TOX, FAM184B, SPP1, IBSP, PKD2, SDCBP, PIGY, LCORL, NCAPG, and ABCG2 were noteworthy. Enrichment analysis revealed biological processes and functional terms involved in growth and lipid metabolism, such as growth (GO:0040007), muscle structure development (GO:0061061), skeletal system development (GO:0001501), animal organ development (GO:0048513), lipid metabolic process (GO:0006629), response to lipid (GO:0033993), metabolic pathways (bta01100), focal adhesion (bta04510), ECM–receptor interaction (bta04512), fat digestion and absorption (bta04975), and Rap1 signaling pathway (bta04015) being the most significant for the carcass primal cut traits. Thus, identification of quantitative trait loci regions and plausible candidate genes will aid in a better understanding of the genetic and biological mechanisms regulating carcass primal cut yields.The importance of meat and carcass quality is growing in beef cattle production to meet both producer and consumer demands. Primal cut yields, which reflect the body compositions of carcass, could determine the carcass grade and, consequently, command premium prices. Despite its importance, there have been few genome-wide association studies on these traits. This study aimed to identify genomic regions and putative candidate genes related to 10 primal cut traits, including tenderloin, sirloin, striploin, chuck, brisket, top round, bottom round, shank, flank, and rib in Hanwoo cattle using a single-step Bayesian regression (ssBR) approach. After genomic data quality control, 43,987 SNPs from 3,745 genotyped animals were available, of which 3,467 had phenotypic records for the analyzed traits. A total of 16 significant genomic regions (1-Mb window) were identified, of which five large-effect quantitative trait loci (QTLs) located on chromosomes 6 at 38–39 Mb, 11 at 21–22 Mb, 14 at 6–7 Mb and 26–27 Mb, and 19 at 26–27 Mb were associated with more than one trait, while the remaining 11 QTLs were trait-specific. These significant regions were harbored by 154 genes, among which  Hanwoo is an indigenous and popular meat-type cattle in Korea, and is particularly renowned for its rapid growth rate and quality attributes such as juiciness, tenderness, characteristic flavor, and extensive marbling of its beef . In receOver the last decade, with the development of high-throughput single nucleotide polymorphism (SNP) genotyping technologies, genome-wide association studies (GWAS) have become an affordable and powerful tool for detecting and localizing candidate genes and causal mutations associated with quantitative traits in different species . SeveralA total of 3,467 Hanwoo steers born between 2008 and 2017 were included in this study. All steers were slaughtered at approximately 24 months of age and were progeny of 442 sires and 3,357 dams. The pedigree data consisted of 15,117 animals after tracing the pedigree file back 10 generations and pruning with SECATEURS software . During n = 3,720), version 3 , and customized Hanwoo version 1 . Individuals (and SNPs) with a call rate of less than 90% and those without a valid phenotype were also excluded. The genotyped animals with Illumina BovineSNP50K version 2 were used as a reference populations to impute target animals  using FImpute V3 software  and slaughter age (days from birth to slaughter) was considered as covariates; Xij is the incidence covariate corresponding to the βj for individual i; Zik is the incidence covariate corresponding to the kth random animal effect for individual i; u =  is the incidence covariate corresponding to the mth imputation residual for individual i and ϵm is the imputation residual; p is the number of genotyped loci; β; i is the ith random residual effect for individual i assumed normally distributed N , and σe2 is residual variance.The single-step Bayesian regression (ssBR) method proposed by β are assigned to the flat priors. In addition, the additive genetic (σu2), residual (σe2), and marker (σα2) variances were assumed to have a scaled inverted chi-square prior with scale parameters α degrees of freedom. The prior means for additive genetic and residual variances were estimated using an animal model. In addition, the prior means was equal to i is the allelic frequency at the lth locus. The degrees of freedom were four for residual and marker variances, and five for additive genetic variance.The effects of The analysis was performed using the JWAS Julia package for whole-genome analyses  to obtai2a) and residual variance (σ2e). The direct genomic value (DGV) that is attributed to markers is estimated as:2m) was estimated using Gibbs sampling technique described by 2m and σ2u. In addition, the phenotypic variance was estimated by summation total genetic and environment variances. The heritability was obtained using total genetic variance divided by phenotypic variance.The markers effect in each MCMC was estimated by using single-step Bayes B in addition to the polygenic additive genetic variance (σThe percentage of genomic variance (GV%) explained by each 1-Mb window in any particular iteration was calculated by dividing the genomic variance of the window by the genomic variance of the whole genome in the same iteration. Similarity, the proportion of additive genetic variance (AGV%) determined using each window markers were also obtained.http://www.ncbi.nlm.nih.gov/gene/), and GeneCards (www.genecards.org). A Manhattan plot was created using the ggplot2 package , 6 were tRNA (RNA transporter), and 53 were pseudogenes  complex and plays an important role in cell–cell interactions. A previous study reported that there were significant effects of copy number variation of the PIGY gene on growth traits in three Chinese sheep breeds (DUSP10. Previous studies have shown that the DUSP10 gene is associated with growth traits (CDK14 gene is located in this region and is associated with fatty acid profile (C18:1 trans-9) in the intramuscular fat of Longissimus thoracis muscle of Nellore cattle  was located on chromosome 19 at position 26–27 Mb. This region explained between 1 and 2.07% of the genomic variance across the traits of interest, and harbors candidate genes, including y cattle , feed efy cattle , and body cattle . The MISy cattle . Two othferation . For QTLf cattle . The ABCy weight  and milky weight , which ip breeds . Brisketh traits  and carch traits  in beef e cattle . AccordiHLX, SOS1, SDCBP, ANXA2, FZD1, and MEPE, were highlighted as the main candidates for the traits under study in at least three biological pathways. The HLX gene, located on chromosome 16 at approximately 25–26 Mb, is a protein-coding gene that is involved in embryogenesis and hematopoiesis. It has also been shown to be associated with intramuscular fat in composite beef cattle  were found to be associated with 10 primal cut traits in Hanwoo cattle using a single-step Bayesian regression GWAS. Within these windows, five QTLs had pleiotropic effects, with the most significant region located on chromosome 14 at position 26–27 Mb. Several candidate genes with potential functions in tissue development, regulation of growth, and lipid metabolism for the related traits were highlighted, among which


Sesame is a rare example of non-model and minor crop for which numerous genetic loci and candidate genes underlying features of interest have been disclosed at relatively high resolution. These progresses have been achieved thanks to the applications of the genome-wide association study (GWAS) approach. GWAS has benefited from the availability of high-quality genomes, re-sequencing data from thousands of genotypes, extensive transcriptome sequencing, development of haplotype map and web-based functional databases in sesame.http://sigedid.ucad.sn/) has been developed to provide access to all genetic and genomic discoveries through GWAS in sesame. We also tested for the first time, applications of various new GWAS multi-locus models in sesame.In this paper, we reviewed the GWAS methods, the underlying statistical models and the applications for genetic discovery of important traits in sesame. A novel online database SiGeDiD (Collectively, this work portrays steps and provides guidelines for efficient GWAS implementation in sesame, a non-model crop.The online version contains supplementary material available at 10.1186/s12870-021-03046-x. Sesamum indicum L, 2n = 2x = 26) which belongs to the Pedaliaceae family is one of the most ancient oilseed crops domesticated from the wild progenitor S. malabaricum in Near East, Asia and Africa over 5,000 years ago )%20AND%20bioproject_sra[filter]%20NOT%20bioproject_gap[filter]) facilitates post-GWAS works particularly for pinpointing candidate genes and their functional analysis. The availability of several mapping populations [http://ocri-genomics.org/Sinbase/index.html), SesameFG (http://sesame-bioinfo.org/SesameFG/) and Sesame HapMap that have been deployed to facilitate genome excavation, comparative genomics, gene expression analysis, are highly useful for post-GWAS investigations [Implementation of GWAS based on high-quality genome sequences results generally in a more accurate prediction and mining of potential causative genes. The high-resolution positioning of SNPs in the genome along the entire chromosomes can unravel the genetic architecture of target traits; hence, GWAS can detect more significant associations, candidate genes, and genomic locations with high power and efficiency. Since 2014, the development of a high-quality draft genome of the sesame genotype ‘Zhongzhi13’ . Progresum Table   (http://sigedid.ucad.sn/). SiGeDiD is a flexible online catalog of all genetic and genomic discoveries including, candidate genes, QTLs and functional molecular markers in sesame , petiole color (SiMYB113 and SiMYB23), oil content (SiPPO), fatty acid biosynthesis (CXE17 and GDSL-like lipase) and yield (SiACS) were identified [SiLPT3 and SiACS8) which may control capsule length and capsule number [SiEMF1, SiGRV2, SiCYP76C7, SiGRF5, SiCCD8, SiGPAT3, SiGDH2, SiRABA1D were detected as potential genes regulating drought stress while for salt tolerance, a total of 120 significant SNPs resolved to 15 QTLs and 241 genes of which of SiLHCB6, SiMLP31, SiPOD, SiHSFA1, SiDUF538, SiCC-NBS-LRR, SiUDG, SiGPAT3, SiNAC43, SiGDH2, SiCP24, SiWRKY14, SiXXT5, SiXTH15, and SiG6PD1 were detected as potential genes [SiABI4, SiTTM3, SiGOLS1, SiNIMIN1, and SiSAM having high potentials to modulate drought tolerance in sesame, were identified [SIN_1022039–SIN_1022045) and LG_08_6621957 containing five genes (SIN_1001936–SIN_1001940), detected simultaneously on LGs 3 and 8, respectively, by employing two different models (GLM and MLM). Hence, the authors suggested that these two simultaneously detected loci have high potentials to control vitamin E in sesame. However, due to the limited numbers of SNPs  and small panel size used in this GWAS, potential loci for this important trait may have been missed [SIN_1016759 (encoding predicted PPO) was also reported in previous GWAS by [SIN_1019016 was one of the candidate genes identified closely associated with phytophthora blight resistance in sesame. The limited SNP numbers called from the GBS approach and relatively small size of sesame accessions used in this study could have affected the GWAS output associated with trait under investigation. More recently, a comprehensive GWAS conducted by Dossa et al. [Big Root Biomass’ (SIN_1025576) which modulates sesame root biomass through the auxin pathway [From 2015, several GWAS projects have been successfully implemented in sesame to uncover the genetic bases of key agronomic traits such as oil content, oil nutrient composition, seed yield, and yield-related components, seed coat color, morphological characteristics, disease resistance salt tolerance, waterlogging resistance, drought tolerance, root traits and nutritional values , 148. Asentified  using the number . Likewise number . A totalal genes . Later oal genes . A totalentified . Their sentified , 136  in sesame. Herein, we tested the applications of new GWAS models in sesame based on quantitative (root length) and qualitative (seed coat color) traits. Natural variation in root length of 350 sesame accessions was collected from a field experiment following the methodology developed by Su et al. , and theSIN_1017810, SIN_101781, SIN_1017812, SIN_1017815, SIN_1017843, SIN_1007064, SIN_1007065, SIN_1020072 and SIN_1017818 are proposed for further functional studies to pinpoint the causative gene (s). Regarding the seed coat color, the potential candidate genes identified in our study include SIN_1007188, SIN_1007221, SIN_1023226, SIN_1023227 and SIN_1023228. Interestingly, three genes detected in this study were previously reported by Mei et al. [Our GWAS results for the two traits showed that a total of 190, 181 and 162 significant SNPs (-log10(p) > 6) associated with root length were detected by FASTmrEMMA, mrMLM and EMMAX, respectively. Similarly, 67, 492 and 143 significant SNPs associated with seed coat color were detected by FASTmrEMMA, mrMLM and EMMAX, respectively Fig. . Of the i et al. .Fig. 5Ap. It is also important to note that significantly associated regions simultaneously detected by more models in GWAS are more likely to be highly associated with the traits under investigation as compared with regions detected only by a single model. Hence, developing diagnostic markers for the co-detected associated regions could speed up sesame molecular breeding programmes.Collectively, the analysis of different GWAS models indicates the potential of using an integrated approach (single and multi-locus models) to improve the capacity and power of GWAS in sesame. This will help to detect more and novel marker-trait associations and candidate genes, particularly when investigating quantitative traitsOver the last five years, GWAS have been successfully implemented in sesame and is illuminating the genetic basis of many important agronomic traits. Even though a list of QTLs (~300) and candidate genes (~250) have been identified for qualitative and quantitative traits, more traits, including chlorophyll-yield, metabolite-GWAS, waterlogging, heat tolerance are under investigation. We envision that all these results will lead to the development of allele-specific diagnostic markers to be used as daily molecular tools in sesame breeding programmes. Though a high-quality sesame reference genome sequence has been developed, more often, there are limitations to find any candidate gene around the peak SNPs from GWAS. To overcome these limitations, we need to use the recently developed sesame pan-genome  for futuThe application of new multi-locus GWAS models and integration of single- and multi-locus models will provide more efficiency and power in future GWAS implementation in sesame. Up to date, very few studies have validated the numerous GWAS findings in sesame. Therefore, follow-up studies are needed for further validating the favorable alleles identified from GWAS in independent populations and using other approaches . Validation of GWAS findings using transgenic approach is also instrumental in several plant species. In sesame, genetic transformation protocols using tissue culture techniques have been reported . More stAdditional file 1 : Table S1. Summary list of total QTLs and candidate genes identified in GWAS for root length and seed coat color along the linkage groups in sesame by multi-locus and single-locus models. Table S2. Summary of QTL and candidate genes detected by each GWAS model. Table S3. Candidate genes detected in each LG for each model.


Mei2-like 4 isoform, known to be associated with seed size and weight regulation. Moreover, the genes that were pinpointed are also good candidates for functional analysis to validate their influence on seed shape and size in common bean and other related crops.Seed weight and size are important yield components. Thus, selecting for large seeds has been a key objective in crop domestication and breeding. In common bean, seed shape is also important since it influences industrial processing and plays a vital role in determining the choices of consumers and farmers. In this study, we performed genome-wide association studies on a core collection of common bean accessions to dissect the genetic architecture and identify genomic regions associated with seed morphological traits related to weight, size, and shape. Phenotypic data were collected by high-throughput image-based approaches, and utilized to test associations with 10,362 single-nucleotide polymorphism markers using multilocus mixed models. We searched within genome-associated regions for candidate genes putatively involved in seed phenotypic variation. The collection exhibited high variability for the entire set of seed traits, and the Andean gene pool was found to produce larger, heavier seeds than the Mesoamerican gene pool. Strong pairwise correlations were verified for most seed traits. Genome-wide association studies identified marker–trait associations accounting for a considerable amount of phenotypic variation in length, width, projected area, perimeter, and circularity in 4 distinct genomic regions. Promising candidate genes were identified, e.g. those encoding an AT-hook motif nuclear-localized protein 8, type 2C protein phosphatases, and a protein  Phaseolus vulgaris L., 2n = 2x = 22) is the most widely consumed legume grain in the human diet , size , and shape (circularity and length-to-width ratio). Finally, we searched within genome-associated regions for candidate genes putatively involved in seed phenotypic variation.P. vulgaris germplasm collections at the Agronomic Institute of Campinas (IAC), Brazil , during the 2017–2018 growing season (December–May). Each plot consisted of 1 pot containing 2 plants. Considering that all accessions were inbred lines grown under optimal conditions, high seed homogeneity was expected. Briefly, seeds were germinated in a biochemical oxygen demand (BOD) incubator at 26°C for 5 days prior to transplanting. Three seedlings were transferred to each 3 l pot containing soil and sand (3:1). After establishment, 1 plant was discarded from each pot. The remaining 2 were fertilized with 5 N–10 P–30 K according to crop recommendations and irrigated periodically until harvesting.In order to evaluate seed size, shape, and weight, 7 specific traits were measured on 50 seeds randomly selected from each plot. Seed size was determined by measuring seed length (LENGTH), width (WIDTH), perimeter (PERIM), and projected area (AREA). Length-to-width ratio (LWR) and circularity (CIRC) were calculated to reflect seed shape. Finally, seed weight was estimated on the basis of hundred seed mass (HSM).Seed size and shape properties were evaluated using high-throughput image processing. Seeds were photographed in a dark room with a supplementary light source and using a digital SLR camera  fixed perpendicularly on an image acquisition station, 35 cm above the seeds. The background color was either white or blue, to contrast with the tegument. A 10-mm crossmark was added to the background to convert from pixels to mm and indicate actual seed size on the image . HSM in Images were analyzed using SmartGrain software , implemented in ASReml‐R software (Likelihood ratio tests (LRT) were run to verify the statistical significance of the accession factor and the Wald test run to verify the significance of the gene pool. The best linear unbiased predictors were applied to compute the genotypic correlations using Pearson’s coefficient, and standardized to perform principal component analysis (PCA). Broad sense heritabilities for all 7 seed traits were estimated as the ratio of genotypic to phenotypic variance, expressed as a percentage using the formula: PRJNA336556, BioSamples SAMN05513252, and SAMN05513251. Bioinformatics procedures for SNP calling were performed by processing GBS raw sequences through the TASSEL-GBS pipeline, with the P. vulgaris (G19833) genome as reference (Genotyping by sequencing (GBS) had already been used to genotype the IAC core collection panel, as reported by α/number of markers) is used. To avoid bias ascribed to double-shrinkage, best linear unbiased estimators (BLUEs) of the genotypic values for each trait were used in the association analysis.GWAS on the 10,362 SNP markers and seed traits were performed using multilocus mixed modeling effect2 and Vpheno is the variance of the adjusted means for each trait.The proportion of variance explained (PVE) by each association (SNP and seed trait) was calculated using the expression: r2) between the significant SNPs and neighboring SNPs (1 MB up and downstream) was computed in order to establish the LD block inherited together with the associated SNP, or inherited more frequently than could be expected by randomness. To do this, we used the LDheatmap R-package  mapping to assign GO terms by running Blas2GO with default parameters. Finally, aiming to assess the expression patterns of these genes during seed development, we consulted the common bean expression atlas  except WIDTH  accounted for 93.58% of the variation, in which PC1 mainly described the variation in LENGTH, PERIMETER, AREA, WIDTH and HSM, and PC2 captured most of the variance in CIRC and LWR , corroboP-values are close to the expected values under the null hypothesis for all traits, indicating that there were no systematic false positive associations.GWAS for the 7 seed traits were conducted on a total of 10,362 SNPs. As shown in the quantile–quantile (QQ) plots , accordiP-values below the Bonferroni threshold (P < 4.82E-06) were considered significant for LENGTH, WIDTH, AREA, PERIM, and CIRC, and were located in 4 distinct genomic regions of 3 chromosomes , with an additive effect of 10.86 mm2, resulting mainly from its influence on seed width. For WIDTH, this SNP also accounted for 23.7% of the total phenotypic variation and the effect of the variant allele on this trait was 0.55 mm. No significant associations were detected for LWR and WEIGHT (AREA-associated SNPs (S1_53011276 and S1_379992973) were located on Pv02 and Pv08 at 805645 bp and 55946412 bp, respectively. For this trait, the effect of S1_379992973 was 6.01 mmd WEIGHT .For the 4 significantly associated SNPs, both the variant and the reference allele were found in both gene pools. However, the minor allele variant always occurred at more balanced frequencies in both gene pools. Corroborating the phenotypic analysis which revealed larger seed size in the Andean accessions than in the Mesoamerican accessions, the alleles with positive effects were also present at higher frequencies in the Andean gene pool, except for S1_507205534 .With the aim of delimitating the genomic window for candidate gene investigation, LD analysis was performed on the borders of the significant SNP–seed trait associations. The genomic intervals varied between 358.4 kb for S1_53011276 and 558.6 kb for S1_507205534 . SupplemP. vulgaris expression atlas for 8 tissues and stages related to seed development , S1 (1.57), and S2 (1.66)  . Finally species , with hi species .. Consistent with the expression atlas, this gene exhibited the greatest expression for FY, PV, PH, P1, P2, and SH, and also the highest Z-scores for most tissues, especially in the early developmental stages , indicating that image analysis of seed properties leads to a slight experimental error, as reported by P = 0.12) were exhibited by the individual gene pools. These findings suggest that the main reasons for the seed size discrepancy between the 2 pools are related to the shape aspect of longitudinal development, an observation supported by the higher LWR of the Andean seeds and the higher CIRC values for Mesoamerican accessions.Even though both domestication events increased seed size and weight compared to their wild ancestors , it is aCorroborating other evidence, the core collection herein evaluated showed strong positive pairwise correlations among seed size traits , LENGTH The GWAS approach is fundamentally based on LD and aimed at identifying genetic markers physically linked to the actual causal variant for a given trait. Nonetheless, several other factors over and above physical linkage may influence LD estimates. In breeding programs, intense selection, admixture of populations and crossing using a limited number of elite genotypes reduces genetic diversity and may strongly impact LD patterns and consequently affect GWAS resolution and statistical power   and belongs to a protein family recognized as a pentatricopeptide repeat-containing protein, a modular RNA-binding protein which intermediates numerous aspects of gene expression, such as RNA processing, splicing, editing, stability, and translation. Already associated with several phenotypes, this family is recognized as an important player in seed development in various crops . Mei2-like proteins with conserved RNA recognition motifs have been identified in several plant species and seem to play a role in plant development (Mei2-like 4 protein (OML4) is phosphorylated by a glycogen synthase kinase 2 and negatively controls seed size and weight. Thus, loss of function of OML4 mutants produces larger and heavier seeds, while the overexpression of this gene leads to smaller, lighter grains , exhibited low transcriptional levels in the 8 tissues considered herein. The plant-specific B3 superfamily encompasses well-studied protein families, including prominent transcriptional elements, such as auxin response factors . The genotypic information is available in https://www.mdpi.com/2073-4425/10/1/5#supplementary [accessed 2020 Sep 10], GBS data that support the findings of this study are available in the GenBank database, BioSample SAMN05513252 and SAMN05513251, included in BioProject PRJNA336556 (G3 online.jkac048_File_S1Click here for additional data file.jkac048_File_S2Click here for additional data file.jkac048_File_S3Click here for additional data file.jkac048_File_S4Click here for additional data file.jkac048_Table_S1Click here for additional data file.jkac048_Table_S2Click here for additional data file.jkac048_Supplemental_Figures_Tables_and_LegendsClick here for additional data file.


Brassica napus L), but its genetic dissection for the formation of high yield remains enigmatic, given the rapid population growth. In the present review, we review the discovery of major loci underlying important agronomic traits and the recent advancement in the selection of complex traits. Further, we discuss the benchmark summary of high-throughput techniques for the high-resolution genetic breeding of rapeseed. Biparental linkage analysis and association mapping have become powerful strategies to comprehend the genetic architecture of complex agronomic traits in crops. The generation of improved crop varieties, especially rapeseed, is greatly urged to enhance yield productivity. In this sense, the whole-genome sequencing of rapeseed has become achievable to clone and identify quantitative trait loci (QTLs). Moreover, the generation of high-throughput sequencing and genotyping techniques has significantly enhanced the precision of QTL mapping and genome-wide association study (GWAS) methodologies. Furthermore, this study demonstrates the first attempt to identify novel QTLs of yield-related traits, specifically focusing on ovule number per pod (ON). We also highlight the recent breakthrough concerning single-locus-GWAS (SL-GWAS) and multi-locus GWAS (ML-GWAS), which aim to enhance the potential and robust control of GWAS for improved complex traits.Yield is one of the most important agronomic traits for the breeding of rapeseed ( Brassica napus L., genome AACC, 2n = 38) is one of the second top-most oilseed crops predominantly grown for protein meal and vegetable oil across the world )GWi7.1, GWi7.2 and to identify novel genes. Moreover, Xu et al. [Triticum aestivum L. As a result, they achieved that ML-GWAS models are very authentic and dynamic [The mrMLM method enhances the identification of loci by more than 55% throughout the covered region. For instance, Misra et al.  determinu et al.  utilizedu et al. . More reu et al.  utilized dynamic . Corresp dynamic  confirme dynamic . Additio dynamic  showed r dynamic . Finally dynamic ,165,172.Brassica napus. Follow-up work can be carried out on the following aspects:The genetic analysis of phenotypic traits has found novel and significant QTNs/QTLs/candidate genes, giving research a chance to progress, more specifically, in the identification of the activity of these genes and further recognize the mechanism of the genetic constitution of the traits of thousand seed weight, ovule number per ovary and seed number per silique through crosswise adverse geographical and climatic conditions.The information collected from previous studies enriches the knowledge of variations in populations in the three above-mentioned yield-related traits; therefore, this warrants the need for supplementary authentication that will deliver markers through the integration into the host plant improvement.The association analysis of the candidate gene method and the development of near-isogenic, transgenic, or mutant plants may be efficiently utilized to identify novel and significant alleles and how they interact with the above-mentioned yield-related traits.Brassica napus.Genomic regions reviewed in the present study merit attention for their further utilization in breeding programs that use marker-assisted selection (or genomics-assisted breeding) on This review presented the robustness and power of QTL/GWAS analyses, candidate gene association studies and superior allele identification in


Pulmonary function progressively declines with aging. Forced expiratory volume 1-second (FEV1) and forced vital capacity (FVC) are predictors of morbidity of cardiovascular diseases and all-cause mortality. Reduced pulmonary function has shown association with elevated chronic low-grade systemic inflammation and low muscle strength, and glycosylated hemoglobin (HbA1c). We evaluated whether FEV1 and FVC share common genetic factors with interleukin-6 (IL-6), high-sensitivity C-reactive protein (hsCRP), body mass index (BMI), handgrip strength, plasma glucose, and HbA1c employing correlated meta-analysis (CMA) in up to 3888 individuals (age range: 26-106). CMA tests whether combining genome-wide association (GWA) P-values from two or more traits enhances the ability to detect variants concomitantly influencing such traits. We considered a variant pleiotropic if the univariate GWA P≤1.0x10-02 and CMA P≤5.0x10-08. We identified pleiotropic loci for FEV1 (or FVC), IL-6 and hsCRP within CYCS, CALN1, TRIM9, AXIN2, MICAL3, and CMIP; FEV1 (or FVC) and BMI within CYP2U1, LINC00871-RPL10L-MDGA2, and LOC105372472; FEV1 (or FVC) and grip strength within XXYLT1 and MAL2; FEV1 and FPG within MAF; and FVC and HbA1c within HDHD3. Some loci were reported as GWAS suggestive (P<10-6) for pulmonary function. Additionally, the identified loci harbor genes with roles in pro-inflammatory cytokine production, immune and inflammatory pathway, T-helper signaling pathway, transforming growth factor-beta, and T-cell receptor-alpha enhancer. Our findings suggest that inflammation is a feature of reduced pulmonary function with IL-6, hsCRP, BMI, low muscle strength, glucose, and HbA1c. Pleiotropic genetic associations across these traits may explain part of the correlated genetic architecture between pulmonary function and aging-related traits.


Humans differ substantially in how strongly they respond to similar experiences. Theory suggests that such individual differences in susceptibility to environmental influences have a genetic basis. The present study investigated the genetic architecture of Environmental Sensitivity (ES) by estimating its heritability, exploring the presence of multiple heritable components and its genetic overlap with common personality traits. ES was measured with the Highly Sensitive Child (HSC) questionnaire and heritability estimates were obtained using classic twin design methodology in a sample of 2868 adolescent twins. Results indicate that the heritability of sensitivity was 0.47, and that the genetic influences underlying sensitivity to negative experiences are relatively distinct from sensitivity to more positive aspects of the environment, supporting a multi-dimensional genetic model of ES. The correlation between sensitivity, neuroticism and extraversion was largely explained by shared genetic influences, with differences between these traits mainly attributed to unique environmental influences operating on each trait. According to the recent evolutionary-inspired theories   and the Recent studies using these scales provide empirical evidence for the validity of these scales –15. For Importantly, while these sensitivity measures were initially conceptualised to reflect one general sensitivity factor, subsequent factor analyses identified three factors, each capturing different aspects of ES: Low Sensory Threshold (LST) reflecting variations in the threshold for reactivity to sensory stimuli; Ease of Excitation (EOE) manifested in being easily overwhelmed by contextual emotional psychological stimuli and Aesthetic Sensitivity (AES) characterised by greater attention to contextual details and aesthetic appreciation , 19, 20.Higher sensitivity has been consistently associated with common personality traits, such as higher neuroticism and openness to experiences, and lower extraversion, with low-to-moderate effects sizes , 20, 22.The current study applies quantitative behavioural genetics methodology in a large sample of 17-year old adolescent twins in order to examine (1) to what degree ES is heritable, (2) whether the heritability of sensitivity reflects a multi-dimensional structure, with genetic influences that are shared across the three components of ES, as well as influences that are distinct to each component and (3) the extent to which genetic and environmental influences on ES overlap with those on the Big Five personality traits.The sample for the current study included a subset of adolescent twin pairs from the Twins Early Development Study (TEDS). TEDS is a large longitudinal epidemiological study of over 16,000 twin pairs born in England and Wales between 1994 and 1996. A detailed description of TEDS recruitment procedures and data has been provided elsewhere . The datα = 0.81 for the main scale (HSC) and α = 0.64, 0.81 and 0.70 for AES, EOE and LST, respectively, , extraversion (α = 0.72), openness (α = 0.63), agreeableness (α = 0.69), and conscientiousness (α = 0.77).Personality was measured using an abbreviated five factor model questionnaire by Mullins-Sweatt et al. , containTo address the first aim of this study, a univariate ACE model was used to estimate the heritability of ES. An ACE model is constructed by using the inter-class correlations of MZ and DZ twins to estimate the contribution of genetic and environmental factors to observed phenotypic variations in a trait. An ADE model was also constructed and examined against the ACE model to determine the best-fitting model. In addition, sex differences in the heritability estimates were examined, using the main four sex-limitation models.The ACE model partitions the phenotypic variance into additive genetic effects (A), shared/common environmental effects (C) and non-shared environmental effects (E). An ADE model, on the other hand, replaces the shared environmental effects (C) with non-additive genetic effects (D for dominance). Importantly, dominance genetic effects are only explored when there is no evidence for shared environmental effects (C). Shared environmental effects are the environmental influences that contribute to the similarity between twins, whereas non-shared environments are the environmental influences that make twins dissimilar such as individual-specific life events. The genetic correlation (rg) between MZ and DZ twin pairs is assumed to be 1 and 0.5, respectively, and the correlation between twins’ shared environments (rc) is assumed to be 1 for both MZ and DZ twin pairs. Higher phenotypic similarity within MZ twin pairs, in comparison with DZ twins, can therefore be attributed to MZ twins’ higher genetic similarity (A). Since C also contributes to the higher resemblance between MZ twin pairs, any variance not accounted for by A can be attributed to C  are indicated). E is what makes twins different from one another and is estimated as the difference between the MZ twin correlations and 1. E also includes measurement error . The sexTo address the second question, we constructed a multivariate common pathway ACE model (as well as a Cholesky decomposition ACE model [correlated factors solution] for comparison) to examine the genetic architecture of sensitivity as a function of its three components. To address the third question, we used a multivariate independent pathway ACE model to examine the extent to which the phenotypic correlation between ES and personality is due to common or specific genetic influences.The multivariate models parse the variance/covariance of the phenotypes of interest into two sets of ACE effects: those that are due to shared ACE effects and those that are due to specific ACE effects for each phenotype. The common pathway model assumes that the shared ACE factors influence the variables of interest via a single psychometric/latent liability factor. The Cholesky decomposition-correlated factors model assumes that the phenotypic correlation between variables is due to correlating ACE influences, rather than via a latent factor. The structural equation modelling package of OpenMx  in R 3131 was usF = 48.58, p < 0.001; EOE: F = 25.56, p < 0.001; AES: F = 14.64, p < 0.001; LST: F = 54.42, p < 0.001) and personality measures of neuroticism  = 0.16.93, p < 0.001), agreeableness  = 11.40, p < 0.001) and conscientiousness  = 7.09, p < 0.05). Mean differences were not statistically significant for openness  = 0.06, p = 0.81) and extraversion  = = 0.10, p = 0.32). Age was not significantly correlated with any of the traits, except for AES .Descriptive statistics including the sample size, mean scores and bivariate correlations for all measures are presented in Table p = 0.98).Cross-twin correlations showed evidence of genetic influences on variability in all traits, with MZ twin correlations being larger than DZ twin correlations in both males and females Table . Twin cop = 0.98; parameter estimates: A = 0.48 95% CI ; D = 0.00 95% CI ; E = 0.52 95% CI ), suggesting additive genetic influences sufficiently captured the heritability of ES.In order to examine dominant genetic effects, an ADE model was compared with the ACE model, but it was not found to be a better fit to the data  versus specific genetic effects (As). The latent factor of sensitivity, as captured by EOE, AES and LST, was heritable , with EOE loading most strongly on the latent factor , followed by LST  and AES  , Common non-shared environmental influences (Ec) explained 0.39  of the variance in EOE, and 0.16  and 0.04  of the variance in LST and AES, respectively. Specific, non-shared environmental influences (Es) explained 0.18 , 0.63  and 0.42  in EOE, AES and LST, respectively.A Cholesky decomposition [correlated factors solution] model was also fitted to the data to compare its fit with the common pathway model  made a significant contribution to explaining the variance in all personality traits, but not in ES . Environmental influences that explained the variance in sensitivity were almost entirely (51/52 = 98%) due to non-shared environmental effects specific to this phenotype . The small, non-significant effect of shared environmental influences on sensitivity (C) was due to effects specific to sensitivity .Overall, these results suggest that the majority of the genetic influences that explain the heritability of sensitivity are shared with the personality traits of neuroticism and extraversion, while the environmental influences that explain individual differences in sensitivity are almost entirely specific to this phenotype. Of the total ACE influences on variations in sensitivity, 37% was explained by ACE effects shared with personality traits. The remaining 63% were due to ACE effects specific to sensitivity, indicating, although shared to a significant degree, a predominately distinct aetiology of sensitivity and personality traits.The current study set out to examine three questions related to individual differences in ES. First, we investigated whether ES is a heritable trait. Second, we examined the genetic architecture of ES as a function of its three components and total score. Third, we investigated the extent to which the genetic and environmental factors that explain variability in the Big Five personality traits are shared with ES.With regards to the first question we found that genetic influences accounted for 47% of the variation in sensitivity, while non-shared environmental influences and measurement error accounted for the remaining 53% of the variance. Our results support theories proposing that sensitivity is a heritable trait, whereby genetic variation explains nearly half of the observed individual differences in sensitivity.In relation to the second question, the results of our analysis show that the genetic and environmental factors that explain variance in EOE and LST do not explain much of the variance in AES. These findings suggest that the genetic aetiology of ES is the function of three heritable components: one that is relevant to general sensitivity, as captured by the common genetic influences across the three components, another that is reflected in specific genetic influences that are involved in variations in the reactivity to adversity (i.e. LST and EOE components), and another that is relevant to processes involved in reactivity to positive experiences (i.e. AES component). Hence, our results support a multi-dimensional genetic model of sensitivity. An important implication of these finding is that the relative presence or absence of the specific genetic factors that contribute to the different heritable components of sensitivity may lead to different sensitivity types . For exaWith regards to our final question, we found that sensitivity was moderately correlated with higher neuroticism and lower extraversion. The majority of the heritability of sensitivity was explained by genetic factors that also influence neuroticism, and to a lesser extent extraversion. A small proportion of the variance in sensitivity was explained by genetic factors that are specific to sensitivity. However, we found no evidence that environmental influences that are involved in the prediction of the Big Five personality traits are also relevant for variation in sensitivity. These findings suggest that the phenotypic similarities between ES, extraversion and neuroticism were largely due to their underlying shared genetic influences, whereas differences between these traits are predominately influenced by unique environmental factors.The current study has several important strengths. These include the use of a twin design to provide a first estimate of heritability of ES in a large, representative sample of adolescent twins. Furthermore, this is the first study to examine shared heritability between ES and the Big Five personality traits. However, our findings have to be considered in light of the following limitations. First, it must be noted that the heritability estimates were based on an adolescent sample, and that these estimates may differ in younger or older samples given that heritability estimate often vary across the life course. Therefore, it cannot be assumed that the same estimates will necessarily apply to ES studies that feature infants and toddlers. Furthermore, it must be acknowledged that the obtained estimates are specific to the ES measure that we used. Although the HSC scale is a promising measure of ES, it may not fully capture systematic or heritable variance in ES and therefore other measures may indicate different heritability estimates. Future studies should seek to investigate and replicate the reported heritability estimates in different samples and at different ages , using additional and alternative measures of ES, as well as estimating heritability with molecular genome-wide methodology . Second,The results of the current study have several implications for future research. First, while our findings do not point to the specific genetic factors underlying variations in ES, they do provide assurance that ES, to the extent that is reflected in the HSC scale, is heritable. This should encourage its use as a proxy phenotype in molecular genetic research applying genome-wide and polygenic approaches. This is especially important given that much of the existing evidence suggesting that differences in ES are influenced by genetic factors is based on the widely criticised candidate gene methodology.Second, we found evidence to suggest that the heritable components of ES is multi-dimensional and consists of three relatively distinct genetic influences: one that pertains to general sensitivity, one reflecting heightened sensitivity to negative and one to positive aspects of the context. Future studies of ES should investigate the existence and distribution of these hypothesized sensitivity components. Third, we found the majority of genetic influences involved in individual differences in ES are also involved in the personality traits of neuroticism and extraversion, encouraging future research on the shared genetic influences on these traits. Finally, we found that environmental factors also play a significant role in shaping ES, emphasizing the need for future research to examine the contribution of early environmental influences in the development of sensitivity.In conclusion, the reported findings support the theoretical proposition that the phenotype of ES has a genetic basis, but that environmental factors play an equally important role. Furthermore, our findings suggest that ES, measured with the HSC scale, reflects a multi-dimensional genetic structure made up of three heritable components. Finally, the genetic overlap between ES, neuroticism and extraversion indicates that similar genetic influences are involved in these different phenotypes.Supplemental Information


N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank  who had multiple micro- and macrostructural MRI metrics measured at each of 180 cortical areas, seven subcortical structures, and 15 major white matter tracts. Linear mixed-effect models were used to investigate associations between PRS and brain structure at global and regional scales, controlled for multiple comparisons. Polygenic risk was significantly associated with reduced neurite density index (NDI) at global brain scale, at 149 cortical regions, five subcortical structures, and 14 white matter tracts. Other microstructural parameters, e.g., fractional anisotropy, that were correlated with NDI were also significantly associated with PRS. Genetic effects on multiple MRI phenotypes were co-located in temporal, cingulate, and prefrontal cortical areas, insula, and hippocampus. Post-hoc bidirectional Mendelian randomization analyses provided preliminary evidence in support of a causal relationship between (reduced) thalamic NDI and (increased) risk of schizophrenia. Risk-related reduction in NDI is plausibly indicative of reduced density of myelinated axons and dendritic arborization in large-scale cortico-subcortical networks. Cortical, subcortical, and white matter microstructure may be linked to the genetic mechanisms of schizophrenia.Recent discovery of approximately 270 common genetic variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. We hypothesized that normal variation in PRS would be associated with magnetic resonance imaging (MRI) phenotypes of brain morphometry and tissue composition. We used the largest extant genome-wide association dataset ( A substantial genetic contribution to the pathogenesis of schizophrenia is indicated by twin and familial heritability estimates of ~80% –3, and sR2 = 0.2%) and not consistently significant between studies [R2 = 0.2%) [The genetic liability for schizophrenia is thought to cause proximal changes in brain structure and function , which t studies , 18. Thi = 0.2%)  but most = 0.2%) .N < 15,000), which were likely underpowered to detect small polygenic effects [The current lack of clear evidence for an association between genetic risk for schizophrenia and brain structure could be attributable to the relatively small sample sizes of prior genetic neuroimaging studies  , grey maN ~ 30,000 participants in the UK Biobank (UKB) to conduct a comprehensive study of PRS association with nine brain MRI phenotypes  to construct schizophrenia PRS for each subject [We therefore hypothesized that macro- and microstructural MRI markers of grey and white matter would be associated with polygenic risk for schizophrenia. We analyzed multimodal MRI and genotype data from pes Fig. . In greyN = 40,680 participants for each of whom complete genotype and multimodal MRI data were available for download (February 2020); Fig. https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=200).Data were provided by the UK Biobank, a population-based cohort of >500,000 subjects aged between 39 and 73 years . We focuhttps://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf) and further processed with Freesurfer (v6.0.1) [https://github.com/ucam-department-of-psychiatry/UKB).MRI data acquisition has been described in detail elsewhere . Minimal(v6.0.1)  using th(v6.0.1)  was alig(v6.0.1) . Probabi(v6.0.1) . DocumenN = 488,377 participants, with DNA acquisition, imputation, and quality control pipelines as described elsewhere [p ≤ 1 × 10−6), variant call rate ≤98%, and an imputation quality score ≤0.4, resulting in 5,366,036 SNPs.Genome-wide genotype data were available for lsewhere . We excllsewhere . We alsoP-value clumping and thresholding method in PRSice 2 [R2 = 0.1, physical distance = 250 kb) [PSNP < 1) to balance signal to noise ratio [A PRS is calculated by multiplying the number of risk alleles by the effect size of each allele and summing the products over all SNPs for each individual . We usedPRSice 2 , 45, 46.se ratio , 47, 48.N = 29,879 participants, using effect sizes for each allele from a trans-ancestry GWAS with N = 69,369 schizophrenia cases and N = 236,642 healthy controls, mostly (~80%) of European ancestry [PSNP inclusion thresholds . We used linear mixed effect  models (Equation 1) to estimate the associations between the scaled PRS and each of the scaled MRI phenotypes with covariates including age, age2, sex, genotype batch, 15 genetic principal components (Data-Field 22006), and x, y and z coordinates of head position in the scanner to control for static-field heterogeneity (Data-Field 25756-25758) [Statistical analyses were conducted in R  . Hemisph6-25758) , 22, 51,PSNP thresholds for PRS construction, and to minimize the multiple testing required for comprehensive brain regional mapping of genetic associations, we decided a priori on a two-step analysis for cortical phenotypes. First, we estimated and tested the association of all eight PRS with each MRI metric at a global cortical scale, using the Benjamini–Yekutieli procedure (BY = 5%) to control for multiple tests over all metrics and thresholds. We thus identified the genome-wide probability threshold for PRS inclusion, PSNP-global, that produced the PRS most strongly associated with each global cortical metric. Second, we used the PRS defined by PSNP-global to test for genetic association with the corresponding metric at a regional scale in 180 cortical areas, covarying for total intracranial volume, with the false-discovery rate (FDR) = 5%.To identify the optimal For analysis of the smaller number of subcortical regions (7 regions), and white matter tracts (15 tracts), we tested for association with all eight PRS at each region, with FDR = 5%, to control for the multiple tests entailed .PSNP thresholds and showed that significant associations between PRS and regional MRI phenotypes were largely conserved across PSNP thresholds  and standardized effect sizes (β) throughout.We report the proportion of explained phenotypic variance by the PRS in percentages  of the causal effect of schizophrenia on NDI, we used the same GWAS summary statistics as for the PRS analyses . Genetices Table .N = 31,722–31,760 for different regional metrics (SI Methods) [P = 5 × 10−8 and clumped with a distance of 10,000 kb and LD r2 of 0.001. Based on these parameters, 40 NDI phenotypes were included and the number of genome-wide significant loci ranged from three to 29 after harmonizing the data  of the causal effect of NDI on schizophrenia, we performed GWAS using fastGWA with sample sizes in the range Methods) . Geneticta Table . Second,as three , 54  MR, which assumes that all SNPs are valid genetic instruments, as the main method to estimate causal effects . The robTo evaluate consistency in effect direction, we additionally calculated genetic correlations between schizophrenia and the same 41 imaging phenotypes using Linkage Disequilibrium Score Regression v1.0.1  and corrN = 27,086–29,878 . Both were microstructural phenotypes derived from DWI or NODDI: FA was significantly negatively associated with the PRS constructed with PSNP ≤ 0.001; and NDI was significantly negatively associated with PRS constructed at PSNP’s ≤ 0.0001, 0.001, and 0.01  were positively correlated with each other and negatively correlated with LGI. Among the microstructural metrics, NDI and FA were positively correlated; and both were negatively correlated with MD and ODI Fig. .Fig. 3ReR2 = ≤ 0.08% with −0.03 ≤ β ≤ +0.03 . The top ten areas where NDI was most strongly negatively associated with PRS were located in association auditory cortex, early auditory cortex, ventral visual stream, posterior cingulate cortex, insular and frontal opercular cortex, posterior opercular cortex, inferior parietal cortex, and superior parietal cortex ; and positively associated with PRS in two regions . The top ten regions where FA was most strongly associated with PRS were all areas of temporal, cingulate, frontal, and insular cortex, where decreased FA was associated with higher risk scores. MD was significantly associated with PRS in 76 regions (66 positive and 10 negative associations). ODI was the microstructural metric least frequently associated with PRS, at 11 cortical regions.Among the microstructural metrics, NDI was significantly associated with PRS in 149 cortical areas .Of the macrostructural metrics, LGI showed the highest number of significant associations with PRS  compared to Vol, SA, and IC, each of which was significant in less than 15 regions  and MD (FA only)  < 0) tended to be the same regions where PRS was most strongly associated with decreased NDI (NDI(t) < 0) and increased MD (MD(t) > 0; Fig. Since the MRI phenotypes were not independent of each other Fig. , we furtly) Fig. . In otheWe also simply counted the number of different MRI phenotypes that were significantly associated with PRS at each of 180 cortical areas. There were genetic effects on at least two metrics in 122 regions Table , at leasR2 = 0.08%, β = −0.03). NDI was also negatively associated with PRS in the caudate, hippocampus, and putamen; and positively associated with PRS in pallidum  and four microstructural phenotypes  at seven subcortical regions. The single strongest effect was a negative association between PRS and NDI of thalamus  followed by NDI of projection fibers  and association fibers . Those associations showed a consistent direction of effect and were significant at all PSNP inclusion thresholds. The strongest negative association between PRS and FA was within the forceps minor , followed by projection fibers , and association fibers . The strongest positive associations between PRS and MD were located in association fibers  followed by limbic system fibers  and the forceps minor   Table .Fig. 5AsP = 5 × 10−8 , suggesting that reduced thalamic NDI may cause increased risk of schizophrenia. Sensitivity analyses using the WM method were also significant . NDI of thalamus showed significant heterogeneity between individual genetic variants (P for Q-test < 0.0001) but the Egger intercept was not significant, suggesting no evidence for substantial pleiotropic effects (P = 0.42) . After correction for multiple comparisons, there was no significant evidence for a causal effect of schizophrenia on NDI metrics (MR (i)), and there was no significant evidence for a causal effect of NDI metrics on schizophrenia (MR (ii)) using genetic instruments identified by genome-wide association significance threshold of  Tables S and S8. 2) Table . In addi2) Table .Genetic correlations were modest and did not survive multiple testing correction. However, 80% of the correlations were in the negative direction predicted by significant negative associations between NDI and PRS scores Table .We estimated the strength of association between PRS for schizophrenia and nine MRI phenotypes measured at 180 cortical areas, seven subcortical structures, and 15 white matter tracts. In strong support of our motivating hypothesis, we found that PRS was significantly associated with microstructural metrics of brain tissue composition at global and regional scales of cortex; in the thalamus, basal ganglia, and hippocampus; and extensively in white matter tracts. First, we interpret and integrate these signals of significant association between genetic risk for schizophrenia and brain MRI metrics, especially NDI; then we discuss post-hoc analyses of causality stimulated by these results.Of all nine MRI metrics considered, NDI was the most robustly associated with PRS. NDI is derived from NODDI, an MRI sequence that was developed to estimate the microstructural complexity of dendrites and axons— collectively referred to as neurites—in grey and white matter of the living brain. NODDI compartmentalizes tissue into three microstructural environments—intra-cellular, extra-cellular, and cerebro-spinal fluid—each of which has different diffusion properties. The intra-cellular compartment refers to the space bound by neurites and allows for the measurement of their density (NDI) and their spatial orientation (ODI) , 58. In Thus, a reasonable interpretation of our results is that polygenic risk for schizophrenia in the general population is associated with decreased axonal and/or dendritic density in grey and white matter. While we cannot ascribe causality using the current methods, this interpretation is consistent with several lines of previous research in individuals with schizophrenia. First, schizophrenia case-control studies have reported abnormally decreased NDI in the cortex, hippocampus, and white matter , 35. SecWhile NDI was the one most strongly associated with PRS, almost all other metrics (except CT) also showed some significant regional associations with genetic risk. We consider that this apparent pleiotropy of genetic effects on brain structure largely reflects correlations between the MRI metrics . For exaOf the macrostructural phenotypes, LGI was positively correlated with MD and positively associated with genetic risk for schizophrenia. Local gyrification is assumed to capture early neurodevelopmental changes that are relatively stable after birth , 71. CasAtypicality in subcortical structures is a frequently reported finding in schizophrenia cases , and in NDI was again the MRI metric most sensitive to genetic association, especially in the thalamus, where PRS was significantly associated with reduced NDI. The thalamus plays a key role in cognitive and emotional processes that are clinically impaired in schizophrenia , 74. ThaAbnormalities of white matter microstructure, consistent with aberrant inter-hemispheric, fronto-temporal, and cortico-thalamic connectivity, have been frequently reported in schizophrenia , 33. We P = 5 × 10−8. However, we did find a significant, potentially causal relationship between lower NDI in the thalamus and increased risk for schizophrenia when we used a less stringent significance threshold to increase the number of genetic instruments and thus boost the power of MR analysis. We consider that this amounts to suggestive evidence that thalamic NDI may causally predict schizophrenia, consistent with the prior observation that thalamic NDI was the regional MRI metric most strongly and robustly associated with PRS for schizophrenia. However, we also consider that the sample size available for GWAS of NDI metrics was underpowered to identify sufficient instrumental variables at the conventional threshold for genome-wide significance . More definitive investigations of the causal relationship between MRI metrics and schizophrenia, on the basis of better genetic instruments defined by larger MRI samples, will be important to pursue in future [Association between PRS and brain MRI metrics, however, statistically significant or anatomically plausible, is uninformative about the underlying direction of causality . In postn future .PSNP-value thresholds for inclusion of risk variants. We also used the largest and most methodologically diverse MRI dataset to date in order to assess PRS associations with brain structure. However, the diagnostic variance in schizophrenia explained by PRS is still relatively small (7.7%) [It is a strength that we used the largest GWAS to date to construct schizophrenia PRS at multiple l (7.7%)  and, in l (7.7%) , 22, thel (7.7%) . The repl (7.7%) . The genPRS for schizophrenia were most robustly associated with significant changes in microstructural MRI metrics in the cortex, the subcortex, and white matter tracts, in a large population sample. These results provide substantial new evidence in support of the pathogenic model that genetic risk for schizophrenia is associated with reduced neurite density, and anatomical dysconnectivity, in cortico-subcortical networks.Supplemental materialTable S1Table S2Table S3Table S4Table S5Table S6Table S7Table S8Table S9Table S10Table S11Table S12Table S13Table S14Table S15Table S16Table S17Table S18Table S19


Egg production traits are economically important in poultry breeding programs. Previous studies have shown that incorporating genomic data can increase the accuracy of genetic prediction of egg production. Our objective was to estimate the genetic and phenotypic parameters of such traits and compare the prediction accuracy of pedigree-based random regression best linear unbiased prediction (RR-PBLUP) and genomic single-step random regression BLUP (RR-ssGBLUP). Egg production was recorded on 7422 birds during 24 consecutive weeks from first egg laid. Hatch-week of birth by week of lay and week of lay by age at first egg were fitted as fixed effects and body weight as a covariate, while additive genetic and permanent environment effects were fitted as random effects, along with heterogeneous residual variances over 24 weeks of egg production. Predictions accuracies were compared based on two statistics: (1) the correlation between estimated breeding values and phenotypes divided by the square root of the trait heritability, and (2) the ratio of the variance of BLUP predictions of individual Mendelian sampling effects divided by one half of the estimate of the additive genetic variance.Heritability estimates along the production trajectory obtained with RR-PBLUP ranged from 0.09 to 0.22, with higher estimates for intermediate weeks. Estimates of phenotypic correlations between weekly egg production were lower than the corresponding genetic correlation estimates. Our results indicate that genetic correlations decreased over the laying period, with the highest estimate being between traits in later weeks and the lowest between early weeks and later ages. Prediction accuracies based on the correlation-based statistic ranged from 0.11 to 0.44 for RR-PBLUP and from 0.22 to 0.57 for RR-ssGBLUP using the correlation-based statistic. The ratios of the variances of BLUP predictions of Mendelian sampling effects and one half of the additive genetic variance ranged from 0.17 to 0.26 for RR-PBLUP and from 0.17 to 0.34 for RR-ssGBLUP. Although the improvement in accuracies from RR-ssGBLUP over those from RR-PBLUP was not uniform over time for either statistic, accuracies obtained with RR-ssGBLUP were generally equal to or higher than those with RR-PBLUP.Our findings show the potential advantage of incorporating genomic data in genetic evaluation of egg production traits using random regression models, which can contribute to the genetic improvement of egg production in turkey populations. Egg production traits are an important component of selection indexes in commercial turkey flocks. Since the heritability of egg production traits changes throughout the laying period , a singlIncorporating genomic data into breeding programs has improved selection accuracy in several species –8. SinglThe use of ssGBLUP based on RR has been shown to increase the accuracy of genomic predictions for longitudinal traits in dairy cows , 13. In Egg production was recorded on 7422 hens from a female line over a 9-year period. The hens laid in individual trap nests and egg numbers were recorded on a daily basis. The trait analyzed was the cumulative number of hatchable eggs laid in trap nests per week over 24 weeks from the date of the first egg for each bird. Thus, hens started laying at different ages, but all hens completed egg laying in 24 weeks. This is similar to milk yield in dairy cattle, which begins at calving and continues until the cow produces less than a certain amount of milk, at which point it is dried off, or until it reaches a certain lactation length, e.g. 305 days. Cows milk for different numbers of days from 240 to 400 days, and begin to milk at different ages at calving, but the RR model accommodates these differences, as well as a variable number of test measures per cow. Similarly, in hens, production starts with the first egg and ends with the last egg (in a specified week), for hens of different ages at first egg, and the RR model can accommodate this. Hens will typically take one to two weeks to begin laying eggs after exposure to longer light periods is initiated (light date) to stimulate the on-set of lay. The production curves of hens are more similar when production is described relative to the first day of lay. Hens that start lay a week or two after the light date are not desired, but that is a different trait than egg production and will be called delay time (DT). Selection against hens with a long DT regardless of their estimated breeding values for egg production is beyond the scope of this paper. Normal practice for commercial producers is to look at eggs produced within 26 weeks from the lighting date regardless of when a hen begins laying, which is adequate when total egg production is the target, but not if egg production curves are needed to identify different shapes of production, as in this study.A phenotypic variance–covariance matrix of order 24 was estimated using the egg production data recorded by week using the R software . CovariaIn total, 3561 animals in this population were genotyped using the Illumina 65 K single nucleotide polymorphism (SNP) panel. Genotyping quality control consisted of filtering out SNPs that had a minor allele frequency lower than 5%, a call rate lower than 95%, or that were localised in non-autosomal regions. In total, 3178 birds and 47,387 SNPs passed quality control and were used for the analysis.th random regression coefficients for additive genetic and permanent environmental effects, respectively; th Legendre polynomial covariate for the observation of individual The data were analyzed with the following RR model:The matrix representation of the model is:The variance and covariance components for the random effects for weekly egg number were obtained as For RR-ssGBLUP, the inverse of the numerator relationship matrix  or genomic breeding values (GEBV) and phenotypes corrected for fixed effects divided by the square root of heritability obtained with RR-ssGBLUP .The second statistic, not previously published in the literature, was developed as follows. The use of genomic data in genetic evaluation aims at improving the accuracy of the Mendelian sampling effect, so that full-sibs may be ranked. Thus, Mendelian sampling effects were predicted by BLUP as:creases) . Thus, tAs the predicted Mendelian sampling effects become more accurate, the ratio should become larger and Means, standard deviations, and coefficients of variation (CV) of weekly egg numbers over 24 weeks are in Table Table 2 = 0.22 ± 0.02) of production and decreased during the last four weeks (h2 = 0.12 ± 0.01).Estimates of heritabilities and genetic correlations for egg production by using pedigree information for the selected weeks are in Table Table Estimates of the genetic correlations between weeks of lay were positive and ranged from moderate 0.67) to high (0.99) (Table 7 to highHere, comparisons of EBV and GEBV were conducted based on validation correlations and based on the variance of Mendelian sampling predictions. The validation correlations are shown in Fig. The highest accuracies recorded in our study were at weeks 20 and 24 for RR-PBLUP and RR-ssGBLUP, respectively. The accuracies of RR-ssGBLUP were lower but not statistically different from those for RR-PBLUP for some weeks. In a previous study conducted on layer hens, genomic information did not improve the accuracy of prediction for longitudinal egg numbers in the early stages of production . These rMendelian sampling variance ratios are shown in Fig. Both the correlation-based statistic and Mendelian sampling statistic indicate that inclusion of genomic information can improve the accuracy of predictions of breeding values and that the increase in accuracy can occur over the entire trajectory of egg production. The correlation-based statistic required the use of validation phenotypes, which include genetic, permanent environmental effect, and residual effects for a generally small subset of the data. Thus, the correlation-based statistic shows more variability over the 24 week trajectory because each time point involves a different set of phenotypic records. The Mendelian sampling statistic makes use of the same information at each time point, and thus, appears to be smooth over the production trajectory.To provide knowledge on egg production traits and maximize the accuracy of EBV for egg production in a turkey population, a single step RR model was compared with a pedigree-based RR model, and variance components were estimated over time. Although the increase in accuracy over RR-PBLUP was not uniform across time, the predictions based on RR-ssGBLUP generally increased the accuracy of prediction, especially in the later weeks of lay. Random regression models require that egg production is monitored from the day of first egg, which may need some adjustments in the procedures applied in a commercial enterprise, but in return they provide a clearer picture of the shape of the egg production curves and allow quantification of persistency of production. The economic benefits versus the cost of obtaining those benefits require further consideration.


We transcribed and thematically analysed the interviews and used an adapted analytic framework to document FOI across four domains—objects of intervention, implementation staff, implementation context and target of implementation. DFS utilisation remained low due to a combination of factors including poor product quality, distribution challenges, ineffective promotion and low awareness amongst end‐user WRAs. Motivation levels were higher amongst district‐level staff compared to frontline staff, who lacked supervisory support and effective incentives to promote DFS. Three typologies of DFS users emerged—‘believers’, ‘thrifters’ and ‘naysayers’—who indicated differing reasons for DFS purchase and its use or nonuse. The implementation of the DFS programme varied significantly from its theorised programme impact pathway. The adapted analytic framework helped document FOI and assess the programme's readiness for impact assessments and subsequent scale‐up. The programme needs product quality improvements, incentivised distribution and stronger promotion to effectively deliver and improve the realisation of its potential as an anaemia prevention strategy.Double fortified salt (DFS) has proven efficacy in addressing iron deficiency and anaemia, thus improving maternal and child nutrition outcomes. However, DFS delivery in large‐scale settings is less understood, with limited documentation of its fidelity of implementation (FOI). We assessed the FOI of the DFS intervention in Uttar Pradesh, India, to improve the design and implementation of such programmes that aim to reduce the anaemia burden, especially in women of reproductive age (WRA). We conducted in‐depth interviews with DFS programme staff ( The double fortified salt (DFS) production, distribution and awareness creation process documented low fidelity, which influenced the perceptions of end‐users and ultimately their utilisation of the programme.The presence of ‘believers’ who used DFS, despite the organoleptic issues reported, suggests the potential for further expansion of coverage and utilisation.Several challenges identified in this programme can be resolved through improvements in product quality, including effective colour masking and encapsulation of the iron‐premix in DFS, and by improving motivation levels of the frontline programme staff.1Anaemia is a widespread public health problem, affecting 1.93 billion people globally and commonly caused by iron deficiency  campaign  of DFS programmes, that is, whether programmes are implemented as intended. Conducting impact evaluations on programmes which have not been effectively implemented   in collaboration with the DFS programme team Figure . DevelopBriefly, the PIP Figure  posited 2.1We selected five programme districts for the midline evaluation Figure , using an = 10), ASHAs (n = 10), end‐users (n = 23) and DCs (n = 5).Finally, we chose one household per village for the end‐user interviews, where the eligible respondent was a WRA (between 18 and 49 years), with a child between 6 and 59 months (same eligibility criteria as the midline survey). We considered code saturation ; (2) Price, examined through the procurement and distribution of the intervention through the PDS; and (3) Promotion, operationalised through awareness creation strategies. Under Domain 2, we used interview data with DCs, FPS owners and ASHAs to examine how their motivations, knowledge and skills, and levels of self‐efficacy influenced intervention delivery, thereby affecting the FOI. For Domain 3, we used emerging themes from interviews with DCs and FPS owners to understand the implementation environment that affected DFS distribution. Finally, Domain 4 focused on the target of implementation, that is, end‐users who were individuals (nested in households and communities), and how they perceived the programme. Specifically, we examined how community norms, household socio‐demographic characteristics and individual perceptions influenced end‐user interactions with the DFS programme. Here, Domain 1 (objects of implementation) affected end‐user experience, Domain 2 (programme staff) influenced their DFS awareness levels and Domain 3 (programme context) affected their motivations for DFS purchase.As a guide for our analysis, we relied on the Implementation Science in Nutrition (ISN) framework  and those with more than four members received two. The government procured and transported PDS rations and DFS to a decentralised network of warehouses. Each programme district had warehouses present in different blocks , and FPS owners purchased their monthly ration and DFS quota from their respective block‐level warehouses. DFS quantity allocation for every FPS was predetermined by the government, and FPS owners were required to deposit the full payment in advance.black” premix particles in DFS, FPS owners found it challenging to sell the product and recover costs. While procurement and transportation costs incurred for high‐demand items like grains and kerosene were easily recovered, DFS sales were neither incentivised nor were expenses reimbursed , ‘thrifters’ (n = 10) or ‘naysayers’ (n = 9). They had similar socio‐demographic characteristics .BQ1“Everyone in our neighborhood is eating that salt. We think that the government is doing it for our benefit …” — ‘Believers’ considered DFS as beneficial to health and adopted mitigation strategies to overcome any adverse organoleptic experiences. ‘Believers’ were mostly convinced that the DFS programme was introduced by the government for their benefit good salt” (BQ 4) and had a positive attitude towards DFS after hearing about its benefits from either the local doctor/ASHA or the FPS owner in their village. While DFS bundling was a reason some of them purchased DFS (BQ 2), all ‘believers’ used DFS in most, if not all, their food preparation. During cooking, they found that DFS caused food to turn “slight[ly] dark in color” (BQ 5) but continued its use because of its benefits (BQ 3). Some ‘believer’ households adopted strategies to mitigate discolouration, for example “add more turmeric” (BQ 12) or sprinkling DFS only on top of prepared food instead of adding it while cooking. One ‘believer’ found that the taste altered “after the dish has cooled down” (BQ 7), but none of the believers perceived any side‐effects from DFS use (BQ 8).They considered DFS to be a “3.4.2.TQ2“We have to use it since we are getting it. We hope that it is for our benefit …” – ‘Thrifters’ purchased DFS only because it was bundled with other commodities and used DFS when they could not afford to purchase other salt in the retail market (TQ 3), most had no awareness about the “tiny black crystals” and considered these to be limestone particles or “Nirma” , based on its texture (TQ 4). ‘Thrifters’ predominantly preferred and used kada namak , a cheap locally available unrefined salt, for all their food and beverage requirements. Crystal salt had to be washed and crushed before use; some families mixed in DFS with the crushed salt, while others simply stored DFS as a back‐up option to be used when they ran out of readily crushed crystal salt (TQ 9). A few of them used “the filter to get the pure salt” from DFS packets by separating the premix particles (TQ 12) and some others kneaded dough with DFS, to make rotis (flatbread) or pooris (deep‐fried flatbread), where discolouration was minimal. They avoided using DFS while cooking vegetables or lentils as the discolouration was more prominent, especially when the food was not consumed immediately after cooking. They hesitated serving discoloured food to guests and relatives, saying “it doesn't even feel good” (TQ 6), and family members were ashamed to open their packed tiffin (lunch) in front of others due to the dark colour. One user also noted that DFS watered down the food and “everything became tasteless” (TQ 7). Some family members in ‘thrifter’ households experienced “itching” after DFS use (TQ 8) and therefore refrained from DFS use. However, many recognised that these rashes may not be caused by DFS use but could be due to external factors .While some knew of DFS' iron content 3.4.3Tata salt” which was available in the retail market : 1).“If something like the Tata salt [retail brand] comes, then everyone will eat it. We don't know what is being mixed in DFS.” –NQ1‘Naysayers’ had apprehensions about DFS and mostly used “the program should take such a step so that the food will look good” (NQ 12). Most of them had no awareness about DFS contents  and considered it to be mixed with impurities, such as “pebbles”. A few ‘naysayers’ also found that cooking with DFS made their food bland as it led to “melting” of vegetables (NQ 7). Some thought that DFS made their food bitter, but one participant mentioned that this could be a perception that “settles in the mind” after seeing the food discolouration (NQ 7). Another participant mentioned that DFS makes her family “unhealthy” (NQ 10) and two ‘naysayer’ households reported that everyone in their family, “even our little child”, suffered from rashes . Unlike ‘thrifter’ households, ‘naysayers’ believed that it was DFS use that caused rashes and completely discontinued its use. In spite of non‐use, these participants had to continue purchasing DFS due to PDS bundling (NQ 3). As DFS stock accumulated in their homes, they mixed DFS in cattle feed (NQ 11) and/or scattered it in agricultural fields to address worm infestations.After initially trying DFS, ‘naysayers’ found the food discolouration unacceptable, saying “4Uttar Pradesh is one of the most densely populated and impoverished Indian states which, despite several efforts, has made little or no progress in its nutritional status in the last decade. The level of anaemia in the state is 53% in nonpregnant WRA and 63% in preschool aged children  required higher investments in the interim. With the UP DFS programme documenting a low FOI, subsequent impact assessments were conducted only in a subsample of intervention districts where the potential for impact was higher, based on DFS utilisation estimates from the midline quantitative survey, carefully desiged to maintain objectivity and evaluation rigour. As new fortification programmes are implemented in multiple contexts, our approach of using the ISN framework to document programme fidelity can be replicated or modified to evaluate implementation outcomes and support rigorous programme design to acheive sustainable impact. We hope that this use of implementation research to course correct programmes can maximise their potential in addressing the anaemia burden in women and children, and ensure that DFS programmes are worth their salt.Authors have no conflicts to declare. This study was funded by the Bill & Melinda Gates Foundation and the project was implemented by The India Nutrition Initiative (TINI). Co‐authors employed by the Bill & Melinda Gates Foundation and TINI provided feedback on drafts of this manuscript. However, TINI and the funders of the study had no role in study design, data collection, data analysis, or the decision to submit the paper for publication.Bill and Melinda Gates Foundation.SC, KK, LMN and MNNM designed the study; SC, KK and MNNM were involved in the acquisition of the data; SC, AWG, RM and MNNM contributed to the analysis and interpretation of the data; SC wrote the paper, all authors provided critical feedback on the draft, all authors approved the final version submitted for publication.Figure S1. The Uttar Pradesh DFS Program Impact PathwayClick here for additional data file.


Triticum aestivum L.) is one of the most widely consumed cereal crops, but its complex genome makes it difficult to investigate the genetic effect on important agronomic traits. Genome-wide association (GWA) analysis is a useful method to identify genetic loci controlling complex phenotypic traits. With the RNA-sequencing based gene expression analysis, putative candidate genes governing important agronomic trait can be suggested and also molecular markers can be developed.Bread wheat , days to heading (DTH), days to maturity (DTM), stem length (SL), spike length (SPL), awn length (AL), liter weight (LW), thousand kernel weight (TKW), and the number of seeds per spike (SPS), of 287 wheat accessions from diverse country origins. A significant correlation was observed between the observed traits, and the wheat genotypes were divided into three subpopulations according to the population structure analysis. The best linear unbiased prediction (BLUP) values of the genotypic effect for each trait under different environments were predicted, and these were used for GWA analysis based on a mixed linear model (MLM). A total of 254 highly significant marker-trait associations (MTAs) were identified, and 28 candidate genes closely located to the significant markers were predicted by searching the wheat reference genome and RNAseq data. Further, it was shown that the phenotypic traits were significantly affected by the accumulation of favorable or unfavorable alleles.From this study, newly identified MTA and putative agronomically useful genes will help to study molecular mechanism of each phenotypic trait. Further, the agronomically favorable alleles found in this study can be used to develop wheats with superior agronomic traits.The online version contains supplementary material available at 10.1186/s12870-021-03180-6. Triticum aestivum L.) is one of the most widely consumed cereal crops worldwide, providing approximately 30% of dietary energy to humans , where VG is the genotypic variance, VE is the environment variance. Verror is the residual error variance, and y is the number of environments. Analysis of variance (ANOVA) was conducted by including the genotypes, environment, and genotype × environment interactions as random factors. Pearson pairwise correlation was calculated for all BLUP traits using the “cor” function and the “PerformanceAnalytics” package [All phenotypic data analyses were conducted using R version 4.4.0 (R Core Team). The mean, standard deviation, median, and coefficient of variation (CV) of quantitative data in each environment were calculated. For each accession, BLUP (best linear unbiased prediction) values across all environments were calculated using the “lme4” package  in R and package  in R.https://wheat.pw.usda.gov/GenotypingLabs/fargo) for use in the Illumina iSelect 90 K SNP Assay. SNP allele clustering and genotype calling were performed with the GenomeStudio Module Polyploid Genotyping 2.0 software (https://support.illumina.com/downloads/genomestudio-2-0.html). Markers with minor allele frequencies < 0.05 and missing data > 10% were removed, giving a total of 30,218 high-quality SNPs for use in the population structure analyses and genome-wide association (GWA) analysis.For the genotyping assay, leaves were sampled from the wheats before the booting stage in the Deokso field and stored at − 80 °C until use. DNA was extracted from a single plant of each germplasm, according to the USDA instructor’s manual using the CTAB method . DNA wasThe model-based Bayesian cluster analysis program STRUCTURE v2.3.4  was usedr2 and syntenic r2, respectively). Syntenic r2 was plotted against physical distance on chromosomes and a smooth line was drawn using the “ggplot2” package [r2 estimates were square root transformed and then beyond the parametric 95th percentile of that distribution was likely to be caused by genetic linkage. The intersection of the smooth line to syntenic r2 with this baseline was considered as the estimate of the extent of LD in the chromosome.The linkage disequilibrium (LD) between the pairs of 30,218 SNP marker was estimated using the TASSEL v5.2.57 with a sliding window size cut off 100. The LD decay was analyzed as per physical distance according to . Briefly package  in R. UnP-value of 0.001 (−log10(P) = 3) was used to declare significant SNPs for GWAS results and the SNPs having P-value less than 0.001 were selected. Furthermore, the entire analysis was conducted in each different environment to validate the consistent performance of the marker-trait associations. Quantile-quantile plots were drawn based on the observed and expected log10(P) values.The BLUP of each accession was used to fit a mixed linear model (MLM) by applying the residual maximum likelihood (REML) algorithm, which was calculated to analyze the phenotypic data and estimate the mean of each individual over different environments. An association test was performed using the GAPIT package v3  in R, anhttp://plants.ensembl.org/index.html), and the annotation of each gene followed the International Wheat Genome Sequence Consortium (IWGSC) Wheat RefSeq v1.0. Furthermore, the gene expression of the retrieved genes was observed using public RNA-seq data. For WSR-related genes, the RNAseq of 2-week cold-treatment wheat seedlings was available at the Wheat Expression Browser (http://www.wheat-expression.com/) and those genes with a more than 2-fold increase were retrieved. For the genes concerning the rest of the agronomic traits, except for LW and TKW, tissue-specific gene expression was observed at the Wheat eFP Browser (http://bar.utoronto.ca/efp_wheat/cgi-bin/efpWeb.cgi) and the genes with TPM (Transcripts Per Million) < 5 were removed. The heatmap of gene expression was created using the Heatmapper (http://www.heatmapper.ca/).To identify genes related to each agronomic trait, the high-confidence annotated genes located ±250 kb proximal to each identified MTA (marker-trait association) were retrieved from Ensembl Plants   and cDNA was synthesized using the PrimeScript™ 1st strand cDNA synthesis kit  according to the manufacturers’ manuals, respectively. Each gene specific PCR primers was designed using the NCBI Primer-BLAST (e) Table . Real-tiescribed . StatistAdditional file 1 : Table S1. The origin of the wheat accessions used in this study and their population structure and principal component.Additional file 2 : Fig. S1. Frequency distribution of the observed phenotypic data in 287 wheat genotypes. (a) survival; winter survival rate (0: 0-10% to 9: 90-100%), (b) DTH; days to heading, (c) DTM; days to maturity, (d) SL; spike length (cm), (e) SPL; spike length (cm), (f) AL; awn length (cm), (g) LW: liter weight (g), (h) TKW; thousand-kernel weight (g), (i) SPS: Number of seeds per spike. E1: Deokso in 2018-2019 season, E2: Deokso in 2019-2020 season, E3: Jeonju in 2018-2019 season, E4: Jinju in 2018-2019 season.Additional file 3 : Table S2. Analysis of variance (ANOVA) of the nine agronomic traits in wheat genotypes (**p < 0.0001).Additional file 4 : Table S3. The information of SNP markers used to analyze marker-trait associations.Additional file 5 : Fig. S2. Q-Q plot and minor allele frequency (MAF) plot of SNPs associated with nine agronomic traits. In the Q-Q plot, X-axis and Y-axis represent cumulative P-values and observed P-values on a -log10 scale, respectively. In the MAF plot, each SNP is shown as circles and X-axis and Y-axis represent the MAF and the significance of marker-trait association, respectively. (a) winter survival rate, (b) days to heading, (c) days to maturity, (d) stem length, (e) spike length, (f) awn length, (g) liter weight, (h) thousand-kernel weight, (I) the number of seeds per spike.Additional file 6 : Fig. S3.In silico gene expression of the putative agronomic traits-related genes (a) under cold temperature and (b) in diverse plant tissues. DTH; days to heading, DTM; days to maturity, SL; stem length, SPL; spike length, AL; awn length, SPS; number of seeds per spike.Additional file 7 : Table S4. The significant marker-trait associations  for nine agronomic traits. he duplicated markers for each trait in different environments are marked in red.Additional file 8 : Table S5. The information of real-time PCR primers used in this study.Additional file 9 : Fig. S4 Scatter plot of the linkage disequilibrium (LD) decay with the critical r2 value and the physical chromosome distance (Mbp) for the whole genome. The red dashed line shows the critical r2 value i.e., 0.34. The pink, green, blue, and red lines indicate LD decay of A, B, D, and all chromosomes, respectively. The LD was estimated as pairwise squared correlations of allele frequencies (r2) in sliding windows of 100 loci.Additional file 10 : Fig. S5. Relative gene expression of winter survival rate-related candidate genes under cold treatment. Error bars indicate the standard error of the mean (n = 3). Asterisks indicate significant difference between the expression level of control and cold-treated plants .Additional file 11 : Fig. S6. Relative gene expression of candidate genes associated with (a) days to heading, (b) days to maturity, (c) stem length, (d) spike length, (e) awn length, and (f) number of seeds per spike. Error bars indicate the standard error of the mean (n = 3). Different letters above each bar represent statistically significant differences (P < 0.05) after an analysis of variance (ANOVA) and a Tukey post hoc test.