During approximately 10,000 years of domestication and selection, a large number of structural variations (SVs) have emerged in the genome of pig breeds, profoundly influencing their phenotypes and the ability to adapt to the local environment. SVs (≥ 50 bp) are widely distributed in the genome, mainly in the form of insertion (INS), mobile element insertion (MEI), deletion (DEL), duplication (DUP), inversion (INV), and translocation (TRA). While studies have investigated the SVs in pig genomes, genome-wide association studies (GWAS)-based on SVs have been rarely conducted.FKBP5 containing the most significant SV locus for almost all traits. In addition, we found several significant loci in intramuscular fat, abdominal circumference, heart weight, and liver weight, etc.Here, we obtained a high-quality SV map containing 123,151 SVs from 15 Large White and 15 Min pigs through integrating the power of several SV tools, with 53.95% of the SVs being reported for the first time. These high-quality SVs were used to recover the population genetic structure, confirming the accuracy of genotyping. Potential functional SV loci were then identified based on positional effects and breed stratification. Finally, GWAS were performed for 36 traits by genotyping the screened potential causal loci in the F2 population according to their corresponding genomic positions. We identified a large number of loci involved in 8 carcass traits and 6 skeletal traits on chromosome 7, with We constructed a high-quality SV map using high-coverage sequencing data and then analyzed them by performing GWAS for 25 carcass traits, 7 skeletal traits, and 4 meat quality traits to determine that SVs may affect body size between European and Chinese pig breeds.The online version contains supplementary material available at 10.1186/s40104-023-00929-x. Genome rearrangements generate an abundance of structural variations (SVs) that, despite occurring mainly in non-coding regions, can determine the binding of transcriptional regulatory elements, mRNA splicing and processing, genome folding and higher order structures, and translational alterations due to their size and location , 2. In gDuring the long process of domestication and selection, which began about 10,000 years ago in Europe (Near East) and Asia (China), pig breeds with independent biological traits and breed-specific genomic variants have emerged . The LarIn recent years, several SV studies on pig genome have been reported –16. NeveIn this context, a high-quality SV map containing 4 SV types was constructed using resequencing data with an average depth of more than 35×. Subsequently, potential causal loci that were screened between breeds were rapidly genotyped in a large population of offspring according to our own designed strategy. Finally, we performed GWAS for 36 traits to determine the effect of SVs on phenotype. To our knowledge, this may be the most comprehensive trait association study of the pig genome using SV markers to date. In conclusion, our study provides a new strategy for SV research at the population scale that has been demonstrated to be reliable and efficient. Meanwhile, this study will provide a new theoretical basis for using SVs as molecular markers or developing marker-assisted selection and deepen the understanding of the potential function of SVs in the pig genome.https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB39374), and information for all individuals is shown in Table S1, Additional file The pigs used in the experiment were all from the Large White × Min pig resource population, raised at the Changping pig farm of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences. The Large White × Min pig resource population included 19 F0 individuals and 513 F2 individuals, among which the F0 individuals included 4 Large White and 15 Min pigs. The 15 Min pigs were collected from the Jilin Academy of Agricultural Sciences (JAAS) and the rural areas of Northeastern China, and the 4 Large White pigs were from the United Kingdom. All F2 individuals were raised to market age (240 ± 7 d) and slaughtered for commercial purposes. Paired-end sequencing was performed using Illumina Hi-seq 2500, with a sequencing depth of > 30× for F0 individuals and 5–7× for F2 individuals. The sequencing data of the F0 and F2 individuals used in this study have been submitted to the Genome Sequence Archive (GSA) with the accession number CRA002451. We downloaded data for additional 11 Large White pigs from the NCBI Sequence Read Archive , published breed genetic resource studies, and the official websites of pig breeds.The F2 population constructed with Large White and Min pigs collected abundant quantitative traits. For the convenience of this study, we divided all the traits into 25 carcass traits, 7 skeletal traits, and 4 meat quality traits. Among them, carcass traits include carcass length, body length, body height, cannon circumference, scapular width, chest width, chest depth, abdominal circumference, waist width, hip width, hip length, hip circumference, bone rate, total weight of front bone, total weight of middle bone, total weight of hind bone, total weight of front lean meat, total weight of middle lean meat, total weight of hind lean meat, total weight of front fat, total weight of middle fat, total weight of hind fat, heart weight, liver weight, and lung weight, skeletal traits include scapula length, humerus length, forearm bone length, hip bone length, femur length, calf bone length, and vertebral number, and meat quality traits include marbling, intramuscular fat, tenderness, and moisture percentage. All phenotypic characteristics were defined according to the Animal Genetic Resources in China (pig) , WikipedThe raw reads were trimmed with Trimmomatic , and highttps://www.girinst.org/), and the three most widely distributed types of ERV, LINE, and SINE were selected for MEI calls, and the reference sequences are displayed in Table S2. Survivor [The primary research approach was to employ several software to detect SVs, essential to maximize the obtained SV loci. Thus, five SV software were selected for SV discovery: Delly v0.9.1, Smoove v0.2.8, Manta v1.6.0, Breakdancer v1.4.5, and MELT v2.2.2. Among them, Delly, Smoove, Manta, and Breakdancer were used to identify DELs, DUPs, and INVs, whereas MELT was used to identify MEIs. Previous studies have relied on the overlap of results from several different SV software, although this strategy does not reliably improve detection and may even aggravate false discoveries . In thisSurvivor  softwareSurvivor , whereasTo control the quality of all SV loci, we also set a strict filter for each SV locus, keeping only loci with QUAL > 200 for DELs, DUPs, and INVs and only loci with "PASS" for MEIs, to ensure the quality of each SV locus. For INVs, variants within 1,000 bp could not be merged because of the reverse position of breakpoints output by different software. We merged the loci with more than 75% overlap between each breakpoint using our own written script. Meanwhile, these loci were divided into two groups, < 100 kb and > 100 kb, and merged separately in order to avoid large variants covering small variants. For SVs of 1–10 Mb, we used Samplot  for visuFST) values of all SVs with the Weir and Cockerham method comparing 15 Min vs. 15 Large White pigs. All SVs within the differentiated region with FST values in the top 5% were selected, and the corresponding genes overlapping with SVs were considered as candidates for breed stratification based on the gene information annotated by Ensemble. The candidate genes were annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis by DAVID online webloci (https://david.ncifcrf.gov/). The wordcloud webloci (https://www.jasondavies.com/wordcloud/) was used to create word clouds for the top 10 significantly enriched terms (P < 0.05) to reveal underlying molecular mechanisms.The sample geographic distribution map was produced using the ggplot2  and ggspIn order to obtain potential causal loci affecting specific traits, a screening strategy for differentially genotyped loci was used. Six relatively purebred Min pigs  and 15 Large White pigs were selected for this screening strategy. Two conditions were set: first, one genotype was selected for one breed and the remaining two genotypes were set for the other breed; second, the frequency of genotypes was more than 80% for Min pigs  and more than 50% for Large White pigs  , Breakdancer [We developed an SV study for the Large White × Min pig resource population, which included 19 F0 individuals and 513 F2 individuals. Among them, the F0 individuals involved 4 Large White and 15 Min pigs. In order to obtain a more comprehensive SV landscape, we collected additional 11 Large White pigs PRJEB39374). Then, whole-genome resequencing data were obtained for the 15 Large White and 15 Min pigs, with a total of approximately 1,396 G data obtained after quality control and an average sequencing depth of more than 35×. The sequencing data were aligned to the Sscrofa11.1 using BWA-mem, and SVs were called by Delly , Manta [374. Thenakdancer , and MELakdancer . All varakdancer , 42, butakdancer . In thisA high-quality landscape with 123,151 SV loci, including 68,121 DELs, 12,045 DUPs, 19,727 INVs, and 23,258 MEIs . All four SV types are located mainly at intergenic and intron positions, with DELs, DUPs, INVs, and MEIs accounting for 96.75%, 95.15%, 97.40%, and 96.18%, respectively , and this gene has been frequently reported to be associated with human height [TFEB , RCAN2 , and ANKS1A , which have been reported to be associated with osteoblast differentiation [MRS2  and downstream of GLP1R , respectively. Among them, MRS2 is associated with Mg2+ expression, and lower Mg2+ levels stimulate osteoclast formation [GLP1R plays a key role in bone strength and quality [We found that SVs may have an effect on pig body size, and the GWAS identified overlapping strong association peaks for carcass length, body length, body height, cannon circumference, and bone rate involving 87 significant loci Fig.  overlappits Fig. , and thin height –71 as wen height , 73. A Dntiation , osteoblntiation , and bonntiation , respectormation . GLP1R p quality . The GWAP = 2.25255E-12), 7_33224291 (P = 6.19796E-09), and 7_30669698 (P = 7.95851E-13) were the most significant SV loci in the three sections, corresponding to the three genes: FKBP5, MDGA1, and ILRUN  of VRTN, which is consistent with a previous study using SNP markers [P = 7.2393E-07) in the exonic region of the ZNF79 gene, which was previously associated with bone mineral density [To verify whether there was position specificity in the effect of SVs on skeleton, we divided the pig carcass into three sections, namely front, middle, and hind, according to the position of 4–5 ribs and the lumbosacral joint after removing the head. We then performed GWAS for total weight of front bone, total weight of middle bone, total weight of hind bone, scapula length, humerus length, forearm bone length, hip bone length, femur length, calf bone length, and vertebral number. The results showed that all three sections of bone weight and six bone length traits showed strong association peaks  and liver weight , which were a 2,681-bp DEL overlapping 3 bp with the NOL10 exon and a 278-bp DEL upstream of MRS2, respectively. Considering that a certain tolerance is needed for the determination of SV breakpoint locations [Among the remaining other traits, GWAS were performed for four meat quality traits: marbling, intramuscular fat, tenderness, and moisture percentage  statistics based on Weir and Cockerham. Table S9. Primers for significant loci of skeletal-related traits.Additional file 2: Fig. S1. Schematic design of SV primers for 1–10 Mb. Fig. S2. Schematic diagram of differential genotype screening. Fig. S3. SV Pipeline. Fig. S4. Validation of DEL variants at 1–10 Mb loci. Fig. S5. Validation of DUP variants at 1–10 Mb loci. Fig. S6. Validation of INV variants at 1–10 Mb loci. Fig. S7. The accuracy of SV genotyping was verified by agarose electrophoresis. Fig. S8. Venn diagram of SV loci in public databases and this study. Fig. S9. PCA plot of the 50k chip in 19 F0 individuals. Fig. S10. KEGG pathway. Fig. S11. Validation of SVs involving carcass traits using Sanger sequencing. Fig. S12. Manhattan plots for scapular width (A), chest width (B), chest depth (C), abdominal circumference (D), waist width (E), hip width (F), hip length (G), and hip circumference (H). Fig. S13. Manhattan plots for total weight of front bone (A), total weight of middle bone (B), total weight of hind bone (C), scapula length (D), humerus length (E), forearm bone length (F), hip bone length (G), femur length (H), calf bone length (I), and vertebral number (J). Fig. S14. Venn diagrams of three bone weight and six bone length traits overlapping with carcass length, body length, body height, cannon circumference, and bone rate. Fig. S15. Manhattan plots for total weight of front lean meat (A), total weight of middle lean meat (B), total weight of hind lean meat (C), total weight of front fat (D), total weight of middle fat (E), and total weight of hind fat (F). Fig. S16. Manhattan plots for marbling (B), tenderness (C), moisture percentage (D), heart weight (E), liver weight (F), and lung weight (G).


Wolbachia spp. induce thelytokous parthenogenesis in certain parasitoid wasps, such as the egg parasitoid wasps Trichogramma spp. To complete the cycle of vertical transmission, Wolbachia displays efficient transovarial transmission by targeting the reproductive tissues and often exhibits strong tissue-specific tropism in their host.The bacterial endosymbiont, Wolbachia distribution patterns that occur during the development of Wolbachia-infected, thelytokous Trichogramma dendrolimi, and T. pretiosum. We used fluorescence in situ hybridization (FISH) to investigate Wolbachia signal dynamics during early embryogenesis (from 30 to 120 min). Wolbachia titers and distributions from the embryo to adult stages of Trichogramma after early embryogenesis were detected by absolute quantitative polymerase chain reaction (AQ-PCR) and FISH. The symmetry ratios (SR) of the Wolbachia signals were calculated using the SR odds ratios in the anterior and posterior parts of the host. The SR was determined to describe Wolbachia tropism during early embryogenesis and various developmental stages of Trichogramma.The present study aimed to describe the basic Wolbachia was concentrated in the posterior part of the embryo during early embryogenesis and the various developmental stages of both T. dendrolimi and T. pretiosum. Wolbachia density increased with the number of nuclei and the initial mitotic division frequency during early embryogenesis. The total Wolbachia titer increased with postembryogenesis development in both T. dendrolimi and T. pretiosum. However, the Wolbachia densities relative to body size were significantly lower at the adult and pupal stages than they were at the embryonic stage.Wolbachia concentration during early host embryogenesis determined Wolbachia localization in adult wasps. By this mechanism, Wolbachia exhibits efficient vertical transmission across generations by depositing only female Wolbachia-infected offspring. The results of this study describe the dynamics of Wolbachia during the development of their Trichogramma host. The findings of this investigation helped clarify Wolbachia tropism in Trichogramma wasps.The present work revealed that posterior  Wolbachia spp. resides in the cells of more than 40% of all terrestrial arthropod species  Wolbachia titers in their germ line  was used to detect dynamic Wolbachia signals during early embryogenesis (30–120 min) in T. dendrolimi and T. pretiosum. Absolute quantitative polymerase chain reaction (AQ-PCR) and FISH were employed to clarify the loading and distribution dynamics of Wolbachia from the embryonic to adult stages of T. dendrolimi and T. pretiosum. The results of the present study provided a detailed description of Wolbachia dynamics during early embryogenesis and throughout different developmental stages of the host. These findings contribute to our understanding of how Wolbachia tropism enhances its vertical transmission across generations of Trichogramma wasps.The egg parasitoid wasps an pests . The sexolbachia . Comparect pests . Our preWolbachia-infected thelytokous isofemale Trichogramma dendrolimi and T. pretiosum lines were reared on eggs of the rice moth Corcyra cephalonica [Stainton]  at 25°C ± 1°C and 75% ± 5% RH and under a 16-h/8-h light/dark cycle. The C. cephalonica host was reared on a semi-artificial diet.Wolbachia signals in the early embryo  and at various developmental stages (24 h (late embryo), 48 h (larva), 72 h (larva), 120 h (pre-pupa), 168 h (pupa), and 240 h (adult)) of T. dendrolimi and T. pretiosum according to previously described procedures  within 5 min. Trichogramma sample size uniformity was established and maintained by fixing the samples in Carnoy’s solution (1:3 (v/v) glacial acetic acid: methanol) for 48 h, rinsing them thrice in 50% (v/v) ethanol for 300 s each time, and treating them with destainer (6% (v/v) ethanol dissolved in perhydrol) for 48 h. The Trichogramma samples were then subjected twice to phosphate-buffered saline (PBS) for 300 s each time and transferred to a hybridization oven  at 45 °C for 45 min. Wolbachia signals were indicated by two 5′-rhodamine-labeled Wolbachia probes (F: 5′-AATCCGGCCGAACCGACCC-3′ and R: 5′-CTTCTGTGAGTACCGTCATTATC-3′) targeting Wolbachia 16S rRNA. The Wolbachia distributions in the Trichogramma samples were photographed under a confocal laser scanning microscope . The Wolbachia densities in the Trichogramma samples were quantified using the area of each Wolbachia signal weighted by the area of each Trichogramma sample via log transformation and with ImageJ software Version 1.53t , Bethesda, MD, USA). To delineate asymmetric Wolbachia distributions in the Trichogramma samples, the early embryos were divided into anterior and posterior parts using the intermediate vertical boundaries of the lines from the anterior to posterior poles of the embryos. The symmetric ratios (SR) of the Wolbachia signals were calculated based on the odd ratios of the Wolbachia densities in the anterior and posterior parts of the embryos. The rates of the initial embryonic divisions were estimated based on the number of nuclei and the mitotic frequencies at various developmental times.FISH was conducted to detect ocedures . An egg Trichogramma progeny were dissected and isolated from their host eggs to evaluate the Wolbachia titers in T. dendrolimi and T. pretiosum at 48 h (larva), 72 h (larva), 120 h (pre-pupa), 168 h (pupa), and 240 h (adult) after parasitization. As Trichogramma embryos are very small, those that developed at ≤ 24 h were not subjected to AQ-PCR. In total, 50 progeny served as a sample replicate. The Chelex-100 method was used to extract total DNA from the Trichogramma samples. The Wolbachia titers in the Trichogramma samples were measured by AQ-PCR and a specific primer pair  that was designed according to the Wolbachia wsp sequences (GenBank Accession No. MG914000) (G914000) . The AQ-Wolbachia density, and SR  < 0.1, and the comparative fit index (CFI) > 0.90. The standardized coefficient was used to estimate the correlation between variable pairs by weighting the units and the changes in the different variables  was used to enumerate the nuclei and determine the mitotic frequency directly or indirectly influenced by the developmental stage, , and SR . The SEMariables . A one-won 4.2.0 .T. dendrolimi and T. pretiosum embryos, we detected more Wolbachia signals at the posterior than the anterior part. The SR values for the T. dendrolimi embryos were 0.070 ± 0.013 (30 min), 0.13 ± 0.038 (50 min), 0.12 ± 0.027 (60 min), 0.30 ± 0.047 (70 min), 0.17 ± 0.026 (90 min), and 0.31 ± 0.030 (120 min). The SR values for the T. pretiosum embryos were 0.16 ± 0.021 (30 min), 0.16 ± 0.022 (50 min), 0.13 ± 0.025 (60 min), 0.12 ± 0.031 (70 min), 0.16 ± 0.028 (90 min), and 0.19 ± 0.035 (120 min)  2.T. dendrolimi embryos was not rejected after one path was excluded . The Wolbachia density, SR, number of nuclei, and mitotic frequency increased with the stage of early embryonic development. The number of nuclei also increased with Wolbachia density, and the latter was positively correlated with the SR. The number of nuclei was positively correlated with the mitotic frequency . Early embryonic developmental time had no apparent effect on the Wolbachia density or the SR. The number of nuclei and the mitotic frequency increased with early embryonic development. Both the number of nuclei and the mitotic frequency increased with the Wolbachia density. The number of nuclei was positively correlated with the mitotic frequency. The correlation between the Wolbachia density and the SR was not significant  and the T. pretiosum (0.46–0.84) offspring were < 1 at all stages (T. dendrolimi offspring was highest at 168 h (0.89 ± 0.19) and significantly higher than those at 24 h , 48 h , 72 h , and 120 h , but not significantly higher than that at 240 h . For the T. dendrolimi offspring, the SR at 24, 48, 72, and 120 h did not significantly differ. For the T. pretiosum offspring, the average SR value was highest at 120 h (0.84 ± 0.16) and significantly higher than that at 24 h . However, the SR at 48 h (0.58 ± 0.082), 72 h (0.67 ± 0.088), 120 h, 168 h (0.52 ± 0.043), and 240 h (0.48 ± 0.031) did not significantly differ  and significantly higher than those at 48 h , 72 h , 120 h , 168 h , and 240 h . The average Wolbachia density of the T. dendrolimi offspring was lowest at 240 h, significantly lower than those at 24 h, 48 h , 72 h , and 120 h , and not significantly lower than that at 168 h . The average Wolbachia density of the T. pretiosum offspring was highest at 24 h (0.86 ± 0.0062) and significantly higher than those at 48 h , 72 h , 120 h , 168 h , and 240 h  , significantly lower than those at 120 h , 168 h , and 240 h , and not significantly lower than that at 72 h . The Wolbachia titers in the T. dendrolimi offspring at 168 and 240 h were significantly higher than those at 24 and 72 h  and 120 h . The average Wolbachia titers in the T. pretiosum offspring were lowest at 48 h (5.17 ± 0.099), significantly lower than those at 120 h , 168 h , and 240 h , and not significantly lower than that at 72 h . The average Wolbachia titers were highest in the T. pretiosum offspring at 240 h, significantly higher than those at 48 and 72 h  and 120 h , and not significantly lower than that at 168 h  (The  = 0.16) Figure 9Wolbachia density and the SR were significantly influenced by the developmental stage in both T. dendrolimi and T. pretiosum. We observed a higher number of Wolbachia signals in the posterior part of each embryo compared to the anterior part. Regardless of Trichogramma species, Wolbachia density decreased with developmental time, while Wolbachia titers increased with developmental time.The Wolbachia presented efficient vertical transmission across host generations, as evidenced by the nearly 100% female offspring deposited by thelytokous Trichogramma females, all of which were infected by Wolbachia. We found that Wolbachia were concentrated in the posterior part of each embryo throughout the early stages of T. dendrolimi and T. pretiosum embryogenesis. Though the SR values varied with the Trichogramma wasp stage, the Wolbachia were nonetheless more concentrated in the posterior than the anterior regions of each embryo. These results provide evidence that the posterior concentration of Wolbachia during embryogenesis dynamically shapes tissue tropism during the postembryonic stages in two Trichogramma species, T. dendrolimi and T. pretiosum.Wolbachia in early embryos has been investigated in Nasonia and Trichogramma wasps, both parasitic wasps. As mentioned in the introduction, different patterns of Wolbachia distribution have been observed in these studies. Though Wolbachia exhibit posterior concentration in both T. dendrolimi and T. pretiosum embryos. The Wolbachia density, SR, and mitotic frequency increased with the developmental time of T. dendrolimi embryos, whereas this was not observed in T. pretiosum embryos. Similar to our findings, the Wolbachia wVitA strain in N. giraulti embryos localizes to the posterior end at a high density and then spreads toward the anterior end. Conversely, in N. vitripennis embryos, the Wolbachia wVitA strain localizes to the posterior end at a low density . ER stress could activate cell defense and immune responses  or abiotic  factors that can affect the transmission rate of Wolbachia (Wolbachia-infected Trichogramma can impact the quality of Trichogramma offspring and their efficacy in pest control. Our findings have practical implications for the future application of Wolbachia-infected Trichogramma in biological control programs.We found that l stages . The obsivisions . Second,esponses . Our stue female . The PI olbachia . The unsWolbachia were concentrated at the posterior part of the embryo throughout early embryogenesis, the SR values were significantly higher at the pupal stage of T. dendrolimi and the prepupal stage of T. pretiosum than at the embryonic stage. We detected Wolbachia signals in the anterior part of the embryo, the 48- and 72-h larvae, and the pre-pupae of T. dendrolimi and T. pretiosum. However, no Wolbachia signals were detected in the heads of pupal or adult T. dendrolimi and T. pretiosum. Our previous study revealed that Wolbachia infection caused memory loss, increased the incidence of superparasitism, and attenuated the host discrimination mechanism in T. dendrolimi (Wolbachia titers in the heads of Trichogramma adults were low, the endosymbiont nonetheless caused behavioral changes in the wasps. The results of the present work suggest that Wolbachia colonization in the nervous tissues of immature Trichogramma progeny may impair central nervous system development and function in this insect host. However, the precise mechanisms by which Wolbachia infection induces behavioral changes in Trichogramma wasps remain to be elucidated.Though ndrolimi . Though Wolbachia concentration during early Trichogramma embryogenesis dynamically shapes Wolbachia localization in this host wasp. In this way, Wolbachia can undergo efficient vertical transmission across generations and ensures that thelytokous Trichogramma females deposit only female Wolbachia-infected offspring. The present work clarified the localization pattern of Wolbachia during the embryogenesis and various developmental stages of Trichogramma spp. These findings may help understand the interactions between Wolbachia and other host insects and provide a fundamental reference for the application of Wolbachia-infected thelytokous Trichogramma.The present study showed that posterior The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.This study was designed by J-CZ, W-NC, HD, and L-SZ. Q-JD, DS, S-FN, and YW carried out the experiments. J-CZ, Q-JD, and DS also help to analyze the phenotype as well as the data. J-CZ, W-NC, and H-HZ also participated in the data analysis. J-CZ wrote the paper. All authors contributed to the article and approved the submitted version.


Saline-alkali stress is one of the main abiotic factors limiting rice production worldwide. With the widespread use of rice direct seeding technology, it has become increasingly important to improve rice saline-alkali tolerance at the germination stage. To understand the genetic basis of saline-alkali tolerance and facilitate breeding efforts for developing saline-alkali tolerant rice varieties, the genetic basis of rice saline-alkali tolerance was dissected by phenotyping seven germination-related traits of 736 diverse rice accessions under the saline-alkali stress and control conditions using genome-wide association and epistasis analysis (GWAES).LOC_Os02g51900 (UGT85E1) and an E3 ligase gene LOC_Os04g01490 (OsSIRP4), while the second pair comprised an ethylene-responsive transcriptional factor, AP59 (LOC_Os02g43790), and a Bcl-2-associated athanogene gene, OsBAG1 (LOC_Os09g35630) for salt tolerance. Detailed haplotype analyses at both gene promoter and CDS regions of these candidate genes for important QTNs identified favorable haplotype combinations with large effects on saline-alkali tolerance, which can be used to improve rice saline-alkali tolerance by selective introgression. Totally, 165 main-effect quantitative trait nucleotides (QTNs) and 124 additional epistatic QTNs were identified as significantly associated with saline-alkali tolerance, which explained a significant portion of the total phenotypic variation of the saline-alkali tolerance traits in the 736 rice accessions. Most of these QTNs were located in genomic regions either harboring saline-alkali tolerance QTNs or known genes for saline-alkali tolerance reported previously. Epistasis as an important genetic basis of rice saline-alkali tolerance was validated by genomic best linear unbiased prediction in which inclusion of both main-effect and epistatic QTNs showed a consistently better prediction accuracy than either main-effect or epistatic QTNs alone. Candidate genes for two pairs of important epistatic QTNs were suggested based on combined evidence from the high-resolution mapping plus their reported molecular functions. The first pair included a glycosyltransferase gene Our findings provided saline-alkali tolerant germplasm resources and valuable genetic information to be used in future functional genomic and breeding efforts of rice saline-alkali tolerance at the germination stage. RecentlAlthough some main-effect loci for saline-alkali tolerance have been identified via GWAS in rice, the genetic architecture of rice saline-alkali tolerance remains unclear since genome-wide epistatic interactions  have been ignored in most GWAS. Epistasis is pervasive and has been recognized as a key aspect of plant genetic architecture . Recent To unravel the complex genetic mechanisms underlying saline-alkali tolerance in rice, we conduct a GWAS by the IIIVmrMLM method to detect the genome-wide main-effect QTNs and QQIs associated with seven saline-alkali tolerance-related traits at the germination stage using a large panel of 736 diverse rice accessions from the 3,000 rice genome project (3KRG). By identifying large numbers of QTNs and their QQIs, our findings greatly enriched the current knowledge on the genetic architecture of saline-alkali tolerance in rice and provided valuable germplasm resources for developing saline-alkali tolerant varieties through molecular breeding.22.1admix (adm) (4 accessions), Aus (160 accessions), Basmati (67 accessions), Geng/japonica (GJ)-adm (8 accessions), GJ-sbtrp (65 accessions), GJ-tmp (58 accessions), GJ-trp (50 accessions), Xian/indica (XI)-adm (78 accessions), XI-1A (56 accessions), XI-1B (59 accessions), XI-2 (61 accessions) and XI-3 (70 accessions)  were used to evaluate saline-alkali tolerance of rice at the germination stage. Based on the known population structure , these a2.22CO3 solution (pH = 11) as the saline-alkali stress treatment at the germination stage and with distilled water as the control. Three biological replicates for each accession were performed. In each replicate, 20 seeds of each accession were evenly placed in a 90-mm diameter Petri dish with filter paper, then 10 mL of 0.15% Na2CO3 solution or distilled water was added. We placed the Petri dishes in a growth chamber under the 14-h light/10-h dark photoperiod (28°C/26°C) with 70% relative humidity conditions for 7 d and then changed the Na2CO3 solution and distilled water daily.To evaluate saline-alkali tolerance of the rice accessions at the germination stage, we measured seven germination-related traits following the method of t/Tt), and mean germination time (MGT) was calculated as MGT = ΣTtNt/ΣNt, where Gt is the accumulated number of the germinated seeds on day t, Tt was the time corresponding to Gt in days, and Nt was the number of newly germinated seeds on day t  and germination rate (GR) of each accession in each replicate were calculated as the proportion of germinated seeds  on day 3 and day 7, respectively. Then, germination index (GI) was calculated as GI = Σ(Gon day t . On day 2.3m) were calculated for claiming significant QTNs for the whole panel (P = 8.18 ×10-8), where m is the number of SNPs. The SNP with the minimum P value at a locus was considered the lead SNP. Based on reported genome-wide linkage disequilibrium (LD) decay in 3KRG  associated with saline-alkali tolerance-related traits during rice germination, we selected the SNPs with  by GCTA  with the/tool/50 .2.5post-hoc tests with the “agricolae” package in R.Candidate genes in a QTN locus associated with saline-alkali tolerance were determined based on the following criteria: (1) excluding genes encoding transposon, retrotransposon and hypothetical proteins based on the annotation of Nipponbare reference genome IRGSP 1.0 ; (2) sig2.6Geng] and ARC 6052 [Xian]) and two saline-alkali tolerant accessions (SACHIKAZE [Geng] and ARC 18597 [Xian]) from the GWAS panel. After 24 h of saline-alkali stress with 0.15% Na2CO3, seed embryos of each accession were sampled under saline-alkali stress and control conditions, respectively. Three biological replicates for each accession were performed. Total RNA was extracted from seed embryos using plant RNA extraction kit (Tiangen Biotechnology), and reverse transcribed using reverse transcription kit (Tiangen Biotechnology). Real-time qRT-PCR analyses were conducted with Taq Pro Universal SYBR qPCR Master Mix . UBQ was used as the internal control and the relative expression levels of the target genes were calculated using the 2-ΔΔCT method  to evaluate the saline-alkali tolerance of rice accessions. A wide range of variation in the saline-alkali tolerance traits was observed among different rice populations and among different accessions within each of the populations  and RMGT, while positive correlation existed between RGR, RGI, RVI, RSL and RRL, though much stronger between RGI, RGR and RVI (3.2). A total of 70 (42.4%) main-effect QTNs co-localized with previously reported QTLs related to alkali tolerance and genes related to saline-alkali tolerance in rice, suggesting that most QTNs detected by the multi-locus random-SNP-effect mixed linear model were reliable . In addition, the most significant main-effect QTN associated with RGR, q1.11, explained 4.49% of the phenotypic variation of RGR.Using the IIIVmrMLM software, 165 QTNs associated with saline-alkali tolerance-related traits at the germination stage were identified based on a multiple-locus GWAS  QTN associated with RGR. The LD block region of q1.11 was estimated from 25.820 to 25.866 Mb  on chromosome 1 (LOC_Os01g45550 (OsPIN3t). Five major haplotypes (n ≥ 15 accessions) were detected using ten SNPs in the 1 kb upstream region of LOC_Os01g45550  and saline-alkali-tolerant Xian accession (ARC 18597) under saline-alkali stress, whereas it was significantly down-regulated in saline-alkali-tolerant Geng accession (SACHIKAZE) under saline-alkali stress (q5.7 was the most significant (P = 1.94 × 10-30) one associated with four of five saline-alkali tolerance traits  except RVI, while the q3.6 (P = 4.71 × 10-12) and q11.16 (P = 8.12 × 10-13) were the QTNs associated with four saline-alkali tolerance traits except RGI and RSL, respectively. The total phenotypic variance explained by this QTN was 2.69% for RGR, 4.68% for RGI, 3.43% for RSL, and 2.59% for RRL. The q5.7 LD block region of lead SNP (rs5_14379574) associated with RGR was estimated from 14.206 to 14.406 Mb  on chromosome 5 , while significantly down-regulated in saline-alkali-tolerant Geng accession (SACHIKAZE) under saline-alkali stress (n ≥ 15 accessions) of LOC_Os05g24770 were detected based on 13 SNPs in its 1 kb upstream region and the coding sequence region (Xian accessions (According to the criteria for candidate gene prediction (see Methods), we identified four and two candidate genes in the important main-effect QTNs, 3.4). Most (90.5%) epistatic QTNs were involved in single pairwise epistasis, and were associated with only a single trait  QQIs occurred between two main-effect QTNs; (II) QQIs occurred between one main-effect QTN and one non-main-effect QTN; and (III) QQIs occurred between two non-main-effect QTNs. The proportion of different QQI types varied considerably across different traits (3.5LOC_Os04g01490 (OsSIRP4) near rs4_436064 and a glycosyltransferase gene LOC_Os02g51900 (UGT85E1) near rs2_31646695. UGT85E1 was significantly down-regulated in saline-alkali-tolerant Geng accession (SACHIKAZE) and saline-alkali-sensitive Xian accession (ARC 6052) under saline-alkali stress  and saline-alkali-sensitive Xian accession (ARC 6052), while significantly down-regulated in the saline-alkali-tolerant Xian accession (ARC 18597) under saline-alkali stress  at rs2_31646695 were significantly different, with the mean RGP values of AA and TT being 0.45 and 0.24, respectively (LOC_Os02g43790 (AP59/OsBIERF3) near rs2_26441823 and a Bcl-2-associated athanogene protein gene LOC_Os09g35630 (OsBAG1) near rs9_20644583. AP59 was significantly down-regulated in saline-alkali-tolerant Geng accession (SACHIKAZE) and saline-alkali-tolerant Xian accession (ARC 18597) under saline-alkali stress, while significantly up-regulated in saline-alkali-sensitive Xian accession (ARC 6052) under saline-alkali stress  and saline-alkali-tolerant Geng accession (SACHIKAZE) under saline-alkali stress, while significantly down-regulated in the saline-alkali-tolerant Xian accession (ARC 18597) under saline-alkali stress  of rs2_26441823 were significantly different, with the mean RGP values of CC and TT being 0.43 and 0.34, respectively  approaches. Based on 10 times repeated 10-fold cross-validations, the main-effect QTN dataset showed significantly higher prediction abilities than the epistatic QTN dataset for all traits except for RGP Figure 744.1LOC_Os01g45550 (OsPIN3t) and LOC_Os05g24770, associated with saline-alkali tolerance at the germination stage in rice. LOC_Os01g45550 (OsPIN3t) is involved in drought stress response in rice and acts in auxin polar transport  and LOC_Os04g01490 (OsSIRP4) were identified as the promising candidate genes. OsSIRP4, highly induced under salt stress in rice, acts as a negative regulator in the plant response to salt stress via the 26S proteasomal system regulation of substrate proteins, OsPEX11-1, which provides important information for the adaptation and regulation in rice  and LOC_Os09g35630 (OsBAG1) were identified as the promising candidate genes. AP59/OsBIERF3 was up-regulated by salt, cold, drought and wounding in rice  after continuous high-temperature treatment and was down-regulated in two varieties (Dagad deshi and IR20) under drought stress  of the identified epistatic QTNs overlapped with previously reported QTLs/genes related to saline-alkali tolerance in rice, indicating a very low false positive rate in the epistatic QTNs and the reliability of the method used in this study, even though most of the identified QQIs appeared to have smaller effects than the main-effect QTNs. Secondly, significantly more (39.2%) main-effect QTNs were involved in QQIs in this study than in previous reports from segregating populations of bi-parental crosses in which most QQIs occur between QTNs that do not have detectable main-effects . Theoret4.2UGT85E1Hap1 × OsSIRP4Hap2 and AP59Hap1 × OsBAG1Hap1 of the epistatic QQIs can be used to improve RGP by selective introgression (The ultimate goal of past theoretical work of rice, including both genetic dissection of complex traits and functional genomic research aiming at cloning and functional characterization of genes controlling complex traits, is to apply these results to accelerate genetic gains in improving complex traits in future breeding efforts. One common strategy is to select and pyramid favorable alleles at many target loci to improve traits such as yield potential and abiotic stress tolerances in breeding. It is also clear that virtually all complex traits are controlled by complex signaling pathways involving large numbers of loci. However, the presence of epistasis would suggest that it is unreliable to predict the phenotypic effects of breeding progeny based on their genotypes at single loci. Thus, it is crucial to discover the epistatic interactions between genes to optimize gene pyramiding experiments . In thes5Through high-density SNPs, extensive phenotyping of the large-scale rice natural population under the saline-alkali stress and control conditions, and recent advancement in GWAS methodologies, we were able to conduct more robust and efficient genetic analysis of rice saline-alkali tolerance to better address QQI effects. Our results suggest that rice saline-alkali tolerance is a very complex trait with epistasis as an important genetic basis. The epistatic QQIs and their frequency distribution in germplasm resources can provide valuable information for future molecular breeding for improving rice saline-alkali tolerance at the germination stage.The original contributions presented in the study are included in the article/FZ and YS conceived, designed the experiments; GZ, JJ, JL, KL, DB, XW and XYZ performed the experiments; GZ, ZB, ML, XQZ, WW and JX performed analysis and interpretation of the data; GZ, FZ and ZL wrote the manuscript; FZ revised the manuscript. All authors contributed to the article and approved the submitted version.


Sussex cattle breed is characterized by their distinctive solid red coat colour and white tail switch. Sussex cattle are known for being easy to handle and manage, making them an ideal choice for cattle farmers. The phenotypic characterization of this cattle breed in South Africa is unknown. Hence, the objective of this study was to characterize the morphological structure, phenotypic and body indices traits of Sussex cattle in South Africa at Huntersvlei farm, Frere State province of South Africa. One hundred and one weaners (n = 101) between 6 and 8 months old  and fifty yearlings between 12 and 15 months old  were used in this study. Body weight at weaning, yearling and linear body measurements such as head length (HL), head width (HW), ear length (EL), ear width (EW), sternum height (SH), withers height (WH), heart girth (HG), hip height (HH), body length (BL), rump length (RL), and rump width (RW) were measured. Moreover, the animals were assessed for coat colour and horn presence. Descriptive statistics, Pearson’s correlation and Principal Component Analysis (PCA) were used to describe the Sussex cattle breed. The results indicated that male Sussex cattle had highly significant (p < 0.01) mean numeric values for the BW and morphometric traits. The results further showed that Sussex cattle had highly significant (p < 0.01) increase for the BW and morphometric traits as age advances in all sexes. Interaction effect of sex and age showed a highly significant (p < 0.01) effect with BW and measured morphometric traits, while moderately significant (p < 0.05) with EW. Male Sussex cattle showed highly significant (p < 0.01) higher mean numeric values for the body index (BI), length index (LI) and compact index (CI) indices. While female Sussex animals showed highly significant (p < 0.01) mean numeric values for area index (AI) and proportionality (PR). Body weight showed a positive highly significant (p < 0.01) correlation with the measured morphometric traits except for the moderate significant (p < 0.05) correlation with EL. Coat colour traits ranged from 15 (9.93%), 103 (68.21%) to 33 (21.85%) for light, moderate and dark colours, respectively. While horn presence traits ranged from 48 (31.79%), 42 (27.81%) to 61 (40.40%) for polled, scur and horned respectively. The PCA results extracted only two components in both sexes of the animals. The morphological variations obtained in this study could be complemented by performance data and molecular markers of single nucleotide polymorphism (SNP) to guide the overall breed characterization, conservation and development of appropriate breeding and selection strategies. Livestock plays an important role in the social, economic and cultural stability of rural households in many parts of the world . MASA 22 reporteThe experimental procedures were conducted following the University of South Africa (UNISA) Ethics code for the use of live animals in research, ethics reference number: 2022/CAES_AREC/171.This study was conducted at Huntersvlei farm also known as Rhys Evans Group (RE) in the Free State Province, South Africa. The farm is situated in Viljoenskroon, Fezile Dabi municipality; the site, temperatures, latitudes, longitude, and rainfall of the study area similar as described by Bila . HuntersAll the animals used in the study were exposed to a traditional management grazing system which allows animals to freely graze in the camps during the day and afternoon. Fresh clean water was always available in the camps. Animals received a routine inspection and dipping for herd health management purposes. The linear body measurements were taken while the animal was in a standing position with head raised up and weighed on all four feet. A functional handling facility with a crowding pen, working crush and head clamp was used for handling the animals to minimize movement during the measuring process.Morphometric traits and live body weight at two different stages were taken. The first stage was at weaning age in one hundred and one (n = 101)  South African Sussex weaner animals between 6 and 8 months old. Second stage yearling age is 50  South African Sussex yearling animals are between 12 and 15 months old. The animals used in the study at weaning age were between six to fifteen months old. The live body weight at the two stages was measured using a balance weighing scale whereas linear body measurements were measured using a measuring tape calibrated in centimetres (cm). The body weight at weaning, yearling and morphometric traits, namely head length (HL), head width (HW), ear length (EL), ear width (EW), sternum height (SH), withers height (WH), heart girth (HG), hip height (HH), body length (BL), rump length (RL), and rump width (RW) were measured following the guideline defined by Lomillos and Alonso , Tyasi eTo prevent individual variations in measurements, only one individual was taking the body weight and morphometric traits. The horn presence was assessed using three point scale  following the guidelines reported by Grobler et al. . Moreove2) test was used to find the significant difference on qualitative traits. Procedure of correlation (PROC CORR) was used to determine Pearson’s correlation matrix. These analyses were then followed by a principal component analysis (PCA) to reduce the dimensionality of the morphometric data using the procedure of principal component (PROC PRINCOMP and PROC FACTOR). Furthermore, to examine the morphometric traits and body indices that have the most discriminating power the stepwise discriminant analysis was applied using the STEPDISC procedure (PROC STEPDISC). The significant difference was observed at P<0.05 and highly significant at P<0.01.The Statistical Analysis System  version The overall summary of the collected data  discoverMeans and standard deviation for the sex effect on body weight and morphometric traits is given in The overall summary of the collected data  discoverMeans and standard deviation for the overall sex effect on body indices is given in In females, the BI showed a positive highly significant (p < 0.01) correlation with LI, while moderately significant (p < 0.05) correlated with PI and insignificant (p > 0.05) correlated with CI . PI showThe overall summary of the collected data  discoverThe frequency and percentage of coat colour and horn presence observed in males and females of Sussex cattle at Huntersvlei farm population are presented in The frequency and percentage of coat colour and horn presence observed in males and females of Sussex cattle at Huntersvlei farm populations are presented in The predicted factor loading extracted by factor analysis, eigen-values and variation explained by each factor are presented in The CDA for morphometric traits of South African Sussex cattle are presented in The CDA for body indices traits of South African Sussex cattle are presented in This study was exclusively based on specific phenotypic and morphometric traits of Sussex cattle at Huntersvlei farm in the Free State, Province, South Africa. Phenotypic variation of local animal resources shows a genetic diversity that may be worth conserving for future uses while better understanding of the external features assists to facilitate the implementation of conservation policies intended to ensure local resources survival . FirstlyFurthermore, the results of the study showed that the coat colour both in male and female populations had a highly significant effect with males showing a maximum incident of dark coat colour while females showing a maximum incident moderate coat colour. On the other hand, the horn presence showed a significant effect amongst the sexes with males showing a maximum occurrence of horn presence and females showing a maximum occurrence of polledness. Bila and Tyasi  showed tThe importance of morphological characterization of cattle breed’s genetic resources cannot be emphasized. The present study is based on morphometric and certain phenotypic traits of Sussex cattle at Huntersvlei farm in South Africa. The study findings displayed the morphological and phenotypic variations between ages and genders of the Sussex cattle breed at Huntersvlei farm. It was found that the Sussex cattle breed in the study area have developed some diverse phenotypic traits such polled, scur and horns. Moreover, the Sussex cattle at Huntersvlei farm showed that the male animals had a much darker red brown colour in comparison to female animals. The correlation results shown that in male BW had highest positive association with HH, BL and WH and hence can be used as markers to predict BW using regression equations. The extracted principal components from different morphometric traits symbolize the general body size and shape of the Sussex cattle population at Huntersvlei farm. Stepwise canonical discriminant analysis showed that head width, head length, hip height, sternum height, heart girth and rump width are the suitable discriminating morphometric traits in the Sussex cattle breed. Furthermore, an investigation on the molecular characterization using molecular markers SNP will complement the results obtained from morphometric variation. Lastly these findings might be helpful in breed characterization, conservation and developing breeding and selection strategies.S1 FileIt is an Excel file with variables fully explained in column P and Q of the sheet.(XLSX)Click here for additional data file.S2 FileIt is an Excel file with variables fully explained in column E, F and G of the sheet.(XLSX)Click here for additional data file.


Quantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data.Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL.Using a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, This proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies.The online version contains supplementary material available at 10.1186/s13040-023-00331-3. Quantitative trait locus (QTL) analysis and genome-wide association studies (GWAS) are widely used approaches in the analysis of complex traits, and are commonly used to explore the genetics of both human diseases and phenotypes in model organisms. However, these methods commonly rely on univariate or interval mapping approaches and an additive genetic model to capture phenotypic variance . Thus, tSaccharomyces cerevisiae identified that of the 61% broad-sense heritability detected, 21% was due to non-additive effects (dominance deviations and epistasis) [S. cerevisiae, of the 91.7% of the phenotypic variance attributed to genetic sources, non-additive effects accounted for 18.7% [A recent large-scale study in istasis) . In a reor 18.7%  FurthermAn alternative approach is to employ automated machine learning (autoML) techniques, which can be designed to detect both additive and non-additive effects while reducing the need to invest significant resources in constructing specific methodologies and selecting specialized parameters. AutoML approaches have the advantage of exploring vast parameter spaces while performing both algorithm selection and hyperparameter optimization without express human interaction post-initialization. Therefore, these approaches can be unleashed to detect patterns in large and complex datasets. Even though, to date, autoML packages specifically designed for phenotype to genotype association in omics level data have not been fully explored, there have been several significant and promising attempts. As initial examples, there have been studies that have identified epistatic interactions using random forest (RF) methodologies for major adverse cardiovascular events  and Alzhcurse of dimensionality where the number of predictors (features or variants) are much larger than the number of observations. To remain computational tractable, autoML approaches to omics-level data must parse and filter predictors based on expert biological knowledge or computationally derived methods to reduce dimensionality. However, optimizing these strategies can prove to be difficult, especially when it comes to universality of the approach. Second, the vast majority of autoML software packages are not specifically designed with quantitative genetics and epistasis as a core part of algorithm and hyperparameter optimization. Quantitative genetics is a field that attempts to describe a complex biological system that includes non-linear pairwise and higher order associations, a continuum of deviations from a purely additive inheritance pattern, and elaborate networks of genes and regulatory elements [Despite the promising examples explored above, the application of autoML approaches to QTL analysis and GWAS has historically been limited for two reasons. The first is best described as the elements , 17–19. Here, we provide a proof-of-concept for an open-source automated machine learning (autoML) method and software, AutoQTL, to automate QTL analysis by building an analytics pipeline optimized for explaining variation in a complex trait given a set of genetic variants. Most AutoML methods are agnostic to the application domain and thus unaware of decisions important to geneticists such as selecting features by allele frequency or feature encoding to capture non-additive genetic effects. Thus, we have developed AutoQTL as a method to automatically explore important aspects of quantitative genetics and the analysis of complex traits. Central to this method is an algorithm designed to make decisions a geneticist might make when planning and executing QTL analysis/GWAS. These decisions include genetics-based encoding of the genotypes for each genetic variant , selection of genetic variants based on genetic principles such as allele or genotype frequency , and the selection of a parametric statistical or machine learning based regression method for relating genotype to phenotype, allowing AutoQTL to explore all sources of variation, including non-additive effects. These decisions are optimized globally in AutoQTL, representing one of its core strengths. Humans intuitively apply optimization strategies one after another in a set order, which limits the search space of what is possible to detect. However, AutoQTL’s algorithm allows it to construct pipelines with operators and hyperparameters in any order, maximizing the propensity for discovering genetic relationships not explored by standard approaches.Key to AutoQTL is the data structure for representing QTL analysis pipelines for optimization. We utilize expression trees as an intuitive graph-based data structure for representing pipelines where a regression method is selected in the root node, feature encoding and feature selection methods can be selected for the child nodes, and hyperparameters selected as terminals of the tree.We selected GP as the search and optimization method for AutoQTL given it is designed to generate, diversify, evaluate, and select optimal expression trees given some fitness or quality metrics –22. BeloAll operators (root nodes and child nodes) have been implemented using the Python programming language  includinWe have implemented operators for the encoding phase that demonstrate an additive inheritance model and alternatives  e.g. doA into fon percentile of features based on an F-regression R2 score (where n can be between 5% – 95% in increments of 5%), and 3.) the genotype frequency (GF) operator removes features if any genotype frequency is below a threshold . Like with feature encoding, multiple feature selection phases may be present in an AutoQTL pipeline, leading to combinatorial effects of feature selection based on multiple criteria and hyperparameters. This global optimization strategy of feature encoding, feature selection, and respective hyperparameters allows AutoQTL to construct pipelines that can explore complex genetic relationships that may not be immediately intuitive to human geneticists and are not inherently included in standard analyses.In the feature selection phase, we remove features based on pre-designed criteria. AutoQTL has three feature selection operators with their own respective hyperparameter ranges and increments: Fig. C. 1.) ThWe have implemented three regression method operators from the scikit-learn package which serve as the root node of a pipeline: LinearRegression (LR), and two machine learning regressors: DecisionTreeRegressor (DT)  and RandomForestRegressor (RF) (ensemble tree-based model). Unlike feature encoding and feature selection phases, only one regression (root) can be selected per pipeline. The phenotype explored in this proof of concept is continuous, but we will expand AutoQTL to also handle discrete phenotypes by adding classifier methods readily available in scikit-learn.Graph-based data structures are nonlinear data structures consisting of two primary components: nodes and edges . Each edWe have adapted the core algorithm comprised of GP and Pareto optimization  from the2 and the difference score (DS), which is calculated as: 2 and DS are the objectives of Pareto optimization performed at the end of each generation. Maximizing both metrics allows for more varied Pareto fronts with multiple optimal solutions. Once all the pipelines are evaluated, the Pareto front is updated.We split the input data randomly into 80%/20% split and use the 20% split as a holdout dataset to evaluate our final Pareto front pipelines at the end of the process to assess for overfitting. We then split the remaining input data in half to produce the training and testing sets—each containing 40% of the original data. AutoQTL pipelines are generated randomly in the first generation and are each assigned a fitness score. Each AutoQTL pipeline is evaluated using two metrics: test split RTo create the next generation, new pipelines (offspring) are generated from the existing population of pipelines by applying mutation and crossover. Those pipelines with higher fitness contribute more phases  of their pipelines compared to pipelines of lower fitness. Mutation of a pipeline can be performed in the following three ways, and each has equal probability of occurring: 1.) Uniform mutation: Either a random AutoQTL operator in a pipeline is replaced with a new randomly selected operator  or the hyperparameter values of an operator is modified . 2.) Insert mutation: A new randomly selected AutoQTL operator is inserted in a pipeline at a random position, adding a step to a pipeline. 3.) Shrink mutation: A random AutoQTL operator is removed from the pipeline. Examples of mutation events are found in File SCrossover is performed by splitting two eligible chosen pipelines at a random point in the pipeline tree and then swapping the subtrees. Eligibility between two pipelines is defined when both pipelines have a phase operator in common . This is known as one-point crossover. This produces two offspring pipelines. The first offspring is selected to populate the next generation while the second is discarded. An example of a crossover event is found in File Sn number of generations set by the user. At the end of nth generation, the Pareto front contains the most optimal pipelines discovered by AutoQTL over n generations.Once offspring are created, a new population for the next generation is produced by selecting pipelines from the present parent population and offspring population with the highest fitness by the selection algorithm Non-dominated Sorting Genetic Algorithm (NSGA)-II . This seFor our analyses, population size is set at 100, offspring size is set at 100, the mutation rate is set at 90%, and crossover rate is set at 10% for a maximum of 25 generations. All these parameters can be changed by the user before initializing AutoQTL but are optimal for analyses we present considering our number of operators and the size of the data.p-values to determine variant effect size and significance, we incorporate a feature importance procedure to compare and rank features by measuring their model contributions. Although not analogous to standard hypothesis-driven statistical metrics used in GWAS and QTL analysis, Shapley values allow the user to select putative QTL (highly predictive features) for further study.To determine each feature’s (locus) importance to each pipeline, we utilize Shapley  values. p > 5.6) for body mass index (BMI), defined as the distance from the tip of the nose to the tip of the tail, in rats (Rattus norvegicus) from a genome-wide association study (GWAS) using 3,400,759 single nucleotide polymorphisms (SNPs) [To test AutoQTL’s GP pipeline processing and optimization, we use genotype data for 18 putative QTL (significance threshold =  − log10 s (SNPs) , 31. We s (SNPs)  using R s (SNPs) . A multi2 (0.0968) for the genotype/phenotype dataset from the 5,566 rat cohort for the 18 putative QTL [P) explained by the main effects in the linear model. Since the 18 putative QTL were identified using standard GWAS approaches (linear regression assuming an additive inheritance model), AutoQTL should generate a final Pareto optimal pipeline with linear regression (LR) as the only phase in each run that captures all the VP (R2) in the dataset. However, since AutoQTL randomly splits the data, we compare each pipeline’s test R2 to the R2 of the test split with basic LR since it will vary from the LR R2 of the full dataset (unsplit). We perform 10 replicates, each with a distinct random seed, ensuring different data splits, and determine if one of the Pareto optimal solutions includes LR as the only pipeline operator phase and captures the entirety of the VP in the test set for that split. We also explore the other Pareto optimal solutions and compared them to LR alone.The multiple LR Rtive QTL , 31 was 2 matching that of the 18 QTL datasets (0.0968) detectable by AutoQTL’s machine learning regressors, we randomly shuffle a percentage of the genotypes to weaken the total signal, partially breaking the strong association of complete penetrance with the phenotype (see File SP (R2) detectable in AutoQTL by machine learning regressors of approximately 0.10 with all nine interactions to compare with the total phenotypic variance explained by multiple linear regression in the 18 QTL experiment. As a result of this shuffling, all main effects total nearly zero VP (multiple LR R2 after shuffling = 0.00480). Thus, the distribution of genotype and allele frequencies remains intact while we alter the genotype–phenotype relationships of the dataset by manipulating two loci at a time  by one or more genes , we creaP explained by LR and machine learning regressors changes in the presence of an increasing quantity of 2-way epistatic interactions, we reduce the significant main effects from the original 18 putative QTL dataset by shuffling columns  and with nine interactions, the maximum test R2 explained by 9 interactions via machine learning regressors was limited to 0.10. We perform this experiment with ten replication datasets per interaction for a total of 100 datasets. We also generate Shapely feature importance for an example run (R random seed = 0 and AutoQTL random seed = 0) across interactions from zero to nine to explore how the difference in feature importance between interacting loci is altered as main effects are replaced by interactions.To illustrate how V2 and DS) in Optuna and use the same hyper-parameter search space in AutoQTL. To make the performance of AutoQTL and Optuna hyper-tuned models comparable, we executed Optuna for the number of minutes (rounded to the nearest minute) closest to the run-time of AutoQTL for each experiment except the 18 QTL experiment  to hyper-tuned random forest and decision trees without genetic operators. Hence, we chose Optuna , an opennt File S. The 18 nt File S. Therefo2 matching that of the test R2 before GP was executed in AutoQTL ) compared to 3.1 LR (10.9%) in final Pareto front on average; example in Fig. 2 evolves more steadily in the XOR experiment than in the 18 QTL dataset and for more generations  increasing in prevalence  of each locus in this dataset in File S2 from LR of the dataset with all main effects removed via shuffling is relatively close to zero . However, after each XOR addition, mean test R2 of machine learning regressors rises steadily with increasing interactions  occurring at nine interactions. The average number of machine learning regressors in the final Pareto fronts increases as soon one interaction is added ; at one interaction, mean of 9.2 pipelines (83.5%); at nine interactions, mean of 22.2 pipelines (83.5%); File SThe model Rons Fig. A. Over tan R2 0.1 occurrinan R2 0.1 occurrinan R2 0.1 occurrin2 using LR alone decreases from 0.0728 at zero interactions to -0.00629 at nine interactions while the mean test R2 of machine learning regressors increases from 0.00847 at zero interactions to 0.0539 at nine interactions . This value falls slightly (0.0378 at four interactions) until again increasing from six to nine interactions. Machine learning regressors replace LR as the most prevalent root regressor in Pareto fronts at four interactions (average of 9.6 pipelines (62.3%)) and remain the most prevalent up to nine interactions (average of 19.4 pipelines (74.8%); Fig. 2 and higher DS selecting two level encoders in the final Pareto front. However, it is important to note that all runs at zero interactions include pipelines with no encoders and high test R2 as seen in the example run in our 18 QTL experiment  and, thus, are more generalizable. This generalizability is observed by these pipelines’ performance on holdout data where the increase in R2 is proportionally higher than more overfit models . Another reason these ranks are different is because AutoQTL provides the user multiple Pareto optimal pipelines, each with different models potentially containing feature selection and/or encoding phases. As we have discussed, encoding changes can alter each feature’s importance, sometimes drastically. Additionally, if a feature is removed from a pipeline, its feature importance is zero, considerably affecting the mean in most cases. Due to the combined effects of potentially variable feature importance values across models and the dissimilar interpretations of feature importance metrics compared to p-values, researchers should compare the results of AutoQTL and GWAS/QTL analysis with caution as both approaches are asking different questions. However, what feature importance metrics offer are new and/or alternative insights into genotype/phenotype associations and putative variant identification. This, coupled with the diverse pipelines produced by AutoQTL, can lead researchers to new discoveries in quantitative genetics.Shapley value-based Feature importance values place SNPs in a different rank order than the ts File S. This isIn the presence of nine XOR interactions, AutoQTL’s final Pareto front is populated by more pipelines selecting machine learning regression (25.3 (89.1%) machine learning vs. 3.1 LR  to detect interactions . Indeed,The vast majority of Pareto optimal pipelines in the nine XOR analysis have at least one feature encoder selected , the Pareto efficient pipelines tell us important information about loci that have strong non-additive effects and loci that could be false positives in the GWAS. Thus, AutoQTL has the capability to elucidate genetic relationships that are extremely difficult or impossible to reveal using standard approaches. Furthermore, our comparison with standalone machine learning models without AutoQTL’s genetic operators, illustrates that feature encoding and feature selection aid in explaining more phenotypic variance and are likely uncovering non-additive genetic variation not describable using standard approaches . How should one approach selecting which model to apply to their data? This is a valid concern that deserves a focused response. In addition to understanding that prediction is wholly distinct from hypothesis testing, machine learning approaches have the potential to overfit the training set and not become generalizable to other datasets. We chose to address this issue by invoking a Pareto optimization strategy which employs two optimization metrics with one specifically tailored to address overfitting (DS). Furthermore, the graph-based expression tree structure of our pipelines allows us to explore more dimensions than standard approaches can including non-additive effects, feature selection, regressor type, and hyperparameter optimization globally. Due to these factors, there are potentially many equally optimal predictive models for any dataset. However, we recommend researchers think about Pareto efficiency when selecting a pipeline (model). That is, ones that are optimized in both scoring metrics like the pipelines we have outlined with arrows in Figs. es File S.As with many autoML methods, AutoQTL is currently limited in the number of features that can be analyzed through its automated workflow to maintain computational tractability. One major benefit of standard QTL analysis/GWAS is that there is no theoretical limit to the number of loci that can be analyzed as these methods employ modified versions of univariate regression. However, AutoQTL uses multiple LR as a base regressor option and can therefore not have more features than observations. To address this limitation, we are currently investigating and developing pruning strategies that identify important features from large GWAS-level datasets using autoML methods, statistical thresholds, and/or a priori expert biological knowledge.We have currently added Shapley values as an AutoQTL feature importance module. However, in the future, we wish to modify our current feature importance strategy to consider both univariate effects and epistatic interactions. This will provide more detailed information of the genetic variance that underlies important traits of interest and aid in identifying novel genetic relationships. Furthermore, we wish to apply encoders on a locus-by-locus basis to produce genotype values indicative of the actual inheritance model of each SNP to better describe each feature’s importance to the model being tested as well as increasing the overall phenotypic variance explained.In addition to the encoder and feature selection operator classes we currently employ, we aim to add other classes of operators that can contain detailed structural and function genomic information. Examples of this would be if a SNP is within a gene model or regulatory region, if the gene(s) associated with a particular SNP is part of a certain biological pathway or biological function, and if there is expert knowledge available on a SNP’s effect on phenotypes, genes, and/or other biological entities. We also aim to explore feature engineering operators that have the capability of adding pertinent information to existing features or transforming features into forms that better describe genetic relationships like epistatic interactions. Operator classes like this will allow AutoQTL to be more informed about genetic variants, potentially enhancing downstream analyses and putative QTL detection.Finally, we are currently developing a feature which will provide researchers with executable Python code of the models generated in AutoQTL’s final Pareto front. This will allow easy application of AutoQTL models to genomic data for further analysis.P in a dataset of putative QTL from real-world experimentation and provide evidence of its ability to detect epistatic interactions in simulated data. In addition, our optimization strategy provides diverse solutions that differ from standard QTL analysis using a set of feature selectors, feature encoders, and machine learning regressors with a varied set of hyperparameters that is optimized globally. This strategy allows AutoQTL to explore genetic relationships not easily detected in QTL analysis. Feature importance adds to this by providing researchers a different perspective on selecting potential targets of further study compared to standard statistical approaches. We intend to further improve the software to both enrich and supplement the analysis of complex traits in both human and non-human systems. It is our aim that AutoQTL can assist in the spaces of precision medicine and quantitative trait analysis by enhancing the exploration of diverse phenotypes, disease models, and non-additive effects in omics-level datasets.We have demonstrated that AutoQTL can both capture Vhttps://github.com/EpistasisLab/autoqtl.AutoQTL is open-source and available on Github: Additional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.Additional file 7.Additional file 8.Additional file 9.Additional file 10.


Cassava (Manihot esculenta) is an annual root crop which provides the major source of calories for over half a billion people around the world. Since its domestication ~10,000 years ago, cassava has been largely clonally propagated through stem cuttings. Minimal sexual recombination has led to an accumulation of deleterious mutations made evident by heavy inbreeding depression.To locate and characterize these deleterious mutations, and to measure selection pressure across the cassava genome, we aligned 52 related Euphorbiaceae and other related species representing millions of years of evolution. With single base-pair resolution of genetic conservation, we used protein structure models, amino acid impact, and evolutionary conservation across the Euphorbiaceae to estimate evolutionary constraint. With known deleterious mutations, we aimed to improve genomic evaluations of plant performance through genomic prediction. We first tested this hypothesis through simulation utilizing multi-kernel GBLUP to predict simulated phenotypes across separate populations of cassava. Simulations showed a sizable increase of prediction accuracy when incorporating functional variants in the model when the trait was determined by<100 quantitative trait loci (QTL). Utilizing deleterious mutations and functional weights informed through evolutionary conservation, we saw improvements in genomic prediction accuracy that were dependent on trait and prediction.We showed the potential for using evolutionary information to track functional variation across the genome, in order to improve whole genome trait prediction. We anticipate that continued work to improve genotype accuracy and deleterious mutation assessment will lead to improved genomic assessments of cassava clones. It is estimated that cassava is a major caloric source for almost half a billion people around the world . AlthougMany generations of clonal propagation have caused cassava to accumulate genetic load that inhibits its potential crop performance. This genetic load is most apparent in the heavy inbreeding depression exhibited in cassava, as observed through low performance of selfed offspring . StudiesPlant breeders have worked on various methods to detect and manage genetic load throughout history. Many crop species exist as polyploids, which enables them to more easily mask recessive deleterious mutations responsible for genetic load . Hybrid During the past decade, plant breeders have seen the emergence of methodical application of genotyping and genomic selection as a method to improve breeding selections and leverage understanding of genomic information. Genomic selection, which uses genome markers and a phenotyped training population to predict unobserved offspring performance, can decrease selection cycle time and improve selection accuracy. Efforts have been made to improve genomic selection by using causative knowledge, however understanding the true causative elements in the genome is not a trivial exercise. Many studies have shown that benefits from including genome-wide association (GWA) hits in genomic prediction can diminish when predicting unrelated material , indicatRegarding genetic load, evolutionary conservation has shown to be an effective method to assess deleterious mutations and explain functional variation  in a popThe purpose of this study is first, to identify likely deleterious mutations in cassava, and second to evaluate their potential impact on genomic prediction for the goal of improving future breeding selections. We sequenced, assembled, and gathered 52 genomes from species that all shared ancestry within the last 50 million years in order to score conservation and detect deleterious mutations.We designed an experiment that uses evolutionary information to augment genomic predictions within and across two different populations of 1048 cassava clones present in two different breeding programs in Sub-Saharan Africa, the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria, and the National Crops Resources Research Institute (NaCRRI), Namulonge, Uganda. By performing phenotype simulations using real genotypic data and generating genomic predictions with known, simulated QTL, we first evaluated the best possible benefit of including causative information in our genomic predictions under different scenarios. We then used genomic and phenotypic data from these cassava clones to test genomic predictions, while including various functional annotations based on deleterious mutations.22.1Utilizing many germplasm resources, we sampled, sequenced and assembled 27 Euphorbiaceae species Figure 12.2We used evolutionary conservation and predicted protein mutation effects to classify the deleterious effects of 66k nonsynonymous SNPs segregating in the two target populations. Firstly, we used the intersection of baseml evolutionary rate and SIFT deleterious scores to classify 2,210 deleterious sites that are segregating in both cassava populations Figure 22.3To validate our methodology and guide our expectations we performed genomic predictions using simulated phenotypes on 1048 cassava clones originating from IITA and NaCRRI breeding programs. These simulations represent some best-case scenarios for genomic prediction, where all QTL and their effect sizes are known.The simulated QTL effects represent a suite of different genetic architectures ranging from highly complex genetic traits controlled by thousands of small effect QTL to oligogenic traits controlled by a handful of large effect QTL. These genetic architectures are represented by the proportion of the 66k variants simulated as causative QTL Figure 42.4Once QTL effects were modeled, we then calculated phenotypes for each of the 1048 clones  offer a possible method of producing enough viable embryos for crops with heavy inbreeding depression like cassava. . Additional short-read sequences were downloaded from SRA corresponding to 11 unspecified Euphorbiaceae taxa  score of < 0.05 . Additiohttps://github.com/churchlab/UniRep).In addition to identifying a binary classification of deleterious, we used a RandomForest model to obtain a quantitative prediction of conservation similar to a previously reported method reported . We usedin silico non-synonymous mutations at every possible site in our gene models. This resulted in over 1 million non-synonymous mutations whose genomic conservation could be modeled. We then used a leave-one-out prediction scheme where each of the 18 cassava chromosomes was left out of model training and predicted by the other 17. This method produced a predicted value between 0-1 for each of the ~66k nonsynonymous, segregating mutations used in this study  in Nigeria, while the second is from a breeding program at National Crops Resources Research Institute National Crops Resources Research Institute (NaCRRI) in Uganda, representing breeding material for West and East Africa, respectively. Genotypes for the associated clones were downloaded from the “East Africa Clones Dart-GBS 2020” genotyping protocol on cassavabase.org containing 23,431 variants. Plant phenotypes for fresh root yield and dry matter percentage were downloaded from cassavabase.org and prepared according to previously described methods .Phenotypic and genotypic data for 1048 cassava clones were downloaded from Ne=100, resulting in ~26M variants. These variants were then filtered down to two genome-wide marker sets, one being a thinned sample of ~135k genome-wide SNPs, and the other being all non-synonymous sites segregating in both populations resulting in ~66k genome-wide variants. The input marker genotypes, the imputed sample, and the imputed non-synonymous sites will be used in genomic prediction analyses.We then performed genotype imputation using the cassava haplotype map using Beagle5 , with an4.5-1, 10-2, 10-3, and 10-4. The effect sizes for these QTL were then sampled from a gamma distribution using the rgamma function in R, with the shape parameter=1, with the ancestral allele set as having a positive effect. Lastly a dominance effect for each QTL was sampled from normal distribution “rnorm”, restricting to dominance<=2 (We used quantitative trait loci (QTL) simulation, replicated 50 times, to model the potential benefits of knowing causative variants in genomic prediction. This simulation begins by sampling QTL across the 66K variant sites from a binomial distribution with the probability of being a QTL varied across possible values of 10ε~N and pnd Satoh  method.i*(1-pi) for the heterozygote, and 2(1-pi)2 for the homozygous alternate allele.Where the entries of D are given as AG & dG matrices, but only utilizing the known QTL sites in the genomic relationship matrices:The second model includes additive and dominance QTL relationship matrices formed in identical manner to the The final model includes weighted QTL matrices based on their effect size:Here the weighted matrices are formed using modified methods of the previously cited methods. The weighted additive matrix given by:i along the diagonal, wi and pi are the weight and frequency for the ith locus, respectively.Where M is the scaled genotype matrix. W is a diagonal matrix with wThe weighted dominance matrix is modified in a similar fashion to the additive matrix:i*(1-pi) for the heterozygote, and 2(1-pi)2 for the homozygous alternate allele.Where the entries of D are given as 4.6The genomic prediction models used for real breeding program phenotypes follow a similar pattern to our simulated scenario, with a few notable differences.https://wolfemd.github.io/GenomicSelectionManual/index.html):First, our ground truth for the phenotype of each clone was the best linear unbiased estimate (BLUE) using a model like those previously used in cassava plot level traits  and thosX. Replications were nested in trials, treated as random, and represented by the design matrix repZ and the effects vector t~NZ and the effects vector b~N were calculated using R:Where n= # folds * # replications and SD=standard deviation. A true confidence interval assumes observations are independent, which is not true for replications of cross-fold validation, however this gives an estimate for variability in cross-validation prediction accuracies.4.7https://bitbucket.org/bucklerlab/cassava_load_and_gp.Genotype and Phenotype data used in this study is available at cassavabase.org. Euphorbiaceae sequence reads and assemblies generated in this study will be available under bioprojects PRJNA608937 on the Sequence Read Archives and PRJEB55682 on the European Nucleotide Archive, respectively. Code used to process data and produce assemblies, simulations, genomic predictions as well as deleterious weights and mutation results are available at Euphorbiaceae sequence reads and assemblies generated in this study will be available under bioprojects PRJNA608937 on the Sequence Read Archives (SRA) and PRJEB55682 on the European Nucleotide Archive (ENA), respectively.EL - Collected samples, performed analysis, and did majority of manuscript writing. MR - Managed and organized germplasm collection and genome sequencing. EB - Mentor and oversaw experiments for measuring evolutionary conservation and deleterious mutations, reviewed and edited manuscript. KR - Mentor and oversaw experiments for genomic prediction and deleterious mutations impact on traits, reviewed and edited manuscript. All authors contributed to the article and approved the submitted version.