It is unclear how cardiac autonomic function, as indicated by heart rate (HR), heart rate variability (HRV), HR increase during exercise, and HR recovery after exercise, is related to blood pressure (BP). We aimed to examine the observational and genetic evidence for a potential causal effect of these HR(V) traits on BP.We performed multivariable adjusted linear regression using Lifelines and UK Biobank cohorts to investigate the relationship between HR(V) traits and BP. Linkage disequilibrium score regression was conducted to examine genetic correlations. We used two-sample Mendelian randomization (2SMR) to examine potential causal relations between HR(V) traits and BP.Observational analyses showed negative associations of all HR(V) traits with BP, except for HR, which was positively associated. Genetic correlations were directionally consistent with the observational associations, but most significant genetic correlations between HR(V) traits and BP were limited to diastolic blood pressure (DBP). 2SMR analyses suggested a potentially causal relationship between HR(V) traits and DBP but not systolic blood pressure (SBP). No reverse effect of BP on HR(V) traits was found. One standard deviation (SD) unit increase in HR was associated with a 1.82 mmHg elevation of DBP. In contrast, one ln(ms) unit increase of the root mean square of the successive differences (RMSSD) and corrected RMSSD (RMSSDc), decreased DBP by 1.79 and 1.83 mmHg, respectively. For HR increase and HR recovery at 50 s, every additional SD increase was associated with a lower DBP by 2.05 and 1.47 mmHg, respectively. Results of secondary analyses with pulse pressure as outcome were inconsistent between observational and 2SMR analyses, as well as between HR(V) traits, and therefore inconclusive.Both observational and genetic evidence show strong associations between indices of cardiac autonomic function and DBP, suggesting that a larger relative contribution of the sympathetic versus the parasympathetic nervous system to cardiac function may cause elevated DBP. Hypertension, characterized by chronically elevated blood pressure (BP), is a main risk factor for serious and disabling diseases and is responsible for an estimated 10 million deaths every year . Non-invThe association between increased HR and elevated BP has long been observed , 4. The In addition to phenotypic evidence from observational studies, there is some genetic evidence for a relation between indicators of cardiac autonomic function and BP. There is a significant negative genetic association between HRV and BP, as was shown in a large community-based family study , which iThus, many studies demonstrated links between markers of cardiac autonomic function and BP on the phenotypic and genetic level. However, it is uncertain whether cardiac autonomic function, as indicated by HRV and HR measures, is causally related to BP. In the current study, we aimed to strengthen the evidence for a potential causal effect of these HR(V) traits on BP using two-sample Mendelian randomization (2SMR), which utilizes genetic variants to strengthen causal inference and minimize bias in observational studies . In addiThis study consisted of three main parts, including (1) traditional observational analyses for which both data from the Lifelines Cohort Study and the UK Biobank (UKB) were used to investigate the phenotypic associations between HR(V) traits and BP; (2) linkage disequilibrium score regression (LDSR) analyses for which the latest publicly available genome-wide association study (GWAS) summary statistics for HR(V) phenotypes and BP were used to explore the genetic correlations; (3) 2SMR analyses for which the same GWAS summary data were used to determine the potential causal relationship between exposure and outcome. Overview of the study design can be seen in Detailed description of Lifelines and UKB is provided in n = 79,217, baseline visit) that underwent a cardio assessment using a stationary bicycle. From these ECGs, resting HR and HRV (15-second ECG), heart rate increase during exercise (6-min ECG) and heart rate recovery post exercise (1-min ECG) were calculated. In Lifelines, HR and HRV were obtained using the same calculation methods from the 10-second 12-lead resting ECG (Welch Allyn CardioPerfect software) results of ∼157k participants  electrocardiograms  at rest, during exercise and recovery post exercise were recorded from a sub-group of participants (icipants . In our Model 1: BP∼HR(V) traitModel 2: BP∼Model 1 + Age + Sex + EthnicityModel 3: BP∼Model 2 + CVD + T2DEach model was applied for each individual HR(V) trait, and for systolic BP (SBP) and DBP separately. A directed acyclic graph was used to inform covariate selection (Descriptive statistics and transformation of variable units are detailed in the election . In addiLDSC software version 1.0.1 (https://github.com/bulik/ldsc) (ik/ldsc) . More deMR uses genetic variants as instrumental variables to strengthen causal inference from observational studies. Due to the fixed nature of genetic variants and Mendel's inheritance laws, estimates from MR are thought to be less sensitive to reverse causation and confounding . There ap < 5 × 10−8) associated with exposure [HR(V) traits] and outcome (BP) were selected from the most recent, comprehensive GWASs. Details of the GWAS datasets used in 2SMR are listed in Genome-wide significant SNPs  meta-analysis of individual SNP-specific Wald estimates. To assess violation of the exclusion restriction assumption due to horizontal pleiotropy, we performed diagnostic tests and adopted four complementary MR methods, namely MR Egger, weighted median, simple mode and weighted mode, that are pleiotropy-robust to varying degrees. Detailed description of the above methods is provided in P-value significance threshold of <0.005  was determined to be statistically significant for all analyses .The MR study followed the STROBE-MR checklist of recommended items to address in reports of Mendelian randomization studies . To accoIn the UK Biobank, we also performed multivariable linear regression models to obtain observational estimates of the relationships between pulse rate and BP. Considering the impact of beta-blockers on HR, we excluded beta-blocker users and re-estimated the observational relationships between HR and BP. Besides the primary HR(V) traits, analyses were also performed for SDNN, corrected SDNN (SDNNc), and HR recovery 10–40 s. We also utilized SNPs from Ramírez et al.  as instrTwoSampleMR (version 0.5.6) R package . All 2SMR analyses were conducted by using the  package .Descriptives of Lifelines and UKB participants are shown in rg range 0.19–0.99). HR showed significant positive genetic correlations with SBP (rg = 0.08) and DBP (rg = 0.24) (rg = −0.13). DBP had a low to moderate significantly negative correlation with all traits except SDNNc. The strongest negative correlation of DBP was with RMSSD (rg = −0.23).Between HR(V) traits, we found modest to near-perfect genetic correlations ( = 0.24) . The othP-value did not survive our multiple testing threshold (P > 0.005). There were robust effects of each HR(V) trait on DBP. In several analyses, the MR Egger estimate was non-significant. However, we were reassured by the comparable effect magnitude from MR Egger, as well as the general absence of evidence for directional pleiotropy  phenotypes are provided in P = 2.17e-28). In contrast, per unit of ln(ms) increase of lnRMSSD and lnRMSSDc, DBP decreased by 1.79 mmHg  and 1.83 mmHg , respectively. For HR increase during exercise and HR recovery after exercise (50s), every SD increase was associated with a lower DBP of 2.05 mmHg  and 1.47 mmHg , respectively,  leads to less favourable outcomes . We struggle to interpret this physiologically, but one possible explanation is that PP is calculated as a linear combination of SBP and DBP . In 2SMR, we found evidence for a lowering effect of HRV on DBP but a null-effect on SBP, which could explain the association between higher HRV and PP. However, not all cardiac autonomic function indicators were associated with PP in 2SMR, and observational results in UKB and Lifelines did not converge with the 2SMR results. We therefore remain cautious in inferring causality between cardiac autonomic function and PP.As one of the main mechanisms involved in the regulation of the cardiovascular system, cardiac autonomic function plays an important role in the development and progression of cardiovascular diseases such as hypertension, arrhythmia, and sudden cardiac arrest , 30. DueOur study suggests that indicators of cardiac autonomic function are related to DBP but not SBP. There was no reverse effect of BP on HR(V) traits, suggesting unidirectionality. MR suggests that resting HR has a positive relationship with DBP, suggesting that a relatively higher activation of the cardiac sympathetic system in the resting state may lead to an increase in DBP. HRV and HR response to exercise were shown to have a negative relationship with DBP. Taken together, a relative shift towards a less favorable sympathovagal balance  regulating cardiac rhythm, both in rest and in response to exercise, may cause higher DBP. There may be two explanations why a larger relative contribution to the HR of the SNS versus the PNS causes elevated DBP but not elevated SBP. First, sympathetic activation leads to higher systemic vascular resistance and, therefore, higher mean arterial pressure (MAP), which is dominated by DBP (MAP ≈ 2/3DBP + 1/3SBP). A second explanation is that SBP is influenced, more than DBP, by short term variations in HRV dampening fluctuations in BP through the baroreceptor reflex, which reduces the long term  correlation .Our study has several implications. It provides evidence of a potential causal effect of cardiac autonomic function on DBP, warranting future etiological investigation on the underlying biological mechanisms. Our study was based on data from the relatively healthy, general population. Therefore, translation to clinical practice is uncertain. An objective for future research could be to investigate the role of autonomic function in hypertensive patients with a predominantly elevated DBP; autonomic function is possibly a suitable treatment target, although our effect estimates predict a modest treatment effect. With regards to prevention, clinicians may consider monitoring indices of cardiac autonomic function to identify those at higher risk of developing hypertension.Our study has several strengths. We used two large samples of different populations for the traditional observational analyses, and provide meta-analytic results. Similarly, GWAS studies used in our study also have (very) large sample sizes, allowing for the identification of multiple strong genetic instruments and precise estimation of their effects. Furthermore, we employed different approaches  and different datasets to address distinct sources of bias. In addition we used a wide range of cardiac autonomic indices. Several limitations of our study should be noted. Firstly, in the main analysis of the 2SMR, we used Evangelou's BP GWAS summary statistics, which included data from UKB and the ICBP consortium . The parBoth observational and genetic analyses show robust associations between indices of cardiac autonomic function and DBP, suggesting that a larger relative contribution of the SNS versus the PNS to heart rate may cause higher DBP but not higher SBP.


KIT, KITLG, NBEA, RELL1, AHCY, and EDNRA), while we also discovered novel genes, such as STIM1, NRXN1, LEP, that may be associated with agronomic traits like poll and big ear morphology. Our study found a set of new genetic markers for genetic improvement in goats and provided novel insights into the genetic mechanisms of complex traits.Goats with diverse economic phenotypic traits play an important role in animal husbandry. However, the genetic mechanisms underlying complex phenotypic traits are unclear in goats. Genomic studies of variations provided a lens to identify functional genes. In this study, we focused on the worldwide goat breeds with outstanding traits and used whole-genome resequencing data in 361 samples from 68 breeds to detect genomic selection sweep regions. We identified 210–531 genomic regions with six phenotypic traits, respectively. Further gene annotation analysis revealed 332, 203, 164, 300, 205, and 145 candidate genes corresponding with dairy, wool, high prolificacy, poll, big ear, and white coat color traits. Some of these genes have been reported previously (e.g.,  As one of the earliest domesticated animals, goats profoundly affect human society . Then, hRecent studies by integrating whole genome datasets and ancient DNA information have identified candidate genome regions and genes during goat domestication . The avaMarkDuplicates module with Picard v2.18.12 (http://broadinstitute.github.io/picard/) and detected short variations using the GATK v4.2.3.0 HaplotypeCaller module  biallelic SNPs, (ii) autosome SNPs, (iii) minor allele frequency (MAF) > 0.05, (iv) call rate >90%. The above analyses were performed by VCFtools v0.1.14  (θπ-Control/θπ-Case) of pairwise populations with contrasting phenotypes to detect changes in genetic diversity due to artificial selection -dependent neurexin/neuroligin complexes (versus small ear goats, 205 genes within 188 genomic regions were identified to be associated with the big ear trait (versus black coat color pair population (AHCY, and EDNRA were identified to be involved in the regulation of coat color (GPR22, SOX5, CLEC12B, AIM1, ITFG1, and LDLRAD4 (We obtained 28.56 million SNPs after filtering in 361 samples. We implemented selection screening in 6 pairs of populations . 515, 30al genes . 332 genal genes . LPL wasin goats . Additio in cows . Similar buffalo . We furt buffalo . The GO  buffalo . In the echanism . One of  327 kDa . Althoug 327 kDa . We dete 327 kDa , and the 327 kDa . For the regions . RELL1, lificacy . RELL1 w in pigs , the sinnd sheep . Furthernd sheep . The cycnd sheep , and mednd sheep . 300 cannd sheep . The higchannels  and NRXNomplexes  (Supplemomplexes . They boomplexes . Furtheromplexes . Enamel omplexes , which mar trait . In addiar trait , potentipulation . The twoat color  (Supplemat color . We also LDLRAD4 .In summary, we collected diverse goats’ germplasm resources and explored functional genes of important phenotypical traits. Our study confirmed previous results and also identified some novel genes involved in the regulation of specific traits. Our study provides deep insights into the genetic mechanisms of complex traits and genetic markers for genetic improvement in goats.


PDGFD) is a candidate gene that has the potential to affect fat deposition and body size in sheep. In this study, we discovered two intronic InDels  with the potential to significantly affect the morphological traits of three different indigenous Chinese sheep breeds. These InDels can be used as DNA markers for sheep marker-assisted selection (MAS) breeding.Among the most important factors in the production of livestock are morphological traits, such as body weight, body length, chest depth, chest width, cannon circumference, and the body index. Platelet-derived growth factor D (PDGFD) is a member of the PDGF gene family, and it plays an important role in the regulation of adipocyte development in mammals. Furthermore, genome-wide association studies (GWAS) have previously identified it as a candidate gene associated with fleece fiber variation, body size, and the fat-tail phenotype in domestic Chinese sheep. In this study, a total of 1919 indigenous Chinese sheep were genotyped to examine the association between nucleotide sequence variations in PDGFD and body morphology. Our results detected both a 14 bp insertion in intron 2 and a 13 bp deletion in intron 4 of PDGFD. Moreover, these two InDel loci had low to moderate polymorphism. Notably, the 13 bp deletion mutation of PDGFD was found to significantly affect sheep body size. Yearling rams in the Luxi black-headed sheep (LXBH) containing a heterozygous genotype  were found to have larger body length, chest depth, and body weight than those with wild genotypes. Furthermore, adult ewes in the Guiqian semi-fine wool sheep (GSFW) containing a homozygous mutation  were found to have smaller chest width than their peers. Moreover, yearling ewes in this group with the same homozygous mutation were found to have lower body weight, chest width, and cannon circumference compared to those of other individuals. This study demonstrates that PDGFD InDel polymorphisms have the potential to be effective molecular markers to improve morphological traits in domestic Chinese sheep.Platelet-derived growth factor D ( China has many extensive and genetically diverse types of sheep that are well adapted to local agricultural and climate conditions, being resistant to common diseases . LanzhouPDGFD), bone morphogenetic protein 2 (BMP2), thyroid-stimulating hormone receptor (TSHR), xylulokinase (XYLB), and fibroblast growth factor 7 (FGF7) [PDGFD gene was discovered to belong to the PDGF family, which encodes for a mitogenic factor used by mesenchymal cells and is located in the blood serum [PDGFD contains a tissue-specific expression pattern. It is expressed more abundantly in the pancreas, pituitary gland, ovaries, and adipose tissue than in other tissues in humans [PDGFD is involved in various intracellular signaling pathways,  and regulates IGF1R, VEGF, and Snail, among other proteins. By affecting these pathways, PDGFD has the potential to regulate multiple-organ fibrosis, atherosclerosis, tissue repair, and cancer in humans, as well as fleece fiber diameter in domestic sheep [Previous selective sweep analyses and genome-wide association studies (GWAS) identified several key candidate genes functionally associated with growth, body size, and tail morphology in sheep, such as platelet derived growth factor D (7 (FGF7) ,10. In rod serum . Moreoven humans . Studiesic sheep ,14,15,16PDGFD is highly conserved amongst different species [PDGFD and DYNC2H1 (dynein cytoplasmic 2 heavy chain 1) was identified by multiple de novo assemblies, which may have biological significance [PDGFD was also shown to affect intermuscular fat deposition in Dianzhong cattle [PDGFD plays a critical role with regard to adipocyte proliferation and differentiation, and it is directly and indirectly involved in fat metabolism as it relates to the tails of sheep [PDGFD gene that have been shown to significantly affect the size of sheep tails [PDGFD is closely related to sheep growth and development. However, it is notable that the functional role of PDGFD polymorphisms in relation to morphological traits of sheep is still poorly understood. Therefore, we aimed to characterize InDel variations of PDGFD in three different Chinese sheep breeds to validate the association between InDel polymorphisms and morphological traits, as well as provide fundamental reference data for use of PDGFD in domestic sheep breeding.Furthermore,  species . In a reificance . PDGFD wg cattle . Furtherof sheep . There aep tails . These s2O after determination of concentration [PDGFD gene, 30 DNA samples from each sheep breed were chosen at random and placed into three different DNA pools [A total of 1919 sheep of three breeds were chosen for this study: Guiqian semi-fine wool sheep , Luxi black-headed sheep , and Lanzhou fat-tailed sheep . Of the 618 LXBH sheep, 37.2% n = 230) were lambs (≤3 months), 44.3% n = 274) were yearlings (4–18 months old), and 18.5% (n = 114) were adults (>18 months). Among the GSFW group, 477 animals already had body size records . Within each sheep breed, healthy sheep individuals raised under similar feeding and management conditions were selected as tested sheep in this study. Morphological characteristics, i.e., body weight, body height, body length, chest circumference, chest depth, chest width, and cannon circumference, were recorded for each sheep in the study ,22. On t0 were lantration . To studNA pools ,27,28.4 were yePDGFD gene. NCBI Primer-Blast was subsequently used to design six pairs of primers (PDGFD reference sequence for sheep (NC_056068.1). These primers were synthesized via Sangon Biotech . PCR was performed using a 13 μL reaction volume and Touchdown program described previously [The Ensembl database was used to find six possible InDel sites within sheep  primers  based oneviously ,26. PCR 2) was carried out. Differences in allele frequencies, as well as the genotypic distribution, for each sheep population were assessed using the χ2 test or Fisher’s exact test. If all expected counts were greater than 5, the Pearson chi-square test was used. If there was a certain expected count <5, the Fisher exact test was used. The SHEsis online platform  was used for linkage disequilibrium (LD) analysis. The association between different genotypes of InDels and morphological traits of each sheep breed was evaluated using the Student’s t-test (only two genotypes) or one-way ANOVA (three genotypes) via SPSS 25 software. The statistical model used was as follows: Yijk = µ + Gi + Sj + Ak + eijk, in which Yijk represents the phenotypic value for morphological traits, μ represents the population mean, Gi represents the fixed PDGFD genotype effect for each group of sheep, Sj represents the fixed effect of gender, Ak represents the fixed effect of the age, and eijk represents the random error.Genotype frequency, allele frequency, and population indices  were all calculated using Microsoft Excel (2019). Population indices were calculated following Nei’s methods . In ordePDGFD gene were detected: a 14 bp insertion (rs590816164) in intron 2, and a 13 bp deletion in intron 4 (rs1092650847)  . Genotyp2650847) , and thr2650847) . The P4-p < 0.05). No significant deviations from HWE were identified for the P5-del-13bp locus in all tested sheep breeds (p > 0.05).Genotypes, allele frequencies, and population genetics analyses are shown in p < 0.05) (2 = 0) population, and a weak linkage state in the GSFW  population.Chi-square test results pertaining to the allelic frequency distribution of both InDel mutation sites amongst all three sheep breeds showed that the allele and genotype frequencies of the P4-ins-14bp locus were significantly different amongst all three breeds ( < 0.05) . Further < 0.05) . Accordi < 0.05) , both P4p = 0.001142); thus, this site was not used for association analysis [p < 0.05), and individuals with wild genotype had a smaller overall body size than individuals with a heterozygous genotype. Furthermore, a homozygous mutation (DD) at the P5-del-13bp locus showed a strong, negative influence on body weight, chest width, and cannon circumference in the GSFW sheep (p < 0.05). The body weight, chest width, and cannon circumference of yearling ewes with DD genotypes were significantly smaller than those of other individuals (p < 0.05). The chest width of adult ewes with a homozygous mutation was significantly smaller compared to other individuals (p < 0.05) (The association between genotype and phenotypic traits (seven body size traits and six body size indices) was analyzed. Significant deviations from HWE were identified for the P4-ins-14bp locus  . Some trPDGF family includes proteins that are both proangiogenic and regulatory factors stimulating connective tissue growth [PDGF comprises four subtypes: PDGFA, PDGFB, PDGFC, and PDGFD [PDGFC is involved in adipose expansion [PDGFD mediates PDGF receptor chain tyrosine kinase (PDGFR-β) dimer formation and activation [PDGFD is associated with angiogenesis required for tissue development and growth due to the inclusion of PDGF receptors on the surface of adipocytes and pre-adipocytes [PDGFD has been shown to play a key role in the formation and development of pre-adipocytes, which can be seen in all species of mammals [The e growth . PDGF cond PDGFD . A priorxpansion . The protivation . PDGFD iipocytes . Further mammals .CREB1) regulates fat metabolism in sheep adipose tissue, with one mutation within its first intron region strongly associated with differences in body measurement traits [PDGFD, which is located on chromosome 15, has also been identified as a candidate gene for the phenotypic traits of sheep  [Adipose tissue is the main means of caloric storage in the body and plays a crucial role in the regulation of metabolism and body shape . The lart traits ,40. PDGFdy size) .KDM3B) not only affected the litter size of Australian white sheep, but was also significantly associated with the body size traits of Lanzhou fat-tailed sheep and Luxi black-headed sheep [PDGFD was associated with litter size for Australian white sheep [PDGFD is highly expressed in the reproductive system  [PDGFD may control multiple traits simultaneously .Related studies have shown that genes affecting animal reproductive traits may also affect animal morphological and growth-related traits. Recently, Kang et al. discovered that a 7 bp InDel variation in intron 8 of lysine demethylase 3B  . These rPDGFD was considered as a potential candidate gene for our study. In this study, we detected two InDel mutations, P4-ins-14bp and P5-del-13bp, both of which were found to be in Hardy–Weinberg equilibrium (p > 0.05) for Lanzhou fat-tailed sheep and Luxi black-headed sheep. These results indicate that no large-scale migration or mutations occurred in the tested population. It is worth noting that the P4-ins-14bp InDel existed in both Lanzhou fat-tailed sheep (2.59%) and Guiqian semi-fine wool sheep (8.31%) populations with relatively low frequency; however, Luxi black-headed sheep populations did not have this InDel. This may be due to different breeding requirements for those sheep varieties or the relatively conservative nucleotide sequence of PDGFD, as its genetic variation has been shown to be species-specific and vary on the basis of population size with regard to analysis and testing [With these studies in mind,  testing . The P5-PDGFD. Studies have shown that intronic mutations may function to regulate transcriptional activity by preventing or increasing transcription factor binding, thus influencing gene expression via splicing, which leads to phenotypic variation [HNF-1A) that caused abnormal mRNA splicing and impaired its activity as a transcription factor [BMPR1B) intron 1, which could control pig prolificacy via the cis-regulation of BMPR1B expression [PDGFD is a critical gene related to morphological traits in sheep.Furthermore, both P4-ins-14bp and P5-del-13bp loci were located in the intronic regions of ariation ,45,46,47n factor . Li et apression . Therefopression . In the PDGFD gene were detected in multiple breeds of sheep. Additionally, the 13 bp noncoding genetic variation had a significant effect on morphological traits  of Luxi black-headed sheep and Guiqian semi-fine wool sheep, indicating the possibility of using these InDel mutation sites as DNA markers to increase the growth and development of indigenous Chinese sheep. However, the concrete mechanisms via which PDGFD gene and the 13 bp deletion affect morphological traits are still indistinct.In this study, a 14 bp insertion in intron 2 and a 13 bp deletion in intron 4 within the


Processing quality is an important economic wheat trait. The marker-assisted selection (MAS) method plays a vital role in accelerating genetic improvement of processing quality. In the present study, processing quality in a panel of 165 cultivars grown in four environments was evaluated by mixograph. An association mapping analysis using 90 K and 660 K single nucleotide polymorphism (SNP) arrays identified 24 loci in chromosomes 1A, 1B (4), 1D, 2A, 2B (2), 3A, 3B, 3D (2), 4A (3), 4B, 5D (2), 6A, 7B (2) and 7D (2), explaining 10.2–42.5% of the phenotypic variances. Totally, 15 loci were stably detected in two or more environments. Nine loci coincided with known genes or QTL, whereas the other fifteen were novel loci. Seven candidate genes encoded 3-ketoacyl-CoA synthase, lipoxygenase, pyridoxal phosphate-dependent decarboxylase, sucrose synthase 3 and a plant lipid transfer protein/Par allergen. SNPs significantly associated with processing quality and accessions with more favorable alleles can be used for marker-assisted selection. Dough rheological properties with significant effects on end-use products can be evaluated by mixograph ,2. MidliDough rheological properties are quantitatively inherited and largely controlled by multiple minor genes ,10,11,12The results of those studies were mainly based on relatively low numbers of simple sequence repeat (SSR) or diversity array technology (DArT) markers ,14 and wTo avoid false positives resulting due to any type of familiarity found amidst members comprising studied collectives alongside similar concerns about establishing appropriate mathematical formulas proves useful. General Linear Model (GLM), which was initially introduced, only considers impacts stemming from social arrangements, henceforth causing some degree of inaccurate outcomes because GLM does not take enough measures aimed towards controlling consequences originating via interactions caused jointly by kin and societal constructs. In contrast, MLM has been developed more recently since this methodology incorporates Q (population structure)-based assessments too—K (kinship); therefore, it better manages situations arising out of overlapping ramifications exerted either way by those concepts contributing toward finalized conclusions drawn downstream. At current times, researchers have implemented applicable usage of all sorts of techniques encompassing crop quantity features regularly utilizing mixed linier regression versions (MLM) ,22,23.Association analysis is a quantitative genetic analysis method based on LD among allelic variants at the same locus, using natural populations as research materials. By investigating the association between group genotype data and phenotypic data, target trait genes can be discovered. The advantage of this approach includes, firstly, that it does not require constructing biparental populations like linkage analysis but uses existing natural populations, high-generation crosses, local varieties and wild species instead to significantly shorten study cycles and improve work efficiency . SecondlThe Yellow and Huai River Valleys Facultative Wheat Region (YHVFWR) is the largest wheat production region in China. Breeding cultivars with superior processing quality could be greatly enhanced using markers developed from single-nucleotide polymorphisms (SNPs). In this study, SNPs associated with processing quality in a panel of 165 elite wheat accessions mainly from the YHVFWR were used to (1) dissect the genetic architecture of mixograph-related traits, (2) identify SNPs significantly associated with mixograp-related traits and (3) search for candidate mixograph-related traits genes for further study.In the current investigation, a comprehensive collection consisting of 165 diversified varieties was utilized; specifically, they were comprised of 143 germplasms sourced from both the Yellow and Huai River Valley Facultative Wheat Region located across mainland China while additionally integrating another 22 samples originating from different nations, such as Italy (9); Argentina (7); Japan (4); Australia (1); along with one sample collected each from Turkish territory. These hexaploid wheats represented all types found worldwide based upon their geographical origin or agronomic performance. Furthermore, every single variety had already received permission before being stored into the national seed bank maintained by the Institute of Plant Industry affiliated with the Crop Science Society of China.The 165 accessions used in GWAS to identify the loci for quality traits assessed by mixograph were grown at Suixi in Anhui province and Anyang in Henan in the 2012–2013 and 2013–2014 cropping seasons. Field trials were conducted in randomized complete blocks design (RCBD). Agronomic management followed local practices. Each plot contained three 2 m rows spaced 20 cm apart, and there were 3 replications.Clean samples of at least 300 grains were tempered overnight to the 14%, 15% and 16% moisture contents normally used to mill soft, medium and hard types, respectively. All samples were milled at 60% flour extraction using a Brabender Quadrumat Junior Mill . Mixographs have several advantages, including small sample amounts , high daily processing samples, simple operation and rich curve information. They are increasingly widely used in research on the rheological properties of dough due to their excellent performance characteristics. Mixograph measurements were taken with 10 g of flour per sample on a 14% moisture basis using the National Manufacturing Mixograph , according to the AACC (2000) method 54-40A. BLUP across environments was analyzed using the PROCMIXED function in SAS v9.3.Cultivars were genotyped by the 90 K SNP and 660 K SNP arrays by CapitalBio . The SNP chip genotyping procedure involves six steps: (1) sample whole-genome amplification at approximately 1000 folds; (2) fragmentation treatment on the diluted PCR product; (3) hybridization between segmented DNAs and chips; (4) single base extension reactions at specific loci; (5) staining; and (6) scanning imaging. Due to common wheat’s hexaploid nature, we use combinations of GeneStudio v2011.1 and GeneStudio Polyploid Clustering V1.0 for genetic typing. First, raw image scans from gene expressions were read out through GeneStudio v2011.1; then, clustering based on ploidy level was performed via GeneStudio Polyploid Clustering V1.0. Filter criteria followed four standards: (1) eliminating markers without differences among parents; (2) homozygous alleles assumed missing data; (3) removing marker datasets where more than 10 percentage points of values have been removed; and (4) one particular variant site should meet either condition that either one or two types account for no greater than 0.7 or equal to or larger than 0.3, respectively.http://pritchardlab.stanford.edu/struc-ture.html) (accessed on 5 July 2022). For each K value ranging from 2 to 12, five independent runs were executed employing an admixture model. Each run consisted of 100,000 Markov Chain iterations that were recorded, preceded by 10,000 burn-in periods. To anticipate the actual count of subpopulations, an ad hoc quantity statistic denoted as ΔK, reliant on the rate of logarithmic probability alteration between consecutive K values, was employed [bh2) of mixograph-related traits was calculated as hb2 = σg2/(σg2 + σge2/r +σε2/re), where σge2, σε2 and σg2 mean the genotype × environment interaction, residual error variances and genotype, respectively. Of these, e and r were the number of environments and the number of replicates per environment, respectively.The Chinese Spring (IWGSC v1.0) reference genome was used for GWAS. Population structure, principal components analysis (PCA), NJ-tree and LD decay analysis were reported in a previous study . Populatemployed . Broad-sp-value) ≥ 3.0 were regarded as MTAs because Bonferroni–Holm correction was too conservative. Manhattan and Q-Q plots were drawn by the CMplot (R 3.6.5).The mixed linear model  was used to avoid the spurious marker–trait associations (MTAs) by Tassel v5.0 . Both thCandidate genes associated with loci consistently identified across two or more environments were pinpointed. The ensuing procedures were undertaken to ascertain candidate genes for noteworthy or steadfast quantitative trait loci (QTL). Firstly, a thorough search was conducted to retrieve all genes located within the linkage disequilibrium (LD) block vicinity surrounding the peak single-nucleotide polymorphism (SNP)  of each significant QTL from the IWGSC V1.0 dataset. Subsequently, all accessible SNPs located within these genes were scrutinized. Genes  harboring SNPs within coding regions, with the potential to induce missense mutations, were designated as candidate genes. Given the substantial regulation of processing quality traits by diverse phytohormones, as well as factors such as glycolysis, signal transduction and cell growth, genes participating in these pathways were classified as high-confidence candidate genes for processing quality traits. Flanking sequences of significantly associated SNPs  were used in BLASTx against the NCBI database and reference genome annotations from IWGSC v1.0 was used to predict candidate genes.After filtering, 259,922 SNPs were used in GWAS of mixograph-related traits [p <0.01) of genotypes, environments and genotype × environment interactions on each processing-related quality trait (h2) estimated for MPW, MPV, MPT and MTxW was 0.75, 0.72, 0.69 and 0.71, respectively. MPT and MPW showed significant (p < 0.01) and positive correlations with MTxW (r = 0.885 and 0.448), and the MPV showed a significant (p < 0.01) and positive correlation with MPW (r = 0.867).All mixograph-related traits showed continuous variation . The meaty trait . Broad-sTwenty-four loci associated with mixograph-related traits were detected. There were nine loci for MPT distributed across chromosomes 1A, 1B, 1D, 2B, 3A, 3D, 4A, 5D and 7D explaining 10.6–42.5% of the phenotypic variances. A single locus affecting MPV was located on chromosome 5D, explaining 13.4–16.8% of the phenotypic variance. Eight loci for MPW on chromosomes 1B (2), 2A, 3B, 4A, 4B, 6A and 7B explained 10.2–15.8% of the phenotypic variance. Twelve loci for MTxW were identified on chromosomes 1A, 1B (2), 1D, 2B, 3D (2), 4A (2), 7B and 7D (2), accounting for 10.5–27.3% of the phenotypic variances. The 1A (506.9 Mb), 1B (553.6 Mb), 1D (407.9–416.5 Mb), 3D (191.1 Mb) and 7D (321.0 Mb) loci showed pleiotropic effects on MPT and MTxW, whereas the 4A locus (610.1–621.6 Mb) controlled both MPW and MPT .TraesCS2B01G535700, TraesCS3A01G451100 and TraesCS3D01G444000) were identified on chromosomes 2B (731.5 Mb), 3A (689.5 Mb) and 3D (553.2 Mb), and another gene encoding lipoxygenase (TraesCS4A01G359800) was detected in the QTL (632.9 Mb) on chromosome 4A. Candidate genes for plant lipid transfer protein/Par allergen (TraesCS4A01G021300), pyridoxal phosphate-dependent decarboxylase (TraesCS1A01G329500) and sucrose synthase 3 (TraesCS3D01G184500) were identified on chromosomes 4A (14.8 Mb), 1A (518.5 Mb) and 3D (170.2 Mb), respectively  and the lengthy testing times involved, these devices prove difficult to apply when confronted with numerous samples during breeding generations. In contrast, the mixograph calls for less material and features shorter test times than the aforementioned equipment. Furthermore, it displays highly significant correlations with key parameters measured by means of the farinograph and rheometer, while simultaneously demonstrating a strong connection with actual kneading times during baking. Consequently, this tool is presently extensively implemented in both domestic and international wheat breeding initiatives ,14.In this study, most of the 165 accessions mainly from Shandong and foreign cultivars were classified into subgroup 1; subgroup 2 consisted of 54 accessions, mainly from Henan, Anhui and Shaanxi provinces, whereas subgroup 3 mainly included the accessions from Henan province. Thus, the MLM model with population structure and kinship matrix settings was performed in this study to avoid spurious results . The LD Qmlt.tamu.1d identified by Yu et al. [Qgcd.tamu.7B, which overlapped with the present locus for MPW on chromosome 7B (551.9–557.4 Mb). Zhou et al. [Dough strength constitutes a prototypical quantitative trait under the influence of multiple minor genes. Numerous QTLs associated with mixograph-related traits have been previously documented ,15,16,17u et al. , who alsu et al.  reportedTraesCS1A01G329500 on chromosome 1A encoding a pyridoxal phosphate-dependent decarboxylase involved in nitrogen metabolism affects the amount and composition of dough development and gluten matrix [TraesCS4A01G021300 on chromosome 4A in an LD decay with a nonspecific lipid transfer protein (LTP) gene promotes the inter-membrane transfer of lipids that plays an important role in dough mixing. Lipids not only influence the dough capacity, but also interact with glutenin and gliadin proteins [TraesCS3D01G184500 on chromosome 3D encodes sucrose phosphate synthase III (SPS III), which plays an important role in the sucrose metabolic pathway in plants [TraesCS4A01G359800, LOX 2) encoding lipoxygenase 2 that catalyzes bound fatty acids [TraesCS2B01G535700 on chromosome 2B (731.5 Mb), TraesCS3A01G451100 on chromosome 3A (689.5 Mb) and TraesCS3D01G444000 on chromosome 3D (553.2 Mb) encode 3-ketoyl COA synthases involved in the fatty acid biosynthesis pathway [Candidate gene n matrix . TraesCSproteins . TraesCSn plants ,37. SPS ty acids ,39. Prevty acids . TraesCS pathway . PreviouWhile conventional wheat breeding practices have contributed to the enhancement of processing quality, the efficacy of early-generation selection remains limited. Additive effects have been discerned between traits associated with processing quality and advantageous alleles recognized through genome sequence variations. The accumulation of multiple favorable alleles, such as those pinpointed in this investigation, holds the potential to enhance processing quality-linked traits. Loci with stable effects across environments should be more appliable in MAS, such as those on chromosomes1A (506.9 Mb), 1B (553.6 Mb), 1D (407.9–416.5 Mb) and 7D (321.0 Mb), showing pleiotropic effects on MPT and MTxW, along with the 4A (610.1–621.6 Mb) locus with pleiotropic effects on MPW and MPT. The loci that have been confirmed through conventional linkage mapping or GWAS in prior research could serve as subjects for subsequent investigations. Finally, accessions with superior processing quality-related traits and larger numbers of favorable alleles, such as Aca 601, Klein Jaba l1, Shanyou 225, Jishi 02-1, Sagittario, Klein Flecha, Wanmai 33, Sunong6, Libero Mantol, ProINTA Colibr 1 and Nidera Baguette 20, are recommended as parental lines for breeding cultivars with superior quality-related traits by MAS .A GWAS for mixograph-related traits was conducted on a panel of 165 varieties using the wheat 90K and 660K SNP arrays. In total, 15 of the 24 loci identified were novel. Seven candidate genes for mixograph-related traits were predicted. SNPs in genes associated with favorable mixograph traits can be converted to selection markers that can be used in the early generations of breeding.


Macropus giganteus) population, and quantified associations of behavior with early survival. Behavior of young was measured while still in the pouch and as subadults, and survival to weaning was monitored. We found consistent variation between offspring of different mothers in levels of activity at the pouch stage, in flight initiation distance (FID) as subadults, and in subadult survival, indicating similarity between siblings. There was no evidence of covariance between the measures of behavior at the pouch young versus subadult stages, nor of covariance of the early-life behavioral traits with subadult survival. However, there was a strong covariance between FIDs of mothers and those of their offspring tested at different times. Further, of the total repeatability of subadult FID (51.5%), more than half could be attributed to differences between offspring of different mothers. Our results indicate that 1) behavioral variation is apparent at a very early stage of development ; 2) between-mother differences can explain much of the repeatability  of juvenile behavior; and 3) mothers and offspring exhibit similar behavioral responses to stimuli. However, 4) we found no evidence of selection via covariance between early-life or maternal behavioral traits and juvenile survival in this wild marsupial.Individual behavior varies for many reasons, but how early in life are such differences apparent, and are they under selection? We investigated variation in early-life behavior in a wild eastern gray kangaroo , and between-mother differences could explain personality variation in juvenile behavior. In addition, mothers and offspring showed similar behavioral responses to stimuli. However, there was relationship between early-life or maternal behavioral traits and juvenile survival. Behavior varies both within and between individuals. Interest in consistent behavioral differences between individuals has generated the field of animal “personality,” and with it substantial research on the causes and consequences of individual behavioral variation . These dPan troglodytes; Crocuta crocuta; Halichoerus grypus; Tamiasciurus hudsonicus; P. troglodytes; Macropus giganteus; Microcebus murinus;Macaca mulatta), baboons (Papio sp.), vervet monkeys (Cercopithecus aethiops sabaeus) , maternal mass is correlated with offspring age at sexual maturity, and with dispersal of sons  Do maternal siblings behave similarly to each other, and differently from non-siblings, in early life?2) Is either offspring early-life behavior or maternal behavior associated with offspring survival to weaning?Further, focusing on the trait of FID in subadults, we asked:3) What is the contribution of variation between offspring of different mothers to the repeatability of offspring behavior ?4) Are maternal and offspring behaviors correlated, and do these associations differ for biological versus adoptive mothers?Vulpes vulpes) and in weather conditions . In this population, adult females usually reproduce annually, with a maximum of one offspring per year. Offspring stay in the pouch for approximately 10 months, and then continue to be nursed until approximately 19 months of age . Most ofnditions . Approxinditions . Capturenditions . We consThis study involved individuals born in four cohorts (2016–2019) and their mothers, with behavioral observations and PY measures made in 2017, 2018, and 2019. In these years, there were 276–336 marked individuals in the study population, comprising 115–140 adult females, about 55 adult males, 50–85 subadults, and 30–55 PY. We estimated the birth dates of PY based on body size measurements . We clasMothers were typically captured from August to November, when PY were aged about 7–9 months and were making only occasional exits from the pouch . MothersWe recorded data on four traits: 1) movement of PY during handling at capture; 2) FID, of subadults aged 13–35 months; 3) FID of adult females; and 4) survival of subadults to weaning at 21 months. We describe these in turn below; summary statistics and sample sizes for each trait are given in 1) PY movementWe assessed PY behavior by scoring body movements across eight stages of handling at the point of capture of the mother: extraction from the mother’s pouch, placement of the offspring inside a cloth bag, weighing, collection of three body measurements , tagging, and being held briefly inside an observer’s jacket (for warmth) while the mother was weighed. We recorded 1/0 for any presence/absence of movement at each stage, summed these values across all points to a maximum of 8, then divided by 8 to estimate an individual score ranging from 0 to 1. For practicality, in 2019, we combined responses for foot and hind-leg length measurements and for extraction from the pouch and placement inside a cloth bag, to give a total of only six stages of handling, but again deriving a 0-1 score. The mean total duration of the handling process across all years was 7.9 (±1.9 SD) minutes. The PY data (PY movement) were assessed for the 2017, 2018, and 2019 cohorts, at 6.4–10.8 months of age. The average score of PY Movement was 0.47 . All analyses of PY Movement included age at capture (mean 8.0 ± 0.8 SD months) as a covariate in the model, to account for any changes in behaviour with age.2) Subadult FID and (3) Adult Female FIDWe measured FID of subadult offspring (age range 13–35 months) and of adult females, as the distance to the nearest metre at which a kangaroo moved away when approached by a human . High va® GPSMAP 64 device, as an east–west coordinate in meters. To determine membership of groups, we followed a “10-m chain rule” .Finally, to account for any habituation via multiple testing of FIDs, we recorded the test number and the number of days since the previous test for that individual and included these variables in our models. Test number indicated whether it was the first, second, or FID measures were available on more adult females than just the mothers of the offspring considered in this analysis. We, therefore, analyzed all available data to provide the best estimates of variation in adult female FID. Ninety-nine percent of subadult FID trials were carried out by one observer, WMC, however 9% of FID trials on adult females were carried out by another trained observer, so we included observer ID as a two-level factor in the model of adult female data.4) Subadult survivalWe monitored subadult survival from 6 to 21 months for each individual, by which age nearly all young are weaned and have passed their second winter . SurvivaSee Population monitoring and captures were undertaken with ethics approval from the Université de Sherbrooke , the University of Melbourne (no. AEC 1312902.1), and the Australian National University (no. A2018/02) and research permits from the Victorian Department of Environment, Land, Water & Planning . Behavioral experiments and observations were conducted with animal ethics approval from the Australian National University .MCMCglmm , and Subadult Survival. Although we had repeated measures of Subadult FID, to facilitate fitting of this complex four-trait model, we averaged the multiple measures on each offspring into a single mean value. Each offspring was, therefore, represented by a single observation for each trait. However, each mother could be represented by multiple offspring and also by multiple observations for her FID  and covariances for offspring traits and individual-level repeatability for Adult Female FID. Maternal repeatability measures the level of consistent differences between non-siblings’ behavior and indicates similarities among siblings: variance between mothers indicates covariance within mothers. For the offspring traits , the residual variance in the model represents within-mother between-offspring variance or differences between siblings. We acknowledge that using mean Subadult FID may lead to some overestimation of maternal repeatability of Subadult FID in this model and that an explicit model of repeated measures with error would have been preferable see , but repFor PY Movement, we fitted fixed effects of age at capture (in months), sex, and year . For Subadult FID and Subadult Survival, we fitted fixed effects of sex and year . For Adult Female FID, we also included fixed effects of GPS location along an east–west axis to capture the spatial variation in the study area , group smaternal repeatability by dividing the estimate of the variance between mothers by the total variance in the offspring traits . For Adult Female FID, we estimated individual repeatability by dividing the between-individual variance by the total variance. Calculations were made on each sample of the posterior distribution to generate posterior distributions of the two repeatability estimates, from which we estimated posterior modes and 95% CIs. As Subadult Survival was a binary trait (0/1), we fitted it specifying the family “categorical” in MCMCglmm. In a binomial model, residual effects have a variance fixed at 1, but can still covary with other random effects. Because parameter estimates from the Subadult Survival model were on the latent (logit) scale, we used the QGicc function in the package QGglmm , posterior modes (for variance–covariance parameters and ratios), and 95% highest posterior density credible intervals (CIs) for each parameter and also for any derived estimates. For each offspring trait, we estimated e QGglmm  to back-We estimated the latent-scale repeatability in Subadult Survival as:The data-scale repeatability was estimated as:where “var_Residual” =1, and “var_Binomial sampling” is the variance related to binomial sampling .We used Model I (the four-trait model) to estimate the covariances between the four traits. In particular, we estimated maternal-level covariance between the Mother ID effects, for all behavioral responses, to test for maternal-level associations across offspring traits , and for associations between maternal FID and offspring average behavior and Subadult Survival . We also fitted residual covariances for offspring traits to test for within-mother–offspring-level associations . For the random effect Mother ID, a covariance matrix between traits was set using an unstructured covariance structure in MCMCglmm . We alsoModel II was a bivariate model for Subadult FID and Adult Female FID, and was used to estimate the contribution of between-mother variation to the repeatability of juvenile behavior, and also the covariance between mother and offspring FID. This model included multiple measures on each offspring for Subadult FID  was <0.05.Both Models I and II were run for 1300 × 10maternal repeatability by dividing the variance between mothers (Mother ID variance) by the total variance, and individual repeatability by dividing the variance between offspring (Offspring ID variance) by the total variance as above. For Adult Female FID, we estimated individual repeatability by dividing the variance between individuals (Mother ID variance) by the total variance. Model II only had Gaussian-family traits, so unlike Model I, we did not need to convert repeatability to the data scale in Model II.For Subadult FID in Model II, we estimated total repeatable variance between individuals from the variance between mothers plus the additional variance between offspring, and hence a total repeatability by dividing this sum by the total variance:In the Subadult FID response, we then estimated Finally, we estimated the proportion of total repeatability  that could be ascribed to maternal-level effects as:We did not have a sufficiently informative multi-generational pedigree to estimate heritability of FID from a quantitative genetic “animal model” . Insteadper _ h2 , noting per _ h2 :Note that both Models I and II provided estimates of the covariance between mothers and offspring in FID and the variance between mothers, and hence an estimate of the regression slope. We opted to use the estimates from Model II only to calculate Finally, we made use of the (rare) occurrence of adoption in kangaroos  and examWe present below the results from the two models addressing our four questions. The main results for Model I (the four-trait model) are in 1) Do siblings behave similarly to each other and differently from non-siblings?We found support for maternal repeatability in both PY Movement and Subadult FID . PY Move2) Is either offspring early-life behavior or maternal behavior associated with offspring survival to weaning?Considering the covariances estimated in Model I, there was no support for any association of either offspring or maternal behavioral traits with Subadult Survival. This was the case both across mothers (From the estimates of the other covariances in 3) Do maternal effects contribute to the repeatability of offspring behavior?Model II focused on the repeated measures of FID in both offspring and mothers . Materna4) Are maternal and offspring behaviors correlated, and do these associations differ for biological vs adoptive mothers?2, estimated from Model II  returned equivalent upper-limit heritability estimates of 0.75  for the biological mother and 1.26  for the adoptive mother. Although the adoptive mother estimate was nearly twice that of the biological mother, the values had very large uncertainty reflecting the small sample sizes, so it was not possible to determine the relative impact of direct genetic effects and maternal environmental effects.The supplementary regressions on the FIDs on the very small sample size of adopted young , n = 6 rOur analyses indicate differences between offspring of different mothers (non-siblings) in early-life behavior and survival. There was no indication of covariance between juvenile or maternal behavior and subadult survival. Mothers and offspring, however, had similar FIDs. Below, we discuss the implications of these results for our understanding of individual behavioral differences.We found evidence of “maternal repeatability,” or differences between offspring of different mothers, in all three offspring traits, including PY movement, subadult FID, and subadult survival, indicative of genetic or maternal effects on these traits. Maternal repeatability estimates the part of total variation in offspring behavior explained by juveniles having different mothers, or a measure of the level of non-sibling differences, and hence sibling similarities, in behavior. It, therefore, incorporates both strict-sense “maternal effects” and genetic effects, where the term “maternal effect” refers to the impact a mother has on her offspring over and above the direct effects of the genes they inherit from her. These maternal effects can in turn be driven by both environmental or genetic differences between mothers . MaternaMarsupials offer an especially interesting opportunity to investigate phenotypic variation at very early developmental stages, but to our knowledge no study has considered the components of variance in behavior in wild marsupial PY. Some studies have addressed the influence of maternal traits on marsupial offspring survival in the brushtail possum  and, in Cebus capucinus; In subadult survival, we found support for maternal repeatability, which accounted for a large part of the total variance , suggesting that there is a large variance in fitness among females relative to other modeled terms. However, there was no evidence of covariance with the maternal behavioral trait. From a life-history evolution perspective, offspring survival is sometimes treated as a component of maternal total fitness , but theIn subadult FID, we found that 55% of the total repeatability was explained by differences in the mothers of the juveniles  compared to less than half being explained by differences between juveniles themselves . To our knowledge, only a few studies to date have tried to isolate such effects during repeatability estimation . This seTaeniopygia guttata) cross-fostered offspring and found that “personality” variation arose primarily as a maternal effect derived from the foster mother, indicating that shared environmental effects may be more important than genes in shaping personality variation in that system (Furthermore, in subadult FID, we found support for covariance with mothers’ FID (arguably a measure of personality; t system . For theLiopholis whitii (Several aspects of the environment could cause mothers and offspring to have similar personalities. Mother and offspring share physical and social environments, particularly when the juvenile is 10–18 months old, as they are closely associated before weaning . As we ds whitii , housed In conclusion, our study revealed that variation in behavior and early survival of juveniles is influenced by differences between offspring of different mothers and that these maternal differences also explained a large part of individual differences in the personalities of a wild marsupial. Juvenile behavior covaried with maternal behavior, with the similarity potentially driven by both genetic and shared environmental effects. Mothers are an important source of variation in behavior and survival of juveniles, and more studies using multivariate approaches will contribute to our understanding of the causes and consequences of variance and covariance between individuals in their behavior at different stages of life.arad070_suppl_Supplementary_MaterialClick here for additional data file.


We used the bivariate causal mixture model (MiXeR) to estimate the total number of shared genetic variants, local analysis of co-variant annotation (LAVA) to estimate local genetic correlations, conditional false discovery rate (cond/conjFDR) to identify specific overlapping loci. The MiXeR analyses showed that 12.7k genetic variants are associated with ASD, of which 12.0k variants are shared with EDU, and 11.1k are shared with INT with both positive and negative relationships within overlapping variants. The majority (59–68%) of estimated shared loci have concordant effect directions, with a positive, albeit modest, genetic correlation between ASD and EDU  and INT . We discovered 43 loci jointly associated with ASD and cognitive traits (conjFDR<0.05), of which 27 were novel for ASD. Functional analysis revealed significant differential expression of candidate genes in the cerebellum and frontal cortex. To conclude, we quantified the genetic architecture shared between ASD and cognitive traits, demonstrated mixed effect directions, and identified the associated genetic loci and molecular pathways. The findings suggest that common genetic risk factors for ASD can underlie both better and worse cognitive functioning across the ASD spectrum, with different underlying biology.Autism spectrum disorder (ASD) is a highly heritable condition with a large variation in cognitive function. Here we investigated the shared genetic architecture between cognitive traits (intelligence (INT) and educational attainment (EDU)), and risk loci jointly associated with ASD and the cognitive traits. We analyzed data from genome-wide association studies (GWAS) of INT ( Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and interaction as well as restrictive, repetitive patterns of behavior, interest or activities . Recent n = 18,381 cases and n = 27,969 controls and identified five genome-wide-significant loci [The pathogenesis of ASD is considered to originate from complex interactions between environmental  and geneant loci . By leveant loci . Howeverant loci . Meanwhiant loci  and showant loci .Intelligence and EDU are highly heritable traits which are major determinants of human health and well-being , 15. Furg) between ASD and cognitive abilities [g = 0.2–0.3 [g, between ASD and high INT, there are likely variants with an opposite effect on ASD and INT as well.Recent studies suggest that 35% of ASD patients have an intellectual disability . Among tbilities , 25, wit 0.2–0.3 , 26. Thi 0.2–0.3 , 28 and  0.2–0.3 . Thus, dWe have previously reported large polygenic overlaps despite low genetic correlation in mental disorders such as schizophrenia, ADHD and depression –32 by usHere, we took advantage of recent large GWAS data to determine the degree of overlapping genetic architecture between ASD and cognitive traits (INT and EDU) by applying MiXeR method. Second, we identified risk loci shared between ASD and the cognitive traits using the cond/conjFDR method. Third, we applied FUMA to annotate the identified loci to determine tissue expression and molecular functions of shared risk variants for ASD and cognitive traits .p values and z-scores) for the relevant phenotypes [n = 5305) [We obtained GWAS results in the form of summary statistics  , includin = 195,653) [general intelligence factor (g-factor). In the majority of cohorts, the g-factor was based on results on 13 different cognitive tests that required verbal and mathematical reasoning  [General Intelligence was based on data from 269,867 individuals across 14 cohorts, primarily consisting of data from the UK Biobank  . These sd=20016) . The incd=20016) .http://csg.sph.umich.edu/abecasis/metal/), of 71 quality-controlled cohort-level results files. The included GWAS data are restricted to individuals of European descent.Educational attainment (EDU) is measured as the number of years of completed schooling . The GWA2snp) for each trait, based on the distribution of z-scores and detailed modeling of LD structure. Polygenicity estimates included the number of ‘causal’ variants required to explain 90% of h2snp to prevent extrapolating model parameters into variants with infinitesimally small effects. Results were presented as Venn diagrams displaying the proportion of trait-specific and shared ‘causal’ SNPs. Dice coefficient as calculated by MiXeR was used to estimate the similarity between genetic architecture of two phenotypes. Model fit was evaluated based on predicted versus observed conditional quantile-quantile (Q–Q) plots, the Akaike Information Criterion (AIC) and log-likelihood plots (Supplementary Methods). A positive AIC indicates adequate discrimination between modeled fit and the comparative model. A negative AIC indicates inadequate discrimination between modeled fit and the comparative model.We applied MiXeR v1.3  to quantp-values in the primary and conditional trait are as small as or smaller than the observed p-values. Next, the conjFDR value for each SNP was calculated as the maximum of the two condFDR values . This represents a conservative estimate of the FDR for the association between each SNP with both traits. SNPs with a condFDR <0.01 or conjFDR<0.05 were assigned statistical significance. Since the complex correlations in regions with intricate linkage disequilibrium [We next applied the conditional(cond)/conjunctional(conj)FDR method, which leverages polygenic overlap between two traits to boost statistical power to identify loci associated with a single trait (condFDR) and loci jointly associated with two traits (conjFDR) . Cross-tilibrium  can biasilibrium  overlappilibrium , 47.z scores from the respective GWAS summary statistics.We defined independent genetic loci according to the FUMA protocol . We evalg) was estimated using linkage disequilibrium score regression (LDSR) [Genome-wide genetic correlation (rn (LDSR) . Local hn (LDSR) . See Supr2 ≥ 0.6 with one of the independent significant SNPs, using FUMA SNP2GENE [We functionally annotated all candidate SNPs in the genomic loci with a conjFDR value < 0.1 having an LD SNP2GENE . We linkSNP2GENE . If the SNP2GENE   and MiXeR-estimated genetic correlation of shared variants of ρβ = 0.24 (SD = 0.01). For ASD and EDU, those values are respectively rg = 0.21  and ρβ = 0.25 (SD = 0.02). This pattern of extensive genetic overlap but weak genetic correlation is indicative of mixed effect directions, supported by the MiXeR-estimated proportion of shared ‘causal’ genetic variants with concordant effects of 0.58 for both ASD&INT and ASD&EDU.Log-likelihood plots are shown in Figs. SWe leveraged this pleiotropic enrichment using condFDR analysis and re-ranked the ASD SNPs conditional on their association with EDU or INT, and vice versa. At condFDR <0.01, there were 9 loci associated with ASD conditional on INT Table S, of whicThe conjFDR Manhattan plots are shown in Fig. Loci were either concordant or discordant as denoted by the sign of the effect, and 68% 13/19) of the shared loci between ASD and INT had concordant allelic effect directions (Table S/19 of thp < 0.05/19) in both ASD and INT and nominally significant local genetic correlation (p < 0.05) (marked with green in Table Sp < 0.05/32) local heritabilities in both ASD and EDU and significant (p < 0.05) genetic correlation between them  with significant local heritabilities  we mapped 104 genes from candidate SNPs within loci shared between ASD and INT , NFR1 (nuclear respiratory factor 1) and vitamin D receptor.Candidate genes were enrichments in 23 immune related gene sets for ASD and EDU, among them, Interleukin-2 and Interleukin-10 pathways, Macrophage Stimulating 1 (MSP1) pathway, EBNA1 anticorrelated, and development of regulatory T cells (Tregs).As seen in Tables SNCKIPSD, CCDC36, IP6K2, PRKAR2A, QRICH1, CCDC71) showed that they were enriched in pathways for inflammatory diseases and blood protein levels  are shown in Figs. Sls Fig. S.FUMA GENE2FUNC of discordant loci  we found trends of similar enrichments as the total set of discordant genes , in line with previous findings [n = 27 (63%) had concordant effect directions with INT and EDU. Thus, the main fraction of common variants shared with ASD is associated with higher INT and EDU. These variants may shed light on mechanisms underlying better cognition in ASD patients [The current findings of bidirectional genetic overlap between ASD and cognitive traits INT and EDU, as revealed with the MiXeR method, has not been shown before. The genetic overlap estimated by Dice coefficient was 0.90–0.91 which is substantial, taking into account the relatively low genetic correlation we found between ASD and INT  , 54, bipRHOA has been targeted for improved learning and memory in ASD animal models [Analyses of brain tissue expression of all candidate genes, including both concordant and discordant showed that they are significantly upregulated in two brain tissues in frontal cortex and cerebellum, which is in line with a recent meta-analysis of post-mortem studies in ASD . In recel models . As expeCKB). This seems in line with that creatine has been suggested as a cognitive enhancer [MST1, which is found in the high intelligence-pathway and plays a role in autoimmunity [Genes associated with concordant loci were enriched in a pathway for extremely high INT , and incenhancer , The conimmunity . This suimmunity  and is cimmunity , 66. Conimmunity , 68.CRHR1, KANSL1, MAPT, and WNT3. CRHR1 encodes a corticotrophin releasing hormone receptor implicated in social behavior [KANSL1 has been associated with autistic traits [MAPT encodes the tau–protein which misfolds and forms a hallmark of frontotemporal dementia and Alzheimer’s disease [WNT3 is a Wnt-signaling gene involved in neurogenesis [Discordant credible genes were enriched in three types of GWAS phenotypes, mental disorders, neurodegenerative diseases and somatic traits. Of these, the enrichment in neurodegenerative diseases as Alzheimer’s and Parkinson’s is of interest since the variants could possibly be involved in mechanisms underlying autistic regression in children and of increased risk of dementia in adults , 66. Amobehavior , 70 and behavior . KANSL1 c traits  and cognc traits . MAPT en disease . WNT3 isogenesis , as wellogenesis . It has ogenesis . TogetheA limitation of our study is that the sample of UK-biobank consists mainly of persons of European ancestries. Another limitation is that the study does not include rare pathogenic variants causing ASD, as only common variants are included in the analyses. Furthermore, the results are based on a common factor for INT, which is not exactly similar with a full IQ score. Furthermore, EDU is not purely a cognitive trait, but it is also influenced by other factors, including socioeconomic status.In conclusion, the current findings show extensive bidirectional genetic overlap between ASD and cognitive traits, with a majority of loci for ASD associated with better cognitive performance. The mixture of effect directions is in line with the large variation in cognitive abilities in ASD. Together, these findings suggest that genetic factors may underlie some of the large variation in cognitive performance in ASD, and highlight molecular mechanisms involved in the two cognitive subgroups within the ASD spectrum.Supplementary information regarding methodsSupplementary Results, Figure S1-S9 and Table 16Supplementary Results, Tables S1-S15


Triticum turgidum for drought tolerance and their quality-related traits compared with common wheat (Triticum aestivum) and to identify the association among agronomic, micronutrients, and quality-related traits, especially under climate change conditions. In this research, grain quality, technological properties of flour, and some agronomic traits were studied in 33 wheat genotypes from six different subspecies of Triticum turgidum along with three cultivars of Triticum aestivum in the field, applying a well-watering (WW) and a water stress (WS) environment during two growing seasons. A high degree of variation was observed among genotypes for all evaluated traits, demonstrating that selection for these traits would be successful. Consequences of water stress were manifested as declined DM, GY, and LASRC; and significantly increased GPC, K+/Na+, WAF, WSRC, SuSRC, and SCSRC compared to the well-watering condition. The reductions in the unextractable polymeric protein fraction and glutenin-to-gliadin ratio indicated a poorer grain yield quality, despite higher protein content. This study showed that the early-maturing genotypes had higher water absorption and pentosan, and therefore were more suitable for bread baking. In contrast, late-maturing genotypes are ideal for cookie and cracker production. Two subspecies of T. turgidum ssp. durum and T. turgidum ssp. dicoccum with high micronutrient densities and quality-related traits, and T. turgidum ssp. oriental due to having high values of grain protein content can be used to improve the quality of T. aestivum through cross-breeding programs. Based on the association of different traits with SRC values and other quality-related traits and PCA results, contrasting genotypes can be used to develop mapping populations for genome studies of grain quality and functional properties of flour in future studies.Few prior efforts have been made to investigate the genetic potential of different subspecies of  Wheat is one of the most important staple-foods that supply about the 20% of the total calories and daily proteins worldwide [wheat yields is the demand of the 21st century to satisfy the increasing food demand of the growing population [orldwide . This crwheat production is considerably threatened by prolonged drought that is predicted to increase with progressive global climate change. The development of drought-tolerant wheat varieties is the ultimate mean of safeguarding the crop against the damaging effects of drought [wheat populations remains the basis for elucidation of the genetic structure and improvement of different traits, including drought tolerance.In arid and semiarid regions, global  drought . In thiswheat germplasm has been drastically narrowed, which could jeopardize future crop improvement. This genetic bottleneck severely eroded allelic variation in cultivated wheat and poses a potential threat of serious crop vulnerability to many adverse events, including global warming and climate changes [wheat. Local wheat cultivars or landraces with higher yield stability and better adaption to climate change represent an extensive reservoir of genetic variation for desirable traits such as tolerance to biotic and abiotic stresses [During domestication of wild emmer and later advancement of modern plant breeding, the genetic diversity of the cultivated  changes . The utistresses .wheat species as healthy grains among the different species of wheat. Triticum dicoccum is one of the ancient hulled wheat species globally and represents a valuable genetic resource to improve resistance to biotic and abiotic stresses in bread and durum wheat [The increasing attention to sustainable and organic agriculture and growing demand for high health-promoting foods has led to a renewed interest in ancient hulled um wheat .wheat is mainly the outcome of independent and interactive actions of several traits, including grain hardness, gluten protein quality, flour color, and starch quality. It is influenced by genetic and environmental conditions and biotic and abiotic stresses [Grain quality of stresses . As a rewheat cultivars with optimized performance [wheat quality. Among them, Solvent Retention Capacity (SRC) is a unique diagnostic tool for predicting flour functionality, which examines the glutenin, gliadin, and pentosan (fibers) characteristics of the flour and the level of starch damage in the flour [wheat flour functionality, but it has also been shown to assess the flour functionality of hard wheat products [Evaluating flour quality is an essential task for breeders, millers, and bakers in selecting good-quality formance . There ahe flour . SRC is products .Triticum aestivum L.), durum (T. durum L.), and emmer (T. dicoccum L.) wheat that can be exploited to accelerate the improvement of wheat for quality traits, especially under water stress condition. Our objectives in this study were to (i) determine the genetic variability and heritability of some quality-related traits in six different wheat subspecies under well-watering (WW) and water-stress (WS) conditions; and (ii) evaluate the association of phenological, agronomic, and quality-related traits to identify the most suitable combination of these traits through the indirect selection of promising genotypes for future breeding programs.A unique feature of the present study is that it compares three genetic background sources, bread (This research was conducted during two years (2019 and 2020) at the research farm of the Isfahan University of Technology, located in Lavark, Najaf-Abad, Isfahan, Iran  on a Typic Haplargid, silty clay loam soil, with pH 8.3. In this region, there is no rain during summer (from late May to mid-October); therefore, crops must be irrigated. Based on 40-year meteorological data, the region’s mean annual precipitation and temperature were 140 mm and 14.5°C, respectively.wheat genotypes, including thirty-three tetraploid genotypes that belonged to six different subspecies of T. turgidum, along with three bread wheat (T. aestivum) cultivars . At the second step, water volumes that should be applied in each moisture environment were calculated by multiplication of the irrigation depth and the total area of plots under each moisture environment. The depth of irrigation (Ig) was calculated as follows (a is the irrigation efficiency (%) assumed as 75% during the growing season. Water was delivered to the field using a drip irrigation system through a pumping station, polyethylene pipes, and drip tapes. The water volume applied under each moisture environment was measured using a volumetric counter.Plants were evaluated under WW and WS conditions. Two replications were allocated to each of these two moisture environments. Under the WW and WS conditions, water was supplied when 40 and 80% of plant available water (PAW) was exhausted from the root-zone, respectively. To determine the amount of irrigation water needed for supplying the soil moisture deficit to the field capacity and detect the irrigation times, soil samples were taken from different sites of each WW and WS environment every 2 days at depths of 0–20, 20–40, and 40–60 cm, using a hand auger and the gravimetric soil–water content was measured. At the irrigation time, the irrigation depth was determined as follows :I=[(θF follows :Ig=I×12); thousand grain weight ; grain protein content ; Zeleny index ; grain hardiness ; Zn (mg/g); Fe (mg/g); Na+ (mg/g); K+ (mg/g); ratio of K+/Na+, moisture of flour ; water absorption of flour ; water SRC ; sucrose SRC ; lactic acid SRC ; and sodium carbonate SRC . Five randomly selected samples of each genotype were used for trait measurement, and the mean value of five plants was calculated and used for analyses. Concentrations of Zn and Fe were determined by graphite furnace atomic absorption spectrometry (GFAAS) . All grain concentrations are expressed on a dry weight basis. For determining the concentrations of Na+ and K+, grain samples (0.2 g) were ashed at 550 ˚C for 3 h. Inorganic ions were then extracted using 10 ml 2 N HCl, and the volume of each sample was standardized to 100 ml. Na+ and K+ concentrations of the solutions were determined by flame photometry . The amount of Na+ and K+ concentrations were estimated using a standard curve, and then the ratio of them (K+/Na+) was calculated.Sixteen agro-morphological, quality, and SRC-related characteristics that were evaluated under the two levels of irrigation included: days to maturity ; grain yield  tests of the investigated genotypes were conducted according to the modified AACC International Approved Method 56–11.02 using 5% lactic acid, 50% sucrose, 5% sodium carbonate, and distilled water, with some modifications described by Duyvejonck et al. . The ameEach solvent diagnoses the functional contribution of specific flour components: lactic acid (LA) SRC is a measure for glutenin network forming capacity; sodium carbonate (SC) SRC is related to solvent-accessible amylopectin in damaged starch; sucrose (Su) SRC is a measure for the swelling of water-accessible pentosan (fiber); and finally, water SRC (WSRC) is related to overall water holding capacity by all network-forming components . All solF-value was significant, mean comparisons were carried out using the least significant difference (LSD) test at p < .05 [2b) was estimated on a phenotypic mean basis averaged over replications, years, and environments according to the following formula , the Kolmogorov–Smirnov test was conducted to examine the normality distribution of data. The Bartlett test was used to test the homogeneity of residual variance. Subsequently, to examine the differences between the genotypes, years, moisture environments, and all possible interactions, and also to estimate the variance components, combined analysis of variance was conducted using Proc MIXED of SAS release 9.4 . The genotype effect was considered fixed, and year was considered as random effect. Where the  p < .05 . Phenoty formula :hb2=σgp is the standard deviation of the phenotypic variance, σg is the standard deviation of the genotypic variance, and μ is the phenotypic mean [2b is the broad-sense heritability, The level of genetic variation was estimated with the calculation of phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) as follows Eqs  and 6:pic mean . Data we+ concentration. The effect of genotype was significant (P < 0.01) for all of the evaluated traits; indicating considerable genotypic variation among the selected genotypes with a broad range for each attribute  between the WW and WS environments for all traits except for ZEL and Ke Tables  and S1. e Tables  and 3. Gs Tables  and S1.+/Na+, WAF, WSRC, SuSRC, and SCSRC in both years of the study. Moreover, TGW significantly decreased and ZEL increased due to WS in 2019, but they were not influenced during 2020. During 2020, Zn significantly increased, and MOF decreased under WS condition; while they were not affected in 2019  for WW and WS conditions are given in ondition . For theectively .Broad-sense heritability estimates and variance components of all measured traits based on all subspecies are given in Phenotypic correlation coefficients between different traits based on the average of all subspecies and two years showed that grain yield had significant and negative correlations with GPC, Zn, and Fe under both WW and WS conditions. Under both conditions, grain protein content (GPC) had significant and positive associations with GH, Zn, Fe, and WAF .+/Na+, and negative correlations with WSRC and SuSRC; while these correlations were not significant under WW condition. Under WW condition, ZEL had significant and negative associations with WSRC, SuSRC, LASRC, and SCSRC; while this was not the case under WS condition. Under both conditions, each of the four SRC-related traits had significant and positive correlations with Na+, K+, and the three other SRC traits. Under both WW and WS conditions, K+/Na+ had significant and negative associations with WSRC, SuSRC, LASRC, and SCSRC. Under WW condition, SuSRC was positively correlated with GH and WAF, and SCSRC was associated with WAF. Moreover, LASRC was positively associated with GH and WAF under both conditions  including G9, G10, G11, G12, G25, and G30 had the higher values for these traits under both moisture conditions (wheat subspecies (T. turgidum ssp. durum), including Iranian genotypes  and International Research Centers CIMMYT (i.e. G5), and ICARDA (i.e. G6) had the higher values for these traits. Moreover, traits related to solvent retention capacity, i.e. WSRC, SuSRC, LASRC, and SCSRC were positively and strongly correlated with GH and WAF under both moisture conditions. Among the studied subspecies, T. turgidum ssp. durum , T. turgidum ssp. polonicum (genotype G21), and T. turgidum ssp. persicum (genotype G31) recorded the higher values for these traits (A multivariate technique (i.e. PCA) was conducted for WW and WS conditions to acquire a unifying view of the relationship between the SRC and phenological, agronomic and quality-related traits. It was shown that the first two principal components explained 56% of the total variation incorporated in the data under both WW and WS conditions . Accordinditions . In addie traits .wheat genotypes for all of the measured traits, emphasizing the high potential within and among the studied subspecies for improving these traits through targeted selection in breeding programs. Large variations in grain Zn and Fe concentrations among wheat genotypes have also been already reported, suggesting that there is a genetic potential to enhance the levels of these in edible parts to meet human nutritional requirements through biofortification programs [A significant increase in food production is required to meet the food demand of the growing world population. Abiotic stresses are major constraints to crop production and food security worldwide. The situation has aggravated due to the drastic and rapid changes in global climate . Moreoveprograms .T. durum which had the highest GY, Na+, WSRC, SuSRC, and LASRC, and T. dicoccum which had the highest GH, Zn, Fe, MOF, and WAF are promising candidates in terms of quality-related traits. Therefore, they can be used in the breeding programs to improve the quality of bread wheat (T. aestivum). Moreover, T. turgidum ssp. oriental with the highest values of GPC and ZEL index can effectively increase the protein content of bread wheat. The high grain quality of emmer is mainly conditioned by high protein content. The high protein content in the emmer grain significantly affects the yield of protein per unit area which can be even higher than that in bread wheat. In the present study, protein content in dicoccum wheat ranged from 13.71% to 17.34%. Suchowilska et al. [T. dicoccum wheat from Poland.Comparison among the studied subspecies revealed that a et al.  reportedIn addition, among the studied genotypes of all subspecies, the higher values of Zn and Fe were observed for G25, G29, and G30; and the lower values were detected for G7 and G13. Insufficient supply of Zn and Fe is a widespread mineral malnutrition problem among the world’s population, which compromises the immune system and retards development in infancy. Moreover, Fe deficiency can cause nutritional anemia, problem pregnancies, stunted growth, lower resistance to infections, long-term impairment in mental function, decreased productivity, and impaired neural development. While, growth retardation, delayed skeletal and sexual maturity, dermatitis, alopecia, and increase in susceptibility to infection are the complications of Zinc deficiency . A sustawheat always produces more damaged starch because more force and pressure are required for grinding damaging the starch granule [wheat genotypes and subspecies quality due to different distribution of grains constituents in different genotypes and subspecies, and can be used to differentiate between them.Moreover, the higher values of LASRC were observed for G7, G8, and G27. The LASRC values are explicitly related to the glutenin content of the flour sample . Hence,  granule . The hig granule . Finally granule . These r+/Na+, WAF, WSRC, SuSRC, and SCSRC in both experimental years compared with WW condition. The study region  is a hot and dry area where summer temperature reaches as high as 45°C and precipitation is null; therefore, as mentioned above reductions are to be expected in some traits. Moreover, WS reduced grain yield and contributed significantly to the low productivity of wheat. This may be ascribed to the stomatal closure in response to low water potential of the soil, decreased rate of photosynthesis, disturbed assimilate partitioning, and disturbance in the grain filling period [Water deficit limits agricultural production by preventing crop plants from expressing their full genetic potential. In the current study, WS significantly influenced most of the evaluated traits. As expected, it caused a significant decline in DM, GY, and LASRC; and significantly increased GPC, Kg period .+/Na+ ratio significantly increased under water stress condition. The ability to discriminate between K+ and Na+ in the soil solution and to preferentially accumulate K+ and exclude Na+ has been described as an important mechanism of salt tolerance in the tribe Triticeae [+/Na+ ratio has been suggested as one of the most important criteria for assessing salinity tolerance in a large number of plant species [wheat showing higher K+/Na+ ratio in salinity-tolerant genotypes than sensitive ones. In durum wheat, Houshmand et al. [+/Na+ ratio and salinity tolerance. There are also reports showing that salinity-tolerant genotypes contain lower Na+ concentration and higher K+/Na+ ratio than salinity-sensitive ones [The effect of water stress on the ionic contents also revealed that Kriticeae ,22. Ther species . There ad et al.  found stive ones .In this study, wide genetic variation was observed for all traits within and between subspecies, indicating a high potential for genetic gain through selection in the studied germplasm. The smaller difference between PCV and GCV will lead to more gain through selection, because it shows the more negligible effect of the environment and as a result higher heritability. In the present study, the smaller differences between these two coefficients were observed for DM, GPC, and WAF, indicating that more gain will result through selecting for these traits.+, and MOF) under both WW and WS conditions, suggesting the presence of some major genes or QTLs affecting them. Traits with high heritability could be improved by recurrent or mass selection [wheat [e.g., wheat, under WW and WS conditions. For most of the traits having high heritability estimates, three subspecies of T. durum, T. polonicum, and T. persicum, showed high genetic variance; therefore, it seems that these subspecies also have high heritability estimates for the related traits. Moreover, for GH and WAF with the heritability estimates of 93.12 and 91.62% respectively, two subspecies of T. durum and T. turgidum, and also T. aestivum had high genetic variance and therefore had probably high heritability estimates for these traits, too.Estimation of heritability, a measure of the phenotypic variance attributable to genetic variance available to the plant breeders for selection, is necessary to design and implement an effective breeding program to maximize genetic improvement. In this study, considering all subspecies, high heritability estimates were obtained for most studied traits Click here for additional data file.S1 Raw data(XLS)Click here for additional data file.


Cervus elaphus), using genomic approaches. A previous study using 93 microsatellite and allozyme markers and linkage mapping on a kindred of 364 deer detected a pronounced QTL on chromosome 21 explaining 29% of the variance in birth weight, suggesting that this trait is partly controlled by genes with large effects. Here, we used data for more than 2,300 calves genotyped at >39,000 SNP markers and two approaches to characterise the genetic architecture of birth weight. First, we performed a genome-wide association (GWA) analysis, using a genomic relatedness matrix to account for population structure. We found no SNPs significantly associated with birth weight. Second, we used genomic prediction to estimate the proportion of variance explained by each SNP and chromosome. This analysis confirmed that most genetic variance in birth weight was explained by loci with very small effect sizes. Third, we found that the proportion of variance explained by each chromosome was slightly positively correlated with its size. These three findings highlight a highly polygenic architecture for birth weight, which contradicts the previous QTL study. These results are probably explained by the differences in how associations are modelled between QTL mapping and GWA. Our study suggests that models of polygenic adaptation are the most appropriate to study the evolutionary trajectory of this trait.The genetic architecture of traits under selection has important consequences for the response to selection and potentially for population viability. Early QTL mapping studies in wild populations have reported loci with large effect on trait variation. However, these results are contradicted by more recent genome-wide association analyses, which strongly support the idea that most quantitative traits have a polygenic basis. This study aims to re-evaluate the genetic architecture of a key morphological trait, birth weight, in a wild population of red deer ( Many quantitative traits appear to be subject to selection and yet show substantial levels of genetic variation in nature . Yet, toSince the development of molecular markers, quantitative genetic studies have sought to identify the individual loci responsible for trait variation . Until recently, the main procedure for mapping genes was through “linkage mapping”, which conducted linkage analyses between markers and QTLs, usually performed using microsatellite markers and large half-sibling families, which limited its application in wild populations , which net al. Until recently, genetic mapping and GWAS have been mainly performed in humans and model organisms. Their results strongly support the idea that most quantitative traits have a polygenic basis . In huma2 = 0.30   to account for potential confounding effects of population structure (2 study area are caught soon after birth (often within 24 h), weighed, tagged, and sampled for DNA (via an ear punch). DNA is also routinely extracted from post-mortem tissues and cast antlers.The red deer living in the population in the North Block of the Isle of Rum, Scotland , have been intensively monitored since 1972. The calving period generally extends over 6 weeks, from mid-May to late June. Most calves born within the 12 kmn = 3,067 individuals were genotyped at 39,587 SNPs.All sampled individuals within the study area have been genotyped at an attempted 51,248 SNP markers (2A) and variance due to cohort effects (σ2C). Models of genetic architecture, especially genomic prediction models, do not easily accommodate different sources of genetic effects . For this reason, we did not fit a maternal effect for birth weight here or in the following methods  of calves caught within 7 days of birth and born before August 1 (following stimates . We know2GRM). The GRM was estimated using the –make-grm function in GCTA v1.90.2 , namely a GRM-based heritability . The SNP-based heritability differs from the GRM-based heritability as it estimates the proportion of variance accounted for by linear regression of a set of genotyped SNPs on the trait value (The BayesR method implemented in the BayesR v0.95 software package (2GRM) . We then predicted genomic estimated breeding values (GEBVs) in the remaining 5%. We repeated this operation with a randomized 95% subset 10 times. The accuracy of the model was estimated as the Pearson correlation between the GEBVs and the observed phenotypic values, divided by the square root of the trait's heritability  directly affects the pattern of LD across the genome. For this reason, genomic prediction shows low accuracy in wild populations with large effective population sizes (Ne) in comparison to domesticated species  between causal loci and genetic markers. The effective population size . This calculation gives the power to detect association when the causative variant is typed. The power at the marker locus thus also depends on the coefficient of LD between the marker and causative variant . We conducted this analysis using an analytical method developed by  variant . Therefopower = 0.72 for h2locus = 0.05, and power ≥ 0.99 for h2locus ≥ 0.10). Therefore, if birth weight has an oligogenic basis  . This GRM-based heritability is very similar to a pedigree-based one with a model also omitting maternal effects  , very similar to the GRM- and pedigree-based heritabilities estimates above.The genomic prediction model had a high accuracy to predict the genomic estimated breeding values (GEBVs) in the red deer population. This is an important finding, as models such as BayesR have not yet been tested and validated on many wild animal data sets see e.g. . The estWe found a positive correlation between the chromosome size and the proportion of additive genetic variance explained by each chromosome . ChromosTwo decades of genetic mapping in wild populations have provided contrasting results about the genetic architecture of fitness-related traits (Three different results provide evidence that birth weight has a highly polygenic genetic architecture: (1) there were no significant SNP associated with birth weight in the GWAS, (2) in the BayesR model, most variation was explained by loci with very small effect sizes and (3) in the same analysis, the variance explained by each chromosome scaled with chromosome size. These findings are in line with a number of recent genomic studies usually showing flat association landscapes , which directly affects LD patterns across the genome (Ne (Ne = 175), which explains this high accuracy in comparison to other wild populations (e.g. in birds; Our results highlight the potential of genomic prediction models to study the trait architecture and evolution in this wild population. Indeed, we report a high accuracy to predict GEBVs that is comparable to that found in a similar wild study system, the Soay sheep of St Kilda (e genome . Here, wThis study uses one of the largest datasets in a wild population, with ∼2,300 individuals phenotyped and genotyped, to explore the genetic architecture of a fitness-related trait. This knowledge of the underlying genetic architecture of quantitative traits is most important to understand the origin of genetic variance and how it evolves. According to theory, the evolution of birth weight, a trait with a highly polygenic architecture, will be driven by subtle changes of allelic frequencies at many QTLs and by covariance among QTLs . This pojkad018_Supplementary_DataClick here for additional data file.