Biology subjects

Lipka, A. E.

Publications and source records attributed to Lipka, A. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Optimizing resource allocation in Miscanthus breeding with sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; GxE interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for GxE interaction presented the highest PA and the lowest MSE for CNN (PA: [~]0.77, MSE: [~]0.5) and YDY (PA: [~]0.70, MSE: [~]1.3) while for TCM and AIL these ranged from [~]0.28 to 0.41 and [~]1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

genomics↗

bioRxiv2026

Multi-trait Multi-environment Genomic Prediction Strategies for Miscanthus sacchariflorus Populations

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (GxExT) and (2) two single-trait multi-environment (STME) models (with and without GxE interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

genomics↗

bioRxiv2026

Weather Characterization for Optimizing Genomic Prediction in Miscanthus sacchariflorus

Environmental factors affect crop growth and development thus their consideration across sites and years become essential for genotypic evaluation. Genomic selection (GS) has been broadly implemented to accelerate breeding cycles by skipping field evaluations thus allowing early identification of outperforming genotypes. In this study, 7,740 phenotypic records corresponding to 516 Miscanthus sacchariflorus genotypes evaluated in five locations across three years were considered for analysis. Additionally, environmental data on six weather covariates was implemented to characterize similarities between locations. Different sets of locations of variable sizes were used for model calibration based on two cross-validations (CV00 and CV0) schemes leaving out one location at a time. Predictive ability across locations of the best model varied between 0.45 and 0.90 for both schemes. These results were compared to associate predictive ability in function of weather patterns between training and testing sets to allow models calibration optimization. We found it is feasible to optimize resource allocation by considering environmentally correlated sets. In most cases, the information from only one and, at most, two locations were enough to deliver better results than using all four locations, reducing training sets by up to 75%. The results obtained shed light on helping breeders make informed decisions considering weather data when designing evaluations.

genomics↗

bioRxiv2026

ENHANCING GENOMIC PREDICTION MODELS IN MISCANTHUS POPULATIONS BY INCORPORATING THE GENOTYPE-BY-ENVIRONMENT INTERACTION

Giant Miscanthus giganteus (Mxg) is one of the most promising perennial crops to generate biomass feedstock for bioenergy and biobased products. It is derived from the natural inter-species hybridization of Miscanthus sacchariflorus (Msa) and Miscanthus sinensis (Msi) species, thus population improvement within these species is crucial. Genomic selection (GS) is an attractive option to accelerate breeding of perennial grasses, such as Miscanthus, which requires up to three years of evaluation to produce reliable phenotypic data. Hence, genotypes are observed in multiple years and locations causing inconsistent response patterns from one year to the next, between location, and/or location-by-year combinations. These inconsistencies are known as the genotype-by-environment interaction effect (GxE). Although GS has been successfully implemented in multiple annual crops where straightforward cross-validation schemes exist to assess the levels of predictive ability that can be reached, for perennial crops new cross-validation schemes will help avoid data contamination. Here, we propose a series of cross-validation schemes to evaluate model performance for perennial crops. We perform a case study by analyzing one panel of each species (516 genotypes of Msa, 280 genotypes of Msi) scored for biomass yield at different locations around the world over several years. The results of the different cross-validation schemes provide insights about the usefulness of GS to accelerate the breeding process of Miscanthus species. In addition, leveraging the GxE effects of different types significantly increases predictive ability (up to 10% in Msa and 30% for Msi) compared to the conventional approaches based on main effects only.

genomics↗

bioRxiv2026

Dissecting genetic variance structure and evaluating genomic prediction models for single-cross hybrids derived from Stiff Stalk and Non-Stiff Stalk maize heterotic groups

The early 20th-century discovery of heterosis and the establishment of heterotic groups transformed maize (Zea mays L.) into a keystone of global agriculture. However, maize breeding faces two significant challenges: the gradual decline of general combining ability (GCA) variance within heterotic groups and the impracticality of testing all possible single crosses in the early stages of a breeding program. Here, we developed genomic best linear unbiased prediction (GBLUP)-based multi-kernel models, using additive and two alternative non-additive genomic relationship matrices, to estimate the variance components associated with the GCA of Stiff Stalk (SS) and Non-Stiff Stalk (NSS) heterotic groups and the specific combining ability (SCA) arising from their crosses. We further applied these models to predict the performance of untested single-cross combinations under varying levels of parental information. We showed that the SS and NSS groups retained significant GCA variance across traits in both early- and late-maturity groups. The SS group, in contrast, exhibited no detectable GCA variance in grain yield for the intermediate-flowering subset of hybrids, highlighting a limitation for future genetic improvement. Furthermore, our results showed that GBLUP-based multi-kernel models effectively identified superior hybrids when parental information was available. In the absence of this information, however, these models underperformed compared to covariance-based approaches. Both types of non-additive matrices produced similar results, affirming the robustness of the inferred genetic architecture. Overall, this study sheds light on the future use of US maize commercial germplasm and demonstrates how GBLUP-based multi-kernel models can improve the efficiency of hybrid breeding programs.

genetics↗

bioRxiv2025

Improving Multi-Trait Genomic Prediction Efficiency Through The Incorporation Of Synthetic Traits Selected Based on Co-heritability

Genomic prediction (GP) is an essential tool in the field of plant breeding to accelerate the cultivar development pipeline by predicting the performance of unphenotyped lines. The precision of prediction is constrained by the heritability of the target trait when applying a single-trait genomic prediction model. To overcome this limitation, a multi-trait genomic prediction model leveraging high-heritability secondary traits co-heritable with the target trait can boost predictive ability for the target trait. However, this is practically challenging because it requires additional phenotyping effort and prior knowledge of trait co-heritability. This study aimed to assess the efficiency of multi-trait genomic prediction models powered by secondary traits derived from high-throughput phenotyping data when predicting important leaf functional target traits, i.e., nitrogen (N) content and specific leaf area (SLA) in diverse sorghum accessions. Since these traits can be predicted from hyperspectral reflectance data, there is significant potential for other wavelengths within the existing dataset to meet the criteria needed to improve prediction accuracy using multi-trait approaches. Therefore, experiments were performed on traditional direct measures of leaf N content and SLA, plus partial least squares regression predictions of them (Leaf N-PLSR, SLA-PLSR), i.e., four target traits in total. Three secondary, "synthetic traits" (S1, S2, S3), each a ratio of two wavelengths within the hyperspectral data, were identified based on high co-heritability with a given target trait. Single-trait GBLUP (Genomic Best Linear Unbiased Predictor) was fitted as a baseline model, followed by three multi-trait GBLUP models using synthetic traits and target traits together. Model performance was assessed using k-fold (k=5) cross-validation (CV), which consisted of single-trait, CV1, and CV2 schemes. The synthetic traits high genetic correlation and heritability met the requirements for their use as secondary traits. There was a significant increase in accuracy when synthetic traits were used in the multi-trait genomic prediction model compared to a single trait alone for all four target traits. It improved prediction accuracy while using secondary traits derived from hyperspectral high-throughput phenotyping data in the multi-trait genomic prediction model, suggesting that this approach could be broadly applied in a post-hoc fashion to many datasets without any additional phenotyping effort. Our analysis highlights a practical approach to improve multi-trait genomic prediction model performance using synthetic traits with no intrinsic biological meaning selected through co-heritability estimation.

genetics↗

bioRxiv2025

Optimizing population simulations to accurately parallel empirical data for digital breeding

The use of computational and data-driven approaches to accelerate and optimize breeding programs is becoming common practice among plant breeders. Simulations allow breeders to evaluate potential changes in breeding schemes in a time- and cost-efficient manner. However, accurately simulating traits that match empirical trait data remains a challenge. Here we tested if incorporating information about the genetic architecture from genome-wide association studies (GWAS) of maize agronomic traits with varying heritabilities into simulations can improve the concordance between simulated and empirical data in a population of hybrids developed from crosses of 333 maize recombinant inbred lines grown in four to eleven environments. Using at least 200 non-redundant top GWAS hits as causative variants, regardless of statistical significance, resulted in mean correlations between simulated and empirical trait data of 0.397 to 0.616 within environments and 0.610 to 0.915 across environments. Reducing the GWAS estimated marker effect sizes in the simulations further improved concordance with empirical data. This study provides valuable insights into methods for simulating more realistic phenotypes for digital breeding to parallel empirical trait distributions, and that these simulated traits are highly concordant with observed variance partitioning (i.e. genotype, environment, etc.), and genomic prediction performance. PLAIN LANGUAGE SUMMARYPlant breeders need to evaluate many possible changes to breeding schemes and resource allocation. One method of optimizing these changes is through computer-based simulations, which can be time- and cost-efficient. The genetic architecture of randomly simulated traits rarely parallels the genetic architecture of real-world traits, which can mislead the interpretation of such simulations. In this study, the genetic architecture of real-world traits was used to inform the simulation of digital traits to develop an optimized simulation pipeline for creating and assessing simulated breeding programs. The utility of these informed simulations is demonstrated through genomic prediction assessment, in which we show that the rank order of individuals and the distribution of traits is similar between simulated and real-world data. CORE IDEAS[bullet] Simulations allow breeders to evaluate potential changes in breeding schemes in a time- and cost-efficient manner. [bullet]Incorporating the genetic architecture of traits in simulations can improve the concordance with empirical data. [bullet]Reducing GWAS marker effect size improves correlation and distribution concordance of simulated and empirical data. [bullet]Increasing the number of causative variants beyond GWAS significance thresholds improves simulation performance. [bullet]Simulated data used in genomic prediction produces results similar to empirical genomic predicted data.

plant biology↗

bioRxiv2025

High throughput screen of NPQ in sorghum shows highly polygenic architecture of photoprotection

O_LINatural genetic variation in photosynthesis and photoprotection within crop germplasm represents an untapped resource for crop improvement. Sorghum bicolor (sorghum) is one of the worlds most widely grown crops, yet the genetic basis of photoprotection in sorghum is not well understood. C_LIO_LIThis study examined genetic variation in non-photochemical quenching traits by screening a field-grown panel of 861 genetically diverse natural sorghum accessions across two years. C_LIO_LIBroad-sense heritability ranged between 0.3 to 0.65 across different chlorophyll fluorescence parameters. A combination of genome- and transcriptome-wide (GWAS and TWAS) identification of genetic correlates with the observed trait variation uncovered a complex genetic architecture of many significant small-effect loci. An ensemble approach based on GWAS and TWAS results and the covariance between different fluorescence parameters was used to identify 110 unique candidate genes. C_LIO_LIThe resulting high-confidence candidates reveal novel genetic associations with photoprotection and offer resources for further genetic studies and crop genomic improvement efforts. C_LI

plant biology↗

bioRxiv2025

Ancestral proteins trace the emergence of substrate specificity and oligomerization within bacterial DEDDy dinucleases

Nucleases play a crucial role in bacterial physiology, influencing processes such as DNA repair, genome maintenance, and host-pathogen interactions. We recently identified a class of nucleases, diDNases, which are encoded on mobile genetic elements and homologous to the house-keeping nanoRNase C (NrnC). Despite their shared structural fold, diDNases and NrnC orthologs exhibit differences. DiDNases form dimers and preferably cleave DNA dinucleotides, whereas NrnC homologs assemble into octamers that do not discriminate between RNA or DNA dinucleotides. Here, we investigate the evolutionary divergence of these enzymes using ancestral sequence reconstruction. Our results show that both diDNases and NrnC orthologs originated from a dimeric ancestor with intermediate substrate preferences. Structural analyses of ancestral and extant dinucleases provide a molecular rational for how gradual changes in conformation gave rise to substrate preferences, oligomeric state, and catalytic efficiency of these related, yet distinct enzyme clades. These findings provide insights into how small structural modifications enable large-scale changes in molecular assembly and functional specialization harnessing a conserved protein fold. In addition, the preference of the early ancestors for DNA dinucleotides and preservation of this activity in all extant enzymes strongly argues for a biological function of DNA dinucleotides.

evolutionary biology↗

bioRxiv2024

Genomic prediction of cereal crop architecture traits using models informed by gene regulatory circuitries from maize

Plant architecture is a major determinant of planting density, which enhances productivity potential for crops per unit area. Genomic prediction is well-positioned to expedite genetic gain of plant architecture traits since they are typically highly heritable. Additionally, the adaptation of genomic prediction models to query predictive abilities of markers tagging certain genomic regions could shed light on the genetic architecture of these traits. Here, we leveraged transcriptional networks from a prior study that contextually described developmental progression during tassel and leaf organogenesis in maize (Z. mays) to inform genomic prediction models for architecture traits. Since these developmental processes underlie tassel branching and leaf angle, two important agronomic architecture traits, we tested whether genes prioritized from these networks quantitatively contribute to the genetic architecture of these traits. We used genomic prediction models to evaluate the ability of markers in the vicinity of prioritized network genes to predict breeding values of tassel branching and leaf angle traits for two diversity panels in maize, and diversity panels from sorghum (S. bicolor) and rice (O. sativa). Predictive abilities of markers near these prioritized network genes were similar to those using whole-genome marker sets. Notably, markers near highly connected transcription factors from core network motifs in maize yielded predictive abilities that were significantly greater than expected by chance in not only maize but also closely related sorghum. We expect that these highly connected regulators are key drivers of architectural variation that are conserved across closely related cereal crop species. Article summaryWe used an approach typically used for breeding to infer the contributions of biological gene networks to plant architectural traits. We found that markers near genes belonging to smaller, specialized gene networks from maize could predict breeding values of leaf angle better than expected by chance for both maize and sorghum.

genetics↗

bioRxiv2023

Regulatory variation controlling architectural pleiotropy in maize

An early event in plant organogenesis is establishment of a boundary between the meristem and differentiating lateral organ. In maize (Zea mays), evidence suggests a common gene network functions at boundaries of distinct organs and contributes to pleiotropy between leaf angle and tassel branch number, two agronomic traits. To uncover regulatory variation at the nexus of these two traits, we used regulatory network topologies derived from specific developmental contexts to guide multivariate genome-wide association analyses. In addition to defining network plasticity around core pleiotropic loci, we identified new transcription factors that contribute to phenotypic variation in canopy architecture, and structural variation that contributes to cis-regulatory control of pleiotropy between tassel branching and leaf angle across maize diversity. Results demonstrate the power of informing statistical genetics with context-specific developmental networks to pinpoint pleiotropic loci and their cis-regulatory components, which can be used to fine-tune plant architecture for crop improvement.

plant biology↗

bioRxiv2023

Importance of genetic architecture in marker selection decisions for genomic prediction

Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy by up to 19%, but it is highly dependent on the genetic architecture of the trait. Differences in prediction accuracy across marker types were more pronounced for traits with high heritability, high number of QTLs, and SVs as causative variants. In these scenarios, using SV markers resulted in better prediction accuracies than SNP markers, especially when predicting untested genotypes across environments, likely due to more predictors being in linkage disequilibrium with causative variants. The simulations revealed little impact of different effect sizes between SNPs and SVs as causative variants on prediction accuracy. This study demonstrates the importance of knowing the genetic architecture of a trait in deciding what markers and marker types to use in large scale genomic prediction modeling in a breeding program. Key messageWe demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait.

plant biology↗

bioRxiv2023

Linking genetic and environmental factors through marker effect networks to understand trait plasticity

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene co-expression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of [~]10,000 non-redundant markers from 400 maize hybrids across nine environments. We demonstrate that networks can be generated using this approach, and that the markers that are co-varying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity, and specific environmental factors that modulate the genome.

plant biology↗

bioRxiv2021

Simulation study evaluating the ability of two statistical approaches to identify variance quantitative trait loci Arabidopsis and maize

Genomic loci that control the variance of agronomically important traits are increasingly important due to the profusion of unpredictable environments arising from climate change. The ability to identify such variance quantitative trait loci (vQTL) in association studies will be critical for future breeding efforts. Two statistical approaches that have already been used to detect vQTL are the Brown-Forsythe test (BFT) and the double generalized linear model (DGLM). To ensure that they are deployed to variance genome-wide association studies as effectively as possible, it is critical to study the factors that influence their ability to identify vQTL. We used genome-wide marker data in maize (Zea mays L.) and Arabidopsis thaliana to simulate traits controlled by variance quantitative trait nucleotides (vQTNs) and then quantified true and false positive detection rates of the BFT and DGLM. We observed that the DGLM yielded similar or higher true positive vQTN detection rates than the BFT, regardless of the effect size or minor allele frequency (MAF) of the vQTNs. Low true positive detection rates were noted for QTNs with low MAFs (~0.10), especially when tested on subsets of n = 500 individuals. We recommend that larger data sets than those used in our study (i.e., n > 2,532) be considered to overcome these low observed true positive detection rates. Such an undertaking should maximize the potential of the BFT and DGLM to highlight which vQTLs should be considered for further study.

genetics↗

bioRxiv2020

Eleven biosynthetic genes explain the majority of natural variation for carotenoid levels in maize grain

Vitamin A deficiency remains prevalent in parts of Asia, Latin America, and sub-Saharan Africa where maize is a food staple. Extensive natural variation exists for carotenoids in maize grain; to understand its genetic basis, we conducted a joint linkage and genome-wide association study in the U.S. maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were correlated expression and effect QTL (ceeQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six ceeQTL also had the largest percent phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these ceeQTL had strongly correlated QTL allelic effect estimates across multiple traits. These findings provide the most comprehensive genome-level understanding of the genetic and molecular control of carotenoids in any plant system, and a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extendable to other cereals.

genetics↗

bioRxiv2020

Genetic variation associated with PPO-inhibiting herbicide tolerance in sorghum

Herbicide application is crucial for weed management in most crop production systems, but for sorghum herbicide options are limited. Sorghum is sensitive to residual protoporphyrinogen oxidase (PPO)- inhibiting herbicides, such as fomesafen, and a long re-entry period is required before sorghum can be planted after its application. Improving sorghum for tolerance to such residual herbicides would allow for increased sorghum production and the expansion of herbicide options for growers. To investigate the underlying mechanism of tolerance to residual fomesafen, a genome-wide association mapping study was conducted using the sorghum biomass panel (SBP) and field-collected data, and a greenhouse assay was developed to confirm the field phenotypes. A total of 26 significant SNPs (FDR<0.05), spanning a 215.3 kb region, were detected on chromosome 3. The ten most significant SNPs included two in genic regions (Sobic.003G136800, and Sobic.003G136900) and eight SNPs in the intergenic region encompassing the genes Sobic.003G136700, Sobic.003G136800, Sobic.003G137000, Sobic.003G136900, and Sobic.003G137100. The gene Sobic.003G137100 (PPXI), which encodes the PPO1 enzyme, one of the targets of PPO-inhibiting herbicides, was located 12kb downstream of the significant SNP S03_13152838. We found that PPXI is highly conserved in sorghum and expression does not significantly differ between tolerant and sensitive sorghum lines. Our results suggest that PPXI most likely does not underlie the observed herbicide tolerance. Instead, the mechanism underlying herbicide tolerance in the SBP is likely metabolism-based resistance, possibly regulated by the action of multiple genes. Further research is necessary to confirm candidate genes and their functions.

plant biology↗

bioRxiv2020

HAPPI GWAS: Holistic Analysis with Pre and Post Integration GWAS

MotivationAdvanced publicly available sequencing data from large populations have enabled in-formative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis such as outlier removal, data transformation, and calculation of Best Linear Unbiased Predictions (BLUPs) or Best Linear Unbiased Estimates (BLUEs). In addition, post-GWAS analysis such as haploblock analysis and candidate gene identification are lacking. ResultsHere, we present HAPPI GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS, and post-GWAS analysis in an automated pipeline using the command-line interface. AvailabilityHAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git). Contactangelovicir@missouri.edu

bioinformatics↗

bioRxiv2020

simplePHENOTYPES: SIMulation of Pleiotropic, Linked and Epistatic PHENOTYPES

MotivationAdvances in genotyping and phenotyping techniques have enabled the acquisition of a great amount of data. Consequently, there is an interest in multivariate statistical analyses that identify genomic regions likely to contain causal mutations affecting multiple traits (i.e., pleiotropy). As the demand for multivariate analyses increases, it is imperative that optimal tools are available to compare different implementations of these analyses. To facilitate the testing and validation of these multivariate approaches, we developed simplePHENOTYPES, an R package that simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in a wide range of genetic architectures, including additive, dominance and epistatic models. ResultsWe illustrate simplePHENOTYPES ability to simulate thousands of phenotypes in less than one minute. We then provide a vignette illustrating how to simulate a set of correlated traits in simplePHENOTYPES. Finally, we demonstrate the use of results from simplePHENOTYPES in a standard GWAS software, as well as the numerical equivalence of simulated phenotypes from simplePHENOTYPES and other packages with similar capabilities. ConclusionssimplePHENOTYPES is a CRAN package that makes it possible to simulate multiple traits controlled by loci with varying degrees of pleiotropy. Its ability to interface with both commonly-used marker data formats and downstream quantitative genetics software and packages should facilitate a rigorous assessment of both existing and emerging statistical GWAS and GS approaches. simplePHENOTYPES is also available at https://github.coin/sainuelbfernandes/siinplePHENOTYPES.

bioinformatics↗