Rht1) and some phenology-related genes such as DRO1, TaMOR, TaLTPs, TaSus-2B on RSA and identified significant associations of these genes with important root traits. There was strong selection pressure on DRO1 gene in cultivated wheat indicating the allele fixed in modern wheat cultivars is different from landraces. The expression of DRO1, and TaMOR were retrieved from an RNAseq experiment, and results were validated using qRT-PCR. The highest expression of DRO1 and TaMOR was found in Chakwal-50, a rainfed cultivar released in 2008, and MaxiPak-65 released in 1965. We conclude that there is a positive historic change in RSA after 1965 that might be attributed to genetic factors associated with favored RSA traits. Furthermore, we suggest root depth and steep angle as promising traits to withstand water-limited environments and may have implications in selection for breeding.We evaluated root system architecture (RSA) of a set of 58 historical spring wheat cultivars from Pakistan representing 105 years of selection breeding. The evaluations were carried out under control and water-limited conditions using a high-throughput phenotyping system coupled with RhizoVision Explorer software. The cultivars were classified into three groups based on release year as cultivars released pre-1965, released between 1965 and 2000, and cultivars released post-2000. Under water-limited conditions a decline in 20 out of 25 RSA component traits was observed in pre-1965 cultivars group. Whereas cultivars released after the 1965, so-called green revolution period, showed a decline in 17 traits with significant increments in root length, depth, and steep angle frequency which are important root traits for resource-uptake under water-limited conditions. Similarly, cultivars released after 2000 indicated an increase in the number of roots, depth, diameter, surface area, and steep angle frequency. The coefficient of correlation analysis showed a positive correlation between root depth and yield-related traits under water-limited conditions. We also investigated the effects of green-revolution genes (The online version contains supplementary material available at 10.1186/s12870-022-03937-7. Triticum aestivum L.) is one of the major staple food crops worldwide supplying 20% calories to the global population. Wheat is sensitive to drought and heat stress, that are critical environmental adversity affecting final crop yields [One of humanity’s greatest challenges in 21 century is feeding the ~ 9 billion people by 2050 with continuously decreasing natural resources , 4. WheaRoot system architecture (RSA) is important for water and nutrient uptake facilitating photosynthesis and improving grain yield. It represents a fair benchmark for manipulation under soils poor in nutrition improving crop productivity , 7. RSA Rht) genes responsible for the green-revolution had a significant impact on root traits and coleoptile length [TaMOR (More roots in wheat) in rice plants resulted in longer main panicle, more crown roots, a higher number of primary branches and an increased grain yield. Root depth is an important trait allowing better access to nutrients and water stored in deeper soil layers thus enhancing yield [DRO1 influences root growth angle by modulating root gravitropic response. The deep rooting allele has been found to increase grain yield under drought stress [DRO2, DRO3, and qSOR1 have been reported to control RSA under water-deficient conditions [Recently, a genetic framework of 38 genes underpinning RSA in cereals was reported . For inse length , 13. Oveng yield . DRO1 int stress . Three onditions . Rambla nditions  reportedThe main challenge in breeding programs is the lack of high-throughput phenotyping platforms for root traits that can offer a proxy for field performance. Despite that, genetic improvement in RSA is an underappreciated route to a more efficient and productive wheat crop. Evidence suggests that historical improvements in wheat productivity are linked to changes in RSA . Hence, A panel of 58 bread wheat cultivars released in Pakistan 1911–2016) was selected for this study. The cultivar name, year of release, and pedigree are given in Table 911–2016 Seeds were surface sterilized with 2% NaOCl  and subsequently rinsed with distilled water three times. Ten seeds of each cultivar were placed on a Whatman quantitative ashless filter paper (Grade No.42) fixed in Petri plate and kept in dark for germination. After the emergence of coleoptiles 5 days after sowing (DAS), five uniform seedlings were transferred to germination papers rolled in cigars configuration. The cigar rolls were then placed in 1 L glass beakers totaling eight cigars in each beaker filled with 300 mL distilled water and kept as control. For drought stress treatment, 150 mL distilled water was maintained every day in each beaker . SeedlinRhizoVision crown hardware  controll2 per plot were recorded as described in our previous study [The same set of 58 wheat cultivars was evaluated for two-year (2018 and 2019) in the field at National Agriculture Research Centre (NARC), Islamabad, Pakistan using a randomized complete block design (RCBD) with two replications. The NARC site is located at 33°43′N 73°04′E and has soil electrical conductivity of 0.24dS/m. The plot size of 6 rows of 6 m length with 16 cm row-row distance was maintained. Phenotyping data for various morphological parameters including tillers per plant (TPP), plant height (PH) in cm, spike length (SL) in cm, spikelet per spike (SNPS), grains per spike (GPS), thousand grain weight (TGW), grain length (GL), grain width (GW), grain diameter (GD), and grain yield (GY) in kg per mus study . The expRht-B1, Rht-D1, and TaSus2-2B, were used from our previous work [2O. The PCR was performed in 384-well formats  following conditions described by [Genomic DNA of cultivars was extracted from leaf tissues using the phenol-chloroform method. The KASP markers for genes ous work . The KASribed by .A subset of 12 wheat cultivars was subjected to RNAseq to identify the genotypic variation in gene expression in seedling leaf and roots (PRJNA863398). For this purpose, seedling leaf and root tissues in triplicate were collected and subjected to total RNA extraction. Briefly, RNA was extracted using EasyPure Plant RNA Kit (ER301–01) following manufacturer instructions. RNA quality was checked on 1% Agarose gel and quantified using Nanodrop 2000 spectrophotometer . The samples were sent to Beijing Genomics Institute (BGI), China for sequencing. In summary,mRNAs were isolated and fragmented from total RNA using the oligo (dT) method for cDNA synthesis. The 50-bp single-end sequencing libraries were constructed, and sequencing was performed on BGISEQ-500 platformusing standard protocols. Adapters with unknown bases (N’s > 5%) and low quality were removed from raw reads to produce ‘clean data’ as FastQ data files using a quality control software, SOAPnuke version 2.1.6.p-value. The threshold value for filtering was set at 0.1. To analyze the expression of DRO1 and TaMOR genes in wheat genotypes, the heatmaps were constructed using pheatmap package in R version 4.1.1.High quality single-end reads were mapped to the bread wheat reference genome  using HISAT2  software version 2.2.1 . AlignmeDRO1 and TaMOR genes, a subset of 12 cultivars selected based on high and least root length was germinated in cigar rolls. A total of five surface-sterilized seeds of each cultivar were placed between two germination papers and rolled in a cigar configuration. Two batches of fifteen cigar rolls totaling ten plants per genotype were placed in beakers containing Hoagland’s nutrient solution. The experiment comprised of two treatments i.e., well-watered (WW) and water-limited (WL) conditions. For well-watered treatment, 200 mL of Hoagland’s solution was added while 100 mL nutrient solution was supplied to maintain WL conditions. The nutrient solution was supplied daily to maintain a volume of 200 mL and 100 mL for WW and WL conditions respectively. The roots were harvested 15 days after germination for RNA extraction from each treatment.For expression analysis of Total RNA was extracted from a set of 12 wheat cultivars using EasyPure Plant RNA Kit (ER301–01) following manufacturer instructions. RNA quality was checked on 1% Agarose gel and quantified using Nanodrop 2000 spectrophotometer . RNA samples with 1.8–2.1 A260/A280 values were selected for cDNA synthesis. Genomic DNA was removed, and cDNA was synthesized using ABClonal ABScript III RT Master Mix with gDNA remover. The reverse transcription reaction system consisted of 4 μl 5X ABScript III RT Mix, 1 μl 20X gDNA remover mix, 1 pg-1 μg of total RNA, and 13 μl nuclease-free water making up to 20 μl. The conditions for the reaction were 37 °C for 2 minutes, 55 °C for 15 minutes, 85 °C for 5 minutes, and 4 °C to hold. The products were quantified using Nanodrop 2000 spectrophotometer  and stored at − 20 °C for subsequent qRT-PCR reaction.DRO1 and TaMOR were retrieved from NCBI and were used to design common primers for all three homoeologues using Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast). A previously designed primer pair for TaActin was used as an internal control. The sequences of primers used are given in Table DRO1 and TaMOR were quantified using Livak method in a CFX384 Real-Time detection system (Bio-Rad). The reaction components were as follows: 10 μl 2X Universal SYBR Green Fast qPCR, 1 μl cDNA product (40 ng/ μl), 0.4 μl forward primer (10 μM), 0.4 μl reverse primer (10 μM) and 8.2 μl nuclease-free water to make a final volume of 20 μl. The two-step reaction conditions were as follows: 1 cycle at 95 °C for 3 minutes and cycles at 95 °C for 5 seconds, 60 °C for 30 seconds. The amplification and melting curves were confirmed after the reaction and then a standard curve for quantitative analysis was made.The coding sequences of p = 0.05 significance level. All statistical analyses were performed using Jamovi version 1.8. PCA analysis of root and yield related traits was performed using two principal components in Jamovi version 1.8. Correlations among root and yield traits were determined using Pearson correlation coefficient using GGally package in R 4.1.1. The student’s t-test was used to investigate the significant association of a marker with the target trait at a threshold probability p < 0.05 [Data were analyzed using a generalized linear mixed model. Mean comparisons were made with Tukey’s test at a p < 0.05 .n = 6), 1965–2000 (n = 19), and post-2000 (n = 33). Descriptive statistics of each group with mean and range of all root traits under controlled and water-limited condition is given in Table Wheat RSA traits were phenotyped in the Rhizovision Hardware Crown platform using the Cigar roll method. The set of fifty-eight wheat cultivars was divided into three groups based on breeding eras as pre-1965 (2000 n = 3. DescriP < 0.05) among genotypes were found for all root traits except maximum diameter (MaxD) and width to depth ratio (WDR). The mean squares of each trait are given in Table Significant variations (P < 0.05) positive correlations were found among various root and yield-related traits. Under water-limited conditions, depth (D) showed significant positive correlation with volume (V), grain length (GL), maximum weight (MaxW), total root length (TRL) and volume diameter (VD), whereas a strong negative correlation was found with WDR and shallow angle frequency (SAF). TRL had significant positive correlations to V, D, MaxW, maximum number of roots (MaxNR), surface area (SA), and network area (NtA). Among yield-related traits, only grain density (GD) was found positively correlated to root traits such as D, MaxW, NtA, and SA.The coefficient of correlations between various root traits are illustrated in Fig. Under control condition, steep angle frequency (StAF) had strong negative correlations to WDR, SAF, and MaxW, whereas D was negatively correlated to WDR and SAF. MaxD also exhibited a negative correlation to GD. MaxW had significant positive correlations to various root traits  and yield-related traits (grain yield (GY), grain weight (GW), GL). Similarly, V showed a significant positive correlation to thousand kernel weight (TKW), SA, NtA, VD, and TRL. A strong positive correlation was also found between D and TRL, SA, VD, and NtA. The TRL was also significantly positively correlated to MaxNR, SA, NtA and VD.The PCA biplots exhibiting relationships among genotypes and various root and yield-related traits are illustrated in Fig. Rht1 genes, Rht-D1a had the highest frequency (89.6%). Among TaSus2-2B, TaMOC, and TaLTPs, Hap-H, A, GC alleles were found dominant with 100% allelic frequency in pre-1965 cultivars. In the other two breeding groups (1965–2000 and post-2000), Hap-L , A , and GC (100% in both groups) were found dominant.The KASP markers were used to identify the allelic variations for 19 functional genes. Allelic effects and frequencies of some important genes are given in Table Rht-B1 and TaMOC had no significant effect on any root trait tested. The TaSus2-2B, TaSnRK2.3-B1, TaSnRK2.9-5A, TaDreb-B1, 1fehw3, and TaPPH were significantly associated with root depth under water-limited conditions. TaDreb-B1 and 1fehw3 also had significant association with root volume. The TaSnRK2.3-B1 and TaLTPs were significantly associated MaxW. None of the alleles were found associated with root traits related to number and diameter. Individual Rht-B1 and Rht-D1 did not show any association with any traits except when combined in haplotypes. Rht1 haplotypes had significant effects on TRL, MaxW, NtA, SA, VD, convex area (CA), average diameter (AD), perimeter (P) projected area diameter (PAD), and surface area diameter (SAD) under control. No significant association of any allele was found with D under control condition.The associations of the alleles with root traits demonstrated remarked differences Fig. . Rht-B1 n = 13) and TGACCACGT (n = 26) were used for the association analysis. The student’s t-test was significant where the former haplotype allele was associated with higher root dry weight  and alternate allele was identified in landraces (46 out of 159), and some older cultivars like Chenab-70, Local White and Kharchia. While AX-110640724 was completely fixed in all 159 accessions with no variation. Four SNPs were identified in TaMOR, of which AX-108991022 was present on both A- and D- homeologue caused missense variations, and only three landraces had the nucleotide change.Since all the SNPs were fixed in the 58 cultivars panel with no allelic variation, therefore 660 K SNP array data was retrieved for 159 cultivars and landraces from Pakistan for in-depth analysis of SNP states. It was revealed that all the SNPs in DRO1 was expressed in both leaf and roots with variations in leaf tissues among genotypes. In roots, the highest expression of DRO1 in all genotypes was found in B homoeologue (TraesCS5B02G210500) with negative expression in A (TraesCS5A02G213300) and D homoeologues (TraesCS5D02G218700) relative to B homoeologue. TaMOR exhibited no expression in leaf tissues in all genotypes, hence, differentially expressed in root tissues. The highest expression in A homoeologue (TraesCS4A02G415400) was found in Chakwal-50 cultivar with 0.70 tpm value whereas in B homoeologue (TraesCS4B02G316200) was found in C-518 (0.88 tpm) following Zincol-16 (0.45 tpm). The highest expression in D homoeologues (TraesCS4D02G312800) was found in Pak-81 (1.10 tpm) following GA-2002 (0.54 tpm). qRT-PCR for DRO1 expression  while the lowest expression was recorded in Dirk with a 0.06 expression ratio.Transcriptome analysis of twelve wheat genotypes showed significant differences in expression patterns of genes Fig. a. DRO1 wDRO1 controlling narrow root angle in rice was described to provide yield benefit under limited water conditions [In this study, we evaluated a set of historical wheat cultivars representing the diversity of Pakistan for changes in RSA over time. Root architectural traits such as lateral root number, volume, root length, density, and surface area aid in water uptake from water-deficient soils . The roonditions . McGrailnditions  also conThe response of root growth to water deprivation usually includes growth enhancement of first- and second-order roots and inhibition of lateral roots growth. When water scarcity is severe, a drought avoidance program is implemented to direct root growth and branching into resource-rich regions . Root biDRO1 into a shallow rooting rice cultivar enabled the cultivar to increase deep-rooting and yield under drought stress conditions [DRO1. This gene enabled the scientists to cope with the drought problems by allowing the roots to penetrate deeper thus aiding in yield enhancement even under limited water supply [DRO1 gene in all cultivars tested that supports our findings of increased depth under drought stress as compared to control in cultivars released after 1965.Root angle is also an important drought-adaptive trait that directs the horizontal and vertical distribution of roots into the soil. A strong link between steep root angle and deep rooting has been reported in wheat . The narnditions . Under wnditions  also idenditions  reportedr supply . In our Rht) on the root system is of prime importance [Rht1 alleles on root systems are less clear, with studies in different growing conditions producing contradictory results. We found a significant association of reduced height alleles (Rht) with some root traits. Rht8 allele is linked to reduced plant height and the highest frequency (90.9%) was recorded in post-2000 cultivars. The results are consistent with our field data where the least plant height was recorded in post-2000 cultivars. The gibberellin (GA-3) insensitive green revolution allele Rht-D1b linked to dwarfism was also frequent (87.8%) in post-2000 cultivars. Similar to our findings, Hurd [Rht-B1 on root architecture that might be attributed to differing growing conditions [Although extensive efforts have been made to identify the association of functional genes with the above-ground phenological traits in wheat, and functional markers have been used by various groups to tag favorable alleles in germplasm . Althougportance . Generalgs, Hurd  reportedgs, Hurd , we founnditions .TaMOR also plays a significant role in wheat root development and improvement of root systems [TaMOR expression in five wheat cultivars released in 1965 of which Zincol-2016, GA-2002, and Chakwal-50 belong to the post-2000 breeding group. In line with our results, Li et al. [TaMOR in rice plants contributed to the larger root system and higher grain yield in rice plants.The low heritability of RSA components traits a major challenge because most of the traits are controlled by many genes with minor effects . Very fe systems . Of the i et al.  reportedRht genes along with other phenology-related genes such as TaLTPs, TaSus-2B, TaMOR, and DRO1 that have significant allelic effects on RSA traits. Furthermore, correlation analysis of root and yield-related indicates a strong association of root systems with grain yield. Thus, we suggest that it is crucial to integrate the knowledge of promising root systems and root traits into breeding programs to develop climate-resilient varieties.It is concluded from our findings that the historical bread wheat cultivars released before the green revolution (1965) have poor root systems as compared to cultivars released thereafter. Cultivars of the post-1965 group have improved root systems under both well-watered and water-limited conditions and thus can tolerate climatic fluctuations. A significant improvement in drought-adaptive traits such as the depth and steep angle frequency was prominent feature of modern cultivars. The favorable allele of a major root biomass QTL, qDRM-5B, was present in low frequency, and its positive selection in breeding could improve drought adaptive RSA. This progressive improvement in RSA is also linked to green revolution Additional file 1: Table S1. The raw data for RT-PCR experiment.


FST and genetic diversity ratio. Then, we identified a set of known and novel candidate genes associated with reproductive performance in pigs. For the novel candidate genes , we obtained public available datasets and performed multi-omics analyses integrating transcriptome-wide association studies and comparative single-cell RNA-seq analyses to uncover the roles of them in mammalian reproductive performance. The genes have not been widely reported to be fertility-related genes and can be complementally considered as prior biological information to modify genomic selections models that benefits pig genetic improvement of litter traits. Besides, our findings provide new insights into the function of ROHet and ROH in mammals.Runs of heterozygosity (ROHet) and homozygosity (ROH) harbor useful information related to traits of interest. There is a lack of investigating the effect of ROHet and ROH on reproductive success and the loss of reproduction in mammals. Here, we detected and characterized the ROHet and ROH patterns in the genomes of Chinese indigenous pigs , revealing the similar genetic characteristics of indigenous pigs. Later, we highlighted the underlying litter traits-related ROHet and ROH using association analysis with linear model in these four indigenous pig breeds. To pinpoint the promising candidate genes associated with litter traits, we further in-depth explore the selection patterns of other five pig breeds  with different levels of reproduction performance at the underlying litter traits-related ROHet and ROH using  Runs of heterozygosity (JH), Chun’an (CA), Longyou Black (LY), and Shengxian Spotted (SX) pigs originated in eastern China and have similar characteristics to other indigenous breeds, e.g., improved meat quality, disease resistance, and high fertility in comparison with intensively-reared commercial pig breeds  China has the most genetic resources of domestic pigs In this study, we performed integrated genomic analyses to explore the genetic mechanisms of reproductive success and the loss of reproduction in mammal. Firstly, we systematically investigated the distribution of ROHet and ROH in the genomes of indigenous pigs to detect and characterize the ROHet and ROH patterns, as well as to reveal ROHet and ROH islands that contain the candidate genes related to breed-specific traits of Chinese indigenous pig breeds. Then, we conducted association analyses to identify the litter traits-related regions, and uncovered the effects of the identified regions and promising candidate genes on reproductive performance in other pig breeds via traits-specific selection signatures. Furthermore, we performed comparative transcriptome analysis to identify important fertility-related genes in human, pig, and mouse.22.1Animal care and experiments were conducted in accordance with the Chinese guidelines for animal welfare and were approved by the Animal Care and Use Committee of Zhejiang University, Hangzhou, China (permit number: ZJU20160346).2.2TND). Herein, NBA was identified as the piglets alive after the farrowing; TND included the number of stillborn piglets and the mummified piglets at birth. We removed records if TNB was equal to zero, or if only one record per sow was available. After data editing, 4742 litter records from 412 sows were available. After grouping records according to sow parity, the farrowing records were 1236, 624, 537, 507, 645, 543, 489, and 161 for parity groups 1, 2, 3, 4, 5, 6, 7, and 8, respectively. The descriptive statistics of TNB, NBA, and TND per parity were listed in Blood samples of JH (N = 193: 41 sires and 152 dams), CA (N = 98: 31 sires and 67 dams), LY (N = 94: 32 sires and 62 dams), and SX (N = 174: 43 sires and 131 dams) were collected from the national or provincial conservation farms in Zhejiang province of China. We collected all sires and dams except for full siblings according to the pedigree records in conservation farms, thus these individuals represent the most comprehensive genetic diversity of each pig breed. Litter traits included TNB, NBA, and total number of piglets born dead (LnVarTNB), NBA (LnVarNBA), and TND (LnVarTND) traits from the records of each traits following α is a vector of fixed effects, including farm-year-season of the farrowing (64 levels) and parity of the sow (8 levels); b is a vector of additive genetic effects and is set as b ∼ N, which contains 50,697 SNPs. Subsequently, quality control was performed using PLINK (v1.9) software WGS) data of five pig breeds  from the NCBI SRA database to uncover the evolutionary history of the regions and potential effects on litter traits in pigs (Sscrofa11.1) using the Burrows-Wheeler alignment (v0.7.17-r1188) tool Animal Genetic Resources in China: Pigs.Furthermore, we obtained whole-genome re-sequencing , sn is the number of SNPs per individual, in is the number of individuals, and het is the proportion of heterozygosity across all SNPs.ROHet and ROH were detected separately for each individual using detectRUNS R package v0.9.6. A ROHet was identified based on a consecutive strategy 2.4FROH) using the formula We categorized the ROHet and ROH for each breed into five length classes . In addition, we estimated ROH-based inbreeding coefficients (FDR) adjusted empirical P-values ROHet island) or highly homozygous regions (ROH island) via extracting SNPs with the FDR adjusted empirical P-values less than 0.01 located in a ROHet or ROH.Furthermore, we generated the false discovery rate  and six reproductions traits using 2.6FSTπ statisticθ). We calculated θπ statistic across the highest frequency ROHet and ROH regions in JH, CA, LY, and SX pigs to measure the variability of polymorphism levels across the regions between these pigs. To get more comprehensive results of genetic differentiation among the extremely prolific breeds, prolific breeds and breeds with the lowest litter size, five pig breeds  were further compared with each other using FST and θπ statistic with the use of a sliding window method (50 kb window and 10 kb step). The θπ statistic between two breeds was calculated as ln(θπ|breed1/θπ|breed2). Moreover, we divided five pig breeds into two groups  and separately conducted FST analysis. Here, we uncovered the selection patterns of the highest frequency ROHet and ROH with the breeds of group 1, then the breeds of group 2 were further used to check the patterns.We investigated selection signatures across the highest frequency ROHet and ROH using VCFtools version 0.1.14 2.7Sscrofa11.1, genes overlapping with ROHet and ROH islands were treated as candidate genes. We downloaded the information of tissue-specific and human-pig homologous genes from the Pig RNA Atlas GWAS) summary statistic and transcriptome-wide association studies (TWAS) research for fertility-related traits in human from the webTWAS database Genome annotations were based on 2.8scRNA-seq) data of in vivo embryos  from human, mouse GDM) and healthy control placentas was collected from the database (GSE154414). We downloaded the datasets and further processed that using FastQC (v0.11.4) software to obtain clean reads with default parameters. Clean reads were then mapped to the reference genome and annotated transcripts  by Hisat2 (v2.1.0) software FPKM) using featureCounts (v2.0.3) software The single cell RNA-seq (2.9GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Here, QTLs and terms with the FDR less than 0.05 were retained.In addition, all ROHet and ROH islands were annotated using the Animal QTL Database . We conducted QTL enrichment analyses using GALLO . We used ClusterProfiler package 33.1sus scrofa chromosome (SSC) 1 and 13, which had the longest chromosome length. To note, the SX population had the highest number of ROHet on SSC 7, showing an extremely different pattern with other populations . For CA pigs, 17 out of 167 candidate genes were specifically expressed in a single tissue. The genome regions overlapping with ROHet islands were mostly significantly related to days to 100 kg, carcass weight (cold), umbilical hernia, backfat at last rib, and shear force at first peak traits (B). For LY pigs, we detected 179 candidate genes, of which had 12 tissue-specific genes. These genes were linked with basophil number, intramuscular fat content, days to 100 kg, blood non-esterified fatty acid level, and fat androstenone level traits (C). For SX pigs, we detected 146 candidate genes, which were associated with average backfat thickness, meat color a*, shear force at first peak, fat androstenone level, and backfat at last rib traits (D). Furthermore, we found that there were 66 ROHet shared by at least two pig breeds, and 41, 99, 658, and 116 unique ROHet detected in the CA, JH, LY, and SX populations, respectively (A).To explore the function of ROHet islands, we conducted tissue-specific genes annotation and QTL enrichment analysis F-I. For k traits A. For JHk traits B. For LYl traits C. For SXb traits D. Furtheectively A.3.2FROH showed that the SX and JH populations had the highest level of inbreeding , followed by the CA (0.11) and LY populations (0.05)  . Neverthpulation .Fig. 2ROB). We found several shared candidate genes overlapping with the ROH islands of the studies breeds, e.g., UHRF1, UBA6, GC, AFP and CHN3. In addition, the QTLs overlapping with the ROH islands were mostly related to meat and carcass traits . Nevertheless, we found some QTLs associated with exterior traits  reached the significant threshold  and litter traits among parities , which r3.4π statistic across the highest frequency litter traits-related ROHet for each breed comparison to the difference in mean breed value for TNB, NBA, and TND traits. One ROHet (44.22–46.11 Mb on SSC 5) was significantly associated with TNB and NBA traits  located on the ROH: 16.60–19.73 Mb on SSC 1  and a set of QTLs of reproduction traits  were located  that have been recorded in the webTWAS database and not been widely reported to be fertility-related genes  between GDM and healthy control placentas , and the differences in expression level of the genes between GDM and healthy control placentas. We observed that the expression level of  species A-C. In a periods . The reslacentas .Fig. 6Co44.1In general, genotyping errors, the minimal SNP density per segment, the minimum length of a ROHet or ROH, the number of genotyping errors allowed and the minimum number of SNPs included in a ROHet or ROH are likely to affect the detection of ROHet and ROH 4.2According to previous studies 4.3ceruloplasmin (CP) gene potential participated in iron transport during spleen development timeless circadian regulator (TIMELESS) gene was linked to cell survival after damage or stress testis specific serine kinase substrate (TSKS) and dynein axonemal heavy chain 17 (DNAH17) genes, were essential for spermatogenesis and fertility in mammals The shared ROHet showed the possibility of similar selection patterns on growth, meat quality, health, and reproduction traits in Chinese indigenous pigs. Here, we noticed the tissue-specific genes located on the shared ROHet, which might reflect the same breed characteristics of indigenous pigs more typically. The ubiquitin like with PHD and ring finger domains 1 (UHRF1) gene involved in porcine adipogenesis GC vitamin D binding protein (GC) gene was associated with bone metabolic pathways alpha fetoprotein (AFP) gene encodes a major fetal protein and may promote fetal ovarian follicular development in Chinese indigenous pigs Likewise, we investigated the tissue-specific genes at the shared ROH to uncover the same breed characteristics of the studied populations. The 4.4calcium/calmodulin dependent protein kinase II delta (CAMK2D) gene participated in GnRH signalling pathway, as one of the most important reproductive pathways UDP glycosyltransferase 8 (UGT8) gene was down-regulated in the endometrium of women affected by implantation failure In this study, we highlighted the genetic characteristics of litter traits in indigenous pigs. The highest frequency litter traits-related ROHet within the genome might be able to achieve optimal production performance in indigenous pigs. We identified three highest frequency litter traits-related ROHet among parity groups and each litter traits. Several promising candidate genes at the ROHet involved in reproductive pathways in mammals. The activin A receptor type 2A (ACVR2A), kinesin family member 5C (KIF5C) and Metabolism of cobalamin associated D (MMADHC) gene in reproductive pathways have been previously reported EPC2 and MBD5) on fertility.High levels of inbreeding are directly related to a higher incidence of ROH, which could result in inbreeding depression In addition, the analyses of association degree between the highest frequency ROHet (or ROH) and litter traits revealed that the genomic structures of piglets born alive and dead were different among parity groups, which were consistent with previous studies 4.5ovochymase 1 (OVCH1) and coiled-coil domain containing 91 (CCDC91) genes were two strong candidates with the peak of selection across the ROHet that were related to TNB and NBA traits. Herein, The OVCH1 gene encodes oocyte extracellular polyproteins and has pleiotropic effects on fertility and the development of muscle CCDC91 gene involved in skeletal development Furthermore, we deeply investigated the roles of the highest frequency litter traits-related ROHet and ROH in pigs via litter traits-specific selection signatures. There was no trend of selection patterns has been observed in comparison of the θπ of JH, CA, LY, and SX pigs across the highest frequency ROHet and ROH. These might be caused by the similar reproduction performance of these pigs. Thus, we used the high-density genotypes of other five pig breeds with different levels of reproduction performance to detect selection signatures. A set of known and novel candidate genes associated with litter size were located on the peak of selection. The microtubule actin crosslinking factor 1 (MACF1) gene has been linked to ovarian function major facilitator superfamily domain containing 2a (MFSD2A) gene was needed in body growth, lipid metabolism and brain integrity As mentioned above, the genes of which were homozygous might lead to fetal mortality. The ROH: 16.60–19.73 Mb on SSC 1 and ROH: 94.43–99.41 Mb on SSC 6 were overlapped with several QTLs of the loss of reproduction traits, e.g., non-functional nipples, number of mummified pigs, and number of stillborn. In addition, the promising candidate genes located across the ROH were associated with embryonic and fetal development. The 4.6CCDC91, MACF1, MFSD2A, EPC2, and MBD5 has a significant fluctuation during the development of early embryo, but showed different trends in human, pig, and mouse. The phenomenon was consistent with previous studies, which stated that the molecular mechanisms of embryonic development shared many similarities, but also several marked differences in mammals MBD5 and SAMD5 likely involved in the occurrence of GDM. The results showed that the promising candidate genes played potential roles in mammalian fertility-related pathways.Previous studies have showed that the homozygous variants could lead to human infertility with early embryonic arrest 4.7To our knowledge, few ROHet and ROH analyses have been systematically conducted in pigs. And amounts of “omics” data were generated and released by some consortiums (e.g., the FarmGTEx Taken together, we present a multi-omics analyses integrating transcriptome and genome analyses that captures the underlying chromosome regions of litter traits. The in-depth analyses provided the evidence for the roles of the promising candidate genes that can be used as biological priors to improve the accuracies of genomic selection and further benefit pig genetic improvement. Besides, the results offer a genome-wide understanding of the relationships between heterozygosity or homozygosity regions and reproductive success and the loss of reproduction in mammals.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


For genetic association studies with related individuals, the linear mixed-effect model is the most commonly used method. In this report, we show that contrary to the popular belief, this standard method can be sensitive to departure from Hardy–Weinberg equilibrium  at the causal SNPs in two ways. First, when the trait heritability is treated as a nuisance parameter, although the association test has correct type I error control, the resulting heritability estimate can be biased, often upward, in the presence of Hardy–Weinberg disequilibrium. Second, if the true heritability is used in the linear mixed-effect model, then the corresponding association test can be biased in the presence of Hardy–Weinberg disequilibrium. We provide some analytical insights along with supporting empirical results from simulation and application studies. Y) on the genotypic data of a single-nucleotide polymorphism , as well as on the covariate data of important environmental factors (Z). When individuals in a sample are genetically related with each other, the linear mixed-effect model (LMM) is the most commonly used method for genome-wide association studies (GWAS)  . The vars (GWAS) . The weis (GWAS) ; FalconeA and a, let p and 1 − p be the population allele frequencies, respectively. Under HWE, paa = (1 − p)2, pAa = 2p(1 − p), and pAA = p2, where paa, pAa, and pAA are the population genotype frequencies of genotypes aa, Aa, and AA, respectively. To quantify the departure from HWE or the amount of Hardy–Weinberg disequilibrium (HWD),δ = 0 indicates HWE holds. We note that a) HWE is also known as Hardy–Weinberg proportion and b) δ is also known as p(1 − p)F, where F is the inbreeding coefficient (paa = (1 − p)2 + δ, pAa = 2p(1 − p) − 2δ, and pAA = p2 + δ based on δ (F as paa = (1 − p)2 + p(1 − p)F, pAa = 2p(1 − p)(1 − F), and pAA = p2 + p(1 − p)F (χ2 HWE testing is based on comparing the observed genotype counts with the expected under HWE (δ for this work to be consistent with the GWAS literature.It is commonly assumed that these regression-based association tests are robust to departure from Hardy–Weinberg equilibrium (HWE) . HWE sta measure , and δ =fficient . Equivalsed on δ , we can (1 − p)F . As the nder HWE ; we thusp-value threshold [e.g., 10–12 for an application of the UK Biobank data by A truly associated or causal SNP can be out of HWE , which iδ > 0, and deflated if δ < 0. The result of 2) is novel, but it is mostly of an academic interest as the true heritability of a trait is often unknown in practice. On the other hand, the result of 1) has important practical implications because if the estimate of a trait heritability is larger than the true value, then it helps explain some of the “missing heritability”  when the heritability is estimated from the data as in practice, although the empirical type I error rate of the LMM is well controlled, the estimated heritability is biased, often upward biased; 2) when the true heritability is known and used, the empirical type I error rate of the LMM is then inflated when ability” ; the insY be a (continuous) trait of interest, and G = 0, 1, and 2, respectively, for the genotypes aa, Aa, and AA of a SNP. Additionally, for notation simplicity but without loss of generality, we assume that there is only one additional covariate, denoted by Z. With a sample of n unrelated individuals, the traditional genotype-based association analysis assumes thaty1, y2,…, yn) is a n × 1 vector for the phenotypic values, 1 is a n × 1 vector of 1’s, g =  is a n × 1 vector for the genotypes of the SNP, z =  is a n × 1 vector for the covariate values, ϵ* is the error term with variance σ*2, and I is the identity matrix.Let H0: β* = 0 can be easily derived asTindep’s connection with Hardy–Weinberg disequilibrium, it is instructive to employ some algebraic tricks and show thatScore-based tests are often used for genetic association analyses . In thisTindep inherently adjusts for departure from HWE through Because the data , TindepY is binary, the classic logistic regression is commonly used. However, Y-dependent sampling, “the score statistics are identical for conditional and full likelihood approaches, and are of the same form as those for ordinary random sampling.” Thus, in terms of association testing (not genetic effect estimation), we can conclude that genotype-based association studies of binary traits in independent samples are also robust to HWD.When Φ, the kinship coefficient matrix, can be estimated using the available genome-wide data to capture the sample relatedness between the n individuals  related with each other even in population-based GWAS . Omittinividuals . The assϵ*) = σ*2I in y is a weighted average of two components, where ΣΦ reflects the sample relatedness; naturally, the model is reduced to the linear model of Φ = I. The weight h2 is interpreted as the heritability of the trait  = 2ϕi,j and ϕi,j is the kinship coefficient between the ith and jth samples.Compared with the linear model used for independent samples, var depends on the correlation between Gk1 and Gk2 between the siblings; see Gk1, Gk2) accounting for kinship coefficient and HWD. Thus,h2 estimate ish2 ⋅ δ/(p(1 − p)).By convention, TLMM through both application and simulation studies.Given the analytical insights provided so far, we then briefly examine the empirical properties of Y of interest is the lung function measurements of the 130 related individuals with CF. In total, there were 570,539 SNPs genotyped using the Illumina 610-Quad Beadchip after applying the standard quality control, including minor allele frequency (MAF) greater than 2%. To stabilize the variance estimation, we additionally required SNPs to have MAF greater than 5%. We then applied TLMM to the remaining 505,172 SNPs. In the application, we treated h2 as unknown and estimated it based on the linear mixed-effect model of We extracted 65 sibling pairs from a cystic fibrosis (CF) gene modifier study . The pheh2 was estimated from the data, our association testing based on TLMM had good type I error control (results not shown), consistent with the empirical observations in the GWAS literature. However, the estimated h2, obtained using the 65-pair sibling data, is When n method .Gcausal with MAF of 0.2, affects Y with h2 = 0.5. Genotype and phenotype values for 65 sibling pairs were then simulated under the assumption of HWE . Among the 100,000 independently simulated replicates, only 4.24% of the heritability estimates were greater than To verify if the large heritability estimate from the LMM method in our application was due to chance, we conducted a proof-of-principle simulation study. We assumed that only one causal SNP, h2 is treated as a nuisance parameter, its estimate based on model  when on model  cross-ree of LMM  cross-reY with h2 = 0.5 and influenced by one causal SNP, Gcausal, with minor allele frequency of 0.2 and with HWD factor, δcausal, ranging from −0.04 to 0.16. A non-associated SNP, Gtested, also has an MAF of 0.2 but with its own δtested, which may not be the same as δcausal in a specific simulation study. The sample size was 65 sibling pairs, chosen to match with the sample size of the cystic fibrosis application study in Consider a continuous trait h2 as a nuisance parameter, and no type I error issue has been reported. Indeed, when h2 was estimated in our simulation study conducted in TLMM was correct at the nominal level  and across the range of δcausal values (from −0.04 to 0.16).Most practical implementations of the linear mixed-effect model  cross-reh2 is treated as unknown, the impact of HWD is on the estimation of h2. Specifically, δcausal < 0, and upward biased if δcausal > 0. The bias can be substantial. For example, when δcausal = 0.10, the estimated heritability h2 ⋅ δ/(p(1 − p)) = 0.5 ⋅ 0.1/(0.2(1–0.2)) = 0.31.However, in this situation, when h2 is the proportion of variance in Y explained by additive genetic variation, 0 ≤ h2 ≤ 1 by definition. However, if δcausal ≠ 0, h2 with a bias of h2 ⋅ δ/(p(1 − p)) for this sib-pair design as shown in δ = 0.In h2 estimate as expected, it does not shrink the bias, as shown in Additionally, although a larger sample that consists of 5,000 sibling pairs shrinks the variance of the Y and the non-associated SNP, Gtested, using the LMM model of h2 = 0.5 is known.Here, we conducted the association analysis between TLMM using the true h2 = 0.5, for a nominal level of 0.05, estimated from independently simulated 10,000 replicates for each δcausal value. , the problem remains, albeit less severe, as long as δcausal ≠ 0.In Y has only one causal SNP, which is unrealistic for complex traits. However, the analytical insight shown in δk to differ.We used a sib-pair design to demonstrate that the linear mixed-effect model can be problematic in the presence of Hardy–Weinberg disequilibrium at the causal SNP(s). To demonstrate that the LMM-based heritability estimate can be biased, as a proof-of-principle, our simulation study assumed that the phenotype h2 is known, we also demonstrated the potential type I error issue of the LMM in the presence of HWD using data that consist of related individuals only. In practice, this issue diminishes if the sample includes a large number of independent individuals or the magnitude of HWD at the causal SNP is small. Additionally, in practice, h2 is treated as unknown, in which case, the type I error rate of the LMM is well controlled; indeed, no increased false positives of the LMM due to HWD have been reported in the literature to the best of our knowledge. However, the estimate of h2 can be upward biased and upwardly so if δcausal > 0, as demonstrated in the simulation study in Assuming the true heritability δcausal = 0.10 on the X-axis), there was a type I error issue even if there was no HWD at the tested SNP . Conversely, δcausal = 0 on the X-axis), then the test is accurate even if there was HWD at the tested SNP (δtested = 0.06).In practice, SNPs out of HWE are typically not analyzed due to concerns for low genotyping quality . HoweverAdditionally, the HWE-based screening practice itself can be called into question because a truly associated SNP is often in HWD . The potWe have not examined the implication of HWD combined with linkage disequilibrium (LD)  on the LΦ), derived from G, as part of the variance–covariance matrix  of the LMM can be problematic because the response variable Y is the phenotype of interest. An alternative approach is to reverse the roles of Y and G in the regression model. Indeed, G of an SNP as the response variable and phenotype values Y of multiple traits as predictors, and uses an ordinal logistic regression applicable to independent samples. More recently, Φ is the kinship coefficient matrix as defined earlier and Σδ is a function of δ that explicitly models the amount of HWD; the use of a linear model for the discrete genotype data G is motivated by the work of Although the linear mixed-effect model is a popular and powerful method for GWAS, conceptually, the use of kinship coefficient matrix (i.e., ΣG, in contrast to the LMM model of Y, the variance–covariance matrix only concerns Gtested, that is, Σg. The modeling and estimation of Σg can account for potential HWD through Σδ, in addition to the genetic correlation captured by the kinship coefficient matrix of ΣΦ, resulting in a more robust association test for related individuals. Indeed, when the method was applied to the same simulated sib-pair data in Interestingly, if the variance and covariance matrices in


Over the past few years, the Florida breed goat improvement program has led to significant genetic progress in milk production. However, there has been a parallel increase in the incidence of mastitis cases in this population. In this study, we try to assess the current situation of the population and estimate the genetic parameters of somatic cell score (SCS) using the dairy test day and single and multiple traits models. Genetic analysis of this score may allow us to select goats that are more resistant to mastitis. The results show that the SCSs have a heritability of medium-high magnitude, which ensures genetic progress if this criterion is used for selection. Finally, from the point of view of selection, the SCS should not be interpreted as an expression of the same trait over all the parturitions, even though they are closely correlated.2) were higher in the multiple traits models, showings an upward trend from the first to the third parity (h2 between 0.245 to 0.365). The genetic correlations of the same trait, as well as between breeding values (GVs) between different parities, were different from unity. The breeding values (EBVs) obtained for both models were subjected to a PCA: the first eigenvector . For the genetic analysis, the SCS records were log-transformed to normalize this variable. Two strategies were used for the genetic analysis: a univariate animal model for the SCS assuming that SCS does not vary throughout the parities, and a multi-character animal model, where SCS is not considered as the same character in the different parities. The heritabilities (h Florida is a native Spanish goat breed distributed mostly in central and southern Spain, although there are herds in other countries such as France, Italy, or Portugal. This breed is raised under a wide variety of production systems, ranging from semi-extensive to intensive systems. The breed has a census of about 125,000 breeding goats, of which 30,000 are registered in the national herdbook [Nowadays, the genetic improvement program for the Florida breed is based mainly on milk production and milk composition traits ,3,4 or mSince there is no clinical evidence in most cases of mastitis , it is difficult to diagnose, which makes treatment and prevention all the more difficult. Nevertheless, the somatic cell count (SCC) has been extensively used as an indirect indicator of mastitis. The somatic cells in milk are leukocytes  derived from blood circulation, as well as cellular debris and mammary epithelial cells, the former being the most common situation in ruminants .SCC levels can be affected by multiple factors, and these data do not allow us to make a clear distinction between chronic or repetitive prevalence . In addi−1 and for goats and sheep, 1,000,000 mL−1. In the European Union (EU) (EC Regulation No. 853/2004) [−1, and there is no legal limit for goats and sheep.Although progress has been made in preventive measures for its control, high levels of SCC may have negative effects on milk processing, which leads to significant economic losses both at the industry level and in the herd itself . The obj53/2004)  the legaThe somatic cell count is a phenotypic expression of the presence or absence of a mammary infection and there is solid evidence that this trait can be a key tool for indirect selection for greater resistance to mastitis . HoweverOn a practical level, two protocols have been established in the case of dairy goats to assess the current situation of herds based on the somatic cell count. According to Renée de Crémoux , in studThe objectives of this study were therefore to determine the incidence of clinical and subclinical mastitis in the Florida goat population (prototype of the Spanish dairy goat breed), by analyzing the somatic cell count ; and to estimate the effects of environmental variation and the genetic (co)variance components of the somatic cell count during the first three parities in this breed, comparing those estimations using single and multiple traits animal model models.3 to >10,000 × 103) were excluded. For this study, a total of 1,031,143 test day (TD) records collected between 2005 and 2019 were accessed from the National Association of Florida Goat Breeders (ACRIFLOR). These TD records belonged to 90 herds and included a total of 58,606 females. All the data were subjected to a data-editing process, and all TDs longer than 305 days, parity numbers greater than 6, and records with Somatic Cell Count (SCC) outside the usual range (<40 × 102(SCC) + 3 [Next, SCC was transformed by SCS = logSCC) + 3 , and anySCC) + 3  and AmerSCC) + 3  criteriaThe data sets for the genetic analysis (PROD set) were created from the TOTAL database, and only the information from the first three lactations was selected, with the goats with less than 3 TD in each of the first three parities, the combinations of herds with test dates (gfc) with less than 10 TD and parents with less than 10 progenies being excluded. After this sequential process, 340,654 TD of the first three parities from 90 herds of 27,479 daughters of 941 sires and 16,243 dams were available, of which 8788 were in the data vector . The resj j = 4 classes: single, double, triple, and four or more), and parity number (pnk k = 1 to 6 or more parities).Records of the dependent variable of the total file were analyzed, using a fixed-effects linear model to obtain initial information on the causes of variation that affect the SCS results. In this first analysis, we considered the fixed effects of herd, year and month of test, litter size  using a single linear model (UVM), assuming that they did not vary throughout parities:Model 3: Evolution of SCS through lactation using SCSTD (with each SCS TD as a different trait)Model 4: Evolution of the average SCS per lactation throughout the different lactations (1 to 3), using SCSPT and considering them as different traits.Multivariate approach. The parameters for SCSTD and SCSPT were estimated with the total data available from each parity (PROD2), using a multiple trait linear model (MTM), assuming that they were not the same trait across parities.The Asreml 3 software were used to estimate the genetic (co)variance components. The PROD1 and PROD2 files and two possible scenarios were compared, based on the considerations and form of expression of the SCS original variable:i is a vector of the dependent variables SCSTD and SCSPT; b is a vector of previously defined fixed effects ; lsj and pnk)), although the levels of i and j were not equal. a, p, and e are vectors of random effects due to the goat genetic effects with data and their ancestors without records ; permanent environment due to the repeated measurements in the goat  and residual error (e with the number of levels depending on the model), respectively.In statistical terms, the general representation was very similar:1, and Z2 are incidence matrices that relate the fixed and random effects with the dependent variables. The expected variances are:a and Wp are matrices of (co)variance of additive genetic and permanent environmental effects; e is the residual effects matrix. I is an incidence matrix with an order equal to the pth number of goats with data and The matrices X, Za = i parities.The elements of the matrices Ga and e depend on this approach and are represented as follows: in the case of UVM: G2) will be estimated by the classic formulas:g) between the same trait in the different parities will be:The heritability values  will be estimated in a similar way to hchaeffer . i) can be used as a weighting factor to estimate a new underlying index that can serve as a guide for the selection of healthier goats in all the parities. This new index, represented by the symbol Ipc, is estimated as follows:1, ev2, and ev3 are the coefficients of the evi and the EBVi are the genetic values of each goat for each parity expressed in a standardized form. The correlations between the original variables and the IPC will be estimated as follows: The large number of EBV results for SCSTD and SCSPT in each parity can be combined in a selection index; however, the economic importance of each expression of SCS is not known, to obtain a solution the procedure presented by Togashi and Lin  was usedIt is widely accepted that mammary inflammation, generally of an infectious origin, is the main factor in increasing the somatic cell count (SCC) , besidesSomatic cell count data usually has a non-normal distribution (a skewed left distribution), as can be observed in 750,000 SC/mL: below this count, a goat is considered healthy750,000–2 million SC/mL: moderate inflammation; subclinical mastitis2 million SC/mL: above this count, a goat has severe inflammation; clinical mastitis.Although in dairy cattle, somatic cells score (SCS) is a milk quality indicator widely used to predict subclinical mastitis levels, few studies currently exist that confirm this association in the case of goats. However, there are currently several study protocols that confirm the correlation between cell count and the presence of mastitis . Such is the case of the French goat , which hIn USA dairy goats , the perThe global analysis by farm using the previous thresholds in the Florida breed  allows uThus, following the criteria of Renée de Cremoux , it can The evolution of these parameters in the period analyzed (14 years) allows us to confirm the gravity of the problem : far frop < 0.001), reflecting different sanitary conditions and the handling of the goats, especially of the milking equipment and routines, which are not homogeneous in all the herds, as occurs in other Spanish breeds.In general terms, the level of susceptibility to mastitis in the Florida breed  is withip = 0.001). The evolution of this variable throughout the analyzed period showed an increase until 2009, with a fall up to 2014, and an approximately linear increase up to the present day, except for last year.In the same way, there was a considerable difference in the average SCSs over the years  is the sharp decrease in the price of milk, which led to a decrease in farm censuses, with a large number of older goats with higher levels of SCS being culled, and fewer replacements; the younger goats, therefore, had lower levels of SCS.Once the population had stabilized, there was a clear increase, which shows the high incidence of mastitis that exists today. In the same way, it shows the existence of a very marked seasonal pattern in SCS levels, with maximum values in summer and minimum values in winter. Jimenez-Granado et al.  demonstrAccording to Jimenez-Granado et al. , the numOur results also showed how the SCS level increases with litter size, with a positive difference of 461.55 between multiple litters of four kids and a simple litter. According to Jimenez-Granado et al. , in the The results of the estimates of the (co)variance components for the single and multiple traits models are presented in 2 and Rep variance components, as well as levels of h and Rep  higher t and Rep . Althougs et al. ,35 in gol et al.  in the Al et al.  for SCSPThe parameters presented in This type of response, as well as the results of the genetic correlations showed in The results of the principal component analysis (APC) of the EBV indicate that the first To exemplify this trend, the best 500 goats were selected according to their EBV for SCSTD and SCSPT in the first parity see , and theThe results for SCSTD and SCSPT indicated that the response will be positive in all parities. However, within these groups of elite goats, it has been possible to identify goats with stable behavior that maintain the same level in the three parities, while in others, which can be considered “sensitive” to the effect of the number of parities, the genetic level decreases in the second and third parity. No similar references to this approach have been found in the study of somatic cells. However, in biological terms, this type of response, where the capacity of the goats can vary along a time scale or environment has been termed plasticity , a term During the period represented, the level of somatic cells showed a decrease of −1.39 std  in the EBV of this studied population. The results show the same pattern, with depressive responses (favorable effects) depending on the year of parity, but greater uniformity when the records of each test day are used. For instance, the publications by Rupp et al. ,47 on dii) of the most important eigenvalues  to the EBV estimates of both expressions of the somatic cell count according to the procedure proposed by Togashi and Lin . This coi, coefficients, while the differences are expressed when the Ipct and correlations are made with the original variables, which show higher values than those previously presented  in the genetic evaluations. The levels of heritabilities and the correlations of this trait in the different parities are consistent with the few available references on this species and follow the same trend as the publications on sheep and dairy cows. Further studies are required to improve the estimates of the genetic (co)variance components through longitudinal models and to consider genetic correlations with other traits of economic interest. In the same way, a more precise definition of the necessary protocols in the testing systems for this type of trait is needed for their subsequent use in the genetic evaluation of resistance to mastitis in dairy goats. In that way, it is also advisable to carry out specific analysis that demonstrate the association of this count with the incidence of subline mastitis in this breed, as has been conducted in French and North American goats. This analysis will allow for the establishment of specific limits for this breed that determine a high probability of suffering from the disease.


This approach does not require a reference genome, making it easier to account for accessory genes. However, a same gene can exist in slightly different versions across different strains, leading to diluted effects.Genome-wide association studies (GWAS), aiming to find genetic variants associated with a trait, have widely been used on bacteria to identify genetic determinants of drug resistance or hypervirulence. Recent bacterial GWAS methods usually rely on k-mers. These covariates capture polymorphic genes as a single entity, improving k-mer-based GWAS both in terms of power and interpretability. However, a method naively testing all possible subgraphs would be powerless due to multiple testing corrections, and the mere exploration of these subgraphs would quickly become computationally intractable. The concept of testable hypothesis has successfully been used to address both problems in similar contexts. We leverage this concept to test all CCSs by proposing a novel enumeration scheme for these objects which fully exploits the pruning opportunity offered by testability, resulting in drastic improvements in computational efficiency. Our method integrates with existing visual tools to facilitate interpretation.Here, we overcome this issue by testing covariates built from closed connected subgraphs (CCSs) of the de Bruijn graph defined over genomic https://github.com/HectorRDB/Caldera_ISMB.We provide an implementation of our method, as well as code to reproduce all results at Bioinformatics online. For example, some bacterial species are known to have large accessory genomes, i.e. sets of genes that are not present in every strain in the species. In spite of their name, some of these accessory genes play a central role for some traits of interest, such as antibiotic resistance. In Pseudomonas aeruginosa, for instance, accessory genes account for 70% of known genetic determinants of resistance to amikacin  look for genetic variants whose presence or absence is associated with a trait of interest, such as the risk for a person to develop a disease, or the yield for a crop. They were originally used on human genomes using single-nucleotide polymorphisms (SNPs) as genetic variants . A CCS is a connected subgraph such that adding any neighbor does not change the created covariate . Non-closed subgraphs are represented by the exact same covariate as their closure, and would therefore be redundant.Here, we propose to test the association between the phenotype and a single covariate capturing the presence of any version of a gene—or any other potential genetic determinant. Concretely, this covariate indicates the presence in each genome of any DBGWAS relies on one covariate for each node of the DBG. This new approach has two potential issues: (i) the number of CCSs grows exponentially with the number of nodes in the DBG, making the task computationally intractable, and (ii) adjusting for multiple testing over this very large number of tests leaves little to no power to detect associations. Our method addresses these two issues by using the concept of testability introduced by non-testable hypotheses in its multiple testing correction. Intuitively, a covariate representing the presence of any k-mer among a growing set that corresponds to larger and larger CCS quickly becomes true for all samples. It thus cannot possibly be associated to any phenotype and can therefore be discarded without being tested or counted toward multiple testing correction. Testability provides a well-grounded and quantitative version of this intuition. Furthermore, since adding nodes to a connected subgraph can only increase the number of present k-mers in the corresponding covariate, we can develop a method that rapidly prunes non-testable CCSs, thereby solving the computational problem.As any CCS may represent a causal variant that exists in several versions in the dataset, we take an agnostic approach and test the association between the phenotype and one covariate for every CCS in the DBG. In contrast, LAMP of COIN  test. We show that combining these contributions with Tarone’s testability-based procedure makes it possible to find all significant CCSs in a large graph, making it suited to bacterial GWAS. We provide the first implementation of a procedure finding all significant CCSs, along with a user-friendly visualization tool derived from DBGWAS. Finally, we demonstrate the advantages of CALDERA over competing methods on both simulated and real examples in terms of computational speed, statistical power and biological interpretation.CALDERANotation and goal for : We consider a set of n samples, p binary covariates describing sample i, i to one population among J. We denote n1 and n2 the number of samples such that iy = 0 and 1, respectively. Furthermore, we consider an undirected unweighted connected graph p binary covariates represented in x. We denote by jv. Conversely, for i-th sample. For any connected subgraph ix by its complement. We now properly define the notion of CCS and the closure operation .Minimal act test  relies oN of them and rejecting those whose p-value ip is smaller than a threshold δ, N null distributions α. It is motivated by a simple union bound: as FWERα. m is the number of testable hypotheses, for which δ than the Bonferroni α—while rejecting more hypotheses and therefore increasing power. Choosing the largest such δ is not a trivial task, as increasing δ also increases the number m of non-testable hypotheses. Let m(k) be the number of testable hypotheses at level m(k) = N and we recover the Bonferroni procedure. More generally, Tarone’s analysis guarantees that k0 be the smallest k verifying this property, α.The FWER is the probability to incorrectly reject at least one null hypothesis. When testing rrection  is a comk0 requires computing the minimal p-values for all p-values to adjust the threshold, leaving the computational problem unsolved. A more efficient strategy has been introduced to compute k0   (C2) (C3) , or written differently, Note that we have CALDERA: repeatedly identifying or maintaining these components would be very costly. It results from the fact that the reduction operation Using (C1–3) in Lemma 3 to check whether v1 or v2 in the example. This procedure is necessary to reconnect all children that include v4 in the example. This recursive exploration is guaranteed to visit each candidate child, but does not ensure that each child is visited only once. A redundant exploration would lose the benefit of building a tree to explore Theorem 1. For any  NULLreturns the set More precisely, reducing any CCS k-th equivalence group Theorem 1 says that To help provide a better intuition of v4, we can construct the connected subgraph i = NULL and v1, v2 and v3. For example, with v3, v1 and v2. This can be expected. When applying the reduction to any CCS that contain v4, we will remove all the nodes containing the biggest sample, that is 4 so we will always remove v4. So no CCS gives Starting from v3, we begin similarly. v4 (Line 4), we construct again If we start from i is NULL (Line 6), we add v1 and v2 (Line 3). We first add v2. However, v1 as well, so we have a new i = 4 is not NULL anymore, so we move to Line 10. k = 1  using DBGWAS, leading to a graph with over 2.3 million nodes and average degree Akkermansia, consists of the Akkermansia muciniphila genomes collected in n = 401 strains has 1.3 million nodes with an average degree of α at which the FWER is controlled and the number of stages explored in the BFS search—a full exploration being too memory intensive. The level is fixed at the lowest value at which 10 CCSs at stage 1 of the BFS (i.e. unitig closures) are found significant. The stage is chosen by stopping when the number of unitigs covered by a significant CCS reaches a plateau—suggesting that further exploration would not bring much novelty.We also rely on two real datasets, where we use compacted DBGs , the set of significant CCSs found is the same regardless of the method. For this reason, we only use COIN+LAMP2 in the speed comparison, since the methods have the same power.On top of DBGWAS tests individual unitigs for association with a phenotype, using a linear mixed-model (LMM). We also benchmark three k-mer-based methods, available via the pyseer pipeline  before finishing, while the parallelized version of CALDERA runs in 6 h. Over all parameter values, the average ratio of runtime for COIN+LAMP2 over CALDERA BFS with 5 cores is 76 and we tested CALDERA on graphs with up to p = 100 000 nodes in 14 h. In terms of memory, CALDERA BFS and COIN+LAMP2 have near identical requirements, while the DFS implementation uses about 40% of the other methods. More details on memory usage can be found in ed 4 see . For exaPseudomonas dataset, even CALDERA is unable to explore the entire -Lmax 2000 option). We also observe that the unitigs covered by the significant CCSs reaches a plateau after the first six stages of the BFS. CALDERA took 3h20 on 4 cores (plus 2h30 to build the graph), using 200 Gb of RAM to complete these stages using batches of size 200 000, leading to COIN+LAMP2 was exploring the tree structure with a running DBGWAS runs in 2h45 . k-mer-based methods benefit from a faster initial step of k-mer counting (1h30) but the statistical test is much longer, even using four cores: 2h30 for the fixed-effect and LMM, 3h30 for the elastic net method. Overall, CALDERA performs slower than DBGWAS but on par with the k-mer-based methods.On the larger CALDERA against the number of BFS stages in We provide a more general analysis of the computational cost of CALDERA’s lower-bound on runtime for imbalanced populationBenefit of : For extreme ratios—below 0.02—the new lower bound allows much more pruning and enumerates an order of magnitude fewer elements of aphs see .COIN+LAMP2 and all versions of CALDERA rely on the same statistical procedures and therefore find an identical list of significant CCSs for a given level α. However, we can compare the power of CALDERA with DBGWAS and k-mer-based methods. We also use the LAMP2 procedure when testing All Unitigs separately, using Fisher’s test—like CALDERA.As mentioned above, Exploration and measure how many of the 367 unitigs of gene B are called significant, when controlling the FWER at a varying level α. For CALDERA, a significant unitig is one that is contained in a significant CCS. Even when controlling the FWER at very low levels  gene, represented by one unitig, and the pHS87b plasmid, represented by 476 unitigs. This allows us to see how the methods handle those different scales. At the default CALDERA find the aac(6′) mutation as one CCS, and finds significant CCSs that covers 96% of the plasmid. Those two components represent 59% of all significant unitigs. In contrast, All Unitigs and DBGWAS do recover the mutation but only 34% and 0% of the plasmid, respectively. Even at All Unitigs and DBGWAS only recover respectively 72% and 8% of the plasmid. Moreover, while it is not possible to compute a false negative rate on a real dataset, we can see that, at this level, the two known sequences—the plasmid and the aac(6′) mutation—only represent 6% and 17% of all significant unitigs.We also apply those three methods to the DBGWAS or CALDERA happens at the component levels: significant unitigs or CCSs separated by only a few non-significant unitigs are displayed as one component. Unitigs can also be annotated using various databases, to enhance interpretation. Components are ranked in order of decreasing p-values, choosing the smallest p-value among all unitigs/CCSs. As such, both the number of components and their rankings will impact the ease of interpretation.Biological interpretation in CALDERA on the Akkermansia dataset. Only one CCS is significant, while DBGWAS returns no significant unitig. All the unitigs in the significant CCS (colored in green) are annotated, using the RefSeq database of all known A.muciniphila proteins as a reference  mutation. DBGWAS always ranks the aac(6′) mutation first but never returns the plasmid as one component, even when controlling the FWER at a level of 0.1 . Moreover, at this level, DBWGAS returns 77 components, making the interpretation much harder.The plasmid returned as the first component of CALDERA, an algorithm to enumerate all significant CCSs. CALDERA is between one and two orders of magnitude faster than previously described exploration methods. It easily scales to large datasets, relying on an efficient structure on CALDERA, leads to increased power and facilitates interpretation, compared to previous methods that perform statistical tests at the node level. CALDERA can better detect low signal caused by variability in genetic elements. It also returns larger and more coherent outputs that are easier to interpret.This article presented CALDERA performs on bacterial GWAS. However, CALDERA can also be used for other tests of association involving a graph structure. We provide in Arabidopsis thaliana genomes and a ‘date to flowering’ phenotype. In that setting, the graph is a regulatory network on the genes and the objective is to identify subnetworks whose disruption by at least one mutation is associated with the phenotype.We extensively discussed how p-values.In settings where the node is a more natural object than the CCS, discrete testing can still be used to take advantage of CALDERA does not scale to datasets with hundreds of millions of nodes that are possible in metagenome-wide association studies. Future work that focuses on incorporating pre-processing schemes before CALDERA would be needed to compact the graph to both reduce its size and facilitate pruning by increasing the average For now, btac238_Supplementary_DataClick here for additional data file.


Understanding the genetic basis of yield related traits contributes to the improvement of grain yield in maize.P < 0.0001) referring to GYP, GL, GW, KNR, HKW and TBN, of which 14 SNPs located in yield related QTLs/QTNs previously reported. A total of 66 candidate genes were identified based on the 59 significantly associated SNPs, of which 58 had functional annotation.Using 291 excellent maize inbred lines as materials, six yield related traits of maize, including grain yield per plant (GYP), grain length (GL), grain width (GW), kernel number per row (KNR), 100 kernel weight (HKW) and tassel branch number (TBN) were investigated in Jinan, in 2017, 2018 and 2019. The average values of three environments were taken as the phenotypic data of yield related traits, and they were statistically analyzed. Based on 38,683 high-quality SNP markers in the whole genome of the association panel, the MLM with PCA model was used for genome-wide association analysis (GWAS) to obtain 59 significantly associated SNP sites. Moreover, 59 significantly associated SNPs (Using genome-wide association analysis strategy to identify genetic loci related to maize yield, a total of 59 significantly associated SNP were detected. Those results aid in our understanding of the genetic architecture of maize yield and provide useful SNPs for genetic improvement of maize.The online version contains supplementary material available at 10.1186/s12870-022-03812-5. As an important cereal and forage crop, maize plays an important role in sustaining global food security. Improvement of grain yield is a major and longstanding breeding goal for maize. Maize grain yield was determined by several yield-related traits, including grain yield per plant (GYP), ear length (EL), kernel row number (KRN), grain length (GL), grain width (GW), 100-kernel weight (HKW), and kernel number per row (KNR) . They th2 population and two QTL controlling KRN in a recombinant inbred line (RIL) population derived from the crossing of the maize inbred lines abe2 and B73. Using an intermated B73 × Mo17 Syn10 doubled haploid population, Zhang et al. [ZmVPS29 regulating maize grain shape was successfully cloned according to genetic population which was constructed with maize inbred lines Huangzaosi and Lv28. Overexpression of ZmVPS29 could make the grain slender and significantly improve the yield per ear of maize [Up to now, some yield related traits genes have been cloned by studying mutants –7. Unforof maize . Howeverof maize . Furtherof maize .As a cost-effective tool for dissecting the genetic architecture of complex quantitative traits, genome-wide association studies (GWAS) provide a high-resolution approach for the identification of QTL and have been widely used for the examination of QTL for yield-related traits in crops . Huang e2:3 population with 266 families and RIL population with 301 families to locate QTLs for tassel length and tassel branch number, detected 15 and 16 QTLs respectively, of which 4 QTLs can be co-located by the two populations. Upadyayulia et al. [ramosa1 (ra1) gene controlling the development of tassel is located in bin7.02 within the QTL interval. Using US-NAM population and CN-NAM population, 63 QTLs controlling the tassel branch number and 62 QTLs controlling the length of tassel were identified by linkage analysis, and 965 QTNs significantly associated with the tassel branch number were detected by association analysis [Under the trend of increasing planting density and higher requirements for light energy utilization efficiency in modern breeding, the plant type of maize, such as tassel branch number (TBN), has a great correlation with the yield of maize . At presanalysis .In the present study, we used an association panel of 291 maize inbred lines to identify the significant SNPs related to yield related traits by GWAS in different environments. The objective of the study was to map SNPs that are significantly associated with yield related traits and identify the candidate genes involved in yield related traits. Our results will improve the understanding of molecular mechanisms underlying maize yield related traits and provide novel molecular markers that may be used by breeders to develop superior varieties.Taking the average value of yield related traits in 3 years as phenotypic data, the six yield related traits were statistically analyzed. The six phenotypic traits GYP, GW, GL, KNR, HKW and TBN of 291 maize inbred lines showed an approximate normal distribution  that were significantly associated with six yield related traits in maize. Among these SNPs, some overlapped with previously reported QTL/QTN intervals. The SNP 9_150257246 (Chr9: 150.25 Mb), 7_162001602 (Chr7: 162.00 Mb) and 1_209009744 (Chr1: 209.00 Mb) of GYP were mapped to the previously detected QTL Yqgypp9 (Chr9: 140.8–158.6 Mb), qgy-7.2 (Chr9: 161.51–165.72 Mb), the QTL detected in RIL population derived from lines DAN340 × K22 (Chr1: 208.36–209.3 Mb) [CN-NAM (chr8: 73-101 Mb), which was positioned by Wu et al. [Crop yield is a complex quantitative trait. Understanding the genetic structure of maize yield is helpful to maize high-yield breeding. GWAS facilitates the identification of QTNs and candidate genes associated with the target traits. In this study, we performed GWAS using the association panels, including 291 inbred lines with 38,683 SNP markers, we obtained 59 significant SNPs (09.3 Mb) –34. the 09.3 Mb) , 34, 35.u et al.  in TBN. u et al. . These yZmvps29 through linkage analysis, which can regulate the kernel width of maize and increase the kernel number per row. GRMZM2G110851 and Zmvps29 both belong to PPR family genes and have the same regulatory effect on maize kernel, suggesting that GRMZM2G110851 has a similar function with Zmvps29. The candidate gene GRMZM2G328197 of SNP 6_77081642 in GW and KNR encoded a RING zinc finger domain superfamily protein, which was previously reported to be significantly related to panicle length in rice and to have a positive role in seed germination in Arabidopsis [Pleiotropism is a common phenomenon that has been found in the QTL mapping and GWAS of multiple crops , 37. Accbidopsis . The pSNZmMYBE1 in the two hybrids was higher than that in their parents, and considered that ZmMYBE1 was related to yield heterosis at the transcriptional level. The candidate gene of SNP 6_67617018 and 6_67479669 were GRMZM2G430902, which encoded a C3HC4 type ring finger family protein. The family genes were expressed in many tissues of Arabidopsis and maize during reproductive development, also played an important role in plant seed development [P-value, which associated gene GRMZM2G174696 encoded a TOM40 protein. TOM40 was relatively conservative and had homologous genes in rice and maize. In Arabidopsis, AtTOM40 was essential for the normal structure of the mitochondrion, and participated in early embryo development and pattern formation through maintaining the biogenesis of mitochondria [LRK (leucine-rich repeat receptor kinase) gene could increase the yield of rice [OsAGSW1 (ABC1-like kinase related to Grain size and Weight) exhibited a phenotype with a significant increase in grain size, grain weight, grain filling rate and 1000-grain weight compared with the wild-type and RNAi transgenic plants in rice [p-value in the TBN, which can explain 14.76% of the phenotypic variation of TBN. This locus was associated with GRMZM2G101664, which encoded a zinc finger protein. NSG1 encoded a member of the zinc finger protein family and was expressed mainly in the organ primordia of the spikelet in rice, which played a pivotal role in maintaining organ identities in the spikelet by repressing the expression of LHS1, DL, and MFO1 [ramosa1 (ra1) gene encoded a zinc finger transcription factor protein, which was involved in the regulation of tassel development in maize [Among these candidate genes in this study, some of them were previously reported to affect grain yield or kernel development, which were considered the top-priority candidate genes. The SNP 3_138419644 and 3_138419203 were both associated with GRMZM5G803355, which encoded an MYB transcription factor. Jia et al.  found thelopment . The SNP of rice . GRMZM5G in rice . The SNPin maize . The SNPin maize  found a In this study, a genome-wide association study (GWAS) method was made on an association panel of 291 inbred lines. Using 38,683 high-quality SNPs, six yield related traits were analyzed by the MLM with PCA method. A total of 59 yield related SNP were detected, involving 66 candidate genes. In the future, it is expected to improve the accuracy of GWAS results by adding more representative inbred lines to expand the association population and identifying high-quality phenotypic data from multiple environmental trials. Our results will improve the understanding of the genetic and molecular mechanisms underlying maize grain yield as well as provide new molecular markers for breeders to develop superior maize varieties.An association panel of 291 wide range of genetic diversity maize inbred lines in China, was collected for GWASs. All the accessions were planted following a randomized block design of two replicates in 3 years  Jinan in Shandong Province . Each material was planted in a row. The field experiments include in a single row 3 m in length, with 0.6 m between adjacent rows and 14 individual plants per row. The Maize Institute of Shandong Academy of Agricultural Sciences has established experimental field bases at Jinan. The field experiments were approved by the Maize Institute, and field management followed local maize management practices. The field studies did not involve endangered or protected species in this study. We declare that all plant materials comply with the ‘Convention on the Trade in Endangered Species of Wild Fauna and Flora’ in this study. The plant materials used in this study were conserved in our lab.The phenotypic traits measured in this study included grain yield per plant (GYP), grain length (GL), grain width (GW), kernel number per row (KNR), 100-kernel weight (HKW) and tassel branch number (TBN). In GYP, the ears of each line were harvested after reaching maturity and 10 ears with consistent growth were selected for evaluation in each replication. In GL, GW and KNR, the phenotypes were represented by the mean values of 10 ears. In HKW, the average weight of three repeated measures of 100 randomly selected kernels from the bulked kernels of each line. TBN was the average number of tassel branch number of 10 random individual plants in each row.H2) by ANOVA in software [Excel 2016 and SPSS16 software were used to make statistical analysis on six traits, including GYP, GL, GW, HKW, KNR and TBN. The average values of each trait of 3 years were taken, and the standard deviation and coefficient of variation of each trait were calculated  method . All samP value (0.05/N) was set as a significance threshold and N was calculated using the simpleM package in R to control false negatives [All the above phenotypic and genotypic data in the above associated population were used for GWAS. Based on high-quality SNPs, TASSEL 5.0 software was used to analyze the population structure of 291 inbred lines. Combined with the material pedigree, iTOL software was used to draw neighbor joining tree . Using Megatives .r2 > 0.2) [https://www.maizegdb.org/).We examined the LD in the genomic region around each significant SNP to establish a supporting interval for the significant association. That supporting interval would comprise the surrounding region in LD (2 > 0.2) . The canAdditional file 1.Additional file 2.Additional file 3.Additional file 4.


Oryza sativa), we have conducted genome-wide association studies to investigate maximum root length and crown root number in 135 10-day-old Japanese rice accessions grown hydroponically. We identified a quantitative trait locus for crown root number at approximately 32.7 Mbp on chromosome 4 and designated it qNCR1 (quantitative trait locus for Number of Crown Root 1). A linkage disequilibrium map around qNCR1 suggested that three candidate genes are involved in crown root number: a cullin (LOC_Os04g55030), a gibberellin 20 oxidase 8 (LOC_Os04g55070), and a cyclic nucleotide-gated ion channel (LOC_Os04g55080). The combination of haplotypes for each gene was designated as a haploblock, and haploblocks 1, 2, and 3 were defined. Compared to haploblock 1, the accessions with haploblocks 2 and 3 had fewer crown roots; approximately 5% and 10% reductions in 10-day-old plants and 15% and 25% reductions in 42-day-old plants, respectively. A Japanese leading variety Koshihikari and its progenies harbored haploblock 3. Their crown root number could potentially be improved using haploblocks 1 and 2.To explore the genetic resources that could be utilized to help improve root system architecture phenotypes in rice ( Oryza sativa) is one of the top three grain crops in the world, and several studies have shown that superior rice RSAs can result in increased yields in stressed soils. For example, rice productivity could be maintained under drought conditions if the depth of the root distribution was increased (Root system architecture (RSA) results from the deployment of various roots in the soil . In geneRice plants have fibrous root systems which consist of adventitious and lateral roots ; which iMany quantitative trait loci (QTLs) responsible for RSA-related traits have been identified in rice. Previously, QTLs associated with root length , 2015b hGenome-wide association studies (GWAS) are powerful tools that can be utilized to identify the genes associated with agronomic traits; and the associations of nucleotide polymorphisms and phenotypic variances are best analyzed using a diverse population 2SO4, 22.75 μM K2SO4, 136.75 μM MgSO4,91.25 μM 2, 45.5 μM KH2PO4, and 4.45 μM FeC6H5O7. The pH was adjusted to 5.5 with 5 mM MES (C6H13NO4S), HCl, and KOH. The hydroponic solution was changed every 3 or 4 d. Room temperature was defined as 30°C for 12 h during the day and 26°C for 12 h during the night in a greenhouse environment. Irradiation using 400 W metal-halide lamps was provided during the day. Poorly growing plants were thinned out prior to measurements. The seedlings were cultivated for 10 d in the greenhouse, and then their maximum root length, crown root number, and shoot length were measured. Data were collected from a maximum of ten individuals from each accession. Two cultivation trials were conducted.To investigate maximum root length, crown root number, and shoot length at the early seedling stage of development, hydroponic cultivation with floating nets was performed. Seeds were surface sterilized with tap water containing fungicide at 15°C and immersed in tap water at 30°C for 2 d. The imbibed seeds were transferred onto a net floating on a 1/4-strength hydroponic solution based on Kimura B solution  were filled with nutrient-poor soil Akadama . Then, a germinated seed was sown at the center of each basket. Water and nutrients were supplied using a hydroponic solution: the 1/4- and 1/2-strength hydroponic solution, in which the pH was adjusted to 5.8 with HCl and KOH. The hydroponic solution was changed every 3 or 4 days. Room temperature and irradiation conditions were as defined previously for the maximum root length experiment. The seedlings were cultivated for six weeks in the greenhouse, after which their crown root numbers were counted. Data were collected from three individuals of each cultivar. Two cultivation trials were conducted.To investigate crown root number in the late seedling japonica rice cultivars. The linear mixed model incorporated the kinship data , Cyanidioschyzon merolae, and Saccharomyces cerevisiae were collected from the SALDA database version 3.0 .To identify the candidate genes affecting the root traits, a GWAS was conducted using the phenotypic data from the 135 rice cultivars. Manhattan plots for the maximum root length , root nuqNCR1 , a gibberellin (GA) 20 oxidase , and a cyclic nucleotide-gated ion channel . All three candidates had missense mutations in the GWAS population   and 50 accessions from the rice core collection of Japanese landraces . Haplobthe GWAS . We founthe GWAS . HokkaidqNCR1, for which three possible candidate genes were also identified, a cullin (LOC_Os04g55030), a GA20ox (LOC_Os04g55070), and a CNGC (LOC_Os04g55080), although any gene in the LD block could be a candidate. The cullin protein assembles multi-subunit Cullin-RING E3 ubiquitin ligase complexes, involved in various functions, such as cell-cycle control, DNA replication, and development  gene family member, OsCUL3b  are currently unclear, GA20ox8 may influence crown root number.A20ox1–8 . The mosLOC_Os04g55080 is OsCNGC8 -induced Ca2+ influxes  for qNCR1. To confirm the identity of the genes involved, it will be necessary to make knockout mutants by CRISPR/Cas9 system and/or conduct complementation tests in the future. It seems, however, that it may be possible to improve root systems by using haploblocks of qNCR1 even though the causal genes were not confirmed in this study. When compared to haploblock 1, haploblocks 2 and 3 appeared to effectively reduce the number of roots . The dis haploblock in Japan showed some bias; as haploblock 1 was rarely identified in the Hokkaido region while haploblocks 2 and 3 appeared with a high frequency in the Hokkaido and Hokuriku regions, respectively (Ctb1 was previously located at approximately 31.8 Mbp on chromosome 4 (qNCR1 (32.7 Mbp on chromosome 4, qNCR1 was spread via the breeding program that focused on cold tolerance via linkage drag. The Hokuriku region is also where the Koshihikari cultivar was developed (Taken together, all three candidate genes, ectively . Hokkaidectively , which iectively  were useeveloped . TherefoqNCR1 was detected in juvenile seedlings with no tillering (qNCR1 did not influence the tiller number at juvenile stage. However, influence of qNCR1 on tiller number at adult stage were not elucidated. Although we need further investigation to elucidate it before qNCR1 will be used as breeding materials, these haploblocks of qNCR1 could be potentially used to improve crown root number traits in rice. Koshihikari and Koshihikari-derived cultivars harbored haploblock 3, indicating that these cultivars could use haploblocks 1 and 2 to increase the root number. Almost all world-wide cultivars harbored haploblock 2 (qNCR1 could be used to breed rice cultivars that are adapted to specific environments.The tiller number has a strong influence on root number (illering , 2B, we oblock 2 , indicatoblock 2 . Therefooblock 2 . ConsequST designed the study, obtained the trait data at young growth period, analyzed all data, and wrote the manuscript. MY provided the SNP and indel data, supervised and conducted the GWAS. YU coordinated the project, designed the study, obtained the trait data at a middle growth period, and revised the manuscript.Supplemental FiguresSupplemental Tables