Coffea canephora L. germplasm to breed new varieties with tolerance or resistance to biotic and abiotic stresses (including moisture stress and pest and disease stresses such as coffee berry borer and rust) and for high yield and improved cup quality. The present study validated 192 published single nucleotide polymorphism (SNP) markers and selected a panel of 120 loci to examine parentage and labeling errors, genetic diversity, and population structure in 400 C. canephora accessions assembled from different coffee-producing countries and planted in a field gene bank in Ghana. Of the 400 genotypes analyzed, both synonymous  and homonymous  mislabeling were identified. Parentage analysis showed that 33.3% of the progenies derived from controlled crossing and 0% of the progenies derived from an open pollinated biclonal seed garden had parentage (both parents) corresponding to breeder records. The results suggest mislabeling of the mother trees used in seed gardens and pollen contamination from unwanted paternal parents. After removing the duplicated accessions, Bayesian clustering analysis partitioned the 270 unique genotypes into two main populations. Analysis of molecular variance (AMOVA) showed that the between-population variation accounts for 41% of the total molecular variation and the genetic divergence was highly significant . Taken together, our results demonstrate the effectiveness of using the selected SNP panel in gene bank management, varietal identification, seed garden management, nursery verification, and coffee bean authentication for C. canephora breeding programs.Accurate genotype identification is imperative for effective use of  Coffea. Of the approximately 124 species in the Coffea genus  and C. canephora Pierre ex A. Froehner  are cultivated the most globally. The two species make up approximately two thirds and one third of the total global production, respectively (C. canephora coffee is a diploid species (2n = 2x = 22) and mainly self-incompatible  compared with Arabica coffee, which is an allotetraploid (2n = 4x = 44) and largely self-compatible (autogamous). C. canephora has increased productivity and higher caffeine content than C. arabica. Additionally, C. canephora tends to grow better at lower altitude and is more resilient to pests, diseases, and drought compared with C. arabica  in anephora . The Conanephora . A subst and CAR . A possiC. canephora, largely due to ecological adaptation reasons. At present, the exact genetic diversity group (Congolese or Guinean group) to which the CRIG C. canephora accessions belong is unknown. Although the genetic base is unknown, the collection at CRIG is made up of introductions made in 1977 and 1992 from the Cote d’Ivoire . Gene bank activities may be affected by improper identification, redundancy, and other errors because the trees in the germplasm plots were collected, exchanged, or otherwise obtained at different times with limited passport data on their correct identity. Moreover, limited knowledge on the level of genetic diversity of the C. canephora germplasm hinders their effective utilization in a breeding program , also known as microsatellite markers, have been developed and are currently available to the coffee research community . The utirization , developrization , and iderization . Althougnotyping .C. canephora, such as diversity analysis and molecular characterization of germplasm  markers are the most abundant marker type and suitable for analysis on a wide genomic scale  in breedermplasm , marker-ermplasm , and genermplasm . Recentlelopment .C. canephora germplasm accessions assembled from different sources over different time periods at the Cocoa Research Institute of Ghana. Meeting the set objectives should provide us with a better assessment of the implications of genetic integrity in a C. canephora breeding program.The objective of the present pilot study was to assess a small subset of SNP markers for their effectiveness in germplasm identification, diversity analysis, and parentage verification in 400 C. canephora trees representing 294 C. canephora clones or families  were derived from controlled crossing (full-sib), whereas one (E139 × C134) was derived from an open pollinated biclonal seed garden  located at the experimental fields of CRIG, Tafo . Each tree was tagged with a plastic label, and young leaves were collected into labeled paper envelopes prior to leaf disk punching and further analysis.A total of 400 families  assembleTM mini plant kit. SNP genotyping was done using the Kompetitive allele-specific PCR (KASP chemistry) . The KASParTM Genotyping System from LGC Biosearch Technologies is a competitive allele-specific dual FRET-based assay for SNP genotyping  were obtained through controlled manual crossing, whereas the third (E139 × C134) was derived from an open-pollinated biclonal seed garden. Data on stem diameter growth of trees of the various families in these trials commenced 3 months after planting. Stem diameter (mm) was measured at 15 cm above ground level using electronic calipers. Growth data (during the prebearing phase) and yield data on the family H234 × H207 are presented in detail to show how mislabeling can negatively affect agronomic performance of coffee trees.r2 ≥ 0.5 were also removed. The filtered data were then used in subsequent analysis.Raw data for the SNP loci and sample calls were organized in Microsoft Excel 2007. Quality control was performed using the Quality Assurance Module from SNP Variation Suite version 8 . Any SNP having more than a 10% no-call rate was removed from the data set. SNPs that were in linkage disequilibrium (LD) with each other at Is), observed heterozygosity (HO), expected heterozygosity (HE), and inbreeding coefficient (FIS). The program CERVUS was used to compute the polymorphic information content (PIC) of each SNP and the probability of identity (PID) of the SNPs. The PID among siblings (PID-sib) is defined as the probability that two sibling individuals drawn at random from a population have the same mutilocus genotype  based on the method of C. canephora genotypes of the same group. The Lynch and Ritland method (2x option) assumes values from −1  to 1 . Values close to 0.5 indicate a full-sibling relationship, and a value of 0.25 indicates a half-sibling relationship. To calculate pairwise relatedness among the various C. canephora groups, we used the allele frequency of the total population of assembled genotypes with 9999 permutations in the pairwise relatedness function in the GenAlEx 6.501 program.Pairwise relatedness  ranging from 1 to 15, each consisting of a burn-in of 100,000 iterations and 200,000 Markov chain Monte Carlo repetitions. The most likely number of clusters was detected using the methods of Earl and VonHoldt, 20121). The program CLUMPP 1.1.2  and pairwise Fst were then used to test the results of STRUCTURE using GenAlEx 6.501 , Is, HO, HE, PIC, and FIS, of the retained 120 SNPs are presented in Out of the 192 SNPs used to genotype the coffee accessions, a total of 187 SNPs successfully amplified . Data filtering was further performed to remove SNPs in LD at C. canephora genotypes were identified following the pairwise multilocus matching analysis. The probability of two individuals having the same multilocus genotype (probability of identity among siblings) based on the 120 SNP loci was 1.8 × 10–19, which indicates that there is almost a null probability of finding two individuals with the same genotype in the population. The average PID-sib of the 120 SNP panel was 0.71 . Synonymous and homonymous mislabeling among the was 0.71 .Of the 400 genotypes analyzed, the frequency of synonymous mislabeling (trees with the same SNP profiles but different names) was 12.8%, whereas the frequency of homonymous mislabeling (trees with the same name but different SNP profiles) was 5.8%. Based on breeders’ records, the synonymous mislabeling was mainly in two forms: (a) trees/clones have been mislabeled at CRIG in the breeding process over time, leading to duplications, and (b) the same clone was introduced from a particular country at different times with different names.Parentage analysis was conducted to identify parent pairs for each tree belonging to two families obtained through controlled crossing and one other family derived from an open-pollinated biclonal seed garden. Generally, among all 32 progeny trees analyzed from the three families, female parental clones were assigned at a high frequency and confidence in all families. However, of 12 progenies derived from controlled manual crossing, only four had parentage (both parents) corresponding to breeders’ records . Six outC. canephora trees, six progenies derived from the cross H234 × H207 were used. Of the six progenies, only four had the expected parentage of H234 × H207. Parentage analysis revealed two were of the pedigree H234 × MMC26 and H234 × A129 (P < 0.05) larger in terms of stem diameter over a 4-year period and produced more berries (P < 0.05) during the matured stages of growth than those with either MMC26 or A129 as the male parent  could be classified into the first genetic cluster, whereas 103 samples (37.0%) could be classified into the second cluster. There were 62 admixed samples (23.0%) that had Q-value < 0.7 in either cluster  was two  based on cluster . The fir cluster .P < 0.001; P < 0.001). The distribution of pairwise relatedness (r) within clusters was similar for clusters 1 and 2. Trees within the admixture group had the least pairwise relatedness of r = 0.03, and the most related pairs were within the Vietnam (cluster 1) group  group . The assK plot also showed that the next highest delta K value is K = 3, the studied C. canephora germplasm could also be classified into three genetic clusters. Under this scenario, each germplasm source/origin was represented by accessions belonging to the three genetic clusters except germplasms from Vietnam, which all belonged to one genetic cluster  selected from high-yielding families in hybrid trials. The identification of mislabeling in the current study emphasizes how a breeding program could benefit significantly from establishment of measures to prevent mislabeling of trees and pollen contamination to ensure varieties replicate expected field performance. The average PID-sib of this SNP panel was 0.71, which was slightly inferior to the average PID-sib (0.519) among the 40 SSR markers developed for C. canephora  and homonymous (trees with the same name but different SNP profiles) mislabeling, were identified. Mislabeling identified in the present study is not unique to calyptus , Sitka scalyptus , oil palcalyptus , and caccalyptus  as well calyptus  and ensecalyptus  crop genA majority of the mislabeling identified in the present study is the synonymous type, suggesting erroneous labeling of plants in the nursery prior to field planting or likely from wrong replacement over the course of time of dead stands in the field with plants meant for other plots. Also, the synonymous mislabeling identified could be a result of introduction of the same clone but with different names from the same or different countries at different time periods. This was mainly the case for introductions from Togo. The lack of passport data for these introductions, however, makes verification of this assertion challenging. The homonymous mislabeling, on the other hand, could mainly be due to erroneous labeling of ramets  at the nursery before field planting.C. canephora in farmers’ plantations established using hybrid seed varieties. The effect of erroneous labeling is likely proportional to the level of mislabeled maternal or paternal trees. Obviously, use of mislabeled clones in variety development leads to poor performance of coffee hybrids in farmers’ plantations with consequent effects on farmers’ attitude toward adoption of recommended varieties. The ability, therefore, to have the genetic identity of parental clones verified or authenticated routinely using molecular markers is very likely to be an important requirement for significant investments in coffee breeding and to ensure that expected varietal performance is replicated in farmers’ farms.Furthermore, synonymous mislabeling was identified in the biclonal seed garden at CRIG, Tafo, where the female parent was largely identified to be a different clone meant for a different biclonal seed garden. This also suggests erroneous labeling in the nursery prior to field planting or replacement over the course of time of dead stands in the field with plants meant for different biclonal seed gardens. In Ghana, hybrid varieties derived from crossing between clones with good specific combining abilities in biclonal seed gardens are recommended for cultivation . TherefoC. canephora accessions likely differ by at least six loci in their multilocus SNP profiles. There are 19 near matched groups (including 60 accessions) that have fewer than five mismatched loci and are all likely synonymous groups too. This suggests that 41 accessions (out of the 60) are duplicated accessions and need to be removed from the collection. Nonetheless, repeated genotyping needs to be applied on these near-matched samples to confirm the synonymous mislabeling as was demonstrated on cacao (It is noteworthy that the synonymous mislabeling (or duplicates) we report here represents the lower bound of the existing duplicates in the collection. This is because we only took account of the fully matched genotypes, whereas the samples with a small number of mismatched loci were not considered as confirmed duplicates. In practical application, because genotyping errors always occur, a threshold of mismatches needs to be developed as reported by on cacao .C. canephora improvement program at CRIG. Therefore, among the set of progenies derived through manual pollinations, the wrong pedigree of some progenies is very likely as a result of errors made by manual pollinators during the pollen-labeling process prior to physical crossing or wrongful labeling of parental trees in the field. Detection of pedigrees that do not match breeders’ records is not unique to our study. Similar to our findings, C. arabica varieties showed that, for seeds that have not moved through formal pathways, pollen contamination may have caused genetic drift, which has resulted in much less genetic conformity (39%) in an important Arabica coffee variety, Geisha.As stated under the results section, parentage of the larger proportion of coffee seedling progeny were observed as not conforming to breeders’ records. This anomaly may be due to mislabeling, pollen contamination, or extraneous seeds in the seed lot prior to sowing. By far, wrong pollen use or extraneous seeds in the seed lot prior to sowing appear to account the most for the source of pedigree error in the set of progenies analyzed from the controlled manual crosses. This assertion is supported by the predominance of errors associated with the male parents. Usually, skilled manual pollinators carry out a number of crosses each season to generate progenies for evaluation in our C. canephora seed gardens, which, in this case, needs to be far more than 200 m away from any coffee planting, particularly that C. canephora is predominantly an outcrossing crop. Considering the inherent difficulties in generating hybrid seed through manual pollinations (due to the short window within which flowers are opened and receptive for pollination), biclonal seed gardens are the more practical means for hybrid seed development in C. canephora.For 20 progenies derived from an open-pollinated biclonal seed garden, none had pedigree (both parents) matching that of breeders’ records as expected. Only four had one parent matching expectation with 3 and 1 matching maternal and paternal parent expectation, respectively. A majority of the probable paternal parents identified from the progenies derived from the open pollinated biclonal seed garden were clones from a distant germplasm plot, which was approximately 200 m away. This observation, however, is not surprising as several studies have shown that pollen from coffee could be carried by pollinating agents (wind or insects) over up to a 2 km distance . This haThe performance of one family analyzed in the current study shows the negative effect of erroneous labeling or pollen contamination on variety development as well as replication of expected variety performance in farmers’ plantations. Two out of six progenies from the intended H234 × H207 cross had the paternal parents wrongly assigned. Both of these parents with pedigrees E139 × E186 (for H234) and A213 × A115 (for H207) were high-yielding ortets (progeny selected for clone development) selected from high-yielding families in previous hybrid trials at CRIG (unpublished data). Given the lack of relationship between the two parental clones (judging from their respective pedigrees), it was expected that they might combine well to give rise to high-yielding progenies. Superior performance was largely observed in the four progenies that had both maternal and paternal parentages correctly assigned compared with the two progenies that had the wrong paternal parents, either MMC26 or A129. In the present study, significant differences were observed in the plots planted with the H234 × H207 family. Our analysis shows that the differences observed were due to the non-uniformity of parentage of the progeny trees in this family. Progeny trees that had the correct parentage (H234 × H207) were more vigorous and yielded 2.6 kg wet cherry/tree more than those that had MMC26 or A129 paternal parentage (through pollen contamination or tree mislabeling). The higher vigor of the progenies that had the correct parentage (H234 × H207) is likely due to the specific combining ability of the parental clones. This early vigor in tree crops has been used as a selection criterion for productive varieties, and parents are, therefore, specifically selected for this trait in tree crop breeding programs . SimilarThe number of progenies assessed for the cross (H234 × H207) in the parentage and pedigree verification analysis is small (six); however, the observed yield loss of 2.6 kg wet cherry/tree from the erroneous parentage is suggestive of a negative impact on productivity due to use of mislabeled clones in coffee cultivar development. Also, a possible increase in inbreeding and loss of diversity could be reduced significantly through such routine parentage analysis in breeding programs.C. canephora improvement in Ghana. To eliminate this problem in biparental C. canephora seed garden establishment, it is important that due diligence at the nursery is done to ensure that generated clones are labeled correctly. Furthermore, replacement of dead stands in the field should be carried out carefully with the same clones as the dead ones. Additionally, to reduce the chance of pollen contamination and its negative impact on agronomic performance of varieties, it is important to incorporate SNP fingerprinting in C. canephora breeding programs. Although SSRs for parentage analysis is well established and widely applied because of their hypervariable nature conferring sufficient statistical power, SNP markers have advantages, such as easier automation and scoring, and are, thus, suitable for high-throughput application at a lower cost. As recently reviewed by It is important that significant steps are taken to correct erroneous labeling of trees and wrong parentage of progenies to guarantee anticipated advances from investments in K calculated by Evanno’s method (Q = 0.70) were highly differentiated . The significant divergence was further supported by AMOVA, which showed a large proportion of among-population molecular variance (41.0%). The existence of a significant substructure in C. canephora germplasm is reported from different studies  as revealed by the r = 0.48) compared with other clusters. This suggests the materials introduced from Vietnam were full-sibs (siblings that share the same parents)  to evaluate the level of relationship due to co-ancestry among individuals of a breeding population, (2) to study and understand the pedigree structure of offspring derived through crossing, and (3) for genomic prediction . Lynch aparents)  and furtC. canephora genotypes.Results of our pilot study demonstrate the effectiveness of using a small SNP panel in gene bank management, varietal identification, and seed garden verification. However, more SNP markers are needed for the selection of an optimum genotyping panel. We are currently validating more SNPs on the same germplasm collection and eventually will select a core set of SNPs and recommend them for the international coffee community. The selection criteria will be based on pairwise LD, MAF, and PIC. Moreover, because a large number of interpopulation (and subpopulation) hybrids and their parental clones are maintained in gene banks , ancestrC. canephora breeding program. The set of 120 SNP markers used in this study are effective to characterize the existing diversity of C. canephora germplasm collections in Ghana, judging from their high statistical significance for individual genotype identification, parentage analysis, and dissection of population structure. The 120 SNPs used is not our final recommendation genotyping panel. We are currently validating more SNPs on the same germplasm collection and eventually will select a genotyping panel for coffee varietal identification. To the best of our knowledge, this study is the first to apply array-based SNP genotyping for molecular characterization of C. canephora germplasm collection. The understanding of genetic diversity and population structure in coffee-breeding programs provides opportunities for selection of positive variants for direct cultivation or as parental material for recurrent crossing and further selection.Through this pilot study, we demonstrate that SNP markers are effective tools in fingerprinting germplasm collections from different sources to guide germplasm conservation and variety development in a C. canephora breeding as regards developing improved varieties that would replicate performance in farmers’ fields. To this end, the set of markers reported in the present study are sufficient for assignment of parentage in C. canephora given the high total exclusion probability of 99.997 × 10–2 obtained from the parentage analysis. The result also suggests that this array-based SNP genotyping can be efficiently used to improve our understanding of the mating system, pollen dispersal, and seed garden quality in C. canephora.With a growing demand for improved coffees, authentication of parentage is recognized as an important requirement in C. canephora breeding program that includes germplasm from multiple sources. The observed level of mislabeling in the present study, which is likely underestimated, is comparable to those in other tree crops (as cited earlier for cocoa) that rely on a seed garden system of variety development. This calls for implementation of measures such as training of manual pollinators, field labeling of clones with metal labels, and routine use of SNP markers to fingerprint germplasm collections to eliminate or reduce such erroneous labeling and its effect on variety development in tree crop breeding. Evidence gathered from the present study revealed the negative effect of mislabeling and/or pollen contamination on a practical coffee-breeding program in which unintended crosses are less vigorous and yield less than intended crosses. This reinforces the requirement to incorporate routine DNA fingerprinting to guide the selection of potential parents to reduce inbreeding and increase genetic gain in a C. canephora improvement program.This study demonstrates the utility of SNPs to detect mislabeling in a The original contributions presented in the study are included in the article/AA, FP, and DZ conceived the experiment. AA, FP, DZ, and LM conducted the experiment, analyzed the data, and wrote the manuscript. All the authors contributed to the article and approved the submitted version.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.


Sorghum bicolor with wild Sorghum halepense can help meet both requirements, generating high-yielding and environment friendly perennial sorghum cultivars. Selection efficiency, however, needs to be improved to exploit the genetic potential of the derived recombinant lines and remove weedy and other wild traits. In this work, we present the results from a Genome-Wide Association Study conducted on a diversity panel made up of S. bicolor and an advanced population derived from S. bicolor × S. halepense multi-parent crosses. The objective was to identify genetic loci controlling biomass yield and biomass-relevant traits for breeding purposes. Plants were phenotyped during four consecutive years for dry biomass yield, dry mass fraction of fresh material, plant height and plant maturity. A genotyping-by-sequencing approach was implemented to obtain 92,383 high quality SNP markers used in this work. Significant marker-trait associations were uncovered across eight of the ten sorghum chromosomes, with two main hotspots near the end of chromosomes 7 and 9, in proximity of dwarfing genes Dw1 and Dw3. No significant marker was found on chromosomes 2 and 4. A large number of significant marker loci associated with biomass yield and biomass-relevant traits showed minor effects on respective plant characteristics, with the exception of seven loci on chromosomes 3, 8, and 9 that explained 5.2–7.8% of phenotypic variability in dry mass yield, dry mass fraction of fresh material, and maturity, and a major effect (R2 = 16.2%) locus on chromosome 1 for dry mass fraction of fresh material which co-localized with a zinc-finger homeodomain protein possibly involved in the expression of the D  locus. These markers and marker haplotypes identified in this work are expected to boost marker-assisted selection in sorghum breeding.The efficient use of sorghum as a renewable energy source requires high biomass yields and reduced agricultural inputs. Hybridization of  Sorghum bicolor (L.) Moench] is the fifth cereal in the world in terms of production and acreage (S. bicolor genome sequence was first released in 2009 (x = 10 (2n = 20) chromosomes and reporting more than 34,000 annotated genes several of which can be harnessed in genetic introgressions to improve biomass production in this crop  Pers.] is one of the wild species of interest; it is a natural allotetraploid (2n = 40) thought to have originated by the spontaneous hybridization between diploids S. bicolor and S. propinquum (Kunth) Hitchc., followed by chromosome doubling  encodes for a putative membrane protein involved in the regulation of cell proliferation in the internodes (Dw2 (Sobic.006G067600) is a protein kinase regulating stem internode length; and Dw3 (Sobic.007G163800), which was the first to be identified, encodes a phosphoglycoprotein of the adenosine triphosphate-binding cassette (ABC) transporter superfamily involved in auxin transport, orthologous to maize br2  has been recently reported  loci, Ma1-6, are reported to control sorghum heading time , which modulates the action of several floral inhibitors and activators  domain lysine methyltransferase (Sobic.002G302700)  domain protein (Sobic.006G004400)  ; a phytoG302700) ; a phytoG302700) ; and GhdG004400) . All of ty trait , althoug_ plant maturity, plant height, and dry mass fraction of fresh material _ using a genome-wide association study (GWAS) approach in the genetic background of two distinct populations _ a set of S. bicolor (Sb) landraces and breeding lines and a set of perennial S. bicolor × S. halepense (Sb × Sh) advanced (fixed) inbred lines derived from several parental lines _ both of which make up a unique diversity panel in our breeding program and were amply described in our previous works  explaining significantly sizeable variability in these complex traits can help expedite marker assisted traits introgression and the development of superior and/or farmer preferred cultivars. This area of research in genetics and molecular breeding is especially needed when traits introgression involves broadening the genetic base of cultivated sorghum with the use of wild relatives which provide an untapped source of useful alleles, but can have a detrimental linkage drag  to selecus works , 2018.S. bicolor population including 181 genotypes, mostly selections derived from landraces from Africa and Asia, and seven commercial hybrid lines included as controls. The second group, Sb × Sh, counted 188 advanced (fixed) recombinant inbred lines derived from several S. bicolor × S. halepense controlled hybridizations  at different levels of filial progeny. Detailed information about the two populations can be found in previous publications , perennial/perennial (P/P) and annual/perennial//perennial (A/P//P) crosses; with annual (A) parents being induced tetraploids (2n = 40), standard diploid (2n = 20), genetic male-sterile, and cytoplasmic-genetic male-sterile inbred S. bicolor lines, and perennial (P) parents consisting of either S. halepense or tetraploid hybrid lines obtained by crossing induced S. bicolor tetraploids with S. halepense. Open-field trials for the two populations were run in four consecutive years from 2014 to 2017 in the CREA Research Center for Cereal and Industrial Crops experimental station of Anzola , using an augmented randomized complete block design , plant height (PH), dry mass fraction of fresh materials (DMC) and aboveground dry mass yield (DMY). Plant maturity was scored as the number of days between sowing and flowering, the latter being identified as the stage at which anthers were extruded in 50% of the plants from 50% of the spikelets on the panicle. Plant height was measured 1 week before harvest by estimating the mean height of the elementary plot using a 5 m telescopic rod. Plots were harvested using a single-row chopper harvester and a composite biomass sample of approximately 0.5–1 kg was collected for each genotype individually; the sample fresh weight was immediately measured, while the dry weight was determined after drying the sample at 80°C in a forced air oven for a few days, until weight was constant. The dry mass fraction of fresh material was calculated as the ratio of dry/fresh sample weights, and DMY was derived multiplying DMC by the fresh weight of the plot’s harvest, expressed in t/ha.t-test. Broad-sense heritability (H2) was estimated as repeatability , respectively; variance components were estimated using the R package lme4, setting genotypes and replications (years) as random effects under the following linear mixed effect model:with yij is the response for genotype i in replicate j, with g and e being the genotypic and residual  effects; in this work, yearly adjusted means were used as genotypic response. Polymorphism Information Content (PIC) was calculated for each SNP using the standard formula:where pi is the frequency of the ith allele of the marker.where To obtain plant material for DNA purification, 5–20 seeds per sample were sown in peat, watered, and treated with a fungicide and an insecticide  to protect young plantlets from pathogens and insects. Alternatively, seeds were treated with a seed-coating fungicide  and allowed to germinate on wet filter paper within petri dishes at a constant temperature of 25°C in a Venticell 111 incubator (MMM group) for 4–6 days. One to three healthy plantlets  or 3–5 germinated seeds were collected for each sample and DNA was extracted using the GeneJET Plant Genomic DNA Purification Kit (Thermo Fisher Scientific), following manufacturer’s instructions. DNA concentration and purity were evaluated by a Tecan Infinite M200Pro spectrophotometer , while DNA integrity was checked through 1% agarose gel electrophoresis with GelRed 10 μl/L (Biotium) as fluorescent dye. Aliquots of 60 μl at a concentration ≥ 10 ng/μl were prepared for each sample and used for downstream analyses.S. bicolor version 3.1.1 , and missing genotypes (NA) ≤ 20%.A genotyping-by-sequencing (GBS) strategy was adopted to obtain genotypic data from all the 376 samples. The methylation sensitive restriction enzyme ApeKI was used for library preparation, and sequencing was carried out on an Illumina HiSeq X Ten platform by BGI Hong Kong Company Limited. Two sequencing runs were performed, the first with 192 and the second with the remaining 184 samples, and sequence reads were aligned to the reference genome of on 3.1.1  to enabl3.1.1 (1  was usedp) values was visualized using Quantile–Quantile (Q–Q) plots to test the fitness of GWAS models for all traits   within tl traits ; signifi markers .Ma, Dw and other genes known to be involved in maturity and plant height-related traits were obtained from published papers . No significant differences were registered among the two groups for dry mass fraction of fresh materials  and yield . Broad sense heritability (H2) was high for PH (0.93) and MAT (0.91), while it was medium for DMY (0.81) and DMC (0.63).The variation of the four analyzed traits within the entire panel and the two populations separately is depicted in the box and density plots in r) values ranging from −0.43 to −0.23. The remaining traits were positively correlated and showed r values ranging from low to medium according to the scale suggested in r = 0.73). To investigate the effect of subpopulations on trait distribution and correlation, the Sb and Sb × Sh groups were analyzed separately; the obtained Pearson correlation coefficients are shown in r < 0.50), while the correlation of MAT with DMC was medium (r = −0.60) in Sb but low (r = −0.22) in Sb × Sh. Plant height maintained the highest positive correlation with DMY in both populations with medium values  of the correlation coefficients.Histograms, scatter plots and pairwise Pearson correlation coefficients for the four traits across the entire panel are reported in Q ≥ 40, MAF ≥ 0.05, missing data <20%). The distribution of SNP genotypes and minor allele frequencies (MAF) are reported in S. bicolor × S.halepense lines formed a distinct population from S. bicolor genotypes.Genotyping-by-sequencing (GBS) of the 376 samples yielded a raw data matrix consisting of 1,252,091 markers, that was filtered to obtain a dataset of 92,383 high quality biallelic SNPs (p) values using Blink, as reflected by low scores following the null hypothesis line, particularly for DMY and MAT traits; the SUPER method produced −log10(p) values relatively higher than expected, yet showing a greater statistical power as reflected by the higher number of significant associations detected.The Genome-wide association study was conducted using two different algorithms, Blink and SUPER, which returned 8 and 66 significant SNP-trait associations, respectively, revealed by a total of 63 significant SNPs of which 55 were identified by SUPER algorithm. Quantile–Quantile plots reported in S. bicolor and the S. bicolor × S. halepense populations, respectively.The complete list of markers significantly associated with phenotype is reported in R2) reported in R2≥5% for dry mass fraction of fresh material (Chr1_8820891 and Chr3_12314731), dry mass yields , and maturity (Chr9_58527007 and Chr9_58584246); all but one (Chr3_12314731 identified by Blink) of these markers were identified using SUPER algorithm. All of the eight markers were polymorphic in Sb and Sb × Sh populations except three  that were polymorphic only in Sb population. The only marker with a major effect (R2≥15%) according to the scale defined in Most of the detected polymorphisms had a minor effect on their associated traits as reflected by the low values of the coefficient of determination (r) for all significant markers identified by GWAS. On Chr 1, four SNPs  were highly associated, with r≥0.78, and covered a very narrow region at 73.85 Mbp. Two out of three significant SNPs on Chr 3 (Chr3_6805616 and Chr3_12314731) showed a medium correlation (r = 0.58) despite being physically distant at 5.5 Mbp. On Chr 5, three out of four markers located at 6.8 Mbp  were very highly associated (r≥0.95) while the fourth (Chr5_6800653) showed a negative correlation with them (R ≤ −0.55), likely identifying an alternative haplotype at the same locus; two other SNPs located in close proximity (Chr5_6903496 and Chr5_10479364) were correlated with the main block with r≥0.50. Two markers were associated on Chr 6 , while on Chr 7 six significant SNPs spanning a 2.6 Mbp region were positively correlated: r≥0.61 was found between Chr7_59810341, Chr7_60308754, and Chr7_60741571, and between Chr7_60741571, Chr7_61501575, and Chr7_61501583. All significant markers on Chr 8 were positively correlated among themselves, with pairwise r≥0.62, despite being distantly mapped (from 16.2 to 57.9 Mbp) on the chromosome. Finally, within the main hotspot of significant markers on Chr 9 different blocks of associated SNPs were detected; particularly, markers placed between Chr9_57420844 and Chr9_58584246 formed a large block with a generally high positive correlation ranging from 0.49 to 0.99 with the exception of two SNPs (Chr9_57687430 and Chr9_58293125) showing lower values of the correlation coefficients with the other SNPs and among themselves.Several blocks of highly correlated markers, likely belonging to a common haplotype, were identified; Dw1 and Dw3 toward the end of chromosomes 9 and 7, respectively. The hotspot on Chr9 covered the region from 54.9 to 59.3 Mbp and included 25 SNPs significantly associated to all the four traits (Dw1 (57.1 Mbp)  and heading date   and SbGA3ox1 (1.2 Mbp) (Dw3 (59.8 Mbp)  . The sam5.0 Mbp) , 2016b; SbGA2ox7 . Another1.2 Mbp) , 2016b. 9.8 Mbp) . No signMa genes did not match with significant SNPs detected by GWAS , plant height, fresh and dry biomass production, retrieved from the Sorghum QTL Atlas2 , as showr2 = 0.1 suggested a region of approximately 500 Mb surrounding each significant SNP as the most likely to harbor genes linked with the SNP and determining the traits of interest, consistently with a previous study on a subset of the same population  selected as being of major breeding interests are located in intergenic regions, while the remaining five fell within the sequence of a gene/transcript. Specifically, Chr3_12314731 (associated to DMC) is located in the first intron of transcript Sobic.003G131300; Chr8_56708385 (associated with DMY) falls within the coding sequence of Sobic.008G138100; the two markers Chr9_57601584 and Chr9_57601601 (DMY) are located in the second exon of Sobic.009G237900; finally, Chr9_58527007 (associated with MAT) fell in the 3′ untranslated region of two possible transcripts: Sobic.009G249900 and Sobic.009G250000. Of the three SNPs located in coding regions, two correspond to silent mutations while one  causes a semi-conservative amino acid change  at position 238 out of 259 of the putative protein.R2 = 7.8%), is located in the first intron of gene Sobic.003G131300 whose function is not annotated yet, although it is reported to be differentially downregulated in upper vs. lower vegetative leaf whorl. Two major breeding interest markers on chromosome 8 had significant effects on DMY; the first, Chr8_41653835 (R2 = 6.9%), falls in a pericentromeric region with a low gene density; only 5 genes are harbored in the 1 Mb interval centered on the SNP position, two of which  are likely associated to transposable elements. The second SNP, Chr8_56708385 (R2 = 7.1%), falls within the coding sequence of Sobic.008G138100, whose predicted protein product shows similarities with the exostosin family.The gene closest to the major effect marker Chr1_8820891 explaining 16.2% of the variation for dry mass fraction of fresh material, is Sobic.001G112500, a putative zinc-finger homeodomain (ZF-HD) protein, a class of transcription factors in which the HD domain binds DNA, and the ZF domain can enhance the protein–DNA interaction . The secR2 = 5.9% and 5.5%, respectively. The predicted product of Sobic.009G237900 is a putative plastocyanin which is a protein involved in the photosynthetic electron transport chain between PSII and PSI (R2 = 5.4%) and Chr9_58584246 (R2 = 5.9%) is 57 Kb, indicating that they might be associated to the same functional gene; this gene dense region includes 10 transcripts, several of which have a possible functional correlation with maturity and biomass-related traits  and advanced inbred lines derived from the interspecific hybridization S. bicolor × S. halepense (Sb × Sh) were evaluated genotypically and phenotypically for biomass yield and biomass production relevant traits. Understanding the genetic base of traits and their correlations is important to improve selection efficiency, especially for those quantitatively inherited traits, like biomass yield for which indirect selection for correlated traits with higher heritability can be useful by expediting the cultivar development process and making breeding for biomass yields more cost-effective. As expected, dry mass yield was significantly influenced by environmental factors as reflected by lower heritability compared with maturity and plant height. In the diversity panel evaluated in this work, plant height and maturity are more suitable targets for marker-assisted selection and indirect selection for biomass yields as they displayed high heritability. The use of S. halepense genome in this study was valuable in several respects. On the one hand, while many previous studies contributed to elucidating the genetic control of biomass-related traits in S. bicolor, the possible contribution of S. halepense remained largely unexplored. Besides its ability to confer perenniality to hybrids thanks to overwintering rhizomes, its effect on other traits has been poorly investigated owing probably to the fact that the mainstream in the breeding community considers that this species transmits mainly weediness-related unfavorable traits. However, recent reports highlighted S. halepense as a possible source of useful alleles even for traits other than perenniality, such as the content in grain antioxidants  involving several parental lines, meaning that GWAS investigation was justified as in previous studies  were included in the diversity panel and belonged to es e.g., . The obses e.g., .S. halepense flowers quite rapidly after emergence, which enables a fast seed development and dispersal , highlighting the strongly quantitative nature of biomass-related traits in which a high number of loci contributing small phenotypic effects are expected rather than a few loci with major effects. Therefore, to search genomic regions for additional candidate genes useful for breeding purposes, we lowered the R2 threshold to 5%, which led to select a total of 8 markers located on chromosomes 1, 3, 8, and 9  locus controlling stalk moisture and juiciness. In addition, midrib color was reported to be highly predictive of sugar yield, albeit not significantly correlated with dry biomass  whose function is disrupted in homozygous recessive (dd) genotypes  of Sobic.001G112500 and explains the highest proportion of phenotypic variation for dry mass content among SNPs detected in our GWAS analysis. Additional studies are needed to determine the expression level of this gene in D- and dd genotypes, which was not observed in a previous analysis  protein . Interes biomass . The genenotypes ; these lenotypes . Based oanalysis , but is analysis . Indeed,A successful use of the Chr1_8820891 marker in marker assisted breeding, has the potential to improve bioenergy conversion efficiency through a better control of the moisture that existed in the biomass at the time of harvesting. Dry mass fraction of the fresh material is an important trait in biomass plant breeding for biofuel production as biomass moisture content can impact both biomass logistics and energy bioconversion. High moisture content of the biomass affects strongly the combustion process such as lowering the flame temperature and/or the boiler efficiency, which can result in several operational problems including incomplete combustion. To overcome these issues, biomass is often dried before combustion, but this strongly influences the economies of the utilization biofuel . MoisturMEL2 (MEL2 is a RNA-recognition-motif (RRM) protein possessing ankyrin repeats and a RING finger motif involved in germ-cell development and meiosis progression, required for premeiotic G1/S-phase transition  or qRT-PCR  are nonetheless required in order to validate the major effects SNPs reported in this work before they are incorporated into breeding technologies such as Marker-Assisted selection.S. halepense can be useful for enhancing biomass production, especially if negative traits transmitted by the wild parent, such as seed chattering will be eliminated by breeding techniques including crossing and/or backcrossing to S. bicolor followed by selection. As an effort toward identifying valuable loci for biomass-related traits in support for breeding programs, we presented a list of significant and major SNP markers uncovered using GWAS analysis based on high quality marker data and 4 years of field trials of phenotypic evaluations. The use high quality marker data and 4-year field trials allowed precise phenotypic data and the power of our statistical analyses. The obtained data provide therefore strong and useful insight into the genetic control of the complex traits evaluated herein. Moreover, we propose the ZF-HD gene Sobic.001G112500 as an interesting candidate for the control of dry/fresh biomass ratio underlying a QTL localized on chromosome 1, whose action is also supported by previous works. Important SNPs and blocks of SNP marker haplotypes were identified and can be used in marker assisted selection for the development of superior sorghum cultivars. Before the major effects SNPs reported in this work are integrated into breeding technologies such as Marker-Assisted selection, they have to be validated through appropriate gene expression experiments such as KASP or qRT-PCR.Our data suggest that hybridization of domesticated sorghum with https://www.ebi.ac.uk/ena/browser/view/PRJEB40970 under the following accessions: Project: PRJEB40970, analyses: ERZ1668234.The full sorghum whole-genome GBS SNP genotyping dataset was archived at EH: conceptualization, methodology, investigation, data curation, supervision, project administration, and funding acquisition. EH, PD, and MD: software, writing – original draft preparation, and visualization. EH, PD, MD, SE, and FB: formal analysis and writing – review and editing. All authors have read and agreed to the published version of the 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.


Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia.The objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia  or predict hypoglycemia .Electronic literature searches  were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data  was pooled with a hierarchical summary receiver operating characteristic model.A total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR.Current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users’ Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients’ risk profiles such as for hypoglycemia and its associated complications  as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings.PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682 Hypoglycemia is a major barrier to achieving the tight glycemic control in patients with diabetes mellitus (DM) that is required to delay the progression of late DM-related complications. Although many patients exhibit symptoms of hypoglycemia such as anxiety, heart palpitations, and confusion, a significant number have diminished ability to recognize these hypoglycemic symptoms ,2, whichHowever, even if CGM advancements enabled patients to continuously track their subcutaneous glucose levels, the statistical disadvantage of the CGM data stream would remain as a major limitation. The autocorrelation of the CGM reading vanishes after 30 minutes, meaning that the projection of blood glucose levels more than 30 minutes ahead would be inaccurate . This fiMachine learning (ML) algorithms have been widely introduced to diabetes research including those for identification of hypoglycemia. The growing use of mobile health  apps, sensors, wearables, and other point-of-care devices, including CGM sensors for self-monitoring and management of DM, have made possible the generation of automated and continuous diabetes-related data and created the opportunity for applying ML to automated decision support systems . CombiniConventionally, ML has been applied to detect abnormalities in blood glucose levels using physiological parameters that are highly correlated with hypoglycemia  . RecentlThe study protocol has been registered in the international prospective register of systematic reviews .We used Dialog to perform the electronic literature searches. The platform allows users to access and search 96 databases of peer-reviewed literature. Publication dates ranged from January 1, 1950, to September 14, 2020. Search terms consisted of 2 elements: (1) thesaurus and text words related to ML and (2) text terms related to hypoglycemia and thesaurus terms related to glucose monitoring or blood glucose. The use of the thesaurus term was limited to 2 databases: EMBASE (EMTREE terms) and MEDLINE (MeSH terms). The above 2 elements were combined using the BOOLEAN operator “AND” . Manual Study inclusion criteria were (1) all participants had DM; (2) study endpoint was hypoglycemia; (3) researchers clarified that they originally trained the ML algorithm using training data to build a model for detecting or predicting hypoglycemia or the same researchers trained the ML algorithm in a previous study; (4) the model’s performance was tested using the test data; and (5) sensitivity and specificity for detection or prediction of hypoglycemia were presented or could be calculated.Exclusion criteria were (1) an event-based study , (2) a case study , and (3) a 2 × 2 contingency table consisting of the number of true positives, false positives, false negatives, and false positives could not be reproduced. If studies met all of the inclusion criteria but did not allow the reproduction of a 2 × 2 contingency table, we asked the corresponding author of these studies for the total number of test data sets  and events (N-hypo) so that we could reproduce the 2 × 2 table. If the same test data were shared by 2 or more eligible studies, we chose the most updated study in which the ML algorithm was considered to show the best performance.The outcome of meta-analyses of diagnostic or prognostic tests is the extent of consistency between an index test and a reference standard. The index test is defined as a new test that is proposed when the method for perfectly diagnosing a target condition in all individuals does not exist or cannot be used. In this meta-analysis, it corresponded to an ML algorithm that classified the input data as either hypoglycemia or nonhypoglycemia. The reference standard is defined by a procedure that is considered the best available method for categorizing participants into having or not having a target condition. In this meta-analysis, it corresponded to methods for diagnosing hypoglycemia in clinical practice, which included measurement of glucose levels, the International Classification of Diseases (ICD) code for hypoglycemia, or experts’ subjective judgment. Evaluation item was the ability of ML algorithms to detect hypoglycemia  or the ability to predict hypoglycemia . In studies that assessed the ability for detection, data used for the index test  and data used for a reference standard  had to be examined at the same time. In studies assessing predictive ability, the data input into the ML algorithm had to be examined before the diagnosis of hypoglycemia.Data were extracted by two authors (SK and KF) Disagreements were resolved by discussion with a third author (HiS). We fundamentally selected 1 datum if there were 2 or more extractable data for a set of test data in an individual study. If an individual study tested 2 or more ML classification methods or 2 or more models for 1 ML classifier, we extracted the datum related to the classifier or model that the study proposed as the best. If 2 or more different results were presented for the same model depending on the prediction window or horizon, we extracted data on the result in relation to the longest prediction window or horizon.The following study characteristics were extracted: first author, publication year, evaluated item , country, type of DM , number of study participants, N-total, N-hypo, mean or range of the patients’ age, time of day of hypoglycemic events, place of supposed hypoglycemic episode , ML algorithm used for classification into hypoglycemia and nonhypoglycemia, threshold of glucose level for hypoglycemia, method for diagnosing hypoglycemia, method for separating the database into training and test data, and profiling data that were input into ML algorithms for performance testing.To evaluate study quality, we used a revised tool to assess diagnostic accuracy of studies (QUADAS-2). The QUADAS-2 consists of 4 domains: selection of participants, index test, reference standard, and flow and timing. All 4 domains were used for assessment of risk of bias and the first 3 domains were used to assess the consensus of applicability. Each domain has 1 query in relation to the risk of bias or applicability consisting of 7 questions  9]. A “. A “9]. 2 values for PLR and NLR based on a multivariate random-effects meta-regression that considered within- and between-study correlations  100% [100%-100%]) and NLR (I2 [95% CI] 99% [98%-100%]). Publication bias was not statistically significant (P=.68).When the analyses were limited to 13 studies that specified type 1 as the DM type ,44,46,47Overall, the PLR and NLR of ML algorithms for detecting hypoglycemia were 4.05 and 0.26, respectively. These estimates were almost unchanged throughout several sensitivity analyses that were limited to studies that shared 1 characteristic in common. According to the Users’ Guide to Medical Literature with regard to diagnostic tests , the PLRThe overall sensitivity, specificity, PLR, and NLR for predicting hypoglycemia were 0.80, 0.92. 10.42, and 0.22, respectively. Applying the above described guidelines for diagnostic tests to these results, it is worth considering the use of current ML algorithms as a tool for alerting patients to impending hypoglycemic events. In addition, it is considered that a test with a PLR over 10 has a particularly strong power to alter posttest probability of the targeted disease compared with pretest probability . If a poSeveral limitations of this meta-analysis should be addressed. First, the principal major limitation is the pooling of studies among which there was much variability in the type of DM, profiling data for detecting or predicting hypoglycemia, time of day when hypoglycemic events occurred, setting of supposed hypoglycemic events, and ML classification methods. In particular, although the ability for predicting hypoglycemia depended largely on the ML classification methods , this meOverall, current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerable ability to predict hypoglycemia in patients with DM receiving hypoglycemic treatments. However, the clinical applicability of these ML algorithms should be evaluated according to patients’ risk profiles such as for hypoglycemia and its associated complications  as well as the average ability of the ML algorithm. Continued research is required to further develop ML algorithms to enhance their feasibility, considering the inaccuracy of CGM in the hypoglycemic range, the increased prevalence of hypoglycemia in the elderly, and increasing evidence for the effectiveness of tight glycemic control in preventing microvascular complications .


Capsicum species. However, due to the limited availability of genome-wide association study (GWAS) data and selection signatures for various traits, domestication-related genes have not been identified in pepper. Here, to address this problem, we obtained data for major fruit-related domestication traits  using a highly diverse panel of 351 pepper accessions representing the worldwide Capsicum germplasm. Using a genotype-by-sequencing (GBS) method, we developed 187,966 genome-wide high-quality SNP markers across 230 C. annuum accessions. Linkage disequilibrium (LD) analysis revealed that the average length of the LD blocks was 149 kb. Using GWAS, we identified 111 genes that were linked to 64 significant LD blocks. We cross-validated the GWAS results using 17 fruit-related QTLs and identified 16 causal genes thought to be associated with fruit morphology-related domestication traits, with molecular functions such as cell division and expansion. The significant LD blocks and candidate genes identified in this study provide unique molecular footprints for deciphering the domestication history of Capsicum. Further functional validation of these candidate genes should accelerate the cloning of genes for major fruit-related traits in pepper.All modern pepper accessions are products of the domestication of wild  Capsicum species), like other Solanaceae family members including tomato and potato, is a New World genus with a primary center of diversity in Bolivia and Peru , fruit weight (FWg), pericarp thickness (PT), and fruit position (FP). Among these,  2 and 3 .CNR, SlKLUH, SUN, OVATE, FAS, and LC have been cloned, and their roles in regulating fruit elongation, locule number, and fruit shape have been well characterized  techniques and the availability of newer populations of tomato, six representative gene families were identified to control fruit size in this crop, including the Cell Number Regulator (CNR), Cytochrome P450 A78 class (CYP78A), IQ domain, Ovate Family Protein (OFP), YABBY, and WOX gene families . Candidacterized . These fcterized .Since the release of the first reference genome of pepper , genome-via genotype-by-sequencing (GBS) and subjected them to GWAS. We obtained candidate genes underlying the QTLs detected by GWAS and characterized their functions, laying the foundation for further functional validation and cloning of candidate genes for major fruit-related traits in pepper.The goals of the current study were to (1) explore the correlations among five important fruit-related traits in pepper and (2) determine the significant genetic regions or genes governing genetic variations in the major fruit-related traits with strong evidence for selection during domestication. We obtained high-quality SNPs Capsicum accessions known as the ‘pepper GWAS population’ was used for analysis, comprising four major domesticated species including C. annuum (230 accessions), C. baccatum (48 accessions), C. chinense (48 accessions), and C. frutescens (25 accessions) (A collection of 351 essions) Table S1essions) .The pepper GWAS population was planted in a greenhouse at the Rural Development Administration (RDA)-Gene bank Jeonju, Republic of Korea . Over a three-year period (2015–2017), six plants per accession were randomly planted, and the phenotypes of three plants per accession were evaluated. Five domestication traits were evaluated using a randomized block design, including four quantitative traits  and one qualitative trait (fruit position [FP]). All quantitative traits were measured using an electronic scale and a ruler. FP was scored as 1 to 3 . The correlation among the five traits was evaluated by Pearson correlation (r) analysis with SPSS software .via double digestion with two sets of restriction enzymes (PstI/MseI and EcoRI/MseI) as previously described  values and the imputed ratio of minor and major alleles. Finally, the major filtering step was performed under the following conditions: minor allele frequency >0.05, SNP coverage >0.6, and inbreeding coefficient (IF) >0.8.Raw 101-bp reads from the libraries were trimmed to a minimum length of 80 bp and filtered to a quality score >Q30. The filtered reads were aligned to the nu.ac.kr  using thnu.ac.kr . For SNPnu.ac.kr . The SNPnu.ac.kr . To obtaTo identify population stratification, principal component analysis (PCA) was performed using the ‘pcaMethods’ library in R software . The val10P-value of the GWAS was determined using P-value  < 0.05) based on the number of independent SNPs in a population. The candidate genes inside LD regions with significant SNPs were investigated. Gene prediction was performed based on gff file v.2.0 of the CM334 v.1.6 reference genome (http://peppergenome.snu.ac.kr), and the function of each gene was predicted using Blast2GO (https://www.ncbi.nlm.nih.gov) were performed. To predict the molecular and biological functions of genes, the NCBI, Solanaceae (https://solgenomics.net), and Arabidopsis databases (https://www.arabidopsis.org) were used.GWAS based on the compressed mixed linear model (CMLM) was conducted using the R package of Genomic Association and Prediction Integrated Tool (GAPIT)  with theBlast2GO  based onC. annuum ‘CM334’ v.1.6, http://peppergenome.snu.ac.kr). In brief, 120 F7–F10 RILs derived from a cross between pungent C. annuum ‘Perennial’ and non-pungent C. annuum ‘Dempsey’ were grown for 3 years  in two locations: Anseong (2011 and 2012a) and Suwon, Korea (2012b and 2014). After SNP calling, sequencing reads were aligned to C. annuum ‘CM334’ v.1.6. A modified sliding window approach was used to investigate recombination breakpoints and to construct a bin map of the RILs. Bins were used as markers to construct a genetic map using the Carthagene program (2) for each QTL was estimated. The 95% confidence interval was used to represent the location of each QTL.To validate the GWAS results, recombinant inbred lines (RILs) were used for QTL mapping as described by  program  with def program , we util program . The pheC. annuum cv. CM334 reference genome v. 1.6, http://peppergenome.snu.ac.kr  Trimming and filtering-out of SNPs with a quality score <30, call rate <10%, and mono or tri-allelic SNPs types resulted in 1,869,524 SNPs (2) value of 0.82. Using this imputed genotype, the final filtering step was performed under the following conditions: MAF >0.05, SNP coverage >0.6, and IF >0.8. This step resulted in a set of 507,713 high-quality SNPs, which were evenly distributed on 12 chromosomes  to analyze population stratification. This analysis yielded four genetic clusters, with 22.26% (PC1) and 13.47% (PC2) of the genotypic variance in the first and second axes. Each cluster was well clustered by species, including Capsicum accessions from four species with maximum genetic diversity  values were recorded for FL (0.8), FWd (0.81), FWg (0.83), and PT (0.72) . Specifically, highly strong positive correlations were detected between FWg and FWd (r = 0.91), followed by FWd and PT (r = 0.90) and FWg and PT (r = 0.88). Although FL had slightly lower positive correlations with these three traits , it was the most highly correlated with FP (r = 0.46) compared to the three other traits (p = 0.00), with a mean difference of 0.08 to 0.17 (η2), which also supported the differences among species , whereas the lowest mean values were detected in C. frutescens  values  values Table 1,C. annuum cluster, 187,966 high-quality SNPs were filtered for use in GWAS following the criteria described above  were used to describe our results , which was observed for only one year. Common SNPs that were consistently correlated for at least two years of investigation exceeding the significance threshold −log10P >6.575 Of the 230 common SNPs, one SNP located on chromosome 4 (S04_227983120) was common both to FWg and PT. Furthermore, five SNPs on chromosome 9  and two SNPs on chromosome 12  were detected for both FWd and FWg. Unlike these eight common SNPs, 222 SNPs were associated with 64 LD blocks distributed on chromosomes 2, 3, 4, 5, 6, 9, 10, and 12 Table S710P-value ranging from 6 to 10.57  on chromosome 3 were detected only in 2017. Two SNPs were identified on chromosome 4, including S04_211838587 only in 2016 and S04_211848210 in all three years of the study. Two and one additional SNP  each on chromosomes 5, 7, and 11 were identified only in 2015 and 2017, respectively ; in these lines, 86 QTLs for 17 horticultural traits were previously mapped (http://peppergenome.snu.ac.kr). Based on the reference genome, we used 444,405 SNPs from 120 RILs and both parental lines to construct a binmap. Using a sliding window approach, all SNPs were grouped into 2,050 bins  using 120 RILs derived from a cross between y mapped . The onl050 bins Table S82) explained by each QTL ranged from 8.3% (PD_FP9) to 38.5% (PD_FWd4).Using the same phenotypic information, a total of 17 QTLs were identified Table 2.2) among four environments. For FP, three QTLs  were commonly identified in two different environments but explained less than 10% of the phenotypic variation. However, the two remaining QTLs detected on chromosome 12 at 41.3 to 44.7 cM explained higher phenotype variation (>18%) in one environment. Except for PD_FWd3, all QTLs for FL, FWd, and FWg had positive additive effects, meaning that RILs with the maternal genotype had higher values, while all five QTLs for FP showed negative additive effects.Based on the mapping results, five minor QTLs for FL were detected on chromosomes 2, 3, 5, and 9 in one environment. For FWd, four minor QTLs were identified on chromosomes 3, 4, and 7. All major and minor QTLs for FWg were detected on chromosome 7 at 29.6 to 30.7 cM, explaining more than 18.4% (LOD >7.5) of the phenotypic variation  values ranging from 19.7 to 30.2. This large variation in R2 values indicates that FWg is highly affected by genotype × environment interactions in this population.Using the same criteria as Based on the GWAS results, we selected 64 significant LD blocks and 230 SNPs related to the five major fruit-related domestication traits to predict candidate genes using Blast2GO. Among the 111 genes identified in the significant LD blocks, 1, 70, 39, and 16 genes were correlated with FL, FWd, FWg, PT, and FP, respectively, with some duplication Table S7CA.PGA v.1.6.scaffold517.20) located in the 211 Mb region of chromosome 4 in H04-0562 was strongly associated with FL. This gene, which is annotated as low-affinity sulfate transporter 3-like, is located approximately 1.7 kb from S04_211848210. A gene in the same mapping region at a 47 kb distance from SNP_211848210, CA.PGA v.1.6.scaffold517.21, is annotated as Agamous-like MADS-box protein AGL104; this gene appears to be an important regulator of FL  and CA.PGAv.1.6.scaffold1387.3 (associated with LD block H12-0570) were predicted to be very important for FP due to their known associations with this trait. These two genes, which are physically positioned between 211 and 215 Mbp on chromosome 12, encode auxin-binding protein ABP19a-like and the protein BIG GRAIN 1-like A, respectively  and two genes on chromosome 12 (CA.PGAv.1.6.scaffold730.39 and CA.PGAv.1.6.scaffold534.6) assembled in seven LD blocks  (CA.PGAv.1.6.scaffold5.32 and CA.PGAv.1.6.scaffold5.22) associated with two stable SNPs in all three years of the experiment , which are annotated as elongation factor 1-beta-like and uncharacterized protein LOC107842678 isoform X1, respectively are also predicted to be important for FWd , which significant SNPs inside their coding regions, are annotated as mRNA cap guanine-N7 methyltransferase 1 and DNA-directed RNA polymerase II subunit 1, respectively , while 34 accessions carrying the G allele had relatively wide fruits  were closely related to significant SNPs commonly associated with FWg  by GWAS. As a result, we identified 111 candidate genes within the 65 LD blocks. Of these, we selected 16 genes as strong candidate causal genes regulating fruit morphology according to the following criteria: 1) developmental genes known to be related to domestication in other plants; 2) genes within LD blocks containing significant SNPs detected in all three years of the study; and 3) SNP-containing genes associated with more than two traits.CA.PGAv.1.6.scaffold517.21, CA.PGAv.1.6.scaffold3.10, CA.PGAv.1.6.scaffold5.16), which are annotated as Agamous-like MADS-box protein AGL104, transcription repressor OFP12-like, and leucine-rich repeat and IQ domain-containing protein 1-like isoform X3, respectively, satisfied the first criterion, as they belong to the MADS domain subfamily, Ovate Family Protein (OFP) family, and IQ domain family, respectively. The OFP and IQ domain gene families include the well-known ovate and sun genes in tomato  are thought to be important candidates due to their regulation by auxin. The gene CA.PGAv.1.6.scaffold1387.3, which is annotated as BIG GRAIN 1-like A, is homologous to an auxin transport protein gene in Arabidopsis. This gene controls the adaxial–abaxial polarity of the pedicel  were chosen as candidate causal genes based on the second criterion: these genes are annotated as Agamous-like MADS-box protein AGL104, low-affinity sulfate transporter 3-like, peroxidase 41-like, elongation factor 1-beta-like, and uncharacterized protein LOC107842678, respectively. The first two genes, which are closely related to FL, are located at 211 Mbp on chromosome 4. In detail, CA.PGAv.1.6.scaffold517.20, a member of sulfate transporter family group 2, might be involved in the internal transport of sulfate between cellular or subcellular compartments within the plant (CA.PGAv.1.6.scaffold422.15 (associated with FWg and located at 194 Mbp on chromosome 6) regulates pollen germination and pollen tube growth  were commonly identified as candidates for both FWd and FWg. Three of these genes  are closely related to plant immune responses and are annotated probable serine/threonine-protein kinase Cx32, probable LRR receptor-like serine/threonine-protein kinase At3g47570, and flower-specific defensin-like, respectively. Besides their major roles, a few studies have focused on their roles in plant growth and development  that is correlated with both PT and FWg. GRFs are plant-specific transcription factors that were originally identified for their roles in stem and leaf development. Recent studies have highlighted their importance in other central developmental processes including flower and seed formation, root development, and the coordination of growth processes under adverse environmental conditions  and two significant SNPs . Inside this common area, two genes were identified ; these genes are annotated as L-ascorbate oxidase and ELKS/Rab6-interacting/CAST family member 1 isoform X1, respectively. Although the functional relevance of these candidate genes requires further validation, based on their putative functions, they represent strong candidate genes involved in pepper domestication. Among the 17 detected QTLs, one major QTL for FWg, PD_FWg7.1, spanning around 68.9 to 73.6 Mbp on chromosome 7 was identified. In this position, we were able to detect a relatively high peak than the surrounding area in GWAS. However, the P-values of those SNPs  did not pass a significant threshold. Some QTL positions for FWg and FWd were corresponding to QTLs reported by A comparison of the QTLs mapped based on the PDRIL and GWAS results from the GWAS population revealed only one common genetic area associated with FP. Region 141.6 to 144.6 Mbp on chromosome 12 contains three QTLs ” Rural Development Administration, Republic of Korea.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.


Thereby, we searched for polymorphisms in the ovine LEPR gene and associated them with three reproductive seasonality traits: the total days of anoestrous (TDA) and the progesterone cycling months (P4CM), both based on blood progesterone level and related to seasonal ovarian function; and the oestrous cycling months (OCM) as an indicator of oestrous behaviour. Two non-synonymous and non-linked single nucleotide polymorphisms (SNPs) in the LEPR gene were involved in the OCM trait (rs403578195 and rs405459906). These findings show for the first time the involvement of LEPR gene in seasonality reproduction in sheep and will help to improve genetic selection programs by implementing the genotyping of reproducers, which might increase the productivity of meat sheep.Seasonality of reproduction is one of the limiting factor of sheep production, with the leptin receptor (LEPR) gene responsible for the seasonal variation of reproductive traits in sheep. Three reproductive seasonality traits were studied: the total days of anoestrous (TDA), the progesterone cycling months (P4CM) and the oestrous cycling months (OCM). In total, 18 SNPs were detected in 33 ewes with extreme values for TDA and OCM. Six SNPs were non-synonymous substitutions and two of them were predicted in silico as deleterious: rs596133197 and rs403578195. These polymorphisms were then validated in 239 ewes. The SNP rs403578195, located in exon 8 and leading to a change of alanine to glycine (Ala284Gly) in the extracellular domain of the protein, was associated with the OCM trait, being the G allele associated with a decrease of 12 percent of the OCM trait. Haplotype analyses also suggested the involvement of other non-synonymous SNP located in exon 20 (rs405459906). This SNP also produces an amino acid change (Lys1069Glu) in the intracellular domain of the protein and segregates independently of rs403578195. These results confirm for the first time the role of the LEPR gene in sheep reproductive seasonality.The aim of this study was to characterize and identify causative polymorphisms in the  The seasonality of reproduction in sheep is a general phenomenon in breeds originating from temperate climates, such as those raised in the Mediterranean basin. Changes in the photoperiod at temperate latitudes provide the main external cue that controls the timing of out-of-season fertility . In RasaLeptin gene polymorphisms have drawn much attention from animal scientists for their possible roles in economically important productive and reproductive traits [leptin receptor (LEPR) gene are associated with reproductive traits [LEPR on reproduction, including puberty, the oestrous cycle, pregnancy, lactation, and even the early stages of embryonic development, have been proven [melatonin receptor subtype 1A (MTNR1A) is considered a key gene that mediates photoperiodic reproductive seasonality in sheep [LEPR was detected in the suprachiasmatic nucleus (SCN), the mammalian “biological clock”, and the pineal gland of ruminant species [e traits . In facte traits . Recent e traits ,23. The n proven ,25. Numen proven ,27,28. Mn proven . Thus, ln proven . Howevern proven . The melin sheep ,17,18,19 species , suggest species ,33,34.Leptin and its receptor have been suggested as markers for enhancing productivity in livestock and are also potential candidates for marker-assisted selection [LEPR have been associated with delayed onset of puberty and with decreased ovulation and lambing rates in prolific Davisdale sheep [LEPR gene and its involvement in seasonality reproduction in sheep. Therefore, this study aimed to identify polymorphisms in several regions of the LEPR gene in Rasa Aragonesa sheep and to test their association with reproductive seasonality traits.election . In sheele sheep , but no All experimental procedures were performed in accordance with the guidelines of the European Union (2003/65/CE) and Spanish regulations  for the use and care of animals in research. No hormonal treatments were applied to the ewes during the study.As described by Martinez-Royo et al. , phenotyThree reproductive seasonality traits were considered and described by Martinez-Royo et al. . BrieflyLEPR gene is located on the chromosome OAR1, covering approximately 99 kb with 20 exons (GenBank acc. number NC_019458). Twelve exons were chosen to characterize the LEPR gene. These exons were selected based on having non-synonymous polymorphisms in the Ensembl Variation database (https://www.ensembl.org/info/genome/variation/index.html) on the Oar 3.1 version of the sheep genome. Primers were designed using Primer3 software version 0.4.0 (http://bioinfo.ut.ee/primer3-0.4.0/), and were designed in the intron regions around the targeted exon. The details of the oligonucleotide sequences, the annealing temperature and expected product size are summarized in 2, 50 mM KCl, 10 mM Tris-HCl, 0.1% Triton X-100 and 1 U Taq polymerase . The cycling conditions were as follows: an initial denaturation step of 94 °C for 3 min, 35 cycles of 94 °C for 30 s, annealing temperature for 30 s, and 72 °C for 30 s except for fragments 5, 6 and 11, which were 45 s, and a final extension step of 72 °C for 5 min. Direct Sanger sequencing of the PCR products from the 12 exons of 33 ewes with extreme values for TDA  and OCM  were used to search for polymorphisms in the experimental population. The PCR products from genomic DNA were purified using the FlavorPrep Gel/PCR purification mini kit  according to the manufacturer’s instructions. The PCR products were sequenced in both directions by STAB Vida  using an ABI 3730XL sequencer . The homology searches were performed using BLAST . For alignment of the sequences, CLUSTAL Omega  and BioEdit [http://www.ensembl.org/Ovisaries/Tools/VEP?db=core) and PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/) [Ovis aries Oar_v3.1. The secondary structure of the protein was determined from the amino acid sequence using CFSSP software (http://www.biogem.org/tool/chou-fasman/) [Genomic DNA was extracted from blood samples using standard protocols. The ovine  BioEdit  softwareu/pph2/)  were usefasman/) .Genomic DNA was extracted from blood samples of 239 ewes from the total ewes of the flock using standard protocols. Six non-synonymous SNPs were selected for genotyping the whole population: one in exon 4 (rs411478947), exon 7 (rs596133197), and exon 8 (rs403578195) and three in exon 20  . Only nohttp://socserv.socsci.mcmaster.ca/jfox/Misc/Rcmdr/) [The Hardy–Weinberg equilibrium exact test was applied and the observed and expected heterozygosities and the minor allele frequency (MAF) for each SNP were calculated using Haploview software v4.2 . Statist/Rcmdr/) . The mod2 within the LEPR were calculated and visualized in Blocks of linkage disequilibrium (LD) were evaluated with Haploview software v4.2 using the 4-gamete rule . D’ and LEPR gene, we sequenced twelve exons that have non-synonymous polymorphisms in the Ensembl Variation database (https://www.ensembl.org/info/genome/variation/index.html). These exons were located at the beginning, middle and end regions of the gene. In total, 18 SNPs were detected: 3 and 8 SNPs in exons 4  and 20 , respectively, and 1 SNP in exons 7 (rs596133197), 8 (rs403578195), 12 (rs407234698) and 16 (rs421946862) and in introns 9 (rs416296450), 10 (rs404892216) and 16 (rs401262081) (To identify polymorphisms in the database  (https:/1262081) . Six of 1262081) . These S1262081) .2 between all pairwise combinations of the six non-synonymous SNPs. The results of the LD analysis are shown in To determine the extent of LD among these markers, we estimated the parameters D’ and rp = 0.0027). Ewes with the CC genotype showed more oestrous records (+0.12) than heterozygous ewes (p < 0.05) in young ewes for snp_ex20_1 after the Bonferroni correction (see p = 0.04). Indeed, young ewes with the GG genotype had higher TDA values than heterozygotes. No significant differences were detected between the AA (n = 2) and AG (n = 14) genotypes in young ewes. It is worth noting that in our population, this SNP showed a low frequency for the A allele (0.10).For the association analyses, we used 239 ewes from which thirty-five ewes (29 adults and 6 young ewes) did not present anoestrous during the experiment based on TDA trait (TDA = 0). Similarly, seventy-seven (60 adult and 17 young) and nine (7 adult and 2 young) ewes were cycling during all the experiments based on P4CM and OCM traits, respectively. All SNPs were in Hardy–Weinberg equilibrium. Only snp_ex8 showed a significant association with OCM  haplotype  in block 2 had higher TDA values than those with 1 copy (ATG) haplotype containing allele A of the snp_ex20_1, showing that young ewes with one copy of h1 had more oestrous events than those without copies  or h8 (GCGATG) haplotypes  showed more oestrous records than those with 1 copy. Notably, the h8 haplotype has the G allele  of snp_ex8. Similarly, the analysis of block 1 showed that haplotype h1 (GCG), which also contains the G allele of snp_ex8, is associated with OCM. However, ewes with haplotype h2 for block 0 (GCCGTG) and block 2 (GTG) carry the G allele of snp_ex20_3, which was associated with less oestrous records (OCM), although this SNP did not show a significant p-value after Bonferroni correction in SNP association studies.Haplotypes h2 and h8 of block 0 were also associated with OCM considering the whole population. Ewes with 0 copies of the h2  promoting a change of arginine to cysteine at position 62 (according to their positions in GenBank acc. number ENSOARP00000011154) was detected, with this substitution predicted as tolerated but with a low SIFT value (0.05) and possibly damaging by VEP and PolyPhen-2 software, respectively  but found a higher frequency for the T allele (0.47). These researchers reported that ewe lambs homozygous for the T allele in the LEPR gene were less likely to attain puberty at 1 year of age than those that did not carry the mutation in Davisdale sheep. Therefore, statistically significant effects were found concerning OCM and the deleterious SNP mutation in exon 8, showing a low MAF (0.06) and no homozygous animals for the putative deleterious allele. Heterozygous animals for this SNP showed fewer oestrous records (OCM trait) than homozygous animals. As OCM indicates behavioural signs of oestrous, it could be inferred that natural selection against homozygous animals for the deleterious allele has led to a low frequency of this allele. Haplotype association analysis confirmed these results. In fact, ewes whose haplotype contains the G allele (deleterious mutation) for snp_ex8 showed less oestrous events.SNP association analysis showed that the non-conservative SNPs found in exons 4 and 7 were not associated with reproductive seasonality traits. These SNPs were predicted as tolerated  and deleterious, respectively, but they showed low MAF values  . Only on2 = 0.51) and snp_ex20_3 (r2 = 0.42) in the predicted haplotype block 2 (G) and block 2 (GTG), carrying the G allele of snp_ex20_3, had significantly lower OCM values after Bonferroni correction, indicating the putative involvement of this SNP of the LEPR gene on the seasonal phenotypes. The SNPs at exon 20 segregate independently from those located in the extracellular domain, and different effects in two different regions of the LEPR protein were found in this study. One of them, snp_ex8, is located in the CRHI/immunoglobulin-like domain of the extracellular domain of the protein, where different amino acid substitutions have been associated with obesity and disrupted pubertal development in humans [LEPR mRNA have been identified encoding an identical ligand binding domain but differing in the length of the cytosolic domain [LEPR transcripts in the hypothalamus [LEPR gene in Angus, Brangus and Charolais cattle and found no associations with reproductive characteristics. The authors reported that blood leptin levels were influenced by this LEPR SNP in late pregnancy but not during lactation. It is important to note that associations between a mutation and the observed phenotypes are not direct evidence that the mutations caused the observed changes in phenotype. The observed relationship could indicate that the SNP is in linkage disequilibrium with the true causative mutation [n = 33) with extreme values for reproductive seasonality traits to look for polymorphisms that could be segregating in this population. This design could increase the power to detect polymorphic SNPs associated with the trait but minimized the probability of detecting other polymorphic SNPs. Furthermore, we did not sequence the complete coding region or regulatory regions, such as the promoter or 5′ and 3′ UTRs.The interaction between snp_ex20_1 and age affected TDA in young ewes. However, the opposite effect was found in adult ewes . This fi block 2 . Moreoven humans . The secn humans . This muc domain ,48. The c domain ,49,50. Tthalamus . Then, sthalamus . In livethalamus  investigmutation . In our Although the relationships between individuals in the sample were unknown and then population stratification cannot be checked, the results with the SNP located in exon 8 are very consistent. This consistency is justified by the significant association with the OMC trait , the in silico prediction of the functional and structural consequences of this non-synonymous SNP (predicted as deleterious and affecting the secondary structure of the protein) and the location in the extracellular domain of the protein, where different amino acid substitutions have been associated with phenotype effects.LEPR gene in reproductive seasonality. In this sense, several studies suggest that LEPR influences GnRH neuron activity and GnRH secretion by crosstalk with kisspeptin [In summary, these results confirmed for the first time the involvement of the sspeptin . Kisspepsspeptin  reportedsspeptin . This fisspeptin ,57 aboutsspeptin .LEPR gene (snp_ex8) was strongly associated with the oestrous cycling months in Rasa Aragonesa sheep, confirming for the first time the role of the LEPR gene in reproductive seasonality in ruminants. Furthermore, another non-linked SNP in exon 20 was associated with this trait, as shown in the haplotype association analysis. This SNP could be in linkage disequilibrium with other SNPs not detected in this study. The G alleles of snp_ex8 and snp_ex20_3 are associated with higher OCM values, which indicate behavioural signs of oestrous in the Rasa Aragonesa breed. Genetic selection programs can be enhanced by implementing the genotyping of reproducers for these alleles related to reproductive seasonality, which might increase the productivity of meat sheep.In conclusion, one SNP predicted as deleterious located in the extracellular domain of the


HSPB1), a member of the heat-shockfamily of protein, is a relatively small (27 kDa) molecular chaperoneprotein associated with cellular development. The relationship betweenHSPB1 expression and muscle growth in beef cattle has previously been reported,but there have been no reports of DNA markers related to meat quantity inKorean native steers. Therefore, the aim of this study was to evaluate therelationship of single-nucleotide polymorphisms (SNPs) within HSPB1 in terms of the carcass traits related tomuscle growth in Korean native steers. Through direct sequencing, wediscovered three SNPs: g.111 T > C SNP (rs208395876) and g.2548C > G SNP (rs483014585) were respectively located in 5Heat shock protein beta 1 ( HSPB1 is a 27 kDa small heat-shock protein that is expressed in manyvertebrate tissues, particularly muscle. The general functions of HSPB1 in muscleare to protect cells from physiological stress, inhibit cell death, andchaperone activity . In previous studies, HSPB1 has also been shownto be relevant in terms of feed efficiency . HSPB1 is alsoinvolved in muscle development in many species, including those reported inDubińska-Magiera et al. (2014). HSPB1 has been shown to be related to musclehypertrophy in vivo . In a recent study, the expression ofHSPB1 was found to be significantly increased during bovine myogenicdifferentiation compared to in the un-differentiation stagein myogenic cells, and it was also shown to be regulated byandrogen-mediated myogenesis .Recently, several studies have evaluated the relationship between heat-shockprotein beta 1 .MyoD, Myogenin, MRF4, andDesmin . These myogenic marker genes are known to beupregulated during myogenic differentiation . HSPB1, a majorfactor of actin polymerization in muscle, is known to play a role inmaintaining muscle structure . HSPB1 also shows chaperonefunction, including preventing protein degradation, inhibiting muscleatrophy, and stabilizing muscle protein . It hasbeen speculated that HSPB1 enhances muscle development by protecting muscleproteins . In a recent study, the inhibition of HSPB1expression during myogenesis was shown to repress the expression of MyoD,Myogenin, and Desmin; formation of myotubes; and protein synthesis . Arelationship has also been reported between HSPB1-10 expression and muscle growthin beef cattle, but there have been no reports of DNA markers related tomeat quantity in Korean native steers. Therefore, the aim of this study wasto evaluate the relationship of SNPs within HSPB1 with carcass traits related tomuscle growth in Korean native steers.Myogenesis is regulated by several myogenic genes, such as 22.1HSPB1 sequence was obtained from the NCBIdatabase (AC_000182.1). The primer sequence was designedusing NCBI Premer-BLAST based on the selected polymorphism sites, and theprimer information is shown in Table 1. The sequencing was performed asoutlined in a previous study , and SNPs were discoveredusing the Sequencer v5.2.4 program . Inorder to map the functional SNPs on DNA, mRNA, and protein sequences, theywere aligned using the Graphical View Legend on the NCBI database.A total of 192 Korean cattle . In order to evaluate the association SNPs andcarcass traits, the data were analyzed using the general linear model (GLM) of SPSS v. 22. The model is as follows:Large-scale SNP genotyping was performed commercially using the Fluidigm3HSPB1 has been shown to be closely associated with cell growth and bovinemyogenesis in certain types of muscles. Thus, based on the results reportedby Kim et al. (2018), we found that SNPs, which are directly regulated bygene expression, were discovered, and then we identified the relationship oftheir SNPs with beef quantity.Bovine HSPB1 gene as well as their alignments are shown in Fig. 1. In the present study, we discovered threeSNPs using direct sequencing. These SNPs were located in 5The position of SNP in DNA, RNA, and the protein sequence of the HSPB1 on carcass traits, such asbeef quantity, are shown in Tables 1 and 2. A previous study (unpublisheddata) demonstrated that the g.2548 C > G SNP (rs483014585) had noeffects on carcass traits, and we thus removed the related data from thepresent study, as we could not use it as evidence to support our result. Asshown in Table 1, g.111 T > C SNP was significantly associatedwith meat ratio  demonstrated that the HSPB1 expression level washigher in the skeletal muscle of bulls than that of steers. Thus, in thisstudy, the steer group in Korean cattle was used. As shown in Tables 1 and2, although there were no significant differences between these SNPs and the rib-eye area, the mean in the group with the CC genotype of g.2352 T > C SNP and combination genotypes of CC-CC, CT-CC, and TT-CC were found to benumerically higher than that in other groups.HSPB1 interact with insulin-likegrowth factor receptor 1 and its signal transducer, the serine/threoninekinase Akt, which together modulate an adipocyte metabolism. Specifically, the previous results suggested that HSPB1 was negatively correlated with an adiposemetabolism . Therefore, our results regarding the leastsquare mean of back-fat thickness and the rib-eye area were found to be similar.The cellular development associated with adipose tissue growth involves bothcellular hypertrophy (increase in size) and hyperplasia (increase innumber). Rajesh et al. (2010) reported that In a previous study, it was reported that g.111. T > C SNP(rs208395876) was functional. In particular, our results identified thatg.2352 T > C SNP was located in the acceptor site of the RNAsplicing region.Therefore, our results suggested that g.111 T > C SNP and g.2352T > C SNP could be causal mutations related to an adipose metabolism in Korean cattle steer.4HSPB1 protein. Animals with a CC genotype of g.111 T > C SNP had asignificantly higher meat ratio, and animals with a CC genotype of g.2352T > C SNP had a significantly higher meat ratio and lower back-fatthickness than those of other genotypes. Moreover, for the combination ofg.111 T > C and g.2352 T > C SNPs, the CC-CC, CT-CC, andTT-CC genotypes' back-fat thicknesses were found to decrease while themeat ratios increased. In particular, a g.2352 T > C SNP wasfound to be located in the acceptor site of the RNA splicing site.Therefore, our results indicate that it could be a causal mutation relatedto an adipose metabolism in Korean cattle steer.We discovered three SNPs, including g.111 T > C, g.2352, andg.2548, which are respectively located in 5