Bemisia tabaci, cryptic species complexes and are efficient vectors of begomoviruses. Bemisia tabaci MEAM1 is the predominant vector of begomoviruses in open-field vegetable crops in the southeastern United States. However, recently B. tabaci MED also has been detected in the landscape outside of greenhouses in Florida and Georgia. This study compared the transmission efficiency of one Old-World (OW) and two New-World (NW) begomoviruses prevalent in the southeastern United States, viz., tomato yellow leaf curl virus (TYLCV), cucurbit leaf crumple virus (CuLCrV), and sida golden mosaic virus (SiGMV) between B. tabaci MEAM1 and B. tabaci MED. Bemisia tabaci MEAM1 efficiently transmitted TYLCV, CuLCrV, or SiGMV, whereas B. tabaci MED only transmitted TYLCV. Percent acquisition and retention of OW TYLCV following a 72 h acquisition access period was significantly higher for B. tabaci MED than B. tabaci MEAM1. In contrast, B. tabaci MEAM1 acquired and retained significantly more NW bipartite begomoviruses, CuLCrV or SiGMV, than B. tabaci MED. Quantitative analysis (qPCR) of virus DNA in whitefly internal tissues revealed reduced accumulation of CuLCrV or SiGMV in B. tabaci MED than in B. tabaci MEAM1. Fluorescent in situ hybridization (FISH) showed localization of CuLCrV or SiGMV in the midgut of B. tabaci MED and B. tabaci MEAM1. However, localization of CuLCrV or SiGMV was only observed in the primary salivary glands of B. tabaci MEAM1 and not B. tabaci MED. TYLCV localization was observed in all internal tissues of B. tabaci MEAM1 and B. tabaci MED. Overall, results demonstrate that both B. tabaci MEAM1 and B. tabaci MED are efficient vectors of OW TYLCV. However, for the NW begomoviruses, CuLCrV and SiGMV, B. tabaci MEAM1 seems to a better vector. Middle East-Asia Minor 1 (MEAM1) and Mediterranean (MED) are two of the most invasive members of the sweetpotato whitefly,  Bemisia tabaci Gennadius Mediterranean , was first detected in the United States in Arizona  reported earlier by Kliot et al. (2014) [al. 2014 . Briefly. (2014) . The DNA. (2014) . n = 10).For virus quantitation, MG, hemolymph, and PSG from 25 whiteflies were obtained for each treatment. MG and PSG were dissected using the procedure described above, and hemolymph was collected using the procedure described earlier by He et al. . After dB. tabaci MED-mediated transmission studies have been conducted prior to the current study were identified  in the Lme4 package with treatments as the fixed effect and replications as the random effect [anova in the lmerTest package and post hoc testing was performed using the multcomp package [B. tabaci MEAM1 and B. tabaci MED at different time intervals. Percent infection and virus accumulation were analyzed using a generalized mixed-effect model and a linear-mixed effect model, respectively. Virus accumulation in whitefly adults and tissues was analyzed using a linear mixed-effect model. Statistical differences were considered significant at p < 0.05.Data analyses were performed in R version 3.4.2 . Data fobinomial . Within-kage car . Post ho package . The medm effect . ANOVA w package ,86. Perc package . One-wayB. tabaci MEAM1 and B. tabaci MED developing on each host plant did not vary significantly . Furthermore, TYLCV accumulation did not differ between tomato plants inoculated by viruliferous B. tabaci MEAM1 or B. tabaci MED  . More than 80% of CuLCrV infection was observed in squash plants when inoculated with viruliferous B. tabaci MEAM1. In contrast, none of the squash plants inoculated with viruliferous B. tabaci MED were infected with CuLCrV . More than 80% of SiGMV infection was observed in the prickly sida plants when inoculated with viruliferous B. tabaci MEAM1. In contrast, none of the prickly sida plants inoculated with viruliferous B. tabaci MED were infected with SiGMV (Percent transmission of TYLCV did not differ between MEAM1 and MED whiteflies ( = 0.75) A,B. Howeh CuLCrV A,C. Simith SiGMV A,D. B. tabaci MEAM1 tested positive for TYLCV, 33% B. tabaci MEAM1 tested positive for CuLCrV, and 66% B. tabaci MEAM1 tested positive for SiGMV  whiteflies decreased over time as indicated by endpoint PCR A–C. At tor SiGMV A–C. Howeor TYLCV A. The perth week B. Similarth week C. B. tabaci MEAM1 and B. tabaci MED that had fed on TYLCV-infected tomato decreased gradually with time    than B. tabaci MED at every time interval  D. Howeve< 0.001) E and SiGinterval F.B. tabaci MEAM1 and B. tabaci MED localized in the midgut   A. More TB. tabaci MED and B. tabaci MEAM1 and in their tissues differed significantly  . More SiGMV accumulated in B. tabaci MEAM1 adults and tissues than in B. tabaci MED adults and tissues (CuLCrV accumulation in < 0.001) B. More C tissues C.B. tabaci MED transmissible and non-transmissible begomoviruses did not reveal consistent mutations of amino acids at any location (B. tabaci MED non-transmissible viruses except for tomato yellow leaf curl China virus (TYLCCV) had valine, whereas B. tabaci MED-transmissible viruses had isoleucine , cucurbit leaf crumple virus (CuLCrV), and sida golden mosaic virus (SiGMV) between B. tabaci MEAM1 and B. tabaci MED. Both B. tabaci MED and B. tabaci MEAM1 were efficient vectors of the OW begomovirus, TYLCV. That was not the case for NW bipartite begomoviruses, CuLCrV and SiGMV. Bemisia tabaci MED was able to acquire and retain TYLCV, CuLCrV, or SiGMV after a 72 h AAP on TYLCV-infected tomato, CuLCrV-infected squash, or SiGMV-infected prickly sida plants, respectively. However, it was only able to inoculate TYLCV. In contrast, B. tabaci MEAM1 was able to acquire, retain, and inoculate non-infected tomato, squash, or prickly sida plants with TYLCV, CuLCrV, or SiGMV, respectively, after a 72 h AAP on the homologous infected hosts. Results from qPCR suggest that CuLCrV or SiGMV accumulated in the midgut of B. tabaci MED, but due to inefficient translocation from MG into hemolymph, CuLCrV or SiGMV failed to accumulate in the PSG, resulting in the inability to transmit CuLCrV or SiGMV. Taken together, results confirm that B. tabaci MED and B. tabaci MEAM1 differ in their ability to transmit NW bipartite begomoviruses, viz., CuLCrV and SiGMV.amentals . However Georgia ,13. ReceB. tabaci cryptic species. For instance, B. tabaci MEAM1 and B. tabaci MED differed in their ability to acquire and inoculate TYLCV isolate Sh2 reported from China [B. tabaci has been reported as a better vector of chino del tomate virus than B. tabaci MEAM1 [B. tabaci MED accumulated and retained a higher amount of TYLCV than B. tabaci MEAM1, although this did not translate into differences in transmission. Similar transmission efficiency between the two cryptic species (MEAM1 and MED) appear to be associated with the ability of whiteflies to acquire the virus particles well above the threshold that would otherwise limit virus inoculation. Upon transfer to virus non-host cotton, TYLCV accumulation declined gradually in B. tabaci MED (5–9%/day) and B. tabaci MEAM1 (8–14%/day). The results of this study are consistent with previous findings on the gradual decline of TYLCV in viruliferous whiteflies once transferred to a virus non-host plant [B. tabaci MED on virus non-host cotton was associated with the presence or absence of an endosymbiont, Hamiltonella [Hamiltonella, Arsenophonus, and Rickettsia were present in both B. tabaci MEAM1 and B. tabaci MED populations . Czosnek et al. (2002) compared the retention of TYLCV between vector (B. tabaci) and non-vector  whiteflies. They reported that B. tabaci was able to retain TYLCV for 15 days after a 4 d AAP on TYLCV-infected tomato, whereas TYLCV was undetectable in T. vaporariorum within a few hours post transfer to a virus non-host [B. tabaci MED acquired CuLCrV or SiGMV from infected plants after an AAP of 72 h and retained CuLCrV or SiGMV for much longer, albeit in lower amounts (>10-fold lower) than B. tabaci MEAM1. CuLCrV or SiGMV accumulation in ectively . This conon-host . In the B. tabaci cryptic species have been associated with differences in their feeding habits. For example, B. tabaci MED has been documented to engage in prolonged and continuous feeding on TYLCV-infected plants than B. tabaci MEAM1, translating into increased acquisition and subsequent inoculation of TYLCV by B. tabaci MED [B. tabaci MED and B. tabaci MEAM1. Therefore, it is possible that the differential transmission observed in this study might be the result of incompatible interactions between NW begomoviruses and B. tabaci MED. To further examine the differential transmission of CuLCrV or SiGMV by B. tabaci MEAM1 and B. tabaci MED, the current study, using qualitative (FISH) and quantitative (qPCR) techniques, traced the tropism of CuLCrV or SiGMV in B. tabaci MEAM1 and B. tabaci MED adults. Both viruses were able to traverse the midgut barrier of B. tabaci MEAM1 and B. tabaci MED adults. However, CuLCrV or SiGMV movement across the midgut of B. tabaci MED was less efficient in comparison with B. tabaci MEAM1. Lower-efficiency translocation across the B. tabaci MED midgut resulted in reduced virus accumulation in the hemolymph and consequently in the PSG, leading to an inability to transmit CuLCrV or SiGMV. Similar results have been reported for cotton leaf curl Multan virus (CLCuMuV), where inefficient movement of CLCuMuV across the MG of B. tabaci MEAM1 and B. tabaci MED adults in comparison with B. tabaci Asia II 1 resulted in reduced transmission of CLCuMuV in cotton [B. tabaci MEAM1 and B. tabaci MED were infected with CLCuMuV, whereas 52% of cotton plants inoculated by viruliferous B. tabaci Asia II 1 were infected with the CLCuMuV [B. tabaci MEAM1 was ~ 100-fold more than in the PSG of B. tabaci MED. Additionally, FISH assay did not result in the localization of CuLCrV or SiGMV in the PSG of B. tabaci MED. The non-detection of FISH signals corresponding to CuLCrV or SiGMV in the PSG of B. tabaci MED might have been the consequence of reduced accumulation of CuLCrV or SiGMV in the PSG of B. tabaci MED than B. tabaci MEAM1. A previous study on TYLCV localization in B. tabaci MEAM1 using qPCR and FISH also demonstrated that qPCR allowed more sensitive and specific detection of TYLCV in whitefly tissues than FISH [Some of the transmission differences between baci MED . In the n cotton . None ofhan FISH .B. tabaci transmissibility [B. tabaci after substitution of glutamine with lysine, histidine with glutamine, and leucine with methionine at amino acid residues at 124, 149 and 174, respectively [B. tabaci Asia 1 and B. tabaci Asia II [B. tabaci MED transmissible and non-transmissible begomoviruses did not reveal any consistent patterns in this study. However, at amino acid residue location 85, all B. tabaci MED non-transmissible viruses except for tomato yellow leaf curl China virus (TYLCCV) had valine, whereas B. tabaci MED-transmissible viruses had isoleucine , form a BtCubam receptor complex that binds to the capsid protein of TYLCV, resulting in clathrin-mediated endocytosis of TYLCV into whitefly midgut cells [B. tabaci MED and B. tabaci MEAM1 efficiently transmitted TYLCV, but only B. tabaci MEAM1 was able to transmit CuLCrV or SiGMV. Therefore, it is very likely that factors required for efficient circulation of TYLCV within whiteflies are present in both B. tabaci MED and B. tabaci MEAM1. However, factors required for efficient circulation of CuLCrV or SiGMV were present only in B. tabaci MEAM1. Thus, it is possible that different begomoviruses interact with different whitefly transmission determinants and these factors could differ between cryptic species. Currently, these whitefly factors remain incompletely understood and warrant further investigation. Moreover, at this time it is not clear whether the longer association between NW begomoviruses with B. tabaci MEAM1 (30–40 years) in comparison with B.tabaci MED (~15 years) provided an opportunity for the NW begomoviruses and B. tabaci MEAM1 to coevolve favoring transmission, or whether these differential interactions are a result of innate transmission determinants. From a limited number of studies conducted, it seems that B. tabaci MED readily transmits OW begomoviruses, whereas B. tabaci MEAM1 seems to be an effective vector of both OW and NW begomoviruses. The establishment of B. tabaci MED in the United States can have severe impacts on OW begomovirus epidemics, such as TYLCV in tomato production. However, similar trends may not be observed for NW begomoviruses, such as CuLCrV and SiGMV, with the caveat being that this study was conducted only with these two NW begomoviruses and under controlled conditions.The current study showed that the circulative movement of OW (TYLCV) and NW (CuLCrV and SiGMV) begomoviruses within  and PSG ,99,100. ut cells . In the


HSPA12A,HSP90AA1,IL33,DIO2,BTNL2,CSN2,ABCG1,CSN1S1,GHR,HSPA8,STAT3, and HCRT. We emphasized on HSPA12A and HSPA8 genes as they were successfully genotyped in all studied flocks in which genotype frequency patterns were identified. Contrary to previous findings, the A allele for HSPA8 SNP was not observed in the heat tolerant breeds, being found exclusively in cold-tolerant breeds. The principal component analysis could not clearly differentiate the breeds, while plot concentration was slightly varied among the three groups, with HSP90AA1 and IL33 SNPs’ loading values significantly contributing to PC1 and PC2. We confirmed previous works that the HSPA12A,HSPA8,HSP90AA1 and IL33 SNPs are potential candidate markers for thermotolerance adaptation in sheep. This research contributes to the genetic variability of SNPs for thermotolerance adaptability in sheep.A study on 51 SNPs belonging to 29 genes related to heat stress was carried out in 720 sheep from 17 different breeds adapted to different climates from Hungary, Bosnia and Herzegovina, Morocco and Romania, using Kompetitive Allele-Specific Polymerase Chain Reaction. Genotype frequency and the Hardy–Weinberg equilibrium were calculated, followed by a clustering using the Principal Component Analysis. We analyzed the polymorphisms in the following genes analyzed:  Furthermore, according to reports from several international organizations involved in climate change research, practically all sectors of the economy and all regimes around the world will suffer from the negative effects of climate change, although to varying degrees, such as the irreversible loss of some natural ecosystems5. Particular attention should be paid to temperature variations as the most significant stressor on the efficiency of livestock production systems, affecting animal development, growth, production outputs and reproduction efficiency.Climate change is a complex phenomenon with disastrous consequences in almost all aspects of human societies, including in the livestock sector. Human activities would have contributed to a 1.0 °C increase in global temperature above pre-industrial levels. If current trends continue, global warming will likely approach 1.5 °C between 2030 and 20528, production parameters , reproduction , sheep health14, and sheep welfare15, lowering farms economic returns and efficiency. Van Wettere et al.16 reported that when temperatures drop below 12 °C or increase over 31 °C, thermoregulatory mechanisms are severely disrupted and sheep's ability to maintain homeothermy is disturbed, with the impacts on performance and welfare being severe.Compared to other ruminants, sheep tend to perform better in harsh environments, nevertheless, heat stress was shown to negatively impact physiology19. In ewes, heat stress reduces estradiol concentration and aromatase activity, negatively influencing estrous occurrence and duration22. For each additional day of high-temperature exposure (> 32 °C) in a week prior to estrous onset, 2.7% of the fertilization rate and 3.5% of the lambing rate reduction were observed16. Heat stress also leads to higher circulating progesterone levels during the luteal phase, implying increased progesterone synthesis and decreased clearance, which impacts the timing and development of pre-ovulatory follicle growth13. Research with Malpura sheep housed in a climatic chamber for 6 h at 42 °C and 54% relative humidity found a significant drop in hemoglobin and packed cell volume levels as more water is transported through the thermoregulation circulatory system. Furthermore, higher plasma cortisol levels and increased cholesterol catabolism were involved in improving gluconeogenesis and providing more energy to heat-stressed rams. Because of the lower Gonadotropin releasing hormone (GnRH) release from the hypothalamus, plasma triiodothyronine (T3), thyroxine (T4) and testosterone levels were lowered, as were the sexual behaviour and semen volume, with less progressive sperm motility and sperm concentration24.Heat stress is cytotoxic as it changes biological molecules, disrupts cell activities, affects metabolic responses, causes oxidative cell damage, and activates apoptosis and necrosis pathwaysA critical component of adaptation to climate change is represented by the animals' genetic capacity to survive under stressful climatic conditions. Since it results in a permanent and cumulative transformation, the selection of thermoresistant animals could represent an efficient method of increasing livestock productivity during periods of high environmental temperature. Identifying and utilizing thermo-tolerant genotypes in sheep is critical due to the changing climate scenario, having the potential to significantly influence sheep productivity.HSP90 and HSP70 genes polymorphism were investigated in Indian sheep using PCR–RFLP method25, FGF2, GNAI13 and PLCB1 melanogenesis candidate genes were studied in Egyptian Barki sheep breed, generated by 50K SNPs Beadchips26, and HSP5 and HSP40 in Brazilian sheep using the 50K SNP Chip27. Given that genetics has a complex role in affecting an individual’s capacity to respond to a stressful situation, more research into mechanisms and the development of more appropriate tools is required.Thermotolerant genes in sheep have been studied by employing various genomic tools, for example, in heat stress protein genes; This study aimed to investigate the polymorphism of 51 SNPs in 29 genes involved in thermotolerance throughout the use of Kompetitive Allele Specific PCR (KASP-PCR) technique in 17 sheep breeds originating from different climatic conditions. The main goal was to identify and describe polymorphisms related to climatic adaptation that could be used to develop future thermal resilience in sheep through genomic selection.HSPA12A,HSP90AA1,IL33,DIO2,BTNL2,CSN2,ABCG1,CSN1S1,GHR,HSPA8,STAT3, and HCRT. Allelic and genotypic frequencies presented in Tables HSP12A, rs588145625-HSPA8, rs588498137-STAT3 and rs602521720-HCRT.A total of 601 animals (83.47%) from the total 720 animals were successfully genotyped and 32 SNPs (62.74%) among the initial set of 51 SNPs were successfully genotyped in this study; 17 of them were found to be polymorphic (33.33%)  and Botosani Karakul .The heterozygote GA for SNP rs588145625-HSPA8 was absent in the heat tolerance breeds, except the Transylvanian Merino, and in breeds with high cold tolerance . The homozygote GG was present in all heat-tolerant breeds and some cold-tolerant breeds . The G allele was dominant in all breeds, ranging from 0.760 to 1, and the A allele has only appeared in cold-tolerant breeds and Transylvanian Merino with frequency ranging from 0 to 0.308.The heterozygote STAT3 and rs602521720-HCRT SNPs.No patterns have been found for allelic and genotypic frequencies for rs588498137-GG genotype and G allele were dominant in all breeds for SNP rs588498137-STAT3, with G allelic frequency varying from 0.750 to 1. Similarly, the CC genotype and C allele for SNP rs602521720-HCRT was dominant in all breeds, with C allelic frequency varying from 0.546 to 1.The x2) test using allelic frequencies, observed and expected genotypes, and P value of the polymorphic genes are summarized in Table S2. Botosani Karakul breed from Romania was found to be the breed with most SNPs deviated from HWE (P ≤ 0.05); rs416259751-IL33, rs411181557-DIO2, rs414917134-BTNL2 and rs420611298-ABCG1.The Hardy–Weinberg equilibrium (HWE) was investigated with Chi-square  to highlight differences between either the breeds Fig. a or the HSP90AA1 (c) and rs397514272-HSP90AA1 (e), whereas rs410259751-IL33 (g) also had a positive contribution  unsuccessful genotypes could be the consequence of sample damage during transportation from the laboratory to the subcontracting laboratory. Furthermore, quality control results in lower data for the analysis.The application of KASP-PCR assay in this study did not show a high assay success rate as in previous studies carried out on goatsHSPA12A and HSPA8 as they are the only ones that showed allelic and genotypic frequency patterns with the climatic characteristics. HSPs are a wide family of chaperone proteins categorized according to their molecular size and amino acid sequence similarity. The HSPA12A and HSPA8 are members of the HSP70 family with a molecular weight of 70 kDa30. It is the biggest, most numerous and most conserved protein family throughout evolution, as well as the most extensively studied protein family across a wide range of species33. It is found on chromosome 15 and is made up of nine exons in sheep. This gene is widely studied for thermal adaptability. It controls cellular survival to heat stress with a highly dynamic nature, and these proteins are responsible for maintaining the organism's equilibrium and acclimating to heat stress35.Only four SNPs were successfully genotyped in all investigated populations, and the focus was given to SNPs HSPA12A affects aspects like cellular senescence and how cells respond to heat stress36. It was found to be more active in ruminants during the summer, which helps them adapt to a hostile environment. This gene was found to be highly associated with heat tolerance in indicine cattle (Bostaurusindicus) and also has an important role in the water holding capacity in beef breeds, which might also act as a mechanism in surviving heat stress through the increased adrenaline and pH change in muscles38. According to the genotype and allele frequency results, the heterozygote TC for rs161504783-HSPA12A was dominant in most of the breeds, except Hungarian Racka, Hungarian Merino, Transylvanian Merino, Botosani Karakul and Sardi. Béni Guil, D’Man and Timahdite, breeds from a hot region, were not in HWE for HSPA12A.HSP70, exon 1 is non-coding, while the following eight exons combine to form the HSPA8 protein, with 650 amino acids of 71 kDa. HSPA8 aids in the day-to-day cell functions of protein folding and unfolding, polypeptide aggregation prevention, disassembly of large protein complexes and protein translocation across cellular compartments39. Given that increasing HSPA8 levels have been discovered to be positively correlated with heat tolerance, this gene has been employed as a candidate for heat resistance in many livestock species42.In GG was carried by all heat-tolerant breeds and some cold-tolerant breeds , while the heterozygote GA for SNP rs588145625-HSPA8 was only found in cold-tolerant breeds . The A allele was absent in heat tolerant breeds, which is opposed to the observation made by25 in a study of gene expression in Indian sheep, which discovered that animals with A allele have better hot climate adaptability, compared to animals with the G allele. The AA genotype is more adaptable to a hot environment and has lower HSPA8 gene expression, than animals with the AG genotype. Similarly, a study by41 showed that the GG genotype has the least ability to survive heat stress in Awassi and Arabi sheep.In this study, the homozygote 44.In this study, Botosani Karakul has shown to be the breed with the largest number of SNPs deviating from HWE. Lower heterozygosity was observed in 10 SNPs, and 4 of them deviated from HWE. One possible reason for this deviation of HWE was because Botosani Karakul is one of the Romanian crossbreeds that has undergone extensive genetic mixing from its German and Austria lines, and other Romanian breeds since its introduction from Russia at the beginning of the nineteenth century49. We acknowledge that the cold breeds used in this study were not from year-around cold regions , which was one of our limitations due to our inability to obtain samples from these regions. However, we strongly believe that the cold tolerance breeds from temperate climates utilized in this study are sufficiently contrasting with the Moroccan heat tolerant breeds.PCA was unable to differentiate each breed in a clear and distinct manner; however, a relative clustering was observed based on the three different categories of climatic regions Fig. b. This cHSP90AA1 and IL33 SNPs significantly contributed to PC1 and PC2. HSP90AA1 has been confirmed to be associated with thermal stress susceptibility of sheep in previous studies51, while IL33 was found to affect the sheep immunity and resistance to gastrointestinal intestinal nematode infection53, which is also associated because heat stress promotes to immune suppression and increases animal vulnerability to illnesses54. Our PCA findings validate these two SNPs as potential candidates for heat adaptability across the sheep breeds investigated in the current study, although more research is needed in order to clarify the relationship between the investigated SNPs and heat resistance.From the loading biplot, HSPA12A and HSPA8 SNPs are potential candidate markers for thermotolerance adaptation in sheep, whereas principal component analysis confirmed that HSP90AA1 and IL33 SNPs were the primary potential candidates. The results contribute to an increase in knowledge regarding the genetic variability of SNPs for thermotolerance adaptation in sheep. However, more studies are needed in order to clarify the relationship between the studied SNPs and heat resistance in sheep.Based on our 17 SNP polymorphism analyses performed on 601 animals, we found that the KASP-PCR method represents a feasible method for investigating polymorphisms in different sheep breeds. Furthermore, based on allele and genotype frequency, we validated that 55 recommended method from hair follicles and56 method from the blood was used for DNA genomic isolation. The DNA was kept at − 20 °C until analysis. The concentration of DNA was determined using a NanoDrop Spectrophotometer . All samples were diluted to a uniform concentration, and genotyping was performed using the equivalent of 50 ng of DNA per sample.Samples were collected from 720 sheep belonging to17 breeds adapted to different climatic conditions, originating from 4 countries  and marker-assisted selection studies across the sheep genomees Table . The OviThe bi-allelic discrimination of the selected 51 SNPs was performed using Kompetitive Allele Specific PCR . SNP Viewer software version 1.99  was used to visualize the results. All genotype data were exported for statistical analysis. Only SNPs that appeared in at least 50% of the breeds were considered. Data quality control of genotyped data included discarding animals with a call rate of less than 50% and the SNPs with call rates < 50%. This led to discrepancies in either the number of animals per breed or the number of SNPs per animal.60The raw allele calls obtained from LGC Genomics were analyzed using LGC Genomics' KlusterCaller program. Gene diversity, allele and genotype frequencies, and their accordance with or deviation from the Hardy–Weinberg equilibrium were determined by POPGENE software version 1.3161 and ggplot262 packages from the R Program63 to visualize the genetic divergences between sheep breeds that were divided according to their climatic characteristics; cold-tolerant breeds , heat tolerance breeds originated from Morocco , and heat tolerant breeds reared in Europe .The Principal Component Analysis was done using FactoMineRThe authors confirm that the experiment complied with the European Union's Directive on Animal Experimentation (Directive 2010/63/EU) and ARRIVE guidelines. All animals in the experiment underwent standard procedures without experiencing any harm or discomfort, and all procedures were carried out in compliance with applicable guidelines and regulations. The study was approved by the Scientific and Ethics Committee of Centre for Agricultural Genomics and Biotechnology, University of Debrecen (Ethics statement No. 07).Supplementary Information.


FOXP4, FOXO3, ZSCAN2, and EBF4, involved in lipid metabolism-related pathways. These results helped us to improve our knowledge about the genetic architecture behind important traits in cattle.Understanding the architecture of gene expression is fundamental to unravel the molecular mechanisms regulating complex traits in bovine, such as intramuscular fat content (IMF) and backfat thickness (BFT). These traits are economically important for the beef industry since they affect carcass and meat quality. Our main goal was to identify gene expression regulatory polymorphisms within genomic regions (QTL) associated with IMF and BFT in Nellore cattle. For that, we used RNA-Seq data from 193 Nellore steers to perform SNP calling analysis. Then, we combined the RNA-Seq SNP and a high-density SNP panel to obtain a new dataset for further genome-wide association analysis (GWAS), totaling 534,928 SNPs. GWAS was performed using the Bayes B model. Twenty-one relevant QTL were associated with our target traits. The expression quantitative trait loci (eQTL) analysis was performed using Matrix eQTL with the complete SNP dataset and 12,991 genes, revealing a total of 71,033 cis and 36,497 trans-eQTL (FDR < 0.05). Intersecting with QTL for IMF, we found 231 eQTL regulating the expression levels of 117 genes. Within those eQTL, three predicted deleterious SNPs were identified. We also identified 109 eQTL associated with BFT and affecting the expression of 54 genes. This study revealed genomic regions and regulatory SNPs associated with fat deposition in Nellore cattle. We highlight the transcription factors  Over the last years, several studies have shown a growing interest in understanding the molecular mechanisms regulating carcass and meat quality traits in beef cattle . CarcassFat deposition in beef cattle depends on several intrinsic and extrinsic factors, such as the stage of growth, physiological maturity, nutrition, and genetics . ResearcUnderstanding the architecture of gene expression is fundamental to unraveling the molecular mechanisms regulating complex traits . In prevThe expression quantitative trait loci (eQTL) mapping effectively integrates genetic variations and gene expression at the whole-genome level . eQTL daThe experimental procedures related to animal handling and care were approved by the Institutional Animal Care and Use Committee Guidelines from EMBRAPA (CEUA 01/2013).A population of 193 Nellore steers, derived from an experimental herd of the Brazilian Agricultural Research Corporation (EMBRAPA), and originated from 34 unrelated bulls representing the principal Brazilian Nellore genealogies, was used in the current study. Between the years 2009 and 2011, the animals were raised in grazing systems and finished in feedlots with the same handling and nutritional conditions. The steers were slaughtered at an average age of 25 months and 452 kg in a commercial slaughterhouse located in Bariri , following the Brazilian Ministry of Agriculture, Livestock and Food Supply (MAPA) regularization. More details are provided elsewhere .Longissimus thoracis (LT) muscle sample of approximately 5 g was collected from the right side of each carcass between the 12th and 13th ribs immediately after the animal’s death and stored in ultra-freezer at −80°C until the analysis. For measurements of intramuscular fat content  and backfat thickness , a beef sample of the LT muscle  was collected 24 h after slaughter. For IMF analysis, beef samples of approximately 100 g were lyophilized and ground, then IMF was achieved using the Ankom XT20 extractor, following the AOCS protocol  was used.For total RNA extraction, a sample of 100 mg of the LT muscle was processed using the Trizol reagent , following the manufacturer’s guidelines. After extraction, RNA integrity was verified using the Bioanalyzer 2100 , and the samples presenting RNA integrity numbers (RIN) greater than 7 were considered for the next analyses. A total of 2 µg of RNA from each sample was used for the cDNA library preparation, according to the protocol described in the TruSeq RNA Sample Preparation kit v2 guide . The libraries were sequenced using the Illumina HiSeq2500 ultra-high-throughput sequencing system with the TruSeq SBS kit v3-HS (200 cycles), as described in Bos taurus ARS-UCD1.2 was carried out using STAR (Spliced Transcripts Alignment to a Reference)   v. 2.7 wference)  v. 0.11.Read counts for each gene were normalized to CPM (Counts per million) using the Bioconductor package edgeR , and theBos taurus dbSNP (release 96) was used as known variants. The HaplotypeCaller algorithm was used to call the variants individually, generating GVCF files for each sample. These files were then merged using the CombineGVCF tool, and the joint genotyping analysis was performed using the GenotypeGVCF. In the end, a VCF file with all samples genotyped was achieved. After the variant calling, we filtered the SNP for variant quality score (QUAL) ≥ 30 and total depth of coverage (DP) > 10, using BCFtools  v. 4.1.0.0 was used in the Genomic Variant Call Format (GVCF) mode . Using tBCFtools  v. 1.9. BCFtools  v. 95.2.Previously to the association study, the filtered SNPs from the RNA-Seq variant calling and the Bovine HD BeadChip were combined into one complete dataset. BEDTools  v. 2.27.y was the vector of phenotypic values, X represented the incidence matrix for fixed effects, b was the vector of fixed effects, K was the number of SNP variants , aj was the column vector representing the SNP covariate at locus j, assumed to be normally distributed N  when δj = 1, but βj = 0 when δj = 0, with δj being a random variable 0/1, indicating the absence (probability π) or presence (probability 1-π) of locus j in the model, and e represented the vector of residuals associated with the analysis. In the model, the contemporary group  was set as a fixed effect and hot carcass weight as a covariate.The GWAS was performed using the GenSel software  with a BThe GenSel program uses Markov-Chain Monte Carlo (MCMC) to estimate the effect of each SNP among all SNPs in each interaction. In this procedure, 41,000 interactions, with the first 1,000 interactions being discarded, were accumulated to obtain the posterior mean effect for each SNP. A map file was used to position the SNPs into 2,502 non-overlapping 1 Mb SNP windows. Manhattan plots containing the variance explained by each SNP window along the autosomal chromosomes were constructed for each trait. Based on an infinitesimal model , it is eBos taurus ARS-UCD1.2 genome, available on Cattle QTLdb. We also checked for previously detected QTL reported by our research group for the interest traits .The Cattle QTL database   and the genes regulated by them, we used Cytoscape software  from Clarivate  with the Mus musculus database.To verify if the SNPs within relevant QTL regions were also affecting the gene expression, an overlap analysis using those SNPs and the list of cis and trans-eQTL (FDR < 0.05) were performed utilizing the GNU/LINUX environment. Considering all the eQTL (cis and trans) within QTL regions, we used PLINK  v. 1.9 tsoftware  to buildBos taurus ARS-UCD1.2 genome. In this analysis, 85.71% of the reads were uniquely mapped to the reference genome. In the current study, the Nellore steer population presented mean phenotypic values of 2.93% for IMF and 6.86 mm for BFT, with genomic heritability of 0.25 and 0.18 for IMF and BFT, respectively. From the high-density genotyping SNP data, we obtained a total of 414,879 SNPs that passed all the quality control filters . Concerning the RNA-Seq data, an average of 18.45 million reads per animal were used as input for mapping to the The GATK variant calling analysis allowed us to identify 123,300 SNPs that passed all the quality control filters . There were 3,251 SNPs that overlapped the SNP panel used for genotype (Illumina HD BeadChip). These were used to validate our SNP discovery and revealed 95.85% concordance between genotypes. Variants in the same position as the high-density SNP panel were removed for the next analysis due to their high genotype similarity, and we proceeded with 120,049 unique variants.Bos taurus autosomal chromosomes (BTA) and the most severe consequences predicted by VEP. Most SNPs were in 3′UTR regions (23.24%), followed by downstream genes (18.82%) and intron variants (12.67%). Besides these, 25.28% were synonymous variants, while 9.81% were classified as missense mutations. The SIFT score predicted 2.85% of deleterious SNPs. A complete overview of the results obtained in the annotation analysis can be seen in Functional annotation analysis of the 120,049 SNPs revealed 9,018 novel variants. Bos taurus genome passed all the quality control filters. To empower the SNP database with coding and regulatory variants, we combined the Illumina panel with transcribed variants obtained from the RNA-Seq calling analysis . As a result, for the GWAS, a total of 534,928 SNPs were associated with the phenotypes. The Illumina bovine HD SNP panels were designed to cover the entire genomes with equally spaced polymorphic SNPs across different breeds. In the current study, 414,879 SNPs from the Starting with IMF, we found eleven relevant QTL windows positioned on BTA1, 3, 7, 10, 13, 19, 20, 21, and 23 . The QTLFor BFT, we found 10 relevant QTL  with 106We identified 71,033 cis-eQTL and 36,497 trans-eQTL (FDR < 0.05) distributed along the genome, with 5,718 SNPs acting both as cis and trans-eQTL. VEP analysis showed that most of the cis and trans-eQTL were located on BTA19 and BTA23, respectively, while BTA20 had fewer local and distant variants . MoreoveARNT, FOXO3, FOXP4, NFYA, ZFP2, ZNF354C, and ZSCAN2. Besides that, 12 eQTL were missense mutations ; followed by the novel SNP 21:22425675 (chromosome: position), a synonymous variant located on the ZNF592 gene, that affected the expression of nine genes (seven genes in trans and two genes in cis), being two TFs (FOXO3 and ARNT). Moreover, within the genes regulated by eQTL associated with IMF, the two genes regulated by the larger number of eQTL were the pseudogene ENSBTAG00000052719 (BTA7) by 21 cis-eQTL, and the TMEM87A (BTA10) by 13 cis-eQTL. In To identify eQTL that could be associated with our phenotypes, we overlapped the eQTL and GWAS results. Our analysis revealed that 231 and 109 eQTL variants were located on relevant QTL associated with IMF and BFT, respectively. Within the 231 eQTL associated with IMF , which is affected by nine eQTL (cis and trans), and the novel gene ENSBTAG00000025383 (BTA12), which is affected by eight eQTL (cis and trans). Associated with BFT, there were 74 cis, 20 trans, and 15 both cis and trans-eQTL. These variants were located on the SNP windows BTA9_46, BTA5_104, BTA17_18, BTA7_3, BTA13_52, BTA7_96, and BTA21_23 see . TogetheAt last, to investigate if the genes regulated and containing eQTL associated with the interest traits were involved in lipid metabolism-related pathways, we did an enrichment analysis of these genes . This analysis revealed some interesting pathways, such as signal transduction, cell cycle, development, and transport, underlining the AKT and WNT signaling. In the present study, we used an SNP dataset constituted by RNA-Seq variants and a high-density genotyping panel to perform an integrative analysis between GWAS and eQTL. The idea was to find gene expression regulatory polymorphisms associated with intramuscular fat and backfat thickness in bovine. Backfat thickness and intramuscular fat deposition are of economic importance to the beef cattle industry. The BFT is the best predictor of overall fatness in the animal’s body, impacting carcass cutability and meat yield . The IMFGWAS has been widely used to identify genetic variants and putative candidate genes associated with complex phenotypes . However2) of 0.21. Here, the two QTL with the highest effect for BFT were detected on BTA9 and BTA1, and each explained 0.45% of Vg, with a slightly smaller h2 of 0.18. In another study using the same population (2 = 0.25), while in this study, the highest effect QTL was detected on BTA23 and it explained 0.84% of Vg (h2 = 0.25).With the incorporation of RNA-Seq-based SNP, we were able to identify QTL associated with IMF and BFT that better explained the genetic variance of the phenotype compared with previous works , even uspulation  with 386Complementary to the GWAS analysis, we identified 71,033 cis-eQTL and 36,497 trans-eQTL in this Nellore population, most of them from the RNA-Seq dataset, showing the importance of adding transcribed variant calling information on eQTL mapping, as stated by There are several advantages in integrating GWAS and eQTL data. Expression quantitative trait loci are essential for understanding the genetic basis of cellular processes and complex traits . MoreoveFOXP4 TF, positively regulated by a cis-eQTL (rs449704044) located downstream of the novel gene ENSBTAG00000054479, and harbored on the most relevant QTL for IMF (BTA23 at 15 Mb), that explained 0.84% of Vg. The Forkhead box (Fox) family genes are expressed in various tissues, acting both in developmental processes as in tissue maintenance during adult life  and the CCAAT enhancer-binding protein α (C/EBPα), major promoters of adipogenesis  located on the exonic region of ZNF592 gene. This SNP also regulates six more genes in trans and two genes in cis . This binding forms a heterodimer that attaches to Xenobiotic/Dioxin response element sequences (XRE/DRE) of different target genes, activating mRNA transcription -regulated transcription coactivator (CRTC) family and plays an important role in lipid and glucose metabolism . In a more recent work, CRTC3 changes the metabolic profile in intramuscular adipocytes, and also promotes adipogenic differentiation of intramuscular and subcutaneous adipocytes through the calcium signaling pathway. We also highlight that in the same genomic region , we found two other eQTL (rs379449619 and rs525656948) located on the CRTC3 gene associated with IMF in our population, emphasizing its relevance for IMF molecular regulation. As for IQGAP1, this gene encodes a ubiquitously expressed scaffolding protein implicated in several cellular processes, including mitogen-activated protein kinase and AKT signaling cascades . Deleterious mutations can be defined as genetic alterations that raise individual susceptibility or predisposition to diseases/disorders. These variations often occur in coding regions and are typically missense, causing changes in the amino acid sequence, and consequently, in the protein (WDR73 gene), which changes an Arginine for a Cysteine in the protein sequence, was the only missense eQTL presenting cis-effects. WDR73 is associated with a lower meat quality index  in Angus-Brahman steers. Although missense mutations on the WDR73 gene were already reported in humans associated with neuro disorders  family exerts a principal role in the extracellular matrix (ECM) maintenance and remodeling, mainly by participating in collagen biosynthesis  harbored on the PCED1A gene  in the FSD2 gene  enzymes are components of the Complex I oxidative phosphorylation system in mitochondria. In mammals, almost all the ATP molecules required by the cells are generated by oxidative phosphorylation in the mitochondrial respiratory chain  with a high-density genotyping panel, allowed us to identify relevant genomic regions and regulatory polymorphisms associated with intramuscular fat and backfat thickness of Nellore cattle. Within the genes regulated by eQTL associated with the interest traits, we highlight that the transcription factors


These results are consistent with the findings of earlier studies on other genetic features . Therefore, our observations suggest that the genomic changes in the mutants generated by gamma irradiation occurred at the same loci as the mutations in the natural soybean population. These findings are indicative of the existence of mutation hotspots, or the acceleration of genome evolution in response to high doses of radiation. Moreover, this study demonstrated that the integration of GBS and GWAS to investigate a mutant population derived from gamma irradiation is suitable for dissecting the molecular basis of complex traits in soybeans.In this study, we performed a genotyping-by-sequencing analysis and a genome-wide association study of a soybean mutant diversity pool previously constructed by gamma irradiation. A GWAS was conducted to detect significant associations between 37,249 SNPs, 11 agronomic traits, and 6 phytochemical traits. In the merged data set, 66 SNPs on 13 chromosomes were highly associated (FDR  Glycine max L.), which is one of the most important agricultural crops worldwide, is a source of proteins, oils, carbohydrates, lipids, and essential minerals [Soybean .Mutations are sudden genetic changes in the DNA of living cells that are not caused by genetic segregation or genetic recombination. Mutations induced by ionizing radiation range from simple base substitutions to single- and double-strand DNA breaks . Howeverneration . Mutationeration . SpecifiTMS5 of the T98S rice mutant irradiated with gamma radiation can convert cytosine (C) to adenine (A) in cds.71 to identify the mutation and act as a mutation hotspot. In addition, Xiong et al. [Mutation is a common phenomenon in organisms, and all gene sites are not equally mutable. The points that mutate at a higher frequency are often called “mutation hotspots”  and are g et al.  reportedA single nucleotide polymorphism (SNP), which is a genetic variation at a specific nucleotide position of a genomic sequence among individuals, is usually caused by natural mutations and stresses  . AdditioAdvances in next-generation sequencing technologies have aided the development of SNP detection methods. For example, GBS has recently emerged as a promising technique for simultaneously identifying SNPs and genotyping highly diverse species with large genomes . This meA genome-wide association study (GWAS), which is a powerful tool for identifying significant marker–trait associations, involves the detection of causative allelic variations at individual SNP markers related to a natural phenotypic variation. To conduct a GWAS, a large number of markers is required to provide adequate coverage of the entire genome. According to one estimate, tens of thousands of markers are necessary for the soybean genome . Recent In a previous study, we constructed a mutant diversity pool (MDP) from 7000 gamma-irradiated soybean seeds and evaluated the genetic similarity among 208 soybean mutant lines using target region amplification polymorphism (TRAP) markers . In the The GBS library constructed from 192 soybean MDP lines was sequenced using the Illumina HiSeq 2500 platform, which yielded approximately 940 million reads, with a mean quality score of 34.65 . The demFinally, 37,673 SNPs were selected after applying the following filtering parameters: minimum depth = 5, minimum genotype quality = 20, and max-missing = 0.6 . The average depth and average genome-wide transition/transversion ratio after filtering were approximately 22X and 1.6, respectively. The number of SNP transitions ranged from 16 (HK-32) to 12,456 (BS-25), whereas the number of SNP transversions varied from 6 (HK-32) to 7979 (BS-74) . The SNPA UPGMA-based dendrogram was constructed to clarify the genetic relationships among the 192 soybean MDP lines. At a genetic distance of 0.092, the 8 wild-type cultivars and their mutants were divided into approximately 6 major groups . Group 1K values ranging from 2 to 15 and the entire panel of high-quality SNPs. The estimated marginal likelihood was highest for K = 8. Each accession was assigned to one or more groups, depending on whether or not its genotype indicated it was admixed. The results of this analysis were consistent with the dendrogram topology. As denoted by different colors, the main membership composition of the eight wild-type lines in the corresponding groups was as follows: wild-type KAS523-7 in Group 1 was 100% orange, BS in Group 2 was 87% orange, 94Seori in Group 3 was 62% red, PD and KAS360-22 in Group 4 were 92% and 43% red, respectively, HK in Group 5 was 89% blue, and DB and DP in Group 6 were 78% and 100% green, respectively. When the dendrogram and the results of the population structure analysis were considered together, 96% of the 192 mutant lines  were grouped with the corresponding wild-type line.The population structure of the 192 soybean MDP lines, based on the genotypes acquired in this study, was analyzed using fastSTRUCTURE. Specifically, the population structure was assessed using The data for five qualitative  and six quantitative  agronomic traits, as well as six phytochemical traits  for the 192 soybean MDP lines were obtained from earlier studies ,41.p < 0.05) with the following four agronomic traits: DF (33 SNPs), FC (16 SNPs), NN (6 SNPs), and SCC (11 SNPs) (p = 2.20 × 10−10). One SMTA was detected on chromosomes 1 (p = 8.22 × 10−5), 7 (p = 0.00011), 11 (p = 3.58 × 10−6), 12 (p = 7.24 × 10−5), 15 (p = 2.50 × 10−5), and 20 (p = 1.52 × 10−5). Two SMTAs were detected on chromosomes 2 (p = 8.55 × 10−6) and 13 (p = 4.30 × 10−5), whereas three were detected on chromosome 19 (p = 5.62 × 10−6) (p < 0.05) with FC. Fourteen major associations were detected on chromosome 13 (p = 1.02 × 10−10). One SMTA was detected on chromosomes 5 (p = 1.64 × 10−5) and 12 (p = 2.51 × 10−5) (p = 2.37 × 10−7). One SMTA was detected on chromosome 9 (p = 1.40 × 10−5). The analysis also confirmed the presence of major SMTAs on chromosome 19  (p = 2.43 × 10−6), whereas three were detected on chromosome 20 (p = 5.92 × 10−6). We confirmed the presence of major SMTAs on chromosome 8  (p ≥ 0.05)  . The ass × 10−6) a. A tota × 10−5) b. The as × 10−7) c. Our an × 10−7) d. In con ≥ 0.05) . Althoug ≥ 0.05) .Glyma.06g198100, Glyma.06g204600, Glyma.06g205600 (RGP3 and RGP), Glyma.06g205900 (GAUT11), Glyma.06g208300 (TET11), and Glyma.06g211600. All of the SNPs were located in genic regions, including four and three nonsynonymous and synonymous SNPs, respectively. The locus most highly associated with DF, Chr06_19,461,588  in Glyma.06g205600, was revealed to contain a nonsynonymous SNP. Specifically, compared with the wild-type sequence, T was replaced by C, which resulted in an amino acid change from Val (GUA) to Ala (GCA). For FC, the 13 detected SNPs were located in 13 different genes, of which the following 12 were on chromosome 13: Glyma.13g070400, Glyma.13g070800, Glyma.13g070900 , Glyma.13g071400 (ATHSP22.0), Glyma.13g072000 (SHT), Glyma.13g072600, Glyma.13g073400 (MYB33 and ATMYB33), Glyma.13g073500, Glyma.13g076300, Glyma.13g076800 (EIN3 and AtEIN3), Glyma.13g078500, and Glyma.13g078800  in Glyma.13g073400, contained a nonsynonymous SNP; the C-to-T change caused the amino acid to change from Ser (UCG) to Leu (UUG). The analysis of the SNPs in the candidate genes for NN indicated that all six SNPs were in genic regions . For SCC, nine candidate genes were identified, among which the following five included six major SNPs and were detected on chromosome 8: Glyma.08g124900 (ZKT), Glyma.08g126500, Glyma.08g136600, Glyma.08g247500, and Glyma.08g249910 (RGP2 and ATRGP2). These SNPs were mainly located in genic regions .To identify candidate genes for DF, FC, NN, and SCC, the genes that were highly associated with SNPs  were ana3g078800 . The locIn this study, we identified SNPs in 192 soybean MDP lines using GBS data. Approximately 980 million reads were obtained, with 30.6 (Gm01) to 49.6 (Gm13 and 18) SNPs per Mb (average of 38.5) . In an eA previous examination of genetic diversity on the basis of 16 TRAP markers divided 208 soybean mutants into four groups . Additiop < 0.05) with DF (33 SNPs), FC (16 SNPs), NN (6 SNPs), and SCC (11 SNPs). We confirmed the existence of a major SMTA for DF on chromosome 6  (p = 7.08 × 10−8) on chromosome 6. Several studies of natural soybean populations [p = 1.02 × 10−10) (p = 2.37 × 10−7) (p = 5.89 × 10−36) on the same chromosome determined by Fang et al. [p = 1.16 × 10−7) (p = 1.20 × 10−17) on this chromosome identified during an earlier GWAS analysis of a natural population [p < 0.05 in seven agronomic and six phytochemical traits  at extremely low doses under natural conditions [TMS5 may be a mutation hotspot in the thermosensitive genic male sterile rice line T98S (induced by 300 Gy gamma irradiation). On the basis of transcriptome sequencing data, Xiong et al. [dm1–dm4). Although all four of these mutants were derived from the winter cultivar Jing411, two were the result of a 250 Gy gamma irradiation (dm1 and dm2) and two were generated following a 1–1.5% EMS treatment (dm3 and dm4). In the current study, we compared the wild-type and mutant lines to detect the presence of 66 SNPs highly associated with specific traits in the mutants. Of the 20 SNPs associated with DF , 19 were detected in all 15 PD mutants and in 39–49 of the 53 DP mutants. Similar results were obtained for the SNPs associated with FC, NN, and SCC. More specifically, all 5 BS mutants had 12 SNPs associated with FC; 29–50 of 60 DB mutants had 14 SNPs associated with FC; 12–15 of 15 PD mutants had 7 SNPs associated with FC; 7–13 of 53 DP mutants had 14 SNPs associated with FC; 1–2 of 4 94Seori mutants had 6 SNPs associated with FC; 25–29 of the DB mutants had 5 SNPs associated with NN; 6–7 of the PD mutants had 5 SNPs associated with NN; 34–35 of the DP mutants had 2 SNPs associated with NN; all BS mutants had 1 SNP associated with SCC; 2–10 of the DB mutants had 6 SNPs associated with SCC; and 1–6 of the DP mutants had 3 SNPs associated with SCC. However, in the HK mutants, the genomic regions corresponding to the 66 SNPs were relatively unaffected by the gamma irradiation, with only 4–5 of 45 mutants with 3 SNPs associated with FC, and only 1 mutant with 1 SNP associated with SCC. Accordingly, our findings may reflect the existence of mutation hotspots. These mutated loci might exhibit accelerated genome evolution in response to high gamma-ray doses. Indeed, all of the M1 seeds of the MDP lines were irradiated with 250 Gy gamma rays using the 60Co gamma irradiator when the soybean MDP was constructed [In this study, we identified candidate genes and validated our GBS approach. A genome-wide association analysis was performed for 11 agronomic traits  and 6 phytochemical traits . A total of 66 SNPs on 13 chromosomes were highly associated FDR × 10−10) a. In a Gulations ,31,32,45× 10−10) b. This r with DF  SNPs, FC× 10−10) . We also × 10−7) c. This Sg et al.  in a pre × 10−7) d, which pulation . Anotherpulation  also detl traits . There a with DF  SNPs, FCnditions ,52. Manynditions  suggeste−10 a. Instructed .Glyma.06g205600  on chromosome 6 was identified as a candidate gene for DF and was predicted to be highly correlated with the flowering time (AtRGP2); the flowering time of the transgenic lines was 1.5- to 2-times longer than that of the wild-type control. Moreover, the transgenic plants were stunted, with a rosette-like growth pattern, and their source leaves exhibited severe chlorosis, increased photoassimilate retention, and starch accumulation, which resulted in increased fresh and dry leaf weights. Thus, we speculate that the protein encoded by the candidate gene Glyma.06g205600 modulates the flowering time because it adversely affects the pollen structure. Ambawat et al. [Glyma.13g073400  associated with FC encodes MYB33. In Arabidopsis, AtMYB33 influences responses to hormones [In the present study, ing time . The prot et al. ,58. Anthhormones , as wellGenome-wide association studies are useful for identifying genetic loci related to traits of interest . The det12 generation were phenotypically assessed for the following 11 agronomic traits: growth type (GT), flower color (FC), seed coat color (SCC), seed hilum color (SHC), and stem anthocyanin (SA) ; and days of flowering (DF), maturity days (MD), seed index (SI), plant height (PH), node number (NN), and ramification number (RN)   used in this study were derived from 2 soybean landraces (KAS523-7 and KAS360-22) and 6 representative Korean soybean cultivars  from an earlier study . The 192m et al. ). The fom et al. ).Genomic DNA was extracted from the leaves of each mutant line using the DNeasy 96 Plant kit . After quantifying the extracted DNA using the NanoDrop ND-1000 spectrophotometer , the DNA concentrations were adjusted to 50–100 ng/µL  for the GBS analysis.ApeKI . Bar-coded adapters were then ligated to the digested fragments. The 192 barcode sequences (4–8 nucleotides long) used for tagging the samples are listed in A GBS approach was used for SNP genotyping. Specifically, a GBS library was prepared by digesting the DNA of individual soybean plants with K = 2… 15. This program has been widely used to calculate posterior inference on the basis of the Bayesian framework [Genetic relationships were investigated by constructing a UPGMA-based dendrogram using TASSEL software  . The popramework . A phyloTo ensure sufficient evaluation of the genetic diversity of the 192 soybean MDP lines, 17 phenotypic traits, comprising 5 qualitative traits , 6 quantitative traits , and 6 phytochemical traits  were included in the GWAS analysis. After filtering SNPs with <10% missing data, 37,249 SNPs were selected and used for the GWAS, which was performed using a compressed mixed linear model . All anaIn summary, we previously constructed an MDP core collection that reflects the diversity among soybean lines in terms of agronomic traits. We focused on identifying loci that were identical to the genetic loci in existing natural populations. We revealed substantial variations in 17 soybean agronomic traits, including 6 phytochemical traits, and demonstrated the utility of GWAS for detecting genetic factors underlying important agronomic traits. The genetic basis of these traits was then dissected in an association analysis using 37,249 SNPs obtained by GBS. A total of 66 SNPs on 13 chromosomes were determined to be highly associated with the examined traits. All of the major SNP markers for four agronomic traits  were detected at the same genomic loci in the mutants and the natural populations. The soybean MDP core collection is a useful resource for future research on soybean genetic diversity, as well as soybean mutation breeding. Furthermore, the SNPs described herein may be applicable as new markers in future soybean studies.


Oryza meridionalis populations exhibit a high level of homozygous polymorphisms relative to domesticated rice, inferring the fixation of distinct wild and domesticated alleles. Alleles of the seed shattering genes (SH4/SHA1 and OsSh1/SH1) present in the shattering-prone O. meridionalis populations are likely to be functional, while the dysfunctional alleles of these seed shattering genes are found in domesticated rice. This confirms that unlike Asian wild rice populations, Australian wild rice populations have remained genetically isolated from domesticated rice, retaining pre-domestication alleles in their wild populations that uniquely allow the impact of domestication on the rice genome to be characterized. This study also provides key information about the domestication loci in Australian wild rice populations that will be valuable in the utilization of these genetic resources in crop improvement and de novo domestication. The domestication syndrome is defined as a collection of domestication-related traits that have undergone permanent genetic changes during the domestication of cereals. Australian wild rice populations have not been exposed to gene flow from domesticated rice populations. A high level of natural variation of the sequences at domestication loci  was found in Australian AA genome wild rice from the primary gene pool of rice. This natural variation is much higher than that found in Asian cultivated rice and wild Asian rice populations. The Australian  Oryza sativa L. and Oryza glaberrima Steud. evolved independently from the AA genome of wild Oryza rufipogon Griff. through the domestication process in the last 10,000 years [Oryza subspecies, namely, Oryza sativa ssp. Japonica and Oryza sativa ssp. indica, evolved through single or multiple domestication events. Numerous research studies based on molecular and archaeological studies have not unanimously resolved this debate. Recently, the analysis of domestication genes has gained much attention for mining more reliable information regarding domestication events [Rice is one of the most important domesticated crops and a model species for cereal crops. Two cultivated rice species, namely, 00 years ,3. The dn events . In the n events ,6,7. Then events ,9. DomesPROSTRATE GROWTH 1 (PROG1) [LONG AND BARBED AWN 1 (LABA1) [BROWN PERICARP AND SEED COAT (Rc) [Shattering (QTL)-1 (qSH1) [SHATTERING 4 (SH4) [PROG1 gene, a 14 bp deletion in Rc coding sequences, and a single nucleotide change (G273T) in the SH4 gene resulted in domestication phenotypes. Many genes including GS3 (GRAIN SIZE 3), GS5 (GRAIN SIZE 5), GW2 (GRAIN WIDTH 2), GL3.1 (GRAIN LENGTH 3.1), GL7 (GRAIN LENGTH 7), GW5 (GRAIN WIDTH 5), GLW7 (GRAIN LENGTH AND WEIGHT ON CHROMOSOME 7), GW8 (GRAIN WIDTH 8), and GIF7 (GRAIN INCOMPLETE FILLING 7) have been identified that are involved in the cell division mechanism to control grain shape and width subjected to selection during the domestication process [The genetic basis of domesticated loci is primarily well studied in Asian rice, as the genome of Asian rice is completely sequenced and mapped . Some of (PROG1) , LONG AN (LABA1) , BROWN POAT (Rc) , Shatter1 (qSH1) , and SHA 4 (SH4) , whose r process ,23,24,25Oryza species have been currently recognized from northern Australia: an annual and perennial Oryza meridionalis and a perennial Oryza rufipogon-type taxa [O. rufipogon was closely related to Asian Oryza rufipogon Griff. and Oryza nivara Sharma et Shastry and subsequently formed a cluster together with cultivated O. sativa, while O. meridionalis is genetically distinct from Asian O. rufipogon [O. meridionalis from the other Oryza species around 4.8–2.41 million years ago (Ma.) [O. rufipogon approximately between 0.4 and 2 Ma. in the Early Pliocene, long before the domestication of O. sativa and the dispersal of O. rufipogon to Australia [O. rufipogon and O. nivara populations with few domestication genes [O. rufipogon-type taxa, and O. meridionalis are novel taxa of the AA genome clade that includes domesticated rice [O. rufipogon, while O. meridionalis Ng. represents a divergent gene pool [O. rufipogon type and O. meridionalis were observed in nature, indicating the ongoing reticulate evolution of these species [Two new AA genome ype taxa . A phyloufipogon . That migo (Ma.) ,29, and ustralia . These tustralia . The anaon genes ,31. Receted rice . The forene pool . Thus, t species .O. sativa ssp. japonica cv. Nipponbare.We aimed to determine the sequence variability of domestication loci in wild rice populations determined as single nucleotide polymorphisms (SNPs) relative to O. sativa genome (Os-Nipponbare-Reference-IRGSP-1.0) exhibited a considerable sequence variation among the 26 samples (O. rufipogon-type taxa (WR24), 20 O. meridionalis, and 5 hybrids between these two species found in the wild . The average sequence read depth (times reference length) varied from 20.7 in WR153 to 46.9 in WR103, while an average mapped sequence read depth (times reference length) varied from 8.88 in WR24 to 46.7 in WR103. The highest and lowest percentages of mapped reads were observed in WR44 (72.0%) and WR24 (24.0%), respectively. Similarly, the highest percentage of mapped bases was 71.9% in WR44 and the lowest in WR24 (23.8%). The total consensus sequences length ranged between 257,716,289 bp (WR37) and 322,074,565 bp (WR24). The total consensus as a percentage of the reference varied from 67.4% (WR37) to 83.0% (WR44). The output of the mapped reads was mentioned in our earlier publication [The alignment of reads to the common high-quality  samples . These slication , and maylication .qSH1 locus, the heterozygous SNPs ranged from 1 to 58 . On the contrary, all the samples from O. meridionalis had a high number of homozygous SNPs ranging between 32 and 53 compared to the O. rufipogon-type taxon (3 SNPs) and early generations of hybrids WR44 (4 SNPs) and WR62 (10 SNPs). The non-synonymous amino acid changes due to homozygous SNP were common in all O. meridionalis samples  showed the highest number of heterozygous SNP, amounting to 22 and 11, respectively. However, only 3 non-synonymous amino acid changes occurred in WR62 (early generation hybrid) by a heterozygous single base substitution in the 729 (G>C), 500 (C>T), and 472 (A>G) base positions resulting in an amino acid change in Gln243His, Ala167Val, and Thr158Ala, respectively  in WR143 and 2 non-synonymous amino acid changes (Leu226Pro for 677T>C and Ala155Ser for 463G>T) were determined in WR265. Non-synonymous amino acid changes due to homozygous SNPs occurred in O. meridionalis ranging between 1 and 4 of which important domestication-related amino acid changes from asparagine to lysine occurred at base position 79 (273T>G) located at 34,232,985 bp of chromosome 4  to 40 (WR44) in hybrids. However, only 2 non-synonymous amino acid changes for homozygous SNPs  were determined in all the studied species, except for WR62 which accounted for 1 non-synonymous amino acid change . No homozygous and heterozygous SNPs were determined in O. rufipogon-type taxa.Unlike  6 SNPs. . As a reAN3-1 gene which controls awn development in rice exhibited a conspicuous SNP variation in Australian wild rice populations (O. rufipogon-type taxa (WR24) for this locus. The number of heterozygous SNPs ranged between 2 in WR44 and 20 in WR161 in hybrid populations, and from 11 in WR280 to 31 in WR184 in O. meridionalis populations .The in AN3-1 B. Among in AN3-1 . No non-dionalis . DespiteRAE1 and RAE2) showed a quite similar trend of SNP variation  and WR126 (4 heterozygous SNPs)  (RAE2 locus. The number of homozygous SNPs was higher in the RAE1 locus compared to the RAE2 locus (RAE1 locus was lower in early generation hybrids (5 in WR44 and 6 in WR62) compared to later generation hybrids . Furthermore, the homozygous SNPs varied from 14 in WR233 to 59 in WR81 in O. meridionalis. A similar pattern of homozygous SNPs for the RAE2 locus was observed in hybrids and O. meridionalis  for the RAE1 locus . It is to be noted that 2 non-synonymous amino acid changes for the RAE2 locus resulted from both heterozygous and homozygous SNPs in the coding region (326G>C and 350A>C), resulting in amino acid changes from Arg109Pro and *117Ser , respectively.Two genes involved in the regulation of awn elongation  . Similar (WR126) . FurtherE2 locus . The numdionalis D. In theE1 locus . In addiGS2 (GRAIN SIZE 2) and GS5 (GRAIN SIZE 5) showed a distinct pattern of SNP variation across the 26 samples , of which 6 SNPs in the coding regions  resulted in non-synonymous amino acid changes: Gln264His, Ala261Ser, Ala237Val, Gly183Ser, Ser180Ala, and Met3Leu, respectively. No heterozygous SNP was determined for both wild rice taxon  that led to synonymous amino acid changes. The number of homozygous SNPs varied from 29 in WR233 to 71 in both WR207 and WR219 in O. meridionalis  which exhibited 71 homozygous SNPs. This gene functions to control grain size. It is likely that the larger grain size in the wild populations may reflect some loss of function of this gene. Mutations can accumulate in a gene that is not functioning as they have no impact on fitness, and the large number for WR207 may indicate a complete loss of the gene function. The maximum non-synonymous amino acid changes (3) were observed in O. meridionalis (WR207): Met467Ile for 1401G>T, Ile250Val for 748A>G, and Ala72Glu for 215C>A. Apart from these, the other O. meridionalis samples and later generation hybrids exhibited one amino acid change (Asp66His) due to a base change from G to C at the base position 196.In the case of the S2 locus . Only onGW8 (GRAIN WIDTH 8) gene corresponds to grain size, shape, and quality in rice [GS2, the highest number of heterozygous SNPs was observed in the early generation of the hybrid (WR44 = 56), compared to the later generation of the hybrid (WR153 = 1). As with the later generation of hybrids, the number of heterozygous SNPs for O. meridionalis was also less, amounting to 4 in WR207; followed by 3 in WR287; and 1 in WR37, WR143, and WR233  compared to the later generation of hybrids  and O. meridionalis (ranging from 19 to 57)  . Only 1 O. rufipogon-type taxa × O. meridionalis) shows a high level of heterozygous SNP variation, while homozygous SNP variation prevails in the later generations of hybrids and O. meridionalis. This is the expected result, as the homozygous SNP’s position in comparison between the species will be heterozygous at these positions, especially in early generation hybrids. It is noticeable that no or few SNPs are observed in the O. rufipogon-type taxa. This may be due to the low percentage of mapped reads unable to extract information of domestication loci. In addition, the small sample size (only 1) may have imposed a limitation. The homozygous loci may result from recurrent backcrossing with one of the parental taxa and become fixed over time in each species. O. meridionalis populations diverge from the other species around 4.8–2.41 Ma. [O. meridionalis populations serve as an ideal source for exploiting these genes for crop improvement.Understanding the natural variation, in terms of genomic polymorphisms , is crucial for the comprehensive analysis of genes and alleles in plant evolution and environmental adaptation . This st2.41 Ma. ,29. Thes2.41 Ma. . TherefoqSH1 and SH4, have the largest effect in the regulation of the abscission process compared to the other seed shattering-related loci [japonica and indica) have a lower level of genetic variation in terms of the nucleotide diversity at both loci than that found in their wild progenitors [O. meridionalis exhibiting a wild allele of shattering locus SH4 (orthologs to SHA1), which results in an amino acid change from lysine to asparagine at position +79. Lin et al. [SHA1) brought about an amino acid change from asparagine to lysine at position +79 in the trihelix DNA-binding domain family of plant transcription factor and a proline-rich region (such as the MYB DNA-binding factor) contributing to the reduction of seed shattering in cultivars. Such genetic changes switch off the cell separation process, but not the formation of an abscission zone. This gene is mapped to a 5.5 kb genomic region. Similarly, another seed shattering locus SH1 (OsSh1) also exhibits a wild allele for the seed shattering habit in O. meridionalis in the study. A non-synonymous base substitution from T to C at base position 70 results in phenylalanine to leucine at base position 24. A study on seed shattering locus OsSh1 reveals a missense variant (C to T) at base position +70 for cultivated japonica rice; however, a wild allele C is present in Asian O. rufipogon [OsSh1 (SH1), while low nucleotide diversity is found in japonica rice and moderate diversity in indica rice.Seed shattering is one of the key habits of wild rice that have been subjected to a domestication process. Wild rice develops an abscissic layer at the junction of sterile lemma and pedicle, which plays a crucial role in seed shattering. Two loci, ted loci . This stgenitors . This stn et al.  divulge ufipogon . They alO. rufipogon-type taxa shows shorter awns (4.6–5.0) mm and O. meridionalis exhibits longer awns (5.9–14.7) mm, while the hybrids exhibit an awn size from 6.9 mm to 11.8 mm [RAE1 and RAE2, have been targeted for artificial selection, and the dysfunctional alleles of these two genes are present in cultivated rice [RAE1 and LABA1 are determined inferring these loci almost get fixed in all O. meridionalis samples compared to the AN3-1 and RAE2 locus. Similarly, the Asian wild relatives contain the functional alleles of RAE1 or LABA1, demonstrating wild awn traits [RAE2 locus exhibits similar homozygous and heterozygous polymorphisms in all samples, inferring incomplete fixation over time. The previous study also postulates that this locus is not completely fixed in cultivated rice (93%) and wild progenitors with dysfunctional alleles of RAE2 exhibiting the awn elongation trait [Awnlessness is a desirable characteristic during the domestication process. The  11.8 mm . Two gented rice . In thisn traits ,42. Thison trait .GS2 locus, which encodes Growth-Regulating Factor 4 (OsGRF4), a transcriptional regulator, increases the grain weight and number in cultivated rice [GS5 encodes a putative serine carboxypeptidase and GW8 encodes the SBP-family transcription factor OsSPL16 positively controlling grain size and shape [O. rufipogon-type taxa is medium (5.81–5.90 mm), and O. meridionalis exhibits long-grain (6.20–6.80 mm) [GS5 locus is associated with grain size variation in rice [Similar to seed shattering and awn development traits, grain size loci also show a high level of genetic variation. The ted rice . GS5 encnd shape . The gra6.80 mm) . A previ in rice . TherefoO. rufipogon-type taxa (WR24), 20 O. meridionalis, and 5 wild hybrids   . These hype taxa . SamplesFresh leaves from each sample were pulverized with tissue lyser  in liquid nitrogen to prevent thawing throughout the pulverization process. The CTAB method  was follA local alignment of the 0.01 trimmed data to a reference Os-Nipponbare-Reference-IRGSP-1.0  was madeMapped reads were used to determine the basic variant analysis using the following parameters via CLC Genomic Workbench version 20 software: Ploidy = 2; ignore positions with coverage above = 100,000; restrict calling to target regions = not set; ignore broken pairs = no; ignore non-specific matches = no; minimum coverage = 10; minimum count = 3; minimum frequency (%) = 25; base quality filter = yes; base quality filter = yes; neighborhood radius = 5; minimum central quality = 20; minimum neighborhood quality = 15; read direction filter = no; read position filter = no; remove pyro-error variants = no.qSH1 (Shattering (QTL)-1, SH4/SHA1 (SHATTERING 4), SHAT1 (SHATTERING ABORTION 1), and SH1/OsSh1 (SHATTERING 1); four awn development loci including AN3-1 (Awn3-1), LABA1 (LONG AND BARBED AWN 1), RAE1 (REGULATOR OF AWN ELONGATION 1), and RAE2 (REGULATOR OF AWN ELONGATION 2); and three loci related to grain size , , and  were investigated. The structure and functions of domestication loci were extracted from the rice reference genome (Os-Nipponbare-Reference-IRGSP-1.0) downloaded at Rice Annotation Project Database (RAP-DB) [.bedtools intersect command line with BEDtools version 2.30.0 [Genome-wide single nucleotide polymorphisms (SNPs) were determined for seed shattering loci, awn development loci, and grain size loci. Four major genes controlling seed shattering including (RAP-DB) . The detn 2.30.0 . An aminNatural polymorphisms in domestication loci  are rich in the Australian wild rice populations. The homozygosity of the polymorphisms at these loci confirms the distinctness of the alleles and suggests that these taxa may be ideal for an analysis of domestication.


G-statistic methods with and without the aid of the parental genome sequences. Our results demonstrate that the genomic region(s) associated with the trait of interest could be reliably identified via the significant structural variant method without using the parental genome sequences.Genomic regions that control traits of interest can be rapidly identified using BSA-Seq, a technology in which next-generation sequencing is applied to bulked segregant analysis (BSA). We recently developed the significant structural variant method for BSA-Seq data analysis that exhibits higher detection power than standard BSA-Seq analysis methods. Our original algorithm was developed to analyze BSA-Seq data in which genome sequences of one parent served as the reference sequences in genotype calling and, thus, required the availability of high-quality assembled parental genome sequences. Here, we modified the original script to effectively detect the genomic region–trait associations using only bulk genome sequences. We analyzed two public BSA-Seq datasets using our modified method and the standard allele frequency and  Bulked segregant analysis (BSA) was developed for the quick identification of genetic markers associated with a trait of interest  that is the same as the reference genome is termed the reference base (REF), while the base that differs from the reference genome is referred to as an alternative base (ALT). Because each bulk contains many individuals, the vast majority of SNP loci in a bulk contain both REF and ALT bases. For each SNP, the number of reads of REF/ALT alleles is termed allele depth (AD). Because of the phenotypic selection via bulking, for trait-associated SNPs, the ALT allele should be enriched in one bulk while the REF allele should be enriched in the other. However, for SNPs unassociated with the trait, both ALT and REF alleles would be randomly segregated in both bulks and enriched in neither. Hence, these four AD values (REF/ALT reads from each bulk) can be used to assess how likely an SNP/InDel is associated with the trait.G-statistic value of an SNP with the four AD values and uses the calculated value to judge how likely the SNP is associated with the trait /InDel is more likely associated with the trait of interest than a nonsignificant SNP/InDel. Theoretically, sSNPs/InDels should be clustered within and around trait-controlling genes and should not present in genomic regions not associated with the trait of interest. Since SNPs/InDels normally are not evenly distributed across chromosomes, we use the ratio of the significant structural variants to the total structural variants to judge if a genomic region is associated with the trait of interest in our method. This ratio is a parameter at the genomic region level, whereas both the allele frequency and the G-statistic value are parameters at the SNP level, which is the key difference between the significant structural variant method and the standard allele frequency and G-statistic methods, and the root cause of our method’s higher statistical power in the detection of the genomic region–trait associations.QTL-seq is the most widely used software program for BSA-Seq data analysis , ERR2696319 , ERR2696321 (the resistant bulk from the F2 population), and ERR2696322 (the susceptible bulk from the F2 population). The maize dataset used in this study was generated by G-statistic, and significant structural variant methods. A graphical overview of the analysis workflow is presented in The rice sequencing data used in this study were generated by https://plants.ensembl.org/Oryza_sativa/Info/Index. Sequencing data preprocessing and SNP calling were performed using fastp, samtools, BWA-MEM, and GATK4  was downloaded from nd GATK4 . When annd GATK4 . The SNPThe SNP dataset generated via SNP calling was processed with our Python script to identify sSNP–trait associations. A single script contains all three methods. The workflow of the scripts is as follows:Read the .tsv/.csv input file(s) generated via SNP calling into a pandas DataFrame , the ΔAF  values , or the G-statistic values (the G-statistic method) using the four AD values  of each SNP in the filtered pandas DataFrame. The P-values of Fisher’s exact tests, the ΔAF values, and the G-statistic values of all SNPs on each chromosome are smoothed by applying a Savitzky–Golay filter  score less than 20 in any bulk;ref + ADalt) greater than six times of the average sequencing coverage in any bulk; andSNPs with the sum of its AD values . This step is carried out before checking the genotype agreement between bulks and the REF/ALT fields. When parental genome sequences are involved, the common SNP set is identified before filtering out the SNPs with a low GQ score in the parental SNP dataset.G-statistic values across the chromosomes. For all three methods, the size of the sliding windows is 2 Mb and the incremental step is 10 kb in rice; the values of these variables are 5 Mb and 10 kb, respectively, in maize.The sliding windows algorithm was used to facilitate the visualization of the distribution of the sSNP/totalSNP ratios, the ΔAF values, and the ref/GTalt” when an SNP contains both the REF and ALT bases in the GT field and the AD values in each bulk/parent is represented as “ADref, ADalt”. The genotype and the AD value of the REF allele are always placed first in both fields. For an SNP locus in the .tsv input file, the allele with the same genotype as the reference genome is defined as the REF allele. However, it is highly unlikely that all of the SNP alleles in a parent are the same as those in the reference genome, except in instances where reference genome sequences used in SNP calling are from one of the parents, as in the case of the cold-tolerance study as mentioned above  in the REF position, and those from the other parent  to the ALT position in the GT and AD fields to make the bulk dataset consistent. Thus, for a particular SNP, if the REF base in the .tsv file is different from the genotype of LD24, its GT/AD values in both bulks would be swapped, e.g., “G/A” to “A/G” and “19,9” (the REF read is 19 while the ALT read is 9 in a bulk in the .tsv input file) to “9,19” (the REF read becomes 9 and the ALT read becomes 19 after AD swapping). AD/GT swapping is performed following SNP filtering and only when the parental genome sequences are used to aid BSA-Seq data analysis. AD swapping ensures that adjacent SNPs have similar ΔAF values. Examples of AD/GT swapping are provided in The tightly linked SNP alleles from the same parent tend to segregate together and should have a similar extent of allele enrichment and thus similar AD values. In an SNP dataset, the genotypes (GT) of each bulk/parent are represented as “GTG-statistic values and the ΔAF values and estimation of their thresholds were carried out as described previously , E is the expected AD under the null hypothesis and is calculated as in the original G-statistic method  and the ALT allele frequency in the population are used to simulate its ADref (smADref) and ADalt  under the null hypothesis. The simulated smADref1/smADalt1 of bulk 1 and smADREF2/smADALT2 of bulk 2 are used to calculate the ΔAF or the G-statistic. This process is repeated 10,000 times, the 99% confidence interval of the 10,000 ΔAF values is used as a significant threshold for the allele frequency method, and the 99.5th percentile of the 10,000 G-statistic values is used as a significant threshold for the G-statistic method. As in the real dataset, the threshold of a sliding window is the average value of all SNPs in it.Threshold estimation of ΔAF/ref1/ADalt1 of bulk 1 and ADref2/ADalt2 of bulk 2. An SNP with its P-value less than α = 0.01 is defined as a sSNPs. For a trait-associated SNP (located in a trait-controlling gene or tightly linked to the gene), the more the gene contributes to the phenotype, the more its REF/ALT allele would be enriched in either bulk and the more likely it would be identified as an sSNP. SNPs are not distributed evenly along a chromosome, thus it is very likely that different sliding windows contain different numbers of SNPs. Therefore, the sSNP/totalSNP ratio is used to measure sSNP enrichment in a sliding window.For each SNP in the dataset, Fisher’s exact test was performed using its four AD values: ADref/ADalt values and calculating the P-value of Fisher’s exact test for each SNP in a sliding window. This process needs to be repeated 10,000 times for a sliding window. Doing so for all sliding windows of the SNP dataset would take a very long time. To overcome this obstacle, we first calculate a genome-wide threshold and use it to identify potential significant peaks, then sliding window thresholds of these peaks are calculated via simulation to verify if the sSNP/totalSNP ratios of these peak sliding windows are really significant.Calculating a threshold requires simulating ADref1/smADalt1 of bulk 1 and smADref2/smADalt2 of bulk 2 are obtained via simulation as above. These simulated AD values are used to perform Fisher’s exact test; an SNP with its P-value less than α = 0.10 is considered an sSNP. This process (starting from sampling SNPs) is repeated 10,000 times, and the 99.5th percentile of these 10,000 simulated sSNP/totalSNP ratios is used as the significance threshold for the detection of potential significant peaks. A higher α value  is used here, resulting in the identification of more sSNPs from the simulated SNP sub-dataset, hence a higher threshold and fewer false positives.The number of SNPs that are the same as the average number of SNPs per sliding window are randomly selected from the entire SNP dataset. For each SNP in this sample, smADref and smADalt of each SNP in each bulk in the sliding window are obtained via simulation, and Fisher’s exact test, identification of sSNPs, and sSNP/totalSNP calculation are carried out in the same way as above. This process is repeated 10,000 times, and again the 99.5th of these 10,000 simulated sSNP/totalSNP ratios is used as the threshold for this sliding window.Estimating a sliding window threshold is very similar to estimating the genome-wide threshold; the only difference is that we do not need to sample SNPs from the genome. Instead, smADG-statistic method, and the allele frequency method with or without the aid of the parental genome sequences.We first test how parental genome sequences affected QTL detection in rice. The original sequence reads of the rice data were 3.9G, 3.8G, 3.4G, and 3.5G; they became 3.8G, 3.6G, 3.3G, and 3.4G after quality control, respectively, in ERR2696318 (parent LD24), ERR2696319 , ERR2696321 (the resistant bulk), and ERR2696322 (the susceptible bulk), which correspond to 8.8×, 8.5×, 7.6×, and 7.9× coverage, respectively  were identified in the parental SNP dataset. However, the GATK’s variant calling tools are designed to be very lenient in order to achieve a high degree of sensitivity (https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels-), and it is possible that some sequencing artifacts were identified as heterozygous alleles. The bulk SNP dataset was processed second. The SNPs with the same chromosome ID, the same genomic coordinate, and the same allele composition in both datasets were considered common SNPs. Common SNPs in the bulk dataset were used to detect SNP–trait associations for all three methods.The SNP calling-generated parent/bulk SNP dataset was processed with the Python script PyBSASeq_WP.py. SNP filtering was performed as described in the P-values less than α = 0.01 were defined as sSNPs. The chromosomal distributions of the sSNPs and the total SNPs are summarized in Materials and Methods section. Two peaks above the threshold were identified: a minor one on chromosome 9 and a major one on chromosome 11. The position of the peak on chromosome 9 was at 1.11 Mb, the sliding window contained 230 sSNPs and 3738 total SNPs, corresponding to an sSNP/totalSNP ratio of 0.0615; the position of the peak on chromosome 11 was at 26.44 Mb, the sliding window contained 675 sSNPs and 1139 total SNPs, corresponding to an sSNP/totalSNP ratio of 0.5926. The sliding window-specific threshold was estimated for each peak via simulation, and the values were 0.0551 and 0.0623, respectively, indicating both peaks were significant. Both values are higher than the genome-wide threshold, probably due to the lower amount of total SNPs in these sliding windows. The average SNPs per sliding window was 5893.Each SNP in the dataset was tested via Fisher’s exact test using its four AD values, and SNPs with G-statistic value of each SNP in the dataset was calculated, and its threshold was estimated via simulation as described in the Materials and Methods section. Using the sliding window algorithm, the G-statistic value of each sliding window, the average G-statistic values of all SNPs in that sliding window, was plotted against its genomic position ; its position was at 26.96 Mb, and its G-statistic value was 8.9119  gene via the allele frequency method. We analyzed the dataset using the allele frequency, G-statistic, and significant structural variant methods. With the aid of the parental genome sequences, results similar to those in Zheng et al. were obtained using the allele frequency methods; a significant peak on chromosome 8 was identified. However, many more QTLs were identified using the significant structural variant method: in addition to the major locus on chromosome 8, significant peaks on all chromosomes were identified , ] or  as its input. The G-statistic method assesses if an SNP is likely associated with the trait via the G-test; the greater the G-statistic value of an SNP, the more likely it contributes to the trait phenotype , ] or . Changing the AD value (REF/ALT reads) order in the array/list does not affect the P-value of Fisher’s exact test or the G-statistic value of G-test, which is why the parental genome sequences-guided AD swapping does not alter the curve patterns of both methods. Therefore, theoretically, parental genome sequences are not required to identify genomic region–trait associations in either the significant structural variant method or the G-statistic method.The significant structural variant method assesses if an SNP is likely associated with the trait via Fisher’s exact test. The greater the ALT proportion differences between the bulks, the less the Oryza sativa ssp. japonica cv. Nipponbare or maize B73, see the Materials and Methods section for details), and the ΔAF values of these SNPs were calculated based on the swapped AD values using When the parental genome sequences were used, AD value swapping was performed for the SNPs in which the genotype of the reference parent was different from the REF base in the reference genome sequences , but not the htSNPs containing only two alleles in an SNP locus . In addition, there are always bulk-specific SNPs (bsSNPs); if a portion of the genome is sequenced in the bulks but not in the parents, the SNPs in this genomic region will be bsSNPs; sequencing artifacts can create bsSNPs as well. Thus, the bulk SNP dataset could contain a significant number of extra SNPs when only the bulk genome sequences were used. In the rice data set, there were 1,345,185 SNPs in the bulk dataset when not using the parental genome sequences, 137,224 of them were htSNPs  while 109,445 SNPs of them were unique to the bulks, and the rest 1,099,516 SNPs were the same as in the dataset with the aid of the parental genome sequences. In the maize dataset, the bulk SNP dataset size was 16,573,961 when only the bulk genome sequences were used, 6,549,258 of them were htSNPs while 3,195,935 of them were bsSNPs, and the rest 6,828,768 were the same as in the dataset with the aid of the parental genome sequences.We used the bulk SNPs that also exist in the parents for BSA-Seq data analysis when using the genome sequences of both the parents and bulks, and the htSNPs in the parents were filtered out as well. Without the aid of the parental genome sequences, we can only filter out htSNPs containing three or four alleles in an SNP locus , and the other bulk should contain 100% recessive homozygous individuals, corresponding to 0% ALT allele frequency. For a QTL, allele enrichment is dependent on how much it contributes to the trait. The major QTL would have high allele enrichment, while the minor QTL would have low allele enrichment. The ALT allele frequency of an SNP in a QTL should be lower than 100% but could be higher than that of a dominant locus. The peak on chromosome 11 in rice  should rG-statistic methods use an SNP level parameter to identify significant sliding windows to detect the genomic region–trait associations. The significant structural variant method, however, uses the sSNP/totalSNP ratio, a parameter at the sliding window level, to measure the sSNP enrichment in a sliding window for the identification of the trait-associated genomic regions. An SNP normally has less than 100 reads because of the cost concern, while a sliding window normally contains thousands of SNPs. Thus, the significant structural variant method has much higher statistical power, which is consistent with our observations. Moreover, we used α = 0.01 for the identification of sSNPs in the real SNP dataset, whereas we used α = 0.1 for the same purpose in the simulated SNP dataset when estimating thresholds, leading to the identification of more sSNPs in the simulated SNP dataset and thus higher sSNP/totalSNP thresholds. For the rice dataset, the genome-wide threshold is 0.0538 with the aid of the parental genome sequences and 0.0535 without the aid of the parental genome sequences with α = 0.1 for threshold estimation  to test the Python scripts.The scripts and their usage can be found on G3 online.jkab400_Supplementary_DataClick here for additional data file.