There is great potential for the genetic improvement of oil palm yield. Traditional progeny tests allow accurate selection but limit the number of individuals evaluated. Genomic selection (GS) could overcome this constraint. We estimated the accuracy of GS prediction of seven oil yield components using A × B hybrid progeny tests with almost 500 crosses for training and 200 crosses for independent validation. Genotyping-by-sequencing (GBS) yielded +5000 single nucleotide polymorphisms (SNPs) on the parents of the crosses. The genomic best linear unbiased prediction method gave genomic predictions using the SNPs of the training and validation sets and the phenotypes of the training crosses. The practical impact was illustrated by quantifying the additional bunch production of the crosses selected in the validation experiment if genomic preselection had been applied in the parental populations before progeny tests.We found that prediction accuracies for cross values plateaued at 500 to 2000 SNPs, with high (0.73) or low (0.28) values depending on traits. Similar results were obtained when parental breeding values were predicted. GS was able to capture genetic differences within parental families, requiring at least 2000 SNPs with less than 5% missing data, imputed using pedigrees. Genomic preselection could have increased the selected hybrids bunch production by more than 10%.Finally, preselection for yield components using GBS is the first possible application of GS in oil palm. This will increase selection intensity, thus improving the performance of commercial hybrids. Further research is required to increase the benefits from GS, which should revolutionize oil palm breeding.The online version of this article (10.1186/s12864-017-4179-3) contains supplementary material, which is available to authorized users. Genomic selection (GS) is an efficient method of marker-assisted selection to improve quantitative traits . It usesElaeis guineensis) is a hybrid crop and is among the most economically important agricultural plant species in the world. Palm oil production is currently over 60 Mt. :For RRS, the 125 A individuals and 125 B included in the progeny tests were randomly chosen from the populations of candidates. For RRS with genomic preselection, the 125 A individuals and 125 B individuals included in the progeny tests were those with the highest genomic estimated GCA among each population of candidates. To investigate how the number of A and B candidates subjected to genomic preselection affected the performance of the selected hybrids, we first considered 125 candidates per parental population and then increased the number from 250 to 5000, with a step of 250. For each level of number of candidates, 20,000 replicates were made by generating random populations of candidates for each replicate.FFB. The two approaches were compared based on the performance of the hybrids selected using genomic preselection μSEL(RRS-GP) minus the performance of the hybrids selected without genomic preselection μSEL(RRS), expressed in percentage of μSEL(RRS), with the formula: 100 × (μSEL(RRS-GP) - μSEL(RRS)) / μSEL(RRS).For both breeding schemes, we selected the 10 best A parents and 10 best B parents on their GCA for FFB estimated from the progeny tests. We considered the selected hybrids were the 100 possible A × B crosses between the selected A and B individuals, and their genetic value was computed as the sum of the true GCA of their A and B parents (after centering around zero) plus μAll analyses were conducted using R software version 3.3.1 .Molecular coancestries between training and validation individuals were similar in Group A and Group B, the mean value of the maximum molecular coancestries between validation individuals and training individuals being 0.42 (range 0.38–0.48) in Group A and 0.44 (0.40–0.47) in Group B.P = 0.007) and for FFB, by 3.1% (P = 0.006). For the remainder of the study, we consequently only used the molecular dataset imputed with the pedigree. In this case, GBLUP prediction accuracy for cross values using all SNPs varied from high to low depending on the trait, from 0.78 ± 0.08 (SD over the six sets of hybrid crosses) for BN (bunch number) to 0.27 ± 0.1 for PF (pulp-to-fruit ratio).Using the pedigrees when imputing the missing SNP data increased the prediction accuracy of cross values for two traits, OP (oil-to-pulp ratio) and FFB (fresh fruit bunches), but did not affect the other traits  term. This was the case for all traits, marker densities and models (GBLUP and PBLUP) (not shown). The proportion of SCA variance between crosses in total genetic variance between crosses reached a maximum for FB (32.4%), followed by OER (26.0%), PF (8.9%), OP (8.5%) and ABW, BN and FFB (< 1% for the three of them). For the remainder of the study, we only present the results obtained for cross values prediction accuracies when the SCA term was not used in the prediction.Prediction accuracy increased with the number of SNP and reached a plateau starting between 500 SNPs (ABW and BN) and 2000 (FB), depending on the trait. When 200 SNPs were used, the prediction accuracy of the cross values ranged from very low to intermediate (0.04 ± 0.05 for FFB to 0.50 ± 0.12 for ABW) Fig. . For ABWP < 0.001). The accuracy of GBLUP with SNP subsets minimizing the percentage of missing data was then also higher than PBLUP accuracy (+13.2%), although the difference was not significant. In the other traits, this method of SNP sampling led to prediction accuracies with 1500 to 3000 SNPs similar to random sampling. The method led to very low percentages of missing data in both parental groups, ranging from 0% to less than 2% when the number of SNPs varied from 200 to 3000 . Defining subsets of SNPs with the lowest percentage of missing data increased GBLUP prediction accuracy in one trait, PF Fig. . For thiThe GCA prediction accuracies were higher in Group A than in Group B, except for FB Fig. . They rar(g′), was on average 0.54 in Group A and 0.76 in Group B. The A and B GS prediction accuracies r breeding scheme can be implemented in oil palm, applying a genomic preselection among parental populations to identify individuals with highest genetic value in hybrid crosses, before progeny testing them to make the final selection on all traits. Genomic preselection increases the genetic gain compared to the current RRS breeding scheme thanks to higher selection intensity. GS can be used for several key components of palm oil yield, in particular bunch production and the percentage of oil in the mesocarp. This required at least 2000 SNPs, with best results achieved when only SNPs with less than 5% missing data were used, imputed taking into account pedigree information. We illustrated the impact of genomic preselection on the bunch production trait (FFB) and showed that a preliminary genomic preselection could have further increased (by >10%) the FFB performance of the hybrids selected from Site 2, compared to the actual method with no preselection for this trait.The efficiency of the genomic preselection observed for several traits resulted from two facts. First, the GS prediction accuracies obtained here when predicting the genetic values of unobserved hybrid crosses and their parents could reach intermediate to high levels (> 0.6). Second, the GS prediction accuracies could be significantly higher than the prediction accuracies of the pedigree-based control model (PBLUP), indicating that GS was able to capture genetic differences within full-sib families of parents (i.e. the Mendelian segregation terms) in addition to genetic differences between parental families. In this case, GS enables identification of the best individuals of the best families, as currently done among the progeny tested individuals.−1 year−1 over a 3–7 year period. With a planting density of 143 individuals ha−1 and applying a correction of 95% standing palms, this extra gain would be 1.43 t ha−1 year−1. Assuming a mill OER (oil extraction rate) value of the selected hybrids of 28%, based on the current performance of similar material sold by PalmElit, this represents and extra oil production of 400 kg ha−1. Finally, considering a crude palm oil price of 640 US$ t−1 , we were able to estimate the economic gain of genomic preselection for growers: in the conditions of Site 2 experiment, genomic preselection on FFB with the genotyping of 3000 individuals would have increased incomes by 256 US$ ha−1 year−1 on average over the 3–7 year production period in plantations with hybrids selected from this experiment, compared to the conventional breeding approach that was actually applied. Considering that most oil palm breeding companies are also large scale palm oil producers with tens of thousands of hectares or more, and given the GBS price per sample, the associated extra genotyping costs would be quickly recovered by the increase in annual income per hectare. The question of the additional gain that would be obtained with conventional RRS by using the extra cost of GS to progeny test more A and B parents remains, but given the much higher costs of progeny tests compared to the GBS price, the increase in selection intensity would likely be negligible compared to what can be achieved with GS.According to the results of the simulation, the extra gain in FFB of selected hybrids obtained by using genomic preselection reached, when for instance 1000 A candidates and 2000 B candidates were genotyped, 10.5 kg palmThis study allowed us to extend the promising empirical results of Cros et al.  and MarcThe additional gain obtained here thanks to genomic preselection compared to the conventional method is close to the values obtained in hybrid cereals by Marulanda et al.  when theWe noted that PBLUP prediction accuracy could be higher for some traits (ABW and BN), as previously observed in Cros et al. , leavingHere we considered rather similar environments, while in real situations the marketing area of the best crosses could possibly involve more contrasted environments. In that case, particularly if genotype by environment interactions exist, it would be necessary to use models combining phenotypic data from various environments, genomic data and environmental covariables . HoweveThe SNP density obtained with GBS was sufficiently high to achieve maximum prediction accuracy with our dataset. However, with a larger or more diverse training set, it might be necessary to use a larger number of SNPs than the number that corresponded to plateaus in our study. This would likely not be a problem with GBS, as it would yield more markers if applied to a larger or more diverse population. This confirms the usefulness of GBS for GS already noted in other species . In oil Marchal et al.  studied We found that using the pedigrees when imputing the missing SNP data enabled an increase in prediction accuracy. This indicated that without pedigree information, BEAGLE 4.0 imputed genotypes could actually be inconsistent with the parental genotypes, and that the weight of those erroneous genotypes was high enough to significantly decrease GS accuracy in some traits.The observed variation in GBLUP predictive ability between SNP samples led us to apply several filtering strategies, in addition to the method minimizing the percentage of missing data presented here: most even genome coverage, lowest linkage disequilibrium (LD) between SNPs, smallest departure from the Hardy-Weinberg equilibrium  and highG, to check whether they could improve GS accuracies: applying corrections to G to account for the specificities of GBS  [G matrices from SNPs weighted according to local LD levels [In addition to these SNP filtering strategies, we also used several methods to compute the genomic coancestry matrices ng data) , 62, andD levels , 64. TheAccording to their pedigrees, the individuals used for validation were more related to the training individuals in Group B than in Group A, but the SNPs showed they were actually related to the same degree. This likely resulted from the fact that the pedigree of Group A did not go back to the founders, whereas it did  for Group B, and because SNPs are able to capture these relationships even when they do not appear in the pedigrees due to their incompleteness. This was already observed in our previous study  using SSOur results show that the phenotypic distribution of the traits affected GS accuracy. Muranty et al.  also obsThe smaller variance of true GCAs in Group A compared to Group B was expected from their respective history. As a consequence, the main driver of the prediction accuracy of cross values was the prediction accuracy of GCA in Group B. Likewise, increasing the number of B candidates was a more efficient way to increase the FFB of hybrid crosses than increasing the number of A candidates.The fact that adding an SCA term to the parental GCAs when calculating the prediction accuracies of cross values did not improve the results was likely the consequence of an insufficient proportion of dominance variance in total genetic variance. Indeed, some traits e.g. ABW, BN and FFB, had particularly low SCA variance, for which it was almost null. This low dominance variance might seem inconsistent with the use of RRS in oil palm breeding justified by heterosis in FFB , but thiGS prediction accuracies reached intermediate to high values for some key yield components (0.60–0.85), in particular for the production of fresh fruit bunches (FFB) and the oil-to-pulp ratio, using at least 2000 SNPs with less than 5% missing data, imputed using pedigrees. This enabled genomic preselection in the parental populations prior to progeny tests, which increased selection intensity. We show this for FFB, for which parental genomic preselection could have increased the FFB performance of the selected hybrids by more than 10%, compared to the current method without preselection for this trait. Preselection for key yield components using GBS is the first possible application of GS in oil palm for which empirical evidence of efficiency is available.However, more benefits are expected from GS in this species. Indeed, the genomic preselection suggested here would not make it possible to increase selection intensity for all traits, and progeny tests would still be required, making it impossible to reduce the generation interval. Further research is therefore needed to enhance GS prediction accuracies for all yield components. In particular, further studies are necessary to enlarge and optimize the training set and to model genotype by environment interactions. This would enable the optimal use of GS, which would revolutionize oil palm breeding. GS clearly has a major role to play in meeting the huge increase in the demand for palm oil expected in the coming decades, in a sustainable way, i.e. by maximizing the productivity of the existing planted area.Additional file 1: Table S1.Characteristics of the experimental designs used in the two sites. Distribution of progeny tested individuals among parental groups and breeding populations. (DOCX 19 kb)Additional file 2: Figure S1.n = 67) or common to both sites (green). (DOCX 51 kb)Pedigree of Group A individuals progeny tested in Site 1  and progeny tested in Site 2 and genotyped  or common to both sites (green). (DOCX 50 kb)Pedigree of Group B progeny tested individuals in Site 1 , progeny tested individuals in Site 2 and genotyped Additional file 5: Figure S3.Physical map of the 5092 SNPs available for Group A. The figures below the chromosomes indicate their number of SNPs. The colors indicate the number of SNPs per segments of chromosomes of 1,000,000 bp. (DOCX 153 kb)Additional file 6: Figure S4.Physical map of the 8311 SNPs available for Group B. The figures below the chromosomes indicate their number of SNPs. The colors indicate the number of SNPs per segments of chromosomes of 1,000,000 bp. (DOCX 156 kb)Additional file 7: Figure S5.Method overview for estimation of prediction accuracies. (DOCX 26 kb)Additional file 8: Table S3.g′), reference (Variance-covariance matrices between the true (


Advances in whole genome sequencing have enabled the investigation of rare variants, which could explain some of the missing heritability that genome-wide association studies are unable to detect. Most methods to detect associations with rare variants are developed for unrelated individuals; however, several methods exist that utilize family studies and could have better power to detect such associations.p value Optimized Nuisance parameter Score Test Extended to Relatives (MONSTER), which is a decorrelation method that instead attempts to adjust for relatedness using a regression-based method. We also considered family-based association test linear combination (FBAT-LC) and FBAT-Min P, which are slightly older methods that do not allow for the weighting of rare or common variants, but contrast some of the limitations of FBAT-RV.Using whole genome sequencing data and simulated phenotypes provided by the organizers of the Genetic Analysis Workshop 19 (GAW19), we compared family-based methods that test for associations between rare and common variants with a quantitative trait. This was done using 2 fairly novel methods: family-based association test for rare variants (FBAT-RV), which is a transmission-based method that utilizes the transmission of genetic information from parent to offspring; and Minimum MONSTER had much higher overall power than FBAT-RV and FBAT-Min P. Interestingly, FBAT-LC had similar overall power as MONSTER. MONSTER had the highest power for a gene accounting for a larger percent of the phenotypic variance, whereas MONSTER and FBAT-LC both had the highest power for a gene accounting for moderate variance. FBAT-LC had the highest power for a gene accounting for the least variance.Based on the simulated data from GAW19, MONSTER and FBAT-LC were the most powerful of the methods assessed. However, there are limitations to each of these methods that should be carefully considered when conducting an analysis of rare variants in related individuals. This emphasizes the need for methods that can incorporate the advantages of each of these methods into 1 family-based association test for rare variants. As sequencing technologies continue to improve, new opportunities arise to detect rare variants in complex human traits. Genome-wide association studies (GWAS) have been able to detect thousands of markers associated with various traits . HoweverUntil recently, methods for the analysis of rare variants typically focused on unrelated individuals. However, family-based studies may be better powered to detect rare variants because of their potential to be enriched for rare variants . Family-p value Optimized Nuisance parameter Score Test Extended to Relatives (MONSTER), which is a decorrelation method that instead attempts to adjust for relatedness using a regression-based method [Using WGS data and simulated phenotypes provided for Genetic Analysis Workshop 19 (GAW19) , we compd method . We alsod method , which an = 146), leaving a total of 813 individuals in 16 families. Of these 813 individuals, we used the 719 for whom simulated phenotypic data existed.The data sets provided consisted of family-based WGS data and 200 replicates of simulated phenotypes. WGS data were provided for 959 individuals, of which 464 individuals were sequenced, while the rest were imputed. We removed 4 pedigrees where no individuals passed quality control (QC) in the first phase of sequencing (MAP4 (in chromosome 3), TNN, and LEPR (both in chromosome 1).The primary phenotypes of interest in this study were diastolic blood pressure (DBP) and a quantitative variable with a null association, Q1. DBP was simulated to have an association with several variants and is used here to assess power, while Q1 facilitates the assessment of type 1 error. To allow for the most optimal association results, in this analysis DBP was adjusted for antihypertensive medication use, sex, age, and sex*age, while Q1 was adjusted for sex and age, which is consistent with how the data were simulated. Analyses were based on the first time point and were replicated using the provided 200 phenotypic simulation data sets. We focused on 3 of the top genes that explained the most variance in the simulated DBP variable: MAP4, 493 in TNN, and 899 in LEPR.Gene-based annotations were performed using ANNOVAR (Annotate Variation)  and the n is the total number of nuclear families and ps is the allele frequency for the sth variant estimated from the sample. This weighting method is similar to that of Madsen and Browning [The transmission-based rare variant analyses were conducted using FBAT-RV, FBAT-LC, and FBAT-Min P. FBAT-RV extends the basic family-based association test (FBAT) statistic, a covariance between the offspring genotype and trait, by collapsing rare variants over a specified region, resulting in a test statistic for that region . FBAT-RVBrowning  and estip value for the maximally significant statistic out of the set of individual statistics [FBAT-LC and FBAT-Min P are both FBAT multi-marker tests and use an empirical variance–covariance matrix to estimate the covariance between the markers. FBAT-LC uses non-informative families to estimate the optimal weights for the linear combination of the single-marker test statistics . FBAT-Miatistics . These 2Because multiple families were in each pedigree, all 3 FBAT tests were computed using the empirical variance option (–e) suggested in the FBAT documentation. The 2 outcomes, DBP and Q1, were adjusted for the aforementioned covariates of interest by putting them into linear regression models and using the resulting residuals as the outcome in the FBAT models.1 = 1 and a2 = 25 [The decorrelation-based rare variant analysis was conducted using MONSTER . This te a2 = 25 .Collapsing methods, using the gene plus 5 kbp on either side as the collapsed region, were employed for each of these approaches, as it has been found that power is increased when the effects of multiple rare variants are combined . Computap values for each gene. Type 1 error was defined as the proportion of p values under 0.05 for each gene when tested with Q1, which was then averaged across all genes to estimate the type 1 error rate of the method. Power to detect an association between each gene and DBP was defined as the proportion of p values under a threshold that made type I error equal to exactly 0.05. This was similarly averaged across all genes to estimate the overall power of the method.All 4 methods were tested with genes that had a known association with DBP and a null association with Q1, based on the GAW19 simulation model. Each method was repeated using each of the simulated replicates, resulting in 200 Type I error rates and power and for all 4 methods used are described in Tables MAP4 (power = 100 %), which was simulated to explain the highest amount of variance in DBP. However, it did not perform quite as well for TNN or LEPR, the latter of which had particularly low power. FBAT-LC had similar overall performance to MONSTER and outperformed FBAT-RV for all 3 genes; FBAT-RV had moderate power for MAP4, but poor power for LEPR and TNN. However, FBAT-RV did have slightly lower type 1 error rates than both MONSTER and FBAT-LC for each gene and overall. FBAT-Min P had the lowest overall power, but performed similarly to FBAT-RV for TNN and LEPR. As would be expected, Tables Results from MONSTER showed that each replicate was able to detect an association between DBP and  power = 00 %, whiMAP4, TNN, and LEPR genes. We found that MONSTER and FBAT-LC had superior performance when compared to FBAT-RV and FBAT-Min P. Although MONSTER and FBAT-LC had similar overall power, when considering each gene separately, they actually performed quite differently. Although MONSTER had much higher power for the MAP4 gene, which explained the highest percent of variance of the genes assessed, FBAT-LC had higher power for the LEPR gene, which explained the lowest percent of variance. While FBAT-RV and MONSTER are 2 relatively novel approaches designed for the analysis of rare genetic variants within families, FBAT-LC was not intended for the analysis of rare variants. However, our results, along with those of Zhou et al [Using family-based data provided by GAW19, we evaluated the power of a decorrelation-based test and several transmission-based tests to detect associations between simulated quantitative phenotypes and rare and common variants in the Computationally, we found benefits to both MONSTER and FBAT. Although FBAT-RV does have a built-in weighting method, MONSTER allows user-defined weights, which can be desirable. Neither FBAT-LC nor FBAT-Min P provides options for use of weights. A practical advantage of MONSTER is that it allows the user to enter multiple genes into a single run and then collapses them accordingly, while FBAT-RV, FBAT-LC, and FBAT-Min P require a separate run for each gene. However, the computation time for FBAT-RV was notably shorter than all other methods, with MONSTER taking 3 times longer to run.One limitation of MONSTER is that it adjusts for the pedigree-based estimation of kinship, which is not robust against population stratification. This could explain why the MONSTER results tended to have slightly elevated false-positive rates . HoweverSaad and Wijsman recently reported that using allelic dosages from imputation as opposed to genotypes leads to higher power . MONSTERBased on the simulated data from GAW19, MONSTER and FBAT-LC were the most powerful of the methods assessed. However, even these methods suffer from low power as the amount of variance explained by genes decreased. Furthermore, there are limitations to each of these methods that should be carefully considered when conducting an analysis of rare variants in related individuals. This study emphasizes the need for more efficient FBATs that can incorporate the advantages of each of the tests assessed and increase the power to detect associations for rare variants with moderate to low effects.


Zea mays L.) has a large influence on nutrition intake for animal feeding. Improving forage quality will enhance the utilization efficiency and feeding value of forage maize. Dissecting the genetic basis of forage quality will improve our understanding of the complex nature of cell wall biosynthesis and degradation, which is also helpful for breeding good quality silage maize.Plant digestibility of silage maize (Acid detergent fiber (ADF), neutral detergent fiber (NDF) and in vitro dry matter digestibility (IVDMD) of stalk were evaluated in a diverse maize population, which is comprised of 368 inbred lines and planted across seven environments. Using a mixed model accounting for population structure and polygenic background effects, a genome-wide association study was conducted to identify single nucleotide polymorphisms (SNPs) significantly associated with forage quality. Scanning 559,285 SNPs across the whole genome, 73, 41 and 82 SNPs were found to be associated with ADF, NDF, and IVDMD, respectively. Each significant SNP explained 4.2 %–6.2 % of the phenotypic variation. Underlying these associated loci, 56 genes were proposed as candidate genes for forage quality.C3H gene (ZmC3H2) that is directly involved in cell wall component biosynthesis. The candidate genes found in this study are mainly involved in signal transduction, stress resistance, and transcriptional regulation of cell wall biosynthetic gene expression. Adding high digestibility maize into the association panel would be helpful for increasing genetic variability and identifying more genes associated with forage quality traits. Cloning and functional validation of these genes would be helpful for understanding the molecular mechanism of the fiber content and digestibility. These findings provide us new insights into cell wall formation and deposition.Of all the candidate genes proposed by GWAS, we only found a The online version of this article (doi:10.1186/s12870-016-0919-9) contains supplementary material, which is available to authorized users. Zea mays L) is not only an important staple crop feeding billions of people but also a main forage resource for animal rearing development. Forage maize is a type of high-energy silage that supplies dry matter, organic matter and cell walls of whole plants for ruminants. The forage feeding value is usually denoted as the utilization ratio of the transformation from forage constituents to energy used by the animal. Improving the feeding value is a major objective in forage maize breeding by increasing forage digestibility, especially cell wall digestibility.Maize  was first proposed by Goering and Van Soest 1970) [0 [7]. NDbm1, bm3 and bm5 are known to increase enzymatic degradability of maize cell walls [bm1, bm2, bm3, and bm4 have been confirmed to encode cinnamyl alcohol dehydrogenase (CAD) [Due to the importance of silage maize in livestock farming, breeders began to select maize based on digestibility levels (preferring high digestibility), which resulted in improved cultivars and germplasm . A set oll walls . Until nse (CAD) , 17, metse (CAD) , caffeicse (CAD)  and folyse (CAD) , respectse (CAD) . Penningse (CAD) , identifse (CAD) –24. Thusse (CAD) , 25–39. se (CAD) . Twenty-Recently, genome-wide association studies (GWAS) have played an important role in dissecting complex quantitative traits in plants due to faster analyses, numerous high resolution markers, and abundant genomic and phenotypic variation . Maize hThe association panel used in the present study contains 368 diverse inbred lines (AM368), including resources from the International Maize and Wheat Improvement Center (CIMMYT), China and the USA. Most of the lines from CIMMYT belong to tropical or sub-tropical germplasm sources. Detailed information about AM368 was provided in a previous study . These iin vitro dry matter digestibility (IVDMD) were detected by NIRS [R2CV) and external validation  were 93.6 % and 94.6 % for ADF, 95.3 % and 96.5 % for NDF, and 90.2 % and 91.2 % for IVDMD, respectively.In the present study, when each inbred line reached physiological maturity (i.e. a black layer appeared in kernels), the ears were harvested by hand. After harvest, the second to fifth internodes above the ground of six plants from each inbred line were collected with garden scissors. All samples were immediately enzyme-deactivated at 105°C for 30 mins in a forced air oven and air-dried for 10–14 days. Dried stalk samples were ground with a mill and screened through a mesh size of 0.1 mm. Acid detergent fiber (ADF), neutral detergent fiber (NDF) and  by NIRS , 44. Bef by NIRS . The coehttp://www.maizego.org/Resources.html.The genotyping panel consisted of two sets: the MaizeSNP50 BeadChip, containing 56,110 SNPs, and 1.03 million high quality SNPs detected using RNA sequencing . Includeyi = μ + el + fi + εli, where yi is the phenotypic value of the “i”th line, μ is the grand mean of the target trait, el is the environmental effect of the “l”th environment, fi represents the genetic effect of the “i”th line, and εli is denoted as the residual error. All the effects were considered as random effects. The variance components were used to calculate the broad sense heritability as H2 = σg2/( σg2 + σε2/e), where σg2 represents the genetic variance, σε2 is the residual error variance item, and e is the number of environments. The 95 % confidence intervals of the H2 were calculated following the method of Knapp et al. [The GLM procedure was performed in SAS to dissect the variance of the phenotypes in different environments. The model used for the analysis of variance was p et al. . The Peayi = μ + gi + ei + εi. In this equation, yi represents the phenotype of the “i”th line, μ is the grand mean value of the target trait in all environments, ei is the environmental effect, and εi is the random error. The grand mean was fitted as fixed effect, and genotype together with environment were considered as random effects. BLUP estimation was obtained by using the MIXED procedure (PROC MIXED) in SAS9.3 (SAS Institute), which should be denoted as the sum of the grand mean and genetic effect of each line. The BLUP values of each line were used as the phenotypic values for association mapping.To eliminate the environmental effect within multiple environments, we fitted a mixed linear model to calculate the best linear unbiased prediction (BLUP) value for each line: http://ensembl.gramene.org/Zea_mays/Info/Index) and in the MaizeGDB database (http://www.maizegdb.org/gbrowse).A genome-wide association analysis was performed by accounting for the population structure and kinship matrix , which w−4 was applied for the detection of significant associations. To identify the most robust and stable associations, another round of GWAS was performed with a subsampling procedure [P < 1 × 10−4 and identified at least 10 times in 100 subsamples (resample model inclusion probability (RMIP) threshold of 0.1) were denoted as significant associations.Background LD between the high-density SNPs used in the present study can be problematic for multiple testing . In addirocedure . Subsamprocedure , and 80 r2) with each SNP significantly associated with forage quality traits in a region of 1 Mb were estimated in TASSEL 4.1.26 [http://gbrowse.maizegdb.org/gb2/gbrowse/maize_v2/). The closest gene of each peak SNP was proposed to be the most likely candidate gene.The linkage disequilibrium measure , 54.7 % to 74.3 % (NDF), and 25.1 % to 56.3 % (IVDMD) with averages of 35.2 ± 2.8 %, 64.1 ± 3.6 %, and 43.2 ± 7.0 %, respectively (Table P < 0.01). Using the phenotypic value across all environments, the broad sense heritability of each trait was calculated. The heritability estimates of ADF, NDF, and IVDMD were 0.81, 0.79, and 0.85, respectively , while IVDMD showed a strong negative correlation with ADF and NDF .The results of an analysis of variance showed that both genotype and environmental effects significantly affected the forage quality traits  ranged from 4.2 -6.2 %. SNPs that were significantly associated with ADF (correspond either to cellulose or lignin), which covered 24 unique significant loci, were distributed on all chromosomes except 3  implied that the population structure and familial relatedness were well controlled in the GWAS of each trait  of the maize B73 reference genome assembly v2 was used to annotate the genes that were near the significant SNPs [The MaizeGDB genome database (ant SNPs . After eGRMZM2G140817(ZmC3H2), which encodes a coumarate-3-hydroxylase that catalyzes hydroxylation reactions of the aromatic ring in the monolignol biosynthesis pathway [ZmC3H2 but did not affect alternative splicing. Relatively moderate LD (R2 < 0.6) was observed between adjacent SNPs with the leading SNP  encodes a kinase associated protein phosphatase (KAPP) that is related to the reaction response to pathogen attack and other stresses [GRMZM2G042627, and the level of LD between these two SNPs was high  is contained in the gene 653406 isSNP Fig. . On chroSNP Fig. . The corGRMZM2G331833, which encodes CLP protease regulatory subunit X, was identified as candidate genes for both NDF and IVDMD. In addition to the LIM transcription factor previously described, the co-localizations between ADF and NDF encode a small ribosomal subunit protein, an aspartic proteinase nepenthesin, an ARR-B-transcription factor, an F-box domain and LRR containing protein and an uncharacterized protein. Furthermore, three more genes were detected as candidate genes for both ADF and IVDMD; the encoded proteins included a GRF transcription factor, a mitogen-activated protein (MAP) kinase, and an uncharacterized protein.In addition to the three co-localized candidate genes mentioned above, nine more genes were also found to be common candidate genes  was found, which suggests that high fiber content genotypes will be difficult to digest.The phenotypic variation of the three traits across seven environments were dissected by an analysis of variance. Both genotype and environmental effects were significant for the three traits. In the previous studies about forage quality traits, heritability estimates ranged from 0.51 to 0.92 , 39, 57.Population structure can create unexpected LD between loci on separate chromosomes and lead to spurious associations for GWAS . AlthougP < 1 × 10−4 and RMIP >0.1 as the significance level, we found 56 unique loci that were significantly associated with forage quality traits. The phenotypic variation explained by each locus ranged from 4.2 %–6.2 %, which revealed that the genetic basis of the three traits in our association panel are mainly controlled by a number of minor effect quantitative trait genes (QTG).GWAS have been a powerful tool for studying the genetic basis of complex traits in maize. Because of the rapid decay of linkage disequilibrium in the association panel, the genome of maize was broken up into small LD blocks. The resolution for identifying an association with ultra-high density SNPs is at the gene level. In the present study, we scanned 559,285 SNPs to identify SNPs associated with ADF, NDF, and IVDMD. With this high-density genotype data, background LD between SNPs may be a problem for multiple testing. The threshold determined by the Bonferroni correction seemed to be too strict for detecting significant associations. To resolve this problem, a suggestive threshold that is lower than the Bonferroni correction with a subsampling procedure were employed in previous studies , 62. In Up to now, hundreds of QTL related to cell wall related traits have been identified across the whole genome of maize , 63, 64.GRMZM2G140817 (ZmC3H2), which encodes a p-coumarate 3-hydroxylase, was found to be related to ADF, NDF, and IVDMD. This enzyme belongs to the CYP98 cytochrome P450 family and catalyzes the hydroxylation reaction of the aromatic rings of p-coumaric acid, then converts the substrate to caffeic acid  branch and guaiacyl (G)/syringyl (S) branch in the lignin pathway. In Arabidopsis, a C3H-deficient mutant was reported to be displaying a reduced epidermal fluorescence (ref) phenotype [C3H in poplar and alfalfa caused an increase of H lignin units and a reduction of the total lignin content [C3H down-regulation was performed in maize in a recent study [ZmC3H1 knock-down maize plants were generated and led to a moderate increase of p-hydroxyphenyl (H) lignin subunits. The authors concluded that ZmC3H2 might compensate for the reduced level of ZmC3H1 in these C3H1 repressed plants. In view of all these results, the two C3H genes may together play an important role in the lignin biosynthesis of maize. The functional validation of C3H genes should be performed to study the mechanism of how these two genes affect lignin accumulation in maize.Identifying the genes associated with forage quality is crucial for understanding the molecular mechanism of cell wall biosynthesis. Among the candidate genes found in the present study, 40817 ZmCH2, whichhenotype . This mu content –70. Othent study . ZmC3H1 cis element contained in the promoter region of most lignin biosynthetic genes. This type of transcription activator is recognized by the DNA-binding domain of R2R3-MYB transcription factors [NtLIM1) has been reported to bind with AC elements and to regulate the expression of lignin biosynthetic genes in tobacco [NtLIM1 led to a reduction in the expression level of PAL, 4CL, and CAD, which caused a 27 % decrease of lignin content in antisense lines [Arabidopsis [GRMZM2G134752) and two NAC transcription factors , five more transcription factor genes  were found to be related to fiber content and digestibility , which was significantly associated with ADF and IVDMD, was also found to be associated with resistance to herbivore attack [Plant cells provide not only mechanical strength for the plant stem but also barriers for attacks by insects and diseases . A stronPAL, ZmC3H1 and F5H were associated with forage quality traits. However, these associations were affected by population structure and multiple tests. Only individual polymorphisms in the 4CL1, CCoAOMT2 and ZmPox3 were stably associated with digestibility [ZmC3H2. In addition, the candidate genes identified in the current study mainly included genes encoding transcription factors, protein kinases, and proteins involved in response to stress and other biological processes. Therefore, we conclude that in diverse association panels, only genetic variants of several key enzyme genes in the lignin pathway may play roles in controlling the genetic basis of forage quality. More phenotypic variation of the forage quality may be attributed to the transcriptional regulation of lignin biosynthesis and genes involved in other cell wall biosynthesis pathways.In previous candidate gene-based association studies, ten lignin biosynthetic genes were investigated to detect associations between polymorphisms and forage quality traits –60. The tibility , 59, 60.C3H gene, which catalyzes a key step in the lignin pathway, was proposed as a candidate gene for all the three forage quality traits. Most of the candidate genes underlying the associated loci were involved in transcriptional regulation of cell wall biosynthesis and the response to stress, suggesting that the transcriptional regulation of cell wall biosynthesis may play a vital role in cell wall formation and deposition. In conclusion, absence of silage maize in the association panel in the present study may be responsible for the lack of cell wall component biosynthesis genes found using GWAS. This study improves our understanding of the genetic basis of forage quality traits and provides insight into potential areas to improve forage digestibility. Future research will focus on cloning and validating the function of the potential candidate genes.In the present study, a genome-wide association study for forage quality traits was conducted with nearly 560 thousand SNPs. There were 24, 14 and 31 loci found to be associated with ADF, NDF, and IVDMD, respectively. Each of these loci contributes a small proportion of the phenotypic variance. Twelve loci were found to be co-localized for the three traits. Excitingly, underlying a significant associated SNP on chromosome 6, a


The resulting metaphenotypes integrate the information regarding the underlying biological process (i.e. thrombus/clot formation). Secondly, we take advantage of a family based Genome Wide Association Study to identify genetic elements influencing these metaphenotypes and consequently thrombosis risk. Our study utilized data from the GAIT Project . We obtained 15 metaphenotypes, which showed significant heritabilities, ranging from 0.2 to 0.7. These results indicate the importance of genetic factors in the variability of these traits. We found 4 metaphenotypes that showed significant associations with SNPs. The most relevant were those mapped in a region near the HRG, FETUB and KNG1 genes. Our results are provocative since they show that the KNG1 locus plays a central role as a genetic determinant of the entire coagulation pathway and thrombus/clot formation. Integrating data from multiple correlated measurements through metaphenotypes is a promising approach to elucidate the hidden genetic mechanisms underlying complex diseases.Traditional genetic studies of single traits may be unable to detect the pleiotropic effects involved in complex diseases. To detect the correlation that exists between several phenotypes involved in the same biological process, we introduce an original methodology to analyze sets of correlated phenotypes involved in the coagulation cascade in genome-wide association studies. The methodology consists of a two-stage process. First, we define new phenotypic meta-variables , named  Considerable efforts have been invested to evaluate hundreds of genetic variants associated with human traits. Despite these efforts, the loci that have been identified only explain a small proportion of the total phenotypic variance. Thus, there is the question of where the remaining heritability resides. For a complex disease, such as thrombosis, traditional single-trait genetic studies may be unable to detect the pleiotropic effect that a given genetic variant could have on the intermediate phenotypes involved with the disease. In particular, the normal physiological process underlying thrombosis is complex and many of its components are involved in the coagulation and fibrinolysis pathways. These components form a collection of intermediate phenotypes that are generally measured in the study of thrombosis. These intermediate phenotypes may reflect more directly the effects from causal genes than disease status. They are also less genetically complex and more strongly associated with susceptibility loci.So far, the genetic analyses of thrombosis have been carried out using one or more intermediate traits separately –7. HowevBoth genetic association and linkage research have focused on statistical and computational techniques to investigate the genetic effects between one genotype and one phenotype including polygenic and multiphenotypic approaches. Several strategies have been applied for the analysis of multiple and correlated traits. These can be divided into three categories: p-value correction methods, regression models and data reduction methods. P-value correction methods consist on combining several univariate tests, one for each trait, accounting for the observed correlational structure of the traits ,15.Regression models make use of mixed effects models for modelling the covariance structure of the phenotypes, as well as population structure . These tmetaphenotypes, which integrate information regarding the underlying biological variability on the thrombus/clot formation. Then, we take advantage of our GWAS to identify genetic elements influencing these metaphenotypes and their relationship with thrombosis risk.In this study, we explore an original methodology to determine the inner correlation within a set of related traits involved in the coagulation cascade, to help understanding the genetic bases of the coagulation cascade consequently of thrombosis risk. We apply Independent Component Analysis, a data reduction method, original in this field, to derive new phenotypic variables, called The GAIT  Project has been described in Souto et al 2000 . Briefly® 317k Beadchip on the Illumina platform . Individuals with a low call rate (<0.5%), a too high IBS (>0.95%) and a too high heterozygosity (FDR <1%) were removed from the sample. In addition, markers with a low call rate (<0.95%) and a low MAF (<0.0064%) were discarded also. A total of 34 individuals and 30,793 SNPs were removed from the study. A clean dataset containing n = 364 individuals and 277,191 SNPs was obtained for further analyses. This procedure was implemented in R using the GenABEL package [A genome-wide set of 307,984 SNPs was typed for all of the participants using the Infinium package .m = 27 phenotypes involved in the coagulation pathway were selected to study their joint biological activity within this metabolic process. These phenotypes were selected as they are defined in the literature [Among the 80 phenotypes in the GAIT sample, terature . The oriTo properly apply the mathematical methods that we used, phenotypic data were freed of missing values. To guarantee this condition, the phenotypic dataset was imputed using a bPCA, a Bayesian method for missing value imputation .metaphenotype is defined as a new phenotypic variable obtained synthetically from a set of traits (phenotypes) using a given mathematical model of dimensionality reduction. Metaphenotypes should be able to capture the original structure of the data to describe them as a whole. Therefore, identifying genetic variants related to these metaphenotypes may help to ascertain the genetic bases of the observed variability of the set of phenotypes, here the coagulation pathway.A metaphenotypes.The coagulation factors in the coagulation cascade show related patterns of activity. It is known that the genes coding for the different coagulation factors share a joint ancestry , so therMetaphenotypes are computed from the correlation among factors. There are several algorithms in the literature that are able to decompose the variability under different criteria. We applied an ICA , an algorithm based on a criterion of minimum shared information. ICA was compared to PCA a reference method for studying the genetic association of correlated phenotypes [enotypes , 21.metaphenotypes and W (m × m) are the weights of the model and E is the error of the model. Note that the metaphenotypes correspond to the axes of the new system of coordinates, and are called “components”. The maximum number of components obtained is the same as the original phenotypes but generally, only a few of them are informative and therefore are taken into account.Both PCA and ICA methods apply a linear transformation to the original phenotypic data and obtain a new system of coordinates of reduced dimensionality, following the expression in metaphenotypes are determined by the characterization of the weights of this linear transformation, either using PCA or using ICA.The metaphenotypes. The independence of the components is guaranteed by finding W that maximizes the non-gaussianity of the metaphenotypes (M).In ICA, the weights W are optimized to guarantee the statistical independence of the metaphenotypes (components) of k = 15, according to a criterion based on cross-validation approximations [Among the several ICA algorithms, the fastICA procedure was applied, using a particular approximation of the negentropy measure for maximizing the nongaussianity . In partimations . As othemetaphenotypes capture the maximum covariance existing between the original phenotypes. In this case, the metaphenotypes explore the correlation that exists among the original traits to capture the variability shared by the collection of original phenotypes.In PCA, the weights W are optimized so that the PCA was used as a reference method. By default, PCA obtained as many components as original variables (k = 27).As the structure of the interrelations among phenotypes is hidden and unknown, both techniques are complementary to unravel the cascade of physiological relationships.PCA and ICA answer different biological questions.metaphenotypes that explain the greatest overall variability or correlation between the original phenotypes. In other words, metaphenotypes built with PCA are a new set of indexes of jointly altered levels of the original phenotypes, capturing the common activity of the original phenotypes.PCA obtains metaphenotypes that are statistically independent. Thus, ICA is able to separate the different (independent) sources of variability captured by the original phenotypes. Let us consider the original traits as statistical mixtures of different sources of variability . If there was a genetic source of variability captured by the set of phenotypes (pleiotropy), the metaphenotypes obtained using ICA will capture it. In other words, ICA is especially useful to detect pleiotropic effects.In contrast, ICA was chosen because it obtains metaphenotypes were estimated using the variance component method implemented in SOLAR [h2r) estimates the total variance of a trait due to additive genetic effects.The heritabilities of the in SOLAR . This meGenome-Wide Association Analyses with the SNPs of the GAIT project were performed using a Likelihood Ratio test based on a linear mixed effects (Variance Components) model described in The model provides a vector of fitted values of the phenotype and an estimate of the variance-covariance matrix for each family , 29, 30.metaphenotype Mi with the age and gender co-variables for testing the association as they present a significant correlation with almost all of the metaphenotypes.i is the metaphenotype, μ is the overall mean, βj is the regression coefficient of the j-th covariate, cji is the j-th covariate, Gi is the random additive polygenic effect  which variance is defined as ΦσG where Φ is the kinship matrix and σG is the additive genetic variance due to polygenes. Finally εi are the residuals of the model.The polygenic mixed model defined in With a comparative purpose, GWAS of the original phenotypes were also computed.All the p-values where corrected using the Bonferroni critertion, with a significance criterion set at α = 0.05 after adjustment.metaphenotypes were obtained with our methodology. All of the metaphenotypes showed a significant heritability ranging from 0.15 to 0.7 (metaphenotypes and univariate phenotypes. For two particular SNPs (rs9898 and rs27311672), concordant results were found among the three GWAS approaches. Both SNPs were significantly associated with both an ICA-based and a PCA-based metaphenotype as well as with the univariate phenotypes corresponding to the proteins coded by their respective closest gene (HRG and F12). Concordant ICA-based and PCA-based metaphenotypes were compared as follows. Associations p-values with all the original phenotypes with the SNPs reported in A total of 15 5 to 0.7 . Signifimetaphenotypes, we plotted them in a simple graph  are influenced clearly by the trait corresponding to the FXII levels  and their scorings . For the metaphenotypes associated with SNP rs2731672 (ICA-C10 and PCA-C10) . This indicates that both metaphenotypes capture different information from the original phenotypes.In addition, PCA-C10) , a clearrs3733159, rs1621816 and rs1403694) on Chromosome 3. The former one corresponds to an intronic SNP in the FETUB gene, whereas the latter two are intronic SNPs in the KNG1 gene. It is important to note that KNG1 is located at a distance of around 40Kb from HRG. However, the SNPs rs1621816 and rs1403694 in the KNG1 gene showed a low amount of Linkage Disequilibrium with the SNP rs9898 in the HRG gene (r2 = 0.22 and r2 = 0.21). These four SNPs showed a significant association with the HRG trait with the univariate GWAS approach.In addition, the ICA-C3 metaphenotype was significantly associated with three other SNPs  and with a PCA-based metaphenotype . It is observed in metaphenotype  and FXII levels [As shown in henotype  and the henotype  are inflrs9898 at the HRG locus at chromosome 3 was significantly associated with an ICA-based  and two PCA-based  metaphenotypes. Comparisons were carried out with the metaphenotype showing a lower p-value. In this case, no correlation was observed between the loadings or the scoring  levels, but previous results also reported that it was associated with Activated Prothrombin Time (aPTT) trait and consequently with thrombosis risk [metaphenotype obtained with PCA is oriented clearly to the HRG trait. However, the HRG levels do not have a high weighting value in the metaphenotype obtained using ICA (metaphenotype obtained with PCA captures the variance due to the more weighted trait (that is HRG), the result obtained through ICA extend our knowledge about the implication of this genetic variant, indicating that this locus has a pleiotropic effect on the set of coagulation-related traits involved with this metaphenotype.Secondly, SNP  scoring . This susis risk . This exsing ICA . In othemetaphenotype, showed a significant association with three other SNPs located in the same genomic region of HRG on chromosome 3 (rs3733159 at the FETUB locus and rs1621816 and rs1403694 at the KNG1 locus). These 3 SNPs were also associated with HRG levels in univariate analyses. The proteins coded by these three genes are Histidine Rich Glycoprotein (HRG), Fetuin-B (FB) and the High Molecular Weight Kininogen (HMWK). All of these proteins are structurally related to a fourth protein, the fetuin A- Heremams Schmide-glycoprotein [KNG1), as well as coagulation Factor FXII (encoded by F12) are, together with prekallikrein (PK), important constituents of the plasma contact-kinin system. This system was first recognized as a surface-activated coagulation system, also known as the Coagulation Intrinsic Pathway (CIP). CIP is activated when blood or plasma interacts with artificial surfaces. A better understanding of this system may lead to insight into mechanisms for thrombosis and, therefore, the contact-kinin system represents a promising multifunctional target for potential thromboembolic therapies, since blocking of distinct members of the kallikrein-kinin system has the potential to become an effective and safe strategy to combat cardiovascular diseases such as myocardial infarction.In addition, the same ICA-based oprotein . TogetheKNG1 locus we observed that SNPs rs1621816 and rs1403694 showed a low degree of linkage disequilibrium with the SNP rs9898 at the HRG locus (r2 = 0.22 and r2 = 0.21). This indicates that they represent an distinct genetic signal. Thus, it is reasonable to suggest that among the results obtained, the association between SNPs located at the KNG1 locus and an ICA-based metaphenotype may have more relevant biological and clinical implications. Our results indicate that KNG1 plays a relevant role in the CIP not only at a molecular level, but also at a genetic level. This result is particularly interesting since allelic variants in KNG1 were previously associated with risk of thrombosis [KNG1 with thrombosis suggesting that KNG1 plays a role in the regulation of CIP, even without the influence of the FXI or the FXII levels, since neither FXI nor FXII levels show a specific weight within this metaphenotype Click here for additional data file.S2 TableSignificant thresholds for heritability estimation: * <0.05, **<0.005, *** <0.0005.(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.


DBH) by production of within‐genotype phenotypic variance may evolve to maximize fitness in stochastic environments. Bet‐hedging is generally associated with parental effects, but phenotypic variation may also develop throughout life via developmental instability (DI). This opens for the possibility of a within‐generation mechanism creating DBH during the lifetime of individuals. If so, DI could in fact be a plastic trait itself; if a fluctuating environment indicates uncertainty about future conditions, sensing such fluctuations could trigger DI as a DBH response. However, this possibility has received little empirical attention. Here, we test whether fluctuating environments may elicit such a response in the clonally reproducing crustacean Daphnia magna. Specifically, we exposed genetically identical individuals to two environments of different thermal stability  and tested for effects on DI in body mass and metabolic rate shortly before maturation. Furthermore, we also estimated the genetic variation in DI. Interestingly, fluctuating temperatures did not affect body mass, but metabolic rate decreased. We found no evidence for plasticity in DI in response to environmental fluctuations. The lack of plasticity was common to all genotypes, and for both traits studied. However, we found considerable evolvability for DI, which implies a general evolutionary potential for DBH under selection for increased phenotypic variance.Diversified bet‐hedging ( If the changes are predictable and infrequent , reversible phenotypic plasticity may represent an option to track the environment, by always changing to express the optimal phenotype  mean fitness in stochastic environments Slatkin, . SpecifiIt has been suggested that DI has evolved as a bet‐hedging mechanism to maximize long‐term fitness in a fluctuating or heterogeneous environment  at a final concentration in the aquaria of 2.4 × 105 cells/ml.The study population originated from Sandtjønna . The latter environment was obtained by keeping the air temperatures at 17°C from 05.00 to 13.00, at 22°C from 13.00 to 21.00 and at 12°C from 21.00 to 05.00 , day 2, and day 4. The experiment was repeated in 12 blocks, and on average, 83% of the clones were present in each of these. One of the 12 experimental blocks showed abnormally low growth for all individuals and was thus excluded from the data set (n = 51). In addition, five animals that failed to grow during the 5 days of the experiment were excluded.Three offspring from a single newborn clutch (<36 hr old) were randomly selected from each of the 20 clones. These were then assigned to one of three treatments. In the first treatment, metabolic rate (MR) and body mass (BM) were measured at 17°C immediately after assignment. This measure provides a baseline level of phenotypic variance at birth. The two remaining individuals were allowed to grow for 5 days (until shortly before maturation) prior to measurements of MR and BM in either (1) a stable thermal environment (mean aquatic temperature 17.8 ± 0.6°C) or (2) a fluctuating thermal environment  that were sealed using adhesive PCR‐film . The decline in oxygen content was then measured optically of up to 20 individuals simultaneously using pO2‐dependent fluorescence technology . Respiration of newborns was measured for 3 hr, while the larger (day 5) animals were measured for 1.5 hr. The lengths of the animals  were measured to the nearest 0.01 mm using photographs from a stereo microscope  and the software ImageJ . Details regarding the respirometric and the BM‐GL regression procedure are given by Yashchenko et al.  and log‐transforming the response variable. Evolvability  represents expected proportional change in population mean trait, for a unit strength (mean‐standardized) directional selection t over t generations, where e is the evolvability and β is the strength of selection on the mean trait value μ  to appr Hansen, .2.5R  using the statistical software k and αj|k are variance terms for random run effects and clone effects nested within run, respectively, and ε corresponds to residuals for individuals i of clone j = 1, …., 20 in run k = 1, …., 33 and treatment l = 1, 2, 3 . In these models, the variance of the residuals (ε) represents our measure of DI . This variance of the residuals was allowed to differ both among clones and treatments using the VarIdent command from the nlme package. Specifically, when analyzing the effect of fluctuating temperatures on DI, we used a subset of the data that exclusively contained animals measured at day 5. Here, we also allowed for an interaction between clone and treatment on the weighted variance, where a significant interaction would indicate a clone‐specific response to fluctuating environments. A common fixed effect for both MR and BM was treatment  nor between the two age classes (p = .22). The metabolic rate intercept was higher at day 5 than at day 0 (p < .001). Furthermore, at day 5 there was a significant effect of temperature regime, with animals at fluctuating temperatures showing a lower MR than those from a stable temperature . However, clones varied significantly in the amount of DI ). However, there was no interaction between clone and treatment on DI (p = .49). There was a significant amount of genetic variation (p < .05) in MR. The broad‐sense evolvability, Eμ, in MR was estimated to 0.09% . There was also variation in MR among runs (p < .01).The mean allometric slope describing the relationship between log MR and log BM . Genetic variance in BM was significant , and the clones differed significantly in DI . Significant run effects in BM were observed (p < .001), but there was no difference in mean BM after 5 days of growth between the temperature treatments .DI in body mass  did not differ statistically between the two temperature treatments on day 5  response. Empirically, plasticity in DI would be observable through differences in within‐clone phenotypic variation among environments that differ in their stability. However, we found no such effects, neither in body mass nor in metabolic rate, when Theoretical models on reaction norms predict that, for a given study system, bet‐hedging, plasticity or genetic evolution have evolved depending on predictability of the cue and on the timescale over which the cue operates . One might speculate that this is related to an asymmetric fitness cost of expressing too low MR at a low temperature  vs. too high MR at a high temperature .The daily thermal range used in the fluctuating temperature treatment exceeded 95% of the daily ranges the Appendix . Thus, tAppendix . HoweverDaphnia. The lack of plasticity was a general property of the population, and for both traits studied (BM and MR). However, we found genetic variance in DI, which implies a general evolutionary potential for DBH under selection for increased phenotypic variance.To conclude, we find no evidence that plasticity in DI, in response to environmental fluctuations, contributes to DBH in None declared.The study was conceived and initiated by SE and ØNK, while the experimental work and initial draft of the manuscript was conducted by ØNK. All authors contributed to the study design, analysis of data, and revisions of the manuscript. All authors approved to submit the final version of the manuscript. Click here for additional data file.


HTT haplotype hap.03, which differs subtly at the sequence level from European HD hap.03, suggesting a different ancestral origin but not explaining the earlier age at onset in these Venezuelans. GWA analysis of the Venezuelan HD cluster suggests both population-specific and population-shared genetic modifiers. Genome-wide significant signals at 7p21.2–21.1 and suggestive association signals at 4p14 and 17q21.2 are evident only in Venezuelan HD, but genome-wide significant association signals at the established European chromosome 15 modifier locus are improved when Venezuelan HD data are included in the meta-analysis. Venezuelan-specific association signals on chromosome 7 center on SOSTDC1, which encodes a bone morphogenetic protein antagonist. The corresponding SNPs are associated with reduced expression of SOSTDC1 in non-Venezuelan tissue samples, suggesting that interaction of reduced SOSTDC1 expression with a population-specific genetic or environmental factor may be responsible for modification of HD onset in Venezuela. Detection of population-specific modification in Venezuelan HD supports the value of distinct disease populations in revealing novel aspects of a disease and population-relevant therapeutic strategies.Modifiers of Mendelian disorders can provide insights into disease mechanisms and guide therapeutic strategies. A recent genome-wide association (GWA) study discovered genetic modifiers of Huntington's disease (HD) onset in Europeans. Here, we performed whole genome sequencing and GWA analysis of a Venezuelan HD cluster whose families were crucial for the original mapping of the HD gene defect. The Venezuelan HD subjects develop motor symptoms earlier than their European counterparts, implying the potential for population-specific modifiers. The main Venezuelan HD family inherits  HTT, which encodes huntingtin. HD involves various neurological manifestations such as involuntary movements, cognitive decline, and personality change; age at onset of diagnostic signs is partly determined by the length of the CAG repeat. However, genome-wide analysis of European HD subjects revealed that age at onset corrected for individual CAG repeat size is modified by other genetic factors, revealing potential disease-delaying pathways that form targets for therapeutic strategies. In this study, we set out to identify genetic modifiers in a Venezuelan HD cluster whose families were crucial for discovering the cause of HD. Through genome-wide association analysis using age at onset corrected for CAG repeat size as the phenotype, we found genome-wide significant (chromosome 7) and suggestive significant association signals (chromosomes 4 and 17). Significant modification signals in Venezuelan HD appear to be population-specific because genetic analysis of European HD subjects did not reveal modification signals at those locations. Nevertheless, established modification signals in European HD at a chromosome 15 locus were also augmented by Venezuelan HD data, supporting the existence of both shared and population-specific genetic modifiers in HD. Together with the full sequence of HTT shared by the majority of these Venezuelan HD subjects, our genetic analysis provides insights into the geographical origin of the founder mutation, meaningful allele-specific sites for gene targeting approaches, and potential targets for disease-modifying intervention in this population.Huntington’s disease (HD) is a dominantly inherited neurodegenerative disorder caused by a CAG trinucleotide expansion in  HTT, which encodes huntingtin .,38.HTT aect data . HoweverThe mean CAG sizes across all haplotypes among the Venezuelans were 45.9 and 19 for longer and shorter alleles, respectively, and the average age at onset was 35.5 . The expHTT locus did not emerge as associated with this phenotype in our GWA study of variations of >1% allele frequency [cis-factor on the hap.03 HTT haplotype.Among European HD subjects, residual age at onset  was not different between HD subjects carrying different common haplotypes , and therequency . For exaHTT that are responsible for earlier onset than in Europeans, we determined the HTT genomic sequence by whole genome sequencing of a representative nuclear Venezuelan HD family from Family 1 . Additional Sanger sequencing analysis of HTT exon 1 revealed that the affected parent of this family , the Venezuelan hap.03 disease chromosome carries alternative alleles at 28 variable sites, including a novel SNP (rs966032869 in intron 56) that was not described previously in public databases . None ofs family  carries s family . These dHTT sequence of the Venezuelan hap.03 HD chromosome to European hap.03 disease chromosomes from HD subjects with Northern European, Spanish, and Portuguese ancestries  to identify normal control subjects who may have identical sequences. Among various populations in the 1000 Genomes Project data, including Africans, Ad Mixed Americans, East Asians, Europeans, and South Asians, only one Mexican individual (NA19774) has sequence identical across 5864 comparable variation sites to the Venezuelan hap.03 disease chromosome, including at rs192188940 but excluding the novel SNP, rs966032869. The presence, but extreme rarity (MAF<0.001 based on UCSC Genome Browser) of rs192188940 on both normal chromosomes in the general population and on the Venezuelan hap.03 HD chromosome is most consistent with the CAG expansion mutation having occurred on a normal hap.03 chromosome that already carried the minor allele of this SNP. This normal chromosome may also have carried the rs966032869 minor allele or the latter may have arisen as a private mutation after CAG expansion. In any event, the most parsimonious explanation is that the major HD chromosome in the Venezuelan HD cluster arose by an ancestral CAG expansion event distinct from that associated with the common European hap.03 haplotype, potentially in an individual already geographically located in South America.HTT allele while leaving the normal HTT allele intact [HTT mRNA  that functions as a bone morphogenetic protein (BMP) antagonist, and plays a role in signaling during cellular proliferation, differentiation, and programmed cell death [SOSTDC1, and also show significant eQTL (expression quantitative trait loci) signals for SOSTDC1 in the GTEx data set     whereas thyroid)  but not thyroid) . This reHTT haplotype on HD chromosomes, currently named hap.01 [HTT CAG repeats [HTT disease haplotype among HD subjects throughout Europe is hap.03. Consistent with our observation of a Brazilian subject with a hap.03 HD chromosome comparable to that in Northern European, Spanish and Portuguese subjects, HTT CAG expansion mutations of HD subjects in Latin America have been hypothesized to be of European origin [Investigation of haplotypes in HD subjects, including family members from the large Venezuelan HD cluster and many families from North America and Europe, originally permitted delineation of the chromosomal localization of the HD mutation –6,35, and hap.01 ,38,48 and hap.01 ,37,38, bd hap.01 –38, indi repeats ,24,49,50n origin . HoweverHTT allele without damaging the normal HTT allele, providing a novel route to developing a treatment. The full sequence of the Venezuelan hap.03 HD chromosome and consequent definition of mutant allele-specific CRISPR/Cas9 PAM sites supports this same approach, ensuring that further therapeutic development of these allele-specific inactivation strategies need not be limited on scientific grounds to European HD subjects, but, if societal hurdles were overcome, could also benefit those in the Venezuelan HD cluster who have contributed so much to HD research. We have also reasoned that more traditional pharmaceutical development for HD could benefit from rational, in-human validated, therapeutic targets discovered by identifying genes that act to significantly modify disease pathogenesis in the subjects themselves. The well-established inverse relationship between CAG repeat length and age at clinical onset of HD pointed both to the CAG repeat size as the driver of the rate of HD pathogenesis and to a role for genetic and environmental factors in modifying the impact of the CAG repeat [HTT are not a common source of HD modification [The discovery of the cause of HD made possible the generation of model systems  and numeG repeat ,30–32,54fication , yet thefication . These mSOSTDC1 (sclerostin domain containing 1) whose protein product functions as a bone morphogenetic protein (BMP) antagonist [https://www.ebi.ac.uk/gwas/), providing a clear example of a phenotype determined by gene-environment interaction.Our initial European HD GWA analysis and follow-up study highlighted the importance of DNA maintenance/repair pathways in modifying the rate of CAG-dependent HD pathogenesis and suggested that somatic instability of the CAG repeat accelerates the disease ,40. Whiltagonist ,55 and ttagonist ,56–58. Ttagonist   and large effect size  made it detectable at genome-wide significance in only a few hundred Venezuela subjects. Population-specific modifiers of a disease have been reported only rarely in the literature  but exameviously  and in aIn summary, our comprehensive genetic investigation of a distinct disease population has revealed intriguing aspects of HD, which are directly relevant for gene-based and traditional target-directed small molecule therapeutic development, both for this population and potentially for HD more generally.The study was approved by the Partners HealthCare Institutional Review Board as Protocol #: 2009P000566/PHS, “Genetic Variation that Modifies HD Phenotype” and performed on DNA samples deposited previously, based upon oral consent, into a Partners HealthCare Institutional Review Board approved repository .et al., 2004 [https://imputationserver.sph.umich.edu/index.html) using the 1000 Genomes Project mixed population as a reference panel. The SHAPEIT phasing program (https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html) was used for pre-phasing study samples. The same QC parameters were applied to imputed data to ensure the quality of genotype data.In a study approved by the Partners HealthCare Institutional Review Board, the Genetic Modifiers of HD (GeM-HD) Consortium carried out genotyping for 3,447 HD subject samples from the Massachusetts Huntington’s Disease Center without Walls repository, which included banked samples from North American, European and Venezuelan families who had previously donated blood samples for HD research, and from the European Huntington's Disease Network (EHDN) Registry study . Data frl., 2004 , were exl., 2004 , we appll., 2004 ; MDS , and the HD disease chromosome haplotypes were further analyzed. One hap.03 disease chromosome haplotype, inherited from the father, was obtained for each of the seven trios, and we merged these to discover any inconsistent alleles for additional QC analysis. Locations and numbers of Mendelian errors and inconsistent alleles are summarized in After data pre-processing, trio genotype data were phased using the BEAGLE program (https://software.broadinstitute.org/gatk/). Additional genotyping for specific sites was performed by conventional Sanger sequencing.In order to validate Venezuelan HD whole genome sequencing data and compare to the hap.03 disease chromosome in European HD subjects, we performed capture sequencing for an additional 8 Venezuelan HD subjects and 7 unrelated European HD subjects. Haplotypes of disease and normal chromosomes were based on computational phasing  of our Ghttp://www.xzlab.org/software.html). We applied an additive model for each SNP whose minor allele frequency is greater than 5% in the study population. GWA analysis using imputed genotypes revealed genome-wide significant SNPs at a locus on chromosome 7. Subsequently, we compared genotypes  of 5 Venezuelan HD individuals who had both sequence and imputed genotype data, revealing a mean concordance rate of 98.7% .Residual age at onset was modeled by SNP, gender, and ancestry covariates  in a linear mixed effect model based on kinship matrix using the GEMMA program . Recombination rate was obtained from HapMap data (http://www.sanger.ac.uk/resources/downloads/human/hapmap3.html), and plotted on the secondary Y-axis of regional association plots. In order to determine dependency of SNPs in a given region, linear mixed effect models were constructed similarly but included the most significant SNP as a covariate. Significances of SNPs in the ordinary association analysis were compared to those in conditional analysis. Also, we performed extreme dichotomous analysis to determine whether a small number of data points drove the significant association. We directly compared SNP allele frequencies of those individuals whose residual age at motor onset was among the 10% extremes of earlier and later than expected onset. Logistic regression analysis was performed using the GEMMA program.Genomic inflation factor was calculated using the GenABEL package (median method) (http://penncnv.openbioinformatics.org/en/latest/). For chromosome 15 region, we meta-analyzed association analysis results of Venezuelan HD and European HD using the METAL program (https://genome.sph.umich.edu/wiki/METAL_Documentation). In order to determine whether a polygenic modification score based on European HD subjects captured individual deviation from the expected age at onset for a given CAG size in Venezuelan HD, we obtained effect sizes and significances of SNPs based on the European HD subjects. Polygenic modification score is defined as the sum of effect allele count X effect size for each HD subject. We used independent SNPs with suggestive significance . This scoring method was applied to European HD and Venezuelan HD to calculate individual modification score. Subsequently, polygenic modification score was used as the independent variable with gender and ancestry covariates to explain residual age at onset in each population.Genome-wide copy number variation was determined from GWA data using PENNCNV program . Among ISPD, SOSTDC1, and LRRC72, ISPD and SOSTDC1 generated significant eQTL signals in GTEx data set, and therefore these SNPs were downloaded for subsequent analysis. 42 and 231 SNPs (non-unique) were significantly associated with expression of ISPD and SOSTDC1 in various human tissues, respectively. For those significant eQTL SNPs, we compared the significance ) in our modifier association analysis to those in GTEx cis eQTL analysis, revealing a correspondence between GWA significance and eQTL significance only for SOSTDC1.For SNPs in the chromosome 7 region, we evaluated the significance of eQTL with genes in the region using the GTEx portal data base (Venezuelan HD subjects were separated into two groups based on chromosome 7 modifier genotype (rs12668183), resulting in rs12668183-carriers and non-rs12668183-carriers. For each group of Venezuelan HD subjects, independent GWA analysis was performed. In order to reduce outlier effects in the continuous analysis, subjects were sorted based on residual age at onset, and top and bottom 40% of subjects were analyzed using dichotomized phenotype data, focusing on comparing allele frequency differences between early and late onset groups. Mixed effect models were used to correct familial relationship.S1 Fighttp://www.internationalgenome.org/faq/which-populations-are-part-your-study/.In order to confirm ancestry of Venezuelan HD study subjects, principal components analysis using genome-wide SNP data of samples was performed together with 1000 Genomes Project data . The first vs. second MDS  values (top row), and the third vs. fourth MDS values (bottom row) were plotted. 1000 Genomes Project samples are represented by colored symbols (A and B), or open grey symbols . Study subjects used for GWA analysis (C and D) or sequencing analysis (E and F) are indicated by open black squares. For detailed description of populations and symbols of 1000 Genomes Project, refer to (PDF)Click here for additional data file.S2 FigFor QC-passed 374 Venezuelan HD study subjects analyzed in GWA analysis, familial relationships were inferred based on genome-wide genotype data. Pair-wise IBD (identity-by-descent) values were estimated by the PLINK program. From 374 unique study subjects, IBD values were estimated for 69,751 meaningful pairs. The mean of PI_HAT value (indicative of IBD) was 0.0253 (A). Based on Z1 and Z2 values from the PLINK IBD estimation, we identified 170 unique parent-child pairs comprising 216 subjects . In addition, siblings and other relationships were also revealed (B).(PDF)Click here for additional data file.S3 FigBased on pair-wise IBD estimations, families were reconstructed. A pair of related individuals whose PLINK PI_HAT value was greater than 0.125 (representing first cousin or closer relationships) were combined, and this process was repeated exhaustively until all related individuals were combined, generating 22 families with variable sizes. Therefore, an individual within a given re-constructed family has at least one relative. Families were numbered arbitrarily and were separated by blue boundary line. The size of each family is provided in a parenthesis, and haplotype of HD disease chromosome is indicated in parenthesis after slash. Unrelated individuals (18) are at the bottom-right of the plot without family name.(PDF)Click here for additional data file.S4 FigFollowing sample QC analysis, 374 Venezuelan HD subjects including 12 homozygote HD individuals were analyzed. Histogram plots show distributions of longer CAG repeats (A), shorter CAG repeats (B), and age at onset of motor symptoms (C). The means of longer CAG, shorter CAG, and age at onset of motor symptoms are 45.9 (A), 19 (B), and 35.5 (C), respectively.(PDF)Click here for additional data file.S5 FigHTT in this family, we performed trio phasing for each trio comprising parents and a child. As indicated by "Trio #", 7 trios were used for phasing analysis. For each trio phasing a small number of Mendelian errors were detected. Diamond symbols were used for children to maintain confidentiality.Nine subjects including one non-HD parent were used for whole genome sequencing. This family inherits the hap.03 haplotype on the mutant chromosome as the majority of Venezuelan HD subjects do. For each subject, haplotypes of mutant chromosome (red) and normal chromosome (black) are indicated. In addition, to determine the phased sequence of (PDF)Click here for additional data file.S6 Fig(A) For each trio, trivial phasing analysis was performed using the BEAGLE program, and 7 phased mutant chromosomes were merged to identify consensus alleles of hap.03 disease haplotype in this family. We detected 6 unique Mendelian error sites, 3,231 unphaseable sites due to missing data, and 7 inconsistent sites. In total 99.02% of bases were determined. Examples of inconsistent sites are shown in panel B. N and D represent missing and deletion.(PDF)Click here for additional data file.S7 Fig(A) Northern (filled blue circles) or Southern European HD subjects  carrying hap.03 as the mutant chromosome were identified using phased haplotypes of GWA data. A scatter plot shows MDS 1 and 2 values from principal components analysis of GWA data relative to control populations such as CEU and TSI.(B) Consensus alleles of the hap.03 haplotype in Venezuelan HD were obtained from trio phasing of family samples. In addition, consensus alleles of the hap.03 disease haplotype in 7 independent Northern European HD subjects were obtained from capture sequencing analysis (7 samples). Direct comparisons of consensus alleles revealed 2 sites (including a novel SNP) where alleles are different between Venezuelan and European hap.03 disease haplotypes. For the known SNP site (rs192188940), imputed GWA data for Spanish (15 samples) and Portuguese HD (43 samples) with hap.03 haplotype are also indicated. Since the novel SNP is not part of the 1000 Genomes Project reference panel, rs966032869 was not imputed in GWA data.(PDF)Click here for additional data file.S8 FigSanger sequencing analysis was performed to validate the genotypes of Venezuelan hap.03-specific SNPs including a novel SNP. A panel of hap.03 HD subjects from Northern Europe (1 sample), Spain (4–6 samples), Portugal (4–6 samples), and Brazil (1 sample) were sequences, and representative sequencing analysis results are shown.(PDF)Click here for additional data file.S9 FigHTT.Fully reconstructed Venezuelan hap.03 sequences were compared to consensus allele of hap.08 allele from 1000 Genomes Project data (phase 3). Haplotype hap.08 represents the most common normal chromosome in Europeans and Americans . A consensus sequence of hap.08 from the 1000 Genomes Project data represents the most frequent allele for a given site. Subsequently, we determined those PAM-altering SNPs that create and eliminate CRISPR/Cas9 PAM sites (NGG) in hap.03 and hap.08 haplotypes, respectively. In the region, 16 variations create NGG PAM sites only in hap.03 and therefore can be used for allele-specific CRISPR/Cas9 targeting aiming at silencing the mutant allele. Red arrows show an example where simultaneous application of CRISPR gRNAs is expected to selectively eliminate the transcription start site and expanded CAG repeat in the mutant (PDF)Click here for additional data file.S10 FigIn order to generate phenotype data for GWA analysis aiming at discovering modifiers of HD subjects in Venezuela, a regression model described in (PDF)Click here for additional data file.S11 FigLevels of genomic inflation were assessed based on QQ plot. The inflation factor was 1.0197 by the median method.(PDF)Click here for additional data file.S12 FigSuggestive modification signals on chromosome 4 (A) and 17 (B) are displayed. X-axis and Y-axis represent genomic coordinate (hg19) and -log10, respectively. Each grey circle is a SNP, and cyan trace represents recombination rates (secondary Y-axis) based on HapMap data. All exons were combined to represent a gene, in red or blue to represent genes on plus and minus strand, respectively.(PDF)Click here for additional data file.S13 FigResidual age at onset of HD subjects carrying 0, 1, or 2 minor alleles of the top SNP  are plotted for Venezuelan HD (A) and European HD subjects (B). Red horizontal lines represent mean residual age at onset.(PDF)Click here for additional data file.S14 FigIndependent suggestive significant or higher SNPs in European HD subjects were used as a training set to construct the polygenic modification score of each HD subject. Briefly, for a given individual, effect sizes of SNPs were multiplied by the number of effect alleles to calculate the sum of the modification score. The same scoring method was applied to European HD subjects to estimate the maximum prediction power of the modification score (A). Next, the effect sizes and scoring SNPs based on European HD subjects were applied to calculate the genetic modification score of Venezuelan HD subjects and subsequently used to explain residual age at onset of Venezuelan HD subjects (B). For each HD population, residual age at onset was modeled as a function of genetic modification score to judge its significance.(PDF)Click here for additional data file.S15 Fig(A) Focusing on the chromosome 7 region that generated genome-wide significant modification signals in Venezuelan HD, frequencies of CNVs are plotted over SNP association analysis results (grey circles). Black, blue, and red lines represent all CNVs, duplication, and deletion, respectively. Primary and secondary Y-axes represent SNP significances and frequencies of CNVs, respectively. X-axis represent genomic coordinate based on hg19 assembly.(B) In order to judge the levels of contribution of CNV to significant modification signals in the chromosome 7 region, samples carrying CNVs were excluded for SNP association analysis in a mixed effect model. X-axis and Y-axis represent association analysis results using all samples (374 subjects) and those using samples w/o CNVs (317 subjects). Significance means -log10.(PDF)Click here for additional data file.S16 FigIn order to determine whether the genome-wide significant modification signals were largely contributed by the main family, we split the data into the main family (218 subjects) and other families (156 subjects). Subsequently, statistical analysis was performed to determine whether residual age at onset in the main family was significantly different from that in the other families (A).(B) Chromosome 7 top SNP  was also analyzed independently in the two groups to assess the significance in modification.(PDF)Click here for additional data file.S17 FigAs a confirmatory analysis of continuous phenotype data analysis, we performed extreme dichotomous analysis. Instead of using continuous residual age at onset data, we generated two groups representing the 10% extremes of age at onset. We then compared allele frequency of the markers between the extreme early onset group and the extreme late onset group. The X-axis represents continuous association analysis results using all samples, and Y-axis represents extreme dichotomous association analysis.(PDF)Click here for additional data file.S18 FigWe attempted to detect genetic interactions with chromosome 7 modifier locus. Venezuelan HD subjects were grouped by a chromosome 7 modifier SNP rs150393409; minor allele-carriers  and non-carriers . For each group, samples with top and bottom 40% residual age at onset were identified. Subsequently, GWA analysis was performed using dichotomized phenotype data in mixed effect model see  section.(PDF)Click here for additional data file.