BCMO1, BCO2, and SCARB1, have been shown to be associated with the amount of β-carotene (BC) in milk. Whole-genome sequencing reads from the coordinates of these 3 genes in 202 non-Indian cattle  and 35 non-Indian buffalo  animals from several breeds were collected from data repositories. The number of SNP detected in the coding regions of these 3 genes ranged from 16 to 26 in the 3 species, with 5 overlapping SNP between B. taurus and B. indicus. All these SNP together with 2 SNP in the upstream part of the gene but already present in dbSNP (https://www.ncbi.nlm.nih.gov/projects/SNP/) were used to build a custom Sequenom array. Blood for DNA and milk samples for BC were obtained from 2,291 Indian cows of 5 different breeds  and 2,242 Indian buffaloes . The DNA was extracted and genotyped with the Sequenom array. For each individual breed and the combined breeds, SNP with an association that had a P-value <0.3 in the first round of linear analysis were included in a second step of regression analyses to determine allele substitution effects to increase the content of BC in milk. Additionally, an F-test for all SNP within gene was performed with the objective of determining if overall the gene had a significant effect on the content of BC in milk. The analyses were repeated using a Bayesian approach to compare and validate the previous frequentist results. Multiple significant SNP were found using both methodologies with allele substitution effects ranging from 6.21 (3.13) to 9.10 (5.43) μg of BC per 100 mL of milk. Total gene effects exceeded the mean BC value for all breeds with both analysis approaches. The custom panel designed for genes related to BC production demonstrated applicability in genotyping of cattle and buffalo in India and may be used for cattle or buffalo from other developing countries. Moreover, the recommendation of selection for significant specific alleles of some gene markers provides a route to effectively increase the BC content in milk in the Indian cattle and buffalo populations.Vitamin A is essential for human health, but current intake levels in many developing countries such as India are too low due to malnutrition. According to the World Health Organization, an estimated 250 million preschool children are vitamin A deficient globally. This number excludes pregnant women and nursing mothers, who are particularly vulnerable. Efforts to improve access to vitamin A are key because supplementation can reduce mortality rates in young children in developing countries by around 23%. Three key genes,  Vitamin A plays a key role in human health. Inclusion of proper amounts of vitamin A in the diet is a key factor for the development and maintenance of healthy vision  could also replace vitamin A, as it can be metabolized to vitamin A after ingestion  and β-carotene oxygenase 2 (BCMO2 or BCO2), which are involved in the cleavage of β-carotene , which is involved in cellular transport  and the Bharatiya Agro Industries Foundation veterinarian from the Bharatiya Agro Industries Foundation research foundation meeting the required standards in India and with approval from the Bill and Melinda Gates Foundation.Bos taurus, 35 Bos indicus) and 61 buffalo  non-Indian animals from several breeds were collected from SRA  database (B. taurus) as part of the 1000 bulls genome project , or from other projects . The list of breeds is reported in Supplemental Table S1 (https://doi.org/10.3168/jds.2019-16361). Because of the high genomic similarity between B. taurus and B. indicus, reads from both species were aligned against the same UMD3.1 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000003055.6) reference genome (GCA_000003055.3). Bubalus bubalis reads were aligned to the most recent buffalo reference genome available at the time of the analysis: MD_CAS-PUR_WB_2.0 (GCA_000471725.1). Coordinates of the BCMO1, BCO2, and SCARB1 genes were retrieved through Ensembl (www.ensembl.org) for B. taurus and through alignment (https://blast.ncbi.nlm.nih.gov/Blast.cgi) of the coding sequence of the 3 genes against the UMD3.1 and MD_CASPUR_WB_2.0 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000471725.1) genomes for B. indicus and B. bubalis, respectively.Reads from whole-genome sequencing of 202 cattle  with standard conditions  were considered. For cattle, the effect of each SNP was evaluated through variant effect predictor .For Comparing the AA changes, we were able to determine if a mutation in the coding region could be synonymous (when no AA change was detected) or nonsynonymous (when the mutation causes AA change in the protein frame comparing to the refseq protein).https://doi.org/10.3168/jds.2019-16361), where DNA was extracted from blood following standard protocols. Each animal was genotyped in triplicate.The SNP located in the coding region or in untranslated region or already reported in dbSNP were considered for a Sequenom panel. For each selected SNP, probes for the panel were designed by Geneseek . The panel (composed of 67 SNP as it will be shown in results) was then tested by genotyping a subset of Indian samples belonging to 5 different breeds . For each animal, information on lactation, milk yield, location, and farmer was also collected if available. The same animals were genotyped with the previously developed Sequenom custom array and for each breed, only SNP with call rate ≥0.90 and belonging to the same species  were considered. For the combined analyses within species, SNP had to have a call rate >90% for all the breeds to be included. The retained missing SNP were then imputed breed by breed using Fimpute 2.2  among SNP of the same genes were calculated with Haploview software  and 2,242 buffalo , as shown in Supplemental Table S3  with animals that were homozygous for the unfavorable alleles with the objective of demonstrating the effect that long-term sustained selection could have on the BC content in milk. These analyses were performed both for each individual gene and across all 3 genes for each species and the combined breeds as well.There is no known history of selection for BC content in the population studied. Because of this, the last step of this analysis was to contrast the hypothetical extreme cases of animals that were homozygous for all the favorable alleles . Then, at locus In addition to the previously described frequentist approach, a Bayesian analysis was also undertaken with a model that considered the non-SNP effects as fixed and all SNP effects as random. It has been recognized that explicit adjustments for multiple tests are not needed when inference is based on posterior probabilities  that humans obtain through foods of plant origin Olson, . Beta-caB. taurus, 2,225 for B. indicus, and 3,824 for B. bubalis (B. taurus (Holstein and Jersey) were considered for the analyses because these 2 are extensively employed around the world and are often crossbreed with B. indicus , 27 SNP for B. indicus  and 23 SNP for B. bubalis . For convenience, they were uniquely named with the name of the gene following by a successive number according with the position in the gene and species identification . Five SNP were overlapping among B. bubalis and B. taurus . The number of SNP detected in the coding regions varied from 16 to 26 in the 3 species, with 5 overlapping SNP between B. taurus and B. indicus. This is due to the high similarity among the 2 species that are often crossed to improve the production. Therefore, all SNP designed on cattle were considered for both breed crosses and B. indicus breeds. The genotypes with the Indian test panel cattle  reported that BCMO1.4 was not successfully genotyped. The other SNP showed a call rate ranging from 75 to 100%, with 29 SNP  with a call rate ≥0.90% in all the considered cattle breeds and crosses. For the buffalo breeds , 17 out of the 24 SNP designed for buffalo were genotyped with a call rate ≥90% in all the breeds. These SNP were 5 for BCO2, 7 for SCARB1, and 5 for the BCMO1 genes.Overall a total of 1,576 SNP were detected across the 3 genes for  bubalis . The difcus FAO, . A totalcus FAO,  and wereLD. The genotyping of the animals was successful in at least one breed for all the SNP except for BCMO1.1 (cattle) and BCMO1.17 SNP . The number of SNP successfully genotyped for cattle breeds ranged from 42 (Gir) to 31 (Tharparkar) with the combined cattle breeds having 28 high quality SNP in common. In buffalo, the number of high-quality SNP ranged from 22  to 20 , whereas the combined buffalo breeds had 20 SNP in common that were successfully genotyped. The pairwise analyses performed on cattle and buffalo SNP revealed a low number of SNP in high LD . For cattle, as expected, mostly of the duplicated SNP in cattle and buffalo are in strong LD for most of the breeds. The BCO2 gene had BCO2.2 and BCO2.6 in strong linkage in the Tharparkar breed, whereas the SCARB1 gene has several SNP in strong LD, where SCARB1.13 and SCARB1.14 were in strong LD in all the considered breeds. The BCMO1 gene had BCMO1.8 and BCMO1.9 that are in high LD within Gir, Sahiwal, and Jersey breeds. As for the buffalo breeds, only the SCARB1 gene reported 4 SNP  in strong LD in all the breeds.BC and Association Analyses. An initial breed and species comparison demonstrated differences in BC level in milk among cattle and buffalo with buffalo showing a lower BC content than cattle  analyses and only one for buffalo (BCO2.18). These markers might represent an important tool for selection for cattle and buffaloes when breed proportions are unknown because they are significant when the breeds of each species included in this research are analyzed simultaneously. These SNP may also be particularly useful when the breed of the animal is unknown or in case of crossbreeds. As for the single breeds, SNP with a significant P-value included Jersey crosses (SCARB1.10), Gir , and Sahiwal (Scarb1.16) for cattle; and Jafarabadi (BCMO.21), Murah (SCARB1.24), and Surti (BCO2.16 and SCARB1.20) for buffalo. Finally, there are significant cases such as the BCMO1 gene for Gir cattle and Jafarabadi where several markers were in LD within the gene and therefore it might be beneficial to select for a specific haplotype in that gene.In cattle, the significant SNP with the largest allele substitution effect was SCARB1.16 for the Sahiwal breed with t distribution centered at zero for the effects of all loci, and so each locus is a priori equally likely to be positive or negative. On the other hand, Bayes Cπ prior assumes the SNP effect is null with probability π or has a normal distribution with probability 1 – π for all loci, where the probability π is treated as an unknown with a uniform prior. Thus, in a Bayes Cπ analysis, the posterior probability (PP) that a locus has a non-null effect can be taken as evidence of an association of the SNP with the trait.In the Bayesian approach, inferences are based on posterior probabilities. The Bayes A prior assumes a Thus, PP that deviate from this prior probability of 0.5 can be taken as evidence of an association of the SNP with the trait. In several cases, the 2 analyses suggested the same markers were significant. However, it is often misleading to just compare results from the different approaches as they should be expected to differ for several reasons, but we include this discussion for completeness of these results. Estimates (posterior means) of the marker effects, obtained from the Bayesian approach, ranged from 0.01 to 2.02 (data not shown). Means for total gain, generally, were similar or smaller than those found with the frequentist approach. Interestingly, even though the frequentist approach is not directly comparable with the Bayesian methods, the number of SNP with important effects was similar; in many cases the different sets of SNP in the 2 approaches with important effects overlapped. When comparing the results of the frequentist approach and the most stringent threshold of both Bayesian approaches, 6 SNP overlapped for the combined cattle breeds. For the buffalo combined breeds, 7 SNP overlapped across the 3 analyses performed. The biggest difference was found in the Jersey cattle breed for which the Bayesian analyses found 5 fewer important SNP than were found with the frequentist approach and total gain with the frequentist approach was nearly 45 μg/100 mL higher when compared with the results of the Bayesian approach.In the combined buffalo breeds the frequentist approach found 7 significant SNP, and the Bayesian approach with a Bayes A prior and a PP > 0.7 found 13. Results from the Bayes Cπ analyses showed a different trend. The SNP that had non-null effects with a PP greater than 0.0 , 0.1, and 0.2 are given in It is important to note that estimates of total gain in the Bayesian analyses tended to be smaller than those in the frequentist approach due to both the generally smaller number of markers found to be significant and due to the expected shrinkage of their effects in the Bayesian analyses (Bhattacharya et al., It is also very important to note that the expression of a phenotype is dependent on the interaction between environment and genotype and most of the animals sampled for this project were under harsh environmental conditions. India is a developing country with rural areas that are often poor, and the animals sampled were under varied and generally suboptimal management and nutritional conditions. Even though a fixed effect for herd was included in our statistical models to account for the different herd conditions found throughout the samples, the generalized less than optimal nutritional, environmental, and management conditions that these animals were kept under might have had an overall negative effect that prevented or decreased the full expression of the phenotypes associated with the concentration of BC in milk. Therefore, improving the aforementioned conditions should go hand in hand with the selection program to successfully and significantly increase the concentration of BC concentration in milk in the cattle and buffalo Indian population.The custom panel designed for genes related to BC production shows applicability in genotyping of cattle and buffalo in India. Among the genotyped SNP, some were significantly associated in several cattle and buffalo breeds, providing markers that may be useful to develop genetic selection strategies that can increase BC content in milk of those populations and could be tested in other developing countries. Moreover, the recommendation of selection of significant specific alleles at the gene markers may provide the direction to effectively increase the BC content in milk in the Indian cattle and buffalo populations. Additional analyses will be required to evaluate a haplotype-based selection for SNP in high LD. Moreover, future genome-wide association studies may reveal additional genes associated with BC or vitamin A in cattle and buffalo. The possible discovery of new candidate genes involved in the BC production would help increase the number of informative SNP in this panel.


Ne), and inbreeding rates based on excess of homozygosity (FHOM) and runs of homozygosity (ROH). A GBS assay was constructed based on the sequencing of ApeKI-digested libraries from 285 American mink using Illumina HiSeq Sequencer. Data of 13,321 SNPs located on 46 scaffolds was used to perform LD analysis. The average LD (r2 ± SD) between adjacent SNPs was 0.30 ± 0.35 over all scaffolds with an average distance of 51 kb between markers. The average r2 < 0.2 was observed at inter-marker distances of >40 kb, suggesting that at least 60,000 informative SNPs would be required for genomic selection in American mink. The Ne was estimated to be 116 at five generations ago. In addition, the most rapid decline of population size was observed between 100 and 200 generations ago. Our results showed that short extensions of homozygous genotypes (500 kb to 1 Mb) were abundant across the genome and accounted for 33% of all ROH identified. The average inbreeding coefficient based on ROH longer than 1 Mb was 0.132 ± 0.042. The estimations of FHOM ranged from −0.44 to 0.34 among different samples with an average of 0.15 over all individuals. This study provided useful insights to determine the density of SNP panel providing enough statistical power and accuracy in genomic studies of American mink. Moreover, these results confirmed that GBS approach can be considered as a useful tool for genomic studies in American mink.Knowledge of linkage disequilibrium (LD) patterns is necessary to determine the minimum density of markers required for genomic studies and to infer historical changes as well as inbreeding events in the populations. In this study, we used genotyping-by-sequencing (GBS) approach to detect single nucleotide polymorphisms (SNPs) across American mink genome and further to estimate LD, effective population size ( Ne) in domestic animals e.g., cattle  refers to the non-random association of alleles at two separate loci within a population . The exi, cattle , sheep  is known as one of the most desired sources of fur in the world. Development of genomic tools can potentially play a prominent role in improving the fur quality, reproductive performance, growth rates, and health traits in American mink breeding programs. Identifying a large number of genetic markers throughout the genome is an essential step to facilitate genomic studies in American mink. Genotyping-by-sequencing (GBS) is a simple and low-cost sequencing-based approach to detect single nucleotide polymorphisms (SNPs) across a reduced subset of genome  to estimate the inbreeding levels in the studied population using genomic data.Recent advances in next generation sequencing (NGS) technologies have provided a large number of SNPs to study the genetic basis of economically important traits in livestock species. Accordingly, SNPs have been widely used to study LD patterns in domestic animals e.g., cattle , sheep , pig Du, and chi markers , howeverApeKI (G↓CWGC) which creates a sticky 5′ overhang. At the next step, barcode adaptors unique to each sample along with a standard Y-adaptor were ligated to DNA fragments to generate DNA sequencing libraries. Three separate sets of 96-plex GBS libraries were prepared to amplify DNA fragments by PCR using primers complimentary to the ligated adaptors. Finally, each library was sequenced on a separate lane of Illumina HiSeq Sequencer at Genome Quebec sequencing center to generate single-end reads of PCR products.Animals were originated from four mink farms in Canada, and were raised at the Aleutian Diseases Research Center (ADRC) . There w1 Cutadapt software  and Dmax is computed as the min  if DAB < 0 or min  if DAB ≥ 0, respectively.where Dr2 was calculated in each bin across all scaffolds and plotted against the median size of bins. We used SNP1101 software to estimate Ne based on the observed pattern of LD across the genome. The historical Ne was estimated at 200, 100, 50, 10, and 5 generations ago to survey the changes in population sizes.The distances between syntenic SNP pairs were categorized in three sets including ≤100 kb, ≤1000 kb, and ≤10 Mb, and these sets were classified using bin sizes of 10 kb, 100 kb, and 1 Mb, respectively. The average ROH) in each individual  was computed for all individuals using procedure implemented in SNP1101 software.where the numerator is the total length of ROHs above a certain length and The GBS analysis generated 681,936,405 reads with an average of 2,133,590 reads per animal, and a range of 0 to 5,306,590 reads among all samples. On average, 97.12% of reads were mapped to reference genome . In totar2 and D′ between adjacent SNPs for all scaffolds. The average distance between two adjacent SNPs was 51 kb across studied scaffolds. The average value of r2 ± SD between adjacent SNPs was 0.30 ± 0.35 for all scaffolds and ranged between 0.18 and 0.54 within the scaffolds. On average, 39.09% and 16.97% of all adjacent markers had r2 > 0.2 and r2 > 0.8 across the whole genome, respectively. In addition, the average D′ ± SD between adjacent markers was estimated to be 0.79 ± 0.30 with a range of 0.60–0.90 among all scaffolds.r2 fluctuated among various scaffolds (r2 = 0.15) whereas the lowest LD level was found on scaffold 8 (average r2 = 0.02). r2 and D′ as a function of distance between SNP pairs up to 1000 kb. As expected, the average of LD parameters declined with increase in physical distances between markers. Whereas the average r2 ± SD was 0.38 ± 0.28 in 0–10 kb inter-marker distances, it was reduced to 0.08 ± 0.12 in marker interval of 900–1000 kb. Similar trend was also observed for D′ values over different inter-marker distances , 1000 kb (using bins of 100 kb), and 10 Mb (using bins of 1 Mb). The average r2 of consecutive bins was presented for three distance sets in The average caffolds . The higistances . FurtherNe were inferred based on the relationship between LD patterns across various marker intervals and the size of population at different generations. Ne from 200 to 5 generations ago, showing that the estimate of Ne was 1,012 at 200 generations ago and was reduced to 116 at 5 generations ago. The Ne was predicted to be 104 for present generation using regression model.The ancestral and recent ROH as a function of criterion lengths from 500 kb to 16 Mb. While the average of FROH > 500 kb was 0.186 ± 0.056 among all samples, the values of FROH for segments >8 Mb tended to be zero. FROH values were in range of 0.02 to 0.36 among individuals. The most inbred animal exhibited 365 ROH >500 kb with an average length of 2.59 Mb per segment. The minimum number of ROH >500 kb detected per individual was 40 with an average length of 1.91 Mb.HOM) for all individuals. The estimates ranged from −0.44 to 0.34 among different samples and had an average of 0.15 over all individuals. High correlation (0.87) was observed between FROH (minimum length > 500 kb) and FHOM across the studied samples. However, the correlation coefficients were declined along with increase in the ROH lengths  of reads aligned to reference genome indicated that library preparation and optimization steps were successfully performed. In this study, GBS analysis provided 52,714 SNPs across 895 scaffolds for genomic studies in American mink. Similarly, r2 and D′. The average LD (r2 ± SD) between adjacent SNPs was 0.30 ± 0.35 over all scaffolds with a high percentage (39.09%) of r2 > 0.2 between markers . However, the LD gradually decayed at longer inter-marker distances and no remarkable change was observed at longer than 6 Mb distances  declined to <0.2 at inter-marker distances of >40 kb, suggesting that at least 60,000 informative SNPs  would be required to capture useful LD information necessary for genomic selection. Furthermore, given that the average LD (r2) decayed to <0.3 at inter-marker distances of >20 kb, the minimum of 120,000 SNP markers would be required for GWAS in American mink (2.4 Gb/20 kb). The average distance of 51 kb between adjacent markers implied that 13,321 SNPs would not provide appropriate accuracy in GWAS and genomic selection for American mink. To our knowledge, this was the first study to determine the patterns of LD decay across the American mink genome. However, r2 > 0.3 extended up to distances of 10–20 kb in Nguni and Drakensberger cattle breeds of South Africa (r2 > 0.3 at distances of >20 kb) were reported at some breeds of sheep  was reported in six Chinese goat breeds (ROH > 1 Mb (0.168 ± 0.052) was estimated for Jinhua Pigs using sequencing data (We defined ROH as the homozygous segments with different sizes (>500 kb to > 16 Mb). The average level of autozygosity was obtained to be 0.132 ± 0.042 on the basis of ROH longer than 1 Mb . Althougt breeds . A slighing data . On the ing data . The difing data .r2 = 0.30) was observed between adjacent markers, the extension of medium LD (r2 > 0.2) was restricted to short inter-marker distances (≤40 kb). In addition, it is likely that the total number and length of ROH obtained for long segments (≥ 8 Mb) in the current study were underestimated due to short extension of scaffolds, which restricted the detection of longer ROH.Our results showed that short extensions of homozygous genotypes (500 kb to 1 Mb) were abundant across the genome and accounted for 33% of all ROH identified. However, only a small number of the long ROH segments (≥ 8 Mb) was detected in this study . WhereasThe extension of ROH can be also useful to infer the recent and ancient inbreeding events in the populations. Longer ROH are associated with recent inbreeding in the population because recombination rates would not be enough to break up long segments over a few generations. On the other hand, short tracts of homozygosity tend to be correlated with ancient inbreeding . The abuIS (−0.150 to 0.005) using a panel of 194 SNPs, which indicated that farms were successful in preventing inbreeding. These discrepancies might be also due to the differences in number and nature of markers, and approaches used in various studies. Furthermore, FHOM values were highly correlated (0.87) with FROH (minimum length > 500 kb) across the studied samples. In accordance with the value estimated in this study, high correlations of 0.83–0.95, 0.89–0.99, 0.91–0.98 and 0.89 were also reported in Italian local cattle breeds  were abundant across the genome and were used to infer the levels of ancient inbreeding. However, the chromosomes-scale information is necessary to infer the recent inbreeding events in American mink populations. This study provided useful insights to determine the density of SNP panel providing enough statistical power and accuracy in genomic studies of American mink. Moreover, these results confirmed that GBS approach can be considered as a useful tool for genomic studies in American mink.This study provided the first draft of LD patterns across the American mink genome using GBS data. The estimated 10.6084/m9.figshare.9757784.The datasets generated for this study can be found in the FigShare Repository: doi: The animal study was reviewed and approved by the Institutional Animal Care and Use Committee.KK and YM carried out the statistical analysis and interpreted the data. AF and SM collected the genetic materials and carried out the laboratory analyses. AF and YM conceived the study and participated in its design and coordination. KK and YM wrote the manuscript. MS provided the software and supported the statistical analysis. YM and AF reviewed the manuscript. All authors read and approved the final manuscript.MS was employed by Select Sires Inc. The remaining 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.


Understanding genetic architecture is essential for determining how traits will change in response to evolutionary processes such as selection, genetic drift and/or gene flow. In Atlantic salmon, age at maturity is an important life history trait that affects factors such as survival, reproductive success, and growth. Furthermore, age at maturity can seriously impact aquaculture production. Therefore, characterizing the genetic architecture that underlies variation in age at maturity is of key interest.P-values: 8.7 × 10−133–9.8 × 10−8), including two very strong signals spanning the six6 and vgll3 gene regions on chromosomes 9 and 25, respectively. Furthermore, we identified 116 independent signals that tagged 120 candidate genes with varying effect sizes. Five of the candidate genes found here were previously associated with age at maturity in other vertebrates, including humans.Here, we refine our understanding of the genetic architecture for age at maturity of male Atlantic salmon using a genome-wide association study of 11,166 males from a single aquaculture strain, using imputed genotypes at 512,397 single nucleotide polymorphisms (SNPs). All individuals were genotyped with a 50K SNP array and imputed to higher density using parents genotyped with a 930K SNP array and pedigree information. We found significant association signals on 28 of 29 chromosomes (These results reveal a mixed architecture of large-effect loci and a polygenic component that consists of multiple smaller-effect loci, suggesting a more complex genetic architecture of Atlantic salmon age at maturity than previously thought. This more complex architecture will have implications for selection on this key trait in aquaculture and for management of wild salmon populations. Characterizing genetic architecture is instrumental for understanding how quantitative traits will change in response to evolutionary processes such as selection, genetic drift, and/or gene flow. Reciprocally, knowledge of genetic architecture can help to elucidate how evolutionary processes lead to particular genetic architectures of quantitative traits, i.e. a few major effect loci (oligogenic) , 2, manyAquaculture-reared Atlantic salmon offer an opportunity to overcome sample size limitations. Tens of thousands of individuals are routinely reared in a common environment, from fertilization to maturation. As Atlantic salmon are recently domesticated (just 10 to 15 generations ago) , the gengrilsing), causes significant losses in revenue [Although beneficial in the wild, variation in age at maturation can be problematic in Atlantic salmon aquaculture. Early maturation, as soon as 1 year post-smoltification , which is also associated with height and age at maturity in humans [vgll3 are observed in higher proportions in late-maturing females than in early-maturing females, suggesting that vgll3 may also be associated with age at maturity in North American salmon [vgll3 region and maturation in some [Quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) have both been used to identify genetic variation associated with age at maturity in Atlantic salmon –17, 19. n humans , 29. Anon humans . This ren humans , 29 and n humans . In Nortn salmon . QTL stun salmon . However in some , 15 but  in some , 17, 19.Here, we conducted a large-scale examination of 11,166 males from a single year-class that were genotyped with a 50K SNP array. We imputed up to a higher density using parents that were genotyped using a 930K SNP array, combined with pedigree information. Using a GWAS, we aimed at further resolving the genetic architecture of maturation timing in male Atlantic salmon and identifying potential candidate genes to provide new insight into the mechanisms involved in determining age at maturity in Atlantic salmon.Fish were sampled from the Norwegian AquaGen Atlantic salmon breeding line. This population is the outcome of a breeding program that began in the 1970’s and stems from crosses of founder individuals that originated from 41 wild Norwegian rivers . PedigreSsal array (dbSNP accession numbers ss1867919552–ss1868858426) that is described in Barson et al. [The 2015 year-class individuals, consisting of 2104 grilse and 9062 non-grilse, were genotyped using a custom 50K SNP Affymetrix array developed for Atlantic salmon. The SNPs on the 50K array are a subset of those included on the 930K XHD Ssal array was filtered to 646,528 SNPs based on genotyping quality (categories PolyHighResolution and NoMinorHom), minor allele frequency (MAF) higher than 0.001, and correct allele segregation in family material of 840 individuals sampled from the AquaGen strain. All SNPs used in downstream analyses were positioned based on the Atlantic salmon reference genome (assembly ICSASG_v2) [The 930 K XHD SASG_v2) .Pre-phasing of the reference panel of 184 parents that were genotyped with the 930K SNP array was performed using Beagle 4 , 34. Inddiff-site-discordance function in vcftools [To assess the accuracy of imputation, we masked genotypes in the 184 parents that had been genotyped with the 930 K array. Masked genotypes consisted of 930K array SNPs that were not on the 50K array. In addition to the parents genotyped with the 930K array that served as a reference panel, individuals with masked genotypes were included as extra individuals in the imputation process described above. We then compared the imputed genotypes to the actual genotypes for the 184 parents and calculated the mean proportion of discordance between imputed and actual genotypes at each site using the vcftools .r2 > 0.8) were identified with PLINK 1.9’s indep-pairwise function [−8 for P-values was determined using the strict Bonferroni correction (α = 0.05/# of association tests). The linear regression beta coefficients and corresponding standard errors from BOLT-LMM were transformed to odds ratios using LMOR [We tested for associations of SNP genotypes with age at maturity of males from the 2015 year-class using the linear mixed model method BOLT-LMM , which aect SNPs . Since itructure . \documemination . SNPs infunction  using a function . The gening LMOR .cojo-slct function [cojo-slct function converts marginal effect sizes from a single-SNP association test to joint effect sizes by incorporating information of covariance among SNPs based on the LD structure. Joint effect sizes and P-values were calculated conditional on other SNPs, using a stepwise procedure, beginning with the top-associated SNP and iterating over all remaining SNPs [P-values and beta values obtained from the BOLT-LMM association test were analyzed with cojo-slct to identify independently associated SNPs. We considered a SNP to be independently associated with the maturation phenotype if the conditioned P value was less than 9.8 × 10−8. Linkage disequilibrium was calculated using the genotyping data  of all 11,166 male offspring.Multiple SNPs within a region can show a significant association due to LD around a causal SNP, but they are not all independently associated with the trait. One approach to account for this LD is to select only the top-associated SNP within the region, but this can fail to identify instances where true secondary signals exist within the region. To overcome this challenge, we performed conditional and joint analyses with the function  implemenfunction . The cojing SNPs . P-valueclosest implemented in BEDTools (v2.26.0) [https://www.ncbi.nlm.nih.gov/gene/) were used to determine the overlap between candidate genes for age at menarche in humans [Any SNP that was significantly associated with age at maturity in the conditional and joint analyses was assigned a candidate gene. A SNP that was located within a gene region was assigned to that gene, otherwise the nearest gene (within 50 kb upstream or downstream) was assigned. If a SNP was located within two overlapping genes, both genes were considered candidates. We used the function v2.26.0)  to assigv2.26.0) . RefSeq n humans , 29 and r2 > 0.8) with a significant SNP was excluded from both sets. We then calculated the variance component for each set of SNPs. The variance in phenotype explained by each variance component was then estimated.Variation in the maturation phenotype explained by the resulting set of significant SNPs was estimated using the Monte Carlo average information restricted maximum likelihood method for variance component analysis implemented in BOLT-REML . The modreml) method implemented in GCTA, which uses SNP-based relatedness estimates to calculate the proportion of phenotypic variance explained by a set of genotyped SNPs. The model was defined as follows:Heritability  . Due to P-values indicated genomic inflation that is characteristic of a polygenic architecture of the trait [Data on 11,166 males from the 2015 year-class imputed to 512,397 SNPs were used for association testing. The mean discordance between masked and actual genotypes for each chromosome ranged from 0.21 to 0.24  ranged from 1.01 to 3.07 (0- to 3-fold). The OR ranged from 0.11 to 0.99 (0- to 9-fold) for SNPs with a minor allele effect that delayed maturation   is a missense variant. An intron SNP on chromosome 16  tags the third gene, rora. Another intronic SNP on chromosome 22  tags the fourth gene, cntn4. The fifth gene, vgll3, on chromosome 25 is tagged by a downstream SNP  . Furthermore, the number of additional regions of the genome that were associated with maturation suggests that this trait has a more polygenic architecture than previously indicated. The 120 candidate genes identified here provide a valuable resource for furthering our understanding of maturation in both aquaculture strains and wild populations. This work broadens the scope of empirical examples for the genetic architecture of quantitative traits, which is valuable for developing analytical frameworks to understand the genetic architecture underpinning quantitative traits in nature.Our sample size was substantially larger than in previous studies on the genetic basis of maturation of Atlantic salmon and focused on a single aquaculture strain, thus improving power and minimizing confounding effects of population stratification. This approach enabled us to look beyond large-effect loci and allowed us to reveal the polygenic component of maturation in Atlantic salmon. We confirmed the importance of a large effect locus in the  studies , 15, butsix6, which has been increasingly recognized as a candidate gene for maturation in mammals [six6 region has been identified as potentially involved in local adaptation in numerous population genetic studies of Atlantic salmon. For example, the six6 region is under divergent selection among Atlantic salmon populations in North America [six6 gene has been associated with variation in run timing [six6 in local adaptation may explain why its association signal was lost after population stratification correction in Barson et al. [six6 gene in determining age at maturity may vary among populations, but it should not be ruled out as a candidate gene for maturation in Atlantic salmon.We observed a “new” highly significant SNP on chromosome 9, with an OR of 0.55, which remained significantly associated with age at maturity after correction for relatedness. This region was also identified in Barson et al. , but its mammals , 31. Pop mammals , but the mammals . Indeed, America , 51 and  America . In addin timing , 52 and n timing , and then timing . The potvgll3 is important for controlling maturation, which agrees with some previous studies [vgll3 region with maturation, which parallels that found in wild European salmon, we are not sure whether the same dominance pattern at the vgll3 gene as observed in Barson et al. [vgll3 gene, this conflict is resolved via sex-dependent dominance [In this study, we also found that  studies , 15, but studies , 19. Thiominance , 55. Chaominance , 57.six6 and vgll3 genes, we identified 114 other candidate loci with varying effect sizes, demonstrating a mixed genetic architecture that underlies maturation in Atlantic salmon . These 114 additional candidate loci included nine loci with moderate to large effects (1.75 < fold) across six chromosomes , and numerous smaller effect loci (1.75 > fold) distributed across 21 chromosomes  to large (two- to ninefold) effect sizes  may not be sufficient to prevent early maturation, as there are a number of other QTL that may modify maturation timing. In addition, considerations of this mixed genetic architecture are valuable for designing effective management and conservation strategies of wild Atlantic salmon. Maintenance of variation in age at maturity is of ecological, economic, and cultural importance [The maturation-associated QTL identified here provide a valuable contribution to our understanding of how aquaculture strains can respond to selection. Multiple smaller effect QTL indicate that there is an opportunity for fine-tuning of the trait via approaches such as genomic selection—an opportunity that would not exist if the trait was controlled by a single large-effect locus. Furthermore, this work suggests that a polygenic approach to selective breeding aimed at optimizing maturation timing may be beneficial. Focusing on just a few QTL  indicates the significance of the SNP after conditional and joint analysis. Beta and its corresponding standard error (Beta_SE) were estimated using BOLT-LMM and indicate the effect size of the minor allele. The odds ratio (OR) and its corresponding transformed standard error (SE_T) were calculated using LMOR. Table S3. List of all 512,397 SNPs in the final dataset. Output statistics from BOLT-LMM  and LMOR  & SE_T) are listed. The p-value indicates significance of the association test performed in BOLT-LMM using a non-infinitesimal model. Beta and its corresponding standard error (Beta_SE) were estimated using BOLT-LMM and indicate the effect size of the minor allele. The odds ratio (OR) and its corresponding transformed standard error (SE_T) were calculated using LMOR. Table S4. Estimated heritabilities from GCTA using the 50K array dataset. Estimates are given on the observed quantitative scale and when transformed on the liability scale assuming population prevalences of 1%, 2.5% and 5%.Additional file 2: Figure S1. Quantile–quantile plot of association P-values from BOLT-LMM showing residual inflation following linkage disequilibrium score regression calibration expected under polygenicity.


Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data.cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource.We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.The analysis code is available at  Genome-wide association studies (GWAS) are a powerful method to analyse common diseases in a large cohort. Taking advantage of affordable large-scale genotyping chip technologies, such studies are now routinely run across cohorts large enough to detect even weak associations between common variants and a phenotype of interest. There are now enough GWAS studies to warrant the existence of specialized databases, such as the GWAS Catalog   term suggestion service to map text where possible to ontology terms]. Search through public GWAS databases: GWAS Catalog, GRASP (r2 correlation >0.7.Linkage disequilibrium (LD) expansion. By default, the 1000 Genomes genotypes The  are usedClustering. Each significant SNP and its LD neighbours form a cluster. Overlapping clusters in the same study are merged.Alignment to known regulatory annotations, indicative of whether an SNP has any regulatory effect, in particular, the Ensembl Regulatory Build .cis-regulatory annotations, indicative of whether an SNP regulates a specific gene, in particular, GTEx  pair by summation of all the scores obtained in steps (4) and (5).The pipeline is coded in Python and was designed to be easily installed locally and run privately, whether against public databases or on a private dataset (provided as summary statistics in a tab-delimited file).The Open Target post-GWAS web browser allows users to browse through the pre-computed results of the pipeline run across all of GWAS Catalog. If searching from an SNP rsID or a gene symbol the browser displays either a genomic view or a table of associations see . If searWe ran our pipeline on all GWAS Catalog studies at the time . This comprised 2092 phenotypes and diseases, described in 3187 publications, and a total of 67 771 significant SNPs. After LD expansion, a total of 923 891 unique SNPs were analysed, each SNP being involved in 290 publications on average. The average run time for each study was 40 min.GWAS is a powerful approach to understanding disease mechanism but requires functional analysis to produce actionable results. The Open Targets post-GWAS pipeline facilitates this process, both through an automated tool and pre-processed results, freeing GWAS analysts from the laborious process of data integration.This work was supported by the Open Targets project and EMBL core funds to D.R.Z. and W.J.Conflict of Interest: none declared.


Analyzing the associations between genotypic changes and phenotypic traits on a genome-wide scale can contribute to understanding the functional roles of distinct genetic variations during breed development. We performed a whole-genome analysis of Angus and Jersey cattle breeds using conditional mutual information, which is an information-theoretic method estimating the conditional independency among multiple factor variables. The proposed conditional mutual information-based approach allows breed-discriminative genetic variations to be explicitly identified from tens of millions of SNP (single nucleotide polymorphism) positions on a genome-wide scale while minimizing the usage of prior knowledge. Using this data-driven approach, we identified biologically relevant functional genes, including breed-specific variants for cattle traits such as beef and dairy production. The identified lipid-related genes were shown to be significantly associated with lipid and triglyceride metabolism, fat cell differentiation, and muscle development. In addition, we confirmed that milk-related genes are involved in mammary gland development, lactation, and mastitis-associated processes. Our results provide the distinct properties of Angus and Jersey cattle at a genome-wide level. Moreover, this study offers important insights into discovering unrevealed genetic variants for breed-specific traits and the identification of genetic signatures of diverse cattle breeds with respect to target breed-specific properties. Manipulating domesticated animals by inbreeding and artificial selection has led to the development of a multitude of individual cattle breeds. As a result, many cattle breeds have become highly specialized for meat or milk production subsequent to strong genetic selection for these traits. In this context, investigating the associations between genetic variations and phenotypes has significant potential for understanding the heritability of complex traits in cattle. Moreover, such a study will identify distinct genetic factors that are likely to relate to breed-specific characteristics. Meat and milk yield are important economic factors for cattle production, and Angus and Jersey cattle are representative breeds for beef and dairy traits, respectively. The Angus breed has been intensively selected over the last few decades to reduce several recessive genetic disorders . Jersey ST) [Several studies have been performed to obtain evidence of selection on a genome-wide level in cattle ,5,6,7,8,ST)  and the ST)  are baseST)  and the ST)  to identST) ,13.In this study, we performed a comparative genome analysis of two cattle breeds, Angus and Jersey, using an information-theoretic approach. The proposed mutual information extractor based on conditional mutual information (CMI) is a principled data-driven method and minimizes the use of prior knowledge to identify discriminative SNPs determining breed-specific traits, such as meat and milk yield. Thus, our analysis focuses on systematically detecting the distinct SNPs that discriminate cattle breeds on a genome-wide scale. Together with detecting new genetic patterns from cattle genome, we found putative genes showing genetic signatures that may have contributed to the development of Angus- and Jersey-specific phenotypes, such as beef and dairy production.We collected whole-blood samples (10 mL) from 10 Angus cattle and 10 Jersey cattle. The Angus and Jersey samples originated from Chamtowoo , the Seoul Milk Cooperative , and the Korea Federation of Livestock Cooperatives . The blood samples were obtained from jugular veins. The DNA was extracted using the Wizard Genomic DNA Purification Kit . The collection of blood samples was performed in accordance with the guidelines given by the relevant agricultural institutions. All methods involving animal works were approved by the Institutional Animal Care and Use Committee of the National Institute of Animal Science (NIAS) in Korea under approval numbers NIAS-2014-093. We produced pair-end reads using an Illumina HiSeq 2000 and isolated DNA from whole blood using a G-DEXTMIIb Genomic DNA Extraction Kit  following the instructions of the manufacturer. We used the Covaris System to generate 3 µg of genomic DNA for generating the ~300 bp inserts. The fragments of the shared DNA were end-repaired, A tailed, adaptor-ligated, and amplified using the TruSeq DNA Sample Prep. Kit . Paired-end sequencing was performed on the Illumina HiSeq 2000 platform using the TruSeq SBS Kit v3-HS  with NICEM . Raw Sequence data are available from the National Centre for Biotechnology Information (NCBI) with the Bioproject accession numbers PRJNA318087 (Angus) and PRJNA318089 (Jersey).A per-base sequence was quality checked with fastQC, which calculates various quality metrics for raw reads. Next, the pair-end sequence reads were aligned to a bovine reference genome (UMD 3.1) using Bowtie2 . We usedp-value using Fisher’s exact test) > 200 were filtered. We used BEAGLE [We used open-source software packages for downstream processing and variant calling. Potential PCR duplicates were filtered using the “REMOVE_DUPLICATEDS = true” option in “MarkDuplicates” of Picard. We then used SAMtools  to generd BEAGLE  to imputInformation theory has provided a theoretical basis in many data analysis and machine learning tasks since it was proposed for communication and compression perspectives ,19. In px and y denote an SNP position and breed variables. Given an SNP position and breed variables, MI between the SNP position and cattle breed, I, is defined with the entropy of x and the conditional entropy of x and y as follows:X and Y denote the SNP position and the breed variable sets, respectively. When I is equal to 0, the SNP corresponding to x is independent of breed, and it means that the SNP does not influence breed in the aspect of a pairwise relationship. It is straightforward to apply MI to select significant genetic factors associated with cattle breed, and MI can be used to calculate the associations between the SNP positions and breeds. Assuming that genetic factors  and class variables  are random variables, MI can formulate the dependency of each genetic factor on cattle trait. In formal, let x1 and x2 are two SNP variables. Since MI is a metric for representing the independency between two variables, it is not trivial to characterize the effects of multiple factors on determining cattle breed. To solve this issue, in this study, we employed conditional mutual information (CMI) among two SNP variables and a breed variable as the criterion for extracting important SNP positions ,23:CI and call it the mutual information extractor in the rest of this paper. Mutual information extractors can be defined from the chain rule for mutual information:sn is the allele value of the n-th SNP xn. We are interested in CI is nonnegative, the same for mutual information, the mutual information extractor is equal to 0 when all three variables are conditionally independent. This property is suitable for detecting discriminative two-locus haplotypes influencing cattle breed. Thus, our method is effective in detecting the discriminative SNPs showing a high dependence between the haplotypes of two adjacent loci and breed.A mutual information extractor quantifies the associations between SNPs at two loci and breeds. Since We performed cross-population composite likelihood ratio (XP-CLR) and cross-population extended haplotype homozygosity (XP-EHH) tests for detecting the selective signatures in Angus and Jersey cattle. These two statistics are representative methods that use different criteria to detect the genomic regions under selection in populations. The XP-CLR statistic is able to detect regions of rapid changes in allele frequency at a locus with random drift . In contThe XP-CLR test is based on the detection of multi-locus allele frequency differentiation across populations, which is not as affected by ascertainment bias . We usedp-value < 1.0 × 10−3), and the selected genomic regions were annotated to the closest genes. In addition, the XP-EHH is designed to find alleles with an increase in frequency to the point of fixation or near-fixation in one of the populations by comparing haplotypes from two populations . It meanWe performed a comparative genome-wide analysis for identifying discriminative genetic variations between Angus and Jersey cattle using enhanced methods based on the information-theoretic approach .The genomes of the Angus and Jersey cattle were sequenced to approximately 15.79× coverage on average, with a total of 840,132,997,679 bp in 8,401,720,919 reads. The pair-end sequence reads were aligned with an average alignment rate of 97.54%, and the reads covered 98.82% of the genome across all of the samples on average . A totalPrincipal component analysis (PCA), a linear dimensionality reduction method, is broadly used to extract the fundamental structure of a dataset via the projection of individuals into a subspace spanned by the largest principal components ,25. In gp-value equal to 2.98 × 10−5). To overcome any bias caused by the small number of samples, we set a strict p-value for estimating statistical significance  compared with those used in other studies [We used a mutual information extractor for the Angus and Jersey breeds to identify the candidate SNPs with discriminative potential. We extracted 126,550 SNPs annotated to as 5874 genes . Table S studies .p-value to total the SNPs across all chromosomes. The result shows that the distribution of SNP extracted by mutual information extractor was different in Angus versus Jersey cattle. These distributions of the SNPs on each chromosome can provide the information on genomic locations that are likely to have received selection pressure and possess the ability to distinguish Angus and Jersey breeds. Hence, the regions containing the extracted SNPs can offer specific candidate areas for a fine-grained mapping of the genes that are important for discriminating between the two breeds.p-values. We found 75 genes that were enriched in a catalog of genes involved in lipid and intramuscular fat-related functions , and 90 genes that were overrepresented in a list of mammary gland/milk-related genes . These overrepresented genes are listed in Many of the genes mapped in the regions of the extracted SNPs were highly associated with functional genes for beef and dairy traits. To evaluate this finding, we collected a list of 185 lipid and intramuscular fat-related genes and 256 mammary gland/milk-related genes from the literature ,32,33,34The identified SNPs that overlap the lipid/intramuscular fat or milk-related genes had dissimilar patterns in terms of heterozygosity in the Angus versus the Jersey breeds . Table 1p-value levels. The candidate regions indicating strong associations between SNPs and phenotypic traits can be genetically important sites for Angus and Jersey selection. Moreover, these regions contain key genes associated with functional roles for beef and dairy production, and the identified genes were validated with a literature review and gene ontology (GO) analysis. We found discriminative SNPs based on conditional mutual information from the genomes of Angus and Jersey cattle with significant Several of the identified genes were strongly associated with lipid metabolism for meat and milk production. FASN, LPL, and SCD, important lipogenic enzymes, have been reported to have an influence on lipid deposition, metabolism, and synthesis and are involved in the mammary regulation of milk fat synthesis ,36,37. IIn addition, we found genes involved in adipogenesis and adipose cellular functions. EBF1 has been reported to inhibit the differentiation of intramuscular adipocytes by increasing anti-adipogenic factors . Recent Furthermore, we identified genes highly specialized for milk production and mammary gland-related processes. CSN1S1 and CSN3 are known to be key milk protein genes. These genes are closely related to milk yield parameters and milk quality, and many studies have reported that polymorphisms of casein genes influence milk composition and milk protein synthesis ,51,52. MIn domesticated animals, research has supported the importance of the conservation of specific alleles or genotypes . In partTo obtain insights into the biological processes involving the genes identified by conditional mutual information, we performed a functional analysis using ClueGO . We idenp-value (< 1.0 × 10−4)  based on our mutual information extractors that act as genetic markers on the mitochondrial genome, discriminating between Angus and Jersey cattle. These SNPs included five genes in a total of 33 SNPs marked with a significant  × 10−4) , and manThree of the five genes  are associated with the development of intramuscular fat content in muscles. In detail, ND1 is highly expressed in oxidative muscles with higher intramuscular fat content , and ND2p-value < 1.0 × 10−3)  . Of thesHH tests . Of thesHH tests . p-value < 0.05 (KEGG pathway analysis was performed by KOBAS 3.0, which is the latest web server for functional sets enrichment of genes . The pate < 0.05 . PIGC, wp-value < 0.05 (The 55 positively selected genes in Jersey compared to Angus were mainly involved in the nervous systems, immune systems, infectious diseases, signal transductions, environmental adaptation, endocrine systems, cell growth and death, and lipid metabolisms with a significant e < 0.05 . GNG11 aOur study provides insights supporting the identification of discriminative SNPs with breed-specific genetic variations from the whole cattle genome. The used mutual information extractor explicitly found several genetic variations influencing beef and dairy traits in Angus and Jersey cattle from large-scale genome data. Genotype profiles using these phenotypic traits from the cattle genome can be analyzed to identify the key functional genes involved in the formation of breed characteristics.In this study, we identified discriminative SNPs between Angus and Jersey breeds, and several analysis results based on the identified SNPs confirmed distinct differences between two cattle. The identified genes, including distinct SNPs, are associated with breed-specific functions, such as meat and milk production, respectively, and the contained SNPs showed clearly dissimilar genotypic patterns. Furthermore, several functional enrichment analyses revealed that distinct functional terms were enriched in the identified genes. Interestingly, the enriched GO terms in the fat-related genes were associated with lipid and energy metabolism, fat cell differentiation, and muscular/bone development-related terms, whereas the overrepresented terms in the milk-related genes were primarily involved in mammary gland development, lactation, and lipid metabolism. The lipid metabolism terms were enriched in both the fat- and milk-related genes because the traits for both meat and milk are closely associated with the lipid activity of cattle adipocytes . These fMoreover, we investigated the allele patterns of the identified SNPs associated with major functional genes. In particular, we found that casein genes clearly exhibited different breed-specific genotype profiles. The frequency of heterozygosity per SNP locus also showed clearly distinguishable patterns in Angus versus Jersey cattle. Interestingly, if the alleles of a candidate SNP are heterozygous in one specific breed, this heterozygosity does not occur in the same SNP locus of the other breed. This pattern indicates that genetic variation is associated with different properties depending on specific cattle breeds, which can influence the distinct traits of each individual. In addition, the distinct breed-specific allelic patterns of the candidate functional SNPs can provide insights for the discovery of new breed-specific hallmarks for discriminating between beef and milk cattle breeds. Furthermore, this approach allows us to understand the distinct genetic mechanisms underlying the formation of breed characteristics in domestic animals.Despite numerous studies on the cattle genome, the details of the genetic variations in the mitochondrial genome of cattle have not been much explored relatively. However, mitochondria are important for metabolism, nutrition, and health in humans and animals. Several mitochondrial DNA mutants are reported in connection with a variety of complex traits, such as human disease, longevity, and so on ,73. MoreST, iHS, XP-CLR, and XP-EHH, have been developed to detect the footprints left by selection in genomes. These methods rely on patterns of variation caused by the changes arising quickly in a population, such as allele frequency and haplotype length, to efficiently detect the genomic regions under selection. In addition, these methods use different time frames. In particular, XP-CLR, which utilizes changes in the phase of the allele frequency distribution between populations, has the power to identify older signatures compared with those based on extended linkage disequilibrium, such as XP-EHH [Finally, our analysis focused on identifying the genetic variations distinguishing each cattle breed and representing the functional traits of cattle from genome sequence data, minimizing the use of genetic assumptions. To compare our method with other statistical methods, we performed an analysis of the selection signatures in the Angus and Jersey cattle using two approaches with different theoretical bases. Many statistical approaches, including Fs XP-EHH .In this study, we analyzed using XP-CLR and XP-EHH with conditional mutual information for providing meaningful results. The selection signature results are not completely consistent, but we found several candidate genomic regions. Frequently, combining different approaches can be more powerful than a single test if each statistic provides distinct information about the selective signatures . GeneticThese statistical approaches are useful for detecting the genomic features that accompany the introduction of evolutionarily selective alleles in genome-wide studies, whereas our proposed method has the advantage of identifying potentially discriminative genetic variations in genome sequences. Our method can also assist with hypothesis formulation for genetic mechanisms in cattle, and thus, it provides a new approach for studying the distinct genomic regions related to breed-specific characteristics. Moreover, conditional mutual information can contribute to investigating the associations between distinct SNPs relevant to traits of interest and can considerably aid in understanding the evolution of cattle.Our results described that beef and dairy cattle clearly show genetic differences at a genome-wide level. These implicate that the identified genes based on the extracted SNPs using conditional mutual information can contribute to discriminating the phenotypes of Angus and Jersey cattle, including beef and milk-yield traits. Moreover, the found SNPs showed that they can be involved in different molecular functions and mechanisms influencing the phenotypic differences between cattle breeds of distinct economic significance. Our analysis may provide potential genetic markers for the improvement in livestock productivity, and show the value of comparative genome study in cattle breeds based on an information-theoretic approach.


The SNP solutions were estimated using a weighted single‐step genomic BLUP approach and imputed high‐density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305‐day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and −0.52, respectively. The results identified three windows on BTA14 associated with 305‐day milk yield and the parameters of lactation curve in primi‐ and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes.The aim of this study was to identify genomic regions associated with 305‐day milk yield and lactation curve parameters on primiparous ( The data were records of 21,068 lactations on primiparous  and multiparous  Holstein cows calving between 2010 and 2018, distributed among 118 herds in four countries . To describe the lactation curve, the MilkBot model developed by Ehrlich (Data in this study were collected as part of Work Package 4 from the Genotype plus Environment (GplusE) FP7‐Project  was defined as: tpeak=-bln2dbdb+1+c, and peak yield was calculated by substitution tpeak in the MilkBot equation. The 305‐day milk, the cumulative milk yield between calving and day 305 of the lactation, was calculated as:In which, 2.2n = 31,895) were genotyped using the BovineLD , BovineSNP50K  or BovineHD SNP panel . Genotypes of animals were imputed to HD with a reference population of 795 (46 M and 749 F) HD individuals using FImpute V2.2 software ; D is a diagonal matrix of weights for SNP variances ; M is the number of SNP, and ip is the MAF of ith SNP. The H matrix was built scaling G based on 22A considering that the average of the diagonal of G is equal to the average of the diagonal of 22A and, the average of off‐diagonal G is equal to average of off‐diagonal 22A. Analyses were performed using AIREMLF90 . The genetic analyses were carried out through the Average Information Restricted Maximum Likelihood (AIREML) method, using a linear single‐trait animal model (for measurements on the primiparous cows). The linear model included fixed effect of country and herd‐year‐season of calving, covariate effects of age at first calving in both linear and quadratic forms, and animal and residual random effects. The complete model can be represented as follows:n et al. .G=ZDZ′∑i2.4ga) and ungenotyped animals (na). The animal effects of genotyped animals are a function of the SNP effects, ag=Zu, where Z is a matrix relating genotypes of each locus and u is a vector of the SNP marker effect. The variance of animal effects was assumed as:D is a diagonal matrix of weights for variances of markers (D = I for GBLUP), σu2 is the genetic additive variance captured by each SNP marker when the weighted relationship matrix (G*) was built with no weight.The analyses were performed using the weighted single‐step genome‐wide association study (WssGWAS) methodology :u^=λDZ′G∗-1a^g; 5. to calculate the variance of each SNP:di=u^i22pi(1-pi), where i is the ith SNP; and 6. to normalize SNP weights to keep the total genetic variance constant; exit or loop to step 2. The effects of markers were obtained by three iterations from step 2 to 6. Accuracies of GEBVs were obtained using the following formula:σg2 is the additive genetic variance of the trait. The most accurate genomic evaluation was provided at iteration 2 which was used for estimating the percentage of genetic variance explained by ith genomic region as follow:ia is the genetic value of the ith region that consists of 50 consecutive SNP, σa2 is the total genetic variance, Zj is the vector of the SNP content of the jth SNP for all individuals, and u^j is the marker effect of the jth SNP within the ith region. The results were presented by the proportion of variance explained by each window of 50 consecutive SNP with an average of 165 Kb.The following iterative process described by Wang et al.  was used2.5r2) was used to map linkage disequilibrium (LD) using PreG SF90  in the UMD3.1 bovine genome assembly and Ensembl Genome Browser (http://www.ensembl.org/index.html) was used for identification of genes. The list of genes inside the chromosome segments that explained more than 0.50% of additive genetic variance for each trait, considered as positional candidate genes, was uploaded to Enrichr for GO enrichment analysis  enrichment analysis can be performed to investigate pathways and biological processes that are shared by candidate genes related to associated regions identified in GWAS  were 0.37 (0.01), 0.34 (0.01), 0.17 (0.01), 0.12 (0.01), 0.30 (0.01) and 0.19 (0.01), respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations (SD) of 305‐day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99 (0.001), 0.63 (0.001), 0.20 (0.001), 0.97 (0.001) and −0.52 (0.001), respectively. Estimated heritabilities (SD) were 0.26 (0.02), 0.23 (0.02), 0.13 (0.02), 0.05 (0.02), 0.21 (0.02) and 0.20 (0.02), respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for multiparous cows. Corresponding values for repeatability estimates were 0.42 (0.01), 0.34 (0.01), 0.15 (0.01), 0.05 (0.01), 0.29 (0.01) and 0.24 (0.01). Genetic correlations of 305‐day milk yield with peak yield, peak time, ramp, scale and decay for multiparous cows were 0.95 (0.005), 0.52 (0.005), 0.15 (0.005), 0.89 (0.005) and −0.49 (0.005), respectively.Descriptive statistics for 305‐day milk yield and lactation curve parameters in both primi‐ and multiparous cows are presented in Table 3.23.2.1General information about the results of ssGWAS for primiparous cows is described in Data 3.2.2General information about all results of ssGWAS for multiparous cows is described in Data 3.2.3ZNF7, ZNF34, RPL8, COMMD5 and C14H8orf33. Three LD blocks consisted of 7, 6 and 4 SNP were found in the identified region on BTA14 position 1.85–2.11 Mb, while the biggest LD block contains genes including MIR2309 and LOC786966. The biggest LD block found in the window identified on BTA14 in position 2.67–2.94 Mb, consisted of nine SNP and contains genes including LOC101905222, JRK and ARC (results not shown).In total, four LD blocks consisting of 15, 5, 3 and 3 SNP were found in the region identified on BTA14 position 1.48–1.68 Mb  to 0.34 (peak yield). The corresponding values in multiparous cows were 0.05–0.23, respectively, for ramp and peak yield. Previous researchers have reported that peak yield is more heritable compared with other parameters of the lactation curve  for all animals. Then, the GEBVs of the genotyped animals are used to estimate effects for the SNP. Finally, SNP effects are used to calculate the percentage of genetic variance explained by sets of consecutive SNP (SNP windows). In this study, the proportion of additive genetic variance explained by windows of 50‐adjacent SNP was calculated and the regions that accounted for more than 0.50% of the additive genetic variance were identified as potential QTL. The present study, identified three windows on BTA14  in both primi‐ and multiparous cows associated with 305‐day milk yield or lactation curve parameters. The identified regions overlap with QTL regions for multiple traits including milk yield and milk composition, somatic cell count, calving ease and average daily gain in cattle  approach described by Wang et al.  was usedMIR2309, MIR1839, OPLAH, HGH1, GRINA, PARP10, MAF1, SHARPIN, CYC1, GPAA1, MROH1, EXOSC4 and SPATC1. The association of genes including OPLAH, GRINA and MF1 with milk yield and lactation performance has been reported in previous studies  was used to map LD in the identified windows. In total, four LD blocks were found in the region identified on BTA14 in position 1.48–1.68 Mb. These strong LD blocks reflect the strong selection pressure towards allele fixation that has been carried out in this part of the bovine genome (Khatkar et al., The square of the correlation coefficient between two loci (5The objective of this study was to identify genomic regions associated with milk yield and the shape of lactation curve in Holstein dairy cows. The lactation curve parameters, the slope of the initial rise of the curve, peak yield, time to peak and the slope of the curve after peak yield were used as new phenotypic variables in the GWAS. Among the parameters of lactation curve, scale and peak yield showed the highest heritability and the highest genetic correlation with 305‐day milk yield which can explain the overlapping regions among these traits. The genomic regions were found to be associated with 305‐day milk yield, scale and peak yield in both primi‐ and multiparous cows. Although during the last decade many animals have been genotyped using high‐density SNP chip panels, no significant impact has been observed on genetic improvement programme (Erbe et al., The authors declare that they do not have any conflicts of interest. Click here for additional data file.


Oryza sativa) crop, becomes catastrophic under severe infestations and causes up to 60% yield loss. The highly disastrous BPH biotype in the Indian sub-continent is Biotype 4, which also known as the South Asian Biotype. Though many resistance genes were mapped until now, the utility of the resistance genes in the breeding programs is limited due to the breakdown of resistance and emergence of new biotypes. Hence, to identify the resistance genes for this economically important pest, we have used a multi-parent advanced generation intercross (MAGIC) panel consisting of 391 lines developed from eight indica founder parents. The panel was phenotyped at the controlled conditions for two consecutive years. A set of 27,041 cured polymorphic single nucleotide polymorphism (SNPs) and across-year phenotypic data were used for the identification of marker–trait associations. Genome-wide association analysis was performed to find out consistent associations by employing four single and two multi-locus models. Sixty-one SNPs were consistently detected by all six models. A set of 190 significant marker-associations identified by fixed and random model circulating probability unification (FarmCPU) were considered for searching resistance candidate genes. The highest number of annotated genes were found in chromosome 6 followed by 5 and 1. Ninety-two annotated genes identified across chromosomes of which 13 genes are associated BPH resistance including NB-ARC  domain-containing protein, NHL repeat-containing protein, LRR containing protein, and WRKY70. The significant SNPs and resistant lines identified from our study could be used for an accelerated breeding program to develop new BPH resistant cultivars.Brown planthopper (BPH), one of the most important pests of the rice ( Nilaparvata lugens (Stål)), an outbreak-prone insect pest, causes heavy yield loss in rice (Oryza sativa) production ecologies. It has become a serious economic threat to rice production in several parts of Asia including India due to the rampant use of nitrogenous fertilizers and prophylactic insecticide applications. The famine death of one million people due to a severe attack of BPH was reported in Japan during 1732 [Brown planthopper (BPH) , 34 Small brown planthopper (SBph) resistance genes and about 100 quantitative trait locus (QTLs) were identified from indica sps., and other wild species of rice [Resistant rice cultivars with different stress tolerance systems reveal distinct resistant phenotypic characters to BPH, and consequently, its succession diversifies BPH populations . So far, of rice ,14,15,16 of rice . The heaindica, japonica MAGIC, MAGIC indica plus, and Global MAGIC [Multi-parent advanced generation intercross (MAGIC) is developed by several generations of inter-mating of multiple founder parents . Increasal MAGIC ,22 to maindica population to identify the marker–trait associations through multiple GWAS models for BPH resistance using high-resolution SNP data. Six GWAS models were employed for the identification of significant SNPs and the results of the models were compared. The GWAS approach identified quantitative trait nucleotides (QTNs) associated with resistance genes and the results are discussed with reference to their utility in the rice breeding programmes.So far, no previous report is available for BPH resistance through the genome-wide association mapping (GWAS) approach in rice using high-density single nucleotide polymorphisms (SNPs). Here, we used a MAGIC indica population, hereafter called the RiMi panel, used in this study was developed at IRRI, Manila, Philippines [indica founders, namely, Fedearroz 50, SANHUANGZHAN-2, IR64633-87-2-2-3-3 (PSBRc82), IR4630-22-2-5-1-3, IR45427-2B-2-2B-1-1, Samba Mahsuri +Sub1, IR77298-14-1-2-10 and IR77186-122-2-2-3 (PSBRc 158) in a half-diallel mating design. A set of 28 F1s were selected to develop 210 4-way crosses by avoiding duplicate crosses. The 4-way crosses were intermated two times and further selfed continuously until the S11 stage (The MAGIC lippines . The pop11 stage . A set o11 stage .The Standard Seed box Screening Technique (SSST)  was usedAround 15 sprouted seeds in each of the 391 RiMi panel lines were seeded in a standard seed tray with a size of 60L × 45W × 10H cm, which was filled with fertilizer-enriched puddled soil. In each tray, 20 MAGIC lines were sown by keeping two genotypes per row with an inter-line spacing of 3 cm. Susceptible check TN1 was covered in the borders and the resistant checks PTB33 and RP2068 were used in the middle rows of the seed tray. The trays with three-day-old seedlings were placed in big plastic trays containing water of 5 cm depth. The phenotyping was started by introducing six to eight second to third instar nymphs into 12 days-old seedlings . The infhttp://snpseek.irri.org/_download.zul) were curated by GBS pipeline using Tassel 3.0.169 [The MAGIC panel was genotyped by the Genotyping-By-Sequencing (GBS) approach  through  3.0.169  by filte 3.0.169 . A set oThe curated 27,041 SNPs and BPH phenotypic data of the RiMi panel were used for GWAS analysis using genetic association and prediction integrated tools (GAPIT) developed by . GWAS waPlants are challenged by numerous herbivorous insects invade their natural environment. In consequence, they express diverse substantial and induced defense mechanisms, safeguarding themselves from herbivore attacks . The firindica and other wild species. Chromosome 4 has 12 genes that are clustered in three regions , and bph22(t) in a 4.1–8.9 Mb region between markers RM8212 and B44; and Bph6, bph18(t), Bph27, Bph27(t), and Bph34 in a 19.1–25.0 Mb region between markers RM16846 and RM6506), chromosome 12 L has eight genes  that are clustered together in a 19.1–24.4 Mb region between markers RM7102 and B122, five genes are on chromosome 6S  in a 0.2–1.7 Mb region between markers S00310 and RM8101, and chromosome 3 has five genes (Bph13 and bph19(t) on 3S [Bph11, Bph14, and Bph31 3 L) [So far, around 38 genes were mapped for BPH by several experiments through biparental mapping approaches. Almost 80% of the resistance genes were mapped in four major clusters on the rice genome using the mapping populations of t) on 3S , and Bphh31 3 L) ,46,47,48Controlling the pest becomes ineffective since surface spray of pesticides could not reach the base of the infestation point. The rampant use of chemicals results in the development of resistance against many insecticides and the evolution of new biotypes. Recently,  reportedScreening at the seedling stage is an effective high-throughput method for the rapid and efficient screening of large germplasms as field screening offers inconsistent results due to improper control over the experimentation. A major difficulty in the screening of rice genotypes for BPH tolerance under field conditions is the lack of efficient screening techniques, problems associated with biotype selection, and environmental factors on the uniform density of the insect population. In SSST-based screening, a reliable phenotyping technique, phenotyping was done when all the TN1 (susceptible check) seedlings were dead. The infestation of the RiMI lines was scored on a 0 to 9 scale . The scaBPH resistance scores in both the years revealed a significant variation available for insect response in the RiMi panel . Lines MThough 38 genes were reported for Biotype 4 from landraces and wild species ,49,51, rThe 391 RiMi lines used in the study were selected for superior agronomic and grain quality traits with BPH resistance. Better combinations of segregants for these traits were achieved in the resultant population since MAGIC provides more rounds of recombination by its design and makes it possible to break genetic drag and leads to novel genetic rearrangements with greater genetic diversity ,52,53.Among the RiMi lines, the identified MIB_4393, MIB_3693, MIB_4077, and MIB_4570, which are similar to PTB33 in resistance, could be used as new sources of resistance for developing varieties without genetic drag in the breeding programmes.A total of 27,041 polymorphic SNPs developed in the RiMi panel were used in this experiment. The SNP set with the MAF = 0.05 and with <30% missing data, was distributed across 12 chromosomes, with an average of 2253 SNPs per chromosome. The highest number of SNPs was detected on chromosome 1 (3495 SNPs) and the lowest on chromosome 9 (1386 SNPs). The grouping pattern of the RiMi panel was inferred using principal component analysis (PCA). The RiMi panel explained the uniform distribution of alleles without any population structure . Principindica plus population of rice, and 57 significant genomic regions for agronomic and bio-fortification traits were detected [MAGIC populations developed in several crops including rice serve as potential genetic resources for the identification of QTLs. In the global MAGIC population of rice, 38 and 34 QTLs for yield and grain quality traits were identified through GWAS and interval mapping, respectively . GWAS andetected .Here, we conducted the GWAS analysis in the MAGIC population using four single-locus models  and two multi-locus models (MLMM and Farm CPU) to identify the common SNPs associated with BPH resistance . Each mop value <10−3 to identify significant SNPs across models and, using this threshold, a total of 248 associated SNPs that were significant for BPH resistance were detected for the mean BPH data. These SNPs were distributed on chromosomes 1, 2, 4, 5, 6, 7, 10, 11, and 12. The order of SNPs detected by various models is as follows: GLM (213), Farm CPU (190), MLMM (167), SUPER (162), MLM (91) and CMLM (80) (We considered the MLM (80) .We have identified a maximum of 213 SNPs by employing the GLM model mapped on chromosomes 1, 2, 4, 5, 6, 7, 10 and 12. GLM detected the highest number of associations since it is the less stringent model among all, and hence, it is prone to detect more false positives. Both the MLM and CMLM methods identified the lowest number of SNPs  compared to other models. CMLM is an improvised MLM procedure by way of grouping the markers while a generating kinship relationship that could have reduced SNP associations over MLM. Among the single locus models used in this study, SUPER was the only method that could identify the marker–trait associations on chromosome 11 in addition to chromosomes 1, 2, 4, 5, 6, 7, 10, and 12. SUPER uses a subset of markers to compute the kinship, hence the statistical power is improved over MLM .The MLMM is one of the multi-locus models, and it uses forward selection and backward elimination of SNPs based on heritable variance. This method identified 167 marker-trait associations that are distributed over chromosomes 1, 2, 4, 5, 6, and 10. Farm CPU is an improvised approach by combining the advantages of MLLM and SUPER to achieve better statistical power and computational efficiency . A totalSince FarmCPU is the comprehensive model among all, we considered the results obtained by FarmCPU for identification of resistance genes associated with the SNPs. The identified 190 significant SNP loci located on chromosomes 1 (20 SNPs), 2 (20 SNPs), 4 (11 SNPs), 5 (39 SNPs), 6 (88 SNPs), 7 (5 SNPs) and 10 (7 SNPs) were used for searching genes in their vicinity . The ManBph13 (t) gene reported in O. eichingeri [Four important SNP clusters were identified on chromosome 1 and 2, where 20 SNPs were distributed in these regions . One cluchingeri   and 23.0 to 24.0 Mb (long arm) were identified on chromosome 5. It is to be noted that the long arm of chromosome 5 was earlier mapped for Qbph5 , indicatChromosome 6 showed one major cluster of SNPs on short arm from 6.0 to 9.0 Mb with 86 SNPs. Bph32 was identified and located 4 Mb away from this region . Five SNLOC_Os06g11010, LOC_Os06g12160, LOC_Os06g12360, LOC_Os06g12610, LOC_Os06g12870, LOC_Os06g13600, LOC_Os06g14510, LOC_Os06g15750 and LOC_Os06g15820 represent stress responsive proteins, eukaryotic aspartyl protease domain containing protein, AAA-type ATPase family protein, pentatricopeptide, auxin efflux carrier component, leaf senescence related protein, HEAT repeat family protein, glucose-6-phosphate isomerase, NB-ARC domain containing protein, and NHL repeat-containing protein, respectively. Twenty-one casual QTNs  were associated with these genes. A gene LOC_Os06g18600 associated with S7_10995384 was also found to encode a stress responsive gene OsFBL37-F-box domain and LRR containing protein. Bph9, Bph18 and Bph26 on chromosome 12 [Bph14 on chromosome 3 [The genes associated with BPH resistance were extracted from the annotated gene search in the Rice Genome Annotation Project (Michigan State University Rice Genome Annotation Project (MSU-RAP) database (Osa1) Release 7), to identify the novel candidate genes. The results are given in osome 12 ,64,65 anOsWRKY13, mediated the gene regulation through Salicylate and Jasmonate-dependent signaling pathways in rice disease resistance [Bph14 resistance gene is associated with WRKY46 and WRKY72 by protecting them from degradation, thus providing resistance [RLCK281 and callose synthase genes.On chromosome 5, eight significant QTNs, S5_23249078, S5_23249119, S5_23249125, S5_23249237, S5_23249605, S5_23310970, S5_23312204 and S5_23314218 are mapped in the genic locations of LOC_Os05g39590 and LOC_Os05g39720 encoding AP2 domain containing protein and WRKY70. These two transcription factors are involved in a stress tolerant mechanism that could contribute to BPH resistance. The AP2 (APETALA2)/EREBP (Ethylene-Responsive Element-Binding Protein) family has an important regulatory function in environmental stress tolerance, that regulates diverse processes of plant development and metabolism. It mainly regulates ABA dependent or ABA independent stress responsive pathways and is also known to be involved in biotic stress response through the Jasmonic acid pathway and in abiotic stress response through the ABA biosynthetic pathway . WRKY, asistance . The Bphsistance . The aboLOC_Os01g24690 is associated with QTN S1_13898444 among 12 annotated genes identified on chromosome 1, encoding 60S ribosomal protein L23A. Ribosomal proteins have multiple functions and differentially regulate at the time of stress conditions [A gene nditions . The hignditions  and the Allelic status of the significant QTNs that contribute to resistance in the agronomically superior RiMi lines  are presented in Over the past few years, substantial developments have been made in rice molecular breeding for high yield, biotic and abiotic stress tolerance, and grain quality, etc. Molecular breeding for BPH pest resistance in rice was, however, constrained because of the complexity of interrelatedness among BPH and rice. BPH is one of the most economically important insect pests of rice. Severe infestation in high-input rice ecologies results in major yield loss in susceptible varieties. Host-plant resistance is the best way to protect the environment and preserve the ecological balance of rice growing areas. The MAGIC panel identified 59 common SNPs on different rice chromosomes through six association mapping models. The gene search associated with QTNs revealed 13 stress tolerance genes that would play a significant role in BPH resistance. The 31 significant QTNs associated with the resistance genes could be used in accelerated marker-assisted breeding to develop BPH resistant varieties. Identified BPH resistant lines could be directly used in varietal release programs based on their agronomic performance and used as donors for BPH resistance to develop new resistant cultivars.