Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize ( 2 levels and achieve higher yields on a warming planet.An affordable and scalable system produces repeatable trait measurements to enable breeding of plants that lower CO Evaluating the information encoded in the shape of a plant as a response to environments is essential to understand the function of plant organs . In partZea mays) in particular, with over 700 Mt of maize production worldwide  and cowpea  and also characterize the complete RC. IR traits include number, angle, and diameters of youngest and second youngest nodal roots. Traits like eccentricity or the distance between whorls characterize the RC. The computed traits are known to be relevant and reported frequently in literature as manual measurements . We alsoWe developed DIRT/3D system to phenotype excavated RCs of maize . The sysThe 3D root scanner  utilizesWe used a panel of 12 maize genotypes with 5–10 replicates per genotype to validate the DIRT/3D pipeline. For our validation trial, the 3D root scanner captured images at pan intervals of 1 degree and tilt intervals of 10 degrees. We developed a method to perform a top-down level set scan of the 3D root model to compute 3D root traits. For a vertically aligned model, we slice the 3D root model from top to bottom at consecutive depth levels . The numIdeally, each root is a closed circle in the level set plane. However, some roots are under-sampled or affected by noise such that the contours of some roots are disconnected. A video sequence of all level set images would result in a flickering effect. Therefore, we use a phase-based frame interpolation technique  to smootThe image sequence of the smoothened level sets is the key to compute the location and size parameters of IRs. Applying the active contour snake model  to each Our algorithm initializes a circle around each IR in each level set as an initial input to the minimization of the active contour snake . During However, maize roots intertangle and adhere to each other, resulting in a complex system. In an image of the level set sequence, the entanglement will be visible as one connected component instead of two distinct components. We use the watershed segmentation to segment the overlapping root . The ideWe developed an IR tracking method by adopting a combination of Kalman filters and the Hungarian algorithm . The algUnder-sampled regions within the point cloud can result from left over soil that blocks the view into the RC. Technically, this may lead to unconnected roots in the 3D model of the RC. As a solution to the disconnection problem, we describe each root segment by its curvature and Euclidean distance between every pair of adjacent roots and root segments. If two close-by root parts have similar curvature value, we connect them by interpolating a curved connection between both segments. We accept two connected segments as a valid solution if the newly connected segment does not deviate from the interpolated curve. Once we connected all root segments that fit the same curve, we adopt the Iterative Back-Tracking method  to conneDuring the sequential processing of all level set images, we calculate the diameters of the minimal bounding circle that covers all points in all level set images in a 2D projection. To test the accuracy and precision of DIRT/3D, we correlated the trait values measured automatically in the 3D point clouds with manually measured traits of the RC. We validated manually measurable traits such as RC diameter, whorl distances, number of nodal roots at certain whorls, nodal root angle, and root dry biomass with a precision scale to correlate it with root volume. The correlation analysis of all validated traits showed  < 0.001 . The res < 0.001  indicateBroad-sense heritability, l traits , is compl traits  to demonean2>0.9 , which iWe computed comparable RC traits with DIRT/2D from images of the same roots used for the 3D analysis. Overall, four traits are comparable between DIRT/3D and DIRT/2D and 17 traits are only available with DIRT/3D. Three of the four traits are aggregate traits that vary dependent on the phenes composing them . Our comA principal component analysis (PCA) of 29 DIRT/2D traits suitable for maize RCs and the 18 DIRT/3D root traits show distinguishable clusters per genotype in the projection on the first and second principal component. In our test data set, the first two principal components explain 48.7% of the overall observed variance in DIRT/2D , which cThe presented 3D system to measure traits in highly occluded RCs is a significant advance in root phenotyping because it measures highly occluded traits such as whorl distances and number of nodal roots in dense maize RCs. A main difference of the presented 3D system to other 3D systems, like X-ray and MRI, is that no special infrastructure such as radiation protected rooms or specialist training for personnel is required. Furthermore, the presented methods introduce a 3D whole root descriptor for plant root architecture. For the test panel of 12 genotypes, a minimum of four traits distinguished all genotypes. In contrast, the whole root descriptor distinguished all genotypes with one mathematical expression. With these data, we have made significant progress on the unaccomplished goal of phenomics to measure the comprehensive appearance of a continuously reshaping phenotype .r2 = 0.87) and median RC width (r2 = 0.88) published in the 2D DIRT system (r2 = 0.88) and RC diameter (r2 = 0.89) on a maize data set. Certainly, comparable results could be expected for the nodal root angles of the second youngest whorl if occluding roots were mechanically removed from the RC. Here, we reproduced the previously reported 2D correlations to a manually measured ground truth. For comparable traits, we found that DIRT/3D and DIRT/2D achieved very similar and high correlations (r2 > 0.84) with the ground truth data. Broad-sense heritability also showed high values of H2 > 0.6 for the compared DIRT/2D and DIRT/3D traits . The use of Singularity/Docker containers will allow for integration with cyber-infrastructures such as CyVerse. These containers can run on any high-performance computing system that has the Singularity environment installed. The scanner design is part of the publication . The sites had drip irrigation. The field management supplemented nutrients other than nitrogen, and applied pest management as needed. We planted seeds using hand jab planters in rows with 76-cm row spacing, 91-cm alleys, 23-cm plant spacing, 4.6-m plot length with 3.7-m planted, or approximately 56,800 plants ha−1. We grew plants in three-row plots and sampled only the middle row. Planting occurred on June 5 2018, and sampling on August 25 2018, 81 d after planting. Two fields provided 1 ha of space for four replicates.Plants were grown at The Pennsylvania State University’s Russell E. Larson Agricultural Research Center  which has a Hagerstown silt loam soil . Fields received fertilization with 190 kg nitrogen haZea mays) genotypes were selected based on previous knowledge of their architectural variation and sampling of a larger set of genotypes. The 12 genotypes included six inbred lines  and 6 hybrid lines . These genotypes represent the extremes of dense versus sparse, large versus small, and maximum and minimum number of whorls selected from a full diversity panel. The lab of Shawn Kaeppler at the University of Wisconsin provided the seeds. We selected 10 representative plants for five of the genotypes , and 5 representative plants of the remaining seven genotypes. Sampling followed the shovelomics protocol .We designed a 3D root scanner  to captuA computing cluster of 10 Raspberry Pi 3 B+ synchronizes the image capture of the 10 cameras using a server-client design . The synThe SfM method requires detected feature points to be visible in several camera views. However, pose inaccuracy mechanically inferred by the scanning device or false feature matching may lead to incomplete reconstructions . As a reWe use a luminance-weighted gray world algorithm  to adjusP calculates for r images out of a set of n total images with the following formula:Given the images of segmented roots, we chose the Visual SfM method  as a basHowever, the computation of feature matches generates a large amount of false feature correspondences in the dense root data. We found that image pairs that are not adjacent in the spherical scanner space are particularly prone to incorrect matching . We obseThe optimization in our algorithm generates a matching pair list inside a specified sliding window . SlidingAs a next step, we utilize the RANSAC (random sample consensus) method to detect and remove the falsely matched pairs. The RANSAC results usually contain only highly distinctive features to track between consecutive images. Given the locations of multiple matched feature pairs in two or more images, we can produce an estimation of the positions, orientations of cameras, and the coordinates of the features in a single step using bundle adjustment .We adopted a top-down level set scan of the 3D root model to compute 3D root traits . This scAll statistics used python 3.7 and the modules NumPy 1.16 and SciPy 1.2.1 . Figureshttp://dirt.cyverse.org on May 4, 2021.DIRT\2D traits were computed with the online platform available at The following materials are available in the online version of this article.kiab311_Supplementary_DataClick here for additional data file.


For many complex traits, the underlying genetic variation is caused by the segregation of one or more ‘large-effect’ loci, in addition to an unknown number of loci with effects below the threshold of statistical detection. The large-effect loci segregating in populations are often necessary but not sufficient for predicting quantitative phenotypes. They are, nevertheless, important enough to warrant deeper study and direct modelling in genomic prediction problems. We explored the accuracy of statistical methods for estimating the fraction of marker-associated genetic variance ( The contributions of individual genes to the phenotypic variation observed for genetically complex traits has been an ongoing and important challenge in biology, medicine, and agriculture. While many genes have statistically undetectable effects, those with large effects often warrant in-depth study and can be important predictors of complex phenotypes such as disease risk in humans or disease resistance in domesticated plants and animals. The genes identified through associations with genetic markers in complex trait analyses typically account for a fraction of the heritable variation, a genetic parameter we called ‘marker heritability’. We discovered that textbook statistical methods systematically overestimate marker heritability and thus overestimate the contributions of specific genes to the phenotypic variation observed for complex traits in natural and experimental populations. We describe the source of the upward bias, validate our findings through computer simulation, describe methods for bias-correcting estimates of marker heritability, and illustrate their application through empirical examples. The statistical methods we describe supply investigators with more accurate estimates of the contributions of specific genes or networks of interacting genes to the heritable variation observed in complex trait studies. The genetic variation observed in nature is frequently caused by genes with quantitative effects –7. TheirThe phenotypic variation observed in a population is customarily partitioned into genetic and non-genetic components to estimate heritability, repeatability, and reliability of the quantitative traits under study , 30, 32.σG2) , 44, 45.σG2) –48.BRCA2 gene can have large effects, are incompletely penetrant, interact with other genes, and are necessary but not sufficient for predicting breast, ovarian, and other cancer risks in women [Bos taurus) [BR, PHY, and HYP) have large effects, interact, and pleiotropically affect several genetically correlated seed biomass traits in sunflower (Helianthus annuus) [BRCA2, BTA19, BR, PHY, and HYP were not reported in those studies. What fraction of the heritability for breast cancer risk, for example, can be explained by the known mutations in BRCA2? Our study explored the accuracy of methods for estimating that parameter.The discovered genes in polygenic systems of genes are often necessary but not sufficient for predicting quantitative phenotypes, e.g., disease risk in humans or yield in domesticated plants and animals , 44, 49.in women . The lar taurus) , and bra annuus) . Despite annuus) , 54, 55.Our surveys and others substantiate that the missing and non-missing fractions of the genetic variance are commonly either not estimated or inaccurately estimated in GWAS and other gene finding studies, e.g., the statistical significance of individual marker loci from sequential regression analyses are typically reported without correcting for the effects of other discovered marker loci through multilocus partial regression analyses or Type III ANOVA , 34, 56.BRCA2 and BTA19 [p and Lande and Thompson  proposeds or QTL , 30, 63.nd BTA19 , 52. The effects , 51, 61. studies , 62.p and p and p and ratios. The ASV methods we applied to the problem are extensions of those described by Piepho [R2) in GLMM analyses. For the AMV and ASV analyses shown throughout this paper, REML was used to estimate the variance components [p and p and Here we show that le sizes –69, and le sizes . While sce (AMV) , 72 and ce (AMV)  methods GLIMMIX’ –79. We iy Piepho  for estimponents , 79. The studies , 80, 81.H2) and REML estimates of p and p ≤ 1.0) must also be upwardly biased. The proof was found through algebraic analyses of the ANOVA estimators of p and The overestimation problem described here was originally discovered in a reanalysis of data from genetic studies in plants where REML estimates of rameters . We initced data  Texts. Ap as a function of the ANOVA estimators of p, and nG entries are phenotyped for a normally distributed quantitative trait using a balanced completely randomized study design with rG replications/entry, nM marker genotypes/locus, and rM replications/marker genotype. The LMM needed for estimating yjk is the jkth phenotypic observation, μ is the population mean, Gj is the random effect of the jth entry, ϵjk is the random effect of the jkth residual, j = 1, 2, …, nG, and k = 1, 2, …, rG. Suppose entries are genotyped for a single marker locus (M) in linkage disequilibrium with a gene or QTL affecting the quantitative phenotype (yjk). The between entry source of variation from LMM M) and entry nested in marker (G : M) sources of variation . The LMM for estimating yijk is the ijkth phenotypic observation, Mi is the random effect of the ith marker genotype at locus M, G : Mi(j) is the random effect of the jth entry nested in the ith marker genotype, ϵijk is the random effect of the ijkth residual, i = 1, 2, 3, The source of the bias was identified by expressing the estimator of MSG = SSG/dfG is the between entry mean square, SSG is the between entry sum of squares, dfG = nG − 1 is the between entry degrees of freedom, SSϵ is the residual sum of squares, dfϵ = nG(rG − 1) − 1 is the residual degrees of freedom, rG is the number of replications per entry [M and entry nested in M variance components from LMM nG:M is the number of entries nested in each marker genotype, MSG:M is the entry nested in M mean square, and MSM is the mean square for marker locus M. The residuals in LMMs p is:The ANOVA estimator of the between-entry variance component , the mean squares are not (MSM + MSG:M ≠ MSG). Because p and kM in their denominators, and not the numerators:Although the SS for sources of variation in these LMMs are additive -(S12) in kM is greater  for interaction than main effects, e.g., for two marker loci, kM1 < kM1×M2 < 1 and kM2 < kM1×M2 < 1, where kM1 is the coefficient for M1, kM2 is the coefficient for M2, and M1 × M2 epistatic interaction (kM for the two-locus interaction (kM1×M2) is larger than kM for the individual marker loci (kM1 and kM2) because the denominator (dfGrG) is constant, whereas the numerators increase and approach the denominator as the degrees of freedom for marker effects increase. Therefore, the upward bias is proportionally smaller for the M1 × M2 variance component than the M1 or M2 variance components for a two locus genetic model. Similarly, for a three locus genetic model, the upward bias is proportionally smaller for the M1 × M2 × M3 interaction variance component than the two-way interaction variance components . These results naturally extend to genetic models with more than three loci. Algebraic results are only shown for three marker loci because we found that the the kM bias problem can be directly solved using average semivariance estimation methods when analyzing more complex genetic models (see below). Although certainly not limited to three marker loci, the methods described herein are primarily designed to study the effects of one to a few genes with large effects, e.g., BRCA2 [The eraction . kM for ., BRCA2 , BTA19 [., BRCA2 , and theManihot esculenta), strawberry (Fragaria × ananassa), and apple   for entries [M) is:ijth entry, μ is the population mean, Mi is the random effect of the ith marker genotype, G : Mi(j) is the random effect of entries nested in M, R) is estimated in the first stage of a two-stage analysis [Gc defined by the genetic model, e.g., different main and interaction effects among marker loci.The AMV methods proposed above for bias correcting ANOVA or REML estimates of GLIMMIX’ , 98. HerGLIMMIX’ . As befo entries , 99. Theanalysis . The betV is the variance-covariance matrix of the phenotypic observations, nG is the number of entries, tr(V) is the trace of V, cth genetic factor in the model , Zc are design matrices for the c genetic factors, R is the residual variance-covariance matrix. The AMV estimator of the genetic variance among entries (G) is:nG identity matrix. From LMM nM identity matrix, nG:M-element unit vector, and uM is a vector of random effects for M. The AMV estimator of the variance associated with the residual genetic variation among entries nested in M is:uG:M is a vector of random entry nested in M effects and nG identity matrix. Hence, the AMV estimators of The AMV estimator of the phenotypic  variance among observations for LMM nG × nG identity matrix, and nG × nG unit matrix [cth genetic factor (uc), where c indexes genetic factors, the genetic factors are marker locus effects and entries nested in marker locus effects, and cth genetic factor is M, ASV, or the average variance of differences among observations, leads to a definition of the total variance that provides a natural way to account for the heterogeneity of variance and covariance among observations , 102. ASt matrix . θP¯ASV G) is:nG identity matrix. Hence, from Eqs M is:kM = dfMnG:M/dfG is the bias correction coefficient, dfG = nG − 1, dfM = nM − 1, and dfG:M = dfG − dfM. This definition of the kM-bias coefficient is identical to the earlier definition with rG factored out p for a single marker locus (M) is:The ASV estimator of p and kM).Similarly, the ASV estimator of +rG-1σ^ϵ2where σ^pkM and that ASV-REML estimates of these parameters form p and kM coefficients shown in kM increased as p and kM bias [kM-bias and selection bias could operate in combination to inflate estimates of the contribution of a locus to the heritable variation in a population  dramatically affected the precision of estimates of rG or Computer simulations confirmed that AMV-REML estimates of  designs  were iderent HM2 . These r effects . Their e in LD with a gene (FW1) conferring resistance to Fusarium wilt in a strawberry (Fragaria × ananassa) GWAS population (nG = 564) genotyped with a genome-wide framework of SNP markers [log10(p) = 6.61 × 10−31 for AX493 and 2.95 × 10−222 for AX396. Genotype frequencies were highly unbalanced for both markers with a scarcity of AA homozygotes (2.8%) for AX396 (16AA : 177Aa : 371aa) and a 1 : 2 : 1 ratio for AX493 (141AA : 282Aa : 141aa). For both loci, the minor allele frequency was >0.05. The kM for these data (kAX493 = 0.62 and kAX396 = 0.47) were calculated as shown in AX396 exceeded 1.0, a telltale sign of kM-bias  affecting seed oil content in a sunflower (Helianthus annuus) RIL population using LMM kM needed for bias-correcting AMV-REML estimates of p and p and p for the BR locus (0.54) was slightly more than double the bias-corrected (ASV-REML) estimate (0.26). Hence, the uncorrected REML estimates of p and Statistics are shown here for an analysis of three marker loci (e (0.41) . Similar content .p could not be estimated for this example because individuals were unreplicated. Variance components were estimated for three SNP markers  on chromosomes 2, 6, and 22, respectively, affecting white spotting (%) in a Holstein–Friesian cattle (Bos taurus) population  [aa for rs10, 78AA : 736Aa : 2, 159aa for rs45, and 237AA : 976Aa : 1, 760aa for rs20. The kM for these data  were calculated as shown in The application of bias-correction is illustrated here for a genetic model with three marker loci, highly unbalanced genotypic data, and a single phenotypic observation per individual— 2, 973) . These Se (0.37) . SimilarOur study was partly motivated by inconsistencies in the statistical approaches applied in candidate gene and other complex trait analyses when testing hypotheses and fitting genetic models for multiple large-effect loci. With the high densities of genome-wide markers commonly assayed in gene finding studies, investigators often identify markers tightly linked to candidate or known causal genes, as exemplified by diverse real world examples , 54, 108The problems that arise in fixed effect analyses of unbalanced data profoundly affect parameter estimates and statistical inferences but have not been universally recognized or addressed in complex trait analyses , 112. Wep and single unambiguous generalized linear mixed model analysis, whereas wildly different parameter estimates can arise among the multitude of fixed effects analyses that investigators might elect to apply in practice when the underlying genotypic and phenotypic data are unbalanced  for LMMs nM = 3 genotypes/marker locus and 21 combinations of study design variables  with balanced or unbalanced data (p and rnorm with known means and variances [n = nGrG phenotypic observations for each study design. Variance components for the random effects in LMMs p and m) and unbalanced data affect estimates of p and rG, nG, and p and lme4::lmer v1.1–21 [p for each study design , a QTL mapping study in oilseed sunflower (Helianthus annuus L.) [Fragaria × ananassa Duchesne ex Rozier) [rG = 2) of nG = 146 recombinant inbred lines (RILs) were phenotyped for seed oil concentration (g/kg) and genotyped for three marker loci  with two homozygous marker genotypes/locus [rG = 1; nG = 2, 973) were phenotyped for white spotting (%) and genotyped for three marker loci  with three marker genotypes per locus [BRh is the hth effect of the BR locus, PHYi is the ith effect of the PHY locus, HYPj is the jth effect of the HYP locus, G : (BR × PHY × HYP)hij(k) is the kth effect of entries nested in the hijthBR × PHY × HYP interaction, and ϵhijkl is the hijklth residual effect. The data for RILs were balanced, whereas the data for marker genotypes were slightly unbalanced. Each of the eight BR × PHY × HYP homozygotes were observed in the RIL population; however, the number of entries nested in each marker genotype (nG:M) varied from nG:BR = 81 : 65, nG:PHY = 60 : 86, and nG:HYP = 70 : 76. Variance components for LMMs lme4::lmer [To illustrate the application of bias-correction methods and the differences between AMV and bias-corrected AMV estimates of nuus L.) , and a G Rozier) . For thees/locus . For theer locus . LMM (2)::lmer . The marrG = 4) of 565 entries (nG = 565) from a genome-wide association study (GWAS) were phenotyped for resistance to Fusarium wilt and genotyped for single nucleotide polymorphism (SNP) markers in LD with FW1, a dominant gene conferring resistance to Fusarium oxysporum f.sp. fragariae, the causal pathogen [AX493 and AX396) that were tightly linked to FW1 [AA : 282 Aa : 141 aa for AX493 and 16 AA : 177 Aa : 371 aa for AX396, where A and a are alternate SNP alleles. The variance components were estimated for LMMs lme4::lmer [For the strawberry study, four replications (pathogen . The reppathogen . Geneticd to FW1 . The gen::lmer . REML esG : M) term because it has the same levels as the residual. The LMM for this case study is:rs10h is the hth effect of the peak SNP (rs109979909) on chromosome 2, rs45i is the ith effect of the peak SNP on chromosome 6 (rs451683615), rs20j is the jth effect of the peak SNP on chromosome 22 (rs209784468), and G : (rs10×rs45×rs20)hij(k) is the hij(k)th residual effect comprising residual genetic effects G : M and residual error. In this experiment, there were k entries and k observations, and because of this we cannot fit LMM For the cattle study, we used a model similar to μ), R, and R, where μ is the population mean. Similarly, for the ACB order, the reduction SS for the main effects were R(A | μ), R, and R, and so on for the other four orders . C, A × B, A × C, B × C, A × B × C), the reduction SS for the main effect of B was R. For comparison, the Type III reduction SS for the main effects were R, R, and R.We used the SAS package PROC GLM  for TypeS1 Tablen = nG × rG), the number of entries (nG), the number of replications/entry (rG), the number of marker loci (m), nM = 3 genotypes/marker locus, the number of entries/marker genotype (nG:M), and marker heritability Click here for additional data file.S1 FignG individuals, families, or strains), five replications/entry (rG = 5), true marker heritability (nM1 = 3). The AMV estimates (shown in red) equal nG = 540 entries (study design 1); (B) two marker loci with interaction  and balanced data for nG = 540 (study design 2); (C) three marker loci with interactions  and balanced data for nG = 540 (study design 3); (D) a population segregating 1:2:1 for a single marker locus with rG:M = 135 entries for both homozygotes and rG:M = 270 heterozygous entries, and nG = 540 (study design 4); (E) one locus with 10% randomly missing data among 540 entries (study design 5); and (F) one locus with 33% randomly missing data among 540 entries (study design 6). Study design details are shown in AMV and ASV estimates of (TIFF)Click here for additional data file.S2 FignG individuals, families, or strains), five replications/entry (rG = 5), true marker heritability (nM1 = 3). AMV estimates of marker heritability (nG = 540 entries (study design 1); (B) two marker loci with interaction  and balanced data for nG = 540 (study design 2); (C) three marker loci with interactions  and balanced data for nG = 540 (study design 3); (D) an population segregating 1:2:1 for one marker locus with rG:M = 135 entries for both homozygotes and rG:M = 270 heterozygous entries, and nG = 540 (study design 4); (E) one locus with 10% randomly missing data among 540 entries (study design 5); and (F) one locus with 33% randomly missing data among 540 entries (study design 6). Study design details are shown in Relative biases of AMV and ASV estimates of (TIFF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.S4 Text(PDF)Click here for additional data file.S5 Text(PDF)Click here for additional data file.


Inbreeding depression is the decreased fitness of offspring of closely related individuals. It is a common problem in the livestock industry as a consequence of intense selection favoring few animals with higher genetic merit. The literature shows that inbred individuals may also exhibit decreased performance for economically relevant traits, such as growth in beef cattle. However, this relationship is not simple. The use of genetic markers can improve the detection of homozygous regions and improve the calculation of inbreeding coefficients. The working hypothesis of this study is that genomic homozygous regions can be used to predict animals’ performance. Moreover, homozygous regions across the genome differ in their effects. Our results indicate that the relationship between homozygosity and decreased performance varies by chromosome. Additionally, the inbreeding coefficient can be used to predict animals’ phenotype when calculated within a chromosome. This research warrants the development of a new approach in genomic selection to increase prediction accuracy by including information on homozygous regions. Additionally, management tools can be developed with the goal of breeding individuals with a purpose to decrease homozygosity only in the relevant regions of the genome.The goal of this study was to evaluate inbreeding in a closed beef cattle population and assess phenotype prediction accuracy using inbreeding information. Effects of inbreeding on average daily gain phenotype in the Line 1 Hereford cattle population were assessed in this study. Genomic data were used to calculate inbreeding based on runs of homozygosity (ROH), and pedigree information was used to calculate the probability of an allele being identical by descent. Prediction ability of phenotypes using inbreeding coefficients calculated based on pedigree information and runs of homozygosity over the whole genome was close to 0, even in the case of significant inbreeding coefficient effects. On the other hand, inbreeding calculated per individual chromosomes’ ROH yielded higher accuracies of prediction. Additionally, including only ROH from chromosomes with higher predicting ability further increased prediction accuracy. Phenotype prediction accuracy, inbreeding depression, and the effects of chromosome-specific ROHs varied widely across the genome. The results of this study suggest that inbreeding should be evaluated per individual regions of the genome. Moreover, mating schemes to avoid inbreeding depression should focus more on specific ROH with negative effects. Finally, using ROH as added information may increase prediction of the genetic merit of animals in a genomic selection program. Precision livestock farming relies on increased quality and quantity of data collected, leading to accurate decision making. With the rapid advancement in high-throughput technologies, the large amount of genomic information being generated can greatly contribute to this endeavor. In the beef cattle industry, with the use of genomic tools, we can dissect important genetic mechanisms and apply them to improve animal performance. A practical application of genomic information is its use in animal evaluation to increase phenotype prediction accuracy in younger animals , improveLine 1 Hereford is a unique and important population for the beef cattle industry. Many Hereford herds in the US and abroad have founders from this population . Line 1 Inbreeding coefficients calculated using runs of homozygosity (ROH) are the ideal approach to evaluate livestock . ROH areTaking together the information from the literature, the working hypothesis for this study is that animals are affected differently by inbreeding, which suggests that homozygosity might result in different effects across individuals. The goal of this study is to evaluate the effect of inbreeding and phenotype prediction accuracy using chromosome-specific ROHs in the Line 1 Hereford population.2 = 0.25) was analyzed based on the results of Sumreddee et al. [Average daily gain (ADG)  were estimated based on the probability of identity by descent that occurred at random loci, calculated with pedigree-based analysis. The tabular method  was used to calculate FPED as implemented in the pedigree R package version 1.4  using R i, μ is the intercept, i calculated based on pedigree information (FPED), ROH (FROH), or chromosome-specific data , a is the regression coefficient of To estimate inbreeding depression and the effect of inbreeding coefficients in the phenotypic studies, the following model was fitted: Inbreeding depression was estimated by regressing trait phenotypes on the ROH-inbreeding coefficient. The prediction ability of each model was assessed as the correlation between the predicted phenotype by the linear model above and the observed phenotype of the animals in the population. The term accuracy was used to describe this prediction ability, while the slope of the regression coefficient for each chromosome was used to estimate the effect of inbreeding on the phenotype. The chromosome-specific inbreeding depression was evaluated as the average slope of the FROH-CHRi for each chromosome.To calculate the prediction accuracy, data were randomly split in two groups, where 80% of the observations were termed training group and the remaining 20% were referred to as the validation group. On average, 538 and 134 individuals were in the training and validation group, respectively. The regression was fit using the training data and the regression coefficients were then used to predict the regression validation values which represent animals with masked phenotypes. The correlation between the predicted values and the observed values on validation animals were then calculated and used to validate the model and demonstrate the ability in predicting phenotypic values, corrected for the systematic effects of year and sex. Using inbreeding coefficients calculated with pedigree and ROH coverage across the entire genome or chromosome-specific ROH, the process was repeated 500 times to decrease potential sampling error and identify highly variable predictors. Mean and standard deviation of accuracy across the 500 samples were used to assess prediction ability. The reason for using 500 samples was the stability of the model. Higher fluctuations on values were observed when fewer samples were included, while convergence was always achieved with 500 rounds. Convergence was assessed as changes smaller than e-04 on the average accuracy.p-value or highest effect were fit in another set of models. For the models with selected chromosomes, the regression coefficient and the p-value used to select chromosomes were calculated using the complete data.The contribution of each chromosome to the phenotype was compared using each FROH-CHRi in a separate model. Another model was fit including all 30 chromosomes as separate variables. For the X chromosome, males ROH were considered missing data, and only females were used in the accuracy computing. Finally, only the chromosomes with higher accuracy, lowest p-values less than 0.05 indicate a significant difference between the pair of models.The difference between the mean accuracy of each group was calculated and the Tukey honest significance test  was usedp < 0.01) on ADG, and inbreeding depression was stronger from FPED than from FROH, −1.3 and −0.9 kg/day, respectively. These values cannot be directly compared with those by Sumreddee et al. [The average pedigree inbreeding was 0.29, and the average ROH inbreeding was 0.32. The correlation between the inbreeding coefficients was 0.6. These values were similar from those obtained by Sumreddee et al. , and thee et al. , becauseThe prediction accuracy for the raw phenotypes reached higher values when sex and year of birth were included in the model. However, the prediction accuracy of pedigree (FPED) and genomic (FROH) inbreeding coefficient approached 0 when corrected phenotypes were the dependent variable. Moreover, the gains in accuracy from inbreeding coefficients on raw phenotypes were marginal when compared to the effects of sex and year of birth, even when significant . Those rFrom these analyses, the lack of prediction power by inbreeding coefficient was clear. On the other hand, the impact of specific chromosomes on phenotype prediction varied across the 30 bovine chromosomes, as shown in Comparison of the similarities between the 30 genotyped chromosomes indicated that the phenotype can be predicted based on the unique contributions of each individual chromosome. Chromosomes 15, 18, 19, and 27 were found to have very low accuracies while the highest accuracies were found on chromosomes 2, 4, 10, and 16. Chromosome X demonstrated intermediate to high accuracy; however, this should be carefully interpreted as only females had meaningful ROH values for this chromosome. There is no evident relationship between the accuracy obtained by individual chromosomes and the number or coverage of QTL in the Cattle QTL Database .The prediction power fitting all 30 chromosomes individually was higher than calculating ROH for the whole genome. Selecting the chromosomes with higher and significant effects increased prediction accuracy even further . Adding The inbreeding depression values for individual chromosomes is presented in Associations between chromosomal regions and growth traits in beef cattle were identified by several authors ,27,28,29Studies with ROH in dairy cattle have shown that chromosomal ROH vary widely ,31,32,33The results obtained in this study conflict with the findings by Doekes et al.  in DutchAn additional aspect that can affect findings is the parameters to define a homozygous region as an ROH. Shorter segments are related to ancient inbreeding and longer segments to recent inbreeding , and theInbreeding coefficients calculated with the entire genome had no prediction ability, despite the significance of the regression model. When fitting ROH calculated in individual chromosomes, they were found to differ in accuracy and contribute differently to inbreeding depression. Results from the present study show evidence that not all homozygous regions affect phenotypes equally, and that should be accounted for when studying inbreeding depression. Moreover, these results suggest that future studies should test individual ROH as prediction coefficients, as they may contribute to increased accuracy in genomic evaluation models. While the prediction accuracy using only ROHs was not high, it suggests that individual-specific inbred chromosomal regions may be used to increase the prediction ability of genomic selection models. Finally, different chromosomes were found to have different inbreeding depression levels, suggesting that the severity of inbreeding effects varies across the genome.


Training set construction is an important prerequisite to Genomic Prediction (GP), and while this has been studied in diploids, polyploids have not received the same attention. Polyploidy is a common feature in many crop plants, like for example banana and blueberry, but also potato which is the third most important crop in the world in terms of food consumption, after rice and wheat. The aim of this study was to investigate the impact of different training set construction methods using a publicly available diversity panel of tetraploid potatoes. Four methods of training set construction were compared: simple random sampling, stratified random sampling, genetic distance sampling and sampling based on the coefficient of determination (CDmean). For stratified random sampling, population structure analyses were carried out in order to define sub-populations, but since sub-populations accounted for only 16.6% of genetic variation, there were negligible differences between stratified and simple random sampling. For genetic distance sampling, four genetic distance measures were compared and though they performed similarly, Euclidean distance was the most consistent. In the majority of cases the CDmean method was the best sampling method, and compared to simple random sampling gave improvements of 4–14% in cross-validation scenarios, and 2–8% in scenarios with an independent test set, while genetic distance sampling gave improvements of 5.5–10.5% and 0.4–4.5%. No interaction was found between sampling method and the statistical model for the traits analyzed. The utilization of DNA marker information for selection in breeding programs has increased over the last two decades and can be attributed to two factors: the decrease of genotyping costs, and the advances in quantitative genetics methodology. Genomic prediction (GP) is an example of one such methodological breakthrough that estimates breeding or genotypic values (depending on the application) by regressing known phenotypes against high density molecular markers , which is an auto-tetraploid and the subject of this article. There is potential for genetic gain in applying genomic prediction to potato . The SolCAP North American potato diversity panel is a compilation of elite potato germplasm from breeding programs across the U.S., as well as historical varieties from the NRSP-6 potato gene bank . Information on these traits were reported in the study by Rosyara et al. , and sucrose content, a trait with intermediate heritability (h2 = 0.67) , tuber fructose and sucrose content , Discriminant Analysis of Principal Components (DAPC) and Analysis of Molecular Variance (AMOVA). In a population with distinct sub-divisions, a significant portion of the genetic variability of the population can be attributed to the differences between sub-populations. AMOVA estimates variance components of various factors, including the contribution of subgroups to a population's total variability : Training set construction is equivalent to taking a subset of a larger set. For simple random sampling, members of this subset are chosen randomly and completely by chance so that each individual from the panel has an equal probability to be selected for the training set.Stratified Random Sampling (STRAT): Using the population analysis results to define strata, this method randomly selects individuals from each sub-population, ensuring that every sub-population is represented in the sample, while maintaining the same strata proportions.nS is the number of individuals in the sample from stratum S, NS is the number of individuals in the population from stratum S, while n and N are the total sample size and total number of individuals, respectively.Genetic Distance Sampling (GD): This method requires as input, the distances between genotypes calculated from the marker data. From the initial pool, one individual is randomly selected and all individuals within a radial distance r are discarded and will no longer be candidates for sampling. This ensures that the next individual sampled will not be genetically similar to the first individual. From the remaining set, a second individual is selected and again, all individuals within a genetic distance of r are discarded. This process is continued until the desired training set size is attained. The size of the sampling radius r, is dependent on the desired sample size. A larger sample size requires a smaller r and vice versa. The method is described in more detail in Jansen and van Hintum  this measure calculates the distance between two individuals X and Y:Yj can be interpreted as the number of alternate alleles at the jth marker in individual Y. The Euclidean distance matrix was converted to the similarity measure, and scaled to fit within the desired range  using the following transformation:Eaccard's Similarity Index does not make any biological assumptions and requires as input the numerical representation of the SNP data. The distance between two individuals X and Y is calculated as:Xj ∩ Yj| is the number of alternate alleles common to both individuals X and Y at the jth marker, while the term |Xj ∪ Yj| refers to the total number of alternate alleles at this same marker for individuals X and Y, without repetition . The resulting output was then converted to a similarity matrix.Josman and Leonard's Genetic Distance differs from previously mentioned genetic distance measures as it takes into account the ploidy level of the individuals , and averaged over all r markers.KThis similarity matrix comprises of pairwise measures of genetic similarity between individuals, which Jansen and van Hintum calculated using the simple matching coefficient. The authors go on to suggest the Jaccard's similarity index as a suitable alternative : The generalized coefficient of determination is a training set selection method based on the maximization of the precision of the prediction of differences (or contrast) between the average value of the entire population of candidate individuals and each individual in the test set , and M is an orthogonal projector on the subspace spanned by the columns of the fixed effects design matrix, X , such that M = I − X(X′X)−X′. A is the additive realized genomic relationship matrix as calculated by VanRaden  and j markers (columns). The term pj is the frequency of the reference allele of the jth marker and W is the numerical marker matrix, centered and scaled such that genotypes coded as allele dosages {0, 1, 2, 3, 4} now become {−1, −0.5, 0, 0.5, 1}. The supporting literature A=QQh2) and though we have phenotypic data and can therefore estimate this value for the traits in question, this may not always be the case in practice. Often the decision of which genotypes are to be put in the field to garner phenotypic measurements, is made before estimates of heritability can be performed, as this calculation requires phenotypic data. Secondly, the individuals to be selected may not have to be chosen on the merit of one single trait, but rather by more traits with varying degrees of heritability. The supporting literature  scheme and the training-test (TT) scheme. The TV scheme follows a typical cross-validation approach where a portion of the individuals are used to train the model (training set) and those not part of the training set, used to evaluate model prediction accuracy . The effect of training set size was assessed by choosing 50, 75, 100, 125, and 150 individuals out of the total 190 with each sample size repeated 100 times. We must consider that the training and validation sets are complementary, therefore the size of the validation set depends on the size of the training set, so comparisons across training set sizes are not equally precise see . AdditioTherefore, a second approach (TT scheme) was used where the composition and size of the validation (test) set, is independent of the composition and size of the training set. In each realization of the TT scheme, we first randomly sampled 40 individuals as test set leaving the remaining 150 as the pool from which to sample the training set. Following the different sampling methods, we chose 25, 50, 75, and 100 genotypes from the remaining 150, as training set see . In turnThe purpose of this study was to uncover a superior training set sampling method based on the accuracy of predictions. These predictions were generated with three different whole genome regression models, in order to investigate the presence/absence of an interaction between training set selection method and genomic prediction model.GBLUP:y is a vector of phenotypic BLUEs, β is a vector of fixed effects (only the intercept in our case), u is a vector of genotypic values with distribution A is the genomic relationship matrix as calculated in Equation 2 and X and Z are design matrices as described previously and ϵ is the vector of residuals with distribution RKHS: The model for Reproducing Kernel-Hilbert Spaces (RKHS) is the same as Equation 3, with one difference in that the genotypic values have a different distribution: A, is replaced by the kernel matrix, BAYES Cπ:W is our matrix of marker information, b is a vector of marker effects. Bayes Cπ assumes that marker effects come from a mixture distribution with a proportion of markers (π) having zero effect and the remainder (1 − π) having non-zero effects, such that for the jth marker:accuracy = f in a full factorial model. To conform to normality assumptions, these correlations (accuracies) were transformed using Fisher's z transformation, As mentioned in previous sections, the ranking of the training set construction methods will be based on a measure of prediction accuracy. For both the TV and TT schemes, the observed phenotypic values of the training set are fed to the statistical models to estimate marker effects, while the phenotypic values of the validation (TV scheme) and the test set (TT scheme), are hidden from the model. Predictions are made on those individuals with hidden phenotypes, and the prediction accuracy is defined as the Pearson correlation between observed phenotypic values and the predicted genotypic values. Factors that may influence prediction accuracy are sample size, statistical model and the training set construction method, as well as various interactions between these factors. To answer this question, an Analysis of Variance (ANOVA) was carried out where the correlation (prediction accuracy) is treated as the response variable such that All analyses were executed in R Core Team , except The 3,763 SNPs were reduced to 3,262 after the following filtering steps. For the 190 phenotyped tetraploid lines, monomorphic markers, unmapped markers, markers with a minor allele frequency of <5% and markers with missing values for more than 30 of the 190 individuals were removed.The classification of the population into the six market classes, gives two subpopulations with <20 individuals. This is not ideal for stratified sampling as parameter estimates from these very small subgroups will produce large standard errors. Furthermore, based on past population structure results for this diversity panel, there are indications that some of these sub-populations can be merged.PCA and DAPC results show that the six market classes can indeed be reduced to a smaller number of groups . PrincipFst = 0.0165, while between Yellow and Chip Processing-Round White table, Fst = 0.0098 . For the remainder of the study, the discrete population structure used for stratification is defined by the three groups suggested in The pigmented class is clearly separated but one question arose: Where does the yellow market class belong? AMOVA analyses found that genetic variation due to population structure was the highest (16.6%), when the yellow class was placed with chip processing and round white table classes, as suggested by the DAPC plot . Other pFour different genetic distance measures were used to perform genetic distance sampling, and the sampled individuals were used to train the model. Prediction was performed on the left out individuals as described in the TV scheme. The similarities (correlations) between the different genetic distance matrices were assessed by a Mantel test .There is very little difference between the distance measures for the material in this study. The lowest correlations (0.927 and 0.941) occurred with the Jaccard distance measure, however this degree of similarity is still quite high.The prediction accuracies from a common GBLUP model were quantified for three different traits , at sample sizes ranging from 50 to 150, for different genetic distance measures . It can After determining a suitable distance measure for genetic distance sampling, methods for acquiring the training set were compared .Each row of The results shown in F-values in The magnitude of the For fructose, when the sample size is tripled (from 50 to 150), simple random sampling and stratified sampling improved by 19 and 23%, respectively, whereas genetic distance sampling and the CDmean method resulted in improvements of 27 and 31%, respectively. For sucrose, the CDmean method showed a 37% improvement by tripling the sampling size while simple random sampling improved by 25%. The relative improvement of using a analytical sampling method was greater for sucrose and fructose content. At the median sample size of 100, CDmean showed an improvement in prediction accuracy of 4, 14, and 13% for tuber length, fructose and sucrose content, respectively, when compared to simple random sampling. The genetic distance sampler for these traits , showed improvements of 5.5, 10.5, and 10.5% in comparison to simple random sampling.As discussed before, the objectives for using Genomic Prediction may vary. In many cases the objective is to predict new breeding lines (or clones) and for this scenario we have randomly selected a test set of 40 out of the 190 individuals. These 40 individuals represent the independent test set, and all sampling methods will construct the training set from the remainder of individuals. The trained model then performs predictions for the test set. In this way, each sampling method predicts the same test set.Similar to the previous section, we looked at the prediction accuracy for three statistical models with sample sizes ranging from 25 to 100, and compared the impact of the sampling method . The accIn comparison to the TV scheme, the results of the TT scheme exhibit a more significant impact due to statistical model, and to test whether there is an interaction with the sampling method an ANOVA analysis was conducted.Similar to the results from the TV scheme shown in Although this paper does not primarily focus on statistical models, it is still interesting to observe the differences in predictive performance . For allApplication of the TT scheme to breeding programs, usually involves a test set of hundreds or even thousands of new potential cultivars. In this study it was impossible to emulate this application, still the impact of increasing the test size was investigated. For this investigation, we conducted the same analyses as seen in TT scheme but used a larger test set . There were no changes in the findings; the analytical methods, especially CDmean, sampled training sets that predicted the test sets with greater accuracy than the random methods (results not shown). Similar to the results seen above, these differences disappeared at larger sample sizes and were only evident at smaller training set sizes, where the overlap of sampled individuals between methods was minimal.Training set construction has been proven to be important for GP in diploids and in this study, shown to be important for GP in tetraploids. Both ploidy levels benefit from incorporating genomic information into analytical methods of sampling the training set, when compared to random methods that do not directly utilize genomic information.Only 190 varieties were included in this study which may limit the extrapolation of results to traditional breeding programs. Breeders often make selections within a particular market group. In these scenarios, one must decide if to train models using only individuals belonging to the target market group or allow for the borrowing of information from other market classes. Our study was too small to answer this question, however it has been shown that combining individuals from both within and across market classes, can lead to predictions that are as good as, and often better than predictions made from exclusively within the market class  and the analytical methods (genetic distance sampling and CDmean method). This separation between methods was not dependent on the statistical model used to make predictions which was confirmed by ANOVA analyses of prediction accuracies. As sample size increased so did prediction accuracy due to the fact that the estimation of marker effects is improved as the size of the training set increases, a finding also reported in studies of diploid crops  in comparison to diploids .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. The handling editor declared a past collaboration with one of the authors RV.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


It has been proven that the random regression model has a great advantage over the repeatability model in longitudinal data analysis. At present, the random regression model has been used as a standard analysis method in longitudinal data analysis. The aim of this study was to estimate the variance components and heritability of semen traits over the reproductive lifetime of boars. The study data, including 124,941 records from 3,366 boars, were collected from seven boar AI centers in South China between 2010 and 2019. To evaluate alternative models, we compared different polynomial orders of fixed, additive, and permanent environment effects in total 216 models using Bayesian Information Criterions. The result indicated that the best model always has higher-order polynomials of permanent environment effect and lower-order polynomials of fixed effect and additive effect regression. In Landrace boars, the heritabilities ranged from 0.18 to 0.28, 0.06 to 0.43, 0.03 to 0.14, and 0.05 to 0.24 for semen volume, sperm motility, sperm concentration, and abnormal sperm percentage, respectively. In Large White boars, the heritabilities ranged from 0.20 to 0.26, 0.07 to 0.15, 0.10 to 0.23, and 0.06 to 0.34 for semen volume, sperm motility, sperm concentration, and abnormal sperm percentage, respectively. The use of artificial insemination (AI) is widely applied in the intensive pig industry as AI incredibly accelerates genetic progress by using semen of superior boars. However, young boars are seldom selected for AI considering semen traits. Even boars with high breeding values regarding production traits will still be culled due to poor semen quality. In addition to this, eliminative boars will receive a lower price in the commodity market . TherefoSome important economic traits of pigs, such as semen trait and body growth, are recoded with the age. In a few cases, the assumption of a repeatability model is invalid; however, a multivariate animal model would be highly overparameterized . Random In order to elaborate a reliable selection program for semen traits, estimating variance components and heritability for semen traits is important. Recently, more and more genetic parameters related to pig semen traits have been reported. However, both genetic and population diversity can influence the variance components. In addition, most of the studies related to semen traits are based on animal models or repeatability models , and theEthical review and approval were not required for the animal study because the data used for this study were collected as part of routine data that are recorded in a commercial breeding program. Semen collections were conducted strictly in line with the Guidelines for the Care and Use of Experimental Animals established by the Ministry of Science and Technology of the People’s Republic of China. All efforts were made to minimize animal suffering.n = 1147) and Large White . Semen volume , sperm motility , sperm concentration , and abnormal sperm percentage  were considered in this study. VOL was measured by weighting each ejaculate and considering 1 g of semen to 1 ml. MOT, CON, and ABN were measured by a microscope (before 2017) and a computer-assisted sperm analysis system (after 2017).Data from seven AI centers of southern China were collected between 2010 and 2019. The total number of ejaculates was 124941 stemming from 3366 AI boars, including Landrace  the range age of boars between 33 and 150 weeks; (b) the first record was excluded, and the interval between two subsequent semen collections was within the range of 1–30 days; (c) animals with minimum ejaculation number (set to 6) were chosen to calculate the within-boar variation of the studies trait; (d) each fixed effect level should have at least 10 ejaculation records; (e) records on the VOL, MOT, CON, and ABN should be within 100 ml–600 ml, 10%–100%, 0.1 * 10^8/ml–8 * 10^8/ml, and 0.01–100%. After data filtering, the clean data of each breed are presented in 1 is the vector of fixed effects; b2 is the vector of fixed regression coefficients; a and p are vectors of random regressions for additive genetic and permanent environmental effect, respectively, X1, X2, Z1, and Z2 are design matrices of b1, b2, a, and p, respectively; and e is the vector of residuals. It was assumed thatThe following random regression model is used to estimate the (co) variance and breeding value:th Legendre polynomial evaluated at age t  indicate the order of the polynomial fitted for fixed effects (a), additive genetic (b), and permanent environmental effects. This resulted in the evaluation of 200 models. Here, random regression models are fitted to be evaluated first through eighth-order polynomial covariance functions for the fixed effects of boar age classification and second through sixth-order polynomial covariance functions for the additive genetic and permanent environmental effects. The fitness of model was tested by Bayesian Information Criterions (BICs)  to perform the Waller−Duncan k-ratio s (BICs) :BIC=−2lThe semen trait change over time for all selected boars can be represented by The estimate effects of the interval between two subsequent semen collections were also obtained from the abovementioned model.The variation of the additive genetic variance, permanent environmental variance, heritability, and repeatability for four semen traits in the two breeds changes over time as shown in The genetic correlation estimate of VOL, MOT, CON, and ABN at different ages of boar is shown in The interval between two subsequent semen collections on semen traits had a large effect . VOL incIt is very quick to obtain the result by using a repeatability model to analyze semen traits because of a substantially lower number of parameters . HoweverThe best model indicated that VOL is a medium heritability trait with heritability ranging from 0.18 to 0.28 and from 0.20 to 0.26 in Landrace and Large White, respectively. These values strongly agreed with using the repeatability model for this trait reported previously by Wolf and Li . Wolf esApart from estimates of heritabilities, the estimates of the additive genetic variance and repeatability are also of particular interest to animal breeders. The additive variance directly determines the response to selection and the opportunities for genetic change by natural or artificial selection . For theAs mentioned above, the estimate of heritability from four semen traits in Landrace and Large White indicated that selection for VOL could achieve reasonable rapid genetic gains. However, for the other three traits, the result indicated that the traditional selection will not gain genetic progress quickly because of low heritability. In addition, the boar semen traits are sex-limited traits, leading to the effect of traditional selection based on phenotype, and genealogical information is not obvious . Genome Genetic correlations between measurements at the age of 33 through 150 weeks are of great differences. Those results indicate that a repeatability model is an unacceptable approach to model variation for semen traits in this population. Genetic correlation decreases with age, which may also be due to limited data and selection of records in the prescribed age range. S.H.Oh estimated that genetic correlations were high between adjacent ages and decreased as the interval between ages increased in the sperm cell trait, and this result is consistent with our discovery . These rIf the interval between ejaculation is too long, sperm function will be significantly reduced. However, if the interval between ejaculation is too short, the VOL will be significantly reduced . Thus, iWe estimated the genetic parameters of VOL, MOT, CON, and ABN in different boar ages for two breeds. The higher-order polynomial of permanent environment effects and the lower-order polynomials for fixed effects and additive effects are the best orders to fit the random regression models. In addition, the best interval for semen collection is 8–10 days.


Vaccinium corymbosum L.) and low-chill Vaccinium species native to the southern United States. In this study, we employed a collection of diverse SHB accessions and performed a genome-wide association study (GWAS) for five phenology-related traits  using polyploid GWAS models. Phenology-related traits showed higher heritability and larger correlation coefficients between year replications, which resulted in the detection of robust phenotype–genotype association peaks. Notably, a single association peak for the CR was detected on Chromosome 4. Comparison of genotypes at the GWAS peaks between NHB and SHB revealed the putative introgression of low-chill and late-flowering alleles into the highbush genetic pool. Our results provide basic insights into the diversity of phenological traits in blueberry and the genetic establishment of current highbush cultivar groups.Genetic variation in phenological traits is the key in expanding production areas of crops. Southern highbush blueberry (SHB) is a blueberry cultivar group adapted to warmer climates and has been developed by multiple interspecific hybridizations between elite northern highbush blueberry (NHB) ( In perennial crops, phenological characteristics are one of the determination factors for cultivation areas and productivity. Annually, perennial plants recur a sequence of plant developmental stages and growers are required to provide preferable growth conditions for plants to ensure stable yield and high-quality fruits . DespiteVaccinium corymbosum L.; 2n = 4x = 48) is a tetraploid species native to North America. Highbush blueberry has been classified into northern highbush blueberry (NHB) and southern highbush blueberry (SHB) depending on their chilling requirement (CR) and winter hardiness (V. corymbosum) with various native low-chill Vaccinium species in the Cyanococcus section, especially Vaccinium darrowii and Vaccinium elliottii  is a powerful tool for deciphering genetic bases underlying the diversity of phenotypes of interest. However, in polyploids, genotype calling is not as straightforward as in diploids, possibly resulting in assigning the wrong genotype to samples . Even wiIn this study, we aimed to explore the genomic regions associated with phenology-related traits  in a SHB population, which are supposed to relate to adaptation to the warmer climates. Several gene action models with polysomic inheritance were assumed to optimize GWAS analysis in polyploid species. The results led to infer geographic distribution and putative derivation of the alleles controlling adaptive traits, providing novel insights into the genetic establishment of the SHB cultivar group.A diverse panel of SHB genotypes and 16 NHB cultivars established at the USDA-ARS Southern Horticultural Laboratory in Poplarville, MS, United States were employed in this study . The panN bases were trimmed, and reads shorter than 35 bp were discarded. Clean reads were mapped to the V. corymbosum ‘Draper’ reference genome   and VarSThe updog package version 2.0.2  was usedHaplotype-based discrete variant call was further conducted using freebayes (version 1.3.1) . VCFg waHere we have evaluated five phenology-related traits, namely, CR, flowering date , ripening date , FDP, and continuous flowering. All NDF and NDR were recorded in Julian days (number of days from January 1). All the measurements were collected from a field at the USDA-ARS Southern Horticultural Laboratory.The accumulation of CH was measured during the winters of 2018–2019 and 2019–2020 using the WatchDog weather station . Temperatures of 7°C and below were here defined as effective, and the number of hours with the effective temperature was measured in the field. For every 100 CH , 2–4 branches of 5–10 cm long with enough flower buds attached were collected from 2 to 4 plants of each genotype. Branches were maintained at 24°C and then rated for floral bud development after 2 weeks (in winter of 2018–2019) or 3 weeks (in winter of 2019–2020) as described by Flowering date and ripening date were evaluated for four plants of each genotype in three (2018–2020) and two (2018–2019) harvest seasons, respectively. For each year, January 1 was defined as a reference date, and NDF and NDR were counted and represented here as the number of days from the reference date. Average values were calculated for each accession and used for the following analyses. FDP was represented by ripening date minus flowering date.Here we analyzed the continuous flowering phenotype, in which the plant flowers more than once at some stage of a season in addition to the spring flowering. Continuous flowering was recorded for each genotype when first flower of a genotype was in 10% bloom from July 1 to September 30. Phenotyping was made based on the observation during multiple seasons in the field, and the genotype that showed the continuous flowering phenotype in at least one season was defined as the continuous flowering genotype. We targeted the genetic factor derived from ‘Sharpblue,’ which is a typical cultivar that shows continuous flowering phenotype in conventional cultivation under various environments. Here the genotype showing continuous flowering phenotype was designated as 1 while the others were coded as 0.H2) was calculated using the following formula:Variance components for each trait were separately obtained by a model summary function of the lme4 package , with al2g represents the genotypic variance, δ2y represents the variance of year, δ2e represents the residual variance, and R represents the number of year replications  lower than 0.1.Genome-wide association study was conducted for each trait and year replication separately. In some genotypes, phenotypic data are missing in a particular year due to sample limitation, and accessions with missing phenotypic data were excluded in this analysis. All the GWAS results were visualized using the Manhattan function implemented in R package qqman . At the The R package GWASpoly version 2.0  was usedGenome-wide association study based on a univariate linear mixed model described by To identify the derivation of the candidate alleles detected by GWAS analysis, we employed the population structure analysis results conducted by p < 0.05) of phenotypic variation between traits and year replications. Kolmogorov–Smirnov test and Bartlett’s test were first conducted to test the normality of distribution and equal variance, respectively. Tukey–Kramer test along with ANOVA or non-parametric Steel–Dwass test was then used to test the significance of differences (p < 0.05) of the phenotypic values among the genotypes at GWAS peak or original locations. For the loci with no reference homozygous or alternative homozygous states in the population , t-test or the non-parametric Wilcoxon rank-sum test was applied (p < 0.05). The Chi-square test was used to examine the differences in the ratio of individuals that showed continuous flowering between original locations.Spearman’s correlation analysis was used to test the significance of the relationship   .Most traits exhibited similar average values in multiple experiment years, except that the flowering date in 2020 was 15 days earlier than in 2019 . As for Southern highbush blueberry in this study are derived from four breeding locations in the United States: from south to north, Florida, Georgia, Mississippi, and North Carolina . In our H2 > 0.5), whereas that of FDP was relatively low, which is consistent with the low correlation coefficient between year replications. Genomic heritability, which refers to the proportion of variance explained by genome-wide markers , the updog package (VCFc and VCFd), and freebayes (VCFh), respectively. These variants were tested for phenotype-genotype association with two GWAS methods: GWASpoly and GEMMA.Genome-wide association study analyses detected a total of 114 variant loci significantly associated with five phenology-related traits (FDR < 0.1). According to the number of associated variants, GEMMA showed the larger detection power than GWASpoly . Among tWe categorized the GWAS models into two groups based on gene actions: Additive and Dominant. The Additive group includes additive models in GWASpoly and GEMMA software, which assume polygenic additive marker effect. The Dominant group includes simplex dominant and duplex dominant models in GWASpoly, which assume dominant gene actions. Among two groups, GWAS models belonging to the Additive group detected the largest number of variants significantly associated with traits . Howeverp (Chr4: 36498180) was detected by GEMMA software with additional 10 significant variants distributed between 34,516,343 and 38,282,633 bp on chromosome 4 (r2 > 0.6) with each other (p by additive model in GWASpoly using haplotype-based genotype (Chr4: 36768530) was observed in the same region with that in winter of 2018–2019 , and found that values of the correlation coefficient for trait pairs were positively or negatively significant in most cases. In addition, V. darrowii × V. corymbosum). Our results supported this association is conserved in a tetraploid cultivated population. The phenological traits analyzed in this study are fundamental factors to determine the suitable production area for blueberries; higher CR is advantageous to avoid early spring frost damage. In fact, this correlation has been reported in several fruit species (Prunus mume), In this study, significant correlations in most pairs of the phenology-related traits in a SHB population were observed . A posit species . It is kH2 > 0.5) except FDP (H2 = 0.59) is smaller than that in a previous study (H2 = 0.86)  , we dete = 0.86) . The resIn this study, GWAS analysis with GEMMA software using continuous reference allele dosage identified larger number of phenotype-genotype associations , generatIn addition, some differences in the results of GWAS using discrete genotype call were observed depending on the genotyping method. In particular, GWAS with haplotype-base genotype call showed significant peaks for NDR and continuous flowering, which was not obtained by the other models . This maAs for the marker effect assumed in association studies, additive models identified a larger number of variant-trait associations . This reChilling requirement is one of the major differences between NHB and SHB , and thuV. darrowii × V. corymbosum) × V. corymbosum] and found one major QTL for CR detected on chromosome 5. Our GWAS peak for CR did not correspond to the result from We here summarized the robust loci detected by GWAS analysis and the differences in allele frequencies at these loci between NHB, SHB from southern breeding locations (SHB-S), and SHB from northern breeding locations (SHB-N). In this section, putative introgression and allele fixation were discussed by categorizing genotype patterns shared within each gene pool .First, we hypothesized the introgression of the low-chill allele into the highbush gene pool  II. HereSecond, allele frequency changes were observed at the variants significantly associated with NDF  I. Here,Third, the alternative allele at Chr7: 10064365 was designated as a late-ripening allele based on the marker effect . AlthougFinally, we designated an alternative allele at Chr9: 8401122 as the short-FDP allele . Both NHVaccinium species that represent subpopulations 4 and 6 is unknown, further population structure analysis including Vaccinium species, such as V. darrowii and V. elliottii, which substantially contributed to the SHB population .The original sequence data presented in the study can be found in the DDBJ Sequence Read Archive (accession number SN, HY, EB, and RT conceived and designed the study. EB and MF collected the phenotype data. KN, SN, and KS developed the bioinformatics approach. KN and SN conducted the data analysis. KN, SN, and EB drafted the manuscript. All authors interpreted the data and approved the manuscript.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This paper quantifies behavioural responses to changes in the jurisdiction of a congestion charge, with a successive focus on (i) an extension and (ii) a reduction in the size of the charging zone. We exploit the unanticipated nature of both the implementation and removal of London’s Western Expansion Zone (WEZ) as quasi-natural experiments to test whether individual responses to policies are asymmetric. We use the UK Department of Transport Annual Average Daily Flow (AADF) data, which records traffic flows for seven transport modes . Using a difference-in-differences approach, we find that the introduction of the WEZ led to a 4.9% decline in road traffic flows in the new congestion charge area. These results are robust to different model specifications. HGVs traffic did not significantly change post-WEZ, which indicates that their road demand is price inelastic. The removal of the WEZ led to no significant variations in traffic. This result indicates asymmetry in behaviour with persistent changes in post-intervention traffic demand levels. NOx) and particulate matter, which have been linked to increased child mortality and premature births [Congestion-related economic and welfare costs are substantial and have gone up in recent decades. In the US, they have doubled since 2000 to reach an estimated $166 billion in 2017 . These ve births , 5. NegaSince its first introduction in Singapore in 1975, congestion charging has been trialled in various cities including Stockholm, Gothenburg and London . The undi.e. individuals go back to the way they used to travel once the program ceases to exist. In the cases studied, e.g. the Stockholm 2006 trial [Previous congestion charge scheme studies mostly assess the responses to their introduction: most empirical analyses show that congestion charging is associated with a decline in both congestion, traffic volumes, accidents and air pollution –15. Howe06 trial , 16, thi06 trial –19.To our knowledge, there are currently no papers jointly studying the implementation and removal of a congestion charge yet. The present paper aims to bridge that gap and to contribute to the literature by providing a complete analysis of the different stages of a congestion charge scheme. This enables us to test whether the implementation and removal of a congestion charge have symmetric effects, which is crucial in informing policymakers on the consequences of alterations that can be made to these programs. In analysing responses to the implementation and removal of a congestion charge scheme, we also can understand both the immediate short-run effects of the congestion charge and the long-lasting effects of road pricing.a priori responses to the withdrawal of a congestion charge. Two opposite effects are at play: firstly, the removal of the congestion charge reduces the costs of travelling by car, which incentivises car use and can, in turn, increase traffic volumes. However, the presence of the congestion charge for a certain amount of time in London is likely to induce a salient decrease in driving habits as individuals shifted away from using cars to avoid additional costs since the scheme started. Therefore, in the long run, changes in habits could present themselves in the form of a decrease in car ownership. Previous studies have shown that the long-run effects of pricing tend to outweigh the short-run effects [It is not possible to know  effects , which hAnother contribution of the present analysis is in the choice of the identification strategy. In this paper, we use causal inference methods which allow us to quantify behavioural responses to changes in the congestion charge scheme and enable us to pinpoint precisely individuals’ channel of response.In this study, we analyse responses to the introduction and the removal of the Western Expansion Zone of the London congestion charge. The initial London congestion charge zone was expanded westwards in 2007 and then removed in 2010 following successive political shifts. The unanticipated nature of these events makes them suitable quasi-natural experiment candidates . This enOur results show that the introduction of the WEZ led to a 4.9% decline in road traffic flows for all motor vehicles in the new congestion charge area. However, the traffic volume of HGVs did not significantly change post-WEZ, which implies their road demand is price inelastic. In contrast, we record no significant changes in traffic after the WEZ is removed. Results are robust to different specifications, to placebo tests and buffer tests. Therefore, estimates suggest that responses to the successive implementation and the removal of a congestion charge are not symmetric. After the implementation of a congestion charge, individuals are disincentivised to use their car in the new WEZ area as the costs of travelling by car in this zone increase. The lack of significant change post-removal seems to indicate that road pricing triggers long-run changes in travel behaviour that persist beyond the policy period. However, the withdrawal of a congestion charge increases financial incentives to use one’s car, yet this incentive might be marginal and insufficient to trigger a significant behavioural change.The paper is structured as follows. Sections 2 and 3 review respectively the institutional background and the existing empirical literature on the CC. Section 4 describes our difference-in-differences method. Section 5 presents the data, and Section 6 presents the results, their discussion and robustness checks. Section 7 concludes.Following a program of extensive analysis and planning , 23, thee.g., a 90% reduction in the fee for residents). Vehicle types exempted from the charge are the following: (i) motorbikes and mopeds; (ii) buses and coaches licensed in the DVLA ‘bus class’, (iii) London taxis; (iv) emergency service vehicles . In addition, Blue Badge holders, NHS staff and patients and fire fighters are also exempted from the CC fee. In addition, alternative fuel vehicles  and electric vehicles had to pay only an annual £10 charge.The original London CC zone used the Inner Ring Road as its boundary. The charge levied in 2003 amounted to £5 per vehicle between 07:00 and 18:30 every weekday (with the exclusion of public holidays) payable on the day either online, by phone or text message. Despite the possibility of getting weekly, monthly or yearly passes, discounts were not available. Failing to comply to the CC scheme led to a £80 penalty . Discoun2, comparable in size to the original CC zone and includes most of the Westminster and Kensington and Chelsea boroughs. Note that detailed maps of the congestion charge area are available at:http://www3.westminster.gov.uk/docstores/publications_store/transportandstreets/western_extension_congestion_charging_leaflet.pdf. The WEZ operated as a single congestion zone with the rest of the initial London congestion charge zone, with a toll of £8 since 2005. London also had uncharged roads that provided an alternative for people who wished to avoid the tax and only wanted to cross central London rather than enter the zone.Since its first introduction, the London CC scheme has undergone several changes  will also have affected pollution levels [A strand of literature studies the nature of responses to the implementation of a congestion charge . To our ng areas , 13. Hown levels .As mentioned previously, a significant decrease in traffic volume is unanimously observed after the implementation of a congestion charge. This implies that individuals respond in shifting their travel decisions towards other routes and towards other transport modes. After its initial implementation in London, the number of potentially chargeable vehicle kilometres reduced by 25% while the number of non-chargeable vehicle kilometres increased by 18%, which imply that individuals are incentivised to travel at earlier or later times in order to avoid paying the toll .The implementation of a CC scheme also led to changes in the choice of transport mode, with studies showing that responses are of all the trips that were no longer made in central London, around half transferred to a form of public transport, a quarter avoided the CC zone, and 10% shifted to other forms of private transport . In addiThis shift would ideally lead to a reduction in the volume of traffic and congestion. Descriptive statistics indicate that the WEZ and the area surrounding it experienced an increase in congestion; however, they could be attributed to road works which effectively reduced road capacity . This reMost studies assess responses after a CC scheme is implemented. However, less attention has been paid to the reduction and/or the complete removal of a congestion charge. Overall, studies of responses to the removal of a CC indicate that the duration of the removal and whether it is permanent or temporary affect the nature of responses. Responses to removals appear to be milder than responses to the implementation of a CC.e.g., car ownership), leading individuals to return to their previous travel habits once the trial ended.Studies of responses to the successive implementation and withdrawal of the six-month CC trial carried out in Stockholm find that traffic reduced by about 22% during the trial and then increased almost completely back to its original pre-trial level after the removal and remained only slightly lower than the pre-policy stage . They atA congestion charge was implemented since 2008 in Milan, but was then suspended for 50 days in 2012. This event is a good candidate for a quasi-experimental analysis as it was unanticipated and happened for political reasons. Moreover, the temporary suspension had no significant effect on the overall traffic flow . Yet, thSimilarly in Trondheim, the toll cordon was removed in 2005 after 15 years of operation, providing an opportunity to analyse responses to a permanent withdrawal of a congestion charge. This removal led to a 12% increase in traffic during charging hours as commuters moved their trip times back to the former charging hours . This isWe use a difference-in-differences methodology (DiD) to identify causal responses to the introduction and the removal of the WEZ. We rely on the assumption that the WEZ reforms were not anticipated by individuals. As car ownership entail sunk costs including an important fixed upfront cost, we assume that car purchases and habits would not have changed until the financial incentives are implemented. Yit is the average annual traffic flow at count point i in borough b at time t, WEZi is a dummy variable equal to 1 if the observation is located in the WEZ and zero otherwise; Xit is a vector of location characteristics, namely the annual fuel retail price, borough population and employment rates at borough level; μ is the constant term; ϕi controls for count point fixed effects, γt controls for time fixed effects, and εit is the error term.We specify two separate regressions for the introduction and the withdrawal of the CC in London. In this study, we prefer estimating separate difference-in-differences models for successive treatments that are the (i) implementation and (ii) removal of the CC rather than consider them jointly in a single model, because there could be effect heterogeneity for at least one of the treatments which could impair the identification strategy. The two main econometric specifications used in this study to assess traffic responses respectively for the introduction and removal of the WEZ are the following:t ≥ 2007, which is the year at which the WEZ was introduced, and zero otherwise. Similarly, t ≥ 2010, which is the year at which the WEZ was removed, and zero otherwise.In this study, We do not use transform outcomes into logs in the DiD specifications in order to keep the observations that are equal to 0: this may arise for some streets that can see no traffic at certain times, such as no cars, or no motorbikes.e.g., number of lanes) and borough characteristics . Controlling for population density and employment rate controls whether the area is residential or more of an office district. We control for time trends to account for temporal traffic trends such as the general increase in bus patronage [Count point fixed effects control for the volume of traffic observed as it can be impacted by both road characteristics  data produced by the UK Department for Transport (DfT). This dataset records information on traffic levels and road characteristics for 2,991 count points in London over the years 2006–2012—including 991 points in the treated area—and totalling 12,827 observations. Each observation contains information for a given street for an average day of a given year, namely the number of vehicles travelling past a specific count point in both directions. Traffic flows are counted for each of the following transport modes: “cars and taxis”, “buses and coaches”, heavy goods vehicles (HGVs), light goods vehicles (LGVs), and the total four-wheeled vehicles .Cars and taxis are not split in the dataset, which implies that we cannot separate the responses of each vehicle type to the introduction and removal of the WEZ in our analysis. We acknowledge that this category contains both treated (cars) and untreated (taxis) vehicles. Finally, we complement the aggregate nature of our data by using Consumer Data Research Centre (CDRC) data. The data consists of raw traffic counts collected annually from different count points in London between 8 and 9 am. The raw traffic counts were carried out on roughly the same day every year which controls for seasonal trends and each observation has a specific date. We apply the model to this data and the results are presented in exp(coefficient) − 1 = Changein% . However, after the WEZ was removed, individuals who wanted to shift back to their pre-WEZ travel habits had to face new costs generated by the car ownership and maintenance .Our results differ from the previous significant 14.5% increase in traffic volume found after the road pricing scheme in Milan got temporarily suspended . As suchTi = 2006 and Tr = 2011. In other words, since the treatment has not been dispensed, we do not expect to find an effect on the treated group post-treatment.We compute placebo tests to verify the validity of the common trend assumption . We chosIn addition to this placebo test using different cutoff dates, we run additional regressions and change the count points that are to be included in the treatment area. We set the treatment a bit westward in a way that includes the borough of Hammersmith and Fulham and also find null results for both the implementation and removal of the WEZ. This further confirms the robustness of the results. Finally, additional regressions have been performed using a two period DiD for respectively the introduction and removal, which yield similar results compared to what is obtained in Tables One of the underlying DiD assumptions (see Section 4) is that treated units do not interact with untreated units. To verify this hypothesis, we perform a buffer test following . To implResults from this study have thus far suggested that the introduction of a CC scheme appears to lead to a significant and substantial reduction in the volume of traffic for most modes of travel, with the largest response being recorded for cars. An important finding is that the introduction and the removal of a congestion charge in a given area do not have symmetrical effects. More precisely, removing a CC appears to have no significant effects on traffic. This suggests that individuals, to some extent, maintain the travel behaviours they adopted under the presence of a congestion charge. These results show that a policy which temporarily implements a CC can cause a lasting shift in travel behaviour decisions under the condition that the withdrawal is unanticipated. This confirms previous evidence from Stockholm  that theThis paper quantifies traffic responses after the introduction and the removal of a congestion charge on traffic volume using a difference-in-difference (DiD) methodology. Results indicate that the introduction of the WEZ led to significant responses, with a 4.9% decline in the total volume of traffic and a 5.1% decline in car traffic in the WEZ area. Heavy Goods Vehicles (HGVs) did not significantly change their travel patterns, which might be explained by their price inelastic demand for road use. Further analysing responses to the removal of WEZ show that responses to the implementation and withdrawal of the WEZ are not symmetrical. Estimates recording changes in traffic volumes are insignificant for all vehicles subject to the change in policy. The withdrawal of a congestion charge increases financial incentives to use one’s car, yet this incentive might be marginal and not sufficient for individuals to increase their propensity to change their car use to an extent that makes a substantial difference in terms of traffic flows. Therefore, the change in behaviour after a congestion charge is implemented is likely to be sustained even after this policy is withdrawn. Results are robust to different specifications, placebo tests and buffer tests.More broadly, our results indicate that congestion charging lead to a persistent change in aggregate travel demand levels even after the removal of the CC. Our results provide grounds for policy intervention in favour of the implementation of a congestion charge since they tend to have lasting effects that persist even after its withdrawal.S1 Table(TIF)Click here for additional data file.S2 Table(TIF)Click here for additional data file.S3 Table(TIF)Click here for additional data file.S4 Table(TIF)Click here for additional data file.S5 Table(TIF)Click here for additional data file.S6 Table(TIF)Click here for additional data file.S7 Table(TIF)Click here for additional data file.