BE500291_5_A_37 on chromosome 5A correlated with six kernel traits. Although most of our SNP loci were overlapped with the previously known kernel weight QTLs, several novel loci for kernel traits in durum were reported. Correlation analysis implied that the moderate climatic variables during growth and development of durum are needed for the large grain size and high grain weight. Combined with our previous studies, we found that chromosome 5A might play an important role in durum growth and development.Durum wheat, genetic resource with favorable alleles is considered as natural gene pool for wheat breeding. Kernel size and weight are important factors affecting grain yield in crops. Here, association analysis was performed to dissect the genetic constitution of kernel-related traits in 150 lines collected from 46 countries and regions using a set of EST-derived and genome-wide SNP markers with five consecutive years of data. Total 109 significant associations for eight kernel-related traits were detected under a mix linear model, generating 54 unique SNP markers distributed on 13 of 14 chromosomes. Of which, 19 marker-trait associations were identified in two or more environments, including one stable and pleiotropic SNP  Triticum turgidum L. ssp. Durum Desf.) is often used as a bridge for transferring favorable alleles into bread wheat [Wheat is the most extensively grown commercial crop in the word . The gload wheat . Wheat gad wheat . Kernel ad wheat , is alsoTaGW2 [TaSus2-2B [TaCwi-A1 [TaCKX6-D1 [TaGS-D1 [TaGS5 [TaTGW6-A1 [TaFlo2-A1 [TaGW2 using CRISPR/Cas9 showed that mutation of TaGW2 homoeologs resulted in the decrease of grain weight by affecting the grain size in bread wheat [TaGW2 by RNA interference [TaTGW6-A1a associated with high grain weight was observed in approximately 80% of cultivars, indicating that it was a positively selected allele in wheat breeding [TaGS5-A1 haplotype was positively associated with high thousand-kernel weight in Chinese modern wheat [Comparative genomics approaches provide a powerful tool for gene discovery in wheat. Several QTLs and genes contributing to grain size and weight have gradually been isolated from wheat by using homology-based cloning of the orthologs in other cereal crops, including TaGW2 , TaSus2-aSus2-2B , TaCwi-ATaCwi-A1 , TaCKX6-aCKX6-D1 , TaGS-D1[TaGS-D1 , TaGS5 [1 [TaGS5 , TaTGW6-aTGW6-A1 , and TaFaFlo2-A1 . In recead wheat , which wrference . Haplotybreeding . TaGS5-Arn wheat . In geneqKW-6A was detected in both RIL population and DH population, suggesting that qKW-6A plays an important role in kernel width of wheat [TaTGW-7A, a major QTL explaining 21.7–27.1% of phenotypic variance for thousand-kernel weight contributed significantly to wheat grain yield [1-FEH w3 gene was identified to be associated with thousand-kernel weight under drought conditions [qKnps-4A, a major stable QTL for kernel number per spike was identified by using the Affymetrix Wheat-660K single-nucleotide polymorphism (SNP) array [High-density genetic linkage maps had been constructed to detect qualitative and quantitative trait loci for identifying candidate genes of many important traits in many species. A stable QTL of wheat . TaTGW-7in yield . A singlnditions . qKnps-4P) array .TaGW-6A underlying thousand grain weight in a panel of European winter wheat varieties [Aegilops tauschii [However, linkage mapping has limitations including the basic requirement to create a bi-parental population segregating for target traits . Anotherarieties . Twenty-arieties . Associaarieties . Based otauschii .In common wheat, GWAS approach has been successfully employed to identify numerous candidate genes controlling a series of traits. Nevertheless, limited studies have utilized GWAS in durum wheat to dissect the genetic basis controlling kernel size and weight. In this study, we analyzed architecture of kernel characters in a panel of 150 durum lines collected from 46 countries and regions. As the result, a number of candidate genes were identified, which provides a useful resource for further functional studies to understand the molecular mechanism underlying grain development.One hundred fifty durum wheat accessions, consisting of 51 landraces and 99 cultivars from 46 countries and regions around the world, were used for association analysis in the study. This set of durum wheat was classified into 11 groups based on their geographic origins: East Asia (15), Central Asia (2), South Asia (6), Middle East (32), North America 33), Latin America (12), Oceania (7), Western Europe (14), Eastern Europe (5), South Africa (4), and North Africa (12). Details information was given in previous study [, Latin AThe kernel traits were measured at maturity. Thirty spikes from the individual plant of each line were randomly collected from the middle row in each plot and sundried. Then, all spikes from three different field experimental trials were mixed together for threshing. About 300 fully filled seeds of every line were randomly selected to obtain kernel parameters using SC-G phenotyping system  . In totaH2) for each trait, and Pearson correlation coefficients analysis among different traits were calculated by using SPSS 21.0 (https://www.ibm.com/support/pages/node/213045). Kolmogorov-Smirnov test was performed to test normal distribution of each trait. Origin Pro2017 (http://www.chem.ox.ac.uk/origin/) was used to draw figures of frequency distribution for the examined traits. In order to calculate the mean values of each trait, the best linear unbiased prediction (BLUP) method was estimated using a mixed-effects model implemented in the lme4 package [Descriptive statistical analysis and analysis of variance (ANOVA) of phenotypic data, broad-sense heritability ( package . Correlawww.Misogynistic.net/tassel), accounting for Q-Matrix of the population structure as a covariate and pair-wise kinship coefficients (K matrix) as random effects [P-value, and the QTL effects were estimated using marker-R2. P = 0.01 was used to declare the significant association signals according to our previous study [SNP genotyping was performed on Illumina Bead Array platform and Golden Gate Assay  at the Genome Center of the UC Davis according to the manufacturer’s protocols. The SNP markers used in this study were developed from the EST database. After the process of quality management, 1366 single nucleotide polymorphisms (SNP) markers covering the whole genome of durum were used to genotype the durum accessions. The rate of change in the Napierian logarithm probability relative to standard deviation (ΔK) suggested that the best structure was K = 2. More details were described in previous study . The mea effects . Signifihttp://www.ncbi.nlm.nih.gov/) for candidate genes prediction. Their functions were predicted based on the level of homology identity with the other species. The durum genome was downloaded from the International Durum Wheat Genome Sequencing Consortium . To obtain the physical positions of the our SNP sequences, and search previously identified QTLs which overlapped with our markers in durum, all EST sequences of the significant SNP markers were analyzed using Nucleotide BLAST to gain information in durum reference genome (https://wheat.pw.usda.gov/GG3/jbrowse_Durum_Svevo).The EST sequence of each significantly associated SNP marker was analyzed using translated nucleotide BLAST software from NCBI , kernel circumference (KC), kernel diameter (KD), kernel length (KL), kernel roundness (KR), kernel width (KW), length/width ratio (L/W), thousand kernel weight (TKW). The frequency distribution of these traits was showed in SD = 12.54 ± 1.60 mm) in year 2014, but from 11.96 to 23.47 mm (17.15 ± 2.01 mm) in 2018. TKW had the highest CV among these traits, whereas KD had the lowest CV (H2 > 60%), indicating that a large portion of phenotypic variance for kernel traits were stable and mainly contributed by genotypic effects.The coefficients of variation (CV) among genotypes for all the phenotypic traits in each environment ranged from 5.53 to 28.45%. However, the variation of each trait was different among years. For instance, the variation for the KA ranged from 7.61 to 17.16 mm  and two significant (p< 0.05) correlations. Moreover, as shown in 2 < 10%, only a few SNPs associated with KA, KC, KL, KR and L/W showed R2 ≥10%  markers covering the whole genome of durum were used to genotype 150 durum germplasm accessions. Details on the population structure and the linkage decay were described in our previous study . Here, a R2 ≥10% . The res R2 ≥10% . No SNP UP model .BE500291_5_A_37 was detected for four consecutive years  values were from 4.92% to 8.99% . Two mar markers .BE500291_5_A_37 in 2015  using linear regression analysis . Therefore, the responsive gene involved in ABA signaling pathway might be correlated with grain development of durum.It is difficult to define candidate genes as the low resolution and large LD in this study. Nevertheless, the SNP markers used in this study was developed from the EST database, these ESTs might be candidate genes. As this study shown, the stable SNP marker olase 12 . The E3 ribution . Consequof wheat . In this with KW , was fou with KW . Therefo soybean , 64. TheBE500291_5_A_37 was mapped on chromosome 5A  was applied to examine the difference of traits among different years. Different numbers indicate statically significant difference at P ≤ 0.05. Phenotypic differences obsered for each trait under five consecutive years of 2014–2018, respectively. (A) KA; (B) KC; (C) KD; (D) KL; (E) KR; (F) KW; (G) L/W; (H) TKW.(TIF)Click here for additional data file.S2 FigAnalysis of variance (ANOVA) was applied to examine the difference of traits between landraces and cultivars. There was no significant difference between the two groups .(TIF)Click here for additional data file.S3 Fig(A) SNP numbers for every kernel-related trait in different years. (B) The range of associated R2-values (variation explained by SNP markers) distributed for each kernel trait detected under five years of 2014–2018. (C) The distribution of R2-values for each kernel trait evaluated by using five-years best linear unbiased prediction (BLUP) values. (D) Linear regressions between number of favorable alleles and mean phenotypic effect on L/W.(TIF)Click here for additional data file.S4 Fig(A) Manhattan plots of P values indicating SNP markers associated with KC in 2015. (B) Manhattan plots of P values indicating SNP markers associated with KL in 2015. The horizontal line indicated P = 0.01 thresholds for significant associations.(TIF)Click here for additional data file.S5 FigTriticum turgidum Durum Wheat Svevo, RefSeq Rel. 1.0).Positions of significant markers projected to the durum wheat genome ((TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S1 Table(XLS)Click here for additional data file.S2 Table(XLS)Click here for additional data file.S3 Table(XLS)Click here for additional data file.S4 TableMarked base representing favorable alleles.(XLS)Click here for additional data file.S5 Table(XLS)Click here for additional data file.S6 Table(XLS)Click here for additional data file.S7 Table(XLS)Click here for additional data file.S8 Table(XLS)Click here for additional data file.


Forest trees like poplar are particular in many ways compared to other domesticated species. They have long juvenile phases, ongoing crop-wild gene flow, extensive outcrossing, and slow growth. All these particularities tend to make the conduction of breeding programs and evaluation stages costly both in time and resources. Perennials like trees are therefore good candidates for the implementation of genomic selection (GS) which is a good way to accelerate the breeding process, by unchaining selection from phenotypic evaluation without affecting precision. In this study, we tried to compare GS to pedigree-based traditional evaluation, and evaluated under which conditions genomic evaluation outperforms classical pedigree evaluation. Several conditions were evaluated as the constitution of the training population by cross-validation, the implementation of multi-trait, single trait, additive and non-additive models with different estimation methods (G-BLUP or weighted G-BLUP). Finally, the impact of the marker densification was tested through four marker density sets. The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib families. Four evaluation batches were planted in the same location and seven traits were evaluated on 1 and 2 years old trees. The quality of prediction was reported by the accuracy, the Spearman rank correlation and prediction bias and tested with a cross-validation and an independent individual test set. Our results show that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions. Genomic evaluation appeared to be advantageous when using an independent test set and a set of less precise phenotypes. Genome-based methods showed advantages over pedigree counterparts when ranking candidates at the within-family levels, for most of the families. Our study also showed that looking at ranking criteria as Spearman rank correlation can reveal benefits to genomic selection hidden by biased predictions. Slow growth and cumbersomeness typical of trees do not facilitate either the conduction of breeding programs, notably with evaluation stages being costly both in time and resources. One of the poplar's particularities is clonality or the possibility of asexual reproduction, which is a powerful tool in evaluation and operational breeding   tree in the wood industry. This study is the first GS study for a Populus species. One of the main objectives of the study was to compare GS to pedigree-based traditional evaluation, by assessing different modeling options including non-additive genetic effects and multiple-trait evaluation. The study also considered the role of marker densification in the performance of GS, by benefiting from a recent imputation study  with small variations in plot orientation and with common genotypes as controls across experimental trials . The firPhenotyping involved seven different measurements over different years , and for five different traits. Growth was assessed as stem circumference and tree height. Stem circumference at 1 m was considered for the second year (circ2). Height was assessed with a graduated rod after 1 (height1) and 2 years of growth (height2). Mean branching angle was scored on proleptic branches at the age of 2 years with a 1–4 scoring scale . The scale for angbranch was calibrated in such a way that resulting measures in the same population of reference resulted in phenotyping distributions being close to normality. Rust resistance was assessed with a 1 (no symptom) to 9  scale  were genotyped using the Populus nigra 12K custom Infinium Bead-Chip  . The Akaike Information Criterion (AIC) was used to assess for each given trait the quality of each model. Two alternative methods were used to calculate genomic estimated breeding values for each trait: the best linear unbiased prediction based on genomic information (GBLUP)  was built from the known pedigree at the moment of the controlled crossings, and denoted hereafter as v Wolak, , and kepv Wolak,  and the v Wolak,  to assurM was a genotyping matrix with m markers in columns and n individuals rows, P1 was a matrix (n × p) containing the minor allele frequency (2pi), at the marker i, and Wa was a matrix of weights described below. Ad hoc scripts in R were used to make the computations for G (R3.3.1 platform). To assess dominance effects, a dominance matrix based on the pedigree information was calculated with the R package nadiv . The genomic dominance matrix was calculated as:where v Wolak,  with expX was the genotyping (n × p) matrix containing code “0” for the homozygous and “1” for the heterozygous, P2 the (n × p) matrix containing the heterozygous frequency (2piqi) according to Vitezica et al.  or a diagonal of w weights (wGBLUP) for additive (Wa) or dominance (Wd) relationship matrices, ĝ the genomic estimated breeding values (GEBV) and ûa, ûd, ĝ, and where g et al. , and acci = 1, Wa, d)i( = I and Gi as Equation (3)Define ĝi using GBLUP approachCompute Compute additive SNP effects with Equation (5) and dominance SNP effects with Equation (6)Calculate SNP weights as waj+1 and wdj+1Scale Gi+1 with Equation (3)Calculate Di+1 with Equation (4)Calculate i = i + 1i = 3.Iterate from 2 until A weighted relationship matrix was obtained from each subsequent iteration, giving respectively, Gw1, Gw2, and Gw3, as three distinct matrices leading to separate evaluation methods. In this study, therefore, eight relationship matrices were tested , and the resulting predictions were compared via cross-validation and by an independent data set.We assessed the impact of the composition of the training (TS) and validation sets (VS) on the performance of the genomic evaluation by trying two TS/VS sizes and two different TS/VS compositions in a 10-fold cross-validation scheme. The two sizes were 50% (T50) and 25% (T25) of the individuals evaluated in the 2000/2001, 2012/2013, and 2014/2016 experimental trials. The last field evaluation trial of 2017/2018 did not contribute to TS and was used as an extra independent validation set (TestSet) for each of the four TS, as it represented a sample of the next generation of selection candidates. Such Testset represents an independent validation experiment without the risk of eventual overfitting that is typical of cross-validation schemes, and it is the result of a mating campaign involving a sample of parents from the breeding population see . The twoIn order to assess the main factors accounting for genomic evaluation performance, we applied the Random Forest algorithm  are shown for all models and traits in the Three out of seven traits  were selected to show the cross-validation accuracies in Accuracies responded greatly to changes in the way the training set was constituted (percentage and composition). The fact of using different families for training than for validation had a large impact on the accuracy when compared to the alternative scenario where the splitting between training and validation occurred mostly within families. Basically, as expected, predicting different families was less accurate than predicting different individuals within the same cohort, with losses in accuracy averaging 13%. This pattern was found for all traits, except for one training scenario for angbranch, where differences between the two compositions were also the weakest. Concerning the percentage, the effect of reducing the training set from T50 to T25 had also an impact on accuracy, although mostly when training and validation involved different families. On average, reduction in accuracy with decreasing training set size was around 4.2% for the training composition based on individuals, and vary depending on the trait (from −18 to 8%) for that based on families.We used a completely independent set of individuals representing the next generation of selection candidates to evaluate the different prediction models with 7K SNP and across two different training scenarios (T25 and T50). Results of accuracies from this independent set are presented for three traits in Accuracies were substantially lower under the new more challenging testing scenario than those already shown for the cross-validation scheme for the same traits see . In geneBy adding a dominance effect to the single trait model for each trait with the 7K SNP panel, we did not observe significant changes in accuracy with respect to the purely additive model . OverallAnother added complexity were the multi-trait additive models, which were also evaluated in terms of accuracies . The advIn summary for the TestSet, the accuracy of unweighted G-based models appeared to be slightly better than with pedigree-based models, although in most cases the Acor model obtained comparable levels of performance to the best G-based method (data not shown). The cross-validation sampling strategy  impacted the accuracy in all cases and for all traits, with individual scenarios having, in general, higher accuracy than family scenarios. The percentage of individuals in the training population (T50/T25) showed a less important impact on accuracy than that of composition. More advanced models involving dominance effects and multiple-traits did not improve the performance of genomic predictions.The same three traits  were used to show the effect of an increase in marker density on prediction accuracy over different modeling approaches on the TestSet in The effects of density were clearly trait-dependent, and the choice of traits illustrated here cover well these differences in behavior. Such densities were also differently exploited according to traits by the different G matrices used in the modeling. For traits like height1 and rust1, densification in the number of SNPs had no clear benefit in terms of accuracy, and the use of weighted G matrices did not exploit the extra density to bring additional accuracy. For traits like budburst, however, densification brought some benefits in accuracy when combined with some weighting in the G matrices, notably after one step of weighting and using the highest densities of 250K.Besides the number of markers, their distribution over the genome seemed also of relevance for accuracy. This is particularly illustrated in the comparison between the 7K and 7K_homo sets, where the latter represents an even distribution sample over the genome. Such even distribution was not beneficial for accuracies across traits compared to the original 7K set. This latter set was seemingly richer for some relevant genes, as the array design from which the 7K set results favored certain regions linked to important traits over a homogeneous distribution.t-test on paired data confirmed the difference to be of significance between the two sets of data. Resulting accuracies under this new evaluation scheme are presented in Phenotypes used as dependent variables in the models resulted from averaging six field replicates that were previously spatially adjusted. To test whether the number of replicates could have an effect on the difference in performance between pedigree and genomic-based evaluations, new evaluations were produced based only on 3 out of 6 replicates. The adjusted clonal means produced were compared to those obtained with 6 Blocks. The correlation between the two clonal mean sets was close but not equal to 1  across models showed that the pedigree-based approaches had the most robust behavior with values always around 1. Contrarily, G-based approaches often showed upwardly biased predictions , 11. ThiWe compared two correlation coefficients: the classical Pearson correlation, on which predicting abilities are based, and a rank-based coefficient like Spearman. Such comparison was made across different tiers of the evaluated sample of candidates: from the 5% tier of best candidates to the totality of the TestSet, with the aim to explain the origin of biases. Results are shown in Differences in Prediction ability at within-family level between genome-based and pedigree-based predictions are shown in The Boruta algorithm was used to evaluate the different features explaining the variability of three performance parameters: accuracy, Spearman correlation and slope. Results in terms of Z-score for all features in the cross-validation are shown in This study was conceived as a proof-of-concept of the genomic evaluation in the black poplar breeding program in order to evaluate feasibility and performance in a situation close to operational conditions for the species. Several main messages could be drawn from this study. Firstly, genome-based models captured higher heritabilities and higher additive variances than their pedigree equivalents, although this did not lead to a systematic advantage in terms of prediction accuracy for the former over the latter. Although G-BLUP obtained in general the best prediction accuracies, it was very closely followed by the evaluation based on a genomically corrected pedigree. Secondly, the benefit of densification of the marker panel for the prediction quality was not obvious, with results dependent on traits and treatment of the G matrix. Finally, the most clear advantages of genome-based methods and of marker densification were found in more challenging validation situations, when observing the ranking among the best 5% elite individuals or when importance was given to selection within families.The genomic evaluation captured generally more genetic variance than pedigree evaluation, regardless of the trait. The number of markers fitted in the model generally increased the proportion of genetic variance explained by the model, but this occurred mostly under G-BLUP. When using a weighted GBLUP variant, the proportion of genetic variance explained by the model decreased with the cycles of weighting and selection of relevant markers. Without variable selection, plain G-BLUP, increasing the number of markers favored a better coverage of all genomic regions, including those close or inside relevant QTLs. Variable selection in weighted GBLUP could have eroded relevant variation, affecting the proportion of captured variation. This type of behavior could reflect an underlying infinitesimal-like trait architecture of the traits studied rather than a few underlying QTLs with a substantial effect  did not show in any case better results. Comparable results with decreasing efficiency of several cycles of weighting were found in other recent studies  on the quality of prediction. Our results are in line with previous studies. Several studies integrated dominance or epistatic effects in the GS. The results on real datasets showed either no improvement in terms of accuracy , the variation at within-family level appeared indeed as notably reduced when compared with between-family differences shown in , and thain vitro tests and later in field trials, with each step implying different selection intensities and notably different selection accuracies. It is important to note that each selection step is done sequentially and conditionally onto the precedent , instead of jointly and simultaneously, leading to inefficiencies with the risk of losing in the first steps important variation for subsequent steps. First steps of selection at the greenhouse and nursery are the less accurate, but the ones that screen most of the variation. Due to a limited field evaluation surface, a small number of individuals per family is kept for the next steps, reducing the phenotypic variance within each family. Conversely, later steps at the lab and in the fields are relatively accurate but screen through a subsample of original variation. Therefore, accuracy and genetic variation do not meet in a single same step for maximum efficiency in the current scheme.The present study took place at a particular step in the poplar breeding program, as illustrated in , specifie per individual, although with large variations due to techniques and depths, while genotyping experienced gradual reductions during data gathering from a starting 94e to late 46e per sample (not including chip design costs).Our test of genomic selection was performed with moderate to high heritability traits, well-evaluated in field trials, and on a relatively reduced set of individuals (with low effective population size) that were the result of two previous steps of selection conducted typically with a low precision and at a relatively high selection intensity see , such a One of the main limitations of the study was probably the use of a training population with a design that did not correspond necessarily to what is routinely done in poplar breeding. Indeed, the factorial mating design, although potentially interesting in terms of the parental variability, was more oriented for cognitive or mapping studies. This was partially overcome by the addition of extra families and crosses, well-connected to the breeding program. In that sense, a training population truly representative of the base population for the breeding program could have made more easily generalizable the results of the study. Resampling in the existing population is a good way to improve the training population and increase the prediction accuracy. For instance, to include 6–8 trees per family and evaluation site appears to be sufficient to guarantee an accurate estimation of genetic parameters for wood density and growth in an open pollinated test of black spruce . We believe that genomic selection could be an interesting option at that early stage, where selection precision is typically poor and genetic variability abundant. Our study also showed that it is important to assess performances by looking at other alternative criteria, like those related to ranking, notably when these criteria respond to the operational context of the breeding program under scrutiny.https://data.inrae.fr/dataset.xhtml?persistentId=doi%3A10.15454%2F4FWANJ&version=DRAFT (doi: 10.15454/4FWANJ).The datasets generated for this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at Populus nigra breeding program. VJ and LS designed the study, discussed the analyses, assisted in drafting the manuscript and obtained funding. All authors significantly contributed to the present study, and read and approved the final manuscript.MP performed the analyses and drafted the manuscript. FM developed the scripts for spatial adjustment of phenotypes. VS contributed to the discussion on analytical models and data preparation, providing as well valuable scripts. CB provided the access to plant material and contributed to the view of the breeding program and ways of optimization as the scientist responsible for the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


P < 6 × 10−9), including loci with links to intelligence and Alzheimer’s disease. Genetic correlations demonstrated that participation bias was common across studies. MR showed that longer educational duration, older menarche and taller stature increased participation, whilst higher levels of adiposity, dyslipidaemia, neuroticism, Alzheimer’s and schizophrenia reduced participation. Our effect estimates can be used for sensitivity analysis to account for selective participation biases in genetic or non-genetic analyses.Large studies such as UK Biobank are increasingly used for GWAS and Mendelian randomization (MR) studies. However, selection into and dropout from studies may bias genetic and phenotypic associations. We examine genetic factors affecting participation in four optional components in up to 451,306 UK Biobank participants. We used GWAS to identify genetic variants associated with participation, MR to estimate effects of phenotypes on participation, and genetic correlations to compare participation bias across different studies. 32 variants were associated with participation in one of the optional components ( Large BioBank studies are commonly used in GWAS, but may be biased by factors affecting participation and dropout. Here the authors show that some of the factors affecting participation may have underlying genetic components. This selection has the potential to result in bias in estimating phenotypic and genotypic associations2. It is well established that large cohort studies tend to have a healthy volunteer bias in initial participation3. There is also a growing body of evidence suggesting that continued engagement in cohort studies may be influenced by a range of factors. Studies have demonstrated age, education, ancestry, geographic location and health status are associated with loss-to-follow-up4. However, as with all observational studies, these associations may be confounded and therefore not causal in nature.Initial participation and continued engagement in cohort studies may be influenced by an individual’s social and lifestyle characteristics5. A study in the UK Biobank6 performed a genome-wide association study of completing the mental health questionnaire, identifying 25 loci associated with survey completion, and strong positive genetic correlations with educational attainment and better health and negative genetic correlations with psychological distress and schizophrenia.In order to assess the causes of non-participation, genetic data on a more complete sample can be leveraged. Analysis of genetic data in the Avon Longitudinal Study of Parents and Children (ALSPAC) demonstrated a number of factors were causally related to participation. Education, agreeableness and openness caused higher participation, whilst higher BMI, schizophrenia, neuroticism and depression caused lower participation7). Selection bias can also occur under other circumstances when only the outcome is related to selection, for example, if exposure does cause the outcome, and the outcome causes selection7. As another example, selection bias can occur if a modifier of the effect of exposure on outcome causes selection8. A comparison of associations between risk factors and overall and cause-specific mortality in UK Biobank and the less-selected Health Survey for England and Scottish Health Surveys showed wide variation in these associations9, with some over-estimated in UK Biobank and some under-estimated. Thus, to understand the impact of selective participation for a particular analysis, we need to identify factors that influence participation.In general, an analysis will give biased estimates if the exposure and the outcome variable (or causes of them) are associated with participation  the percentage of food frequency questionnaires (FFQ) completed, (b) acceptance of the invite to wear a physical activity monitor, (c) acceptance of an invitation to participate in the mental health questionnaire (MHQ) and (d) the completion of the aide-memoire. We used two-sample Mendelian randomisation (MR) approaches to explore the role of over 80 predictors on participation in the UK Biobank. Finally, we also explored genetic correlations between participation in the UK Biobank and the ALSPAC study to test between study consistency. If the same factors affect participation in studies that vary by geography, time period and design, then those studies will suffer the same bias, and thus replication of results across studies becomes meaninglessP < 6 × 10−9), including loci with known links to intelligence and Alzheimer’s disease. Genetic correlations demonstrated that participation bias was common across studies, whilst MR provided evidence that longer educational duration, older menarche and taller stature increased participation, whilst higher levels of adiposity, dyslipidaemia, neuroticism, Alzheimer’s and schizophrenia reduced participation. Our effect estimates can be used for sensitivity analysis to account for selective participation biases in genetic or non-genetic analyses.This study identified 32 variants associated with participation in at least one of the four optional components , female sex , lower body mass index , lower levels of deprivation , higher fluid intelligence , never smoking , higher self-reported physical activity using the International Physical Activity Questionnaire (IPAQ) , higher measured physical activity , no depression (MHQ and aide-memoire) and no type 2 diabetes . There was some evidence that the aide-memoire variable captured a different aspect of participation, with associations in the opposite direction to the other participation measures. For example, a longer duration in education was associated with lower odds of completing the aide-memoire, but higher odds of participating in the other three components. This was further evidenced by strong observational associations and genetic correlations between three of the participation variables, whilst completing the aide-memoire was not as robustly correlated with participation in the other optional surveys  was associated with younger age, male sex, lower body mass index, lower levels of deprivation, higher fluid intelligence, never smoking and a lower prevalence of type 2 diabetes.We also generated a binary variable to compare participants who were invited to participate in at least one the optional surveys  versus those participants not invited  and cognitive performance (rs1342859813). For both variants, the allele associated with higher intelligence or cognitive performance is associated with completing more FFQ. A further two variants (rs9261655 and rs147412694) were associated with blood cell traits14. Here, the alleles associated with higher blood cell counts were associated with completing fewer FFQ, whilst the reason behind this unknown, higher white blood cell counts have previously been associated with poorer cognition16. Four of the eight variants were also in high LD (r2 > 0.8) with genome-wide significant (GWS) signals from behavioural GWAS, including ADHD, risk tolerance, smoking and alcohol consumption. As expected from previous work on participation and our understanding of risky behaviours, alleles associated with a higher risk of ADHD, higher-risk tolerance and a higher risk of smoking or consuming alcohol associated with lower FFQ participation (Table Two of the variants identified for FFQ participation were previously identified in GWAS of intelligence rs112108711087112 ar2 = 0.52) and completing the food frequency questionnaire (r2 = 0.32). This variant was previously identified as associated with multiple sclerosis17, with the allele associated with higher odds of multiple sclerosis associated with lower odds of participation in physical activity. In the UK Biobank, this variant is also associated with adiposity related traits18. The allele associated with higher adiposity is also associated with lower odds of participation in physical activity monitoring.The locus identified for participation in actigraphy (rs55714359) was in partial linkage disequilibrium with the variants identified for participation in the mental health questionnaire (6 we replicated 15/25 (60%) at P < 6 × 10−9 and 22/25 (88%) at P < 5 × 10−8 in this larger sample of related individuals. The three missing variants  were directionally consistent and approaching GWS . Of the 21 variants associated with MHQ participation at the stringent threshold, four were previously associated with cognitive function and intelligence measures  and a further three were in high LD with variants identified associated with intelligence outcomes. For all variants, the allele associated with higher intelligence or cognitive performance was associated with higher odds of completing the MHQ.Of the 25 loci identified in a GWAS of MHQ participation by Adams et al.APOE (rs429358) was associated with MHQ participation. The C-allele is a marker of the APOE-ε4 genotype which is a major risk factor for Alzheimer’s disease19, and here, was associated with lower odds of participation in the MHQ. Further analysis in the unrelated subset tested whether individuals with APOE-ε4ε4 haplotype were less likely to participate in the MHQ compared to those with the APOE-ε2ε2 haplotype. Lower odds of MHQ participation was observed in the APOE-ε4ε4 haplotype carriers 0.89  in all individuals and in those who were less than 50 years old at recruitment ). This suggests that individuals with early signs of cognitive impairment had reduced capacity to participate in the MHQ.A missense mutation in 20 and isoleucine levels21. The G allele raises both isoleucine levels and bone mineral density (BMD) and was associated with lower odds of completing the aide-memoire. Previous studies have demonstrated that BMD is inversely associated with cognition22 and Alzheimer’s disease, indicating those with higher BMD may have a better memory.The variant rs58101275 has previously been associated with bone mineral densityP < 6 × 10−9, 6 were either top signals for MHQ participation or in high LD (r2 > 0.8) with variants for MHQ participation , we observed strong positive genetic correlations between three of the four participation measures  and qualifications, fluid intelligence, years spent in education. Strong inverse genetic correlations were noted between three of the four participation measures  and obesity-related traits. Completing the aide-memoire, was strongly inversely correlated with risk-taking, ever smoking, driving fast, having fractured bones in the last 5 years and schizophrenia. It was positively associated with suffering from nerves and experiencing nervous feelings.After Bonferroni correction , participation in MHQ , and physical activity participation  .There were positive genetic correlations between the ALSPAC participation measures and FFQ completion  analyse, with 8 at more stringent P < 0.0001; Supplementary Table In all individuals, Mendelian randomisationLonger duration in education and higher intelligence predicted higher odds of participation in the FFQ, MHQ and physical activity monitoring Fig. . For exaHigher adiposity caused lower odds of participation in the FFQ, MHQ and physical activity monitoring. For example, the odds ratios for participation in the MHQ and PA monitoring per one-SD higher waist:hip ratio were 0.85  and 0.83 , respectively  was the only biomarker tested with some evidence of a causal association, with a twofold higher CRP causing higher odds of completing the MHQ ; Supplementary Data Higher genetic liability of cancer and non-cancer diseases and poorer metabolic health generally caused lower odds of participation  and 1.07  higher odds of completing the MHQ and physical activity monitoring, respectively , ORmen: 1.01 ), Pinteraction = 0.07).Generally, results were consistent when analysed in men and women separately . These include lower intelligence and educational attainment, higher adiposity and increased liability to ADHD and neuroticism and schizophrenia. Many of these were previously identified in the ALSPAC studyAPOE locus. The allele that raises participation in the MHQ (T) is associated with lower odds of Alzheimer’s disease19, heart disease, inflammation and dyslipidaemia26. Further analyses indicated that the APOE-ε4ε4 haplotype carriers were less likely to participate in the MHQ and high genetic liability for Alzheimer’s disease lowered odds of participation in the FFQ, physical activity monitoring and the MHQ.GWAS identified a number of loci robustly associated with the different participation measures. A number of genome-wide significant loci were shared across the participation traits, suggesting a general role in influencing participation. Although further analyses using colocalization methods would be necessary to more formally test whether these shared loci represent the same signal. Many of the variants identified were in or near loci which had previously been identified as associating with, intelligence and cognitive function or behaviour-based traits. Alleles associated with higher intelligence or risk aversion were consistently associated with completing the MHQ and more of the FFQ. In the MHQ GWAS, the top signal was in the highly pleiotropic 27. A factor that is positively associated with both being invited and participation is likely to have its association with participation biased towards the null when conditioning on having been invited, assuming that being invited and participating are positively correlated (as demonstrated here) and that there are no interactions  between the variable and others that also influence invitation/participation. Here, we demonstrated that some variants were associated with higher odds of both being invited to participate and completing the MHQ. This suggests that here conditioning on being invited to participate could have resulted in the Mendelian randomisation analyses for these variables being biased towards the null, if they were in truth positively associated with participation. However, if there are non-linearities or interactions in the effects of the risk factors on invitation/participation, then the direction of the bias cannot be predicted. Similarly, a factor that affects being invited, but does not in truth affect participation, could appear to have a positive or negative spurious association with participation, conditional on being invited.In addition to performing GWAS of our four participation measures, we also performed a GWAS of invitation to participate in at least one of the three optional components. Because only those invited can participate, the fact that not everyone is invited could result in collider bias in our analysis of participation1 were strongly correlated with the participation measures derived in the UK Biobank. This fits with a previous study where strong genetic correlations were noted between UK Biobank mental health participation and participation in follow-up in Generation Scotland6. These results suggest that similar genetic factors are driving participation in follow-up and optional components of studies, regardless of study design, recruitment strategies and the population demographics of the study. The similarity of factors affecting participation across different studies is potentially important for comparisons of results between studies—if similar factors cause participation in different studies, then collider bias will have the same impact on the results from each study. Thus, results from different studies would be subject to similar biases, causing replication of results across studies to become meaningless.Using genetic correlation analyses, we have demonstrated that these participation issues are not specific to the UK Biobank. Two participation measures from the ALSPAC study7. Thus when examining the factors affecting physical activity, all the factors that we have shown here to be related to participation in the physical activity monitoring , should be either included in the analysis model or used in other strategies such as inverse probability weighting (IPW) or multiple imputations (MI). Where a selection is related to the underlying concept(s) measured by the optional component, then this concept will be missing not at random and analyses where it is the outcome will likely be biased7. On the other hand, a participator-only analysis of a model that involves only characteristics that are unrelated to participation will not be biased by conditioning on participation.These results are important for informing analysis strategies and the likely direction and magnitude of bias due to conditioning only on those who participate. For the participator-only analysis for a given model to be unbiased, it is necessary for the outcome variable to be independent of missingness, given the variables in the analysis model9. It should be noted that this simple estimate of the direction of bias depends on assumptions about the underlying selection model, and cannot be verified with only UK Biobank data—e.g., an interaction between smoking and socioeconomic position in their effect on participation could change the size and direction of any bias. We have similarly shown previously that some effect estimates were different when calculated on only those continuing to participate in ALSPAC, compared to all those participating at baseline1. It has also been suggested that selection bias may (at least in part) be responsible for overestimates of the protective effect of moderate alcohol consumption29.Selection of the type demonstrated here may cause bias in estimates of effects, and the size and direction of bias cannot  be exactly determined. Previous work has shown that estimated effects of risk factors on mortality and cause-specific mortality differ between UK Biobank and the less-selected Health Survey for England—with some being moved towards, and some away from the null. This could imply that selection into UK Biobank is causing bias in estimating these effects. For example, we have shown that smoking is negatively associated with participation in UK Biobank. If a factor that causes lung cancer is also negatively associated with participation , then selecting on participating in UK Biobank would induce a negative association between smoking and lung cancer (assuming an additive model), which would bias the estimated effect of smoking on lung cancer towards the null. This is indeed what is seen in the comparison of estimates for this effect between UK Biobank and HSE-SHS30, but the validity of this depends on correctly specifying the selection model. If there is an unmeasured factor that affects selection and is related to the variables in the analysis model, then this may mean that inverse probability weighting is not unbiased31. IPW as a solution also depends on having data on all the variables affecting selection and their distribution in the population in which we wish to make the inference. Solutions using IPW to infer bounds on estimates have been proposed, although these can result in wide bounds, or depend on underlying assumptions about associations of unmeasured factors with selection33. Over-sampling of under-represented subgroups of the population is used, for example in the Millenium Cohort Study34. However, this solution will only remove bias due to selection into those specified subgroups (not any other selection bias). In addition, if the selection in those subgroups now differs according to other factors—e.g., the participators from the hard-to-reach groups are comparatively healthier than those in the easier-to-recruit group, then new biases may be introduced.Strategies to investigate or minimise the impact of selection on a given estimate depend on the data available on the population not selected into the study. Inverse probability weighting (IPW) has been suggested to overcome mortality biasA key advantage to the genetic analyses presented here over the observational analyses usually reported . Our sensitivity analyses using MR-Egger and Median MR, which are more robust to horizontal pleiotropy were generally consistent, although often had much wider confidence intervals that crossed the null.In summary, we demonstrate that genetic variants are associated with participation in several aspects of the UK Biobank study and that a wide range of traits cause differences in participation. This builds on previous work in the ALSPAC study and here we demonstrate strong genetic correlations between the UK Biobank participation measures and ALSPAC highlighting that these issues are likely to be seen in many studies. Our findings highlight the potential for introducing bias into both genetic and non-genetic analyses. All studies need to consider the importance of selection bias and use sensitivity analyses to assess the robustness of their conclusions.https://www.ukbiobank.ac.uk/ethics/). Details of the patient and public involvement in the UK Biobank are available online (www.ukbiobank.ac.uk/about-biobank-uk/ and https://www.ukbiobank.ac.uk/wp-content/uploads/2011/07/Summary-EGF-consultation.pdf?phpMyAdmin=trmKQlYdjjnQIgJ%2CfAzikMhEnx6). No patients were specifically involved in setting the research question or the outcome measures, nor were they involved in developing plans for recruitment, design, or implementation of this study. No patients were asked to advise on interpretation or writing up of results. There are no specific plans to disseminate the results of the research to study participants, but the UK Biobank disseminates key findings from projects on its website.This study was conducted using the UK Biobank resource, which has ethical approval and its own ethics committee  from across the UK between 2006 and 2010. The UK Biobank37.Percentage of food frequency questionnaires (FFQ) completed, based on the number of invites  and the number of acceptances . A binary variable was also created that represented sent a food frequency questionnaire but never accepted (0) and sent a food frequency questionnaire and completed at least one (1). This variable is based on the online requests which were sent out every 3–4 months a total of four times between February 2011 and June 2012 to participants who provided an email address at recruitment38.Participation in physical activity monitoring, a binary variable defined using data fields 110005 and 110006. 0 represents invited but not accepted and 1 represents invited and accepted. Between February 2013 and December 2015, a random sample of participants with a valid email was invited to wear the accelerometer. Participants from the North West region were excluded due to participant burden concernsParticipation in a mental health questionnaire (MHQ), a binary variable defined using data fields 20400 and 20005. 0 represents invited but not accepted and 1 represents invited and accepted. Participants with a valid email were invited to complete the MHQ. The UK Biobank’s contact approach was to (a) send an initial invitation email, (b) send a reminder email to non-responders , (c) send a reminder to partial responders (2 weeks after they started the questionnaire) and d) the last chance invitation after 4 months.Aide-memoire completed, a binary variable derived from data field 111 which represents compliance to a request from the UK Biobank prior to attending the assessment centre to fill out specific information to help with the questionnaire. 0 represents non-compliance and 1 represents compliance.Four participation phenotypes were derived in the UK Biobank:With the exception of the aide-memoire, which was requested by everyone, the remaining variables relied on UK Biobank participants being invited to take part. The general UK Biobank protocol was to invite everyone to participate in the optional questionnaires and surveys, although as detailed above these invitations were generally sent via email. To investigate the impact of this strategy, we also created a variable to represent whether participants were invited to participate in at least one of the optional surveys above (coded as 1) or not (coded as 0).39. Variants were excluded if imputation quality (INFO) was <0.3 or the minor allele frequency (MAF) was <0.1%. This quality control process resulted in 6,930,712 variants for association analyses. Lead SNPs were defined as those with the smallest P value and locus boundaries were defined using a ±0.5 Mb distance from the lead SNP.We used imputed genotypes available from the UK Biobank for association analysesLogistic regression analyses were used to explore the relationship between participant demographics and the four participation measures, plus the invitation measure. The Pearson correlations and overlap between the four participation measures were also investigated. Chi-squared analyses were used to explore the overlap of the binary participation measures.40 v2.3. This software applies a linear mixed model (LMM) to adjust for population structure and individual relatedness. From the ~805,000 directly-genotyped (non-imputed) variants available, we identified 524,307 good-quality variants  which BOLT-LMM used to build its relatedness model. A number of covariates  were included at runtime. Here in the main paper, we only report variants that reached a stringent P < 6 × 10−9 cut-off based on simulations41. The results from the GWAS of receiving an invite to at least one of the three optional surveys is also reported.All individual variant association testing was performed using BOLT-LMM42 as implemented in the LD Hub software43, available at http://ldsc.broadinstitute.org/ldhub/, to quantify the genetic overlap between the four participation traits and 832 traits with publicly available GWA data. This method uses the cross-products of summary test statistics from two GWASs and regresses them against a measure of how much variation each SNP tags (its LD score). Variants with high LD scores are more likely to contain more true signals and thus provide a greater chance of overlap with genuine signals between GWASs. Correlations were reported if they reached a Bonferroni corrected P value .We used a method based on LD score regression1. The LD score regression method used summary statistics from the GWAS meta-analysis of the 4 participation measures in UK Biobank and the participation measures of ALSPAC mother and children, calculates the cross-product of test statistics at each SNP, and then regresses the cross-product on the LD score.We also used the LD score regression to explore the genetic correlation between our participation measures and those available in the ALSPAC studyFinally, we utilised LD score regression to explore the genetic correlation between not receiving an invite to participate in the various optional components and the four participation measures.Genome-wide genetic correlations do not provide evidence of causality, which we tested with Mendelian randomisation using specific sets of variants. Instead, they likely represent a complex mixture of direct and indirect causal associations in both directions, pleiotropy and residual stratification. These likely properties of genome-wide genetic correlations mean they provide a way of projecting a phenotype measured in one study into another to test between study consistency , or, when comparing different traits within one study, potentially a measure of correlation that is more representative of biological processes than observational correlations, although we note that observational correlations were usually very similar to genetic correlations.We undertook two-sample MR analyses to further test the causal relationships between 80 exposure traits (decided a priori on the grounds that they are common exposures and used in current MR pipelines) 25; Penalised weighted median (PWM)25. We have presented the IVW approach as our main analysis method, with the MR-Egger, WM and PWM representing sensitivity analyses to account for unidentified pleiotropy, which may bias our results. Horizontal pleiotropy occurs when the genetic variants related to the exposure of interest independently influence the outcome. IVW assumes there is either no horizontal pleiotropy under a fixed-effect model or, if using a random-effects model after detecting heterogeneity amongst the causal estimates, that the strength of the association between the genetic instruments and the exposure is not correlated with the magnitude of the pleiotropic effects (the InSIDE assumption) and that the pleiotropic effects have an average value of zero. MR-Egger estimates and adjusts for non-zero mean pleiotropy and therefore provides unbiased estimates if just the InSIDE assumption holds24.The two-sample MR analyses used summary-level data from the BOLT-LMM GWAS of the participation traits. Known SNPs for each exposure trait (Supplementary Table To explore the role of smoking heaviness on participation in the different smoking strata we performed one-sample MR in the unrelated subset of Europeans in the UK Biobank. We performed analyses in all individuals and stratified by smoking status into never, former, current and ever smokers. Here, for our binary participation measures, we first assessed the association between the cigarettes per day and the smoking GRS. The predicted values and residuals from this regression model were saved. Second, the predicted values from stage 1 were used as the independent variable and the participation measures as the dependent variable in a logistic regression model. As the FFQ participation measure was continuous we utilised the ivreg2 command in Stata.All analyses were performed in Stata version 14 or R version 3.5.0.Further information on research design is available in the Supplementary InformationPeer Review FileDescriptions of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


The progeny of solid stem type F1 plants from hollow stem parents indicated the complementation of favorable alleles dispersed among the parents. Non-confirmation to digenic complementary (9:7) model of inheritance and polynomial distribution of the trait in all F2 populations indicates multiple factors complementation in the additive fashion for stem solidness.Stem solidness in wheat is an important architectural trait to support the erect behavior of the plant. The varieties with high yield potential due to increased sink strength tend to lodge either because of poor anchorage or weak stem. The solid stem can partially counter the tradeoff between biomass driven yield gain irrespective of the plant height. Stem solidness being a complex trait with highly variable expressivity, understanding its genetic behavior in different genetic backgrounds is highly essential to integrate this trait in the breeding program. In this study, the expressivity of a solid stem in different internodes was investigated in nine F However, declining yield gain in bread wheat breeding poses a big challenge for meeting the requirement of the populous country by 2050, particularly under the constraints of increased competition for the cropped area, depleting water resources and changing climate2. The harvest index (HI) under the optimum plant population and the best management conditions3 is already optimized at about 0.45 and a further increase in HI will require a complete change in plant architecture. An increase in plant biomass can still be explored for furthering the yield gain in the future. However, increased biomass will further the risk of lodging unless we bring about structural changes both in stems and roots. The abrupt increase of temperature in the desert of Rajasthan and snowfall events in lower and Himalayan states brings tropospheric changes resulting in unwarranted rain and or storm generally between the second fortnight of January - middle of March causing large scale lodging in the wheat crop in grain filling stage2. The uncertainty in crop production regime due to erratic environmental conditions poses a serious threat to further realization of high productivity genotypes due to wheat vulnerability to lodging3. The lodging in wheat can occur both in the stem as well as the root level. A weaker stem or tiller will bend permanently due to wind force exerted on plant compared to a stiffer and broader diameter stem which will resonate along with the wind and will come to its original position sooner owing to its better elasticity6. The force exerted on the stem will pass the turning moment to the root zone and a weaker root system will fail to cause the root lodging. To support each 10 cm increase in height, the diameter of the bottom internode and the root plate spread must be increased by 0.36 and 3.7 mm, respectively7. Therefore, integration of stem solidness especially in lower internodes with better root plate seems to be a plausible option to relatively meet the requirement of biomass and the requisite strength to hold heavier spikes6. Stem solidness confers resistance to wheat stem sawfly and has been suggested as an important component for conferring lodging tolerance9.Wheat is the principal staple food of the world with a sizeable contribution from India, is the most important crop for global food supply. The North Western Plain Zone (NWPZ) of India is optimal for wheat production due to the accessibility of supplemented irrigation and good management practices11. A similar study based on monosomic analysis of solid-stem reported that at least five chromosomes carried genes promoting pith production and at least three chromosomes carried genes inhibiting pith production12. A significant solid-stem × hollow-stem parent interaction was observed for stem solidness score suggesting epistatic gene action in the inheritance of this trait13. The trait has also been reported to be negatively correlated with yield in older reports15; however, later studies confirmed no such association of genomic regions explaining the variation of solid stem trait with decreasing the yield potential16. Moreover, the development of high yielding, solid-stem cultivars are not limited by undesirable associations between degree of stem solidness and other agronomic traits20.The stem solidness has been reported as a simply inherited genetic trait controlled primarily by major gene(s) with various levels of expressivity. There are different reports regarding the inheritance of the solid-stem trait ranging from one major gene with several minor, modifying genes to four genes, with one being epistatic to the others, based on two solid- stem × hollow-stem crosses1 plants from hollow stem parents. Moreover, most of the past genetic studies are based on a single bi-parental population implicating to limited genetic background. To address those shortcomings of the past studies, a comprehensive genetic study was carried out in multiple genetic backgrounds comprising of nine F2 populations  which included both the hollow × hollow and solid × hollow stem type of cross combinations to understand the expressivity of stem solidness in different genetic combinations, to find out better parental combinations and superior recombinants from the progenies. Additionally, the frequency of superior recombinants with at least three internodes of stem solidness and frequency of spikes with more than twenty spikelets were also determined with the aim that stem solidness can be favorably used for the development of high yield potential genotypes.Besides, cross combinations of solid versus hollow stem parents, there are no reports of getting solid stem F1 seeds were sown during 2016–17 growing season in 0.5 m long and 0.25 wide paired rows where seeds were sown at 5 cm apart in order give enough space for each plant for optimal growth. At least 10 representative tillers from each of the 413 crosses were evaluated for the stem solidness trait. Out of total F1 combinations, 34 expressed solid stem , New Delhi, India. The soil type is a moderately alkaline sandy loam with pH = 7.5–8.5 and electrical conductivity ranging from 0.4 to 0.6 dS/m. Moreover, the organic carbon content was low 0.41%), and the available Nitrogen (N), Phosphorus (P) and Potassium (K) content at 0–15 cm depth were <280, 25–30, and 120–280 kg/ha, respectively. The recommended dose of NPK fertilizer 150: 60: 40 kg/ha was applied to ensure optimum crop growth. For all the field experiments, the sowing was done during the third week of November, and standard agronomic management practices were followed with five supplementary irrigations scheduled according to the growth stage of the crop. A total of 413 cross combinations were made during 2015–16 in winter season with about 10 spikes per combination, and the F%, and thThe solid stem was characterized into three categories based on the expression of pith formation in different internodes Table . The pitOf the 34 crosses, nine combinations as described in Table corplot function along with hierarchical clustering with alpha = 0.05 using the R software package. The variables were standardized before analysis using scale function for data in R software.The percent of tillers in each of solidness classes representing the expressivity were plotted against different internodes. The regression equation was also established using the best model of regression explaining maximum variability for each class using the plot function in MS Excel. The interclass correlations based on Pearson correlation coefficients were determined by using the 18. The total variance explained was plotted against two principal components on the x and y-axes. The optimum number of K clusters was determined by the elbow method wherein the within-cluster sum of squares (WSS) was plotted up to 10 groups, and point after which there is no significant reduction in WSS was taken as the threshold. The R software packages K means and factoextra were used for summarizing the outputs.The K means clustering using the Hartigan-Wong algorithm was applied for determining the population structure using centroid from the matrix of standardized variablesthe ggplot function in the ggplot2 package in R software. In each population, the scale of desirable recombinants could be changed depending upon the frequency of the same. A general criterion could be the identification of plants having a height of over 90 cm, the number of spikelets equal to or more than 20 and a grade of solidness equal to or more than 8, which means at least two internodes are solid and third could be semisolid or solid.The desirable recombinants could be visualized based on the pair-wise scatter plot of height versus spikelet number, and grade total as an indicator of the solidness was used in a unique color scheme using 1 plants from 413 different cross combinations, 34 were found to express solidness in lower internodes  number of tillers in the fifth internode. The solid, semisolid and hollow lines did not intersect in this combination. The other two combinations viz. PBW725 × PBW707 and K1301 × HD2851/Yr10 were having the expression of solid types in 40% tillers in three internodes. All the categories showed binomial distribution except semisolid types in K1301 × HD2851/Yr10 wherein it was polynomial indicating some complex interaction of factors.The fact that HD2329 was the only parent with a solid stem phenotype could combine differentially with other genotypes resulting in the expression of all three categories of solidness. This indicates the absence of dominant type gene action and confirms the presence of complementary gene interaction. The solidness expressed variably even within a plant as all the internodes within a tiller vary for solidness  transcription factor has been found associated with solid-stem phenotypic variation25. In addition to the genetic components, the stem solidness is subject to variable expression due to different external non-genetic factors, such as seeding rate and N- application28. There is also a possibility of different allelic interactions of main factors as well as transcription factors or the main effect QTL on 3BL and minor QTLs including Qss.msub-3DL30. However, with the available information on the genetic loci and markers linked with solid stem expression, it is possible to select parents for making crosses more precisely25.The pithiness expressivity is variable within a tiller as well as among the tillers of a single plant in F26. The solidness is expressed based on the genomic constitution of wheat lines as different genomes interact to give a different degree of expression of solidness30. The haplotype-based comparison indicated a clear binomial distribution in DH lines from Durum solid × hollow combination while in case of common wheat the distribution was near binomial with more tendency towards being hollow to semisolid9. It may be due to some transcription factors/modifiers which modify the expression of pith production based on certain non-genetic factors including gravity. It is highly likely that stem solidness might interact and maintain the trade-off with other plant architectural traits, such as stem elongation and tillering capacity.The best-fit regression equation has been used to express the relative change in solidness31. The DAPC or PCA based clustering is a strong tool being used in genetic studies based on both genotypic and phenotypic data33. The method gives an idea of possible phenotypic categories in a population of the variables used in the study. In this study, the nine populations could be optimally classified in four to five clusters indicating the role of at least two or more quantitative factors with a variable degree of contribution for solidness. This can be substantiated by the theory of two QTLs, one being major and other being minor governing the major portion of solidness29. The remaining part of variance can be attributed to the other possible minor QTLs and interaction effects of QTL × genotypic background9.The K-means based clustering, which utilizes the Euclidean distance and centroid information to classify the individuals into different groups, is considered as a powerful tool to visualize and compare the underlying differences among individuals. These groups are graphically depicted on two principal components axes that explain the maximum variability. This method is also known as discriminant analysis of principal component (DAPC)34. Studies with solid-stem × hollow-stem wheat crosses have also shown that stem solidness tends to be negatively correlated with plant height35. However, in the segregating population of HD2967 × RAJ 4422 the frequency of favorable segregants with optimum plant height, increased spikelet number, and more stem solidness were quite high. Nevertheless, it is imperative to say that the recovery of useful recombinants is determined not only by the segregating population size but also it depends upon the biological tradeoffs among these traits. Favorable recombinant in this population indicates the possibility of yield gain with decreased incidence of stem lodging due to stem solidness and is supported by other workers35 also. Similar results have also been reported in solid stem cultivars developed at the Montana Agricultural Experimental Station, the USA is an example of this20. Interestingly, the landmark green revolution varieties in India, Kalyan Sona and HD2329 were also solid stemmed. Therefore, the genotypes with high yield potential can be derived from crosses having favorable solidness and sink size with supporting height and biomass.The present study is based on different genetic combinations and could find almost all kinds of associations reported in the past. In the segregating population of the cross K1301 × HD2851, segregants were either with low spikelets number coupled with solidness or reduced height with increased spikelets number and solidness, again putting a ceiling for yield gain due to lower sink in one case and lower source in another case. Similarly, in the case of WH1105 × HD2329, segregants with a significant reduction in height with increased spikelets and solidness were found. The significant negative association between solid stem and yield were reported by many workers in the pastLodging has become one the biggest stumbling block in minimizing the gap between realized and potential yield in the high yielding environment of NPWZ of India. The solid stem was never been explored by Indian breeders because of no major threat from stem sawfly, borer insects and comparatively lower incidence of lodging. The understanding of the negative correlation between stem solidness and grain yield also played a role in keeping the breeding aim at bay. However, the role of stem solidness towards improving plant strength to support lodging tolerance and providing better mobilization of stem reserves under stress conditions besides sawfly resistance have been advocated in the recent past. The current study is a proof of concept that the solid stem genotypes can be developed from a large number of hollow but improved genotypes by combining the dispersed favorable alleles for stem solidness in different genetic backgrounds. Our results indicate that increased phenotypic expression of stem solidness was large because of quantitative factors with additive nature rather than dominance or recessive kind of gene action. The occurrence of fewer transgressive segregants for improved sink and stem solidness in some of the crosses indicates the possibilities of their selection by increasing the size of segregating populations. A better understanding of the molecular basis of variable traits expression in the current segregating population of this study and newer targeted cross by integrating already available information on the role of transcription factors and genomic regions can speed up the yield gain by combining improved sink with lodging resistance.Supplementary Dataset 1.Supplementary Dataset 2.


Genome-wide association study and two meta-analysis based on GWAS performed to explore the genetic mechanism underlying variation in pig number born alive (NBA) and total number born (TNB).Single trait GWAS and two meta-analysis  were used in our study for NBA and TNB on 3,121 Yorkshires from 4 populations, including three different American Yorkshire populations  and one British Yorkshire populations (n = 874).The result of single trait GWAS showed that no significant associated single nucleotide polymorphisms (SNPs) were identified. Using single-trait meta analysis and multi-trait meta analysis within populations, 11 significant loci were identified associated with target traits. Spindlin 1, vascular endothelial growth factor A, forkhead box Q1, msh homeobox 1, and LHFPL tetraspan submily member 3 are five functionally plausible candidate genes for NBA and TNB. Compared to the single population GWAS, single-trait Meta analysis can improve the detection power to identify SNPs by integrating information of multiple populations. The multiple-trait analysis reduced the power to detect trait-specific loci but enhanced the power to identify the common loci across traits.In total, our findings identified novel genes to be validated as candidates for NBA and TNB in pigs. Also, it enabled us to enlarge population size by including multiple populations with different genetic backgrounds and increase the power of GWAS by using meta analysis. China produces more than 50 million tons of pork each year, accounting for over 50% of the total global production . Even a https://www.animalgenome.org/cgi-bin/QTLdb/SS/index), until now, there are 2,135 QTLs reported for overall traits linked to reproduction including 6 QTLs for endocrine, 1,013 for litter traits, 668 for reproductive organs, and 448 QTLs for reproductive traits. Among them, 522 QTLs identified for TNB (350 QTLs) and NBA (172 QTLs) compared with 8,962 QTLs for fatness or 1,745 for growth traits.Exploring the loci and genes affecting sow reproduction performance is necessary for understanding the genetic characteristics of these traits and increasing the speed of genetic improvement . On one Most of those studies mainly utilize one pure breed or an intercross population, with the result only reflecting one specific breed’s linkage disequilibrium character. As the most popular commercial pig breed, Yorkshire is usually used as terminal dam line in pig hybrid production, and the improvement on their reproduction traits is therefore very important. There were two main objectives in our study. The first one was to detect significant SNPs and candidate genes in four populations from different genetic backgrounds using conventional single-trait GWAS respectively. The second one is to improve the power of GWAS by enlarging population size through implementing a meta-analysis for multiple traits within a population or for same trait across populations.The whole procedure for collecting ear tissue samples was carried out in strict accordance with the protocol approved by the Institutional Animal Care and Use Committee (IACUC) at the China Agricultural University. The IACUC of the China Agricultural University specifically approved this study (permit number DK996).A total of 3,121 Yorkshire pigs used in this study were sampled from four pig breeding farms , including 2,247 progeny of three different American Yorkshire populations and 874 progeny of one British Yorkshire population (XD). Animals from LM and ZX are descendants of American Yorkshires but from different breeding companies, while LM and FJ came from the same breeding companies. The progeny of American Yorkshires were born in 2013 through 2018 and came from 222 sire families (8 to 76 offspring in each family with an average of 15), and the progeny of British Yorkshires were born in 2007 through 2013 and came from 129 sire families (10 to 71 offspring in each family with an average of 7). There was no genetic connectedness between LM, XD, and ZX according to the pedigree information. Phenotypic records included two reproductive traits, TNB and NBA. The populations and phenotypes information are presented in http://cnsge.nahs.org.cn/); afterwards, corrected phenotypic values were calculated as EBV plus the estimated residual for each individual in each population.Breeding values for NBA and TNB were routinely estimated by the breeding companies using a standard animal repeatability model which was separately implemented in each population, and were obtained from the National Swine Genetic Improvement Center of China (−6) were removed. After genotype quality control, 3,121 individuals and 49,839 SNP remained for further analysis.Genomic DNA was extracted from blood samples using a TIANamp Blood DNA Kit . Genotyping was performed using a PorcineSNP80 BeadChip , which includes 68,528 SNP across the entire pig genome. Genotype quality control was carried out using PLINK 1.9 software  separate2 greater than 0.2 were further pruned after genotype quality control, and in total 29,229 SNPs were used in PCA. The linkage disequilibrium within each population was calculated using PLINK software [Because the genetic backgrounds of four Yorkshire populations in this study are different, a principal component analysis (PCA) was carried out to detect the population stratification using GCTA software . In ordesoftware  as well.Single-population GWAS through linear mixed model was carried out in each pig population separately. Based on single-population analysis, the meta-analysis within population and cross populations were conducted, respectively.A linear mixed model was implemented to detect the association of SNP with growth and fatness traits. The model in this study is a single SNP regression model. The model includes a random polygenic effect to account for shared genetic effects of related individuals and to control population stratification. The statistical model is described below:yc is the vector of phenotypes ; 1 is a vector of ones; μ is the overall mean; b is the average effect of the gene substitution of a particular SNP; x is a vector of the SNP genotype ; g is a vector of random polygenic effects with a normal distribution g ~ N, in which σa2 is the polygenic variance and G is the genomic additive relationship matrix and was constructed using all markers following VanRaden [Z is an incidence matrix relating phenotypes to the corresponding random polygenic effects; and e is a vector of residual effects with a normal distribution N, in which σe2 is the residual variance. The software GCTA [in which VanRaden ; Z is anare GCTA  was usedAfterwards, Bonferroni correction at a significance level of 0.05 was used to identify significant SNP. There were 52,173, 52,804, 52,526, and 52,267 qualified SNPs in the four populations , respectively. The p values of the 5% genome-wide and suggestive significant thresholds were equal to 0.05/SNPs number and 1/SNPs number, respectively, in four populations.X2) using the formula:Based on the results of GWAS separately in four populations through single-population analysis, and the meta-analysis based on Fisher’s method was carried out to combine P-value probabilities from each test into one test statistic .in which χIn the present study, the traits NBA and TNB reflecting similar fertility function can be considered as different traits with some common genetic components. Therefore, the meta-analysis was performed within reproduction traits, TNB and NBA.An approximated chi-square statistic  was applti was a vector of signed t-values of the ith SNP for all studied traits, ti, V−1 was an inverse of the correlation matrix where the correlation between a pair of traits was estimated from the correlation of summary statistics over the SNPs in the analysis. Afterwards, Bonferroni correction at a significance level of 0.05 was used to identify significant SNP as same as genome-wide association study for a single trait in a single-population (SS-GWAS).where http://www.ensembl.org/Sus_scrofa/Info/Index; Sscrofa 11.1 genome version). Gene ontology analysis was carried out using the DAVID bioinformatics resource (https://david.ncifcrf.gov/). Pathway analysis was conducted using the online KEGG (http://www.kegg.jp/kegg/pathway.html) and GeneCards (http://www.genecards.org/) tools.To identify functionally plausible candidate genes near the significant SNP, the genes located in or overlapping the region between the 0.5 Mb upstream and 0.5 Mb downstream of the significant SNP were obtained using Ensemble , 9.47×10−7 (1.89×10−5), 9.52×10−7 (1.90×10−5), 9.57×10−7 (1.91 ×10−5) in these four populations , respectively. A total of 13 SNPs, of which six SNPs reached the genome-wide suggestive level for NBA and seven SNPs reached the genome-wide suggestive level for TNB, as shown in All significant SNPs associated with TNB and NBA traits in single population analysis are illustrated in Manhattan plots for meta-analysis across populations are presented in Genomic correlation matrices between TNB and NBA were constructed in four populations, respectively. The absolute of genomic correlation was 0.89, 0.85, 0.87, and 0.89 for each population of LM, FJ, XD, and ZX, respectively. Manhattan plot for MM-GWAS within four populations are presented in https://www.ncbi.nlm.nih.gov/).Based on 11 common significant SNPs associated with TNB and NBA identified by two meta-analysis methods, 11 genes which located within the region between the 0.5 Mb upstream and 0.5 Mb downstream of the significant/suggestive SNP were found and annotated  to 4. Whhttps://www.animalgenome.org/cgi-bin/QTLdb/SS/index). After comparing these QTL with the regions of 11 common significant SNPs, 6 SNPs were identified located in 5 QTLs which were identified before in Sus scrofa chromosome 6 (SSC6), SSC7, and SSC9 for TNB and NBA traits . This implies the functional genes such as msh homeobox 1 (MSX1), vascular endothelial growth factor A (VEGFA), forkhead box Q1 (FOXQ1), and LHFPL tetraspan submily member 3 (LHFPL3) around these SNPs are likely candidates for TNB and NBA traits.Until now, there were 522 pig QTLs identified for reproduction traits TNB (350 QTLs) and NBA (172 QTLs) in the pig QTL database , VEGFA, FOXQ1, and LHFPL3 are highlighted as promising biological candidate genes for reproduction traits. Except MSX1 and VEGFA, SPIN1, FOXQ1, and LHFPL3 are reported as related to reproduction in pigs for the first time. MSX1 is associated with the utero embryonic development and some classical pathways in embryo development such as Wnt/Hedgehog/Notch pathway. Daikoku et al [Msx1 was expressed in the preimplantation mouse uterus, and are critical for fertility in mice. Cha et al’s research [MSX1 had been already reported relevant with NBA and TNB in pigs [SPIN1 is necessary for normal meiotic progression in mammals [SPIN1 may play an important role in meiosis II (MII) arrest as well as in the regulation of early embryonic development. As to VEGFA, has key function in uteroplacental vasculogenesis during embryonic implantation and provides the vascular network to the placenta [VEGFA gene has been reported to be associated with TNB and NBA [LHFPL3, a member of the family of LHFP-like genes. A direct link between LHFPL3 and reproductive traits has not been reported before, but Ptacek et al [LHFPL3 might have a key function in conceiving and maintaining pregnancy. FOXQ1 has not been defined in pigs, whereas in mice, it is involved in patterning the early embryonic mesoderm [FOXQ1 for embryo development in pig reproduction.According to the results of genes annotated, a total of 11 genes of which five are relevant with two traits of reproduction, these candidate genes could regulate or influence TNB and NBA through different kinds of biological processes and pathways. ku et al  and Nallku et al  found thresearch  suggeste in pigs . SPIN1 i mammals . In a pr mammals  revealedplacenta . In prev and NBA ,24, and ek et al  reportedmesoderm  and exprmesoderm . Our finPopulation stratification is a major factor in false positives in GWAS for significant SNPs . At presIn our study, two meta-analysis were conducted to explore the genomic loci for TNB and NBA based on the results of single trait GWAS in four pig populations. The results of GWAS for a single trait in a single population showed that no common significant SNPs were detected in the four populations  for TNB No locus was identified significant by single population GWAS for TNB and NBA, with most significant SNPs detected across the two meta analysis approaches. In addition, compared to GWAS, 11 novel significant SNPs were detected using meta-analysis. Besides that, meta-analysis made the p-value smaller and more significant which were also consistent with Guo et al  and Le eSignificant SNPs were not found in single population GWAS, which showed the enhanced capacity of multi-trait approaches for detecting SNPs, particularly when the phenotypes are correlated . Our finSPIN1, VEGFA, FOXQ1, MSX1, and LHFPL3 are five functionally plausible candidate genes for TNB and NBA. Our findings further revealed that the meta-analysis and the multi-trait method can be used to increase the power of GWAS to identify genes relevant with important traits of interest.In summary, we conducted GWAS separately in four Yorkshire populations, which have distinct genetic backgrounds, to identify genomic loci for reproduction traits of TNB and NBA. Based on results of GWAS, two kinds of Meta-analysis were implemented to improve the power of gene identification. Compared to the single population GWAS, single-trait meta analysis can improve the detection power to identify SNPs in a larger multiple population. The multiple-trait analysis reduced the power to detect trait-specific loci but enhanced the power to identify the common loci across traits. In total, 11 significant loci were identified associated with target traits.