P = 0.004 and <0.001, respectively). Both total and pattern VF deviation were significantly worse in the MvD+ than in the MvD– group . Same results were obtained in subgroup analyses for eyes with thick and thin RNFL thickness . These data suggest that parapapillary MvD is associated with poorer function of the remaining axons in eyes with POAG.The degree of visual field (VF) loss can vary widely at a given level of retinal nerve fiber layer (RNFL) thickness. The cause of this variability is not fully understood. This cross-sectional study investigated whether the presence of choroidal microvasculature dropout (MvD) influences on the structure-function relationship among glaucomatous eyes. Seventy-one primary open-angle glaucoma (POAG) patients with choroidal MvD as determined by optical coherence tomography angiography (MvD+ group), and 71 age- and inferotemporal (IT) RNFL thickness-matched POAG patients without MvD (MvD– group) were included. VF sensitivity within the region corresponding to the IT RNFL sector was averaged using the total and pattern deviation fields. The slope of log-scale RNFL thickness versus VF defect was significantly steeper for the MvD+ than the MvD– group, as determined by both total and pattern deviation maps ( The disease is diagnosed and its severity assessed based on structural and functional evaluations. Therefore, understanding the relationship between structure and function is important. This is particularly because the clinical relationships between measures of structure and function differ markedly among individuals. For example, at a given level of retinal nerve fiber layer (RNFL) thickness, the degree of VF loss can vary widely. In clinical settings, this poses a diagnostic dilemma regarding the likelihood of disease and whether the severity of glaucoma should be primarily gauged on the basis of structure or function or a combination of both3. Inter-individual variations in the proportion of the two components5 may result in inter-individual differences in the number of axons included within a given thickness of the RNFL. In addition, media opacities and loss of RGC function before complete axonal atrophy may contribute to inter-individual variability in structure-function relationships6.One possible explanation for this variability may be the difference in the proportion of axons in the RNFL. The RNFL can be divided into two components: RGC axons and a residual component composed of glial cells and retinal vessels9 demonstrated a parapapillary microvasculature dropout (MvD) in eyes with primary open angle glaucoma (POAG) using optical coherence tomography (OCT) angiography (OCTA). The parapapillary deep-layer MvD was associated with the thinner RNFL thickness and worse VF mean deviation8. Our group demonstrated that the parapapillary choroidal MvD defined by the OCTA corresponds to the true perfusion defect determined by indocyanine green angiography (ICGA)10. The parapapillary choroidal vasculature is downstream from the posterior ciliary artery15, which also perfuses prelaminar and laminar tissues15. Parapapillary choroidal MvD may be attributable to vascular compromise in the common arteriole before it divides into choroidal and optic nerve head (ONH) branches. If so, blood supply to the ONH may be insufficient in eyes with choroidal MvD. In addition, the prelaminar region is supplied by fine centripetal branches from the peripapillary choroid17. Because ICGA rarely detects choroidal vessels within the MvD9, the presence of parapapillary choroidal MvD may be considered a regional compromise of vessels supplying the ONH. Therefore, vascular supply to the deep ONH may be insufficient in eyes with choroidal MvD. Ischemia to the ONH affects the mitochondria18, which produce the high energy required for nerve conduction in the axons and for the maintenance of optimum neuronal function19. Therefore, hypoperfusion in the deep ONH tissues will reduce the function of the involved RGCs20. In addition, ischemia in the area of the MvD may disrupt the blood–optic nerve barrier, resulting in the release of vasoactive or toxic substances into the ONH21. These substances may also have a negative effect on the function of optic nerve axons.Recent studies8. Therefore, it is unknown whether MvD affects the structure-function relationship. The purpose of the present study was to compare the structure-function relationship between POAG eyes with and without MvD, as determined by OCTA.Based on the concepts outlined above, we hypothesized that MvD may affect functional status at a given level of structural damage. Although it has been demonstrated that eyes with MvD had worse VF damage compared to eyes without MvD, the RNFL thickness were also thinner in eyes with MvDInitially, the study included 335 eyes with POAG from 335 patients. Of these, 13 were excluded because of poor quality angiography images. There was excellent interobserver agreement in the detection of MvD (κ = 0.930) and in measuring MvD extent [intraclass correlation coefficient (ICC) = 0.926], parapapillary atrophy (PPA) area (ICC = 0.964) and disc area (ICC = 0.977).Of the 322 eyes of patients with POAG, 193 (59.9%) had parapapillary MvD. Most  MvDs were located in the inferotemporal (IT) sector, with six (3.1%) located in the superotemporal, two (1.0%) in the temporal, and two (1.0%) in the inferonasal sector.P < 0.001), whereas the use of antihypertensive drugs was significantly higher in the MvD– group (P = 0.016). There were no significant between-group differences in age, gender, RNFL thickness, disc area, IOP, axial length, spherical equivalent, blood pressures (BPs), migraine, and self-reported cold extremities. Representative cases are shown in Fig. The clinical and ocular characteristics of the RNFL thickness-matched POAG patients with and without MvD are shown in Table 2 = 0.412, 0.440, respectively, both P < 0.001) and pattern deviation map . A univariate regression analysis showed that age, beta-zone PPA area, and MvD had a significant or marginal influence of P < 0.1 on the structure and function relationship between ln(RNFL thickness) and total deviation average (Table P < 0.1) on the relationship between ln(RNFL thickness) and pattern deviation average . The structure-function relationship was significantly steeper in the MvD+ than in the MvD− group, on both total (y = 17.25 ln(x) − 79.30 vs. y = 9.09 ln(x) − 42.58, P = 0.004) and pattern (y = 16.67 ln(x) − 76.00 vs. y = 7.75 ln(x) − 36.61, P < 0.001) deviation maps  after accounting factors which had an influence (P < 0.1).A significant correlation between IT RNFL thickness and VF defect at the corresponding area was found for both MvD+ and MvD− group at the total  and pattern (P = 0.002) deviation maps  and pattern (62.50 µm) deviation maps. The MvD+ group showed a steeper slope below the tipping point on both the total  and VF pattern deviation  were significantly worse in the MvD+ than in the MvD− group. The comparison of VF deviation was also performed in subgroups with RNFL thickness greater (Thick RNFL group) and smaller (Thin RNFL group) than the above mentioned tipping point . Thirty-eight eyes and 33 eyes were included in the Thick and Thin RNFL group, respectively in each MvD+ and MvD− group. VF sensitivity of each group is shown in Table P ≤ 0.027).VF total deviation  difference in VF deviation in eyes with thinner RNFL is at least partly due to larger extent of MvD in these eyes.27. These possibilities should be considered when evaluating OCTA images in future studies. Third, there is possibility that eyes with larger PPA may have more visible MvD. Further study is needed to clarify the relationship between the PPA and MvD.The current study had several limitations. The first was its cross-sectional design. We compared the slopes of the structure-function relationship of the two groups. In a longitudinal study, the natural characteristics of MvD would have been more apparent. However, OCTA was commercialized relatively recently, and longitudinal studies of glaucoma usually take years. Our study provided meaningful results and can indicate the direction of future research. Second, precise evaluation of the MvD may have been hampered by flow projection artifacts caused by the moving shadows cast by flowing blood cellsIn conclusion, at the same level of peripapillary IT RNFL thickness, eyes with MvD had poorer VF test results and steeper structure-function relationships than eyes without MvD. This finding indicates that parapapillary MvD negatively affects the function of the remaining axons in eyes with POAG.This study involved POAG patients who were enrolled in the Investigating Glaucoma Progression Study (IGPS), an ongoing prospective study of glaucoma patients at the Glaucoma Clinic of Seoul National University Bundang Hospital, and with OCTA results. All subjects provided written informed consent. The study protocol was approved by the Institutional Review Board of Seoul National University Bundang Hospital and followed the tenets of the Declaration of Helsinki.All participants underwent comprehensive ophthalmic examinations, which included assessments of best-corrected visual acuity (BCVA), Goldmann applanation tonometry, a refraction test, slit-lamp biomicroscopy, gonioscopy, stereo disc photography, red-free fundus photography , central corneal thickness (CCT) measurement , and axial length measurement . Peripapillary RNFL thickness was measured by spectral-domain OCT . Other ophthalmic examinations included standard automated perimetry  and OCTA . Spectral-domain OCT, SAP, and OCTA were performed on the same day. Intraocular pressure (IOP) was measured at the time of OCTA. Clinical history was also taken from participants, including demographic characteristics and the presence of cold extremities and other systemic conditions. Systolic and diastolic BP were measured at the time of OCTA. BP was measured with an automatic sphygmomanometer in seated subjects after a 5-minute rest. Mean arterial pressure (MAP) was calculated using the expression MAP = diastolic BP + 1/3 (systolic BP − diastolic BP), and ocular perfusion pressure (OPP) was calculated using the equation OPP = MAP – IOP at the time of OCTA.POAG was defined as the presence of an open iridocorneal angle, signs of glaucomatous optic nerve damage , and a glaucomatous VF defect. A glaucomatous VF defect was defined as a defect conforming with one or more of the following criteria: (1) outside normal limits on a glaucoma hemifield test, (2) three abnormal points with a <5% probability of being normal and one abnormal point with a <1% probability by pattern deviation, or (3) a pattern standard deviation of probability <5% confirmed on two consecutive reliable tests .28. Eyes with advanced glaucoma (VF mean deviation worse than −15 decibels (dB)) were additionally excluded as RNFL thinning may reach horizontal asymptotes in advanced stage of disease30. Because MvD was found almost exclusively at the IT sector, the between-group structure-function relationships were compared only for the IT sector. Patients with MvD in the IT parapapillary area were assigned to the MvD+ group. Eyes with MvD at other sectors were excluded from the analysis. If a subject had IT MvD in only one eye, that eye was included in the analysis. If both eyes had IT MvD or neither eye had MvD, one eye was randomly selected for inclusion in the study.The exclusion criteria were eyes with a BCVA worse than 20/40, a spherical equivalent of <−6.0 D or >+3.0 D, a cylinder correction of <−3.0 D or >+3.0 D, a history of intraocular surgery with the exception of uneventful cataract surgery or trabeculectomy, or retinal or neurological diseases. Eyes with a tilted (ratio of minimum to maximum optic disc diameter <0.75) or torted (angle of tilt >15°) disc were also excludedTo compare the slopes of the structure-function graphs in the MvD+ and MvD− group, patients in the two groups were matched 1:1 by IT RNFL thickness and age.32. Only scans with a quality score ≥20 were included. The Spectralis OCT software (version 6.0) allows for automatic segmentation of the upper and lower borders of the RNFL to calculate the RNFL thickness. This software divides peripapillary RNFL thicknesses into 4 quadrants, with the superior and inferior quadrants further divided into nasal and temporal sectors.RNFL thickness was measured using by spectral domain OCT  circumpapillary RNFL circle scan, which has shown excellent reproducibility in measurements of peripapillary RNFL thicknessThe optic nerve and peripapillary area were imaged using a commercially available OCTA device. Each image set consisted of two raster volumetric patterns , which covered a 4.5 mm × 4.5 mm area centered on the optic disc. The OCTA images were coregistered with OCT B-scans obtained concurrently, to enable simultaneous visualization of vascular signal and structure.33. The area covered by large retinal vessels was included as part of the MvD when the MvD extended beyond the vessels; if not, the area covered by the large vessels was not included in the MvD. The extent of MvD was measured as the angular extent of the MvD using Image J software  ] Fig. . This me36. Two experienced ophthalmologists (J.A.K. and E.J.L.) blinded to the clinical characteristics of the subjects measured PPA and disc area using the built-in caliper tool of the Spectralis OCT system. Each observer measured each PPA area and disc area, with the mean of the two measurements included in the analysis.The area of β-zone PPA which defined as the area without retinal pigment epithelium and with intact Bruch’s membrane and disc area were determined using the software provided by the Spectralis viewer , as previously described37, and the total and pattern deviations in this region were evaluated.The structure-function relationship was evaluated only for the IT sector. The RNFL thickness of the IT sector (271–310°) was obtained from the printout of the SD-OCT measurement of peripapillary RNFL thickness. The VF region corresponding to the OCT IT sector was determined using Garway-Heath maps Fig. 37, and t10 (1/L) before averaging the data within each sector. The averaged data were then converted back to dB units39.VF sensitivity is expressed conventionally on a dB (logarithmic) scale. To calculate the mean total and pattern deviation of the IT sector, the dB levels in the location of the total and pattern deviation fields were converted to a linear scale using the equation dB = 10 log41. The interaction term between RNFL thickness and group was included, allowing evaluation of the difference in slopes between the two groups. Factors influencing the structure-function relationship were identified by regression analysis. The slopes for the structure and function were compared after accounting for the factors which had an influence of P < 0.1 on the structure-function relationship in the univariate regression analysis.Because log(RNFL thickness) and VF sensitivity have a linear relationship, a logarithmic (y = a*ln(x) + b) regression model was fitted, as described previouslyt-tests, chi-square test were used to compare the group differences as applicable. Interobserver agreement on the presence or absence of MvD was assessed using kappa statistics , and interobserver agreement on the extent of MvD and the area of PPA and disc area were assessed by calculating ICCs. Logarithmic regression for the interaction between RNFL thickness and group was modeled as y = a*ln(x) + b*I*ln(x) + c + d*I, where I = 1 if MvD+, = 0 if MvD−. The coefficients a and c indicate the slope and intercept of MvD− group. The coefficients b and d refer to the difference in slopes and intercepts between the two groups. For the ‘Hockey-Stick model’, a tipping point was calculated using Davies’ test. All statistical analyses were performed using SPSS software  and R statistical packages version 3.3.3  with P < 0.05 defined as statistically significant. The data are presented as mean ± SD values except where stated otherwise.Independent sample


Seed size and weight are key traits determining crop yield, which often undergo strongly artificial selection during crop domestication. Although seed sizes differ significantly between oil flax and fiber flax, the genetic basis of morphological differences and artificial selection characteristics in seed size remains largely unclear. Here we re-sequenced 200 flax cultivated accessions to generate a genome variation map based on chromosome assembly reference genomes. We provide evidence that oil flax group is the ancestor of cultivated flax, and the oil-fiber dual purpose group (OF) is the evolutionary intermediate transition state between oil and fiber flax. Genome-wide association studies (GWAS) were combined with LD Heatmap to identify candidate regions related to seed size and weight, then candidate genes were screened based on detailed functional annotations and estimation of nucleotide polymorphism effects. Using this strategy, we obtained 13 candidate genes related to seed size and weight. Selective sweeps analysis indicates human-involved selection of small seeds during the oil to fiber flax transition. Our study shows the existence of elite alleles for seed size and weight in flax germplasm and provides molecular insights into approaches for further improvement. Linum usitatissimum L.) has a worldwide distribution and is considered as an economically important crop in the world because of its vast fiber and seed oil (Linum bienne) (Flax (seed oil . It has  bienne)  and has  bienne) . The dif bienne) . Typical bienne) . FindingGL4 gene during African rice domestication resulted in the loss of seed shattering and smaller seeds than their wild ancestors. Selection of the loss of seed shattering allows small seeds to be selected together into African cultivated rice , etc (Linkage mapping and association mapping are popular methods for the identification of underlying genetic variations in agronomic traits . LinkageLs), etc . HoweverLs), etc . CompareLs), etc . Most alLs), etc . In theoLs), etc . As a poLs), etc .During the past decades, genetic studies on flax agronomic traits based on association mapping have been reported. In this study, we investigated 200 worldwide flax cultivars, representing a wider geographical distribution and a richer phenotypic variation. We performed more than 12× depth genome-wide resequencing based on chromosome assembly reference genomes and developed 1,179,402 informative SNP markers to estimate the genetic diversity and population structure. Subsequently, we attempted to identify candidate regions related to seed size and weight by GWAS combining with LD Heatmap. Thirteen candidate genes were finally obtained by screening candidate region variation sites and detailed gene annotation. We also detected the polymorphism patterns and characteristics in 13 candidate genes and revealed signatures of artificial selection. This genetic resource could contribute to the development of molecular markers and the breeding of flax varieties in future.A total of 901 flax germplasms were collected worldwide to construct a germplasm resource bank. 541 accessions with wide geographical distribution and rich genetic diversity in phenotypic traits were selected to construct an original population. Then 200 accessions were used to construct a core collection for genome variation analysis and GWAS in this study. Among the 200 accessions, 87 were from the core collection of Plant Gene Resource Centre in Canada (PGRC), 105 from the United States National Plant Germplasm System (U.S.NPGS), and 8 from the Chinese Crop Germplasm Resources Information System (CGRIS) , Dali, Yunnan, China, in October of 2016 (2016DL) and Urumqi experimental base , Urumqi, Xinjiang, China, in May of 2017 and 2019  as well as at the YiLi State experimental fields of the Institute of Agricultural Sciences , YiLi, Xinjiang, China, in March of 2019 (2019YL). A randomized complete block design with three biological replicates was used for field experiments. Each cultivar was planted in five rows with a length of 2 m, and spacing of 25 cm between rows. The experimental area was surrounded by 1.5m wide isolation plot, and the field management was carried out in a conventional way. Seed length (SL), seed width (SW) and 1,000-seed weight  were measured after harvest. The 1,000-seed weight was the weight of 1,000 mature, full and clean seeds counted by manual. High-resolution scanner was used to capture the image of seeds. Image J 1.8.0 assisted to accurate measure seed length and width manually by measuring the distances between the longitudinal axis and horizontal axis of twenty seeds, respectively. Three replicates were performed in the same environment.https://www.ncbi.nlm.nih.gov/nuccore?LinkName=pubmed_nuccore&from_uid=22757964) using bwa-mem with the -M option of BWA software (Flax seedlings were planted in greenhouse until 25-day-old. Genomic DNA was extracted from leaves with the plant genomic DNA extraction kit (TIANGEN). The qualified DNA samples were randomly fragmented by enzyme digestion and Covaris S2. The fragmented DNA was used for a DNA library construction with the NEBNext DNA Library Prep Reagent Set for Illumina (BioLabs). The DNA library was sequenced using an Illumina HiSeq X Ten , and a total of 762.94 Gb of genomic sequence data were obtained. All reads were mapped against the reference genome   and set the K value to 2~8. The most likely group number was determined according to the variation rule of an ad hoc statistic △K, and the genetic structure map was constructed. In addition, we performed principal-component analysis (PCA) using the software GCTA  in the Haploview 4.20 software  between SNPs for the oil flax, oil-fiber dual purpose, and fiber flax accessions was evaluated using squared Pearson’s correlation coefficient  were used to perform GWAS for seed size and weight in 200 accessions. GWAS was performed using a mixed linear model (MLM) with the GEMMA software package . For they is a vector of phenotype; X represents genotype; S is a population structure matrix built up based on the top three PCs, and K is a kinship matrix built up based on simple matching coefficients calculated from the nucleotide polymorphisms; α and β represent fixed effects, and μ represents random effects; Where e is a vector of random residual effects. The significance thresholds (1/n) was set to control the genome-wide type I error rate of GWAS by modified Bonferroni correction ≥ 4 mapping to flax reference genome  was performed for functional annotation. (iii) We classified all the SNPs in the candidate region into three groups. Group I included SNPs significantly associated with trait variation in the GWAS (−log10(P) ≥ 5), and located in the exon, intron and promoter regions. It should be reasonably expected that such polymorphism will have higher probability in functions variation or be more closely linked to genes. Group II included SNPs significantly associated with trait variation as group I and located on outside coding regions. Group III included SNPs not significantly associated with trait variation. (iv) Using Group I and Group II as a major and assistant screening site respectively, and combining with detailed gene function annotation, candidate genes were obtained. The SNP recombination within the candidate gene divides the varieties into distinct haplotypes, and we compare the phenotypic differences in the distinct haplotypes by non-parametric t test.Rapid identification of new genes associated with agronomic traits by GWAS was performed as described previously  with slie genome  (Annotathttps://sourceforge.net/projects/mev-tm4/files/mev-tm4/).To display the relationship between population genetic structure and phenotypic distribution, we generated graphical phenotypes. Because the phenotypic data are distributed in different orders of magnitude, here we used zero-mean normalized phenotypes values of 7 major agronomic traits, including plant height (PH), technical length (TL), number of branches (BN) and capsules (CN), 1,000-seed weight , seeds length (SL) and width (SW). To schematically display structural variation of candidate genes, we also generated graphical genotypes. In regions of candidate genes, SNPs were converted to numeric genotypes as 1, −1 and 0, which corresponded to major and minor polymorphisms and miss sites, respectively. Clustering the tested population was carried out using the hierarchical clustering algorithm based on candidate gene haplotypes, and the clustering information was used to generate graphic genotypes. In each polymorphic locus, the major and minor alleles were expressed in red and green, respectively, and the miss sites were represented in black. Phenotype and genotype visualization graphics conducted using the MeV4.9.0 software  is often used to measure the level of population diversity . We usedAbundant phenotypic variation is the basis for efficient genome wide association study (GWAS). The association mapping population in this study highly conserved the genetic diversity of the original population, and the three traits  showed abundant variations in 200 flax cultivated accessions Table 1.Whole-genome resequencing was performed with the 200 flax association mapping population, and obtained a total of 762.94 Gb of sequences. The high quality clean data was 761.45 Gb, with an average 3.80 Gb for each accession. The average depth was 12.28× and covered 94.42% of the reference genome (chromosome assembly reference genomes) . After aπ = 0.703 × 10−3) than in the OF group (π = 0.482 × 10−3) and the fiber group (π = 0.357 × 10−3). These results indicated that the oil flax group is the ancestor of cultivated flax, and the OF group is the evolutionary intermediate transition state between oil and fiber flax.A subset of 1,179,402 SNPs was used to construct neighbor-joining tree, and genetic population structure, and to carry out principal component analysis (PCA) to quantify the population structure and genetic relationships between the 200 flax cultivated accessions. The cultivated flax could be divided into oil flax, oil-fiber dual purpose (OF) flax and fiber flax groups    (r2) was measured as the physical distance at which the r2 dropped to half of its maximum value, which was consistent with this study. This indicates that flax has obvious advantages in using LD compared with other crops, and the low LD extent makes it relatively easy to screen genes that regulate important agronomic traits.Population structure and linkage disequilibrium (LD) extent are two main factors affecting the correctness and accuracy of GWAS identification of candidate genes . Diversictively)  and cultctively) , and eve, 20 kb)  and wild, 27 kb) . In the The variation of flax seed shape is very small, and seed length, seed width and 1,000-seed weight were significantly positively correlated (P) = 11.64), overlapping with 2017UR-SL, 2016DL-SL, 2016DL-SW and 2019YL-SL peaks  , which showed the highest -log10(P) value  = 5.70), encoded a ubiquitin carboxyl-terminal hydrolase   regulates seed size by promoting cell proliferation  = 7.07) of 2017UR-SL on chromosome 12, which was also detected on 2017UR-SW and 2017UR-1000 SW and showed a weak association signal  (Lus10043126 (LusUCH-2). Here we investigated why this peak was weak and had only one SNP support. We focused on the local LD heatmap surrounding the peak, which showed a week linkage  Table 2.Lus10043126 was a candidate gene of seed size on chromosome 12.Next, we constructed a schematic map of the structural variation of candidate genes. The major and minor alleles at each SNP locus were expressed in red and green, respectively, and the miss sites were represented in black  and fiber flax groups with obvious morphological differences , and the highest diversity was found in oil groups and the lowest in fiber groups . The whole region of Lus10008949 showed strong selective sweeps, while the local area of Lus10008438 showed obvious selective sweeps  among oil flax, OF flax and fiber flax. The diversity of candidate genes was significantly different among three groups  was higher than that of OF (π = 0.482 × 10−3) and fiber groups (π = 0.357 × 10−3), and LD extent was lower than that of OF and fiber groups  = 11.64) was detected in the LusUBC locus (Lus10036372) on chromosome 11, which encodes the ubiquitin-conjugating enzyme E2 (UBC)  significantly reduced the silique length and the seeds number, and nearly 90% of ovule abortion  . In Arababortion . The Araty acids . So in oLus10022567). RING/U-box protein as E3 ubiquitin ligases can transfer ubiquitin from E2 to specific substrate proteins and has been reported to play an important role in plant morphology, hormone signaling and stress response . In Arabidopsis, CIP8 as a ubiquitin E3 ligase (Lus CIP may play the role in ubiquitin E3 ligase for regulating seed size in flax.We detected nine environment and traits that showed overlap on chromosomes 15 (response . The RINbidopsis . In ricend yield . Similarnd yield . Candida3 ligase . This meLus10008949 and Lus10043126) in the peak regions on chromosomes 9 and 12 that were annotated as ubiquitin carboxyl-terminal hydrolase (UCH) , which regulates seed size by promoting cell proliferation (SOD2 results in seed and plant enlargement. WIDE AND THICK GRAIN 1 (WTG1) encodes an otubain-like protease with deubiquitination activity. Wtg1-1 mutant showed wide, thick, short and heavy grains and overexpression of wtg1 resulted in narrow, thin and long grains by regulating cell expansion influences grain size and shape in rice  Table 2.feration . It shou in rice . These rLusRPN locus (Lus10035470) on chromosome 7, which encodes 26S proteasome regulatory subunit .All sequencing data generated for this study can be found in the NCBI using accession number PRJNA590636  and High Technology Project of Xinjiang Uygur Autonomous Region in China (201517108).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Populus populations: a linkage mapping population comprising a full‐sib family of 1,200 progeny and an association mapping panel comprising 435 unrelated individuals from nearly the entire natural range of Populus tomentosa. We mapped 311 quantitative trait loci (QTL) for three growth traits at 12 timepoints to 42 regions in 17 linkage groups. Of these, 28 regions encompassing 233 QTL were annotated as 27 segmental homology regions (SHRs). Using SNPs identified by whole‐genome re‐sequencing of the 435‐member association mapping panel, we identified significant SNPs (P ≤ 9.4 × 10−7) within 27 SHRs that affect stem growth at nine timepoints with diverse additive and dominance patterns, and these SNPs exhibited complex allelic epistasis over the juvenile growth period. Nineteen genes linked to potential causative alleles that have time‐specific or pleiotropic effects, and mostly overlapped with significant signatures of selection within SHRs between climatic regions represented by the association mapping panel. Five genes with potential time‐specific effects showed species‐specific temporal expression profiles during the juvenile stages of stem growth in five representative Populus species. Our observations revealed the importance of considering temporal genetic basis of complex traits, which will facilitate the molecular design of tree ideotypes.In perennial woody plants, the coordinated increase of stem height and diameter during juvenile growth improves competitiveness (i.e. access to light); however, the factors underlying variation in stem growth remain unknown in trees. Here, we used linkage‐linkage disequilibrium (linkage‐LD) mapping to decipher the genetic architecture underlying three growth traits during juvenile stem growth. We used two  This revealed remarkable phenotypic variation in stem growth during the juvenile growth phase , basal diameter (BD), and stem volume (V) during growth of 1,200 juvenile se Table a–c. The se Table a‐c. The se Table a. Considse Table a revealsP. tomentosa, including three sub‐populations from different climatic zones, the southern (S), north‐western (NW) and north‐eastern (NE) climatic regions , 309 simple sequence repeat (SSRs) and 32 insertion‐deletions (InDels) on the linkage map  dynamic QTL , 430 genes encoding long noncoding RNA (lncRNA) from 121 overlapping genomic loci and 28 genes for precursors of microRNA (pre‐miRNA), covering all genomic sequences of the 27 putative SHRs Figure . For insWRKY, AP2/ERF, bZIP and MYB TF families (Data Gene ontology (GO) analysis of the 640 PCGs revealed that the significantly enriched categories (FDR ≤ 0.05) mainly include ‘transferase activity, transferring glycosyl groups’ and ‘transcription regulator activity’ Data . The acties Data , suggestcis‐target pairs , was predicted as the cis‐target of lncRNA TCONS_00256202 and TCONS_00256202 is the target of Pto‐MIR1985  were considered as potential s Figure . Also, 4s Figure . For exa−1 (Table dN/dS = 0.34) of the synonymous diversity.To validate these dynamic QTL across populations, we next identified SNPs within the SHRs in the association mapping panel. Analysis of the re‐sequencing data of the association mapping panel identified 52 949 high‐quality SNPs from the 27 SHRs Table . The SNP−1 Table . 64.9% S−1 Table . The nuc−1 Table , where nr2) for all pairwise SNP combinations within each SHR. This found 684 high‐LD blocks  and the population‐differentiation statistic (Fst) values of each pairwise comparison between climatic regions  located on SHR22, encoding zinc finger proteins and disease resistance‐responsive family proteins, which showed very strong selection signals in the NE vs. NW comparison , located on SHR26, showed significant signals of selection in the NE vs. S comparison  of allelic variants within SHRs of QTL for the same three growth traits over a longer period of growth  in the association mapping panel.P = 1.9 × 10−5  association mapping approach to examine the additive/dominant effect between 52 949 high‐quality SNPs and 27 phenotypic sets . As shown in the quantile‐quantile and Manhattan plots for the 27 phenotypic data Figures , a rangeP = 9.4 × 10−7 , we detected 652 (BD), 179 (H) and 1,758 (V) associations among the nine timepoints, representing 411 unique SNPs for BD, 156 for H and 985 for V   Figure .P < 0.001, Data In summary, we found that 60.9% of the associations were exclusively related to dominant effects , and 12.0% were related to both additive and dominant effects  were detected for V and BD at multiple timepoints  or high‐LD clusters of multiple top‐ranked associated SNPs underlie the stem growth phases in Populus. In total, 22 lead SNPs were detected within nine SHRs that may represent the possible causative signals for each trait at the nine timepoints , was the peak associated variant for BD1‐03  and BD1‐04   Figure b.Potri.008G113300  in pairwise combinations were observed for the lead SNPs and top‐ranked associated SNPs.In addition, we screened six intergenic SNPs that located close to (approximately 0.3–7 kb) the four PCGs, which were the peak associated variants for growth traits at multiple timepoints Table . For exaP. tomentosa (Table LATERAL ORGAN BOUNDARIES 42 (LOB42), which had the most significant SNP for H5 , JAG (encoding a C2H2 and C2HC zinc fingers superfamily protein), PPC4 (encoding phosphoenolpyruvate carboxylase 4 protein) and an unknown PCG (Potri.016G073400), where the major SNPs with presumably mutant alleles were more likely to be found at high frequency in the high latitude northern China  novel genes. We next chose several SHRs and candidate genes involved in specific growth phases to investigate the potential functional polymorphisms capable of affecting the stem growth processes.WRKY3 (Potri.004G120800), which produces a Lys > Arg substitution, is the lead SNP for BD4  and V5  with similar genetic effect  in the 4‐ to 5‐year‐old P. tomentosa clones  in SHR23 that simultaneously associated with the V4 (P = 1.79 × 10−15) and V5 (P = 1.47 × 10−16) (Table Potri.016G073400), with significant regional selective signals , BD5 (P = 1.02 × 10−13), H4 (P = 4.10 × 10−8), H5 (P = 5.46 × 10−10) and the expression of Potri.016G073400 (P = 2.78 × 10−6) , a nonsynonymous variant within exon 4 of a key TF gene, ect Data . The wilnes Data , suggest6) Table . The SNPs Figure . This pl) Figure , suggestPotri.016G12400 (DUF594), is the lead SNP for BD3 . This SNP shows obvious regional differentiation, and the mutant allele (C) was mainly distributed in S climatic regions in southern China , as well as DUF594 expression , is the lead SNP for H2  and H3 (P = 7.05 × 10−9) , in exon 1 of a Figure , to confnes Data .Potri.016G013200), and partially overlapped with a cluster of lncRNA genes (TCONS_00138008), had the most significant association signals for V2  and V3  , a noncoding SNP variant located in intron 2 of a novel gene (5) Table a,b. A siR2 = 36.8%, LOD = 6.12; Figures P = 3.16 × 10−6−1.19 × 10−8) was detected in the 2.5‐kb region upstream of Potri.004G120900 (DUF3598) that significantly associated with V1‐04  and/or H1‐04   significantly associated with H1‐04  significantly associated with V1‐04  for V_04 (stem volume at the fourth timepoint) in the linkage population  from the P. tomentosa association panel to investigate the temporal and spatial expression profiles among five representative Populus species in the northern hemisphere . As shown in Figure Populus orthologs were more closely related to orthologs from Salix purpurea in the phylogenetic tree than to each other from the perennial dicot subdivisions, suggesting that these novel genes originated from lineages that split off after Salicaceae speciation. All five genes exhibited distinct expression profiles within the vascular cambium and the SAM at the nine growth timepoints among five Populus species , showed potentially similar regional differentiation patterns, as the mutant allele of each SNP increased in frequency from the S to the NW or NE regions , is the major SNP for BD and V traits at the four and 5‐year‐old growth stages , which had the highest association signal for H1‐05 (1‐year‐old stem height at the fifth timepoint), and also as a significantly top‐ranked locus for H1‐04 and H1‐03  with associated variants in SHR21 , located 138 bp upstream of 03 Table . RPL15 ewth Data , which ss Figure b. The prs Figure , suggestPotri.016G013200, a novel gene in the SHR21 locus, was detected as the most significant association signal for V2 and V3  with an Arabidopsis gene (AT5G20130). AT5G20130 encodes an enzyme of the sulphate adenylyltransferase subunit, which is part of a bifunctional polypeptide chain associated with the adenosyl phosphosulphate (APS) kinase that catalyses the primary step of intracellular sulphate activation  as the female parent and clone ‘LM50’ (P. tomentosa) as the male parent . The distribution zone from which these samples were collected was divided into the southern , north‐western , and north‐eastern  climatic regions , which were randomly sampled from a collection of 1,047 natural s Huang, . Total get al.  as the cut‐off. The potential QTL were initially detected employing the Interval Mapping algorithm. Then, markers with the highest LOD value were selected as cofactors and the final set of markers associated at P < 0.02 after automatic cofactor selection were further implemented in the Multiple QTL Model (MQM). Co‐factor selection and MQM analysis were repeated until the best possible set of QTL was declared, then each QTL was characterised by its maximal LOD score and PVE. The QTL confidence interval was designated in centiMorgans (cM) and corresponded to a LOD score (>3.0) drop of one on either side of the likelihood peak  in 19 LGs with an average marker interval of 2.3 cM  corresponding to the confidence interval of each QTL, based on a comparison of the anchoring gene‐derived markers on the linkage map with the reference genome using BLASTX with a cut‐off E‐value < 1.0e‐10 , and the FDR  < 0.05 was set as threshold to identify significant GO terms.The PCGs within the SHRs were annotated on the basis of the gene models of the P. tomentosa as previously described  via complementary base pair interactions, with expectation ≤ 5.0. Two independent algorithms were used to predict the potential target genes of lncRNAs according to their cis‐ and trans‐ regulatory actions, and the detailed methods are provided in Method The pre‐miRNA and lncRNA genes within the SHRs were annotated based on our transcriptome database of predicted lncRNAs and miRNAs in et al., Populus reference genome v3.0 using SOAPaligner  with the default options , which are provided in Method Populus individual was calculated using SOAPsnp v1.03 with default parameters  was used to capture the genotype data of 106 663 candidate SNPs within the 27 SHRs (Table http://www.maizegenetics.net/tassel) with the selection criteria of missing data > 10% and MAF < 0.05. All high‐quality SNPs within the SHRs were assigned as noncoding RNA‐derived SNPs, genic , or intergenic annotation sets based on their locations relative to our annotated PCGs and noncoding RNA genes. To include SNPs in the promoter or flanking regulatory regions for each gene, we included the full‐length gene sequences 2 kb upstream and downstream of each gene.To obtain high‐quality SNPs, we selected the reads that could be matched to a unique genomic location to perform SNP calling. The genotype likelihood of the genomic site for each Rs Table  followinw , in which the latter was the reference population and the former was the object population. Selection sweeps were evaluated using an adjusted method for reduction of diversity (ROD) statistics according to Xu et al. , by usinr2) values across pairs of SNP loci within each SHR were calculated in TASSEL v5.0 with 105 permutations. A visual software package, Haploview v4.2 , according to Wen et al. (P = 9.4 × 10−7 (significant P = 0.05/n) was subsequently calculated on the basis of an empirical estimate of the number of independent tests for the entire population, based on a nominal level of 0.05  within each significant SHR locus and used a cut‐off of <0.2 for the LD statistic r2 . Each pool was a mixture of the same tissue samples from 10 random P. tomentosa individuals at a particular timepoint. The details of total RNA extraction, cDNA synthesis and RT‐qPCR programme and reaction are described in Methods Actin1, Accession number: EF145577) are listed in Table Actin1 using the 2−ΔΔCt method. All reactions were performed with three technical replications and three biological replications.Expression analysis was conducted to investigate the tissue or temporal expression patterns of the potentially associated genes in the population of To test whether the most significant SNPs and InDels affect the relative transcript abundance of their corresponding genes, we quantified the mRNA levels of the different SNP and InDel genotypic classes in the association mapping panel by RT‐qPCR. The tested timepoints and tissues were selected based on the spatial association signals; the stem vascular cambium was generally used for validating the variants underlying BD traits and SAM was used for variants associated with H. For each genotypic class, tens or hundreds of individuals from the association mapping panel were sampled by obtaining cDNA from the fresh stem cambium or SAM tissue.CRA000903 that is publicly accessible at http://bigd.big.ac.cn/gsa. All data corresponding to the QTL and linkage disequilibrium mapping are provided in Supplementary Raw sequence data of genome re‐sequencing have been deposited in the Genome Sequence Archive in BIG Data Center, Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, under accession number D.Z. planned and designed the research. Q.D., X.Y., J.X., M.Q., L.X., W.L., J.T., C.G., J.C., and B.L. performed experiments, conducted fieldwork, analysed data etc. Q.D. wrote the manuscript.Figure S1 Growth trajectories of tree height, basal diameter and stem volume in Populus linkage population and association mapping panel.Figure S2 The dynamic curves of the stem height to diameter ratio in the Populus linkage population and association mapping panel.Figure S3 Annotation and target gene prediction within all 27 putative SHRs.Figure S4 Estimates of the squared allele‐frequency correlations for all pairwise SNP combinations within each SHR.Figure S5 The diverse patterns of linkage disequilibrium decay with physical distance between the common SNPs separately for each SHR.Figure S6 Manhattan and quantile‐quantile plots resulting from the association results between all SNPs of 27 SHRs and basal diameter at the nine timepoints.Figure S7 Manhattan and quantile‐quantile plots resulting from the association results between all SNPs of 27 SHRs and stem height at the nine timepoints.Figure S8 Manhattan and quantile‐quantile plots resulting from the association results between all SNPs of 27 SHRs and stem volume at the nine timepoints.Figure S9 The significance of 27 SHR‐wide scans for basal diameter, tree height and stem volume at the nine growth timepoints of Populus.Figure S10 Structural networks that represent significant gene, lncRNA and miRNA loci for basal diameter, tree height and stem volume at the nine growth timepoints of Populus.Figure S11 Identification of the association hotspots related to the protein‐coding genes and noncoding RNA genes at the suggestive and significant P‐values respectively.Figure S12 The genetic effect and phenotypic contributions of Chr04_11343447 and Chr16_5505403 over the 2‐ to 5‐year growth phases.Figure S13 Gene–gene interactions formed uniquely interconnected networks for basal diameter at the nine timepoints.Figure S14 Gene–gene interactions formed uniquely interconnected networks for tree height at the nine timepoints.Figure S15 Gene–gene interactions formed uniquely interconnected networks for stem volume at the nine timepoints.Figure S16 SHR08 contains causative signals underlying growth‐stage‐specific stem height and basal diameter growth.Figure S17 The allelic distributions of 12 lead SNP sites among individuals from the three climatic regions.Figure S18 SHR23 may contain a causal SNP (Chr16_5505403) associated with the fourth and fifth year of growth in Populus.Figure S19 Interspecific expression profiles for the five potential time‐specific genes in the genus Populus.Table S1 Descriptive statistic for each trait at each single timepoint in the linkage population and association mapping panel.Table S2 Quantitative trait locus detection and segmental homology region identification for three growth traits at 12 timepoints in a Populus interspecific linkage population.Table S3 Annotation of dynamic QTL and corresponding segmental homology regions for three growth traits over 12 timepoints in Populus.Table S4 Annotation and diversity assessment of genes and SNPs within 27 segmental homology regions in the association mapping panel of Populus.Table S5 Summary of potential selective signals and genes within the SHRs in the association mapping panel.Table S6 Functional annotations of these selective protein‐coding genes among all pairwise climatic regions of Populus .Table S7 Summary of the number of all significant SNP within SHRs associated with three growth traits over nine time points of Populus.Table S8 Summary of pairwise SNP and gene epistasis for three traits at nine time points in association mapping panel of Populus.Table S9 The mean value for each trait at the nine time point in the three climatic regions of the association mapping panel.Table S10 Primers used for reverse transcription quantitative PCR for the candidate genes.Method S1 Identification and target prediction of noncoding RNA genes.Method S2 Library construction for genome re‐sequencing.Method S3 Statistics formulas for Fst, θw, π and Tajima's D calculations.Method S4 Total RNA extraction, cDNA synthesis and reverse transcription quantitative PCR.Click here for additional data file.Data S1 Annotation of all protein‐coding genes in the 27 SHRs of Populus.Data S2 Summary of all long noncoding RNA genes in the 27 SHRs of Populus.Data S3 Summary of all MicroRNA genes in the 27 SHRs of Populus.Data S4 Gene ontology (GO) term analysis of all genes in the 27 SHRs at the top‐ranked enrichments (FDR ≤ 0.05).Data S5 Prediction of lncRNA‐target gene pairs within all 27 SHRs.Data S6 Prediction of microRNA‐target gene pairs within all 27 SHRs.Data S7 Summary of all SNP variants in the association mapping panel of Populus.Data S8 Summary of potential selective signals within the SHRs among pairwise climatic regions of Populus.Data S9 Summary of potential selective genes within the SHRs among pairwise climatic regions of Populus.Data S10 The allelic distributions of 109 representative SNPs within the selective genes among the three climatic regions.Data S11 Identification of significant additive and dominance associations for BD, H and V at the nine timepoints.Data S12 Significant SNP–SNP epistatic interactions for BD, H and V across the nine timepoints in the association mapping panel.Data S13 Pairwise epistatic interactions between these significant additive/dominant SNPs detected for V and BD at multiple timepoints.Data S14 Expression data of several time‐specific genes at the corresponding timepoints or tissues in Populus.Click here for additional data file.


The use of prior information to rank genomic regions and perform SNP selection could increase the power of GWAS. In this study, we propose the use of gene expression data from RNA-Seq of multiple tissues as prior information to assign weights to SNP, select SNP based on a weight threshold, and utilize weighted hypothesis testing to conduct a GWAS. RNA-Seq libraries from hypothalamus, duodenum, ileum, and jejunum tissue of 30 pigs with divergent feed efficiency phenotypes were sequenced, and a three-way gene x individual x tissue clustering analysis was performed, using constrained tensor decomposition, to obtain a total of 10 gene expression modules. Loading values from each gene module were used to assign weights to 49,691 commercial SNP markers, and SNP were selected using a weight threshold, resulting in 10 SNP sets ranging in size from 101 to 955 markers. Weighted GWAS for feed intake in 4,200 pigs was performed separately for each of the 10 SNP sets. A total of 36 unique significant SNP associations were identified across the ten gene modules (SNP sets). For comparison, a standard unweighted GWAS using all 49,691 SNP was performed, and only 2 SNP were significant. None of the SNP from the unweighted analysis resided in known QTL related to swine feed efficiency  compared to 29 (80.6%) in the weighted analyses, with 9 SNP residing in feed intake QTL. These results suggest that the heritability of feed intake is driven by many SNP that individually do not attain genome-wide significance in GWAS. Hence, the proposed procedure for prioritizing SNP based on gene expression data across multiple tissues provides a promising approach for improving the power of GWAS.The “large  Genome-wide gene expression data from thousands of studies have been accumulating and made available through public repositories such as the Gene Expression Omnibus GEO; . RecentlThe U.S. Meat Animal Research Center (USMARC) Animal Care and Use committee reviewed and approved the use of animals in this study.ad libitum access to a standard corn/soybean meal-based diet that met or exceeded NRC requirements (via the FIRE feeders and pigs were weighed at the beginning (d0) and end (d 42) of the feeding trial. Twenty-two cohorts of pigs had individual feeding events recorded.Feed intake and body weight gain were measured on cohorts of growing pigs reared at USMARC. All pigs were sired by either Landrace or Yorkshire boars sourced from 5 different genetic suppliers and produced out of Landrace-Yorkshire cross sows. Two different genetic suppliers are represented in each group of pigs. Pigs entered the barn at approximately 95 days of age at the beginning of the feeding trial and had irements . Pigs inDifferent numbers of animals from the population were used in different stages of the study. Feed intake data was collected on a total of 4,200 animals across the 22 cohorts. Four of these 22 cohorts  were used to select 30 animals with extreme feed efficiency phenotypes for RNA-Seq. Lastly, GWAS was performed using data from the 2,813 animals that were both genotyped and phenotyped. Detailed descriptions of each stage of the study are provided in subsequent sections.Feed efficiency phenotypes were determined for each pig in four cohorts  by dividing average daily body weight gain (ADG) by average daily feed intake (ADFI) to determine the gain to feed ratio (Gain : Feed). From each cohort of pigs, a selection criterion was applied to select animals for further study that included ADG within ± 0.30 SD of the mean and the greatest and least ADFI (n = 7 or 8 per cohort). The descriptive statistics are presented in Tissue collection and RNA extraction were performed using the same procedures in each contemporary group. Sample collection time frame was consistent across cohorts. Pigs identified as high and low feed efficiency were euthanized with barbiturates in accordance with the American Veterinary Medical Association guidelines . The heaTotal RNA was isolated from the tissue samples using the RNeasy Mini Plus kit and QiaShredder columns . Briefly, 800 ul of RLT buffer with β-mercaptoethanol were added to 50–100 mg of tissue samples and homogenized for 40 sec using an Omni Prep 6-station homogenizer . The homogenate was centrifuged in a QiaShredder column on full speed for 3 min. Genomic DNA was removed from the total RNA with the Qiagen RNeasy Plus mini-kit, according to the manufacturer's protocol, and the total RNA was eluted in 50 ul of RNase free water. Total RNA was quantified with a NanoDrop One spectrophotometer . The average 260/280 ratio was 2.05, with a range of 1.94–2.09. An Agilent Bioanalyzer RNA 6000 nano kit  was used to determine the RNA integrity number (RIN). Only samples with a RIN of 8.0 and higher were used for the RNA sequencing. The average RIN was 9.1, with a range of 8.1–9.9.Samples were prepared for RNA sequencing with the Illumina TruSeq Stranded mRNA High Throughput Sample kit and protocol . The libraries were quantified with qRT-PCR using the NEBNext Library Quant Kit  on a CFX384 thermal cycler , and the quality of the library was determined with an Agilent Bioanalyzer DNA 1000 kit . The libraries were diluted with Tris-HCL 10 mM, pH 8.5 with 0.1% Tween 20 to 4nM samples . All libraries were paired-end sequenced with 150 cycle high output sequencing kits for the Illumina NextSeq instrument. Bases of the paired-end reads for all sequenced libraries were identified with the Illumina BaseCaller, and FASTQ files were produced for downstream analysis of the sequence data. Sequence data is available for download from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) BioProjects PRJNA528599 , PRJNA528884 (duodenum), PRJNA529214 (ileum), and PRJNA529662 (jejunum).www.bioinformatics.babraham.ac.uk/projects/fastqc), and reads were trimmed to remove adapter sequences and low-quality bases using the Trimmomatic software .Three-way clustering of multi-tissue, multi-individual gene expression data was performed using an adaption of the method described by F is defined entry-wise as Tr ≥ 0 or Tr ≤ 0, by thresholding values in to 0. The appropriate sign of was selected to maximize Tr. This constraint on eases the interpretation of the interaction at the tissue level. Non-zero tissue loading values indicate that the module is “active” in the tissue. Without constraining values in to a single sign, it is possible (in fact likely) that contains two expression modules, one for the tissues with positive loading values and one for the tissues with negative loading values. Consequently, gene and individual loadings become less informative since they cannot be explicitly assigned to either the positive or negative loading module. Note that the constraint on used in this work is slightly different that of Tr.Complete details of the algorithm used for tensor decomposition can be found in Gr exhibit strong relationships with individuals and tissues in the r-th component, while these relationships are stronger in the individuals with larger Ir-values and tissues with larger  Tr-values. The loading vectors Gr, Ir, and Tr will be referred to as eigen-genes, eigen-individuals, and eigen-tissues, respectively, throughout the remainder of the manuscript.Genes with large values in Sus scrofa GO annotation as background was utilized. Data from PANTHER was considered statistically significant at FDR-corrected P ≤ 0.05.Enrichment analysis of gene ontology GO terms was performed using the PANTHER classification system  for all j = 1, 2,…, nI. After the model was fit, the proportion of variance explained by each covariate  was calculated using ANOVA.Sources of variation in individual loadings were analyzed by fitting the following linear model:Tr) be the tensor projection of Ω through the eigen-tissue Using the notation from above, let εj~ N. The ADFI-effect was assessed by testing Then, the following linear model was used for each gene tested,Twenty-two cohorts of 196 pigs had individual feeding events recorded in a building fitted with Osbourne FIRE Feeders. The animals and facilities were previously described in Section 2.1. Records were removed for animals with incomplete data due to one of the following reasons: animal removed from the study due to health, failure of the electronic ID eartag, or failure of the FIRE Feeder for a majority of the test. As a result, 4,200 animals remained in the study. Aberrant feeding events were removed if they did not conform to a logical length of meal time , amount of feed consumed (20 g < feed consumed < 3 kg), and consumption rate (rate < 2 kg/min). Once aberrant feeding events were removed, feeding parameters were computed for each pen and day of test to determine if a feeder was not operating properly. Statistics used to remove a pen x day included number of aberrant feeding events recorded, amount of feed distributed, and total number of events for each day. After all suspicious records were removed, the amount of feed consumed by each pig for each day of test was calculated, resulting in a total of 164,660 records of the 184,800 possible daily intake records.Data were analyzed with WOMBAT . Genotyping was conducted using three platforms: the NeoGen Porcine GGPHD chip , Illumina Porcine SNP60 v2 chip , and NeoGen GGP-Porcine chip .Ancestors of the pigs having intake records were identified from USMARC pedigree records to create a 7,009 animal pedigree. Phenotyped pigs and their ancestors genotyped with a SNP assay, Illumina Porcine SNP60 v1 or v2 , Illlumina Porcine SNP50 , NeoGen GGP-Porcine chip , and NeoGen Porcine GGPHD chip  were identified. The SNP were ordered according to the Sscrofa11.1 genome assembly and available pedigree was used to impute genotypes to 49,695 SNP from at least one assay for the 4,632 genotyped animals  using findhap .i the frequency of the B allele for the ith SNP, and M* a centered genotype matrix (M) weighted by a diagonal matrix of weighting factors (D)log10(P), where P denotes the P-value obtained from testing the ADFI-effect in Equation (5) in the gene module of interest. If a SNP did not reside in a gene, it was assigned a weight of zero.Following t, and (3) a weighted analysis using only SNP with weight > t. Arbitrary thresholds of t = 2 and t = 5 were evaluated.For a given weight threshold, t, three G for each of the 10 sets of gene weightings were evaluated: (1) a weighted analysis with all SNP where all SNP had non-zero weightings (min = 0.00001), (2) an unweighted analysis using only SNP with weight > 2) of test intake attributable to pedigree relationships and each weighted genomic relationship matrix. Phenotypic variance should remain constant; all estimates of phenotypic variance from these data using different unweighted G were similar. Weighted G resulted in additive variance estimates much greater than phenotypic variance from unweighted G, and residual variances were similar to estimates using unweighted G. Assuming the residual variance estimate is appropriate for variation not explained by weighted G and phenotypic variance equal to that estimated with unweighted G, the amount of variation explained by weighted G should be the difference between phenotypic variance from unweighted G and residual variance from weighted G, and corrected heritability that difference divided by phenotypic variance. That is,Pu denotes the phenotypic variance from unweighted G, and Ew denotes the residual variance from weighted G.The average information restricted maximum likelihood (AIREML) algorithm implemented in WOMBAT was used to estimate heritability  a weighted analysis with all SNP, (2) an unweighted analysis using only SNP with weight > 5, and (3) a weighted analysis using only SNP with weight > 5. Removal of low weight SNP resulted in SNP sets ranging in size from 101 to 944 markers Table 5.The removal of SNP with weight < 5 and leaving SNP unweighted in the model decreased performance in all 10 modules Table 5,Output from the association analyses for feed intake is shown in The most widely used approach to GWAS has been to assign equal prior probability of association to all sequence variants tested. Recent findings suggest that incorporating prior information can increase the power for identifying associations. Such prior information can be obtained from several different sources, including but not limited to linkage analysis , gene exWe applied our method to identify genetic markers associated with feed intake in swine. The gut-brain axis is comprised of bidirectional communication between the central and enteric nervous systems, linking cognitive centers of the brain with peripheral intestinal functions. The gut-brain axis modulates short-term satiety and hunger responses to regulate the delivery of nutrients and transit of nutrients through the gastrointestinal tract . RNA-Seqvia successive rank-1 approximations, if more expression modules were desired the algorithm could simply be applied to the residual tensor. Module I captured baseline, global gene expression common to all samples in all tissues, indicated by the flat distributions of the eigen-tissue and eigen-individual loading values. Other gene modules captured expression specific portions of the gut-brain axis, including the hypothalamus, the proximal and distal small intestine, the entire small intestine, and the individual components of the small intestine.The top ten gene modules from the tensor decomposition were considered. Note that since the clustering algorithm generates expression modules A tensor projection model was used to identify ADFI-associated genes within each of the ten modules. The P-values obtained from testing the ADFI-effect were used to weight SNP in order to conduct a weighted GWAS. P-values were chosen over regression coefficients for weighting in order to rank SNP according to the significance of their respective genomic regions rather than simply an effect size. Results from both the weighted and unweighted analyses are shown in There was a common pattern to the change in heritability estimates as the SNP prioritization changed. When using all 50K unweighted SNP, the heritability increased from 0.269 using genomic relationships to 0.366 using pedigree. In all ten modules, the use of weighted SNP restricted to those with weight > 2 resulted in a heritability slightly lower, but comparable to that from the usual unweighted, genomic model. Randomization of SNP weights Table S4To investigate if a more stringent SNP weight threshold could increase model performance, SNP with weight < 5 were removed from the analysis. This resulted in an average 21-fold drop in the number of SNP included in each analysis Table 5.ROBO2 (2 SNP), PLA2G4A (4 SNP), and MEGF10 (1 SNP) were previously identified as candidate genes for residual feed intake and feed conversion ratio in swine (Across the ten gene modules (weight > 5), 36 unique SNP were identified as having significant effects, while only 2 SNP were significant in the unweighted analysis utilizing all 50K SNP. Neither SNP from the unweighted analysis resided in known QTL related to swine feed efficiency  compared to 29 (80.6%) in the weighted analyses, with 9 SNP being located in feed intake QTL Table S4in swine . Hence, Our integrated methodology, at present, is obviously partial to genotyped SNP within genes. Because most available biological resources are biased toward genes, SNP pertaining to known genes likely have more relevant prior information. Consequently, the resulting weights may be more effective for associated SNP residing in or close to known genes. Therefore, results derived from our method can still be informative regardless of their intrinsic bias. Future work will focus on extending the scope of the tensor decomposition step to leverage data from other genomic sources, including but not limited to expression of non-coding RNA, miRNA expression, transcription factors, methylation targets, and miRNA binding. Additionally the method will be extended to prioritize variants from whole genome sequencing for assay development based on functional effects.The datasets generated for this study can be found in the NCBI SRA Accession Numbers PRJNA528599, PRJNA528884, PRJNA529214, and PRJNA529662.BK conceived of the study, and all authors participated in its design and coordination. WO and AP were involved in the acquisition of data, and BK, WS, LK, and GR performed data analyses. BK drafted the manuscript, and AP, WO, LK, WS, and GR contributed to the writing and editing. All authors read and approved the final manuscript.Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because all or part of an individual’s income is derived from any public assistance program.  Persons with disabilities who require alternative means for communication of program information  should contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, D.C. 20250-9410, or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA is an equal opportunity provider and employer.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


TRIB3 was reported to associate with pig growth, GRP, TTR, CNR1, GLP1R, BRD2, HCRTR2, SEC11C, and ssc-mir-122 were reported to associate with growth traits in human and mouse. The newly detected candidate genes will advance the understanding of growth related traits and the identification of the novel variants will suggest a potential use in pig genomic breeding programs.Improvement of the growth rate is a challenge in the pig industry, the Average Daily Gain (ADG) and Days (AGE) to 100 kg are directly related to growth performance. We performed genome-wide association study (GWAS) and genetic parameters estimation for ADG and AGE using the genomic and phonemic from four breed  populations. All analyses were performed by a multi-loci GWAS model, FarmCPU. The GWAS results of all four breeds indicate that five genome-wide significant SNPs were associated with ADG, and the nearby genomic regions explained 4.08% of the genetic variance and 1.90% of the phenotypic variance, respectively. For AGE, six genome-wide significant SNPs were detected, and the nearby genomic regions explained 8.09% of the genetic variance and 3.52% of phenotypic variance, respectively. In total, nine candidate genes were identified to be associated with growth and metabolism. Among them,  Growth rate is a vital economic trait and significantly affected pig production  in pure Duroc population  were reported that associated with ADG and AGE  has been widely used for mapping candidate genes of complex traits  for mapping candidate genes underlying ADG and AGE traits. Estimation of genetic parameters, such as heritability was also carried out. The detected candidate genes, potential breeding markers, and the better understanding on genetic architectures of ADG and AGE will benefit breeding programs.Animals used in this study were from two isolated farms, and composed of four pure breeds, including Duroc, Yorkshire, Landrace, and Pietrain . ADG andwhere correction factors are different for sire and dam, and the formulas are shown below:ADG was calculated by following equation:In total, 31,173 phenotypic observations from farm 1 and 24,374 phenotypic observations from farm 2 were recorded. Tail and ear tissue samples were collected and preserved with 75% alcohol and were stored in −20°C freezers.ng/μl, quantity ≥ 1 μg, and passed gel electrophoresis test were used for genotyping. A total of 4,865 DNA samples, including 1,595 samples from farm 1 and 3,270 samples from farm 2 were genotyped by Illumina PorcineSNP50 Bead Chip, which includes 50,697 SNPs, and all SNP markers were mapped to Sus scrofa genome build 11.1  in an R procedure  . The genomic locations were downloaded from https://www.animalgenome.org/pig/ and genes within the candidate region are considered as candidate genes.The candidate genes nearby the genome-wide significant SNPs were identified by Ensembl database using the gene annotation information of Sus scrofa genome version v11.1 , and ssc-mir-122 were identified in the nearby candidate regions on chromosome 1. GRP is a regulatory neuropeptide that stimulates gastric G cells to release gastrin and regulate gastric acid secretion and intestinal movement, which affects the food intake and may lead to anorexia, bulimia, and obesity if lacked of the genes  and plays an important role on growth and energy metabolism  and FTO (Fat mass and obesity-associated protein) that located on chromosome 6 significantly affected muscle development and obesity, respectively . The science ethics committee of the HZAU approved all the animal experiments that conducted in this study.XiaL, SZ, and XinL conceived the study. ZT, LY, and JX wrote the codes and did the data analysis. XiaL and ZT drafted the manuscript and all authors contributed to finalizing the writing.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Sequencing using 10 Ion Proton runs generated a total of ∼943.4 million raw reads, out of which ∼881.6 million reads remained after trimming for low-quality bases. After alignment, 794,297 polymorphic SNPs were identified, and filtering resulted in LMD50 SNPs  for each sub-population. Every data point was supported by actual sequencing data without any imputation, eliminating imputation-induced errors in SNP calling. Genotyping substantiated the impacts of novel breeding strategy revealing: (a) the donor introgression patterns in ILs were characteristic with variable introgression frequency in different genomic regions, attributed mainly to stringent selection under abiotic stress and (b) considerably lower heterozygosity was observed in ILs. Functional annotation revealed 426 non-synonymous deleterious SNPs present in 102 loci with a range of 1–4 SNPs per locus and 120 novel SNPs. SNP-typing this diversity panel will further assist in the development of markers supporting genomic applications in molecular breeding programs.This study demonstrates genotyping-by-sequencing-based single-nucleotide polymorphism (SNP)-typing in 11 early-backcross introgression populations of rice (at BC Rice is considered as one of the world’s most important staple foods and is the key to food security especially under the threats of climate change in the coming decades. Currently, rice is planted in 166 million hectares worldwide, nurturing some four billion people, and is harvested annually with a total worth of USD 203 billion .The predicament caused by climate change and a burgeoning population is leading to increased food insecurity and poverty. It is imperative to hasten the rate of genetic improvement efforts to meet the challenges of these biophysical and socio-natural constraints. Breeding improved rice cultivars using cutting-edge biotechnological tools and delivering them efficiently within shorter time frames is the fundamental solution to this.Cost-effective next-generation sequencing has been successfully employed for whole genome sequencing, gene expression, and single-nucleotide polymorphism (SNP) discovery . SeveralGenotyping-by-sequencing (GBS) has also emerged as a powerful breeding tool with the continued increase of sequencing output, the development of reference genomes, and improved bioinformatics. Plant scientists are getting deep into connecting phenotype to genotype using sequencing outputs. Deciphering interactions among heritable genetic factors and phenotypes will aid in harnessing the benefits of genomics-assisted selection in plant breeding. GBS has the potential to discover novel or population-specific polymorphisms. One approach to using GBS is to incorporate polymorphisms discovered via GBS into a closed platform, like an array, which can then be used to genotype an entire population of interest . These aConventional GBS cGBS;  involvesHowever, cGBS comes with some complexities such as yielding relatively fewer reads per site and having high levels of missing data across samples. Low read depths per site limit the ability of cGBS to identify heterozygous loci in diversity panels while high levels of missing data require imputation that limits the ability of cGBS to detect rare alleles.®) technology  can be tuned. For example, in GRL1, GRL2, and GRL3, only 1/4th, 1/16th, and 1/64th of all of the restriction fragments are sequenced. To achieve these levels of genome reduction, about one to three additional nucleotides are added up at the end of a selective polymerase chain reaction (PCR) primer. These GRLs concentrate the available sequencing reads at fewer sites of the genome, thereby increasing read depth and enhancing the accuracy of SNP calling, including in heterozygous individuals, as well as the discovery of rare alleles  and the corresponding 12 parents, is extensively described. It is the first report on the application of tGBS® in rice and its application for SNP-typing. Another nested goal of this study is to serve as a positional reference for chromosomal introgressions to the ongoing molecular breeding work of the authors. Subsequent publications based on the detailed analysis of the tGBS® results briefly summarized here will follow describing the fine mapping of various abiotic and biotic stress tolerance loci, their transcriptome and metabolome analyses, and their utilization in our marker-assisted breeding programs.In this study, the application of tGBS1s were backcrossed once also with WTR-1 (Table 1). Subsequently, the F1BC1s were self-pollinated, and their seeds were bulked to create 11 BC1F2 populations that were screened for three rounds beginning in the 2011 wet season under varied biotic and abiotic stresses such as drought, low input, salinity, submergence, tungro, and standard irrigated conditions, among others.Eleven donors (elite cultivars selected from different rice agro-ecologies) were crossed with Weed Tolerant Rice-1 (WTR-1), and their FFigure 1. This novel early-backcross breeding technique has been proven successful in exploiting favorable genes hidden in diverse germplasms to develop ILs that are tolerant to multiple stresses  were selected using a previously published approach as illustrated briefly in stresses ,b, 2013.1F5 sub-populations, each of which was derived from one of the 11 different donor parents (DPs). These 564 ILs, 11 DPs, and the common RP were genotyped via tGBS® . All the lines were grown in a greenhouse at the International Rice Research Institute (IRRI), Los Baños, Philippines, in 2015. The harvested leaves were stored at -80°C before extracting the genomic DNA using QIAGEN® DNeasy Plant Mini Kits following QIAGEN®’s standard protocol   . DNA qua1 [Rice (Oryza sativa) Reference Genome on Phytozome]. The typical workflow of tGBS® analysis with a reference genome is illustrated in Supplementary Figure S1.All 576 samples were sequenced using 10 Ion Proton runs. The rice reference genome (Osativa_204_v7.0.fa) was downloaded from the Phytozome websiteNspI and BfuCI). Ligation followed with a single-stranded barcode oligonucleotide in one site and an oligonucleotide complementary to amplification primer in the other site (Library preparation and sequencing was performed using the previously published protocol by Data2Bio (11). In short, all DNA samples were digested using two restriction enzymes . Of the total LMD50 SNPs discovered in all 11 sub-populations (Table 1), ∼40% were located in genic regions . This frequency of SNPs in the coding region is higher than that reported in earlier studies , a low-missing dataset (LMD50) was identified . SNP distribution varied within chromosomes for all the 11 sub-populations. The analysis revealed major SNP hotspots across all chromosomes and genomic regions where no SNPs were identified. The variation in the distribution of polymorphisms on chromosomal basis has frequently been reported in rice and model plants (The LMD50 SNPs were not uniformly distributed across chromosomes (l plants . This lol plants  and natul plants .Table 1). Using the unique alignments of each read from the 122 samples relative to the public reference genome, 4,669 high-quality SNPs (LMD50) were identified (Table 1).Sub-population 1 consisted of 122 samples . This display of LMD50 SNPs allows the reader to more easily observe clustering of SNPs that are heterozygous or homozygous for the DP allele in specific chromosomal regions. It is notable that the introgression patterns do not appear to be random, providing suggestive evidence of selection during backcrossing and in subsequent inbreeding generations. Most of the 11 sub-populations exhibited similar introgression patterns, noticeably corresponding to the breeding strategy for their development. Similar displays of LMD50 SNPs for the other 10 sub-populations are provided in Supplementary Figures S8.The LMD50 SNPs for each sample in sub-population 1 were plotted by their physical ordering on concatenated chromosomes, providing a clear visualization of genotypic patterns . Additionally, to visualize the unexpected high DP allele frequency presumably implying to selective sweep on chromosome 4 in comparison with chromosome 10, the same window scanning parameters were used and average DP allele frequency of each window was plotted .The recurrent parent allele frequency was also plotted by SNPs and selective window scanning .Recurrent parent allele frequency for each population by SNPs and selective window scanning (a window size of 10 SNPs and a step size of 5 SNPs were used) were plotted and aggregated for a broader visualization. The average recurrent parent allele frequency ranged between 0.7 and 0.8 for those populations . The 12 groups correspond to the 11 DPs of each of the 11 sub-populations (Table 1) and the RP. The genetic distance between the RP and the 564 ILs can be explained by the novel breeding strategy used in this study  is consistent with the hypothesis that the introgressed genomic regions contributed to the strong phenotypic responses in resulting ILs, rendering them tolerant to multiple biotic and abiotic stresses during stringent selection. The favorable phenotypic effects get fixed in the population through positive selection leading to uniqueness and deviance from the RP, both genotypically and phenotypically.The phylogenetic analysis based on the LMD50 SNPs revealed a clear differentiation of the 576 genotypes into 12 distinct groups in a neighbor-joining tree , and the corresponding details of amino acid and functional change are found in Supplementary Tables S2.Of the 102 affected loci, 24 were predicted to be responsive to biotic (six loci) and abiotic (18 loci) stress. These loci contained deleterious SNPs that substitute the amino acid and change the function of protein either positively or negatively. The identified abiotic and biotic stress-responsive loci are represented in Circos diagrams (Dp: Haoannong) and 4 (DP: Y-134), a G/A deleterious SNP at position 349,360 in the Os01g01689 locus alters an amino acid from Ala to Thr (Supplementary Tables S2). This locus is associated with the abiotic stimulus. The DP Haoannong of sub-population one is known for its drought and salinity tolerance , 10 (DP: M-401), and 11 (DP: X-21) at position 6,884,254 in the Os11g12330 locus and the changed codon A/C changes the amino acid from Lys to Asn (Supplementary Tables S2). The presence of this SNP was observed in the DPs while there was no variation in the RP at this position, suggesting that this allele was introgressed from the DPs in the sub-populations. High variability was observed at position 16,687,362 of the Os12g28250 locus within sub-populations 7 (DP: BG-300), 9 (DP: Basmati-385), and 10 (DP: M-401) as these three DPs have “G” at this position, the same as in the reference genome (MSU7), while the RP has SNP “C” (Supplementary Tables S2). The presence of this allele precisely defines the background of the RP (WTR-1) in the sub-populations (Supplementary Datasheet S2).In sub-populations 1 .Through the novel early-backcross introgression-breeding strategy used in this study, many important tolerance alleles were combined due to selections made simultaneously in different stress conditions over three rounds using these sub-populations  the donor introgression patterns in ILs were characteristic with variable introgression frequency in different genomic regions, attributed mainly to stringent selection under abiotic stress and (b) considerably lower heterozygosity was observed in ILs. The development of SNP markers through further detailed analysis of the sequencing results summarized here will help in the identification of novel gene and QTL resources for biotic and abiotic stress tolerance in rice for use in marker-assisted breeding programs.This study discussed the utility of tGBSJA bred the genotypic population and conceived the research. JA, UA, and CM-N prepared the DNA samples and sent for genotyping. PS, DL, UA, RT, JA, and VM analyzed the data and prepared the results. All authors prepared, read, revised, and approved the manuscript.PS and DL are employed by company Data2Bio, LLC, Ames, IA, United States, where we carried out the tunable genotyping by sequencing for our materials. Both read the MS and placed their inputs. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The average inbreeding coefficients obtained from runs of homozygosity (ROH) and pedigree analyses were 0.119 and 0.0001, respectively. Low correlation was observed between the inbreeding coefficients possibly as a result of genetic recombination effect accounted for the ROH estimates or caused by pedigree identification errors. A large number of long ROHs might indicate recent inbreeding events in the studied population. A total of 36 homozygous segments were found in more than 30% of the population and these ROH harbor genes associated with reproductive traits. The population stratification analysis indicated that this population was possibly originated from two distinct populations, which is a result from crossings between the eastern and western breeds used in the formation of the line. Our findings provide support to understand the genetic structure of swine populations and may assist breeding companies to avoid a high level of inbreeding coefficients to maintain genetic diversity, showing the effectiveness of using genome-wide SNP information for quantifying inbreeding when the pedigree was incomplete or incorrect.Single nucleotide polymorphism (SNP) markers are used to study population structure and conservation genetics, which permits assessing similarities regarding the linkage disequilibrium and information about the relationship among individuals. To investigate the population genomic structure of 300 females and 25 males from a commercial maternal pig line we analyzed linkage disequilibrium extent, inbreeding coefficients using genomic and conventional pedigree data, and population stratification. The average linkage disequilibrium (r The availability of low-cost single nucleotide polymorphism (SNP) panels has become an efficient tool for evaluation of the genetic structure in livestock populations, such as cattle, poultry, and pigs. The knowledge of the linkage disequilibrium (LD) pattern can reveal the population history, as well as characterize the genetic diversity .Controlling inbreeding has fundamental importance in animal production and conservation genetics because it leads to higher homozygosity, which leads to reduced genetic diversity and fitness . Loss ofInbreeding coefficients also can be estimated using genotype data, based on genomic-pedigree information  or calcuPrevious studies have used SNP chip data to evaluate genetic diversity and conservation of commercial and local pigs –9. StudiTissue samples were collected from ear puncturing of the 300 females and 25 males of a maternal line of pigs. The animals were obtained from a swine breeding program located in Santa Catarina, Brazil. This line was initially developed from the crosses of eastern and western breeds and the expected proportion of the genes were 50% Landrace, 12.50% Meishan, 12.50% Xia Jing, 8.33% Hampshire, 8.33% Pietrain and 8.33% Large White. Genomic DNA was extracted from 200 mg of the harvested tissue, with PureLink Genomic DNA Mini Kit  following the manufacturer protocol. DNA quantity, quality, and integrity were assessed using the NanoDrop 1000 Spectrophotometer  and the samples read with 260/280 nm between 1.75–1.95 were diluted to a final concentration of 50 ng/μl.The animals were genotyped using the Illumina PorcineSNP60-v2 BeadChip, comprising 61,565 SNPs distributed throughout the porcine genome. Genotype quality control was performed using PLINK v.1.9  to remov2 equation:2 measures were calculated using the parameters—ld-window 60,000—ld-window-r2 0 of PLINK [2 was calculated for all adjacent SNPs in 60,000 Kb windows.The LD was measured using the robertson . For allof PLINK . The r2 PED) on three generations of a swine maternal line population was estimated following the methodology proposed by Wright (1931) [ROH) for autosomal SNPs by means of the PLINK v.1. [The inbreeding coefficient using pedigree data (Ft 1931) . InbreedINK v.1. . SNPs loINK v.1. . The ROH31 . InbrROH) was estimated using the following equation:ROHL is the sum of all ROHs identified for each individual by length (L) of the pig genome . The ROH frequencies were calculated for all ROHs identified in the population and then compared to verify how many individuals shared the same ROHs (same start and end of the ROH positions).From the identification of ROHs, the inbreeding coefficient for each genomic individual (Fhttps://david.ncifcrf.gov/home.jsp) and the results were considered statistically significant at FDR cutoff < 0.20. For visualization and reduction of redundant GO-terms, the REVIGO software [The ROH regions observed in more than 30% of the samples were investigated to identify possible conserved regions and determine the genes that might be associated with adaptive traits such as survival and disease resistance. Genes located on ROH regions were identified using the UCSC genome browser (Scrofa 10.2) and Ensembl ID gene was used for the enrichment analysis. Gene ontology (GO) enrichment analysis was performed using David 6.8 database  was obtained using the ADMIXTURE software , which tAfter the genotype quality control, one animal and 3,795 SNPs were excluded due of the low call rate. Therefore, 57,770 SNPs were analyzed, including 1,498 SNPs on chromosome X (SSCX), 15 SNPs on chromosome Y (SSCY), and 4,076 SNPs with no defined position .2) between all adjacent SNPs, with distance less than 100 Kb. The average and standard deviation of estimated r2 was 0.29 ± 0.31, indicating high variability around the mean value estimated for the LD. It was observed that as the distance between markers increased, the LD values decreased, and for distances greater than 1 Mb, the mean r2 was less than 0.20  between FROH and FPED  and most of these segments (78%) were located on SSC17. Other ROHs observed in more than 30% of the population were on SSC1, SSC4, SSC7 and SSC14.IL25 (interleukin 25) and MALT1 (MALT1 paracaspase)  ; Fig 5, LIF (Leukemia inhibitory factor) associated with reproductive traits. Two other genes identified on this same ROH segment, the ZNRF3 (zinc and ring finger 3) and KREMEN1 (kringle containing transmembrane protein 1), were associated with waist-hip ratio and fat distribution. The METTL3 (methyltransferase like 3) gene found on ROH located on SSC7 is involved in the adipogenesis. The most shared ROH of the population (~47%) was located on chromosome SSC17 , with length of 2.89 Mb, harboring 9 genes, of which the TMX4 (thioredoxin-related transmembrane protein 4) and PLCB1 (phospholipase C beta 1) were enriched in the superclusters negative regulation of hormone secretion and cerebral cortex development, respectively  on SSC14 was shared among 97 individuals (30%) and harbor the gene ectively .Two possible genetic group structures were revealed, represented by the delta-k value  S1 Tabl. The larThe results of the principal component analysis for all SNPs and for SNPs with LD less than 0.20 were similar. The population was divided into two clusters, based on the first principal component . The pri2 equal to 0.32 between markers with distance 0.5 Mb, excluding the SNPs for minor allelic frequency (MAF) and Hardy-Weinberg equilibrium criteria [2 equal to 0.19 for markers spaced 0.5 Mb in Landrace populations. These values are in agreement with the average r2 obtained in this study (0.29) for a Landrace-based pig line. Furthermore, Badke et al. [2 for distances with less than 1 Mb. The LD decreasing pattern as the distances increase is expected because as the distance between markers on the same chromosome increase and the possibility of occurrence of recombination events (breaking of the linkage between the markers) increase as well.A previous study with Landrace using the Illumina Porcine 60K BeadChip reported average rcriteria . Badke ecriteria  used theThe low correlation between inbreeding coefficient estimates based on ROH and pedigree could be the result of genetic recombination effect accounted for by the ROH estimates, once this effect cannot be considered to estimate the inbreeding coefficients from pedigree data. Moreover, estimates based on pedigree records neglect the effects of inbreeding accumulated over generations and it does not consider pedigree errors accumulated from past generations .ROH and FPED found in this study (0.04) was lower than the 0.24 reported by Zanella et al. [PED has been underestimated because the pedigree analysis considered three generations and possible animal identification errors may occurred.The correlation between Fa et al.  in a LanA high number of long ROHs (> 5 Mb) may indicate that the population has not undergone recent crossings; otherwise, the crossing between different breeds would have caused these segments to breakdown. Herrero-Medrano et al.  reportedROH. Fisher [The high prevalence of long ROHs can also indicate recent inbreeding of the population according to our results found for F. Fisher  noted thROH resulted in a reduction of total milk yield to 205 days postpartum of 20 kg, increases in days open of 1.72 days and a decrease in some linear-type traits [Inbreeding can lead to increased homozygous frequencies and risks of deleterious recessive genes to be co-expressed, causing inbreeding depression. The deleterious effects of inbreeding depression have been evaluated in several livestock species. Saura et al.  reportede traits .The high number of ROHs shared on SSC1 occurs because it is the largest chromosome of the pig genome, with a higher number of markers compared to shorter chromosomes ; Fig 3. PLCB1 and TMX4, located on the most frequent ROH, were enriched in the superclusters negative regulation of hormone secretion, cerebral cortex development and locomotory behavior, which indicates possible selection for behavioral traits  was lower than the results reported by Herrero-Medrano et al.  in domesThe results for K indicated the separation between the two genetic groups could be explained by the crossing between the Eastern and Western breeds used to form the studied population. The first cluster Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 FigRed = Cluster 1; Green = Cluster 2; Blue = Cluster 3.(TIF)Click here for additional data file.S3 FigRed = Cluster 1; Green = Cluster 2; Blue = Cluster 3; Yellow = Cluster 4.(TIF)Click here for additional data file.S4 FigRed = Cluster 1; Green = Cluster 2; Blue = Cluster 3; Yellow = Cluster 4; Pink = Cluster(TIF)Click here for additional data file.