold to call associated loci, whether they are due to a single effect or to a joint effect.The stepwise conditional regression implemented in cojo-GCTA corrects −3 to 5 × 10−8, several SNPs became significant at the genome-wide significance threshold due to the joint effects of neighboring markers. The SNPs identified using the Norwegian LD reference sample were then tested for validation using the German LD reference sample. SNPs were considered to be validated if the joint p values passed the genome-wide significance threshold (5 × 10−8) with both LD references and if the joint p values did not change between the two LD references. The procedure is summarized in Supplementary Fig. We first tested the effect of lowering the significance threshold for the stepwise regression on the level of validation when the Norwegian or German samples were used for LD reference Table . When th−7, we observed that the increase in additional loci identified by joint effects in the Norwegian sample did not lead to an increase in loci validated using the German sample as LD reference. This is probably because by lowering the significance threshold, we identified more signals due to population-specific LD patterns.By decreasing the threshold to <10−7 estimated from either the Norwegian or the German cohort were concordant with the frequencies for the list used for the PGC-SCZ data  using CLUMP9. A total of 108 independent SNPs were identified as significant by the PGC-SCZ CLUMP analysis , represents the MHC region. For comparison with the cojo-GCTA results, we excluded this SNP and used the remaining 107 markers.Next, we compared our results with independent signals identified in the publicly available PGC-SCZ discovery data , four did not reach significance in the PGC-SCZ discovery sample (Table Out of the 85 significant independent SNPs identified by cojo-GCTA (Supplementary Table le Table . One of −11) and rs4702 (2.30 × 10−12), substantially increased to 5.44 × 10−18 and 2.06 × 10−24 respectively, after combined analysis. The third locus, on chromosome 22, contains the signals rs61298040 (3.90 × 10−8) and rs1023500 (5.04 × 10−8). The significance levels of these two markers were improved to 4.02 × 10−12 and 5.59 × 10−12, respectively.In addition, SNPs in three loci showed increased significance after joint model analysis . A total of 86 loci were identified.We next analyzed the same GWAS summary statistics with multi-SNP. This methodpj value <10−7. As many as 84 of the 86 multi-SNP loci corresponded to loci identified by the cojo-GCTA procedure, i.e., 87% of the loci identified by cojo-GCTA are validated by multi-SNP (Supplementary Table We then compared the 86 multi-SNP SNPs with the 96 cojo-GCTA markers with joint 11), and (2) the statistical method used to calculate the joint effects and the power of the studies, since the multi-SNP meta p values were calculated using smaller discovery and validation sets.The additional 13 regions identified with cojo-GCTA but not with multi-SNP analysis are most likely explained by differences in (1) the external LD reference  and the multi-SNP method from Ehret et al.6 on the meta-statistics from the latest analysis of SCZ by the PGC3, to try and capture allelic heterogeneity and loci of joint effects in this GWAS. At present, there is no “gold standard” for conditional regression analysis of metastatistics, but these approaches have successfully identified additional independent signals in GWAS of height . In the body mass index (BMI) GWAS, Yang et al.5 did not find additional SNPs, while Ehret et al.6 found 10 new signals. These results suggest that the portion of the missing heritability due to multiple independent effects per locus is not insignificant, but varies across human traits, which might be due to different polygenicity level or to power.In this study, we have used the conditional regression method from Yang et al.p value <10−7 to select SNPs in stepwise regression, cojo-GCTA identified 96 independent signals, 85 of which passed the genome-wide significance threshold. The PGC-SCZ study, using the CLUMP method, identified 107 SNPs (excluding the MHC region) in independent loci in the discovery sample. One reason for this difference is that the two methods correct for LD and assess the range of LD in different ways. For a single locus, cojo-GCTA can account for longer range LD effects up to 10 Mb, while CLUMP has so far been limited to 500 kb. Therefore, with cojo-GCTA, we can correct for LD effects in larger regions. The other main difference is that cojo-GCTA adjusts the β values of neighboring markers, thus taking into consideration both LD and the direction of effects between SNPs. While the validation of cojo-GCTA by multi-SNP was up to 92%, the validation of cojo-GCTA by CLUMP was 76%. Taking these findings into consideration, it appears that the set of independent SNPs identified by cojo-GCTA is more conservative than that identified by CLUMP. Thus, in future studies and in analyses of independent markers in GWAS, we would recommend applying the cojo-GCTA and/or multi-SNP methods as complementary approaches to the standard CLUMP procedure, since the tools are easy, versatile, and computationally fast. Both methods have shown good agreement with each other and with the CLUMP analysis. In contrast to cojo-GCTA, the multi-SNP protocol suggests splitting the summary statistics into discovery and validation sets. This avoids selection bias, but at the same time, the sample sizes are decreased, and therefore there is less power to identify signals of association27. These secondary analyses of GWAS were performed recently with cojo-GCTA for several phenotypes , and will most likely become more common in future GWASs.Using the strict threshold of 5 found that the significance of 29 SNPs  was greatly improved, while 2 SNPs for BMI were improved at the threshold 5 × 10−6 and none at the genome-wide significance level. Similarly, the number of loci with multiple independent SCZ-associated SNPs was relatively low, i.e., only two 1 Mb loci had possible allelic heterogeneity. In comparison, Yang et al.5 identified 36 loci showing allelic heterogeneity for height in the GIANT samples, but none for BMI. We have provided comparison of cojo-GCTA results across several traits with complex polygenic architecture in the After including cojo-GCTA, four additional SNPs became significant in the joint effect model, and the level of significance of three of them increased substantially. The significance level was unchanged for the majority of independent markers. Applying the same method to a GWAS of height, Yang et al.TCF4, which has previously been shown to have potential allelic heterogeneity, since other markers in the gene are associated with phenotypes related to SCZ18. The locus on chromosome 22 encompasses several genes and has been reported previously to be associated with SCZ31 and Alzheimer disease32. More work will be needed to understand the relationship between these independent markers and whether they target the same genes .The two loci with evidence of allelic heterogeneity in SCZ are located on chromosomes 18 and 22. The locus on chromosome 18 encompasses the gene C4 structure is crucial for the development of SCZ and might explain the main signal of association to this region in SCZ33.We excluded the MHC region from the analysis because this region presents a complex LD structure. Although conditional regression analyses have successfully identified the number of independent associations in other complex phenotypes associated with the MHC region, these analyses require access to genotypes, which were not available to us. Identifying the number of independent associations in the MHC was not the purpose of our investigation, and other studies have successfully focused specifically on this region. For instance, a recent study showed that the Contrary to our expectations, we found that the number of markers showing genome-wide significance was lower when independent signals were identified with cojo-GCTA than with CLUMP, and we show that this is likely due to long-range LD. However, we confirm the association of 85 independent loci with SCZ (in addition to the MHC locus), and we identified two loci with multiple signals that should be further examined. Thus, the systematic analysis of independent markers located at the same loci with the methods we used here can enrich our current understanding of complex disease architecture and provide insights into designing further tools for post-GWAS studies.Supplementary DataSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 5


To investigate structure–activity relationships, the effects of individual DP fractions of VGOS were evaluated. Moreover, the obtained results with GOS were compared with Caco-2 monolayers incubated with fructo-oligosaccharides (FOS) and inulin.The direct effects of galacto-oligosaccharides (GOS), including VivinalCaco-2 monolayers were pretreated (24 h) with or without specific oligosaccharides or DP fractions of VGOS (DP2 to DP6) before being exposed for 12 or 24 h to the fungal toxin deoxynivalenol (DON). Transepithelial electrical resistance and lucifer yellow permeability were measured to investigate barrier integrity. A calcium switch assay was used to study the reassembly of tight junction proteins. Release of CXCL8, a typical marker for inflammation, was quantified by ELISA.In comparison with PGOS, FOS and inulin, VGOS showed the most pronounced protective effect on the DON-induced impairment of the monolayer integrity, acceleration of the tight junction reassembly and the subsequent CXCL8 release. DP2 and DP3 in concentrations occurring in VGOS prevented the DON-induced epithelial barrier disruption, which could be related to their high prevalence in VGOS. However, no effects of the separate DP GOS fractions were observed on CXCL8 release.This comparative study demonstrates the direct, microbiota-independent effects of oligosaccharides on the intestinal barrier function and shows the differences between individual galacto- and fructo-oligosaccharides. This microbiota-independent effect of oligosaccharides depends on the oligosaccharide structure, DP length and concentration.The online version of this article (doi:10.1007/s00394-016-1234-9) contains supplementary material, which is available to authorized users. Human milk oligosaccharides (HMOs) play an essential role in the postnatal growth and development of the intestinal and immune system . As the GOS and FOS differ in origin and structure, and it has been suggested previously that the biological function of oligosaccharides is influenced by their structures, molecular weight and type of glycosidic linkages , 13. GOSIn the current study, we aimed to compare direct, microbiota-independent effects of different galacto-oligosaccharides, on dysregulated intestinal epithelial barrier function and the related inflammatory response. Monolayers of the human intestinal epithelial cell line, Caco-2, served as a model system for intestinal barrier function, while the fungal toxin deoxynivalenol (DON) was used as a model compound to impair the intestinal integrity as previously described by Akbari et al. . ConsidePurified DON  was dissolved in pure ethanol  and stored at −20 °C. The mycotoxin DON was selected as model compound to impair intestinal barrier integrity in the cell culture experiments. DON was diluted to a concentration of 4.2 μM in complete cell culture medium and added to the apical side as well as to the basolateral side of the transwell inserts for either 12 or 24 h. This DON concentration was selected on the basis of our previous results and did not affect the viability of the Caco-2 cells .® GOS syrup  and purified Vivinal® GOS  without the monomeric sugars glucose, galactose and lactose were provided by FrieslandCampina Domo . Fructo-oligosaccharides  and inulin (Orafti®LGI) were obtained from Beneo Orafti . The detailed composition of the applied oligosaccharides is summarized in Online Resource 1  were used. For the purification, Vivinal® GOS syrup was enzymatically treated with a lactase to hydrolyze the lactose into glucose and galactose, after which the monosaccharides were removed by nanofiltration on laboratory scale  were prepared by dissolving in complete cell culture DMEM. Subsequently, the cells were preincubated with the different oligosaccharides or DP fractions of VGOS from apical side as well as basolateral side of the transwell inserts for 24 h before being challenged with DON in the presence of the different oligosaccharides or DP fractions of VGOS for another 24 or 12 h, respectively. Prior to the functional assays described below, the potential cytotoxicity of the different oligosaccharides or DP fractions of VGOS in Caco-2 cells had been evaluated by measuring lactate dehydrogenase (LDH) leakage . These control experiments confirmed that none of the above oligosaccharides induced any cytotoxicity in Caco-2 cells at the concentrations used in the assays (data not shown).The integrity of the Caco-2 cell monolayer grown on inserts was investigated by monitoring the TEER across the monolayer. A Millicell-ERS voltohmmeter connected to a pair of chopstick electrodes was used to measure the TEER values either 12 or 24 h after DON stimulation with or without pretreatment with the different oligosaccharides or DP fractions of VGOS for 24 h. Results are expressed as a percentage of the initial value.Transport studies from the apical side to the basolateral side of a Caco-2 cell monolayer were performed using a membrane-impermeable molecule, lucifer yellow . LY was added at a concentration of 16 μg/ml to the apical compartment (350 μl) in the transwell plate for 4 h, and the paracellular flux was determined by measuring the fluorescence intensity in the basolateral compartment with a spectrophotofluorimeter  set at excitation and emission wavelengths of 410 and 520 nm, respectively.N,N,N′,N′-tetraacetic acid (EGTA)  in calcium- and magnesium-free Hanks’ balanced salt solution (HBSS)  to disrupt tight junction proteins as previously described , 20β and purified Vivinal® GOS (PGOS)), and their related DPs, as well the plant-derived oligosaccharides FOS and inulin on intestinal epithelial cells. It can be concluded that the tested oligosaccharides have different capacities to regulate the DON-disrupted epithelial monolayer and the related immune response. VGOS showed the most significant protective effect on all tested parameters in a concentration-dependent manner. Overall, differences in oligosaccharide structure and size result in significant changes in the direct, microbiota-independent effects.In summary, this study for the first time compared direct, microbiota-independent effects, of defined oligosaccharides, including GOS Supplementary material 2 (PDF 85 kb)Supplementary material 3 (PDF 45 kb)Supplementary material 4 (PDF 256 kb)Supplementary material 5 (PDF 229 kb)Below is the link to the electronic supplementary material.


PRKAG3. In this study, we used whole-genome SNP genotypes of over 700 AI boars; after a quality check, 42,385 SNPs remained for association analysis. All the boars were purebred Finnish Yorkshire. To account for relatedness of the animals, a pedigree-based relationship matrix was used in a mixed linear model to test the effect of SNPs on pH measured from loin. A bioinformatics analysis was performed to identify the most promising genes in the significant regions related to meat quality.One of the most commonly used quality measurements of pork is pH measured 24 h after slaughter. The most probable mode of inheritance for this trait is oligogenic with several known major genes, such as PRKAG3). A conditional analysis with a significant SNP in the PRKAG3 region, MARC0083357, as a covariate in the model retained the significant SNPs on chromosome 3. Even though linkage disequilibrium was relatively high over a long distance between MARC0083357 and other significant SNPs on chromosome 15, some SNPs retained their significance in the conditional analysis, even in the vicinity of PRKAG3. The significant regions harbored several genes, including two genes involved in cyclic AMP (cAMP) signaling: ADCY9 and CREBBP. Based on functional and transcription factor-gene networks, the most promising candidate genes for meat pH are ADCY9, CREBBP, TRAP1, NRG1, PRKAG3, VIL1, TNS1, and IGFBP5, and the key transcription factors related to these genes are HNF4A, PPARG, and Nkx2-5.Genome-wide association study (GWAS) revealed three significant chromosomal regions: one on chromosome 3 (39.9 Mb–40.1 Mb) and two on chromosome 15 (58.5 Mb–60.5 Mb and 132 Mb–135 Mb including Based on SNP association, pathway, and transcription factor analysis, we were able to identify several genes with potential to control muscle cell homeostasis and meat quality. The associated SNPs can be used in selection for better pork. We also showed that post-GWAS analysis reveals important information about the genes’ potential role on meat quality. The gained information can be used in later functional studies. PRKAG3  [RN gene [RYR1 [CAST [PHKG1 [The most common quality measurements of pork are pH and color. Both depend on post-mortem biochemical processes where lactate is produced from glycogen through glycogenolysis and anaerobic glycolysis, consequently lowering the pH and affecting the color of meat. The degree of glycolysis depends on the glycolytic potential, i.e. the amount of lactate that can be produced from glycogen in muscle at the time of slaughter . Differesubunit) , formerl[RN gene . An R200[RN gene . Additione [RYR1 , CAST [7T [PHKG1  among otGenome-wide association study (GWAS), based on abundant SNP markers, is an effective tool to find chromosomal regions that explain at least a moderate proportion of the genetic variance of the studied trait in a certain population. Since the launch of the commercial SNP chip for pigs (Illumina PorcineSNP60 Genotyping Beadchip), several GWAS for pork meat quality traits have been published –15. These) of commercial pork breeds is relatively small; e.g. in Finnish Yorkshire, the pedigree- and marker-based estimate of Ne is only 60 animals [in silico validation of the most probable group of candidate genes in these regions and increases our understanding of the molecular mechanisms of the studied trait [The effective population size , based on 45,639 loin pH observations.The animals used in this study were AI boars of Finnish Yorkshire origin. The studied trait, pH of loin, was measured at the slaughterhouse 24 h post mortem using a Knick 752 pH meter and an Ingold 460 electrode  for the AI boars were calculated by Figen Oy  using a single-trait animal model and the Mix99 program package  or semen (boars born after 2008). Whole-genome SNP genotyping was done at FIMM  or at GeneSeek  using the Illumina PorcineSNP60 BeadChip . Only animals with call rate (CR) >0.9 were included into the statistical analysis, 703 animals in total. Additionally, SNPs with CR <0.9, minor allele frequency (MAF) <0.1, and Associations between loin pH and SNPs were tested for each SNP separately. Because the animals were related, the following linear model was applied to the data:yi is the deregressed EBV of an AI-boar i; μ is an intercept; b is the regression coefficient; and xi is the corresponding allele dose of the tested SNP with values of 0 , 1 (heterozygote), and 2 ; ai is the polygenic effect with ai ~ N; and ei is the residual effect with ei ~ N. Matrix A is the pedigree-based additive relationship matrix and I is the identity matrix with diagonal elements of σ2e/wi, where weights (wi) were calculated based on the reliabilities of the EBVs of the animal i itself and its parents. The SNP effect (b) and additive genetic (σ2a) and residual (σ2e) variances were estimated for each SNP separately using the Restricted Maximum Likelihood (REML) method of the DMU program package [where  package .t-test with H0: b = 0, HA: b ≠ 0, and Bonferroni-corrected P-values. Because of the strong LD between the SNPs in Finnish Yorkshire [P-value of 2.0E-06 based on the assumption of 25,000 independent tests, as a limit value for statistically significant association [The statistical significance of the SNPs was based on a two-sided orkshire , the numociation . Haplotyociation  using deociation .https://www.ncbi.nlm.nih.gov/gene/). The two-sided hypergeometric test of the ClueGO plug-in of Cytoscape [For post-GWAS analysis, three lists of genes were formed based on genes within the significant regions: on chromosome 3 (39.4 Mb–40.1 Mb) and on chromosome 15 (58.5 Mb–60.5 Mb and 133.3 Mb–134.2 Mb). Additionally, we formed a separate list of genes relating to the significant SNPs on chromosome 15 outside the above regions, using 22.2 kb of the 5′ and 3′ flanking sequences . The annotations were based on the genome build Sscrofa10.2 at the NCBI website (ytoscape  was usedhttp://bioinfo.lifl.fr/TFM/TFME/) was used to identify the TF related to significant SNPs and regions. The TFM-Explorer takes a set of gene sequences and searches for locally overrepresented TF-binding sites (TFBS). The search protocol of the TFM-Explorer utilizes weight matrices from the JASPAR vertebrate database [P-value ≤ 0.01) of the clusters (regions of the input sequences associated with a factor) was based on a score function, as described by Touzet and Varré [The TFM-Explorer program  and a literature review. The most likely candidate genes were then identified using the NetworkAnalyzer tool within Cytoscape. Based on TFBS , the most connected genes were determined within each list of genes to form a gene-TF network.The obtained list of TFs was analyzed for overrepresented gene ontology (GO) terms using the BiNGO  against the chromosomal positions with the validated genes from the Reference Sequence database  in Fig. The Manhattan plots of GWAS are presented in Fig. a = 0.045 used in national breeding value evaluation for loin pH). In this region, the frequencies of unfavorable and favorable alleles were equal (MAF = 0.5). This region harbors three validated genes: TNF receptor-associated protein 1 (TRAP1), deoxyribonuclease I (DNASE1), and clusterin-associated protein 1 (CLUAP1).The statistically significant region on chromosome 3 was located at around 39.9 Mb–40.1 Mb and had four significant SNPs with an average allele effect of 0.018 pH units (SE = 0.004), corresponding to 0.4 SD of the total additive genetic effect and approximately 8% of the additive genetic variance (σMAK16), has been validated in this region  had six statistically significant SNPs. The allele frequency of the minor allele of the best SNP (ASGA0069650) was 0.11 and the SNP effect was -0.031 pH units (SE = 0.006), corresponding to 0.7 SD of the total additive genetic effect and approximately 9% of the additive genetic variance. Only one gene, MAK16 homolog  was 0.041 pH units (SE = 0.005), corresponding to 0.9 SD of the total additive genetic effect and approximately 20% of the additive genetic variance. Also, a single significant SNP (ALGA0106581) was observed 10 Mb from this region.The second region on chromosome 15 (approximately 130 Mb–134 Mb) included  as here , 31. ThePRKAG3 region had far stronger association with pH than the other SNPs on chromosome 15 or on other chromosomes, MARC0083357 was included as a covariate in the model, and GWAS was repeated. MARC0083357 was chosen because it was in complete LD with the most significant SNP ALGA87116, is located very close to PRKAG3 (62 kb from PRKAG3), and had a call rate of 1.0. The conditional GWAS revealed the same significant SNPs on chromosome 3 as the original GWAS. However, the significance of the SNPs in the first region on chromosome 15 (58.5 Mb–60.5 Mb) in the conditional analysis differed from the original GWAS depending on the LD between the SNPs and MARC0083357; e.g., the P-value for H3GA0044376 in the conditional GWAS was only 0.017 whereas the original P-value was 4.910E-07 (r2 with MARC0083357 was 0.16). This reflects the long-distance LD found between MARC0083357 and several SNPs in the region 50 Mb–100 Mb , were segregating independently from MARC0083357, and the association with meat pH in the conditional GWAS for these SNPs was still relatively significant  between all other SNPs in the region on chromosome 3, forming four haplotypes  of the first region on chromosome 15 (58.5 Mb–60.5 Mb) is presented in Fig. 06 Table . Also haWe also attempted a haplotype analysis for the second region of chromosome 15, but no further information was attained beyond the results presented for single SNPs. The reason for this is the length of the significant region, which created a very large number of possible haplotypes depending on the way haplotype blocks were defined.CREBBP and ADCY9) and on chromosome 15  share statistically the most significant pathway, the Hedgehog signaling pathway. Other highlighted network pathways include Endocytosis, ErbB signaling, Faconiani anemia, RNA degradation, Non-homologous end-joining, Lysosome, and Primary bile acid biosynthesis.Based on the database annotation, a total of 56 genes were located next to the significant SNPs or regions identified in GWAS  and provides an overview of shared TFs and genes across the single significant SNPs and regions on chromosomes 3 and 15.In addition, the four sets of genes in the significant chromosomal regions were used as input for the TFM-Explorer. Twenty-five TFs were identified through this analysis in each set. Based on the biological processes overrepresented in the BiNGO as well as on a literature review related to meat pH, we selected eight key TFs Table  to constP-value < 2.0E-06) chromosomal regions: one on chromosome 2 and one on chromosome 15 (PRKAG3 region) [PRKAG3 also gave the smallest P-value in this study. The SNPs on chromosome 2 found significant in the previous study [P-value of 5.77E-05 (ASGA0009838) that is still very close to significance and can be considered as “suggestive” finding. In the previous study, the best SNP on chromosome 3 (ASGA0014287) had a P-value of 5.89E-05. The best SNP on chromosome 15 at 58.5 Mb–60.5 Mb (ASGA0069650) also had an almost significant P-value (5.75E-06) in the previous study. Thus, increasing the number of observations from 470 to 703 changed the results somewhat. Results based on larger data are obviously more reliable than those based on smaller datasets.This article presents the results of GWAS and post-GWAS of pH measured from loin using the Finnish Yorkshire pig breed. A previous GWAS with 470 animals which were also included in the current study revealed two statistically significant ( region) . As in tus study  now reacPRKAG3 region and the other SNPs on chromosome 15. Including MARC0083357 in the model reduced the effect and significance of all SNPs on chromosome 15. Thus the significance observed in the first region on chromosome 15 may be “reflection” of the PRKAG3 region due to LD. However, the question why LD between these regions is so strong remains unsolved. Altogether, the mechanism on chromosome 15 that reduces pH in muscle post mortem is currently not yet fully understood. Beyond the original 200Q allele of PRKAG3, the functional variations could be due to other genes or to interaction between genes and transcription factors. Several studies have given evidence that I199V is highly associated with loin pH [PRKAG3 expression and meat quality, and Zhang et al. [PRKAG3 could be responsible for the association found in this region.Long-distance LD was observed between the SNPs in the  loin pH –35. Also loin pH  consider loin pH , I199V i loin pH  reportedPRKAG3, ADCY9, CREBBP, PLCD4, IHH, WNT10A, and STK36). The hedgehog signaling pathway has been cited as a key factor in the regulation of human adult tissue homeostasis and repair. It acts via multiple different routes to regulate distinct cellular outcomes, including the maintenance of plasticity [ADCY9 is a type 9 adenylyl cyclase, which is involved in the main pathways promoting muscle relaxation by a cAMP (cyclic AMP) messenger [PLCD4 is connected in the network through the calcium signaling and thyroid hormone signaling pathways. The phospholipase C enzyme promotes smooth muscle relaxation [All the detected significant regions contain several genes. To find the most promising candidate genes within these regions and to understand the possible relationship between the candidate genes, a post-GWAS analysis was performed. Hedgehog signaling was the most significantly enriched pathway in the main functional group network Fig.  comprisiasticity . Among tessenger . PLCD4 ilaxation  and genelaxation .CREBBP and ADCY9) involved in cyclic cAMP signaling. cAMP signaling mediates the effects of metabolism-controlling hormones, such as glucagon and epinephrine, and regulates energy homeostasis in multiple tissues [ADCY9 (adenylate cyclase 9) [ADCY9 displays high expression in skeletal muscle and responds to beta-adrenergic receptor activation [2+ release from the sarcoplasmic reticulum. Ca2+ release is a major contributor to pork meat quality, as is the case in the mutation R615C found in the pale, soft, and exudative (PSE) meat-related ryanodine receptor gene RYR1 [CREBBP [RYR1 and consequently regulates calcium release in skeletal muscle [ADCY9 on meat quality.The significant region of chromosome 3 contains two genes ( tissues . cAMP isclase 9) . ADCY9 dtivation , which m [CREBBP . Togethe [CREBBP , which c [CREBBP . Moreovel muscle , providiHNF4A, followed by PPARG and Nkx2-5. HNF4A encodes to hepatocyte nuclear factor 4α, which is essential to control the basal expression of genes involved in lipid metabolism and is indispensable for maintaining normal lipid homeostasis [PPARG is the most studied isoform of the nuclear receptor superfamily, and performs an important role in regulating lipid and glucose homeostasis, in adipocytes differentiation, and in fatty acid storage [Nkx2-5, which is a homeobox transcription factor known to be required for homeostasis of cardiac myocytes [Nkx2-5 knockout mice presented a reduced expression of ryanodine receptor 2 (RYR2), through which calcium is released from the sarcoplasmic reticulum. The corresponding skeletal muscle-related ryanodine receptor gene is RYR1. The most relevant biological processes of these TFs that affect meat pH are the homeostatic process (HNF4A and PPARG) and the regulation of calcium ion transport via voltage-gated calcium channel activity (Nkx2-5).Each of the four sets of genes was also used to explore the promoter regions for enriched TFs, of which we selected the most relevant ones for meat pH to generate gene-TF networks. These networks were merged, enabling the visualization of the genes and the TFs that are common between genes in the different regions. The most connected TF (linked with a high number of genes) in this network is eostasis . PPARG i storage . Moreove storage . The thimyocytes . Briggs myocytes  observedHNF4A and PPARG are mainly connected with the genes on chromosome 3 and in the second region on chromosome 15, while genes identified in the first region of chromosome 15 are mainly connected with Nkx2-5. In the significant region on chromosome 3, TRAP1 is the most connected gene in the gene-TF network. TRAP1 encodes a mitochondrial chaperone protein that interacts with a calcium-binding protein, sorcin, and is thus involved in intracellular calcium concentration [LOC102164569/NRG1 . This gene is a member of the neuregulin growth factor gene family that is involved in the differentiation of embryonic muscle cells [PRKAG3 in the second region on chromosome 15 that are highlighted in the gene-TF network, such as VIL1, USP37, RQCD1, and CRYBA2. Among them, VIL1 has also been associated with meat pH and color in crossbred commercial pigs [PRKAG3 is TNS1 (tensin 1), which belongs to a focal adhesion gene family and interacts with actin filaments [TNS1 has been identified as a candidate gene in GWAS for meat pH in Chinese Laiwu pigs [IGFBP-5, 2 Mb from PRKAG3, is a member of the IGF gene families. It has been suggested to be associated with meat quality, especially with pH in pigs [TNS1 and IGFBP5 are the most connected genes in the whole gene-TF network, and might thus be considered very stronger candidate genes for meat pH.In the gene-TF network, ntration  and mighle cells . It has le cells  and to tle cells . We obseilaments . Recentl in pigs . Moreove in pigs . TNS1 anPRKAG3 proved significant. The significant regions harbored several genes, including two genes involved in cAMP signaling: ADCY9 and CREBBP. Based on post-GWAS analysis using functional and TF-gene networks, the most promising candidate genes for meat pH are ADCY9, CREBBP, TRAP1, NRG1, PRKAG3, VIL1, TNS1, and IGFBP5. The key TFs related to these genes are HNF4A, PPARG, and Nkx2-5. To conclude, we succeeded in identifying several genes which, based on SNP association, pathway, and transcription factor analysis, have potential to control muscle cell homeostasis and meat quality. However, functional studies are still needed to warrant the role of each of these genes in pork meat quality.Three regions, one on chromosome 3 (39.4 Mb–40.1 Mb) and two on chromosome 15 (58.5 Mb–60.5 Mb and 133.3 Mb–134.2 Mb), were found to be highly associated with meat pH in Finnish Yorkshire. Additionally, several other SNPs up to a distance of 3 Mb from the known meat quality gene