PvGUX1_1 differed significantly between sutured pods and non-sutured pods. In addition, PvGUX1_1 was also a domesticated locus that diverged from PvGUX1_2 during an early stage. The results obtained in this study can provide important information for the improvement of pod quality in snap beans.Using 138 snap bean accessions as plant materials, we investigated their suture strings across two years. With the goal of identifying the gene(s) responsible for the formation of suture strings, we conducted a genome-wide association study. A strong association signal was found in a 266.19 kb region on Chr02. Within the region, 23 candidate genes were identified. Importantly, the sequence and gene expression of Phaseolus vulgaris L.). The St locus on chromosome 2 has been described as a major locus associated with suture strings. However, the gene and genetic basis underlying this locus remain unknown. Here, we investigated the suture strings of 138 snap bean accessions across two years. A total of 3.66 million single-nucleotide polymorphisms (SNPs) were obtained by deep resequencing. Based on these SNPs, we identified a strong association signal on Chr02 and a promising candidate gene, PvGUX1_1. Further analysis revealed that the 2 bp deletion in the exon of PvGUX1_1 was significantly associated with stringlessness. Comparative mapping indicated that PvGUX1_1 was a domesticated locus and diverged from PvGUX1_2 during an early stage. Our study provides important insights into the genetic mechanism of suture string formation and useful information for snap bean improvement.Suture strings are a particularly important pod trait that determine the quality and texture of snap beans ( Phaseolus vulgaris L.) is a type of common bean that is harvested before the seeds mature and eaten as a vegetable. Immature snap bean pods are succulent and rich in protein, carbohydrates, vitamin C, vitamin K, and carotenoids [Snap bean  mapped near the St locus was predicted to be the St gene. However, there was incomplete co-segregation of PvIND and the St locus and a lack of polymorphisms with dehiscent/indehiscent phenotypes, suggesting that PvIND was not the St gene [PvMYB26 was identified as the best candidate gene based on mapping and gene expression patterns [PvPdh1 on chromosome Pv03 and QTLs on Pv08, Pv05, and Pv09 [Reducing suture strings in snap beans is crucial, and the key to this effort lies in understanding the genetic basis of the formation and development of suture strings. The inheritance analysis of suture strings revealed that stringlessness was governed by a dominant gene, mon bean . Quantitmon bean . Working St gene . Recentl St gene . In the patterns . In addiand Pv09 .The first common bean reference genome was published in 2014 , which mOne hundred and thirty-eight snap bean accessions obtained from the Institute of Vegetables and Flowers at the Chinese Academy of Agriculture Sciences (CAAS), including landraces, elite lines, and breeding lines, were grown between March and June in 2019 and 2020 . These sYoung leaves at the unifoliate growth stage were collected from each accession were collected, flash-frozen in liquid nitrogen, and stored in an ultra-low-temperature freezer. Genomic DNA was isolated for each genotype using the Plant Genomic DNA kit  following the manufacturer’s instructions. The integrity of the gDNA was determined on 1% agarose gels. The concentration and quality of the gDNA were measured using a NanoDrop2000 Spectrophotometer . The DNA library was constructed according to the manufacturer’s instructions for the TruSeq nano DNA kit (Illumina). The libraries were genotyped using an Illumina HiSeq 2000 (125PE) sequencer at the facilities of Berry Genomics Co. Ltd., Beijing, China, as previously described .At the green mature stage, 10 fresh pods from different plants of each accession were randomly chosen to measure the pod suture strings. The 10 pods from 10 individuals served as technical replications. The strings were evaluated for both qualitative traits and quantitative traits. For the qualitative traits, the pod strings were scaled 0–1 . For the quantitative traits, the ratio (string length/pod length) of each pod was measured. The average ratio value of 10 pods and the scale rating of pods were both used for the GWAS analysis.PvGUX1_1 were compared with those of β-actin, and the expression fold changes were calculated using the 2−△△Ct method. Each qRT-PCR reaction was performed in triplicate. The sequences of the primers used for qRT-PCR in this study are shown in Three stages  of R02 (non-sutured) pods and R05 (sutured) pods were sampled. The total RNA of the three stages of the pods in sutured pods and non-sutured pods was extracted and converted to cDNA using a Reverse Transcription Kit  according to the manufacturer’s instructions. Quantitative real-time PCR was performed with SYBR Green , and the data collection was performed on the QuantStudioTM 6 Flex system  according to the manufacturer’s instructions. The primers were synthesized by Sangon Biotech . The relative expression levels of The raw paired-end reads were initially filtered by fastp (v0.20.0) software with the following parameters: -q 30. Next, the clean reads were aligned with the common bean reference genome V2.1  using thhttps://broadinstitute.github.io/picard/, accessed on 22 September 2020). In addition, realignment around InDels was conducted by RealignerTargetCreator and IndelRealigner in GATK (v3.2) [The SortSam and MarkDuplicates tools in PICARD (v1.127) were used to sort the mapping results and mark the duplicate reads (K (v3.2) . The varK (v3.2) .The functional annotation of variants was performed using ANNOVAR software (Version:2017-07-17) .2), where SNPs with a strong linkage disequilibrium (LD) (r2 > 0.2) within a 50 kb window were discarded using plink (v1.90b6) software with the following parameters: -indep-pairwise 50 10 0.2 [https://itol.embl.de/, accessed on 5 October 2020).To analyze the population structure, the reduced SNPs were employed based on the value of the correlation coefficient (r0 10 0.2 . To esti0 10 0.2 . A neigh0 10 0.2  software2) of pairwise SNPs within a 1000 kb window from all chromosomes was used to estimate LD decay, which was calculated and plotted using PopLDdecay software [https://github.com/BGI-shenzhen/LDBlockShow, accessed on 15 October 2020).The correlation coefficient (rsoftware . LDBlock10P) was Bonferroni-corrected as −log10P = 7.86. The Manhattan plot was run by the CMplot package in R 3.6.1 .High-quality SNPs were used for the GWAS analysis in the R package GAPIT . To reduPhaseolus vulgaris V2.1) and cowpea (Vigna angularis V1.2) was downloaded from phytozome 13. The genomes of soybean (Glycine max 109)  and pea (Pisum sativum)  were downloaded from public databases. The analysis of collinearity and synteny between the four legumes was implemented with MCScan (Python version) . Proteins with a similarity of over 90% on PvGUX1_1 in the common bean, soybean, cowpea, and pea were identified using BLASTP with an e value < 10−5. The neighbor-joining tree from the orthologue gene of PvGUX1_1 was constructed using MEGA X [The genome sequence information of the common bean (g MEGA X  with defp = 0.00015) between 2019 and 2020.The pod suture strings of 138 snap bean accessions were investigated based on the ratings and ratios . For theThe whole-genome resequencing of 138 accessions produced a total of 3.08 billion raw paired-end reads and 0.92 Tb bases, which had an approximately 11.4-fold sequence depth, ranging from 10.2- to 13.5-fold. After being filtered, 2.71 billion clean reads were retained. Mapping against the common bean genome V2.1 resulted in 5,130,030 SNPs and 1,524,528 InDels. Further filtering  identified 3,656,683 high-confidence SNPs and 626,853 InDels. Among these variants, 3,589,978 SNPs and 618,666 InDels were placed on chromosomes; 66,705 SNPs and 8187 InDels were on scaffolds. The distribution of these SNPs across the genome was uneven . Most SNTo investigate the distribution regions of these variants across the genome, we carried out variant annotation and found that 146,882, 512,153, 279,102, and 244,805 SNPs and 4180, 53,742, 30,812, and 28,930 InDels were located in exons, introns, upstream, and downstream, respectively. Furthermore, of these SNPs in exons, 65,001 nonsynonymous, 718 stopgain, and 171 stoploss SNPs were annotated, which resulted in amino acid changes, premature stop codons, or longer transcripts. Similarly, of these InDels in exons, 697 frameshift insertion, 1091 frameshift deletion, three stoploss, and 49 stopgain InDels were annotated, which also influenced protein sequences.2 ≥ 0.2), 97,841 SNPs were generated and used to implement a population structure analysis with Admixture. The use of K = 2 divided the 138 genotypes into two genetic groups, which was in agreement with two domesticated genepools (Andean and Middle American)  . Among t2 dropped to half of its maximum value), which was faster than that of common bean (107 kb) [We further analyzed the LD decay across the genome . The ave(107 kb) , cultiva(107 kb) , and cul(107 kb) . In addi10(P) = 7.86 was set as a genome-wide significance threshold based on Bonferroni correction. Strong association signals were used to identify candidate regions and screen candidate genes.To find out genetic loci controlling pod stringlessness, we implemented GWAS for four traits  using 3,656,683 SNPs . The Q2 10(P) > 7.86 for ST1-2019. Of 205 SNPs, 204 were located at Chr02, and 1 was located at Chr11 (10(P) = 10.08), accounting for 14.53% of phenotypic variation. The major locus Chr02:44248269 (−log10(P) = 8.60) was significantly associated with ST2-2019. Furthermore, strong signals were both found at Chr02:44194640 for ST1-2020 (−log10(P) = 8.49) and ST2-2020 (−log10(P) = 9.62). Taken together, the peak SNPs for the four traits were all located in adjacent physical regions in chromosome 2, which suggested that pod stringlessness was under the control of a major locus.A total of 205 loci were identified with −logat Chr11 . The peaTo identify the candidate regions associated with the significant SNPs, we carried out haplotype analysis in the whole genome. We found that these peak SNPs for the four traits all resided on the same linkage disequilibrium (LD) block (Chr02:43998258–44264446) . These gPhvul.002G270800, an ortholog to AtGUX1, which is responsible for secondary wall deposition in Arabidopsis. The 2 bp deletion introduced a premature stop codon that truncated the protein to 64 amino acids. To verify the deletion, we cloned the gene from two suture and non-suture accessions (−16)  and the function of this gene was unclear, suggesting that Phvul.002G276100 maybe not be the key gene.A 2 bp deletion in the exon region was identified in cessions . The resns (−16) . We idenPhvul.002G271700. Phvul.002G271700, encoding a NAC domain protein, carried eight nonsynonymous SNPs. Among these SNPs, K120I was significantly associated with pod stringlessness (1.39 × 10−8), whereas other SNPs exhibited a weak association.The most abundant nonsynonymous SNPs were found in PvIND (Phvul.002G271000). The SNPs T32C and C604 showed a weak association (p = 6.42 × 10−8 and 6.79 × 10−6) with pod stringlessness, while C737T showed no association (p = 0.24).Three nonsynonymous SNPs, T32C, C604T and C737T, were identified in Glycine max), cowpea (Vigna angularis), and pea (Pisum sativum). The common bean, cowpea, and soybean are members of the Phaseoleae tribe, whereas pea belongs to the Fabeae tribe [To identify the function and relation of the candidate gene, we performed a syntenic analysis within the candidate region of the common bean against three legumes, including soybean (ae tribe . AmongstPvGUX1_1), the Phvul.002G270800 protein was used to conduct a BLASTP search against cowpea, soybean, pea, and common bean. Specifically, we identified another common bean protein, Phvul.009G148800 (PvGUX1_2), which shared a strong sequence homolog to PvGUX1_1. PvGUX1_2 encoded 636 amino acids, whereas PvGUX1_1 encoded 221 amino acids. The best hit of PvGUX1_1 in cowpea, Vigun03g064600, encoded 629 amino acids, which exhibited larger sequence differences than PvGUX1_1. To verify the relationship between PvGUX1_1, PvGUX1_2, and GUX1, we performed a phylogenetic analysis of PvGUX1_1, PvGUX1_2, and other orthologous genes. PvGUX1_1 and PvGUX1_2 were placed in two different sub-branches , pod development , and pod maturation  for sutured (R05) and non-sutured pods (R02)  D. The qRred pods E. Furthered pods C.PvGUX1_1 was the best candidate gene due to its function and polymorphisms, which was consistent with dominant inheritance.Understanding the genetic mechanism of suture string development will facilitate the study of the domestication and plant breeding of snap beans. Here, we identified a strong signal on Chr02 that determined the formation of pod stringlessness based on large-scale resequencing. Within these putative genes in candidate regions, As the common bean is a selfing species, effective recombination and heterozygosis in the common bean are significantly reduced, which results in the generation of a large LD and slow LD decay. Generally, LD decay is slower in selfing species than in outcrossing species because of the loss of recombination, which potentially leads to be homozygosity . The natSt), which is mapped on chromosome 2 [The inheritance of pod stringlessness is complex due to genotypic and environmental factors . Since tmosome 2 ,8,39. MoPvCUC2 . In order to identify more loci, we conducted GWAS only on stringed snap beans for ST1-2019. Strong association signals were identified on chromosome 7  of the string might be more complex. This characteristic was also observed in pod shattering. Previous reports showed that at least two additional loci were likely relevant to the level and mode of pod shattering . In our mosome 7 . These lPhvul.002G270800 is the orthologous gene of AtGUX1 (AT3G18660). In Arabidopsis, AtGUX1 belongs to Glycosyltransferase Family 8, which participates in the synthesis of plant cell walls [AtGUX1 is responsible for the decoration of xylan, an important component of the secondary cell wall [AtGUX1 led to a decrease in glucuronoxylan content and microsomal xylan in the cell wall [AtGUX1 gene in Arabidopsis, which has only one copy, two copies are found in the common bean. This result indicated that GUX1 underwent a broad expansion in the common bean as compared to Arabidopsis. The phylogenetic analysis based on protein sequences classified the two copies into two clades. The differences might be associated with biological function differentiation. Furthermore, more than 400 aa residues located at the N-terminal were lost in PvGUX1_1 compared with other GUX1 homologous proteins, suggesting its specific function. Taken together, these findings indicate that the functions of two PvGUX1 genes have diversified during evolution. This phenomenon has been found in Arabidopsis, in which the members of the GUX family have different functions and distinct expression patterns [A total of 23 genes within the LD block surrounding the high association signal were identified. Among them, several of the genes are orthologous genes involved in cell-wall biosynthesis, pod shattering, and fiber formation. ll walls . AtGUX1 ell wall . Silenciell wall . Unlike patterns .PvGUX1, causing a premature stop. The 2 bp deletion was significantly associated with pod stringlessness. Although Phvul.002G271600 also possessed 2 bp deletion in the exon and caused a premature stop codon, it was weakly related to pod stringlessness. Therefore, we propose Phvul.002G270800 as the best candidate gene for the St locus. In addition to Phvul.002G270800, another gene of interest was Phvul.002G271000, the orthologous gene to AtIND. AtIND, as a b-HLH transcription factor, plays a crucial role in pod shattering in Arabidopsis [PvIND (Phvul.002G270800), a previous study reported that it was not the St gene controlling the suture strings [PvIND, these SNPs only showed a weak association with the suture strings. Therefore, PvIND may not be directly involved in suture development. Other interesting genes included NAC transcription factor Phvul.002G271700 (PvVNI1) and MYB transcription factor Phvul.002G269900 (PvMAMYB). Many studies have suggested that an NAC transcription factor is correlated with pod shattering and secondary cell wall development [MYB26 [MYB46 [MYB63 [PvVNI1 and PvMAMYB need to be further studied in future research to determine whether they are related to suture string development or pod shattering.In our study, a 2 bp deletion was found in the exon region of bidopsis ,45,46. Helopment ,48,49. Ielopment . Likewist [MYB26 , MYB46 [6 [MYB46 ,52, and 6 [MYB63 , are inv6 [MYB63 . Therefosin-2) is regarded as the key gene responsible for the stringless trait in pea [St gene. In the synteny block, the orthologs of GUX1 were not detected in pea, indicating that the genetic mechanism of the stringlessness of the two legumes may be different.The loss or presence of suture strings is not an important factor for many legumes whose dry seeds are consumed. In legumes, reports on the stringless trait are currently only found for the common bean and pea. In pea, pod stringlessness arose from spontaneous mutation . The rect in pea ,56. In cSt gene in the common bean would accelerate the mining of sin-2 and improve the understanding of the genetics of domestication under parallel selection in the future.Although the same regulation gene may not be shared by both the common bean and pea, there are many parallels, including seed dormancy, growth habit, and earliness, between the common bean and pea that have occurred in the process of crop domestication . The idePvGUX1_1 resulted in a truncated protein in stringed pods and was significantly associated with pod stringlessness. Moreover, the gene expression of PvGUX1_1 was also significantly different in sutured pods and non-sutured pods. Taken together, the results suggest that PvGUX1_1 is the best candidate gene for pod stringlessness. This study provides useful insights into the molecular mechanism of suture string formation and valuable information for snap bean improvement.In the present study, the suture strings of 138 snap bean accessions were investigated across two years. The whole-genome resequencing of these accessions generated 3.66 million single-nucleotide polymorphisms (SNPs). The analysis of population structure indicated that a high degree of intercrossing between Andean and Middle American gene pools occurred within Chinese snap beans. GWAS identified a strong candidate region for pod stringlessness. Within the region, a total of 23 putative genes were annotated. The 2 bp deletion in the exon of


ZNF652 gene can increase body weight (BW), reduce eviscerated carcass weight (ECW), and increase abdominal fat percentage (AFP).With intensive selection in broilers, excessive abdominal fat accumulation is also present and causes economic concerns. Abdominal fat percentage (AFP) is one of the main indices of abdominal fat traits. We identified key SNP and candidate gene affecting AFP by a genome-wide association study (GWAS). Additionally, the main findings show that rs312715211 on the ST). A 0.15 MB region associated with AFP was located on chromosome 27 and included nine significant single nucleotide polymorphisms (SNPs), which could account for 3.34–5.58% of the phenotypic variation. In addition, the π value, genotype frequency, and dual-luciferase results identified SNP rs312715211 in the intron region of ZNF652 as the key variant. The wild genotype was associated with lower AFP and abdominal fat weight (AFW), but higher body weight (BW). Finally, annotated genes based on the top 1% SNPs were used to investigate the physiological function of ZNF652. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that ZNF652 may reduce AFW and BW in broilers through the TGF-β1/SMad2/3 and MAPK/FoxO pathways via EGFR and TGFB1. Our findings elucidated the genetic basis of chicken AFP, rs312715211 on the ZNF652 gene, which can affect BW and AFW and was the key variant associated with AFP. These data provide new insight into the genetic mechanism underlying AF deposition in chickens and could be beneficial in breeding chickens for AF.Abdominal fat percentage (AFP) is an important economic trait in chickens. Intensive growth selection has led to the over-deposition of abdominal fat in chickens, but the genetic basis of AFP is not yet clear. Using 520 female individuals from selection and control lines of Jingxing yellow chicken, we investigated the genetic basis of AFP using a genome-wide association study (GWAS) and fixation indices (F Carcass traits of chickens are economically important, and include abdominal fat , body weight (BW), eviscerated carcass weight (ECW), etc. Broilers typically contain 150–200 g of fat per kg of BW. Abdominal fat accounts for 22% of body fat, but is generally considered waste, due to its low economic value ,4. CurreAFP has relatively high heritability, ranging from 0.53 to 0.71 ,7. This Genome-wide association studies (GWAS) have become the main approach to studying economically important traits in poultry . NumerouST) analyses, in order to explore the genetic basis of AFP. We hope that effective variants can be identified and used in the practical breeding programs to improve the economics of broiler production.A total of 520 female Jingxing yellow chickens  were used in this study; among them, the selection line was selected for 16 generations according to intramuscular fat (IMF) content and body weight (BW) . In prevn = 252; control line: n = 268) were bred for intramuscular fat (IMF) and used in this experiment. After 16 generations of breeding, IMF and AFW in the selection line were significantly increased, and breeding programs were as described in Zhao et al. [A total of 520 female Jingxing yellow chickens . More details about the genotyping and filtering steps are available in the work of Liu et al. [https://bigd.big.ac.cn/gsa/ (accessed on 10 July 2022).DNA was extracted from the blood of 520 female Jingxing yellow chickens using the standard phenolic method, and DNA concentration was measured using nano-Drop1000 nucleic acid protein analyzer . The determination standard was A260 nm/A280 nm (1.8–2.0), Qualified samples were sent to the Beijing Boao Jingdian Company for whole-genome resequencing with a depth of 10×. Double-terminal sequencing (PE150) was used based on the Illumina Novaseq 6000 sequencing technology platform. Whole genome resequencing data were filtered using FASTP (0.19.5) for quality control. The phred mass value was 30, each read had ≥75 bases, and the base mass was removed <30% base. The clean reads were compared with the chicken 6.0 reference genome using BWA software ), 495 animals, 8,940,029 SNPs, and chromosomes 1–28 were retained.The original sequencing data were first filled with SNP loci using Beagle 5.0 software . PLINK 1n = 252) and control (n = 268) lines, using GEMMA software [p-values using a derived Wald test. The Bonferroni correction multiple test was used to determine the significance threshold after LD linkage modified SNPs. The sums of the independent LD blocks plus singleton markers were used to define the number of independent statistical comparisons [p-value thresholds, including genome-wide significance ) and suggestive association ). The Manhattan plots of GWAS for AFP were produced using the “qqman” package in R (https://www.r-project.org/) (accessed on 10 July 2022).We applied a linear mixed model (LMM) to conduct a GWAS examining the traits underlying AFP in the selection  [ST analysis, a single SNP was used as the step size for genome-wide scanning. The top 5% of FST values were defined as selected loci between the selection and the control lines. Then, π values were calculated for the significant SNPs screened by FST. During π analysis, the window 40,000 and step 10,000 were used to calculate the region.Selection signals combined with nucleotide polymorphism analysis can reveal the genetic mechanisms underlying population evolution. Based on the SNPs remaining after quality control, FV0.1.13) . In FST ZNF652, we constructed vectors based on PGL4.18 plasmids. According to the chicken 6.0 reference genome sequence, we constructed a 1500 bp promoter sequence upstream of the transcription start site and designated it PGL4.18-ZNF652-pro. We also constructed wild and mutant luciferase reporter vectors of rs312715211 and designated them PGL4.18-SNP-TT and PGL4.18-SNP-AA. Next, chicken embryo fibroblasts (DF1) cells were cultured on a 24-well plate and were co-transfected with 250 ng of PGL4.18-ZNF652-pro and 250 ng of PGL4.18-SNP-TT or PGL4.18-SNP-AA. After 24 h, 100 μL lysate was added according to the Dual Luciferase Reporter Gene Assay Kit  and centrifuged for 15 min. Firefly and Renilla activities were measured using 20 ul supernatant, and each group was replicated three times.To quantify the interaction of rs312715211 and its potential target gene https://bigd.big.ac.cn/gsa/ (accessed on 10 July 2022) (accession data code CRA006031).Transcriptomic sequencing was performed on 98-day-old Wenchang chickens’ abdominal fat, including 18 samples. After eliminating abnormal individuals, high and low AFP groups were selected. For detailed sequencing steps, please refer to previous studies . Then, Rhttp://www.genome.jp/kegg) (accessed on 10 July 2022) [p value) ≤ 0.05 were considered significantly enriched. The results were drawn using the “ggplot2” package in R.The top 1% of SNPs were selected for gene annotation, and these genes were enriched. The genes of the significant modules analyzed from the WGCNA were also annotated and then enriched. The KEGG database (ly 2022)  is an imt-test. Confidence limits were set at 95%, and p < 0.05 (*) or < 0.01 (**) was considered significant. Data are presented as mean ± standard error. ASReml 3.0 software was used to estimate genetic variance, genetic correlation, and heritability. Genetic parameter estimation was based on the animal single trait model using restricted maximum likelihood (REML) and the model:R 4.0.4 and SAS 9.4  were used to generate descriptive statistics and for normal distribution tests of AFP and to test the significance of the differences between the groups using Student’s Y = Xb + Za + e, where Y represents the observed value of traits, b represents the fixed effect vector, and X represents the incidence matrices of fixed effects. a represents the additive genetic effect vector of the individual, Z represents the incidence matrices of the additive genetic effect of the individual, and e represents the random residual effect vector.β represents the effect value for the GWAS result, MAF represents SNP minor allele frequency, and N represents the number of individuals included in the GWAS analysis.The phenotypic variation explained (PVE) can be estimated using equation :PVE=2β2Mp < 0.01) increased in the selection line, compared to the control line, resulting in no significant difference in AFP (AFP correlates with the AFW/ECW ratio) between the selection and control lines.Descriptive statistics for AFP, ECW, and AFW for both the selection and control lines are presented in 10. The potential significance level threshold was −log10. These analyses showed that the significant SNPs associated with AFP phenotype were located on chromosomes 1, 14, and 27. An approximately 0.15 MB region on chromosome 27  was strongly associated with AFP and included nine significant SNPs with AFP, which were annotated on IGF2BP1, ZNF652, GIP, UBE2Z, and ETV4, respectively. The most significant SNP (rs312351828) accounted for 5.35% of the observed phenotypic variance. Taken together, all of the SNPs that reached the GWAS threshold explain 3.34−5.58% of the observed phenotypic variance.To find significant variation at the genomic level, we performed GWAS of AFP. The GWAS results are summarized in ST and π values of all SNPs located on chromosome 1, 14, and the candidate region  were calculated. FST is calculated with a single SNP as the step size, and the FST threshold was 0.1. These data are summarized in ST analysis showed that SNPs reaching the GWAS threshold line on chromosomes 1, 4, and 14 were not selected , ZNF652 promoter activity was not changed  of individuals carrying wild or mutant genotypes of rs312715211 SNP. According to these results, rs312715211 caused significant differences in the AFP, AFW, and ECW phenotypes between the selected and control lines. We also confirmed that rs312715211 can increase AFW and reduce ECW, thereby affecting AFP B. Both tp < 0.05)   . Among tnd bound . We also < 0.05) . Among tZNF652 was expressed less in high AFP group than in the AFP low group, consistent with the dual-luciferase result (p < 0.05) . Of these, the MAPK and FoxO signaling pathways were consistent with our above results  C. Althou results D. In conExcessive abdominal fat in broilers not only reduces reproductive performance and causes metabolic diseases, but also reduces meat quality ,27. An iST, and π analysis identified one significant SNPs on chromosome 27, and the SNP differed significantly between wild and mutant individuals in phenotype (p < 0.05). This is consistent with Zhang [With the application of GWAS and selection signals, AFP in chickens has been fully utilized and SNPs and QTLs associated with abdominal fat have been identified . Accordith Zhang  and suggZNF652 promoter activity after mutant. In order to improve chicken breeding and increase its economic benefits, we should select wild homozygous individuals from rs312715211 for breeding and eliminate mutant homozygous individuals, so as to achieve increased body weight and reduce abdominal fat.As a result, rs312715211 conformed with our expectation. The SNP was mapped to ZNF652 and may simultaneously regulate abdominal fat deposition and growth development in chickens. rs12715211 can increase AFP, AFW, and BW in broilers after mutant. After selection, AFW significantly increased and BW significantly decreased. Moreover, rs312715211 significantly increased ZNF652 and IGF2BP1 may be important for the control of abdominal fat deposition in broilers. IGF2BP1 (insulin-like growth factor 2 mRNA-binding protein 1) can bind IGF2 mRNA and is a member of the single-stranded RNA-binding protein family [IGF2BP1 can function by affecting cell proliferation, migration, and apoptosis [IGF2BP1 knockout mice can reduce Ramp3 mRNA expression; thus, IGF2BP1 indirectly affects glucose and lipid metabolism [IGF2BP1 lead to high weight [IGF2BP1 can increase body weight and affects feed efficiency in ducks [IGF2BP1 is also associated with tumors, cancer, dwarfism, and other diseases, but its mechanism of fat regulation requires further study.In the target region, n family . IGF2BP1poptosis . IGF2BP1tabolism . In chich weight . IGF2BP1in ducks , promotein ducks . IGF2BP1ZNF652 is the classical C2H2 zinc finger DNA binding protein [ZNF652 may be involved in human lipid and carbohydrate metabolism, e.g., sex hormone binding globulin (SHBG) [ZNF652 can increase the expression of EGFR and TGFB1, and EGFR can regulate fat deposition by regulating fatty acid synthase [EGFR expression can significantly reduce BW and subcutaneous and abdominal fat mass in mice, and more importantly, SREBP-1 and FASN expression decreased after EGFR inhibition [EGFR is an epidermal growth factor receptor that regulates fat metabolism genes, especially PPAR, and the inhibition of EGFR can reduce the expression of adipose synthase [TGFB1 (transforming growth factor β) [SMAD family. Our studies showed that AFW and BW decreased after ZNF652 expression increased, consistent with the results after EGFR inhibition. Previous reports suggest the inhibition of the TGFB1 gene by PPARG activation, and PPARG can inhibit cell proliferation through the TGF-β1/Smad2/3 signaling pathways [ZNF652 was associated with EGFR, TGFB, and PPARG by targeting the TGF-β1/SMad2/3 signaling pathways and MAPK/FoxO signaling pathways, resulting in reduced proliferation of adipocytes and accelerated fatty acid oxidation. protein  and an i protein . ZNF652 n (SHBG)  and may n (SHBG) ,40. Howesynthase ,41. The hibition . EGFR isactor β)  can bondpathways . TherefoOur findings elucidated the genetic basis of chicken AFP. rs312715211 in the ZNF652 gene is the key variant associated with AFP of chickens, and the wild genotype is the favorable genotype to lower AFW and heighten BW. Additionally, ZNF652 is the key gene related to AFP by affecting BW and AFW. These data provide a new insight into our understanding of the genetic mechanisms underlying abdominal fat deposition in chickens and will aid in the breeding of broilers with lower AFP. In the future, the further study of these genetic variations may be applied in marker-assisted selection to reduce abdominal fat deposition in broilers.


Mineral malnutrition is a major problem in many rice-consuming countries. It is essential to know the genetic mechanisms of accumulation of mineral elements in the rice grain to provide future solutions for this issue. This study was conducted to identify the genetic basis of six mineral elements  by using three models for single-locus and six models for multi-locus analysis of a genome-wide association study (GWAS) using 174 diverse rice accessions and 6565 SNP markers. To declare a SNP as significant, −log10(P) ≥ 3.0 and 15% FDR significance cut-off values were used for single-locus models, while LOD ≥ 3.0 was used for multi-locus models. Using these criteria, 147 SNPs were detected by one or two GWAS methods at −log10(P) ≥ 3.0, 48 of which met the 15% FDR significance cut-off value. Single-locus models outperformed multi-locus models before applying multi-test correction, but once applied, multi-locus models performed better. While 14 (~29%) of the identified quantitative trait loci (QTLs) after multiple test correction co-located with previously reported genes/QTLs and marker associations, another 34 trait-associated SNPs were novel. After mining genes within 250 kb of the 48 significant SNP loci, in silico and gene enrichment analyses were conducted to predict their potential functions. These shortlisted genes with their functions could guide future experimental validation, helping us to understand the complex molecular mechanisms controlling rice grain mineral elements. Being a staple food for half of the world’s population, the nutritional quality of rice can have a large impact on human nutrition. The lack of nutritional quality mainly affects countries where rice is eaten primarily as a staple food. Mineral malnutrition is one of the more serious problems for rice-eating societies, especially in Asian countries . More thA number of linkage mapping studies have investigated QTLs controlling mineral accumulation in rice grain. Over 200 QTLs have been identified for micro  and macro  nutrients elements . HoweverAssociation mapping based on linkage disequilibrium (LD), as an alternative approach to linkage mapping, is a powerful method for dissecting the genetic basis of plant traits . This apTo conduct GWAS, several statistical models have been widely used, including the general linear model (GLM) and the mixed linear model (MLM) . The MLMp-value, such as 0.01, is used to select the SNPs in the first step. Each of these multi-locus model differs in terms of algorithms utilized in the two steps [Multi-locus models have been proposed as an alternative to overcome the issues with the single-locus model GWAS. These multivariate models consider all loci simultaneously; as a result, multiple test corrections are not needed. So far, several multi-locus GWAS models have been developed and used to study GWAS, such as MLMM (multi-locus mixed-model), FarmCPU (Fixed and random model Circulating Probability Unification), mrMLM (multi-locus random-SNP-effect MLM), FASTmrMLM (fast mrMLM), FASTmrEMMA , pLARmEB , pKWmEB , ISIS EM-BLASSO (iterative modified-sure independence screening expectation-maximization-Bayesian least absolute shrinkage and selection operator), and GPWAS (Genome-Phenome Wide Association Study) . All thewo steps ,17,19.indica and 239 japonica accessions, largely of Chinese origin, to study 11 minerals in rice grains grown in field trials in China [indica and aus and 59 japonica accessions, to study 16 minerals in rice grains grown in Beaumont, Texas, of which 9 had significant QTLs detected in the GWAS [indica germplasm and identified QTLs across 12 mineral elements in brown rice grain harvested in field trials in the Philippines [Several recent GWAS publications have investigated mineral element concentrations in rice grains using various sets of diverse rice accessions. One study employed 575 rice accessions, including 294 in China . Anotherthe GWAS . Anotherlippines . AlthougThe objectives of the current study are: (1) to identify loci that are significantly associated with six mineral elements  by using single-locus and multi-locus GWAS methods using a novel rice diversity panel; and (2) to compare the performance of these methods in terms of detection of trait-associated SNP markers. The findings will accelerate the development of new mineral-rich rice varieties by facilitating marker-assisted breeding (MAB), identifying candidate genes, and providing insight into the molecular mechanisms underlying mineral accumulation in rice grain.A total of 174 accessions, including 151 diverse global accessions from the USDA-GRIN germplasm collection and 23 US-released varieties were used in the study. The included accessions flowered in 80–130 days after emergence (DAE) in previous field evaluation to minimize the effect of extremely early and late heading times on rice grain mineral content. These accessions originated from 31 countries, where the highest number of accessions were from Bangladesh (19) followed by Russia (18), Uzbekistan (16), India (14) .The field experiment was conducted at the Texas A&M AgriLife Research Center, Beaumont, Texas  in heavy clay soils during from late April to September 2018, which can be considered an average growing season, without any major extremes in weather or conditions. The lines were directly seeded and followed standard practices of keeping a constant flood in the field until all the accessions reached their full maturity along with applying standard fertilizer. A randomized complete block design with two replications with a two-row plot for each replication for each accession was used in the field experiment. Plots were harvested based on individual accession maturity. After reaching the maturity stage, plants in the middle of each plot were bulk harvested and air-dried for 3 months in the drying room. Then, around 120 g of rough seeds were dehulled with electrical dehuller to make brown rice.3 (12.1 N), and 3 mL of 30% (v/v) H2O2 [We used brown rice for mineral content determination for this study. At first, brown rice of all the samples were dried for 72 h at 65 °C, followed by sterilization with 70% ethanol to remove contaminants and/or debris from the surface . Then, s/v) H2O2 . The dig/v) H2O2 .After digestion, the solutions were allowed to cool to room temperature and then were filtered through Whatman No. 1 (11 µm pore size) filter paper into a 25 mL volumetric flask. The volume was brought to the 25 mL mark with ultrapure water. Next, 100× and 1000× diluted samples were prepared from the solution to determine Cu, Fe, Mg and Mn content and K and Zn content, respectively. Inductively Coupled Plasma Mass Spectrometry (ICP-MS)  was used to quantify the Cu, Fe, K, Mg, Mn and Zn content. Samples were run in total seven batches; each batch composed of three blanks (digestion reagent with no samples), standard reference material (Rice Flour SRM 1568B), and experimental samples were included. Rhodium (Rh 103) was used as internal standard to monitor ICP-MS machine drift while running. During calculation, blanks were averaged and subtracted from experimental samples per batch. Five technical replications were generated for each sample and were averaged. The average elemental concentration of two biological/ field replications of each accession was used during GWAS analysis.Indica and Japonica, to determine the phenotypic variation. To know the effect of population structure on phenotypic variation, we used ANOVA using the general linear model (GLM), where population structure was set as the fixed variable. In addition, correlation analysis among the minerals was completed. All the analyses were conducted using JMP pro15.Basic statistics, including mean, standard deviation, coefficient of variation (CV), and analysis of variance (ANOVA) were conducted on the whole panel as well as on the two subspecies, We used 6565 high quality SNPs  from the 7K SNP array data  for GWAShttp://taylor0.biology.ucla.edu, accessed on 10 January 2021) was used to determine the best k value using the method of Evanno, Regnaut [Population structure and kinship analysis were conducted to control the false positive results in the GWAS analysis. STRUCTURE 2.3.4  using Ba Regnaut  by submi2) values between the pairs of markers of 6565 SNPs calculated by PopLDdecay 3.41 [2 value was used as the estimate of r2 of a bin. The LD decay was calculated as the chromosomal distance at which the average r2 dropped to half of its maximum value [The LD decay distance across the whole genome was measured by squared allele frequency correlations . R2 values for significant SNPs were obtained from the respective software used for the GWAS analyses, except for the GEMMA software which does not provide R2 values. To calculate R2 values for the GEMMA model, we used the following equation:The GWAS was conducted using nine models that can be divided broadly into two groups; single-locus models: CMLM (compressed mixed linear model), ECMLM (Enriched CMLM) and GEMMA , and multi-locus models: mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO. All six multi-locus models are implemented in mrMLM R package . At firsple size .https://rapdb.dna.affrc.go.jp/, accessed on 20 January 2021). To check the in silico expression levels of the mined genes, Nipponbare (japonica) and IR64 (indica) gene expression data were downloaded from the MSU Rice Genome Annotation Project  and the OryzaExpress database , respectively. A heatmap of the gene expression for each trait was created with ComplexHeatmap R package.We mined the genes within the LD decay distance on either side of the significant SNPs by using RAP-DB database  decay across all chromosomes was estimated to be 250 kb, defined as half the maximum of mean r2 values  were regarded as the same significant locus .A SNP was declared as significant using −logBased on these criteria, a total of 147 significant SNPs for six mineral elements were identified using nine models .−10–26.83% of the phenotypic variation. For K, 13, 7 and 3 SNPs were identified by only single-locus, only multi-locus and both models together, explaining 6.03–23.80%, 3.28–34.37% and 5.86–18.56% of the phenotypic variation, respectively. For Mg, 35 SNPs were found by only single-locus models, explaining 5.83–25.51% of the phenotypic variation. Only two SNPs were detected by only multi-locus, and this explained 8.79–16.40% of the phenotypic variation. Five SNPs were identified by both models that explained 3.62–26.75% of the phenotypic variation. For Mn, single and multi-locus models each detected three SNPs separately, in total six SNPs, that explained 5.94–14.16% and 6.54–10.92% of the phenotypic variation, respectively. For Zn, single-locus, multi-locus and both models identified 11, 7 and 4, in total 22 SNPs, explaining 6.11–49.90%, 5.32–24.44% and 6.15–50.06% of the phenotypic variation. It can be noted that different algorithms per model resulted in different R2 per SNP across the different models. However, 147 significant SNPs for all six minerals were reduced to 48 after applying the 15% FDR multi-test correction  are associated with Fe, three SNPs  with Mg and one SNP  is also found for Zn. Similarly, 15 SNPs have an effect on both Fe and Mg and one SNP on both Fe and K. In addition, seven and one SNPs are associated with controlling both K and Mg and K and Zn, respectively. Two SNPs influence both Mg and Zn elements. Moreover, SNP-6.2196821 and 6,285,634 SNPs are involved in affecting Cu, Fe and Mg, whereas the 1,202,195 SNP has an effect on the Fe, K and Mg elements. Once multi-test correction was applied, 32 SNPs decreased to 18 SNPs. Among the 18 significant SNPs, five SNPs appear to control more than one trait, suggesting a pleiotropic effect. Among the thirteen SNPs that affect K, three SNPs  are associated with Mg, and one SNP (SNP-1.41998191.) is found for Zn. Similarly, 6,496,457 SNPs have influence on both Mg and Zn .In terms of SNP detection ability of the different models used in this study across the six mineral elements, the single-locus GWAS method outperformed the multi-locus GWAS method based on the first level of cut-off value. The single-locus and multi-locus GWAS method identified overall 125 and 37 SNPs, respectively, where 110 and 22 SNPs were identified by single-locus and multi-locus method only, respectively, and both methods shared 15 SNPs A. In addhttps://rapdb.dna.affrc.go.jp/, accessed on 20 January 2021), we found 43, 36, 273, 197, 85 and 353 genes for Cu, Fe, K, Mg, Mn, and Zn, respectively. To investigate which genes are more likely to be responsible for rice grain mineral traits, we selected only those genes expressed in both reproductive and vegetative stages or only in the reproductive stage by using Nipponbare (Japonica) and IR64 (Indica) gene expression data in normalized FPKM values. 9, 6, 111, 51, 23, and 98 genes were found to be expressed in Nipponbare, whereas, in IR64, 13, 9, 98, 59, 16, and 116 were expressed for Cu, Fe, K, Mg, Mn, and Zn, respectively, and were used for gene enrichment analysis  than multi-locus models (37 SNPs) at the first significant cut-off value, but its number reduced to 21 once a 15% FDR multiple test correction was applied to the single-locus models. Multiple test correction is not required for multi-locus methods, which is an obvious advantage, so the number of SNPs detected by these methods will be same. Therefore, the performance of the multi-locus models was better compared to the single-locus models in our study. Similar results were also reported in the previous studies using both real and simulation datasets where the multi-locus approach was more powerful than the single-locus approach ,17,35. HAs the main goal of this study is to find as many significant SNPs as possible without losing any potential causal SNPs, we applied two of three approaches necessary to determine stable QTLs: one was applying multiple approaches by using single and multi-locus GWAS models and another was verification by previously reported QTLs, which will be described in the next section . By applTo evaluate the reliability of our QTLs, we compared our detected SNPs for the six minerals with the genes/QTLs and markers related to mineral content identified from previous linkage mapping and association mapping studies. To compare with the QTL/marker locations of the previous studies, the surrounding 250 kb of our associated SNPs were regarded as potentially the same locus for any particular trait when this region was found between the borders of previously identified QTL/marker locations. In the case of SNP marker comparisons, the markers of past studies that were located within the 250 kb region of our significant associated SNPs were considered as the same locus for the particular trait. Thus, with these parameters, 43 (~29%) of the 147 of the significant SNPs identified by the first significance threshold of this study coincided with previously reported genes/QTLs and/or markers for the six minerals and the remaining 104 (70%) SNPs could be considered as novel. Out of the verified 43 SNPs, 17 were found for Fe, followed by eight for Mg, seven for K, five for both Cu and Zn and the remaining one was found for Mn. However, after applying a multiple test correction to the single-locus models, 43 significant SNPs that verified by co-location with previously reported QTLs now reduced to 14 verified SNPs (~29%), supporting the fact that the multiple test correction can eliminate true QTLs identified in the single-locus GWAS study. Out of the established 14 SNPs, 5 SNPs were detected for K, followed by 3 for Zn and 2 SNPs each for Cu ang Mg. Both Fe and Mn had one SNP .NAS gene family is involved in the accumulation of Fe, Zn and Cu in rice endosperm [OsNAS3 at SNP-7.29385457. in chromosome 7 associated with Fe at our first threshold value for the significance test, where two SSR markers were also reported before [COPT transporter gene family for Cu was not identified by our GWAS analysis. Interestingly, a known gene, OsIRO2, an iron-related bHLH transcription factor 2 that regulates Fe uptake from soil, transport during germination, and translocation to the grain, was co-located at 1,305,247 SNP in chromosome 1 associated with Zn in our study, where Bollinedi et al. (2020) also found a SNP at almost the same position, supporting the fact that a single gene may control the molecular mechanism of multiple elements simultaneously  and other plant development programs . AlthougJaponica and Indica, respectively, without sharing any common function. The genes in indica genetic background were categorized as mainly antiporter and transporter activity of K and Na, whereas japonica genes were involved in structural molecule activity and binding (drug and cyclosporin). The genes for Fe in japonica and indica were classified into 15, and 30 molecular function GO terms with 14 common functions between them. The common functions mostly consist of activity  and binding . For K, nine molecular function terms were assigned for each group of genes with eight common functions, revealing that both genomes could follow similar molecular pathways for K. However, for Mg, the two genomes might have different molecular mechanisms because 48 molecular functional terms were identified for indica genes, whereas only one term was assigned for japonica genes. Among the 48 functions, some functions are well known for Mg roles, for example, DNA-binding transcription activator activity, RNA polymerase II-specific, NADPH binding, and catalytic activity, indicating the possibility that some candidate genes in the indica genetic background might not be present in japonica. For Mn, 15 and 16 molecular function terms were enriched for the japonica and indica genes, with three common functions between them that were mostly involved in mannosyltransferase activity. For Zn, the japonica and indica genes were enriched into three and seven molecular functional terms, with one common between them . For Cu, the analysis revealed that the genes were significantly enriched at 5% FDR in three and four molecular function GO terms for een them . OverallA potential limitation of this study is using one year/location data that cannot provide assurance of the stability of the significant SNPs identified in our study. However, we tried to validate our findings by comparing with previous studies, showing 29% of our significant SNPs co-located with the previously known gene/markers. Future research can be used to further validate our findings by (1) conducting additional GWAS studies in multiple locations and years and (2) using gene-editing tools to characterize the candidate genes identified in this study.japonica and indica, respectively, were used for enrichment analysis to know their potential functions. Thus, they could be identified as candidate genes for controlling accumulation in rice grain. These shortlisted genes could be used for future studies to further investigate the gene expression levels, followed by functional gene characterization, to better understand the complex molecular mechanisms controlling rice grain concentration of these six mineral elements.This study reported the GWAS of six mineral elements using 174 global rice accessions and 7k SNP array genotype data. A total of 48 SNPs affecting mineral elements were identified by single-locus and multi-locus methods. GEMMA along with five multi-locus models  were the best models to identify significant SNP in the single-locus and multi-locus GWAS method, respectively, used in this study. While 14 SNPs matched with previously reported genes/QTLs and markers, 34 SNPs were novel. After mining genes within a 250 kb region of these SNPs, a total of 987 genes were found. Among these genes, 298 and 311 genes in


Pecten are found in Europe: the north Atlantic Pecten maximus and the Mediterranean Pecten jacobaeus whose distributions abut at the Almeria–Orán front. Whilst previous studies have quantified genetic divergence between these species, the pattern of differentiation along the Pecten genome is unknown. Here, we mapped RADseq data from 235 P. maximus and 27 P. jacobaeus to a chromosome-level reference genome, finding a heterogeneous landscape of genomic differentiation. Highly divergent genomic regions were identified across 14 chromosomes, while the remaining five showed little differentiation. Demographic and comparative genomics analyses suggest that this pattern resulted from an initial extended period of isolation, which promoted divergence, followed by differential gene flow across the genome during secondary contact. Single nucleotide polymorphisms present within highly divergent genomic regions were located in areas of low recombination and contrasting patterns of LD decay were found between the two species, hinting at the presence of chromosomal inversions in P. jacobaeus. Functional annotations revealed that highly differentiated regions were enriched for immune-related processes and mRNA modification. While future work is necessary to characterize structural differences, this study provides new insights into the speciation genomics of P. maximus and P. jacobaeus.Two commercially important scallop species of the genus  How genomes diverge during speciation is a central question in evolutionary genetics. Speciation is generally considered as a continuum where separate genetic lineages accumulate differences over time after a vicariant event due to mutation, genetic drift, and selection ,2. DurinFST [dxy [Relative measures of genetic divergence, such as FST , are senFST . This isFST . In contFST [dxy , are indFST [dxy  proposedPecten maximus and P. jacobaeus are two Pectinids inhabiting different parts of the coast of Europe. P. maximus has a north Atlantic distribution and can be found from Norway to the western Mediterranean [P. jacobaeus is restricted entirely to the part of the Mediterranean that lies to the east of the Almeria–Orán front (AOF), a recognized biogeographic boundary [Paracentrotus lividus [Donax trunculus [Mytilus galloprovincialis [Stramonita haemastoma [Dicentrarchus labrax [P. maximus and P. jacobaeus remains unclear.The commercially exploited scallops erranean . In contboundary  known to lividus , the clarunculus , the musincialis , the snaemastoma ), and sps labrax ). HoweveP. maximus and P. jacobaeus separated around five million years ago (MYA), shortly after the Messinian salinity crisis [These two scallop taxa are currently considered as two separate species on the basis of morphological traits and nonoverlapping geographical distributions, but this view has been put into question by a suite of genetic studies published over the last two decades with different levels of genetic resolution offered by a variety of genotyping approaches including allozymes ,20, mitoy crisis .P. maximus samples collected from 12 sites along the Atlantic coast of Europe and P. jacobaeus samples collected from two sites along the Mediterranean coast. Clear genetic differences were found between P. jacobaeus and P. maximus yet the two species were estimated to have diverged much earlier than originally thought, sometime around the end of the Pleistocene . Climatic conditions strongly fluctuated during that period and many bivalve species went extinct or shifted their distributions [Pecten species might have begun to experience novel environmental pressures, leading them to diverge from populations located in their native range. Moreover, it is likely that divergence was also promoted by genetic drift and the consequent potential increase in reproductive isolation caused by the rise, for example, of Bateson–Dobzhansky–Muller incompatibilities [P. maximus is composed of two major genetic lineages, one located along the coast of Norway and the other along the rest of the Atlantic coast of Europe, which diverged more recently during the last glacial maximum . This finding was recently corroborated by Hollenbeck et al. [P. maximus populations inhabiting different regions along a European latitudinal gradient.Vendrami et al.  examinedibutions . It is tbilities ,26,27,28bilities , in linebilities , showed k et al.  who alsoP. maximus was published by Kenny et al. [P. maximus and P. jacobaeus and investigate its likely determinants. Our aims were, (i) to compare a series of demographic models of speciation to understand which are the mostly likely circumstances under which the divergence between P. maximus and P. jacobaeus occurred; (ii) to characterize and quantify relative and absolute divergence along the genome while also identifying, if present, any fully diagnostic loci; and (iii) to characterize the regions of these species’ genomes showing the highest levels of differentiation, as these may underlie important adaptive divergence.Recently, a high-quality chromosome level genome assembly for y et al.  and thisy et al. . Here wey et al.  by mappiy et al.  to charaP. maximus), collected from 12 locations from the Atlantic coast of Europe, and Mediterranean scallops (P. jacobaeus), collected from two locations in the Mediterranean Sea , which revealed that trimming was unnecessary as read quality scores were uniform along the entire length of the reads. Sequencing reads were then mapped to the P. maximus reference genome [https://github.com/isaacovercast/easySFS, accessed on 28 November 2022) to be used for demographic modelling , of the P. jacobaeus lineage (Npja), and of the ancestral lineage that later split into the two Pecten species (Nanc). Moreover, in all models except SI, we also estimated the gene flow as the proportion of migrants per generation from the P. maximus lineage to the P. jacobaeus lineage (mmj) and vice versa (mjm). mmj and mjm were estimated independently from one another to allow for the possibility of asymmetrical gene flow to occur. All priors for these analyses are given in We implemented demographic analyses using the coalescent simulator simcoal2  in combiodel SI,  represenodel IM,  represenodel IM,  represenodel SC,  represendel SCS,  represenfastsimcoal2 runs with 100,000 simulations and 40 cycles of the likelihood maximization algorithm. The run associated with the highest maximum likelihood value for each model was then selected to compute Akaike’s information criteria (AIC) as described in Varin and Vidoni [For each model, we implemented 50 independent d Vidoni  to allowd Vidoni . SpecifiFST values within sliding windows using VCFTools. Specifically, a 100 kb window was used, as recommended by Hohenlohe et al. [FST was above the 95th percentile were considered as highly divergent genomic regions. For comparison, we repeated the same analysis where FST values were calculated between the Atlantic and Norwegian genetic lineages within P. maximus. Next, separately for the two scallop species, we used VCFTools to quantify nucleotide diversity within the same sliding windows used to calculate FST and tested for the association between these two measures by performing a Spearman correlation test. Subsequently, we employed the software pixy [dxy between the two scallop species within the same genomic windows described above to verify whether genomic regions characterized by elevated FST values also showed high divergence regardless of local levels of genetic diversity. The correlation between these two measures was assessed using a Spearman correlation test. Moreover, we compared highly divergent genomic windows against the remaining genomic areas at their levels of divergence quantified using both FST and dxy. Agreement between these two measures would imply differential gene flow across the Pecten genome, while disagreement would indicate that heterogeneous patterns of FST along the genome were primarily caused by differential selection [In order to evaluate the degree of genetic differentiation between Mediterranean and great scallops along their genomes, we calculated e et al.  for RAD are pixy , in combelection . FinallyP. maximus and P. jacobaeus, we calculated r2 values among all pairs of loci located within the same chromosome, separately for the two species, using PLINK [r2 under drift recombination was expressed according to Hill and Weir [Pecten genome were characterized by a different LD decay pattern. Subsequently, we employed the R package ‘LDheatmap’ [In order to evaluate how linkage disequilibrium (LD) decays with physical distance along the genome in ng PLINK . LD decaand Weir . Next, wheatmap’  to generFST values were above the 95th percentile as well as on diagnostic SNPs (from now on collectively referred to as ‘highly divergent SNPs’). First, we used the great scallop reference genome annotations provided by Zeng et al. [p-value < 0.05 were considered significantly enriched. We then repeated the GO enrichment analysis considering only SNPs located within genes. Finally, we used the program SnpEff v5.1 [In order to functionally characterize highly divergent genomic regions of the scallop genome, we focused on the SNPs located within windows whose g et al.  to annotg et al. . For SNPg et al.  to invesEff v5.1 , togetheP. maximus and P. jacobaeus. RAD sequencing produced a total of 823,811,871 50 bp SE sequence reads. Alignment rates to the P. maximus reference genome were high (>90%) for both scallop species. The aligned reads were used to call a total of 1,009,368 raw SNPs. Application of the filtering criteria described in the materials and methods resulted in a final dataset consisting of 45,704 SNPs genotyped in 262 samples, including 235 great scallops and 27 Mediterranean scallops.We used the RADseq data of Vendrami et al.  to charafastsimcoal2 together with the empirical folded joint SFS to implement demographic reconstructions and to infer the divergence history of P. maximus and P. jacobaeus. All of the models that included gene flow between the two lineages received higher support than the SI model  generations ago. After a relatively long period of initial isolation, secondary contact occurred 14,799  generations ago, with gene flow continuing until 58 (95% CI: 20–286) generations before present. Gene flow occurred in both directions but was more pronounced from the P. jacobaeus lineage to the P. maximus lineage.We used the coalescent simulator SI model . Among tFST values between P. maximus and P. jacobaeus within 4460 100 kb sliding windows containing at least five SNPs. The resulting FST values revealed a highly variable divergence landscape between the two scallop species, with FST values ranging from −0.013 to 0.967 across the genome . These were located on chromosomes 1–3, 7, 9–15, and 17–19, with chromosomes 2 and 10 carrying the largest numbers of highly divergent genomic regions  and having the highest chromosome-wise mean FST values  and we could retrieve only a handful of peaks of high divergence, which were localized within comparably short genomic regions.We calculated e genome a and an of 0.118 . This paof 0.118 , implyinT values . In contrcentile . This ge maximus b. Here, FST values. Genome-wide nucleotide diversity (π) was 1.8 × 10−5 in P. maximus and 1.69 × 10−5 in P. jacobaeus . Third, highly divergent genomic windows, when compared to the remaining windows, were characterized by higher levels of divergence as quantified by both FST and dxy , while the genotypes characteristic of each species can be found in Comparison of the genotypes from the two scallop species uncovered a total of 62 (0.13%) fully diagnostic SNPs . NotablyP. maximus chromosomes. SNPs located within unplaced scaffolds were excluded from this analysis. In P. maximus, LD was found to decay rapidly, with r2 reaching the background level (r2 = 0.01) by around 2 kb  that was reached more slowly, at around 3 kb . ,21. P. mi  this would imply that the two lineages split between around 0.6 and 1.5 million years ago (MYA) and that secondary contact started approximately between 30,000 and 75,000 years ago and lasted until a few centuries before the present. This is consistent with previous studies that estimated the split between the two Pecten species to have occurred in the Pleistocene during a period of fluctuating climatic conditions [Our demographic analysis suggests that  maximus   and absolute (dxy) measures of genomic divergence provides evidence that differential gene flow occurred after secondary contact and supports the presence of reproductive barriers within the Pecten genome. This implies that the observed highly divergent genomic regions are not simply a by-product of locally reduced genetic diversity [Pecten species likely accumulated important adaptive divergence and were reproductively isolated. As a consequence, gene flow during secondary contact could not homogenize allele frequencies at these regions, resulting in a mosaic of highly and lowly differentiated genomic regions.Genetic incompatibilities may also have increased during the prolonged period of isolation. The fact that we obtained consistent patterns of divergence using both relative , thruleatus, ), rough axatilis ), and, n maximus ). In the maximus ) and seqP. maximus and P. jacobaeus and tested whether SNPs located in highly divergent regions exhibited higher levels of LD. We found that, whilst overall LD decays rapidly in both species, when only divergent SNPs are considered in P. jacobaeus, LD extends beyond 400 kbp, indicating the likely presence of structural variants, such as chromosomal inversions [Pecten to maintain adaptive variation at different scales, both within different P. maximus lineages and also between Pecten species, where they act as intrinsic barriers to gene flow and maintain locally adapted variation.Here we focused on the comparison between versions . Consistversions  suggest Chlamys farreri [P. maximus and P. jacobaeus evolved might have shaped patterns of genetic divergence between these two species. Furthermore, toll-like receptors also participate in development [Drosophila [Pecten, divergence at genes associated with this pathway could potentially promote reproductive isolation, as larvae of one species dispersing within the distribution range of the other, may not be adapted to survive and develop under contrasting environmental conditions.In support of the argument for locally adapted variation, we found that highly divergent genomic regions between the two species appeared to be enriched for several biological processes. Furthermore, the genes enriched for GO-terms were distributed throughout the genome, discounting the possibility of this signal being produced by tandem repeats of the same gene. Among them, we found the “MyD88-dependent toll-like receptor signalling pathway” which is involved in the innate immune response of nonmammal species , includi farreri . The evo farreri , while p farreri . It is telopment ,64 and hosophila . If thisGenes involved in RNA modification also appeared to be over-represented in our enrichment analysis (GO terms: “snRNA 3’-end processing” and ”RNA (guanine-N7)-methylation”). In recent years, mRNA modifications have been implicated as regulatory mechanisms that control gene expression , which iAnguilla rostrata) and the European eel (A. anguilla), and by Jones et al. [Similar enriched GO terms were obtained when repeating the GO enrichment analysis considering only SNPs located within genes, corroborating our conclusions. Whilst highly divergent loci are located in low-recombination genomic regions, such that these SNPs could potentially be associated with any gene in the region where recombination is restricted, the fact that the majority of SNPs are outside coding regions suggests that most of the identified loci are likely to have regulatory roles rather than producing changes at the protein level. Similar results were obtained by Pujolar et al.  when stus et al. , who invs et al. . Regulats et al. ,73,74,75P. maximus and P. jacobaeus. Consistent with this, we are not aware of any studies that have identified hybrid individuals based upon genetic data and this is also true of the current study [P. jacobaeus and P. maximus hybrids can be produced artificially [Our demographic analyses further suggest that there currently is no gene flow occurring between nt study , althougficially  and measficially ,78, oppoficially  and wateficially . Moreoveficially ,81. ThisP. maximus and P. jacobaeus revealed a highly heterogeneous genomic divergence landscape between the two species. Our analyses indicate that this pattern likely originated as the consequence of a long period of isolation after divergence, during which the two lineages accumulated both adaptive divergence and reproductive barriers, followed by secondary contact, during which only those genomic regions permeable to gene flow were homogenized. Highly divergent genomic regions were associated with reduced levels of genetic diversity, which is indicative of the occurrence of selective sweeps and is consistent with the notion that positive selection imposed by contrasting environmental conditions played an important role in determining the pattern of genomic divergence observed today. Moreover, our analyses indicate that highly divergent genomic regions are located in areas of low recombination, hinting at the possible presence of chromosomal inversions, which are known to be important for maintaining adaptive genetic variation and promoting reproductive isolation. In line with this, highly divergent genomic regions also showed elevated dxy values indicating that differential gene flow across the genome occurred during secondary contact. Finally, the genes located within highly differentiated regions are related to immunity, development, mRNA modification, and the biosynthesis of molybdenum cofactors, with the majority of SNPs likely affecting regulatory elements. Taken together, these results provide new insights into the divergence history of these two scallop species, while also identifying specific genomic regions that potentially contribute to the maintenance of adaptively important variation.RADseq data for the commercially exploited European scallop species


We describe a precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to determine the mechanisms by which SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 378 UC patients we map the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. With unsupervised clustering algorithms we group these patient-specific networks into four distinct clusters driven by PRKCB, HLA, SNAI1/CEBPB/PTPN1 and VEGFA/XPO5/POLH hubs. The pathway analysis identifies calcium homeostasis, wound healing and cell motility as key processes in UC pathogenesis. Using transcriptomic data from an independent patient cohort, with three complementary validation approaches focusing on the SNP-affected genes, the patient specific modules and affected functions, we confirm the regulatory impact of non-coding SNPs. iSNP identified regulatory effects for disease-associated non-coding SNPs, and by predicting the patient-specific pathogenic processes, we propose a systems-level way to stratify patients. Single Nucleotide Polymorphisms (SNPs) affect cellular regulatory networks, and SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Here the authors introduce iSNP, a precision medicine pipeline that combines genomics and network biology approaches to uncover patient specific pathways affected in complex diseases. With IBD, the interlinked facets leading to disease are a dysfunctional immune system and response to environmental triggers, including constituents of the intestinal microbiota and dietary factors, in a genetically susceptible host3. Focusing solely on genetic susceptibility, genome-wide association studies (GWAS) and subsequent fine mapping of identified regions defined causal disease-associated single nucleotide polymorphisms (SNPs)5. However, the clinical impact of these SNPs has yet to be realised. A promising approach to assess the functional role of SNPs, and advise clinical practice, is to examine patient-specific sets in combination with systems-level approaches6.Precision medicine is a key clinical goal for the effective treatment of heterogeneous, complex diseases such as inflammatory bowel disease (IBD). Complex, multilayered, integrative techniques are required to identify the individual patients’ complex pathogenic pathways7, IBD5 and lung cancer8. Computational workflows prioritise such coding SNPs for further analysis9. These approaches include artificial intelligence methodologies  to identify and quantify deleterious regulatory impacts of SNPs using chromatin accessibility and transcription factor binding affinities10, and high-throughput chromatin interaction studies11. This allows for the identification of SNPs of interest. However, understanding the function of SNPs in non-coding regions of the DNA remains challenging, principally because many disease-causing SNPs are in areas yet to be annotated5.Exome sequencing and protein structural biology have already contributed to the functional annotation of SNPs in protein-coding regions , and how they impact diseases such as obesity12. These coding SNPs are not causally related to impaired intestinal barrier function or inflammation that are hallmark pathognomic features of UC13. Understanding of the phenotypic effects of SNPs in IBD has involved the study of monogenic IBD in paediatrics that analysed the deleterious nature of non-coding SNPs14, although in adult-onset IBD these rare individual phenotypic SNPs have not been identified15. A broader and deeper understanding of the function of SNPs in this complex genetic disease is therefore needed.In ulcerative colitis (UC), a form of IBD, coding SNPs comprises less than 10% of the total UC-associated SNPsWe propose that functional annotation at the molecular and systems-level of the remaining 90% SNPs located in non-coding regions would expand the utility of these disease-associated SNPs. The proposed gap-filling systems-level analysis is essential, as individual SNPs may have subtle phenotypic effects, but in combination, they may have a pathological impact. Integrated analysis of these non-coding SNPs allows the identification of novel pathogenic pathways, and potentially patient-specific disease susceptibility, thus facilitating precision therapy.20, microRNA-target sites (miRNA-TS)21, splicing25 or transcription factor (TF) binding sites (TFBS)26 in promoter regions and within the first introns27, which has been reported in complex diseases such as diabetes, schizophrenia, coronary heart disease and Crohn’s disease31. In this study, we focused on two regulatory effects as examples; SNPs occurring in transcription factors binding sites and in miRNA target sites as they can be validated by published studies.For functional annotation of SNPs in non-coding regions, a key question is whether the SNPs affect gene expression by, for example, affecting long non-coding RNAs32. Using network biology approaches that we have previously exploited to uncover novel and important proteins in cancer biology33, we aimed to further understand the pathogenic pathways of UC and to identify novel and previously hidden disease-associated proteins. These proteins are often undetected or hidden in conventional mutation and expression screens as they mostly act as direct interactors (first neighbours) of the proteins affected by a disease-associated SNP. Using first neighbours gives an optimal trade-off to keep specificity while reconstructing a connected graph. Similar studies have utilised the concept of first neighbour proteins in both type 2 diabetes34 and juvenile idiopathic arthritis35. Systems biology approaches have been utilised with predictive network models that identified proteins involved in the pathogenesis of IBD in general38 but these approaches are unable to account for regulatory and downstream effects of non-coding SNPs. Therefore, by identifying first neighbour proteins in UC, we aimed to expand current research and identify additional pathogenic pathways of pharmacological use in UC that have been previously undetected or hidden due to a lack of connection with non-coding UC-associated SNPs. As UC is highly heterogeneous, we used individual patient data to identify patient cohorts with similar or different pathogenic pathways of UC.To identify the effect of non-coding SNPs, we have built on the concepts identified by Boyle et al. to track the cumulative effects of multiple regulatory SNPs as an ‘omnigenic’ model39. Integrating systems genomics and network biology data and analysis offers unique biological insights and enables the scalable examination of patient-specific datasets for precision medicine.Connecting non-coding SNPs to pathways, especially in a patient-specific manner, is a much needed but highly challenging approach. To achieve this, we developed a workflow, named the integrative SNP Network Platform (iSNP) by combining systems genomics and network biology approaches into a scalable system. We demonstrated its applicability by analysing a UC-associated signalling network and by identifying patient clusters with distinct pathomechanisms contributing to UC. Within these clusters, we highlighted cluster-specific key players, identifying known and additional proteins as well as patient-specific pathways to the disease. These predicted pathogenic effects were then validated using transcriptomic data from an independent patient cohortThe integrative SNP Network Platform (iSNP) was developed to assess the regulatory effects of non-coding SNPs. The iSNP workflow constructs an integrated network based on identifying the proteins whose expression could be directly affected by the SNPs (termed as SNP-affected proteins) and their interactors (first neighbours) through protein–protein interactions. Fig. . We usedPatients from this cohort had a total of 40 individual UC-associated SNPs from which we identified 22 UC-associated regulatory SNPs localised within TFBS or miRNA-TS. These SNPs were annotated to occur within 20 TFBSs and 4 individual miRNA-TSs Table . 11 of t41 and were regulated by 169 TFs and 247 miRNAs. To uncover the larger effect space of the non-coding SNPs, we identified the first neighbour interactors of the 33 SNP-affected proteins. In total, the UC-associated signalling network consisted of 686 protein nodes, 6808 protein–protein interactions resulting in 758 regulatory interactions  calcium homeostasis; (2) cell motility and adhesion; (3) stress regulation. Two additional modules were identified, one containing HLA receptors involved in antigen-presentation and one containing other proteins such as MAPKs or HDAC7.PRKCB gene through a miRNA -TS whereas rs913678 affects PTPN1, CEBPB and SNAI1 genes through a TFBS in an enhancer region.The network visualisation shown in Fig. The UC-associated signalling network uncovered interesting regulatory feedback loops Fig. . In thesWe then investigated how the UC-associated signalling network was different in each of the 378 UC patients. Based on the set of SNPs present in each patient, we defined patient-specific UC-associated signalling networks, called ‘network footprints’. Unsupervised hierarchical clustering using different linkage algorithms of 378 patients stratified the patient-specific network footprints into four distinct clusters Fig. . The disPRKCB gene had the largest effect in clustering the patients, as it has 305 interactor partners in the network. PRKCB has been implicated in the pathogenesis of IBD due to its effects on the colonic mucosa42, colonic microbiota43 and cell junction complexes45. This SNP divides the patient cohort into two different clusters -induced colitis, it was shown that SNAI1 augmented the effects of MIST1 on the inflammasome protein NLRP3, promoting inflammation47. PTPN1 is a phosphatase that inhibits many tyrosine phosphate receptors such as EGFR48 or PDGFR50. Inhibiting PTPN1 increases angiogenesis and decreases inflammation51. CEBPB is a transcription factor overexpressed in both DSS- and beta caryophyllene-induced colitis52. Tertiary drivers are the SNP rs477515 affecting TFBSs in the enhancer region of HLA genes, and the SNP rs943072 affecting TFBS in a shared enhancer region of VEGFA, XPO5 and POLH.SNP-affected proteins with many protein interactions drove the clustering of patients, often designated as hub proteins in network biology. In our analysis, we defined these proteins as ‘cluster driving proteins’ and the SNPs affecting them are identified in Table ers Fig. . The sec53 and STRING54) to validate the clustering of the patients. From the 48 SNP-affected proteins 23 were present in Reactome and 33 in STRING. The UC-associated signalling networks were not similar, due to the complementarity of the three used networks  and 20 juvenile controls. The data were coming from the PROTECT study56. We defined whether a gene is differentially expressed in the UC patients compared to controls using fold change as a simple metric and developed three validation approaches  UsiPRKCB and two HLA genes, HLA-DQB1 and HLA-DPA1, were differentially expressed in all patients in the validation cohort, whereas VEGFA and CEBPB were differentially expressed in 97.6% and 93.2% of the patients, respectively. This validation analysis demonstrated that the SNP-affected genes we have functionally annotated (predicted) were also differentially expressed in an independent cohort of UC patients.The first validation approach revealed that the SNP-affected genes were differentially expressed on average in 63.24% patients (SD = 39.58%) Fig. . Of the The second approach Fig.  used theThe third validation approach showed that the biological functions which we have identified using the UC-associated signalling network were also differentially regulated in the independent cohort. We identified the Gene Ontology biological processes that were overlapping between the differentially expressed genes and first neighbours of SNP-affected proteins Fig. . These iOur validation approaches confirmed that the iSNP analysis identified the known genes involved in active UC. Moreover, with the increased coverage from the first neighbours of the SNP-affected proteins, iSNP enabled the identification of those genes and proteins that are involved in UC pathogenesis that would not have been identified by conventional genetic or transcriptomic analysis alone.We have designed an integrated systems genomics workflow Fig. , termed 57, giving individual patient alleles present at SNP sites. This allowed us to functionally annotate UC-associated SNPs on a patient-by-patient basis. A binary approach was used for determining whether a SNP-affected the regulation of a gene or protein, allowing us to identify when a SNP weakly affects the binding of a transcription factor (TFBS) or miRNA target site (miRNA-TS), but does not eliminate the site completely, giving a broader overview of SNP functional annotation.There are significant challenges in designing and executing computational pipelines for functional analysis of genetic data, particularly on an individual patient basis (see Supplementary Discussion for more detailed discussion). To overcome input challenges, we accessed high-quality individual patient genetic information from the UK IBD Genetic Consortium. This comprises preprocessed and quality-controlled immunochip data59 and Find Individual Motif Occurrences (FIMO)60. We considered the length of the TFBS query sequence to include promoters and enhancers. We acknowledge that not all TFBS in enhancer regions will be active, and that recently artificial intelligence techniques have integrated predictions of chromatin interaction with SNP data to identify SNPs in areas of active chromatin11. A switch mechanism to identify which TFBS were active or inactive was not available during the development or expansion of the iSNP workflow so we adopted a simple approach: If a TFBS was affected in an enhancer site by a SNP with a target gene in the Human Enhancer Disease Database (HEDD) it was retained within the network.For functional annotation of SNPs within TFBS, we utilised the two widely cited, validated tools, Regulatory Sequence Analysis tools (RSAT)61 and TargetScan62 were trialled for inclusion in the pipeline. Both performed well; however, as TargetScan requires genome assembly to work, it was not plausible to integrate it into a functional annotation pipeline. Although SNPs may impact other parts of miRNA biogenesis and action, we utilised the site of SNP impact with the largest wealth of experimental data.In terms of the miRNA-TS identification algorithm, both MIRANDAThe UC-associated signalling network identified mechanisms of transcriptional and post-transcriptional regulation impacted by non-coding SNPs. There was more transcriptional regulation of SNP-affected genes than miRNA-based regulation Fig.  due to t63. iSNP mapped both the specific and pleiotropic regulatory changes one step further using a protein–protein interaction network. This has an inherent risk of increased noise within the network, and to reduce this we utilised the sparse OmniPath which integrates experimentally validated protein–protein interactions from 44 sources40. From studies of cancer-related signalling networks, we have shown that information regarding pathogenic pathways to disease can be gleaned from the direct protein–protein interactors for a protein of interest33. By integrating the protein–protein interaction and regulatory SNP effects, the iSNP method highlighted key pathogenesis pathways including calcium homeostasis, cell adhesion, stress response and cytokine signalling  and etrolizumab (b7 integrin subunit inhibitor). Neither gene was affected by a SNP within the network, nor in the first neighbours, but cell motility and adhesion pathways feature in a distinct subset of patients indicating a potential mechanism and explanation by which therapies that impact these pathways may be more or less successful in certain subsets of patients. This needs to be examined more closely and validated in a large clinical cohort, as it may be a means for personalising therapeutic strategies based on patient-specific underlying pathogenic mechanisms in UC.69 as they identify where a SNP has a wider effect on signalling pathways, and from this, we can identify particular pathways unique to clusters of patients which aim to correlate with therapeutic response or disease process. However, no significant differences based on the cohorts  using distance metrics within a hierarchical agglomeration method. This allows us to identify structures within the networks, which were hitherto unknown. One limitation of this approach is a potential bias towards promiscuous hubs, which have high numbers of curated interactions within the interactome networks. An example of this is PRKCB. Conversely, these large hub proteins are very important to the network71 (HLA-DQB1 with Crohn’s72) with HLA-DQB2 and HLA-DPA1 being associations identified here. Unexpected cluster-driving proteins were identified that have clear links with IBD such as PRKCB, and VEGFA74 as well as proteins that have not been previously associated with UC including Exportin 5 and DNA polymerase eta. The involvement of Exportin 5 , as well as the multitude of miRNA-TSs identified, adds weight to UC being a disease whose pathogenesis is intrinsically complex, with multiple small impacts on upstream gene regulation as opposed to singular high impact phenotypic mutations.Our analysis identified multiple genes whose translated proteins were hubs within the network including NFKB1 which is a central player in inflammatory signalling cascades, immune-mediated processes and in tight junctions regulation, but in our network was shown not to be a cluster-driving protein. The HLA proteins were cluster-driving proteins within the network, but did not include the known IBD HLA serotypeshttps://github.com/korcsmarosgroup/iSNP.Whilst we have used UC as a use case study for iSNP, the pipeline is not disease-specific. We have made iSNP accessible and tailorable, accounting for the importance of functional annotation and downstream analysis of non-coding SNP effects for complex genetic diseases. iSNP is a dockerised pipeline that can be interfaced using the command line. Each of the analytical modules of the pipeline can be run independently of each other or run from start to finish. The parameters for each analytical module can be tuned by the user based on the input data. It is available on GitHub at The integrative SNP Network Platform (iSNP) is a workflow to functionally annotate non-coding SNPs, identify the first neighbour interactions within a disease-specific network and identify signalling pathways in which these SNPs and interactors are over-represented. iSNP has the functionality to allow this to be done on a broad scale to identify disease-associated pathways, and on an individual level to identify patient-specific affected pathways. Using UC as an example of a complex genetic disease, iSNP has identified how patients have differing mechanisms of pathogenesis. We identified pathways regulating the cellular response to stress, cell motility and calcium homeostasis as being over-represented in the UC-associated signalling network. Further work now needs to be done on larger cohorts and with multi-omics datasets to confirm the potential for iSNP to be used for precision therapy based on patient-specific genetics.12 and the Broad Institute Repository76. If no fine mapping was available for an index SNP (the immunochip finemapped SNP had an R2 < 0.8), then the highest proxy partners (based on tightest linkage disequilibrium and distance) were assessed using a SNP proxy search and were included in the analysis. Each SNP was annotated using Ensembl from the rsID using the genome map GRCH38.p7. Disease-associated SNPs were retrieved from the original data source.UC-associated index SNPs were identified from the UK IBD Genetics Consortium Immunochip data77. For the list of analysed SNPs and their effect, see Supplementary Data After obtaining ethics approval from the University of East Anglia Faculty of Medicine and Health Science ethics committee ref 02-01-16), anonymised individual patient immunochip data and clinical parameters for 378 patients were retrieved from the UK IBD Genetics Consortium from seven centres across East Anglia, UK . Informed consent of the patients was obtained by the IBD Bio-resource team. The patients have consented to the IBD Bio-resource consent form version 2. We included patients between 16 years and 83 years of age at diagnosis to account for the bimodal age prevalence of UC  matrix-scan58 was used to search for potential TFBS in the ancestral and patient-specific mutant alleles. The background model estimation was determined by using residue probabilities from the genome version GRCH38.p7 sequences of all promoters based on the UCSC genome table browser79 5KB before the TSS and all enhancers from the HEDD database80. In calculating the background probabilities we used a Markov order of 1. The search was subject to both strands of the sequences. Hits with a P-value ≤1e-05 were considered binding sites. Other parameters were set at default values.From the JASPAR database, 746 human transcription factors’ binding profiles represented by Position Specific Scoring Matrices (PSSMs) were downloaded60. FIMO predicts the transcription factor targets sites using a matrix-based sequence scanning algorithm without a hidden Markov model, unlike the previous tool RSAT matrix-scan. It calculates the log-odds scores comparing random and test sequences followed by a Benjamini-Hochberg-based false discovery correction of the P-value. The false discovery rate cut-off was 0.1.As a complementary TF binding sites prediction algorithm, FIMO was used81. HEDD contains the enhancers from ENCODE82, FANTOM584 and the Epigenomics RoadMap85. To assess the effect of the SNPs on miRNA-TSs, the 22 bp sequences of mature miRNAs were retrieved from miRBase87. The flanking sequences of SNPs were assessed for the presence of miRNA-TSs using miRanda88. Hits occurring in the seed region (2’–8’) of the miRNAs, and with alignment scores ≥90 and energy threshold ≤ −16 kcal/mol were considered as TS. Other parameters were set to default settings. TSs in the coding region or in the first intronic region were kept. A final manual check was performed to ensure that the SNPs overlapped with the predicted TFBS or miRNA target sites. For the miRNA-TS predictions, miRanda was chosen as it predicts and characterises miRNA binding sites using entropy-based binding energy scores instead of traditional conservation-based methods88. Gain or loss of the regulatory interactions between TFs and protein-coding genes were also considered where the protein-coding gene was in the promoter or in the enhancer region. We defined the promoter regions as 5 kb upstream from the transcription start site and downstream to the first exon of the gene. This information was retrieved using the feature retrieval function of the UCSC genome table browser79. The effect of SNPs on the uncovered TFBS or miRNA-TSs was classified into either a gain or loss of binding site/target site or a neutral change. Only those sites identified as loss or gain regarding sites corresponding to the ancestral allele were considered for subsequent analysis. We referred to genes corresponding to such SNPs as ‘SNP-affected genes’.To increase the coverage of the TF binding sides, enhancer regions were added using the Human Enhancer Disease Database (HEDD)41. For the STRING network, we used stringent parameters using only the physical protein–protein interactions: values >0 in the experimental and database channel in the physical links downloaded on 28 October 202154. For the Reactome interactions, we used the Homo sapiens mitab interaction file downloaded on 28 October 202189. All interactions were translated to UniProt Accession numbers90 using the UniProt mapping tool with a python script. For each patient, the set of proteins encoded by SNP-affected genes and their first interactors (first neighbours) were defined as the UC-associated network footprint of a particular patient. The union of all network footprints, the UC-associated signalling network, was analysed and visualised in Cytoscape 3.3.091 using the inverted self-organising map layout. We retained only those SNP-affected genes which were present in the OmniPath resource and which formed a giant component with their interactors. Patient-specific networks were constructed using the Cytoscape CyRestClient 0.6 in Python 3.692.Protein–protein interactions of the proteins encoded by SNP-affected genes were obtained from OmniPath on 10 January 202093 implementing the GLay clustering method94, which is an implementation of the Girvan-Newman clustering algorithm95. Briefly, the clustering method deletes the highest betweenness edges from the network until the network collapses to non-connected components and these components form the clusters. We used this clustering method due to being algorithmically quick and giving biologically meaningful clusters. (For further discussion see Supplementary Discussion). We call the network clusters ‘modules’, to distinguish them from patient clusters.Module analysis was carried out using the Clustermaker2 1.1.0 Cytoscape appscikit-learn (v 0.23.) package was used for hierarchical clustering of the patient-specific clusters96. The constructed distance matrix between patients was based on the Hamming distance97. If a protein was directly or indirectly affected by a SNP, it was assigned a value of “1” for a patient. If the protein was not affected, it was scored as “0”. The cluster similarity was measured using the adjusted rand index from the python Scikit-learn package96.The 98. The gene ontology biological processes were visualised using REVIGO99. For the overrepresentation test, the background was the giant component of the specific network resource . The tests were false discoveries corrected by the Benjamini-Hochberg method. We considered a Gene Ontology Biological Process term representative for a cluster if it was enriched with a corrected q < 0.05.The Gene Ontology analysis was performed using the GORILLA tool55. After examining the metadata, the study GSE10914239 was used as it had annotated source tissue and an adequate number of patients and controls . Expression tables were assembled from the gene-specific expression values remaining those genes expressed in 10 or more read counts and the samples were normalised using the limma package (version 3.50.1)100 which implemented voom101. The log2 normed counts were used for further analysis. On a patient to patient basis, the fold change values were calculated by comparison with the average of the control samples. If the absolute differential expression was >1 then the gene was considered to be differentially expressed in that patient. This binary matrix was used for clustering and visualisation.The TAMMA transcriptomics collection datasets were downloaded on 14 June 2021t-test using the standard limma analysis pipeline100.For case one, only the SNP-affected genes in the OmniPath database were used Table . For casFurther information on research design is available in the Supplementary InformationReporting SummaryPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7