Zea mays L.). Investigation of these traits would help to develop high-yield varieties in maize. Genome-wide association study (GWAS) uses the linkage disequilibrium (LD) in the whole genome to determine the genes affecting certain phenotype. In this study, five ear traits  were investigated across multi-environments for 2 years. Combining with the genotype obtained from Maize SNP50 chip, genetic diversity and association mapping in a set of 292 inbred lines were performed. Results showed that maize lines were clustered into seven subgroups and a total of 20 SNPs were found to be associated with ear traits significantly (P < 3.95E-05). The candidate genes identified by GWAS mainly encoded ubiquitin-activation enzymes (GRMZM2G015287), carotenoid cleavage dioxygenase (GRMZM2G446858), MYB-CC type transfactor, and phosphate starvation response protein 3, and they were associated with kernel length (KL) and ear diameter (ED), respectively. Moreover, two novel genes corresponding to RNA processing and fructose metabolism were found. Further, the SNPs detected by GWAS were confirmed by meta-QTL analysis. These genes and SNPs identified in the study would offer essential information for yield-related genes clone and breeding program in maize.Kernel and ear traits are key components of grain yield in maize ( Zea mays L.) is one of the most significant cereal crops worldwide and plays a crucial role in sustaining food security. In addition, forage crop and industrial energy require maize as a raw material. The wide demands of maize make grain yield a major breeding target. In the past century, maize grain yield has increased eight-fold with the majority of the yield being attributed to selection and hybrid breeding  have been verified to be a powerful approach for identifying genes, alleles or haplotypes related to a certain agronomic traits under complex environments , which iectively . These iZmMADS-RIN using the homologous cloning method, which has sufficient homology with the gene OsMADS6 related to kernels development in rice. Based on the yield-related gene OsGW2 in rice, ZmGW2-1, which encoded E3 ubiquitin ligase protein and probably regulated the development of ear. A CLAVATA receptor protein locus on chromosome 4 was cloned, and its mutation led to the increase of meristem and ear row number  gene on chromosome 4 was found, which was responsible for the quantitative variation in kernel row number (KRN) by regulating UB3 expression (OsGS5) in rice, a 981 bp gene segment ZmGS5 was obtained using homologous cloning method, which encoded protein sequence that belonged to serine carboxypeptidase in maize  in rice. At present, most of yield-related genes of maize come from the homology genes in rice, so the study of GWAS and QTL mapping for maize yield-related traits is imperative.Recently, some kernel related genes had been cloned. With Zheng 58 as the plant material, w number . Using apression . Accordiin maize . And thiIn this study, the phenotype of five ear traits for an association mapping panel consisting of 292 maize inbred lines were collected in 2015 and 2016. Then, QTLs associated with the five traits were identified with GWAS method, and candidate genes were also predicted. This study would provide useful insights into the genetic basis of related traits, and supply molecular tools for improving kernel size and grain yield in maize.Supplementary Table S2) were analyzed in the study, which were derived from four subgroups of China, Reid, Lvdahonggu, P group, and Sipingtou, as well as some tropical lines and sweet-waxy maize. All the materials were collected or bred by the maize molecular breeding team, Qingdao Agriculture University, China and were commonly used in maize breeding program.A total of 292 maize inbred lines , Luoyang, Henan Province 2015 (LY15), and Jiaozhou, Shandong Province 2016 (JZ16). Experiment was arranged in a randomized complete block design with three replications, and each inbred line was grown in a single row with 15 plants, 3 m in length, 0.6 m between adjacent rows, and 0.2 m between adjacent plants. The measure method of KL and KW were as described in When harvest, five well-developed ears in the middle of each row were selected so as to minimize the boundary effect. A digital caliper  was used to measure KL, KW, EL, EW, and CD, in which EW and CD were measured at the middle of each ear and cob, KL and KW were measured with ten mixed and randomly selected kernels from each inbred line. To ensure accuracy, the data of each trait were determined with the average value of three replications.SPSS20 statistical software  was used to calculate the phenotypic data, including Normal analysis of each trait and Pearson correlation analysis between traits and environments.h2) was calculated using the following formula:The broad-sense heritability  method . A total1  .2 (r2). The calculation result was imported into Excel to create the LD decay plot.Genetic distance of the 292 inbred lines and LD of each chromosome were calculated with Cladogram and LD functions of Tassel 5.2.312 , respect3  of the population was inferred according to lnP (D) value in Structure V2.3.4. With the optimum K-values ranging from 1 to 10, the strong Markov Chain Monte Carlo (MCMC) after the non-repeated iteration was set to 10000 times at the beginning, and then set to 50000 times with the number of iterations set at 7. The probability of each inbred line grouped into a subgroup to determine the genetic composition of materials.The molecular markers used for subgroups division by the Structure V2.3.4 software3  were selP = 1/total number of SNPs = 3.95E-05 (Fixed and random model Circulating Probability Unification (FarmCPU)  was adap3.95E-05 . Further3.95E-05 . The CML4 according to the physical positions of the SNPs. Then, the functional annotations of candidate genes were predicted in NCBI5. The significant association regions were scanned for putative genes using IGV downloaded6, and LD analysis of the linkage SNPs in 310-kb window were conducted using Haploview v.4.27.Based on the SNP locus which were significantly association with target traits, the genome sequence of the maize line B73 was used as the reference genome for selecting candidate gene . The gen8 and NCBI, which included the size and type of the mapping population, the markers’ genetic distance, mapping function, and the position, LOD value, contribution rate, and confidence intervals of QTLs. Because the maximum likelihood value, confidence interval, and contribution rate are important information of QTL  in five models of QTL given by the simulation operation, and the optimal model was judged by the value of the minimum Akaike-type criteria value (AIC) . And theFigure 1A). And 96.75% of the relationship values between two lines ranged between 0.25 and 0.75, and only 0.06% of them was zero, 3.19% >1, and 38.48% <0.5, which mean that most of the materials are relatively relevant to each other, with a few irrelevant.Based on the 25331 SNPs for GWAS, we made relationship diagram among 292 maize inbred lines .The LD level of the whole genome of inbred lines was estimated using 25331 SNPs. Results showed that LD decayed differently in ten chromosomes, with chromosome 2 had the most rapid decay rate and chromosome 4 had the slowest. At a cut-off value of Figure 2). The entire materials were divided into seven groups, namely, Tangsipingtou, PA, PB, PC, Lvdahonggu, Lancaster, and an integrated groups. Tangsipingtou group mainly consists of inbred lines such as Chang 7-2, Lx 9801, H 21 as well as hybrids selected from Chang 7-2 hybridizing with other materials in this study. The PA group tends to Reid, including Zheng 58, Ye 478 etc. PB group mainly includes Ex, Qi 319, P 138, Qi 318, X 178 and so on. PC group tends to BSSS, mainly including B73. Lvdahonggu group mainly contains E28, Dan 340 and other materials. Most of the inbred lines were clustered into their corresponding subgroup, and the tropical lines and sweet-waxy maize derived from Philippines and Mexico were clustered into the integrated group. However, a few inbred lines belonging to Lancaster subgroup, such as Qi 205 and Ji 846, were diffused in other subgroups.The genetic distance between 292 maize inbred materials was calculated by Tassel 5.2.31, and a neighbor-joining clustering graph was constructed .The genetic diversity results analyzed with the Structure software are the same as that of NJ clustering. When Table 1. Abundant and large variation of the five traits, KL, KW, EL, ED, and CD, was observed in each location. But the variation values of each trait were different at different environments. For example, the variation for the KL in LY15 ranged from 65.04 to 135.96 mm (mean ±SD = 98.54 ± 10.40 mm), but it ranged from 67.30 to 117.95 mm (92.71 ± 9.04 mm) in JZ16. The H2 of the five traits was relatively high, ranging from 68.86 for EL to 85.88% for ED (Table 1), indicating that a large portion of phenotypic variance for ear and grain traits could be attributed to genotypic effects. All the phenotypic data of every trait follow Normal distribution, as the absolute values of kurtosis and skewness among these environments were less than 1, thus they were suitable for QTL mapping .The descriptive statistics for ear and kernel traits under three natural environments in 2 years are presented in Table 2). Among all the positive correlations in 2015, the highest value was found between ED and CD, while the lowest value was between ED and KW. In 2016, significantly positive correlations were obtained between all traits except KL and CD in Jiaozhou (Supplementary Table S1).To verify the accuracy and consistency of the results, ANOVA was conducted to reveal the significant correlation among the five ear traits in 2 years  and -lg , respectively .Using FarmCPU, the phenotype data of five ear traits and the genotype data of 25331 SNPs were analyzed for GWAS. Quantile–quantile (Q–Q) plots implied that the population structure and kinship relationship were well controlled in the GWAS for each trait. The horizontal and the verticle axises show the values of -lg (Transformed expected Table 3). Among them, four SNPs, associated with KL dispersed on chromosome 3, 6, and 7 (2), were detected. Especially, the SNP locus PZE_107042407 on bin7.02 was detected both in JZ16 and LY15. For KW, three SNPs were detected only in LY15 and distributed on chromosomes 6 and 10 (2), respectively. Additional, one SNP locus associated with EL, seven loci associated with ED, and five associated with CD were also identified. Among all the SNPs related to ear traits in this study, the one related to CD on chromosome 1 was the most significant among them. All the details of SNPs and candidate genes were shown in Supplementary Table S3.Twenty SNPs, significantly associated with the five ear-related traits, have been detected and distributed on eight chromosomes of maize . In the Q-Q plots of association studies, the signal above the Bonferroni correction line by FarmCPU was better than that by CMLM model in GAPIT, which suggested that candidate genes were difficult to be distinguished from the background noise by CMLM. Namely, CMLM reduces the detection efficiency of the associated sites with known candidate genes when compared with FarmCPU . In our study, and the significantly association loci decreased to 26 when the CMLM was employed.We also analyzed the five traits using CMLM model in GAPIT package (Table 4). SNP locus, PZE_107042407, which was detected in two environments of JZ16 and LY15 simultaneously, was located within the interval of gene GRMZM2G173943, whose functional annotation is MYB-CC type transfactor. Reported genes, Arabidopsis AtPHR1 and algal PSR1, are members of MYB-CC gene family and regulated phosphorous hunger signal pathway (Table 5). Two linkage genes at chr: 72.21–72.53 Mb were identified in the same LD block with PZE_107042407 .Candidate genes containing SNPs associated with the five ear-related traits were identified, and their function were predicted basing on MaizeGDB and NCBI ( pathway . BesidesTable 6) were collected and used to construct the consistency map . In the meta-analysis, the model with the minimum AIC value was chosen as the optimal one, then 20 meta-QTLs were obtained according to the selection criteria of at least three initial QTLs existing within one location. These meta-QTLs are mainly distributed on chromosomes 1(4), 3 (3), 4 (3), and 9 (5), respectively, with confidence intervals ranging from 4.2 to 15.13 cM (Table 7). Then, these meta-QTLs and the SNPs associated with ear traits in this study was mapped according to the physical distance of SNP on both sides of MQTL tag, Results showed that five of the detected SNPs was located within the intervals of these meta-QTLs, such as locus PZE_101048890 in MQTL4 interval, PZE_103171163 in MQTL8, SYNGENTA6857 in MQTL12, and PZE_110067406 and PZE_110061773 were both in MQTL20 interval. These results further i verified the accuracy of the SNP loci related to ear traits in this study.Information of 293 QTLs related to the panicle traits , three for KW at bin 6.07, 10.00, and 10.07, one for EL at bin 7.03, seven for ED at bin 1.10, 3.09, 4.05, 5.03, 8.03 (2), and 10.03, and five for CD at bin 1.05, 1.08, 1.10, 1.12, and 8.03, respectively. Especially, the SNP for KL at bin 7.02 was common in two environments. Besides, another SNP at bin 8.03 (PZE_108042082) was detected both for ED and CD, indicating that ED and CD may have same genetic basis, which was supported by the significant correlations of the two traits in our research materials. Therefore, our results would provide important information for further fine mapping yield-related genes, thus to reveal corresponding molecular mechanisms.Usually, a lot of QTLs for a certain trait have been detected with different populations in different environments, and most of them are not consistence with each other. In previous studies, GW2 in rice could decrease cell division, associated with grain width, grain weight and growth period, codes a RING-type protein with E3 ubiquitin ligase activity that locating in the cytoplasm and degradating zymolytes by anchoring them into proteasomes and ultimately decreasing cell division.In addition, four functional genes detected are related to ubiquitin (Ub), which is widely existing in eukaryotes and highly conservative. The most important gene, GRMZM2G015287, encoded ubiquitin-conjugating enzyme E2N, and the other three separately encoded ubiquitin-activating enzyme E1 2, ubiquitin carboxyl-terminal hydrolase 13, and E3 ubiquitin protein ligase DRIP2. Ubiquitination pathway is an important regulatory process in plant biological activities including growth and development, response to biological and abiotic stress signals, etc. The covalent attachment of Ub to target proteins involves a three kinds of enzymes  and a series of reactions via the transfer of thioester linkage between these enzymes . PreviouZmGS3-CHR1-1, ZmGS3-CHR1-2, ZmGS3-CHR2, ZmGS3-CHR7, ZmGS3-CHR4, and ZmGS3-CHR5 that homologous with ring E3 ubiquitin-protein ligase gene GS3 and GS2 in rice by integrating meta-QTLs of ear row number and grain weight reported in major journals over 1994–2012. The gene GW2 in rice could decrease cell division, and loss of GW2 function would result in a larger and wider spikelet hull, which accelerated grain milk filling rate and enhanced grain width, weight and yield  protein which can inhibit the activity of rice tiller buds, maintain their dormancy and participate in the dark-induced leaf senescence process and hydrogen-induced leaf cell death process  encoding peptide chain release factor PrfB1 in chloroplast (AtPrfB1) and GRMZM2G103843 encoding fructokinase-2 were found. PRFB1 responses to the peptide chain termination codon UGA, which is necessary for the proper translation and stability of UGA-containing polycistronic transcripts in chloroplasts. So AtPrfB1 participates in the biological processes of plastid organization  encode peptide chain release factor PrfB1 in chloroplast (AtPrfB1) and GRMZM2G103843 encoded fructokinase-2. Besides, a MYB-CC type transfactor and a gene encoding phosphate starvation response protein 3 were found to be associated with KL and ED, respectively. These results would be helpful for understanding the relationship between yield and the ear-traits in maize.Genetic diversity and GWAS of maize ear traits were performed in a panel of 292 inbred lines. And 20 significant SNPs associated with kernel size and grain yield were detected using FarmCPU software. Among them, a candidate genes on chromosome 1 was related to ubiquitin, and two novel candidate genes, (GRMZM2G398608 and GRMZM2G103843) related with ED and CD, were also explored in the study. Bioinformatics analysis showed that gene X-YSo and M-AZ conceived and designed the study. M-AZ, X-MZ, X-YSh, Y-HP, X-MG, and JL performed the experiments, phenotyped the populations and field work. X-MZ and X-YSh contributed to genotype and phenotype data analysis. X-YSh conducted the meta-analysis. X-MZ drafted the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Brucella melitensis, whereas gene-by-gene comparison through core-genome multilocus sequence typing (cgMLST) has not been explored so far.The use of whole-genome sequencing (WGS) using next-generation sequencing (NGS) technology has become a widely accepted method for microbiology laboratories in the application of molecular typing for outbreak tracing and genomic epidemiology. Several studies demonstrated the usefulness of WGS data analysis through single-nucleotide polymorphism (SNP) calling from a reference sequence analysis for  Brucella melitensis, whereas gene-by-gene comparison through core-genome multilocus sequence typing (cgMLST) has not been explored so far. The current study developed an allele-based cgMLST method and compared its performance to that of the genome-wide SNP approach and the traditional multilocus variable-number tandem repeat analysis (MLVA) on a defined sample collection. The data set was comprised of 37 epidemiologically linked animal cases of brucellosis as well as 71 isolates with unknown epidemiological status, composed of human and animal samples collected in Italy. The cgMLST scheme generated in this study contained 2,704 targets of the B. melitensis 16M reference genome. We established the potential criteria necessary for inclusion of an isolate into a brucellosis outbreak cluster to be ≤6 loci in the cgMLST and ≤7 in WGS SNP analysis. Higher phylogenetic distance resolution was achieved with cgMLST and SNP analysis than with MLVA, particularly for strains belonging to the same lineage, thereby allowing diverse and unrelated genotypes to be identified with greater confidence. The application of a cgMLST scheme to the characterization of B. melitensis strains provided insights into the epidemiology of this pathogen, and it is a candidate to be a benchmark tool for outbreak investigations in human and animal brucellosis.The use of whole-genome sequencing (WGS) using next-generation sequencing (NGS) technology has become a widely accepted method for microbiology laboratories in the application of molecular typing for outbreak tracing and genomic epidemiology. Several studies demonstrated the usefulness of WGS data analysis through single-nucleotide polymorphism (SNP) calling from a reference sequence analysis for  Brucella melitensis, which infects primarily sheep and goats, is the most frequent agent of brucellosis in humans, and it leads to the most severe manifestation of the disease  status. The disease, however, still persists in several countries in the Mediterranean area. In Italy, despite implementation of the brucellosis eradication program for over 50 years, ovine and caprine brucellosis remains endemic in several southern provinces, in Sicily in particular   has been considered the most efficient typing method for emiology . Accordilineages , 21. Moriew/907) . Howeveriew/907) .With advances in and decreased cost of whole-genome sequencing (WGS), new methods of pathogen typing, including gene-by-gene comparison using core genome multilocus sequence typing (cgMLST), as well as single-nucleotide polymorphism (SNP) calling based on a reference sequence analysis, are considered to be a suitable and more informative replacement of the gold standard typing schemes –26. cgML–B. melitensis and to assess the performance of cgMLST and a whole-genome SNP-based approach against the traditional MLVA-16 typing method using a set of animal outbreak-associated isolates and a set of isolates with unknown epidemiological status.The aims of our study were to develop a cgMLST scheme for B. melitensis strains isolated during a single outbreak in 21 farms from the provinces of Frosinone, Rome, Isernia, and Campobasso in central Italy  according to the manufacturer's instructions. All isolates were stored at −80°C. Epidemiological data are reported in protocol . Brieflyescribed . DNA froSamples were genotyped using the MLVA-16 panel described by Le Flèche et al. , 16. TheTotal genomic DNA was quantified with the Qubit fluorometer , and library preparation was performed using the Nextera XT library preparation kit  or Kapa high-throughput library preparation kit  according to the manufacturers' instructions. The libraries were sequenced using the Illumina NextSeq 500 platform, producing 150-bp paired-end reads, or Illumina MiSeq, producing 250-bp paired-end reads. After demultiplexing and removal of adapters, reads were trimmed from 5′ and 3′ ends to discard the nucleotides with quality scores of less than 20. Reads shorter than 70 bp and average Phred mean quality of <24 were automatically discarded. Read coverage ranged from 18× to 356×, with an average of 155×. All scaffolds were assembled with SPAdes version 3.11.1 with the –careful option selected , 29.B. melitensis bv. 1 strain 16M reference genome (NC_003317.1 and NC_003318.1) from the cgMLST scheme: a minimum length filter that discards all genes shorter than 50 bp, a start codon filter that discards all genes that contain no start codon at the beginning of the gene, a stop codon filter that discards all genes that contain no stop codon or more than one stop codon or if the stop codon is not at the end of the gene, a homologous gene filter that discards all genes with fragments that occur in multiple copies within a genome (with identity of 90% and more than 100-bp overlap), and a gene overlap filter that discards the shorter gene from the cgMLST scheme if the two genes affected overlap by >4 bp. The remaining genes were then used in a pairwise comparison using BLAST, version 2.2.12 , with the query chromosomes of one representative for each of the other two B. melitensis biovars (B. melitensis bv. 2 strain 63/9 [NZ_CP007788.1 and NZ_CP007789.1] and B. melitensis bv. 3 strain Ether [NZ_CP007761.1 and NZ_CP007760.1])  function of the SeqSphere+ software, v5.0.90 , with default parameters. These parameters comprised the following filters to exclude certain genes of the 7760.1]) . Using aBrucella melitensis bv. 1 strain 16M (GenBank accession numbers NC_003317.1 and NC_003318.1). Clean unique variants used in further analysis are listed in Tables S1 and S2 in the supplemental material.SNPs were identified using In Silico Genotyper (ISG), version 0.16.10-3 . We usedB. melitensis task template in Ridom SeqSphere+ (http://www.geonames.org) for geocoding and Natural Earth  for drawing vector maps.MLVA-16 allelic profiles and SNP matrix data were analyzed using the goeBURST algorithm implemented in PHYLOViZ, version 2.0 . MinimumqSphere+ , 36. MSThttp://www.comparingpartitions.info/?link=Tool  and adjusted Wallace (AW) test of congruence using an online tool available at ink=Tool , 38.PRJNA448825 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA448825).All generated data  were subThe outbreak-related isolates were detected in 21 different farms in three Italian provinces over a period of 1.5 years. The culture-positive samples belonged to 37 animals that were investigated as a part of the within- and among-farm epidemiological investigation .MLVA-16 revealed the presence of 13 different genotypes, divided into two groups formed by single-locus variants and one double-locus variant . MST shoB. melitensis 16M reference genome. The cgMLST clustering divided the isolates into two different genetic complexes, grouping the two farms from the province of Rome (complex 2) separately from the remaining 19 farms (complex 1). The genetic division measured with the cgMLST panel was for 164 different genes . A within-farm genetic variation was also observed.The SNP analysis identified 3,390 SNPs, of which 3,146 were classified as clean unique variants and included in further analysis. The tree split the samples into two genetic clusters with a distance of 244 SNPs between them . We obseThe comparison of discriminatory power of MLVA, cgMLST, and SNP typing showed that the SNP-based approach was superior to the other two methods, with an SDI of 0.922 and 95% confidence intervals (CI) of 0.866 to 0.978. SDI of cgMLST was calculated to be 0.815 , and SDI of MVLA-16 was 0.674 . SNP typing was a good predictor of cgMLST, with an AW of 0.788 . The correspondence of the typing results, however, was not bidirectional, as the cgMLST to SNP AW was 0.295 . Comparison of the remaining pairs of typing schemes showed that there was no congruence between clusters they predicted (the AW of each pair did not exceed 0.03).MST calculated using the MLVA-16 typing results showed a distance between directly linked nodes not exceeding 9 VNTR loci . Fifty-oB. melitensis isolate and no relatedness to one another.MLVA profiles tend to be conserved between epidemiologically linked strains; therefore, the strains from an outbreak are likely to have a similar MLVA profile. Three MLVA-16 profiles, 10 (samples ItBM_41 to ItBM_44), 15 (samples ItBM_93 to ItBM_96 and ItBM_98), and 24 (ItBM_55 and ItBM_89 to ItBM_91), were identified in more than three strains, suggesting close relatedness of samples within these profiles. The method also allowed identification of two clear outliers. Samples ItBM_38 and ItBM_39 showed a distance of 9 alleles from the nearest According to our MLVA-16 data, only three out of six human cases could be linked to a specific animal source analyzed in our study. Human samples ItBM_41 and ItBM_43, isolated from two patients in the city of Salerno, shared the same MLVA-16 profile as two animal isolates from a farm in Salerno province (samples ItBM_42 and ItBM_44), all collected in 2011. Human isolate ItBM_50 and two animal isolates (ItBM_51 and ItBM_52) were assigned MLVA-16 profile 42, but interestingly, ItBM_50 was isolated 4 years later than the animal strains. The other three human samples did not show sufficient relatedness to any of the animal isolates to reliably trace the source of infection. The number of variable loci, in these cases, ranged from 2 to 9 in relation to the closest neighboring MLVA-16 profile.To increase the discriminatory power of the investigation, we analyzed 71 assemblies using a cgMLST scheme. The genome assemblies exceeded 98% of good targets (with a mean of 99.4%). Isolates ItBM_38 and ItBM_39 were clear outliers, separated from the closest neighbor by 1,227 genes, and 1,096 loci from one another .Based on the analysis of epidemiologically related isolates, we used 6-gene difference as a threshold for a potential complex of related cases. Thirteen complexes were assigned in the MST data analysis. Gene-by-gene analysis confirmed relatedness of genotypes with MLVA-16 profiles 10 and 15; however, according to cgMLST two other isolates were at a distance of 0 to 1 gene away from the samples of MLVA-16 profile 15, as was one other isolate of profile 10. ItBM_55, classified as MLVA-16 profile 24, was shown not to be closely linked to other isolates with the same MLVA-16 alleles when examined with a gene-by-gene approach.Using cgMLST, four of the human isolates  were found in the distance not exceeding 2 alleles to the closest animal strain. Two of the human samples originating in Piedmont (ItBM_99 and ItBM_78) were genetically different from the animal samples, with 156 and 195 allele differences from the closest isolate, and could be identified as outliers, although they were distantly related to other Italian genotypes. Divergence of these two samples was not evident in MLVA-16 typing .B. melitensis 16M reference strain. Out of these, 6,027 were considered high-quality discriminatory SNPs and were used to infer the relationship between the strains. We applied the threshold of 7 SNPs to detect the clusters of closely related cases, and in accordance with cgMLST analysis, we identified 13 complexes  could not be traced to any of the analyzed animal strains of The SDI for the three typing schemes were calculated to be 0.986  for MLVA-16, 0.988  for cgMLST, and 0.992  for SNP typing. AW test showed the highest congruence between SNP- and cgMLST-based clusters when the SNP method was used as a primary typing method . When we used cgMLST as the primary method, however, the AW value dropped to 0.573 . MLVA-16 was a poor predictor of SNP  and of cgMLST .B. melitensis detected in the context of a national surveillance program.Our study compared the performance of two WGS-based typing methods, SNP analysis and cgMLST, with the gold standard MLVA-16 in an analysis of the phylogenetic relationship between isolates of We found that all three typing schemes generally performed equally, and although SNP analysis had the highest resolving power in terms of differences detected between the isolates, the number of predicted genotypes in the surveillance scenario was comparable for all examined methods , and the SDI were similar. However, SDI test applied to samples from epidemiologically linked sets showed that SNP analysis was superior in differentiating between closely related samples. This suggests that while WGS-based approaches could be used as standalone tools in establishing phylogenetic relationships, MLVA-16 optimally should be supported by either SNP or gene-by-gene typing results.B. melitensis, while the outliers were members of the East Mediterranean and American lineages  to the source of human infection but not directly involved in the transmission event. In these cases, observation based on WGS typing indicates that strains of B. melitensis collected from an ibex (Capra ibex ibex) in Gran Paradiso National Park, located in the Graian Alps in Italy (sample ItBM_100). This demonstrated that while all applied schemes could be used to identify very distant genomic outliers within the Brucella population, WGS-based schemes were superior in identifying unrelated cases belonging to the same lineage. Additionally, within the clusters of similar genotypes, cgMLST performed equally to the SNP analysis, but some discrepancies were observed in MLVA-16 analysis. For instance, seven isolates from Sicily had profiles differing by a maximum of two SNPs or one gene (samples ItBM_92-ItBM_98), suggesting that they were very closely related. However, while five of these isolates shared MLVA-16 profile 15, one belonged to type 8  and another to type 12 . The interpretation of WGS results therefore suggests that these were actually strains from the same complex, while MLVA-16 typing would not necessarily lead to the same conclusion. A similar observation was reported by Dallman et al. . Together, these observations show that MLVA-16 profiling might not provide enough resolution to accurately predict phylogenetic relationships between isolates involved in an ongoing outbreak or strains that have been circulating over the years with no direct link to one another.For distantly related genomes from the same lineage, cgMLST as well as SNP analysis provided higher phylogenetic distance resolution than MLVA-16, and therefore spotting divergent genotypes unlikely to be connected to the other circulating strains was possible with greater confidence. This was particularly apparent in the case of two clinical isolates (ItBM_99 and ItBM_78) and in the case of n et al. , who sholleagues  demonstrBrucella species and to map the geographic distribution and global spread of B. melitensis  or 7 SNPs. These findings highlight the potential criteria necessary for inclusion of an isolate into a brucellosis outbreak cluster that we would therefore suggest to be ≤6 loci in the cgMLST and ≤7 in WGS SNPs analysis.SNP analysis has successfully been used to discriminate between litensis , 39, 41.Jackson et al. argued that a general cutoff value applied in SNPs or cgMLST could not always reliably predict whether samples were epidemiologically related and that isolates with SNP differences ranging from 10 to 30 were frequently linked . Thus, wUsing a typing approach that offers maximum resolution is particularly important for tracing the spread of a disease during an outbreak. SNP analysis potentially has the highest discriminatory power among the typing methods, as polymorphisms can be discovered in both coding and noncoding regions of the genome. However, the choice of a reference genome can significantly influence the number of identified SNPs and the accuracy of the reconstructed phylogenetic relationships . cgMLST Brucella populations and to gather in-depth information which can be used for source tracing in case of outbreaks within animal holdings, zoonotic or foodborne infections, and illegal animal movements. Moreover, WGS data facilitate the assessment of the possible extent of an ongoing outbreak and the reliable prediction of the routes of its spread.In conclusion, WGS/NGS data can be used effectively to gain a better understanding of epidemiology and dynamics of B. melitensis genetic diversity, and we demonstrated that the performance of the gene-by-gene approach was comparable to that of the SNP analysis. On the basis of these results, we believe that MLVA-16 typing of B. melitensis in Italy can now be successfully replaced by the more informative WGS analysis.In accordance with the One Health approach, public health agencies can implement WGS to aid in disease control and eradication plans. In our study, both cgMLST and SNP analysis performed well despite the restricted level of


STF) and absolute (xyd) genetic differentiation near genes involved in reproduction, particularly genes associated with the brain‐pituitary‐gonadal axis, which likely control timing of spawning, contributing to prezygotic isolation. We also found genetic differentiation associated with heat shock proteins and other genes of functional relevance, with complex patterns that may point to multifaceted selection pressures and local adaptation among spawning populations. We provide a high‐resolution picture of U.S. Atlantic cod population structure, revealing greater complexity than is currently recognized in management. Our genome‐scan approach likely underestimates the full suite of adaptive differentiation among sampling locations. Nevertheless, it should inform the revision of stock boundaries to preserve adaptive genetic diversity and evolutionary potential of cod populations.Selection can create complex patterns of adaptive differentiation among populations in the wild that may be relevant to management. Atlantic cod in the Northwest Atlantic are at a fraction of their historical abundance and a lack of recovery within the Gulf of Maine has created concern regarding the misalignment of fisheries management structures with biological population structure. To address this and investigate genome‐wide patterns of variation, we used low‐coverage sequencing to perform a region‐wide, whole‐genome analysis of fine‐scale population structure. We sequenced 306 individuals from 20 sampling locations in U.S. and Canadian waters, including the major spawning aggregations in the Gulf of Maine in addition to spawning aggregations from Georges Bank, southern New England, the eastern Scotian Shelf, and St. Pierre Bank. With genotype likelihoods estimated at almost 11 million loci, we found large differences in haplotype frequencies of previously described chromosomal inversions between Canadian and U.S. sampling locations and also among U.S. sampling locations. Our whole‐genome resolution also revealed novel outlier peaks, some of which showed significant genetic differentiation among sampling locations. Comparisons between allochronic winter‐ and spring‐spawning populations revealed highly elevated relative ( Many studies have identified genomic signals of adaptive divergence among cod populations against a background of low genome‐wide differentiation  and, in total, account for around 7% of the genome  Atlantic, variation in the frequency of the chromosomal inversions has been found to underlie much of the population structure at both large  c22.1The study area includes sampling locations from all known, primary cod spawning grounds in U.S. waters, including those in eastern and western GoM , the northeastern peak and western flank of Georges Bank, the Great South Channel area, southern New England waters off Cox Ledge, and samples from Canadian waters on the eastern Scotian Shelf (management unit 4VsW) and St. Pierre Bank off Newfoundland  with a highly cost‐effective protocol based on Illumina's Nextera reagents, as described in Therkildsen and Palumbi (DNA was extracted from fin clip samples with Qiagen® DNeasy Tissue Kits (Qiagen) or with Omega EZNA Tissue DNA kits (Omega Bio‐Tek), following the manufacturer's protocols. We prepared a separate, dual indexed library for each individual  to remove nonuniquely mapped reads and reads with a mapping quality score less than 20. We removed duplicate reads using the Picard v2.9.0 MarkDuplicates tool (http://broadinstitute.github.io/picard/). We then merged the bam files from individuals that had been sequenced twice using samtools, deduplicated again, and clipped redundant sequence from overlapping ends of each mapped read pair using the bamutil v1.0.14 clipOverlap tool . SNPs were called with this data set, 172 historical samples , and 15 additional lab‐reared samples from the west coast of Nova Scotia that were later dropped due to a high number of half‐siblings among those individuals. Thus, SNPs were called across a total of 505 individuals. We set the minimum number of individuals' threshold to 100 . We excluded bases with a base quality score <20 (‐minQ 20) and applied a maximum total depth threshold of 1,000 to remove loci from repetitive regions (‐setMaxDepth 1000). This threshold was chosen since our target sequencing depth was 1X, and it was deemed unlikely that each individual in the data set would have received approximately 2X coverage at a given site if it were not from a repetitive region  and retained only SNPs with a minor global allele frequency ≥1% (‐minMaf 0.01). Major and minor alleles were inferred from genotype likelihoods across all individuals (‐doMajorMinor 1), and these were then set for downstream analyses (‐doMajorMinor 3). Per‐individual inbreeding coefficients were estimated using PCAngsd v0.93 .We used ANGSD v0.912  to accurately estimate minor allele frequencies, leading to very low expected heterozygosity for this sampling location. All further analyses were conducted with the 306 remaining individuals from 20 sampling locations, all of which included 11–15 individuals each, except for Stellwagen and the northeastern peak of Georges Bank, which included 23 and 25 individuals, respectively, after we combined two sets of samples from each location Table .2.4STF was estimated between each sampling location using ANGSD. First, we estimated site allele frequency likelihood for each sampling location (‐doSaf 1), supplying the reference genome and setting the minimum number of individuals threshold (‐minInd) to either (a) three or, (b) two‐thirds of the number of individuals in the sampling location to test the effect of missing data on pairwise STF estimates. We then estimated the 2D site frequency spectrum for each pair of populations  and calculated the average pairwise weighted STF . Classical multidimensional scaling (MDS) was applied to the pairwise STF matrix using the cmdscale function in R  among the inversions using the LD function from the genetics package in R. We calculated LD separately for each of the groups outlined below, to avoid influences of population structure on LD calculations.To investigate the influence of the genomic inversions on LG 1, 2, 7, and 12, we also used PCAngsd to perform PCAs separately for each genomic region containing an inversion. Within inversion regions, individuals formed three clusters along PC1 and we used the position of individuals along this axis to genotype them as homozygous for the noninverted haplotype, heterozygous, or homozygous for the inverted haplotype . To reduce the number of pairwise comparisons to investigate, we grouped certain sampling locations based on our a priori knowledge of population structure . This effectively removed all SNPs in any STF outlier window found between any pair of groups, leaving only putatively neutral regions of the genome. We then estimated the weighted average pairwise STF values among groups using the same methods in ANGSD described previously and visualized the result using MDS, as before. In addition, to evaluate putatively neutral patterns of population structure among sampling locations rather than among groups, we subsetted the stringent SNP data set for the neutral SNPs identified above and calculated pairwise STF among all sampling locations using the neutral, stringent SNP data set. Finally, we performed an individual‐level PCA using the covariance matrix calculated from the neutral SNP dataset in PCAngsd, as described above.To evaluate putatively neutral patterns of population structure among these groups, we created a putatively neutral SNP data set . We first removed all SNPs within the boundaries of the well‐known chromosomal inversions, calculating the boundaries using ngsLD  winter spawners versus eGoM as pairwise STF suggested they were surprisingly similar; and (e) spring spawners versus the Great South Channel group as the Manhattan plot between these showed some relatively high peaks of differentiation. We identified the genomic windows that were in the upper 99.9th percentile of the windowed STF distribution for each comparison, after excluding the inversions, and extracted all gene annotations from the gadMor2 reference genome  to investigate the function of each gene. For those peaks that appeared to have functional relevance or particularly elevated peaks compared to the genomic background, we estimated pairwise STF and Tajima's D .To investigate regions of elevated differentiation outside of the chromosomal inversions, we chose to focus on five of the 28 pairwise comparisons among groups, which were (a) wGoM spring versus wGoM winter spawners to investigate the genomic basis of alternative spawning times; (b) St. Pierre Bank versus Cox Ledge as these are at the extremes of the geographic and thermal distribution of our samples; (c) Georges Bank versus Cox Ledge as these are currently in the same management unit but pairwise STF values in the population pairwise comparison where the peak appeared the most significant (Supp. File 1). Then, based on genotype likelihood data at these SNPs for all sampling locations, we performed PCA at each of the outlier regions using with the software PCAngsd and recorded the average score along PC1 for each sampling location . We rescaled the average PC1 score so that the populations with the maximum and minimum scores were assigned 1 and −1, respectively, and visualized this rescaled PC1 score on a heat map.Finally, we investigated how each of the identified peaks was driving genetic differentiation among all sampling locations. To do so, we first selected the SNPs that underlaid each peak. For inversions, we included all SNPs inside the inverted regions. For each outlier region outside of the inversions, we located all SNPs within the region, and among those SNPs, we selected those with the 1% highest 3The 306 individuals that were included in the final data set received on average, 7.7 million reads each . After adapter trimming, 92% of raw bases were retained. On average, 58% of the raw reads mapped uniquely to the gadMor2 reference genome. Our average duplication rate was 1.9% , and after deduplication and clipping of overlapping read ends, we retained 45% of our raw bases for downstream analysis. The average individual coverage was 0.67X when calculated across the entire reference genome, including regions that we could not map to . Mean depths and inbreeding coefficients across sampling locations were relatively consistent Figures  and S3 aSTF matrix calculated from genotype likelihoods for the full SNP data set allowed us to visualize the population structure  wGoM spring spawners versus wGoM winter spawners; (b) St. Pierre Bank versus Cox Ledge; (c) Georges Bank versus Cox Ledge; (d) winter spawners versus eGoM; and (e) spring spawners versus Great South Channel group  between wGoM winter and wGoM spring spawners and overlies three genes: FSHR, RASD1, and PEMT . The magnitude of the haplotype frequency differences between our U.S. and Canadian samples were significant against low, neutral differentiation, suggesting that selective pressures acting on these inversions are likely important for local adaptation Figure . The invSTF) and absolute (xyd) genetic differentiation on LG 18 in a region surrounding the FSHR gene. This gene codes for a receptor for follicle‐stimulating hormone (FSH) and may also bind luteinizing hormone (LH) , ESR2 has similarly been associated with timing of spawning in spring and autumn‐spawning populations  and may have been difficult to detect with low‐density SNP screening, highlighting a key advantage of the low‐coverage, full genome sequencing approach used here  and winter‐spawning cod  revealed strong peaks of genetic differentiation that likely underlie the mechanisms regulating timing of spawning Figure , Table 1 et al.,  in Atlan et al., . Finallyxyd) at the peaks on LG18 and LG21 suggests that these peaks have been targets of selection and may be resistant to introgression, possibly by contributing to prezygotic isolation between winter‐ and spring‐spawning cod. Prezygotic isolation does not evolve easily under gene flow as recombination tends to break up associations between traits involved in assortative mating and the preferences for those traits. However, prezygotic isolation can evolve under gene flow through a “one‐allele mechanism” whereby a single allele controls assortative mating  coding genes (HSPB1 and HSPB8). These are both members of the small Hsp family and are known to interact with one another (Sun et al., Danio rerio; Mao, Bryantsev, Chechenova, & Shelden, Oncorhynchus mykiss; Mosser & Bols, xyd, however, could instead suggest that this is a region of low recombination where background selection against deleterious mutations has created an STF peak (Charlesworth, Morgan, & Charlesworth, STF may be the result of recent selection where too little time has passed for sequence divergence to accrue.Other peaks with gene functions unrelated to reproduction were also apparent in our pairwise comparisons Figure , Table 1STF peaks (Burri et al., STF peaks identified in this study is therefore warranted to determine the exact process underlying their creation. Furthermore, in the marine realm, local adaptation in quantitative traits is likely to evolve through polygenic architecture, creating high variation in allele effect size (Gagnaire & Gaggiotti, STF may also miss targets of recent selection, since genetic differentiation around the target of selection will initially be limited to a narrow window (Ravinet et al., Variation in recombination rate throughout the genome can create 4.1STF values were all close to zero within U.S. waters (Tables STF is not straightforward (Conover et al., The results presented here highlight the uniqueness of the wGoM spring spawners from the other U.S. spawning aggregations. We found large haplotype frequency differences at both chromosomal inversions and regions housing genes with reproductive functions Figures , 4, 5 ins Tables  and S13,s Tables , and is Georges Bank appeared intermediate between the wGoM winter and spring spawners using the full SNP data set Figure , consistThe eastern GoM nonspawning samples appeared most like the wGoM winter spawners with a very little putatively adaptive differentiation among them Figure . HoweverThe complex patterns of population structure and putatively adaptive diversity among cod spawning populations that we show here are not recognized in current cod management units Figure . Cod in The preservation of adaptive diversity is important for the conservation of threatened species (Funk et al., None declared. Click here for additional data file. Click here for additional data file.


A set of 404 individuals from three Iranian buffalo populations were genotyped with the Axiom Buffalo Genotyping 90K Array. Average r2 and |D'| between adjacent SNP pairs across all chromosomes was 0.27 and 0.66 for AZI, 0.29 and 0.68 for KHU, and 0.32 and 0.72 for MAZ. The LD between the SNPs decreased with increasing physical distance from 100Kb to 1Mb between markers, from 0.234 to 0.018 for AZI, 0.254 to 0.034 for KHU, and 0.297 to 0.119 for MAZ, respectively. These results indicate that a density of 90K SNP is sufficient for genomic analyses relying on long range LD (e.g. GWAS and genomic selection). The persistence of LD phase decreased with increasing marker distances across all the populations, but remained above 0.8 for AZI and KHU for marker distances up to 100Kb. For multi-breed genomic evaluation, the 90K SNP panel is suitable for AZI and KHU buffalo breeds. Estimated effective population sizes for AZI, KHU and MAZ were 477, 212 and 32, respectively, for recent generations. The estimated effective population sizes indicate that the MAZ is at risk and requires careful management.Accuracy of genome-wide association studies, and the successful implementation of genomic selection depends on the level of linkage disequilibrium (LD) across the genome and also the persistence of LD phase between populations. In the present study LD between adjacent SNPs and LD decay between SNPs was calculated in three Iranian water buffalo populations. Persistence of LD phase was evaluated across these populations and effective population size (Ne) was estimated from corrected r B. bubalis) is an important livestock resource in many regions of the world, particularly in tropical and subtropical countries. Water buffalo produce milk and meat, and are used as draught animals in developing countries [Bos taurus) belong to the sub-family, Bovinae. While cattle were domesticated between 8000 and 10,000 years ago [The water buffalo  and |D'|; (ii) the consistency of the LD phase across studied populations; and (iii) the effective population size in relation to LD decay.The objectives of this study were to assess: (i) LD between adjacent SNPs and LD decay according to physical distance between bi-allelic SNPs in three Iranian indigenous water buffalo populations using the statistics  and Khuzestani (KHU) breeds is described in Mokhber et al. . Samples-6) were removed. Individuals with more than 5% missing genotypes, were excluded from data set. After quality control for each population, genotypes from the three breeds were merged into a single file, and SNPs that were common across all three populations were retained for further analyses. The Axiom Buffalo Genotyping 90K Array was designed based on alignment of buffalo sequences to the bovine UMD3.1 genome assembly [Genomic DNA was extracted from blood by the modified salting out method  and fromassembly , therefo2 [A and a at the first locus and B and b at the second locus, freq A, freq a, freq B and freq.b denote frequencies of alleles A, a, B, and b, respectively, and freq AB denote frequency of haplotype AB in the population. The r2 and |D'| were calculated between adjacent markers and SNP pairs with physical distances from 0 to 15 Mb for each population, using SnppLD software  [The LD between two SNPs was evaluated using the statistics r2  and the 2 , which w Canada) .2 and |D'| of adjacent SNPs were estimated for each chromosome. SNP pairs were grouped by their pairwise physical distance, based on their position in the UMD3.1 reference cattle sequence, into intervals of 100 Kb (from 0 to 15 Mb). Average r2 for SNP pairs in each interval was estimated [2 of adjacent marker pairs on each chromosome [rij is the correlation of phase between rij(A) in population A and rij(B) in population B, SA and SB are the standard deviation of rij(A) and rij(B) respectively, and rA and rB are the average rij across all SNP i and j within the common set of markers.The average rstimated . The conromosome . The conromosome :rij=∑ in intervals of 100 Kb (from 0 to 15 Mb). The historical Ne was investigated at 150 time points from recent to 500 generations in the past.where After quality control for each population, a set of 63824, 62667 and 58588 SNPs remained for AZI, KHU and MAZ breeds, respectively. SNPs that were in common across all breeds, were merged in single file. The final data set comprised 57212 SNPs from 396 individuals (253 AZI and 118 KHU and 25 MAZ) which was used for further analyses. After removing SNP with a MAF less than 0.05, the mean MAF observed in the Iranian populations was 0.333, 0.321 and 0.299 for AZI, KHU and MAZ, respectively, for the common SNP set. As the buffalo genome available is highly fragmented SNPs were mapped to the bovine sequence (version Btau UMD3.1). A summary of SNP numbers for each bovine chromosome with MAF in each population is shown in Distribution of SNPs with the distance between adjacent SNPs as mapped to the bovine genome sequence (version Btau UMD3.1), is shown in The average estimated physical distance between adjacent markers in the common set was 46 Kb, and covered 2.65 Gb of total genome length . The hig2 and |D’| statistics. Average r2 and |D’| between adjacent SNP pairs were 0.27 and 0.66 for AZI, 0.29 and 0.68 for KHU, and 0.32 and 0.72 for MAZ , using the same SNP set [2 and |D’| values between adjacent SNP pairs reported here are similar to those for Holstein cattle [2 and |D’| values are related to breed diversity, such that populations with lower diversity have higher average LD between adjacent loci. It should be noted that the level of LD differs across chromosomes e.g. Nelore cattle genotyped using a high-density bovine SNP marker panel gave a wide range of LD estimates across different chromosome regions, ranging from 0.17 to 0.24 for r2 and from 0.55 to 0.72 for |D'| [The average r SNP set . The aven cattle ,14, but n cattle  and the n cattle . Breedinn cattle  reportedfor |D'| . These dThe consistency of LD is high between the AZI and KHU breeds, indicating the close genetic relation of these breeds, whereas the comparison of AZI with MAZ, and KHU with MAZ show lower preservation of LD. This is consistent with Colli et al  who repo2 between adjacent SNPs is higher, as the makers are more likely to predict the alleles at adjacent QTLs. Marker spacing giving an r2 of at least 0.2 is recommended to estimate genomic breeding values [2 of 0.3 and above has been suggested for genome wide association studies and QTL mapping [2 values between adjacent markers obtained from using the Axiom Buffalo Genotyping 90K Array were between 0.27 and 0.327 for the populations studied here, which is around the threshold of 0.3.The level of LD between markers is important in the design and success of genome wide association studies and genomic selection : genomicg values ,53,54 wh mapping . The aveOne of the factors impacting on the accuracy of genomic breeding estimates across-populations is the persistence of LD phase, which reflects the genetic relationship between the populations . The mai2 values, which are less sensitive to allele frequency and a small sample size than |D'| [Effective populations size (Ne) is related to the history of a population  and is ahan |D'| . In the han |D'| . The highan |D'| . Artifichan |D'| . The MAZ2> 0.3) or to estimate genomic breeding values (r2> 0.2). The calculated Ne from LD decay indicates that the AZI and KHU have a sufficiently large effective population size to be sustainable, while the MAZ has a low effective population and needs careful management to ensure its survival.The average distance between adjacent SNPs in the current Axiom Buffalo Genotyping 90K Array is 30 Kb, based on alignment with the bovine genome. After filtrating for quality and MAF the between-marker distance for the 57212 common SNPs was 46 Kb. The level of LD in Iranian buffalo using this set is above that recommended for genome wide association studies Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


Based on these findings, we have identified a direct interaction between CILP-2 and PEPCK and suggested that CILP-2 plays an important role in the regulation of hepatic glucose production.Genetic association studies have implicated that cartilage intermediate layer protein 2 (CILP-2) confers the risk susceptibility for type 2 diabetes (T2DM). However, it is still unknown whether CILP-2 is involved in the regulation of glucose homeostasis and insulin resistance (IR). In the current study, we initially observed that CILP-2 as a secreted protein was detected in both conditioned medium and lysates of cells transfected with an overexpressed vector. We then found that circulating CILP-2 levels had a progressive increase from normal to impaired glucose tolerance (a pre-diabetic status) and then to diabetes, which was correlated positively with waist-to-hip ratio, triglyceride, fasting blood glucose, 2-h blood glucose after glucose overload, HbA1c, fasting insulin, 2-h plasma insulin after glucose overload, and homeostasis model assessment of insulin resistance but negatively with HDL-C. CILP-2 expression was increased in the liver and muscle but decreased in adipose tissues of obese mice or T2DM patients. Furthermore, we demonstrated that CILP-2 circulating levels were affected by OGTT and Exenatide. CILP-2 overexpression resulted in impaired glucose tolerance and hepatic IR  Cartilage intermediate layer protein 2 (CILP-2) is a cartilage intermediate layer protein and expressed most abundantly in cartilaginous tissues . As a glSeveral genetic association studies have demonstrated that CILP-2 genetic polymorphisms are associated with low-density lipoprotein cholesterol (LDL-C), triglyceride (TG) levels, and coronary heart disease risk in European population , high-dein vivo and in vitro and identified a direct interaction between CILP-2 and phosphoenolpyruvate carboxykinase (PEPCK) in the regulation of hepatic glucose production (HGP).In the current study, we first identified CILP-2 as a novel secreted protein in human blood. We then analyzed the circulating CILP-2 levels in healthy and impaired glucose tolerance (IGT) individuals as well as newly diagnosed T2DM patients (nT2DM). We also examined CILP-2 expression in fat and muscles from T2DM patients and in insulin targeted tissues from animal models of IR. Furthermore, we evaluated whether the circulating CILP-2 levels were impacted by overnight fast (a low-insulin and hypoglycemia condition), oral glucose challenges (a hyperglycemic and hyperinsulinemic condition), euglycemic-hyperinsulinemic clamp , and anti-diabetic agent  in T2DM patients or healthy individuals. Finally, we evaluated the impacts of CILP-2 overexpression on hepatic glucose fluxes and insulin signaling We first overexpressed pIRES2-EGFP-CILP-2-Flag (CILP-2-F) cDNA in HEK-293T cells. As shown in Figure n = 163), defined by waist circumference   , had sigP < 0.01 vs. 0 h). During the EHC, circulating CILP-2 levels had a slight non-statistically significant increase  and EHC in normal individuals. As depicted in Figure e Figure . InteresP < 0.05 or P < 0.01, Figure P < 0.05 or P < 0.01, Figure We examined the expression of CILP-2 in different tissues of mice. The results showed that CILP-2 was mainly expressed in muscle, including myocardium, fat and liver tissues . TherefoCILP-2) or recombinant adenovirus encoding enhanced green fluorescence protein (Ad-GFP) via tail-vein and found that the hepatic mRNA and protein expressions of CILP-2 were markedly increased in liver of Ad-CILP-2 mice . These results indicated that CILP-2 overexpression further decreased whole-body insulin sensitivity in HFD-fed mice. To examine the impact of CILP-2 overexpression on hepatic insulin sensitivity, hepatic glucose kinetics was assessed by tracer dilution methodology. When fed with HFD, the suppression of HGP by hyperinsulinemia was 73% in Ad-GFP mice, but only ~48% in Ad-CILP-2 mice relative to NC-fed Ad-GFP mice  and Akt kinase (Akt) was found to be significantly decreased in Ad-CILP-2 treated HepG2 and MPHs  were transfected with Ad-s Figure G. In adds Figure . Consists Figure G.CILP-2 or Ad-GFP by bilateral gastrocnemius muscles and found that the expression of CILP-2 mRNA was markedly increased in the muscle of Ad-CILP-2 mice -luc. The dual luciferase assay showed that the transcriptional activity of CILP-2 to regulate PEPCK promoter was significantly increased in pGL3-PEPCK (–2112), pGL3-PEPCK –1257), pGL3-PEPCK (–1097), pGL3-PEPCK (–857), pGL3-PEPCK (–457), and pGL3-PEPCK (–217), but not in pGL3-PEPCK (–57)  to investigate whether CILP-2 may be a secretory protein. The results showed that most of the 5′ methionine residue of CILP-2 is immediately followed by a typical signal peptide sequence as predicted according to the method described previously . The preTo test the speculation, we assessed the effect of an oral glucose challenge on circulating CILP-2 in healthy subjects. A notable finding a significant increase circulating CILP-2 levels until 2-h after an oral glucose challenge. This pattern is different from those of insulin and glucose during OGTT. The results of OGTT and positive correlation of HbA1c and HOMA-IR with circulating CILP-2 implicated an effect of hyperglycemia or hyperinsulinemia on CILP-2 release.in vitro study, we also found that high glucose stimulated the expression and secretion of CILP-2 in HepG2 cells. Therefore, hyperglycemia may stimulate the secretion and release of CILP-2, as hyperglycemia promotes insulin release.As the increase in glucose after an oral glucose challenge is accompanied by an increase in insulin levels, it is unclear whether short-term hyperglycemia or hyperinsulinemia is responsible for the CILP-2 release. To address this question, we further performed EHCs to control blood glucose levels and investigated the impact of hyperinsulinemia alone on CILP-2 levels in healthy individuals. Our results indicate that acute hyperinsulinemia failed to lead to changes of circulating CILP-2 levels. In the in vivo. In an interventional study, we evaluated whether Exenatide, a GLP-1 analogue, would affect circulating CILP-2 levels in T2DM patients. After 12 weeks of Exenatide administration, we observed a significant descent in circulating CILP-2 levels, following improved glycemic control and insulin sensitivity. Based upon this finding, we speculate that the effect of Exenatide treatment on circulating CILP-2 may be due to a result of metabolic changes. However, future studies are needed to evaluate the direct effect of GLP-1 analogue on CILP-2 secretion by acute injection.Oral glucose ingestion stimulates the incretin release, while incretin release is represented by a postprandial elevation of GLP-1 . MoreoveTo further understand CILP-2 physiology, we investigated CILP-2 expression in human and mouse tissues. Firstly, we found that CILP-2 mRNA and protein are more expressed in the muscle, fat, and liver in mice, suggesting that three insulin target tissues can secrete CILP-2 into circulation. In addition, we found that the mRNA expression of CILP-2 is the highest in muscle, followed by fat and liver. Therefore, we speculate that in these tissues, the muscle tissue secretes most CILP-2, followed by fat and liver. We recognized that these tissues might be important contributors to CILP-2 systemic levels. CILP-2 might be a novel myokine, adipokine or hepaticokine related to IR, like irisin, adiponectin, or fibroblast growth factor 21 (FGF21) . AlthougIn IR mice (including HFD-fed ADI-KO and db/db mice) and T2DM patients, the CILP-2 expression was significantly increased in muscle and liver but decreased in fat. This scenario is similar to some adipokines, such as adiponectin and Zinc-α2-glycoprotein . We specin vivo, EHCs were performed to examine the metabolic response of CILP-2 overexpression in IR state. We found that CILP-2 overexpression in both liver and muscle tissues further impaired glucose tolerance, reduced glucose utilization in peripheral tissues and blunted the ability of insulin to inhibit HGP in vivo. The changes in glucose output occurred at similar insulin levels and accounted for the effect of CILP-2 on whole-body glucose metabolism, suggesting its role in the mediation of hepatic and peripheral glucose homeostasis. Parallel to the results of in vivo study, the expression of PEPCK, a key gluconeogenic enzyme, was significantly increased in Ad-CILP-2 treated hepatocytes, indicating that increased CILP-2 expression promotes gluconeogenesis and increases HGP. Therefore, we believe that hyperglycemia increases the expression and secretion of CILP-2, which in turn promote glyconeogenesis and lead to a vicious cycle to promote the progression of diabetes.To understand the direct impact of CILP-2 on HGP and insulin sensitivity in vivo, the data from the study in vitro also indicate that CILP-2 overexpression blunts hepatic insulin signaling.The insulin signaling pathway is activated when insulin binds to an InsR, resulting in phosphorylation of insulin receptor substrate-1 and consequent activation of phosphatidylinositol 3-kinase/PDK1/Akt . TherefoTo understand the molecular mechanism that CILP-2 increases PEPCK expression, we demonstrated that CILP-2 positively regulated the transcriptional activity of PEPCK promoter. Furthermore, the detailed deletion analysis of PEPCK promoter revealed that the region of the PEPCK promoter responsible for CILP-2 activation was located within −217 bp to −57 bp upstream of the transcriptional start site. The results thus revealed an interaction between CILP-2 and PEPCK, by which HGP was regulated.in vivo.It has been reported that some hormones, such as insulin and glucocorticoids, regulated the transcriptional activity of the PEPCK gene , while aIn summary, the current study provided the first evidence that CILP-2 is a novel secreted protein and demonstrated that circulating CILP-2 levels were elevated in both IGT and nT2DM subjects and associated with IR. CILP-2 overexpression promoted hepatic IR in HFD-fed mice, increased PEPCK expression and suppressed insulin signaling in hepatocytes. Thereby, we have identified a direct interaction between CILP-2 and PEPCK in the regulation of HGP.BglII and EcoR sites of pIRES2-EGFP vector to generate CILP-2-F. To generate Ad-CILP-2, CILP-2-Flag were recombined into the Gateway-based pAd-AdEasy™ XL vector, according to the manufacturer’s instructions. The sequences from a mouse cDNA library (GI: 28913720) and encoding FLAG tag were listed in GFP; Clonetech) was prepared as described previously (Sequence encoding FLAG tag was introduced to full-length CILP-2 cDNA fragment (3471 bp) and cloned into the eviously .in vitro study as previously described , 102 IGT subjects (IGT group), and 105 age-matched normal controls (NGT group), were recruited in this study. The diagnosis of IGT and T2DM was based on World Health Organization 1998 diagnostic criteria . The ind2 as previously described  aged 18–35 years and with BMI 18–24 kg/mescribed .2, and HbA1c 7%–8.5%. Blood samples for CILP-2 and biochemical measurements were obtained at 8 h prior treatment and on Day 2 of the last administration.Thirty patients  received subcutaneous Exenatide (5 μg) injection twice daily for 12 weeks. Inclusion criteria included age 40–70 years, BMI 25–35 kg/mPlasma glucose, HbA1c, HOMA-IR, and HOMA of insulin secretion (HOMA-IS) were detected and calculated as previously reported . CirculaGastrocnemius muscle, abdominal fat, and/or liver were obtained from 10 T2DM patients and 10 healthy subjects undergoing surgical procedures and also from mice. For each, 200 mg of tissues was mobilized and excised. Tissue samples were immediately frozen by liquid nitrogen and stored at −160°C until use.CLIP-2 or Ad-GFP (1 × 109 pfu in 100 μl of PBS) once a week via tail vein or bilateral gastrocnemius muscles (intramuscular injection) at weeks 11 and 12 of HFD feeding. Three days before GTT or EHC experiments, catheters were placed into the right internal jugular vein and left carotid artery. GTT and EHC experiments were performed as previously described  male mice were fed either a standard chow diet  or a high-fat diet  for 12 weeks. For glucose tolerance tests (GTTs) and EHC study, male WT mice were injected with Ad-escribed .CILP-2 or Ad-GFP followed by treatment with or without 100 nM insulin for 10 min -Luc and PEPCK promoter truncations were constructed by PCR amplification. The primers were given in TM 2000 Reagent (Gibco BRL) according to the manufacturer’s instruction. The transfected cells were washed three times with phosphate-buffered saline and lysed with Passive Lysis Buffer. Luciferase assays were performed using the Dual- Luciferase Reporter Assay System (Promega).For the dual-luciferase reporter assay, HEK-293 cells were grown in 24-well plates and co-transfected for 24 h with CILP-2-F or pIRES2-GFP (0.38 μg), 0.38 μg/well PEPCK-luciferase reporter plasmid, and 0.04 μg pRL-SV40 (Promega) by using Lipofect Aminet-test or paired t-test. Normal distribution of the data was determined by Kolmogorox–Smirnov test. Some values were skewed and logarithmically transformed to obtain a normal distribution. Simple and multiple regression analyses were used to examine the association between circulating CILP-2 and the values of other biomarkers. The association of circulating CILP-2 with IGT and T2DM was examined by logistic regression analysis. P-values < 0.05 were thought as significant.All analyses were performed by using PASW Statistical Package (version 20.0 SPSS Inc.). Data were expressed as mean ± SD or SEM. Comparison analyses between groups were carried out by using ANOVA, unpaired mjz016_Supplementary_materialClick here for additional data file.


However, only a small number of studies have been reported in horticultural crops, and in polyploid species in particular. In this paper, we have developed a versatile forward simulation tool, called polyploid Sequence Based Virtual Breeding (pSBVB), to evaluate GS strategies in polyploids; pSBVB is an efficient gene dropping software that can simulate any number of complex phenotypes, allowing a very flexible modeling of phenotypes suited to polyploids. As input, it takes genotype data from the founder population, which can vary from single nucleotide polymorphisms (SNP) chips up to sequence, a list of causal variants for every trait and their heritabilities, and the pedigree. Recombination rates between homeologous chromosomes can be specified, so that both allo- and autopolyploid species can be considered. The program outputs phenotype and genotype data for all individuals in the pedigree. Optionally, it can produce several genomic relationship matrices that consider exact or approximate genotype values. pSBVB can therefore be used to evaluate GS strategies in polyploid species , or to optimize experimental designs for association studies. We illustrate pSBVB with SNP data from tetraploid potato and partial sequence data from octoploid strawberry, and we show that GS is a promising breeding strategy for polyploid species but that the actual advantage critically depends on the underlying genetic architecture. Source code, examples and a complete manual are freely available in GitHub  Traditionally, polyploid species have been classified into autopolyploids, caused by one or more genome duplication events in a single species, and allopolyploids, the result of hybridization between closely related species (Genomic selection (GS)  or a text file with genotypes coded to 0 up to h (where h is the ploidy level). For diploids, the vcf genotype format is of the kind 0/0, 0/1, and 1/1 for the three possible genotypes in a biallelic SNP. The polyploid vcf format is an extension of the type 0/0/0/0, 0/0/0/1 and so on in the case of an unphased tetraploid genotypes. Phased genotypes are represented by vertical bars,  . No missing values are allowed. Phased genotypes are needed in pSBVB to identify which chromosomes are passed to offspring. A number of accurate phasing algorithms for diploids are available such as beagle (https://lauzingaretti.GitHub.io/pSBVB/). A list with QTNs (Quantitative Traits Nucleotides) positions, a list of SNP positions to be used for GS, a pedigree file and a parameter file are also necessary. The pedigree file is used to perform the gene dropping simulation, i.e., genotypes’ of the descendants along the pedigree are generated following Mendelian rules and a pre specified pairing rate between homologous and homoelogous pairs; for autopolyploids, pairing is at random. While performing gene dropping, pSBVB stores only the recombination breakpoints, which results in an efficient algorithm to recover marker genotypes and phenotypes.As input, pSBVB needs genotypes in vcf format  or the whole sequence. For polyploids, p is a m-dimension vector with marker allele frequencies. Note that As output, pSBVB produces phenotype and marker data of individuals obtained from the pedigree-based gene-dropping procedure. In addition, pSBVB can also compute molecular relationship matrices hat h=2) .i.e., that a given marker allele behaves as fully dominant. To accommodate this, pSBVB allows computing a modified Assessing the genotype for polyploids can be inferred from fluorescence intensity in SNP arrays or from read count in sequence data , which expresses the increased probability of pairing between homologous chromosomes  minus 1 can technically be defined. However, this results in an over-parameterized model that is of no practical use. Here instead we define the In a diploid organism, the phenotype for e.g., There are currently numerous statistical methods that address the large p small n problem and use genome-wide markers to predict breeding values e.g., , as detahttps://github.com/lauzingaretti/pSBVB. The manual includes a full tutorial of all functions at the program and a user guide with the installation guidelines and examples to simulate polyploid organisms. The software is accompanied by R scripts. With this option, new base population individuals are obtained via randomly generated pedigrees. A new base population with 100 founders was obtained. The total pedigree size was 700, with 250 founders was obtained. The total pedigree size was 700, including 250 founders  and four generations with 100, 100, 100 and 150 observations, respectively.Next, to generate linkage disequilibrium in the randomly phased dataset, we included additional dummy founders using the “EXPAND_BASEPOP” statement in the parameter file  and test  population. In this example, PA was reasonably high  from 47 strawberry cultivars. Genotype-by-Sequencing libraries were prepared by Heartland Plant Innovations (http://www.heartlandinnovations.com/). Samples were multiplexed and sequenced 92 cycles on the Illumina MiSeq at the Oklahoma Medical Research Foundation. Data quality was checked by FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). To obtain reasonably realistic genotypes based on these data, we applied the following pipeline. GBS reads were aligned against Fragaria vesca (diploid strawberry) reference genome (F. vesca-genome.v2.0.a1), bam files were filtered setting minimum base and mapping qualities to 37 and 20, respectively, and parsed with snape  and inferred its genotype from the nearest possible integer to 2x, 4x, 6x and 8x, respectively.This software requires as input the number of diploid individuals in the pool, which was set to four. Polymorphic positions with fewer than 20 high quality reads were removed, as well as those where more than 60% of the cultivars were not covered. Logically, only allele counts 0, 1, to 8 are allowed in an octoploid genome SNP, whereas the number of reads per position follows a quasi-continuous distribution. To convert number of reads to genotype score, we computed the fraction of alternative allele reads divided by the total number of reads : 100 SNPs were randomly chosen as causal among the SNPs in the sugar pathway associated genes ± 10 kb.• Diploid QTNs in sugar associated Pathways (DQP): 100 SNPs were randomly chosen as causal among the diploid SNPs in the sugar pathway associated genes ± 10 kb.• Random QTNs Genome-wide chosen (RQG): 100 SNPs were randomly chosen as causal among all detected SNPs.http://pathways.cgrb.oregonstate.edu, In the first two architectures, we aimed at mimicking a trait of economic interest such as sucrose content. The gene information was obtained from FragariaCyc  . Each phSimulated PAs are in i.e., number of copies of the alternative allele, was known without error and all SNPs were used. In this approach Equation uation M  has elemerror. M  has elemploid. M  has elemNumerator Relationship Matrix (P-BLUP): The breeding values were predicted using the pedigree relationship matrix.G (GT) did not always outperform the rest of GBLUP methods considered. In fact, this was observed only when the architecture was fully additive and the QTNs were segregating in more than one homeolog group. In these cases, GT-BLUP was G2*-BLUP. G2, which employs only diploid SNPs, should be preferred to GT-BLUP only if QTNs are exclusively diploid. A relevant result is that G2* -BLUP, which treats markers as dominant, was a quite robust strategy, in particular with complete dominance and with the exception of DQP scenario .G2-BLUP might actually perform worse than P-BLUP when QTNs segregate randomly (RQP and RQG) and genic action is additive. If true SNP genotypes could be known without error (GT), the increase in accuracy compared to P-BLUP would vary between G2*-BLUP, increase in accuracy was between Finally, note that the advantage of GBLUP over P-BLUP is not always guaranteed. At least in the breeding scenario analyzed here, vcf file. More examples combining a set of different parameters  are available on GitHub.The genetic file used as input includes 1500 SNPs from the whole G can be computed and any alternative GS method can be evaluated. There are some limitations though. An important one is that epistasis cannot be modeled in pSBVB -in contrast to the diploid version (SBVB)- but this limitation stems from the lack of realistic modeling on epistasis for polyploids rather than out of computational constraints.Certainly, polyploid sequence data will be increasingly available, which will be used to achieve a better understanding of complex trait genetics and to optimize GS strategies. To help in the latter task, here we have developed an extension of SBVB software (pSBVB) that feeds from real sequence data of polyploid organisms. It uses efficient forward algorithms and allows simulating meiosis in polyploid species, suited for both auto and allopolyploid organisms. Further, pSBVB generalizes genetic modeling in polyploids to generate phenotypes and incorporates several options to compute predefined molecular relationship matrices that are specific to polyploid organisms. Note though that, since pSBVB can print the whole SNP dataset, any custom-made To the best of our knowledge, there are no simulation tools that allow estimate genetic matrix in polyploids organisms with a range of options like the one described here. Among the available forward-time simulation tools, only simuPOP  and PediP-BLUP, but this will depend on the true genetic architecture of the trait, as also shown by To conclude, we have developed a flexible GS simulation tool capable of using real sequence data from polyploids. We show the tool capabilities using potato and strawberry real datasets. With potato genotypes, we illustrate how new base population individuals can be generated and show that accuracy can be relatively high even with modest population sizes. Among the molecular relationship matrices proposed, assuming that only diploid genotypes can be identified seems overall a good compromise in terms of performance, at least in strawberry data. Our study suggests that GS may increase response to selection compared to


Newcastle disease virus (NDV) is a highly contagious avian pathogen that poses a tremendous threat to poultry producers in endemic zones due to its epidemic potential. To investigate host genetic resistance to NDV while under the effects of heat stress, a genome-wide association study (GWAS) was performed on Hy-Line Brown layer chickens that were challenged with NDV while under high ambient temperature to identify regions associated with host viral titer, circulating anti-NDV antibody titer, and body weight change. A single nucleotide polymorphism (SNP) on chromosome 1 was associated with viral titer at two days post-infection (dpi), while 30 SNPs spanning a quantitative trait loci (QTL) on chromosome 24 were associated with viral titer at 6 dpi. Immune related genes, such as CAMK1d and CCDC3 on chromosome 1, associated with viral titer at 2 dpi, and TIRAP, ETS1, and KIRREL3, associated with viral titer at 6 dpi, were located in two QTL regions for viral titer that were identified in this study. This study identified genomic regions and candidate genes that are associated with response to NDV during heat stress in Hy-Line Brown layer chickens. Regions identified for viral titer on chromosome 1 and 24, at 2 and 6 dpi, respectively, included several genes that have key roles in regulating the immune response. Paramyxoviridae, is a single-stranded, negative-sense RNA virus with several different strains that is defined based on their pathogenicity [Newcastle disease virus (NDV) is a pathogen that is known to infect over 200 different species of birds, including chickens . The mosgenicity . Typicalgenicity ,4. Althogenicity . Geneticgenicity ,7,8,9, sIn addition to the devastating potential of NDV for poultry production, environmental factors, such as heat stress, exacerbate losses in poultry production. Heat stress alone is estimated to result in over $165 million U.S. dollars annually in global poultry losses . PhysiolPrevious studies have revealed candidate loci associated with differences in response to NDV infection on several chromosomes in broiler chickens and in intercrossed lines ,18,19. H7 EID50 of the La Sota strain of NDV through both ocular and nasal passages. The treated group refers to birds that were under the effects of both heat stress and NDV infection, while non-treated birds were mock-inoculated with 200 μL of 1X phosphate-buffered saline (PBS) and under non-heat stressed conditions. The experimental trial was performed according to guidelines approved by the Institutional Animal Care and Use Committee at the University of California, Davis (IACUC #17853).Hy-Line International Brown laying chickens  were utilized in this experiment. Three replicate trials of mixed-sex chicks  were conducted using a similar protocol as described by Wang et al. . In brie5 to 102 EID50 of the virus and used to calculate the viral titer of the sample. Viral clearance was measured as the difference between viral titer at 2 and 6 dpi, divided by the viral titer at 2 dpi. Chicken lachrymal fluid was collected at 2 and 6 dpi on treated birds. Viral RNA was extracted using the MagMAX-96 viral RNA isolation kit . Quantification of viral titer was conducted using quantitative RT-PCR (qRT-PCR) with the TaqMan Newcastle Disease Virus Real-Time PCR kit (Life Technologies Cat#44006874) and measured on the ABI 7500 fast Real-Time PCR system . A standard curve was generated from a log copy number dilution of 10Anti-NDV antibodies were measured in serum collected at 10 dpi using the IDEXX NDV ELISA kit for chickens . The sample to positive ratio (S/P) was calculated as the average absorbance of each sample divided by the recorded measurement of the provided kit control. Body weight measurements (g) were taken at day 1, 13, 14, 21, 27, and 30.Genomic DNA was extracted from blood preserved on Whatman FTA cards . A total of 526 individuals that met concentration and DNA quality requirements were genotyped on the Axiom 600 K Genome-Wide Chicken Array Kit  by Geneseek . SNPs were then filtered for a call rate greater than 95% and minor allele frequency of greater than 0.01. After quality filtering, 304,500 SNPs were used in the downstream analysis from 526 individuals. SNP positions were mapped to the galGal 5 reference genome.t-test in R [Y is the dependent variable of the analyzed trait, μ is the mean of the individual trait, sex (S), and chamber nested within replicate (Rep(C)) were fitted as fixed effects. Random effects included animal genetic effects (A), using a genomic relationship matrix based on Reference [e). For viral titer measurements at 2 and 6 dpi, dam anti-NDV antibody level was added as a covariate. Heritability was estimated as the ratio of the animal variance to phenotypic variance and phenotypic and genetic correlations were estimated using corresponding bivariate analyses.Statistical significance of differences in body weight measurements between the treated and untreated groups were done using a Student’s est in R . ASReml est in R . The anieference , and resGenome-wide association analysis was performed using the R package GenABEL , using tp-values for the 10 and 20% genome-wide significance thresholds were 2.04 × 10−6 and 4.07 × 10−6. SNPs were then filtered using the 10 and 20% genome-wide significance thresholds and their Affymetrix SNP IDs were converted into SNP ID’s using National Center for Biotechnology Information’s dbSNP database [r2 for all pairs of SNPs were estimated using the linkage disequilibrium function in PLINK [To establish significance thresholds, the number of independent tests was determined as the number of principal components that accounted for 95% of the variance in SNP genotypes, as described by Waide et al. ,26. The database . SNPs wedatabase  using a in PLINK  with a 110 viral copy number was 4.77 ± 0.022 and 2.76 ± 0.026 at 2 and 6 dpi, respectively (10 (sample to positive ratio) of the population was −0.10 ± 0.016, and 1.04 ± 0.037 on the untransformed scale  in BWG were observed between treated and non-treated individuals across all recorded time points . However, prior to both heat and NDV treatment, differences in BWG existed between the two groups at day 13, although the two groups of birds were randomly selected from each dam family and assigned to treatments (Body weight gain (BWG) of both treated (either heat or combination of heat and NDV infection) and non-treated individuals was measured at 14, 21, 27, and 30 days of age in order to assess the effects of heat treatment and both heat treatment and NDV infection on growth rate , Table 1ASReml data analysis software was used to estimate the heritability for all traits . Viral tp-value = 2.035 × 10−6) and 20%  genome-wide significance, based on the number of independent tests that accounted for 95% of the variation in SNP genotypes, was used to identify suggestive QTL. GWAS was performed for each trait and results are presented as Manhattan plots  on chromosome 1 was the only suggestive association found for the 2 dpi viral titer. The viral titer at 6 dpi had the strongest signal detected, with 30 and 29 suggestive SNPs at 20% and 10% significance, respectively. These SNPs were all located on chromosome 24, in a region from 0.4 to 1.4 Mb  at 0.1 to 1.0 Mb distance. Other measures in the Hy-Line layer lines by Abasht et al. [r2) over these same distances. Based on this, gene annotation in a 1 Mb region around each significant SNP was searched to identify functionally relevant genes that may be responsible for the SNP associations for each trait. A table containing a summary of the known functions of the genes identified in this study can be found in the CDC123, CAMK1d, and CCDC3. SNPs associated with viral load at 6 dpi identified a 1 Mb QTL on chromosome 24, with 43 genes located within this region, including candidate genes ST3GAL4, TIRAP, FLI1, and ETS1, which are involved in functions related to the inflammatory and innate immune response (ST3GAL4 and TIRAP) [(FLI1), and the expression of cytokine and chemokine genes (ETS1) [ETS1 was also located within the QTL region identified for viral clearance. QTL analysis for anti-NDV antibody levels revealed two genes, POU1F1 and CHMP2B, that were in close proximity to the significant SNPs. No genes were found within the 1 Mb region for SNPs identified for BWG at day 13.The average linkage disequilibrium (LD) in the Hy-Line Brown layer chickens ranged from 0.55 to 0.58  ,32, B ces (ETS1) ,34. ETS1To the best of our knowledge, this study provides the first estimates of heritability for NDV viral titer in lachrymal fluid while under the effects of heat stress. Overall, heritability estimates were lower than those reported in investigations into other populations of chickens of similar phenotypic traits ,35,36, sCDC123, CAMK1d, and CCDC3) located within the 1 Mb QTL region identified on chromosome 1 centered around the suggestive SNP associated with the viral titer 2 dpi. CDC123 is a gene that is highly expressed in leukocytes and plays a role in eIF2 regulation and internal host-pathogen interaction [CDC123 to be significantly up-regulated in NDV infected and heat stressed Fayoumi chickens compared to non-treated birds. CAMK1d has been associated with regulation of granulocyte function and neutrophil activation during influenza virus stimulation, while CCDC3 negatively regulates the TNF-α-induced pro-inflammatory response via inhibition of TNF-α-induced NF-κB activation [CAMK1d may protect against influenza infection [CAMK1d gene is also a target for microRNA silencing by viruses and, thus, inhibition of this gene can facilitate viral invasion into the host cell [This study revealed suggestive QTL for NDV viral titers at 2 and 6 dpi on chromosomes 1 and 24, respectively. Although the significance thresholds used in this study deviate from the traditional cutoffs typically used, the results generate candidate regions for functional association between SNPs and phenotype. QTL for anti-NDV antibody levels were previously reported on chromosome 1  but theseraction . Unpublitivation ,41. In aETS1, TIRAP, FLI1, ST3GAL4, and KIRREL3, provide potential candidates that may be associated with a viral titer at 6 dpi. ETS1 had five significant SNPs located within 100 kb of it. ETS1 is a transcription factor that functions as a regulator of cytokine and chemokine gene expression [TIRAP, FLI1, and ST3GAL4, could be critical in terms of the broader response to the virus at 6 dpi. TIRAP is a Toll-interleukin 1 receptor adaptor protein that is part of the microbial pathogen recognition system of Toll-like receptors and functions in pathogen recognition within the host [TLR4 signaling and initiates genes such as NF-κB, MAPK1, MAPK3, and JNK for cytokine secretion and inflammatory response. TIRAP plays a larger role in modulating the early innate immune response and TLR4’s antiviral response through viral envelope glycoprotein detection [FLI1 is a DNA-binding protein with a similar binding domain to ETS1. Reduced expression of FLI1 in transgenic mice was reported to cause deficiencies in proliferative response of B cells to mitogens [ST3GAL4 has been demonstrated to be the major sialytransferase involved in producing selectin ligands, which promote leukocyte trafficking and mobilization [KIRREL3 gene had the largest number of significant SNPs (7) identified within a gene in this study. KIRREL3 is involved in developmental anomalies and defective KIRREL3 expression has been associated with failures in immune response [KIRREL3  were also significantly associated with the viral clearance QTL that was identified in this study, which provides further evidence of the potential role this gene may play in regulation of the immune response to NDV while under heat stress.The QTL affecting the viral titer at 6 dpi had the largest number of SNPs identified in this study. The approximately 1 Mb region on chromosome 24 contained 29 and 30 SNPs that were significant at the 10 and 20% genome-wide significance levels, respectively, and encompassed seventeen genes. Several of these genes, including pression  and was the host . TIRAP cetection . FLI1 ismitogens . ST3GAL4lization . These cresponse . SeveralPOU1F1 and CHMP2B genes are located within the 1 Mb QTL window centered around the suggestive SNPs found on chromosome 1. The CHMP2B gene was found to be differentially expressed between NDV infected and heat stressed, and non-treated Fayoumi chickens in unpublished results from our group. This suggests that CHMP2B is related to NDV resistance, as Fayoumi birds have been previously shown to be relatively more resistant to NDV [Suggestive SNPs associated with anti-NDV antibody levels identified in this study overlapped with regions on chromosome 1 that were identified in a previous study . The POUt to NDV . In addit to NDV . Interest to NDV ,18,19. SNDV infection and/or heat stress appeared to significantly reduce body weight gain of treated individuals compared to the non-treated individuals across all measured time points. The chicks used in the non-treated and treated groups were randomly selected from within dam families in order to maximize the representation of all sixty dams within the population. However, this random selection within each dam group unexpectedly resulted in significant differences in BWG between the treated and non-treated groups prior to the treatment at day 13. Although the differences in BWG between the two groups continually increased with time of treatment, as expected , this poKIRREL3, TIRAP, ETS1, CCDC3, and CAMK1d) that may play important roles in the chicken’s response to NDV during heat stress. The variants identified in this study warrant further investigation to understand the underlying molecular mechanisms of action. These results further the overall goal of the Feed the Future Innovation Lab to develop genetically enhanced, resilient poultry and thus mitigate the impacts of NDV and climate change across the globe.Estimates of heritabilities and the QTL regions that may be putatively controlling traits related to NDV infection during heat stress that were identified in this study provide insights into the genetic control of these traits and provide genetic markers that can be used for selective breeding programs to enhance NDV resistance in chickens in hot environments. Specifically, suggestive QTL regions found on chromosomes 1 and 24 revealed several candidate genes (