Hypoglycemia evaluation is expected in every encounter with diabetic patients. However, self-monitoring and self-management may not be complete at home, and limited by geriatric syndromes. Furthermore, hypoglycemia risk increases with age, and rurality may limit access to frequent monitoring. We identified 112 rural veterans with high hypoglycemia risk, using the local medication database (sulfonylureas and insulin), combined with age and glycated hemoglobin (HbA1c). Statistical analyses were conduct using SAS 9.4 . We used Chi-square, Fisher’s, One-way ANOVA for baseline variables, and a multivariate logistic regression model to assess the association of hypoglycemia and risk factors, including age, HbA1c%, self-monitoring, and knowledge. Hypoglycemia was reported in 30.4% of cases, of whom the majority were younger than those not reporting hypoglycemia . Baseline HbA1c% was higher in cases with hypoglycemia compared to those without . There were no significant differences between pharmacologic regimens, self-monitoring, and general knowledge. Veterans who knew hypoglycemia symptoms were 6 times more likely to reported hypoglycemia, compared with veterans who did not know any symptoms. We contacted primary care teams (PCT) for whom medications were adjusted. Hypoglycemia risk is high in the older population, and telemedicine programs can support primary care teams to improve management of their patients. Poor symptom knowledge needs to be addressed, while considering special attention for hypoglycemia unawareness in the oldest age group. We are implementing a project using continuous glucose monitoring in this high-risk population.


ARRDC5, DCC, ALK, and ITGA1, which have been found previously associated with CRC, and E2F3 and NID2, which are potentially related to CRC since they have known associations with other types of cancer. We performed pairwise and three-way interaction analysis on the 44 SNPs using information theoretical techniques and found 17 pairwise (p < 0.02) and 16 three-way (p ≤ 0.001) interactions among them. Moreover, functional enrichment analysis suggested 16 functional terms or biological pathways that may help us better understand the etiology of the disease.Colorectal cancer (CRC) has a high incident rate in both men and women and is affecting millions of people every year. Genome-wide association studies (GWAS) on CRC have successfully revealed common single-nucleotide polymorphisms (SNPs) associated with CRC risk. However, they can only explain a very limited fraction of the disease heritability. One reason may be the common uni-variable analyses in GWAS where genetic variants are examined one at a time. Given the complexity of cancers, the non-additive interaction effects among multiple genetic variants have a potential of explaining the missing heritability. In this study, we employed two powerful ensemble learning algorithms, random forests and gradient boosting machine (GBM), to search for SNPs that contribute to the disease risk through non-additive gene-gene interactions. We were able to find 44 possible susceptibility SNPs that were ranked most significant by both algorithms. Out of those 44 SNPs, 29 are in coding regions. The 29 genes include  The goal of most genome-wide association studies (GWAS) is to examine common genetic variations across the entire human genome in order to identify single-nucleotide polymorphisms (SNPs) that are associated with diseases or phenotypic traits . GWAS suVarious statistical and computational approaches can be used to identify genetic variants associated with human diseases. Those approaches can be divided into two categories: uni-variable and multi-variable analyses. In the uni-variable analysis, single-locus tests examine each SNP independently for its association with the phenotype. The effect size (or penetrance) of a variant is calculated and scored based on its significance of association with the disease phenotype. Many single-SNP-based methods were used for initial GWAS analyses, but had limited success in detecting genetic risk factors and explaining disease heritability .Recent research has shifted toward using or developing multi-variable approaches that examine interactions among genetic variants . It is mHLA-B gene , to search for risk susceptibility SNPs associated with colorectal cancer (CRC). We used a CRC GWAS dataset collected from the Canadian province of Newfoundland and Labrador. Both algorithms were able to capture the non-linear gene-gene interactions associated with the disease status, and to rank SNPs based on their disease associations through either main effects or interaction effects. We identified 44 SNPs ranked as the most significant by both algorithms, including both known CRC related genes such as The colorectal (CRC) GWAS case-control data were collected from CRC patients and healthy individuals with matching age, gender, and geographical distributions within the province of Newfoundland and Labrador, Canada. The CRC Transdisciplinary (CORECT) consortium coordinated the genotyping of data. Genotyping was conducted using a custom Affymetrix genome-wide platform (the Axiom CORECT Set) on two physical genotyping chips (pegs) for two datasets with around 1.2 and 1.1 million SNPs . The firr2 > 0.6, LD window size 2,000). Missing genotypes were imputed using the most frequent alleles in the population. Last, the genotypes were re-coded using numerical values {0, 1, 2} with 0 standing for homozygous reference, 1 for heterozygous variant, and 2 for homozygous variant. After the steps of quality control and imputation, the final preprocessed and balanced dataset had 186,251 SNPs and 944 samples (472 being cases and 472 being controls).Next, we performed per-sample and per-marker quality control steps and linkage disequilibrium (LD) pruning on the CRC data using PLINK. In the per-sample quality control, sex check was performed, sex chromosomes were excluded, and samples with more than 1% missing genotypes and outlier heterozygosity rate were removed. In the per-marker quality control, SNPs with missing call rates higher than 5%, with minor allele frequencies (MAFs) less than 5%, or with Hardy-Weinberg equilibrium values greater than 0.0001 were removed. Then, we pruned SNPs that were dependent on each other  with the approval number HIC 01.70. Our study was recognized by HREA as the use of secondary data which have already been collected and de-identified, and did not require a clearance.Most computational methods in informatics find it prohibiting to analyze high dimensional GWAS data  due to tFeature selection is frequently used as a data filtering step in machine learning when the original data contain noisy or irrelevant features, or attributes, that could compromise the prediction power of learning algorithms . FeatureIn our previous study , six feaMany preliminary GWAS employed uni-variable approaches where interactions between multiple variables could be overlooked . MoreoveEnsemble learning methods use an aggregation of predictors known as base learners. To produce a final prediction, the predictions of the base learners are weighted and the overall predictions are decided through majority voting for classifications and averaging for regressions. It has been shown that ensemble learning methods are powerful at reducing variance and overfitting by utilizing a collection of diverse base learners such as classification and regression trees (CART) . In thisbagging (short for bootstrap aggregating) that uses bootstrapped samples of the training data to train classification or regression models separately  predictor variables are randomly picked from the total M variables in the original data. Then the best variable/partition-point among the mtry variables is picked and used to split the node into two daughter nodes. Each tree grows to its maximum size, that is, when there is only one sample in the leaves. To make a classification for a new testing sample, each tree casts a vote for the predicted class and the majority vote will be the final prediction result. Intuitively, reducing mtry will reduce the correlation between any pair of trees in the ensemble. For classification, the default value of mtry is In random forests, arameter .The random forests algorithm is effective in uncovering interactions among genes that do not exhibit strong main effects . The algmtry (number of random variables to make best split at each tree node) and ntree (number of trees), and using greater values, work well generally for large GWAS datasets.mtry and ntree in our implementation. For mtry we selected the values of {100, 200, 300, 500, 1,000}, and the selected values for ntree were {500, 1,000, 2,000}. Other parameters used the package default settings. Therefore, we had 15 different combinations of mtry and ntree.In this study, we used a very fast implementation of random forests provided in an R package called “ranger” . The “raaverage CV accuracy using a specific combination of parameters was computed as the averaged accuracy across all testing samples. The parameter combination with the best average CV accuracy will be picked.In addition, we performed a 10-fold CV. The original dataset was partitioned into 10 equal subsets, and for each of the 10 iterations, one subset was picked as the testing data and the rest nine subsets served as the training data. Thus, each sample was used in the testing data exactly once, and the CV accuracy was computed as the percentage of correctly classifying all the testing samples. For each parameter combination and each fold of the CV , we repeated the random forests algorithm 10 times given the stochastic nature of bootstrapping and selecting variables to grow the trees. Since each execution of the random forests will yield a unique predictive model, we computed the accuracy of classifying each testing sample as the fraction of it being correctly predicted by the 10 models resulted by the 10 executions of the algorithm. Then the When the best combination of parameters was picked, the predictive models learned using it were then used to rank features according to the importance of the features in classification. For random forests, we used Gini Index as the quantification of feature importance . Gini Inboosting where the base learners evolve over time and make weighted votes , where m = 1, 2,…,n.trees, denote the approximation that maps the input variables x to the desired output y. At iteration m, a new approximation mF(x) is constructed through improving on the previous one m−F1(x) by adjusting it using the gradient of the loss function ∇L).Gradient boosting machine is a boosting machine learning algorithm in which a weighted combination of predictors are used to make the final prediction . A set on.trees (the number of trees), interaction.depth , and shrinkage (the learning rate or step-size reduction). The testing values of these parameters were as follows: n.trees = {100, 500, 1,000, 2,000}, interaction.depth = {1, 2, 10}, and shrinkage = {0.001, 0.01, 0.1}. Therefore, we had 36 different combinations of the three parameters. Other parameters of GBM, such as n.minobsinnode , bag.fraction (the fraction of the training set samples randomly selected to construct a new tree), and train.fraction (the fraction of samples used to fit a GBM model), were set to default values as 10, 0.5, and 0.5, respectively.For implementation, we used an R package called “gbm” . SimilarSimilar to random forests implementation, we used a 10-fold CV and repeated the algorithm 10 times for each combination of parameters and each fold of the CV. The best combination of parameters found was then used to rank the features. Feature Importance was estimated based on the number of times a feature is selected for splitting and the improvement to the model as a result of each split, weighted by the number of observations each split operates, and averaged over all trees . Again, Both the random forests and GBM algorithms provide importance estimations on each SNP based on their contribution to the accurate classification. Such importance scores by the ensemble algorithms help to identify the top-ranked SNPs that are most possibly associated with the disease of CRC. It has been suggested that tree-based ensemble learning algorithms are able to detect interaction effects among multiple SNP features. Therefore, we employ an information gain method , 2013b tI between SNP A and the disease outcome D as the main effect of the individual SNP A, since it captures how much information of the disease status the genotype of SNP A provides. Then the information gain IG of combining two SNPs A and B on explaining the disease status D is computed by subtracting the main effects of A and B, that is, I and I from the total mutual information I by joining A and B to explain D. Essentially, the information gain IG captures the gained information of combining A and B on explaining D, that is, the synergistic and non-additive interaction effect between SNPs A and B.The information gain method considers the genotype of each SNP and the disease outcome as random variables. It first computes the mutual information A, B, and C, are considered, the information gain IG is computed by subtracting all the main effects and pairwise interaction effects from the total mutual information I between joining A, B, and C together and the disease status D. Such an information gain metric measures the pure three-way synergistic interaction among three SNPs on explaining the disease outcome.Similarly, when three SNPs, p-value was assessed for each computed information gain by counting the portion of permuted datasets that had a greater value of the information gain than the real data.Permutation testing can be used to assess the significance level of a computed information gain value. In each permutation, the disease status labels of the data are randomly shuffled, and the information gain values are computed again for the permuted data. In this study, we performed a 1,000-fold permutation testing, that is, 1,000 permutations were collected to provide a null distribution of the assumption that there was no association between the genotypes and the disease outcome. Then the http://www.ensembl.org) and the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/) databases. The databases provide biological information on the allele, chromosome, and gene information for each SNP.We also annotated the identified top SNPs with functional information using online resources including the ENSEMBL  bioinformatics tool  to perfoIn this section, we first show the result of feature selection and then the application results of the two ensemble learning methods on the CRC GWAS dataset. We performed the parameter tuning and used the parameter combinations of the random forest and the GBM algorithms that yielded the best classification accuracies. Meanwhile, both algorithms can rank the SNP variables based on their contributions to the classification. We identified the set of the best-ranked SNPs by both algorithms as our key possible susceptibility genetic markers, which were further investigated and validated through gene-gene interaction and pathway enrichment analyses.We ran the TuRF feature selection method on all 186,251 SNPs. Each SNP was assigned a score based on its contribution to the disease status. Since TuRF is a multi-variable feature selection algorithm, such a contribution may include both the individual main effect or the effect of interacting with other SNPs. mean + 3SD was chosen which filtered the top 2,798 SNPs. The feature subset was an appropriate size such that the training of the ensemble learning algorithms could be finished in a reasonable time frame, meanwhile, enough features were included in the subsequent analysis to identify possible gene-gene interactions.To reduce the computational overhead of the downstream ensemble learning analysis, we then picked a score threshold and only included the potential SNPs with TuRF importance scores higher than the threshold. The threshold mtry = {100, 200, 300, 500, 1,000} and ntree = {500, 1,000, 2,000}. Thus, there were 15 combinations of parameter values. Recall that we ran the algorithm 100 times for each combination, the average CV accuracy and area under the curve (AUC) were computed over 100 runs.The parameter tuning for the random forests algorithm included adjusting the values of two parameters, ntree, and different curves represent using different values of the other parameter mtry. The highest CV accuracy of 75% and AUC of 0.84 were achieved when mtry = 100 and ntree = 2,000. That is, the algorithm performed the best when ntree was set to the maximum value and mtry was set to the minimum value. This suggests that greater values of ntree and lower values of mtry are preferable for analyzing GWAS data. Both increasing the number of trees and reducing the number of randomly picked variables to construct each node of a tree enhance the diversity of trees in the ensemble. The result suggests that the genetic explanation of complex diseases may be highly heterogeneous, that is, a large number of diverse classification models that use different combinations of SNP attributes are needed to approximate the mapping from the genotypes to the disease outcome.n.trees = {100, 500, 1,000, 2,000}, interaction.depth = {1, 2, 10}, and shrinkage = {0.001, 0.01, 0.1}. Again, for each of the 36 combinations of parameter combinations, 10 runs were collected for each of the 10-fold CV, and the average CV accuracy and AUC were computed over 100 runs.The GBM algorithm had three parameters, that is, shrinkage value, and shows the average CV accuracy (left) and AUC (right) as a function of the parameter n.trees. The highest CV accuracy was 74% and the CV AUC was 0.82, achieved when n.trees was set to 2,000, interaction.depth was 10, and shrinkage was 0.1. This suggests that, for GWAS data analysis, GBM performs better when greater values of interaction.depth, n.trees, and shrinkage are used.mtry = 100 and ntree = 2,000, and of the GBM algorithm, that is, n.trees = 2,000, interaction.depth = 10, and shrinkage = 0.1, to assess the importance of the SNP features. In random forests, the Gini Index importance of a feature was estimated based on the impurity of splitting testing samples on the tree node using the feature. Since we repeated the algorithm 100 times (10 times for each fold of the 10-fold CV), the final importance score (between 0 and 1) of a feature was computed as the averaged impurity score over 100 runs. The averaged importance score of each feature using GBM was computed similarly.After parameter tuning, we chose the optimal parameter combinations of the random forests algorithm, that is, ARRDC5 is ranked as the most important by both algorithms.p-values of SNPs and their ranking among all 186,251 SNPs are also shown in the table. These 44 SNPs and their chromosome and gene information are listed in p < 0.02) with their information gain measures and the p-values. The maximum information gain was 1.3% for the interaction between SNPs rs2010907 and rs3760948 (p = 0.002), meaning that the synergistic interaction effect between the two SNPs explained 1.3% of the disease outcome. Six pairs of coding SNPs, that is, SNPs from genes, had significant interaction effects.We calculated the information gain between all p ≤ 0.001) SNP trios with their information gain measures and p-values. The strongest three-interaction was found among SNPs from genes OTOP1, EPHX2, and LINC with a strength of 2.55% and a significance level of p < 0.001, suggesting that the pure three-way synergistic effect among the three SNPs can explain 2.55% of the disease outcome. There were six trios of significant interacting SNPs all from coding regions of DNA.We then calculated the three-way information gain among all p < 0.05. tobacco use disorder with 14 out of 29 genes in the category and a significance level of 3.9 × 10−5.We submitted the identified 29 genes to the DAVID software, and chose categories of Disease, GO, and Pathway for a functional enrichment analysis. We set the gene count threshold as five, that is, only functional categories including more than 4 of the 29 identified genes were considered, and the significance cutoff as 0.05, that is, Fisher’s exact test Identifying genetic markers associated with complex human diseases helps us better understand the disease etiology in order to better diagnose, treat, and even prevent diseases. Given the complexity of human diseases, especially cancers, the causing factors are more plausibly interactions among multiple genetic attributes instead of individual contributions. However, searching for combinations of attributes imposes a significant challenge for bioinformatics and genome-wide association research, since thousands to a million possible genetic attributes can be included for investigation.Powerful machine learning algorithms have been used for mining high-volume data in fields such as engineering, finance, and social sciences, and have started to see applications in analyzing high-dimensional biomedical data as well.DCC, ALK, ITGA1, E2F3, and NID2, and unknown but potential CRC association genes.In this article, we explored the application of two ensemble learning algorithms, random forests, and GBM, in identifying interacting genetic attributes associated with CRC. We studied a GWAS CRC dataset collected from the Canadian province of Newfoundland. We performed data preprocessing and filtering using the TuRF feature selection algorithm. By parameter tuning, we optimized the parameters for both random forests and GBM for GWAS data analyses. Both ensemble learning algorithms produced rankings on the importance of SNP contribution to the disease classification. By comparing the rankings provided by both algorithms, we identified a set of 44 top ranked SNPs from both coding and non-coding regions of DNA . The codALK, JPH1, and DCC. In addition, functional enrichment analysis on the set of the 29 identified genes suggested 16 significantly enriched functional terms including four diseases: tobacco use disorder, chemical dependency, metabolic diseases, and cardiovascular diseases, and 12 GO terms  was ranked the highest by both random forests and GBM (GLIS3) and non-coding SNP rs1816647 (SNP rs3760948 from gene arrestin domain-containing 5 ( and GBM . It was  and GBM  and the s1816647 . This isDCC) is a tumor suppressor in CRC . Anaplastic lymphoma kinase (ALK) gene is directly related to colorectal adenocarcinoma, and may affect treatments for advanced CRC  and significant (p = 0.001) three-way interaction. Gene α1-integrin (ITGA1) and transcription factor E2F3 have been found related to the prevention of tumor progression in colon cancer patients (NID2) has been found to be associated with lung cancer (The gene “deleted in CRC” (r in CRC  and is wnced CRC . Genes Dpatients . Gene nig cancer .INPP5D) had strong and significant pairwise interactions with another enzyme gene 1-Phosphatidylinositol-4,5-bisphosphate phosphodiesterase beta-4 (PLCB4) and gene cadherin-4 (CDH4), as well as a non-coding SNP rs11985944. We have not seen any published literature associating INPP5D with CRC and related diseases, and it can be a possible risk association gene to investigate in biology.Our results also suggested novel genes associated with the disease of CRC. For instance, enzyme gene phosphatidylinositol-3,4,5-trisphosphate 5-phosphatase 1 (tobacco use disorder (p = 3.9 × 10−5)  . In a st × 10−5) . Another cancers . The tumOur future studies include: (1) utilizing more computational power in order to include more SNP attributes in the classifier training and feature importance analysis and (2) exploring or combining other machine learning algorithms, including neural networks and evolutionary algorithms, for the search of gene-gene interactions.Machine learning algorithms have seen increasing applications in bioinformatics and computational biology thanks to their powerful abilities of automatic learning and modeling complex relationships among a large number of features. Applications of machine learning techniques require domain-specific tailoring and careful design since an application area has unique problem definitions and challenges. In the context of GWAS, we need algorithms that are able to detect the non-linear, non-additive interactions among multiple genetic factors that contribute to the disease outcome. This article demonstrates a novel design of an informatics framework of using two ensemble learning algorithms to search for interacting genetic factors associated with cancer and validating the results through statistical interaction analysis and biological functional enrichment analysis. With further biological experiments, our bioinformatics findings may help us better understand the disease etiology of CRC. We also hope our study can inspire more bioinformatics tool developments for human disease association studies.


The comparison of QTL detection performed on an elite panel and an (elite Selection of stable varieties producing the highest amount of extractable sugar per hectare (ha), resistant to diseases, and respecting environmental criteria is undoubtedly the main target for sugar beet breeding. As sodium, potassium, and The online version of this article (10.1007/s00122-019-03406-0) contains supplementary material, which is available to authorized users. Beta vulgaris L. ssp. vulgaris) is an important European crop for sugar production; it is also used as a source for bioethanol and animal feed. It is one of the youngest domesticated crops and probably originated from a relatively limited range of fodder beet types approximately 200 years ago (Fischer http://www.aker-betterave.fr/en/). AKER proposes to increase the genetic variability of sugar beets by searching for interesting new alleles from exotic resources around the world. The introgression of these new alleles in elite material will produce new varieties with a high potential for use by the industry. Wild crop relatives are a source of potentially adaptive genetic diversity for crop breeding programs  from the Florimond Desprez company. Two successive backcrosses with another elite pollinator also from the same company were then completed, leading to 187 individuals that constituted the (elite An exotic accession of The (elite https://CRAN.R-project.org/package=SpATS). The SpATS method allows us to consider the local trends, thanks to a smooth bivariate function f represented by 2D P splines, where u is the numeric vector of rows and v is the numeric vector of columns. For this experiment, additional terms were included in the SpATS model to account for other sources of environmental variation and genotype effects. We assumed a model including random independent factors for rows . We can notice that the potassium content was low in BAR601 and MEM601, the sodium content was particularly high in UPI601, and the ® beet genotyping array was developed along with Affymetrix (http://www.affymetrix.com), CA (USA). This array carried 33,621 high-quality SNPs from which 88% were recently generated through next-generation sequencing of the 16 beet accessions, selected into the AKER project. The SNPs put on the chip were chosen in respect to the technical constraints required by Affymetrix and to be distributed homogeneously along the genome. In brief, the NGS reads (http://www.retsch.com) for 150 s at 30 frequencies per s Magnetic bead DNA extraction was performed on a robotized platform. All individuals were genotyped using the GeneTitan®microarray automated scanner following the manufacturer’s recommendations . Genotyping crude data were analyzed with the Axiom® 1.1. analysis suite software package . For further analyses, only the highest quality SNPs corresponding to “Poly High Resolution” and “No Minor Homozygote” categories were used.A proprietary 35K AxiomOnly SNPs whose parental allele provenance was known without ambiguity were kept. The missing genomic data were then imputed for each linkage group using Beagle software (Browning and Browning Beta vulgaris L.), resulting from many different crosses in Florimond Desprez’s breeding program, was analyzed in this study. This population was already studied in Mangin et al.  in 2009, 2010, and 2011 as described in supplementary material of Mangin et al. . TestcroPhenotypes were first adjusted per environment, and then the mean phenotype was calculated as described in supplementary material of Mangin et al. . These mThe structure of the elite panel was also analyzed. The optimal cluster number of the hierarchical clustering on principle components analysis was set to two. Clusters contained 676 (Panel A) and 1425 individuals (Panel B).Boxplots of potassium content, The genotyping of this population was completed as described in Mangin et al. . A 0.05 http://www.aker-betterave.fr/en/), 16 exotic accessions have been identified as representing the maximum of the genetic diversity not already present in elite lines. Four of these exotic accessions were crossed with an elite line, and two successive backcrosses were realized with the same elite line, leading to four progenies. Each of these progenies was genotyped with the same 33K Axiom® beet genotyping array as the studied .Association mapping was performed for the QTL detection in both populations. In the  is used to find the best model in the elite panel GWAS . However, this criterion is not strict enough for model selection in large model space . If a detected SNP cannot be merged with another, it alone represented a QTL. For the 33 distinct detected SNPs, 31 were mapped on the consensus map; one SNP associated with the Association studies were performed for each of the three impurity traits with the A model and with the AD model on the elite panel, and on each of its two clusters . Table The majority of SNPs were detected using the A model. The AD model added only five new SNPs. Fourteen were found by both models. Several SNPs were found in common between two or three traits , but none were detected by the AD model only. Finally, a total of 177 distinct SNPs were detected for all traits. Association study results on the entire panel, the panel A and the panel B were illustrated with Manhattan plots .Detected SNPs were then merged into QTLs. A QTL groups together SNPs that are on the same chromosome, with no more than 5 cM between two consecutive SNPs, and with linkage disequilibrium greater than the predefined significance threshold (0.12 for the elite panel). If a detected SNP cannot be merged with another, it alone represented a QTL. Of the 177 distinct detected SNPs, 171 were mapped on the consensus map. One SNP detected associated with the potassium content, one linked with Figure In this work, we compared the detected QTLs found for three impurities traits in two different populations, an . On several chromosomes, the only QTLs detected were found in the elite panel, and when QTLs were found in the . We alsi et al. b, and thRegarding the results, the comparison of the detected QTLs in both populations allowed us to highlight interesting regions in the  progeny and an elite panel allows the detection of new favorable alleles and genomic regions brought by the exotic accession. Their introgression in a sugar beet elite germplasm is therefore an interesting approach to increasing the genetic diversity that is useful in breeding programs.BM, EG, BruD, KH designed the experiment, PPE, BM, and EG performed all the statistical analyses, KH provided the genetic resources, NH provided the phenotypic data, OG built the genetic map, BriD and PD realized the genotyping of the worldwide population, GW provided the genomic data of the elite population, PPE wrote the draft manuscript with the help of BM, EG, OG, PD, NH and BruD, and all authors read and approved the final manuscript.Supplementary material 1 (pdf 2029 KB)Below is the link to the electronic supplementary material.


Coevolution has been used to identify and predict interactions and functional relationships between proteins of many different organisms including humans. Current efforts in annotating the human genome increasingly show that non-coding DNA sequence has important functional and regulatory interactions. Furthermore, regulatory elements do not necessarily reside in close proximity of the coding region for their target genes.We characterize coevolution as it appears in locus-gene interactions in the human genome, focusing on expression Quantitative Trait - Locus (eQTL) interactions. Our results show that in these interactions the conservation status of the loci is predictive of the conservation status of their target genes. Furthermore, comparing the phylogenetic histories of intra-chromosomal pairs of loci and transcription start sites, we find that pairs that appear coevolved are enriched for cis-eQTL interactions. Exploring this property we found that coevolution might be useful in prioritizing association tests in cis-eQTL detection.The relationship between the conservation status of pairs of loci and protein coding transcription start sites reveal correlations with regulatory interactions. Pairs that appear coevolved are enriched for intra-chromosomal regulatory interactions, thus our results suggest that measures of coevolution can be useful for prediction and detection of new interactions. Measures of coevolution are genome-wide and could potentially be used to prioritize the detection of distant or inter-chromosomal interactions such as trans-eQTL interactions in the human genome.The online version of this article (10.1186/s13040-019-0195-y) contains supplementary material, which is available to authorized users. Advances in DNA sequencing have enabled the assembly of high quality and complete genomes of many different organisms, as well as the reassembly and refinement of whole genomes. As of particular interest, the human genome has been through many iterations improving its quality and completeness. Even though the entire sequence representing the population is known, the identification of functional units and regulatory elements, and the interactions between these elements is largely incomplete.Comparative genomics is used to predict functional units, and it is one of the primary whole genome scale methods. High rates of conservation have been shown to be indicative of functionality , and sevIn the literature, pairwise rates of conservation have been used commonly to identify protein-protein interactions (PPIs) and characterize protein function . ProteinIn this paper, we stipulate that the functional interactions that can be captured by coevolution may include locus-gene regulatory interactions. It has been increasingly shown that non-coding DNA sequence has function, and like protein-coding sequences, highly conserved non-coding sequences are likely to have functionality . MotivatCoevolution refers to the observation that the evolutionary conservation of two or more functionally associated cellular elements is correlated across a range of species. A plausible explanation to such correlation is that there is a functional relationship driving the selective pressure to conserve the pair of units together . CoevoluBLAST [In order to assess protein coevolution, the evolutionary histories of the proteins need to be constructed. A common and simple way of representing evolutionary histories is to use phylogenetic profiles, which represent the level of conservation of the protein in a set of organisms. Typically, phylogenetic profiles comprise alignment scores between a protein of interest and its homologs. These alignment scores are obtained using either already known sets of protein families or a search tool like the protein specific version of BLAST , by compBLAST E-values can also be used in place of a simple boolean for hit or miss in a genome [An important distinction between different methods for coevolution detection is based on the resolution at which the profiles are generated and or comparisons are performed. The resolutions break down to three general levels: 1) whole protein , 2) domaa genome .Once the phylogenetic profiles are generated, the process of identifying coevolution becomes the process of identifying similar patterns. One common way of defining similarity for this process is that a pair of profiles with high mutual information  is likelmirrortree use multiple sequence alignment to build a phylogenetic tree for each protein. These methods then measure the correlation of the phylogenetic trees rather than vectors to quantify and detect coevolution. In practice, rather than actually constructing the phylogenetic tree for the protein, the methods generate a distance matrix that stores all pairwise alignments between protein sequences that are incident on the multiple sequence alignment. In the more recent methods, tree generation software is applied to the multiple sequence alignment and distance matrices are extracted from these generated trees. These distance matrices that are generated could be used by any tree generation method to construct a tree, but mirrortree methods instead directly compare the distance matrices of pairs of proteins rather than pairs of trees that would have been generated from those matrices. MATRIXMATCHMAKER is another method that was developed to detect coevolution between pairs of proteins that also utilizes distance matrices, but is more computationally intensive [MATRIXMATCHMAKER method was improved upon to help address the increased computational complexity [In order to better represent a protein-specific phylogeny, phylogenetic tree based methods were also developed , 20. Forntensive . The MATmplexity .The principle of coevolution has been further studied beyond interactions between pairs of proteins or genes. It has been shown that physical interactions between proteins and DNA sequence exhibit coevolution, such as those between transcription factors and their binding sites . There hRecently, direct coupling analysis (DCA) based approaches have been used to predict protein residue contact sites , 25 and An important consideration for coevolutionary analyses is the selection of organisms from which to generate the phylogenetic profiles or distance matrices. Typically, as the number of genomes used in the analysis increases, predictive power also increases . BesidesAn eQTL interaction between a genomic locus and a gene represents the statistical association between the genotype of the SNP at that locus and the mRNA-level expression of the gene. There are two sub-classes of eQTL interactions, cis-eQTL and trans-eQTL interactions. The definition of these sub-classes relies entirely on the genetic distance between the SNP and the target gene. A cis-eQTL interaction is between a SNP and a gene that is on the same chromosome and is nearby. On the other hand, a trans-eQTL is between a SNP and either a gene on the same chromosome that is distant or a gene that is on a different chromosome. For example, the GTEx project, which is the primary repository of detected eQTL interactions in the human genome, considers SNPs that are within 1 Mbp of the target gene to be a cis-eQTL .12, comparing nearly all SNPs against all genes. This large number of performed association tests necessitates the use of multiple hypothesis testing (MHT) correction. One of the methods used for MHT correction is the Benjamini-Hochberg procedure which is a Bonferroni-like correction process which is not as conservative as a Bonferroni correction. The method can be implemented in an iterative manner. Initially a strict p-value threshold of significance is calculated from a specified false discovery rate and the number of tests performed. Hypotheses with nominal p-values that exceed this threshold are rejected, and the significance threshold is relaxed slightly for each of these rejected hypotheses. In turn, more tests can pass this relaxed threshold. The process of rejecting hypotheses and adjusting the threshold continues until no more tests exceed the adjusted threshold.Two contemporary tools that are used to perform large numbers of association tests are Matrix eQTL  and Fastmirrortree-based method to characterize the coevolution patterns of those same pairs. Further we show that we can leverage these coevolution patterns to aid in the prioritization of eQTL detection.In this work we explore the evolutionary relationship between SNPs and genes involved in cis-eQTL interactions. We examine the co-conservation patterns between them, and we use a In this section we present our results which are broken down into three subsections. The first subsection focuses on the relationship between the conservation status of SNP and gene pairs that form eQTL interactions. The second subsection characterizes the coevolutionary relationship of the interacting SNP-gene pairs, as assessed using their phylogenetic histories. Then we explore using this coevolutionary relationship to prioritize eQTL detection in the third subsection.We profiled the genomic conservation of the flanking sequence for SNP loci and transcription start sites that take part in eQTL interactions. Below, in the “p< 2.534×10−9) and furthermore SNPs involved in eQTLs are less conserved than SNPs in general (p< 2.2×10−16). Both of these p-values were calculated using the one-sided Kolmogorov-Smirnov (KS) test.We found that there is typically a conserved region surrounding the locus that harbors the SNP and that the locus that harbors the SNP is undergoing accelerated evolution (negative PhyloP conservation score). SNPs are less conserved than transcription start sites . This result suggests that for eQTL interactions the conservation status of a SNP is correlated with the conservation status of its target gene, with high conservation SNPs interacting with high conservation genes.To examine the evolutionary interplay between the SNPs and their target genes in eQTL interactions, we compared their levels of conservation as classified based on the patterns identified using cluster analysis. Table mirrortree based method to calculate a measure of coevolution for each SNP-gene pair. While DCA based methods have been used recently to quantify coevolution in locus-locus interactions, they have been used on relatively small sequences compared to that of the human genome. The primary data source for phylogenetic information is a library whole genome multiple sequence alignment of 100 vertebrate genomes provided publicly by the UCSC Genome Browser [mirrortree over DCA is that we are quantifying the coevolution between between pairs of sequences rather than pairs of loci alone. This process is described in detail in the “To further characterize the evolutionary relationship between SNPs and their target genes in eQTL interactions, we also examined and compared their phylogenetic histories. For this purpose, we used a  Browser . Subsequp-values for all pairwise comparisons of these distributions.To examine the relationship between our conservation metrics described above and the coevolution metrics we describe here, we calculated the coevolution scores for all eQTL pairs that we classifed using the clustering approach. Figure p< 1×10−300, two-sided KS-test). The higher levels of correlated phylogenetic history suggests that eQTL interactions may impart evolutionary pressure towards co-conservation.We quantified the coevolution of the SNPs and genes involved in eQTL interactions, and compared the distribution of these scores against a background distribution. The background distribution was generated by first identifying all SNPs and all genes that take part in at least one eQTL interaction. We scored all possible pairs of these identified SNPs and genes, namely about 511 million SNP-gene pairs, to form the background distribution. Figure p< 1×10−300, two-sided KS-test). Notably, the SNP-promoter background distribution is shifted towards higher rates of correlation than the SNP-gene background distribution, which is consistent with how each recorded score was chosen as the highest correlation of a set of pairs.Motivated by this result, we also explored the coevolutionary relationship of eQTLs with regard to known regulatory elements of the target genes. For this analysis, rather than comparing the SNP phylogenetic history with that of the gene, we compared them against the phylogenetic history of promoters associated with the target gene. The promoters are those that were discovered as part of the ENCODE project, and we acquired them through the Ensembl project . A challWe examined the relationship between minor allele frequency and coevolution score, to see if there was a correlation between high variation within the human population and coevolution scores. We found that there was no discernable difference between the distributions of minor allele frequencies for different ranges of coevolution scores.p-values of all cis-eQTL tests performed and not just those that are deemed significant. Raw data for the analysis as well as genotype data was provided via dbGaP, phs000424.v6.p1. With this data we are able to apply the multiple hypothesis testing (MHT) correction process on the original dataset. For each cis-eQTL SNP-Gene pair tested in the GTEX project, we calculated the genomic coevolution between the SNP and the target gene. We prioritized eQTL detection by focusing on SNP-Gene pairs that exhibit high levels of coevolution. By thresholding on the coevolution score we identified sets of the SNP-Gene pairs for different levels of coevolution. For each of these sets, we applied the MHT correction process to them and reported the p-value threshold of significance. A property of this MHT correction process is that the ratio of the significance threshold relaxed by prioritizing with coevolution score and the non-prioritized significance threshold is directly proportional to the level of enrichment that SNP-gene pairs with that coevolution score have for significant eQTL interactions. We expand on this property in the “As shown above, eQTL interactions are more likely to exhibit correlated phylogenetic histories as compared to random pairs of sequences. Motivated by this observation, in order to explore the utility of genomic coevolution in detecting regulatory interactions, we applied coevolution to the prioritization of eQTL interactions. We used data made available by the GTEX project , V6, as p-value significance threshold. We also tested whether random downsampling to reduce the number of tests performed affected the p-value threshold of significance and confirmed that it does not affect the significance threshold due to how the Benjamini-Hochberg method of MHT correction works. The significance threshold was 5.61×10−4 for the full dataset and when reducing the number of tests by a factor of 10 using the random downsampling, the significance threshold was nearly unchanged at 5.59×10−4. We found that when prioritizing the eQTL tests with coevolution the significance threshold is relaxed, and we found that the stricter the coevolution thresholds that are used the more relaxed the significance threshold becomes. This effect became apparent for SNP-gene pairs with coevolution scores > 0.75, indicating strongly correlated phylogenetic histories. At the highest level of coevolution threshold, the significance threshold was relaxed to 2.11×10−3. This shows that SNP-gene pairs that have very high levels of correlated phylogenetic histories are enriched for eQTL interactions by over 7 times, SNP-gene pairs with coevolution score of 0.95 or greater are more than 7 times as likely to be a significant eQTL interaction as compared to an arbitrary SNP-gene pair.Figure A side effect of prioritizing eQTL tests by genomic coevolution is that due to a correlation between genomic distance and observed rate of coevolution there is a reduction in the average distance of the tested pairs of SNPs and genes. Prioritizing eQTL tests by genomic distance is effective, as it has been shown that the regions closer to the transcription start sites are enriched for SNPs that participate in eQTL interactions . Taking We characterized the co-conservation and coevolution of locus-gene regulatory interactions in the human genome. We discovered with high confidence that there is interplay between the conservation status of SNPs and their target genes in eQTL interactions. Furthermore, using subsequences of a whole genome multiple sequence alignment we were able to asses the correlation between the phylogenetic histories of those same eQTL interactions, and we discovered that pairs of SNPs and genes that appear coevolved are enriched for eQTL interactions. We applied this property to the prioritization of eQTL association tests and found that lower signficance eQTL interactions could be identified.Coevolution can be used to create genome-wide metrics, and could potentially provide utility in identifying distant and inter-chromosomal interactions such as trans-eQTL interactions in the human genome. Further work in this area includes assessing trans-eQTL coevolution as we did for cis-eQTLs, and examining the interplay between coevolution and the robustness of eQTL detection with regards to sample set size. Furthermore, it may be beneficial to explore other metrics of coevolution such as mutual information or direct coupling analysis in the context of locus-gene interactions in the human genome.We compare the genomes of a large set of organisms to quantify the levels of conservation of human DNA sequence and the levels of coevolution between pairs of sequences. In order to accurately map the evolutionary history of each DNA segment we use a whole genome multiple sequence alignment. This process is computationally intensive, so we use a publically available dataset provided by the UCSC Genome Browser . The datWe first performed a co-conservation analysis of the eQTL interaction data in a manner similar to an analysis done for miRNA binding . For thimirrortree based method.As we discussed above, state-of-art methods for analyzing coevolution make use of distance matrices that are generated from multiple sequence alignments of homologous proteins. We applied this principle to genomic sequence using multiple sequence alignments of homologous DNA sequence rather than protein sequence. One method of acquiring the homologous DNA sequences is to use BLAST against a library of genomes, but this process can produce a number of false positives. This can be exacerbated when the library of genomes is large and when the query sequences are small. To remove the effect of querying relatively small sequences we use whole genome multiple sequence alignments to identify homologous sequences. In this manner the number of homologous sequences for a DNA segment is stable when querying different size segments. In order to create a distance matrix for a genomic segment, we extract a subsequence from the whole genome multiple sequence alignment and further extract all the pairwise alignments in order to calculate the distance matrix. The process of extracting the subsequences of the whole genome multiple sequence alignment is performed serially, and requires just a single pass through the alignment file. And each distance matrix is generated by comparing all pairs of sequences that are contained in that subsequence of the multiple sequence alignment which can subsequences from as many as 100 genomes. In our generated matrices, we store the alignment scores of all pairwise alignments that are induced by the multiple sequence alignment, so it is more accurate to state that we are generating similarity matrices as high nominal values indicate high sequence match rates. We evaluated coevolution with these matrices using a mirrortree, that we refer to as BIPARTITEMIRRORTREE. BIPARTITEMIRRORTREE uses the sparse correlation of similarity matrices in order to measure the rate of coevolution between the pairs of sequences those matrices described; BIPARTITEMIRRORTREE can characterize the coevolution between all pairs that can be formed from two distinct sets, such as SNPs and genes. Since we characterized eQTL interactions, which are associations between SNPs and genes, we used BIPARTITEMIRRORTREE to characterize the coevolution of all previously identified eQTL interactions as well as all possible pairs of SNPs and genes. The general runtime complexity of BIPARTITEMIRRORTREE is O(sgl2), where s is the number of SNPs, g is the number of genes, and l is the number of genomes in the multiple sequence alignment used to create the similarity matrices. However, for cis-eQTL detection all SNPs are not paired with all genes; genes are only paired with SNPs that are within 1mbp. In this instance the number of SNP-gene pairs is much less than all possible pairs, sg, but it is still much greater than the size of the similarity matrices, l2. For these cis-eQTL analyses, the practical runtime complexity of BIPARTITEMIRRORTREE is O(n), with n being the number of SNP-gene pairs examined.In order to evaluate the coevolution of pairs of sequences that belong to two distinct classes we developed a modification to mirrortree use full correlation as the measure of coevolution. One characteristic of using full correlation, is that when there are large amounts of missing alignments (due to either poorly conserved sequence or poor mapping) in both matrices, the correlation of the two matrices will be inflated. For example, as we are using similarity matrices a missing alignment or a poor alignment would lead to a pairwise similarity score of 0, and a large number of them in a pair of matrices can lead to many spurious matches between the two matrices. These spurious matches will in turn inflate the correlation between the pair of matrices, and they will appear to be coevolved when they might not be. Therefore, BIPARTITEMIRRORTREE uses sparse correlation which better captures the idea of coevolution and does not become inflated with missing data. One of the reasons we used similarity matrices as opposed to distance matrices, is that they lead to an elegant definition and implemenation of sparse correlation: while calculating the correlation between a pair of similarity matrices ignore instances when the corresponding values in both matrices are 0.As we stated above we use sparse correlation as our measure of coevolution. Previous implementations of α be the desired false discovery rate Let H0...Hn be the set of hypotheses (e.x. locus-gene pairs to be tested for eQTL interactions)Let P0...Pn be the set of corresponding nominal p-values Let p-valuesSort the hypotheses (e.x. locus-gene pairs) in ascending order of Find the largest K such that H0...HK (e.x. identify locus-gene pairs H0...HK as significant eQTL interactions)Reject hypotheses The way the Benjamini-Hochberg procedure performs multiple hypothesis testing correction gives insight into the enrichment exhibited by prioritization. The unprioritized procedure works as follows: p-value significance threshold becomes, P = β be the fraction of all significant interactions that exceed that threshold (e.x. eQTL interactions with high coevolution scores), and let γ be the fraction of all hypotheses that exceed that threshold (e.x. locus-gene pairs with high coevolution scores). During prioritization we can then express the p-value significance threshold as a modified version of the significance threshold for non-prioritized hypotheses, β>γ then P′>P, thus the significance threshold is relaxed. Furthermore, in this case P′ and P provides a measure of the enrichment a given prioritization threshold has.In this case, the Additional file 1Figure S1: First Singular Vector of SVD analysis compared to the High Conservation Centroid of the clustering analysis. Figure S2: Centroids when using different values of K during clustering. Figure S3: Within-Cluster Distance for different values of K during clustering. Figure S4: Distributions of the distance between pairs of SNPs and target gene promoters. (PDF 120 kb)


Gray leaf spot is a maize foliar disease with worldwide distribution and can drastically reduce the production in susceptible genotypes. Published works indicate that resistance to gray leaf spot is a complex trait controlled by multiple genes, with additive effect and influenced by environment. The aim of this study was to identify genomic regions, including putative genes, associated with resistance to gray leaf spot under natural conditions of disease occurrence. A genome wide association study was conducted with 355,972 single nucleotide polymorphism markers on a phenotypic data composed by 157 tropical maize inbred lines, evaluated at Maringá –Brazil. Seven single nucleotide polymorphisms were significantly associated with gray leaf spot, some of which were localized to previously reported quantitative trait loci regions. Three gene models linked to the associated single nucleotide polymorphism were expressed at flowering time and tissue related with gray leaf spot infection, explaining a considerable proportion of the phenotypic variance, ranging from 0.34 to 0.38. The gene model GRMZM2G073465 (bin 10.07) encodes a cysteine protease3 protein, gene model GRMZM2G007188 (bin 1.02) expresses a rybosylation factor-like protein and the gene model GRMZM2G476902 (bin 4.08) encodes an armadillo repeat protein. These three proteins are related with plant defense pathway. Once these genes are validated in next studies, they will be useful for marker–assisted selection and can help improve the understanding of maize resistance to gray leaf spot. GLS hra zeina ,6. With  America ,8. One o America . GLS symA more effective and ecologically correct method for controlling GLS is the use of host plant resistance. However, published works indicate that GLS resistance is a complex trait controlled by multiple genes, each one with additive effects ,9 and stBipolaris maydis [Exserohilum turcicum [Sphacelotheca reiliana [Puccinia sorghi [The use of single nucleotide polymorphisms (SNPs) identified by the new method of genotyping-by-sequencing (GBS) is a great tool in genome-wide association studies (GWAS) to identify QTLs that explain the phenotypic variation of traits of interest, leading to more effective strategies for the use of molecular markers in crop improvement . Associas maydis , northerturcicum , maize hreiliana  and comma sorghi .i) measure phenotypic variation among tropical inbred lines of common maize and popcorn for GLS resistance, ii) identify SNPs and putative genes associated with a response to gray leaf spot under natural inoculation, and iii) characterize the putative genes by comparing them to genes from previously published studies of GLS resistance.To date, a genome-wide association studies involving GLS resistance have been reported by , where tThe UEM core maize panel was assembled with a total of 183 tropical inbred lines (98 are field corn and 85 are popcorn genotypes), which have already been genotyped . Only 15Cercospora zeina, under natural conditions of disease infection. Evaluation was performed 25 days after the flowering period, during the kernel milk stage. The disease reaction of each genotype was scored with the scale proposed by [The inbred lines were evaluated for GLS severity, caused by posed by  with ninGLS resistance was evaluated as the average of the plants in a row. An arcsine transformation was performed to correct non-normality of the errors and heterogeneous variances. The mean value for each inbred line was calculated with R software  using thYij = μ + Gi + Bj + εij where μ is the general mean; Gi is the fixed effect of the genotypes, Bj is the fixed effect of the blocks and εij is the random effect of the error, assuming NID .Zea mays L. genome reference, version AGPV3 (B73 RefGen v3 assembly) [The 183 DNA samples were sent to the Institute of Genomic Diversity at Cornell University to undergo genotyping-by-sequencing (GBS), as described by . Reads assembly) , resultissembly) . SNP marssembly)  using 60Association analysis between the SNPs and the GLS means was performed with a compressed mixed linear model (MLM) implemented in the GAPIT R package . A kinshY = Xβ + Wα + Qv + Zu + ε where Y is the observed vector of means; β is the fixed effect vector (p x 1) other than molecular markers effects and population structure; α is the fixed effect vector of the molecular markers; ν is the fixed effect vector from the population structure; u is the random effect vector from the polygenic background effect; X, W, and Z are the incidence matrixes from the associated β, α, ν, and u parameters; and ε is the residual effect vector.h2) was estimated using the formula:Components of variance for the means of disease severity were estimated with the residual maximum likelihood (REML) method. Broad sense heritability  was used to estimate and control the proportion of false positive results among all discoveries, adjusted to a p-value of 0.05. To determine the amount of variance explained by the top significant SNPs, a linear model was fitted using R software with the lm procedure [rocedure .To identify gene models for GLS resistance, the physical position of the SNPs were compared with the MaizeGDB database according to version 3 (RefGen_v3) from the reference genome of the maize B73 inbred line, available at the MaizeGDB database. Linkage disequilibrium (LD) between the significant SNPs and the SNPs inside or near the gene models was evaluated using TASSEL version 5.2 , with thThe phenotypic means from 157 inbred lines evaluated at Maringá were considered significant (p<0.05) for the PIFA. The average severity of GLS reactions can be considered similar for popcorn and field corn inbred lines, but the popcorn group showed a higher presence of outliers and large mean variations . This reResults from the variance components and broad sense heritability are shown in flint grain together, the number of field corn genotypes was not enough to show different subgroups.The PCA resulted in two groups based on the eigenvalues of accumulated variances among PCs , which mThe results of the marker-trait association for gray leaf spot severity are illustrated in the quantile-quantile plot QQ-plot–. QQ-plotR2 proportion between 1.48% and 37.50% was explained by these SNPs  available at MaizeGDB  was usedEstimation of population structure and within-group relatedness, in maize genomic association studies, must be included in order to reduce the risk of false-positives, especially when a small number of genotypes are involved . The twoAmong the seven significant SNPs found in this study, six SNPs were located inside, close (< 250 kpb) or with LD information with gene models responsible for protein synthesis and RNA polymerase subunits. Only one significant SNP was located on a gene model with unknown function. Three of these significant SNPs are related with functions pertinent to gray leaf spot resistance.ccp3 and encodes a protein known as cysteine protease 3. This gene model is related to a smut disease in maize caused by Ustilago maydis, a biotrophic pathogen that colonizes maize ears and causes significant yield losses. According to [ccp3 gene expression is also present in leaves after the flowering period [The SNP S1_152600619 is close (243.4 kb) to the functional gene model GRMZM2G073465 (bin 10.07), but no LD was found between this SNP and any SNPs near the gene . Howeverg period ,36,37, a2 rice population, [Xanthomonas oryzae pv. oryzae.The SNP S2_22772409 is inside to the gene model GRMZM2G007188 (bin 1.02), which encodes protein 8B from the ADP-ribosylation factor-like gene (ARF). Although this gene model has not been elucidated its function in maize metabolism pathway, RNA-Seq results have demonstrated its expression as 107.84 FPKM in leaves at eighteen days after pollination . This exulation,  mapped ar2 = 0.10; p-value = 0.01) with the closest SNP to this gene model  to the gene model GRMZM2G476902 (bin 4.08), which encodes a putative armadillo repeat-containing protein (ARM). This significant SNP is located in a region in LD , GRMZM2G073465 (bin 1.02) and GRMZM2G476902 (bin 4.08) may be useful for developing genotypes with resistance for gray leaf spot, through the use in molecular marker assisted selection.S1 TableChromosome locations (AGPv3 coordinates), SNP position, R2, false discovery rate (FDR), genes containing SNP, allele effect and other summary statistics.a: The physical position based on B73 reference genome v3  of 5%.(DOCX)Click here for additional data file.S1 Appendix1NaN: Not a number, code used in R software, in order to substitute the values of the inbred lines which were genotyped but not phenotyped for gray leaf spot.(DOCX)Click here for additional data file.S2 Appendix(RAR)Click here for additional data file.


Trio studies, which involve the testing of samples from a proband and both parents, are often used by clinical laboratories to help with the classification of genetic variants, including copy number variants. In order for the results of the trio study to be valid, the mother and father must be the true biological parents of the proband. As such, non-paternity and sample mix-ups are potential sources of error. To address these potential issues, we developed a computer script to accurately assess maternity and paternity using single nucleotide polymorphism (SNP) data generated by Agilent chromosomal microarrays, a platform-of-choice for clinical copy number testing.We assessed the performance of the script on 10 putative trios tested at our laboratory, and found that the numbers and proportions of discordant SNPs were useful for determining parental relationships. The results of the assessment also confirmed maternity and paternity in the 10 trios tested, and by doing so essentially excluded pre-analytical sample switching in these 30 samples.Computational analysis of SNP data can be implemented as a quality control measure for trio testing performed on Agilent microarrays.The online version of this article (10.1186/s12911-018-0684-9) contains supplementary material, which is available to authorized users. Array comparative genomic hybridization (aCGH) is a first-line diagnostic tool for the post-natal evaluation of patients with intellectual disability, autism spectrum disorders, and complex syndromes with multiple congenital anomalies . A signiThe scripts were written for R (version 3.2.2), a free statistical computing software. In order to develop and test our algorithm, we identified 10 putative trios  who were tested in our laboratory over a period of several months on the Agilent Human Genome CGH + SNP 4x180K Microarrays . Briefly, the testing platform determines (in addition to copy number data) the genotypes  at approximately 30 thousand polymorphic SNP loci across the genome, and records the data as a text table file.The scripts assess the relationships between a proband and the putative parents in the following manner: 1) data files from a proband and one of his/her putative parents (either the mother or father) are merged; 2) SNP loci are then filtered to only include those that are homozygous in both the proband and the parent ; 3) for each remaining (2-copy) homozygous locus, the scripts determines if the genotypes of the proband and parent are identical. If these are identical , the SNP locus is “concordant.” If these are not identical, the SNP locus is “discordant” . The premise is that correct proband-parent pairings  should have very few discordant SNP loci , while incorrect proband-parent pairings  should have significantly greater numbers of discordant loci, since SNPs included are polymorphic. Since the numbers of SNP loci remaining after filtering (homozygous in both the proband and the putative parent) may vary depending on a number of factors (e.g. may be higher when there is a higher degree of relatedness), we considered the proportions of discordant SNPs (rather the absolute numbers of discordant SNPs) to be the most reliable values for assessing parental relationships.Two versions of the script were developed, a test script for assessing performance on putative trios (as described above) and a clinical script for potential clinical use. In the clinical script, a true-pair is called only if the proportion of discordant SNP loci is < 0.015  using real patient data from the 10 putative trios, and determined the numbers of 1) discordant SNP loci, 2) concordant SNP loci, and 3) proportion of discordant SNP loci across 20 possible putative true-pairs  and 180 possible putative false-pairs . This analysis  and SNPTrio [Our clinical script performs quality control functions that overlap with the quality control modules of the Affymetrix GeneChip Genotyping (GTYPE) Analysis Software  the degree-of-relatedness between the putative parent and the proband, 2) the frequencies of the alleles for each SNPs on the array in different populations, and 3) the quality metrics of the specimens, which may vary from laboratory to laboratory. In view of these considerations, we cannot guarantee that the script will reliably distinguish parents from close relatives in all conceivable laboratory settings. Also, the script may be unreliable in other situations. For example, the algorithm will fail to detect a sample switch between the proband and his/her brother or sister, since sibling-parent pairs are true-pairs. Overall, we would recommend that clinical laboratories consider independent internal validation studies (possibly involving samples from close relatives) prior to using script; further, any unusual results reported by the script  should be treated with caution.A second potential issue is that some aspects of our relatively simple script may not be optimized. For example, the cut-off for calling true-pairs (proportion of discordant SNPs < 0.015) in this study is not the result of the statistical analyses of well-defined distributions. The value was selected based on the proportions of discordant SNPs observed for the 20 true proband-mother and proband-father pairs  and protocol/instructions are available as Additional files Computational analysis of SNP data, originally intended for the assessment of absence of heterozygosity, can be implemented as a quality control measure for trio studies performed using Agilent microarrays.Project name: Trio-R: A Script for Assessing Maternity and Paternity in Trio Studies Performed on Agilent Chromosomal Microarrays.Project home page:https://sites.google.com/site/trioscript/Operating system: Platform independent.Programming language: R.Other requirements: R (developed using version 3.2.2).Any restrictions to use by non-academics: none.Additional file 1:Protocol for implementing the R-script. (DOCX 167 kb)Additional file 2:Clinical version of the R-script. (R 7 kb)


The genome-wide association study (GWAS) represents an important tool for the detection of candidate genes, haplotypes or single nucleotide polymorphisms (SNP) markers related to characteristics of economic interest. The present study included information for 280 cows in three dairy production systems in Mexico: 1) Dual Purpose (n = 100), 2) Specialized Tropical Dairy (n = 76), 3) Familiar Production System (n = 104). Concentrations of CH4 in a breath of individual cows at the time of milking (MEIm) were estimated through a system of infrared sensors. After quality control analyses, 21,958 SNPs were included. Associations of markers were made using a linear regression model, corrected with principal component analyses. In total, 46 SNPs were identified as significant for CH4 production. Several SNPs associated with CH4 production were found at regions previously described for quantitative trait loci of composition characteristics of meat, milk fatty acids and characteristics related to feed intake. It was concluded that the SNPs identified could be used in genomic selection programs in developing countries and combined with other datasets for global selection.Genomic selection has been proposed for the mitigation of methane (CH CH4 is colorless and odorless with a global warming potential 25 times higher than carbon dioxide (CO2) [Increase in demand for animal products, particularly meat and milk , has stide (CO2) .4, including the reduction of herd size, increased milk yield, diet manipulation, use of CH4 inhibitors, immunization against methanogenic archaea, and direct and indirect selection for CH4 emission in a breeding program [4 emissions between individuals fed on the same diet [4 [4 is synthesized by microorganisms (Archea being the most important) from compounds released during VFA production, mainly from substrates such as H2 and CO2 in addition to formate, methylamines, and methanol from demethylation of plant polymers [4 production and milk composition has been suggested [In dairy cattle, many strategies have been proposed to mitigate CHame diet , which same diet . Studies diet [4  indicatepolymers . Moreovepolymers ; therefouggested .The genome-wide association study (GWAS) represents an important tool for the detection of candidate genes, haplotypes or single nucleotide polymorphisms (SNPs) related to characteristics of economic interest . Advance4 emission has been reported to be low [4 emission from animals in different environments and production systems in developing countries is scarce. Consequently, the scope for breeding animals with low CH4 emissions in developing countries is unknown, even though these countries account for the majority of cattle in the world [Bos taurus indicus breeds and crosses, and reliance on locally grown forage crops. Therefore, the genetic markers for CH4 emissions established for Bos taurus taurus cattle in developed countries might not be applicable in developing countries. The implicit scope of the present study was to investigate characteristics that could be associated with CH4 production in order to indirectly select against CH4 production in the future. Therefore, the aim of the present study was to identify genomic regions associated with CH4 emissions by dairy cattle in temperate and tropical areas. The hypothesis of this project was that by using CH4 measurements in three dairy systems, general markers for characteristics associated with CH4 production would be identified.The heritability of CHo be low , which mhe world . Cattle n = 100, herds = A, B, C), composed of Bos taurus taurus  and Bos taurus indicus crosses (Zebu); 2) Familiar Production System , composed mainly of Holstein cattle and finally, 3) Specialized Tropical Dairy , mainly Holstein, Brown Swiss, and their crosses. A total of 280 cows kept in 10 commercial cattle dairy farms with three different production systems across Mexico were used: 1) Dual Purpose , Tanzania and Mombaza (Megathyrsus maximus), Pangola (Digitaria decumbens) and Brachiaria spp. Supplemented with concentrates containing 200 g CP/kg at the time of milking. Feeding in the DP and ST systems consisted of grazing forage grasses such as Star of Africa  or grazing plus supplementation with concentrates at time of milking moment . The ave4 emissions were estimated for individual cows during milking using an infrared sensor system, , calibrated and used according to Garnsworthy et al. [4 were measured continuously. These measurements were used to calculate the emission of CH4 per minute per liter of air. The feeders were designed to minimize air currents inside the Perspex head boxes and to obtain reliable measurements without altering daily milking routines. CH4 estimations are reported as mg CH4/L of air sampled at the time of milking (MEIm) [CHy et al. . During g (MEIm) .Tail hair, including the hair follicles, was sampled from all the animals in the study. Samples were individually identified and sent to GeneSeek Laboratory  for DNA extraction and genotyping using high-density panels. Two different high-density chips were used for genotyping due to breed variability (both with the reference genome Bos_taurus_UMD_3.1.1): the GGP Bovine LD V4 chip with 30,125 SNPs was used for FS (only Holstein), and the GGP Bovine 150k chip with 138,962 SNPs was used for DP and ST .n = 26,145 SNPs) were used for the genomic analysis. Prior to GWAS, a quality control analysis of genotypes was carried out, in which all SNPs that had a call rate of less than 0.95, a minor allele frequency of less than 0.05 and those that failed to be in Hardy-Weinberg equilibrium in the experimental population (p < 0.0001) were not included. Individual animals with a call rate of less than 0.95 were also eliminated. After the quality control analysis, 21,958 autosomal and mitochondrial SNPs from 280 cows were used to carry out genotype association tests. Subsequently, a genotype association test was carried out, including a false positive detection test and Bonferroni test.Only SNPs common to both chips k = CH4 production during milking residual in the k-th observation in the i-th herd and j-th months in milk.εCHsoftware . The modThe effects of days in milk, average milk yield and dairy system were also evaluated in the model, however, herd and months in milk fit the model significantly better.The effects included in the model explain part of the environmental variation. The residuals represent the proportion of the variance not explained by the model effects, including genetic variance , for thaijg be a matrix of genotypes for SNP i and individual j, where i = 1 to M and j = 1to N. We subtract the row mean iμ = (∑j gij)/N from each entry in row i to obtain a matrix with row sums equal to 0. We then normalize row i by dividing each entry by ip is a posterior estimate of the unobserved underlying allele frequency of SNP i defined by ip = (1 + ∑j gij)/(2 + 2 N). The resulting matrix is X. We compute an N × N covariance matrix Ψ of individuals, where Ψjj´is defined to be the covariance of column j and column j´ of X. The kth axis of variation is the kth eigenvector of Ψ. The axes of variation reduce the data to a small number of dimensions, describing as much variability as possible; they are defined as the top eigenvectors of a covariance matrix between samples. This method allows the detection and correction of population stratification on a genome-wide scale, maximizing the detection of true associations and minimizing erroneous associations [A principal component analysis (PCA) was applied to the genotype data to infer continuous axes of genetic variation. Let ciations . Qualityciations . SNP & Viê is the vector of residuals from (1), μ is the mean, g is the vector of markers corrected by PCA, k is the vector of markers effects, and e is the vector of random residuals.We use residuals from Equation (1), which are now adjusted for polygenic covariation and fixed effects, as a novel quantitative trait for association analyses with each of many markers using classical methods for unrelated individuals (‘‘population-based design’’). These residuals are used as the dependent trait in a simple linear regression for each SNP.4 emissions on DP and FS were similar (P < 0.05) and lower than ST (P < 0.05). Milk yield was different among the three dairy production systems, as presented in The descriptive statistics of MEIm  for the Bos taurus taurus while the cattle crossed with Bos taurus indicus have the lowest values.The data were corrected of population stratification on a genome-wide scale. The PCA analysis for the dairy production systems and breeds is shown in The quantile–quantile plot  did not 4 emission in dairy cattle. Chromosomes 1, 3, 13 and 20 have most SNPs associated with CH4 emission with 11, 7, 5 and 4 SNPs, respectively  have been associated with characteristics of meat composition, milk fatty acids and characteristics related to feed intake [The PCA stratification could be the result of crossbreeding, which is predominant in DP and ST systems. The SNPs associated with genetic variation in MEIm  between SNPs, strong association between SNPs and phenotypes, and systematic bias .p-values) [p-value) ranged from 3.3 to 6, using either no multiple testing correction, a Bonferroni correction, the false discovery rate, or genomic control [As trait complexity increases, the number of loci affecting the trait increases along with environmental interactions with an expected decrease in heritability. Conversely, for complex traits, a higher number of loci affect the trait, there is more interaction with the environment, and there is an expected decrease in heritability. For a trait with a low heritability, the threshold value for significance of associating loci with a trait would have low −log10  . In publ control .4 is synthesized by archaea from CO2 and H2 released during VFA production [4 and fatty acids in milk, finding both positive and negative correlations [4 emissions [SNPs identified to be associated to methane emission in this study on chromosomes 13 , 14  and 19  were, in other studies, associated with milk fatty acid composition for C6:0, cis-9-C16:1 percentage  and seveoduction . Previouelations ,35, and missions .SLC9A9 gene and the other in the LOC104971015 gene. On chromosome 13, a marker associated with MEIm was found within the QTL associated with the percentage of caproic acid in milk [SLC23A2 gene   and cont4 has been correlated with dry matter intake (r = 0.46) [r = 0.36) [Additionally, relationships were found between our markers on chromosome 1 at position 107,388,026 for dry matter intake and on chromosome 14 at position 7,744,264 for residual feed intake . The pro = 0.46)  and resiPPM1L gene. In addition, on chromosome 1 (MCF2L2 gene and is very close to the QTL peak. LOC107132190, LOC104970931, LOC100138913, NAALADL2, MECOM, and PPM1L.On chromosome 1  a QTL remosome 1 , we founSLC2A13 gene. On chromosome 13, we found a marker associated with MEIm, that is, in the QTL related to the content of the fatty acids C22:1 and trans-12-C18:1 [On chromosome 3 , we foun12-C18:1 , this is4 production per unit of dry matter intake [Finally, our GWAS results concur with those previous observations of markers on chromosomes 4 and 20, associated with CHr intake .4 emissions by dairy and dual-purpose cattle in temperate and tropical areas of Mexico. This aim was achieved and the SNPs identified to provide a solid foundation on which to develop further studies leading to genomic selection for low CH4 emissions in developing countries. Of course, selection for low CH4 emissions must be in parallel with selection for feed efficiency and other traits of economic importance. The secondary aim of this study was to compare the SNPs identified to the markers reported to be associated with production traits previously related to CH4 emissions. This aim was also successful, and the associations discovered provide confidence that the results of this study concur with known biological pathways. Furthermore, the associations provide confidence that the results could be combined with datasets generated in developed countries to provide tools for genomic selection on a global basis.The primary aim of this study was to identify genomic regions associated with CH4 are expected, and correlations between CH4 and the fatty acids in milk have been reported, so similar relationships might be expected between CH4 and the fatty acids in meat. The SNPs identified in this study may be incorporated into genomic selection programs for low CH4 emissions in developing countries and could be combined with other datasets to provide tools for genomic selection on a global basis. A future direction for this work could be to sequence the specific regions where the significant markers are found and compare between breeds. This could give stronger results for the characteristics associated with methane production.In this study, 46 SNPs that have significant associations with MEIm were identified. Several SNPs associated with MEIm were found at regions previously described for QTLs associated with composition characteristics of the meat, milk fatty acids, and characteristics related to feeding intake, such as residual feed intake. Relationships between mammary synthesis of fatty acids and ruminal synthesis of CH


Litopenaeus vannamei farming, which is caused by Vibrio parahaemolyticus. AHPND has led to a significant reduction of shrimp output since its outbreak. Selective breeding of disease-resistant broodstock is regarded as a key strategy in solving the disease problem. Understanding the relationship between genetic variance and AHPND resistance is the basis for marker-assisted selection in shrimp. The purpose of this study was to identify single nucleotide polymorphisms (SNPs) associated with the resistance against AHPND in L. vannamei. In this work, two independent populations were used for V. parahaemolyticus challenge and the resistant or susceptible shrimp were evaluated according to the survival time after Vibrio infection. The above two populations were genotyped separately by a SNP panel designed based on the target sequencing platform using a pooling strategy. The SNP panel contained 508 amplicons from DNA fragments distributed evenly along the genome and some immune-related genes of L. vannamei. By analyzing the allele frequency in the resistant and susceptible groups, 30 SNPs were found to be significantly associated with the resistance of the shrimp against V. parahaemolyticus infection . Three SNPs were further validated by individual genotyping in all samples of population 1. Our study illustrated that target sequencing and pooling sequencing were effective in identifying the markers associated with economic traits, and the SNPs identified in this study could be used as molecular markers for breeding disease-resistant shrimp.Acute hepatopancreatic necrosis disease (AHPND) is a major bacterial disease in Pacific white shrimp  Litopenaeus vannamei, which naturally distributed along the Pacific coasts of Central and South America, has become the primary cultivated shrimp species into many regions of the world (Vibrio parahaemolyticus carrying the Photorhabdus insect-related (Pirvp) binary toxin was the pathogenic agent for AHPND  and maintained in filtered seawater at a temperature of 26 ± 1°C with continuous aeration. Before the experiment, the shrimp were kept in the aquarium for 3 days to make them acclimate to the laboratory conditions. The shrimp used in this study were the progeny of mating of two breeding lines. The two breeding lines were bred by 7 years of artificial selection, respectively. The progenies produced by the cross of the breeding lines were genetically similar. Although two populations were consisted of multiple families, their genetic backgrounds were very similar.V. parahaemolyticus was isolated from the diseased shrimp. The Vibrio strain was cultured in tryptic soy broth with 2.0% sodium chloride liquid media. The cultured bacteria were further checked by positive polymerase chain reaction (PCR) amplification of PirAvp and PirBvp genes (4 colony-forming units (CFU)/g (body weight). A total of 236 shrimp with a body weight of 3.98 ± 1.9 g and a body length of 6.89 ± 1.35 cm were used as the first population. About 10 μl bacterial suspension in phosphate-buffered saline (PBS) with 1.0 × 105 CFU of V. parahaemolyticus was injected into each shrimp at the site between the IV and V abdominal segments. A total of 270 shrimp with a body weight of 5.25 ± 2.9 g and a body length of 7.45 ± 1.56 cm were used as the second population, and each shrimp was injected with 1.3 × 105 CFU of pathogenic bacteria. At the same time, 60 shrimp were set aside as the control group and injected with an equal volume of sterile PBS. The mortality was recorded for 8 days. The dead shrimp were stored at −80°C until DNA extraction. Susceptible and resistant phenotypes were evaluated according to the survival time after challenge ] were selected as the resistant group.vp genes . Bacterihallenge . In eachV. parahaemolyticus in shrimp, 150 healthy shrimp with a body weight of 5.15 ± 1.6 g were injected with 2.5 × 104 CFU/g (body weight). To determine the pathogenic bacteria count in surviving shrimp, six shrimp were randomly chosen from the tank at 0, 1, 2, 3, 8, and 12 dpi. Various tissues were collected from each shrimp, including hepatopancreas, gill, and muscle. Three repetitions were used each time, and two shrimp were mixed together in each repetition. Tissues were dissected aseptically and then homogenized in PBS. Serial 10-fold dilutions of the supernatant with PBS were produced, and 100-μl solution was inoculated onto thiosulfate citrate bile salts sucrose (TCBS) agar. Plates were incubated at 30°C for 18 h. Colonies were counted to estimate the number of viable bacteria per pool of shrimp tissue . The primer sequences of the amplicons are displayed in DNA was extracted from the muscle of shrimp with the Tiangen Plant Genomic DNA Extraction Kit  according to the manufacturer’s instructions. DNA concentration was assessed using a NanoDrop 2000 spectrophotometer . A SNP genotyping panel based on the AmpliSeqV. parahaemolyticus. Every DNA pool was generated by combining an equal amount of DNA from 30 susceptible or resistant shrimp. These DNA pools were amplified and next-generation sequencing libraries were constructed using DNA Seq Library Preparation Kit for Amplicon Sequencing-Illumina Compatible . The amplicons were sequenced using Illumina Hiseq 2500. The raw reads were filtered and then mapped to the target sequence using BWA  and P values were corrected using the false discovery rate (FDR) according to the method described by P was set to 0.05 (5% FDR correction). In view of the consistency of favorable alleles in the two populations, SNPs that showed opposite allele frequencies in two populations were filtered out. Statistical tests were performed using the R statistical software  were amplified by individual genotyping method in all samples of population 1. Primers were designed based on the flanking sequence of SNPs (LvALF6) and phosphatidylinositol 3-kinase (PI3K) regulatory subunit α isoform X2 (LvPI3K), were selected for further analysis. Their expression patterns in the shrimp during V. parahaemolyticus infection were analyzed. Healthy shrimp with a body weight of 8.39 ± 2.2 g and a body length of 8.89 ± 0.78 cm were injected with 1 × 105 CFU of V. parahaemolyticus. The hepatopancreas, hemocytes, and gill of shrimp were collected separately at 1, 6, 24, 48, and 72 hpi. Nine shrimp were collected at each time point and three individuals were put together as one sample. The detailed steps of total RNA extraction and cDNA synthesis were the same as described previously by Based on the annotation of SNPs located in the genes, two candidate genes, including anti-lipopolysaccharide factor  and the Q–Q plot were supplied in the supplementary file (10(adjusted P) of three SNPs with the most significant difference, including loci Marker15416_294, Marker8720_486, and Marker1077_61 located in the fragment of Marker15416, Marker8720, and Marker1077, respectively.A total of 1566 SNPs were kept after filtering out SNPs with a minor allele frequency of less than 0.05. The raw data were submitted to the SRA database with accession numbers from SRR9016244 to SRR9016251. Through association analysis on the two populations, 40 significantly different SNPs were identified. After filtering out 10 false-positive sites that showed opposite favorable alleles in two separate populations Figure 22 test showed that the shrimp carrying allele T were more resistant than those carrying allele A (P = 0.0204). Another SNP in the same gene was ALF6-1__22575_510_224, which also showed significant difference in 219 individuals. The percentage of G in the susceptible group was lower than that in the resistant group (P = 0.0293). For Marker2060_197, T was the favorable allele (P = 0.031).To further validate the association result, three SNPs were genotyped by Sanger sequencing in all individuals of population 1. The genotype at the SNP site was determined based on sequencing chromatograms, which is shown in LvALF6. The SNP Unigene19157_All__1806_348_223 was located in gene LvPI3K. In addition, Marker66_168 and Marker66_291 were near the ubiquitin carboxyl-terminal hydrolase. Some other genes, such as low molecular weight phosphotyrosine protein phosphatase-like and calpain-B-like, were also found as candidate genes.The corresponding flanking sequences of the above 30 SNPs with significant difference were obtained by blasting the SNP sequence with the assembled reference genome  to improve the resistance of AHPND infection. However, no research about the selection of AHPND-resistant broodstock was reported for L. vannamei until now. Currently, MAS was known as a method that can greatly accelerate the process of breeding. In red sea bream, it suggested that the RSIVD-resistant trait was controlled by one major quantitative trait loci and could be useful for MAS  were reported to be associated with resistance and susceptibility to Vibrio alginolyticus , the National Key R&D Program of China (2018YFD0901301 and 2018YFD0900103), and China Agriculture Research System-48.Author HH was employed by company Hainan Grand Suntop Ocean Breeding Co.,Ltd.Wenchang, China. The remaining 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.