Biology subjects

Pook, T.

Publications and source records attributed to Pook, T..

17 recordsLinked to original sources

bioRxiv2026

Genetic Dissection of Grain Yield and Correlated Proxy Traits Under Suboptimal Conditions

Securing sustainable crop production requires the genetic improvement of abiotic stress tolerance. Due to the broad range of environmental factors causing abiotic stress and complex genotype-by-environment interactions, it is crucial to understand the genetic basis of crop yield under suboptimal conditions. Here, we developed a dent maize Multi-parent Advanced Generation Inter-Cross (MAGIC) population comprising 388 doubled haploid (DH) lines. The population was derived from eight founders with varying stress tolerance, selected from a dent diversity panel evaluated for yield performance across a wide range of European environments. The MAGIC DH lines were genotyped via whole-genome sequencing ([~]5X coverage) and evaluated in seven testcross and 14 line per se trials, for grain dry matter yield, leaf senescence, leaf rolling, anthesis-silking interval, and six additional agronomic traits. Genetic dissection identified 22 grain yield QTL, explaining 45% of the genetic variance. Under heat and drought stress, testcross grain yield correlated significantly with leaf senescence and leaf rolling measured in line per se trials. Bivariate multi-trait analysis showed that alleles for delayed senescence and reduced rolling at detected QTL generally exhibited positive effects on grain yield, suggesting that accumulating these favorable alleles could enhance yield performance. Incorporating these proxies into multi-trait genomic prediction models improved yield prediction accuracy, although gains were constrained by modest trait correlations. Given the comprehensive data, we also provide recommendations for optimizing sequencing depth and QTL mapping strategies in experimental maize populations. Key messageThis eight-founder MAGIC population represents a powerful resource for dissecting complex traits in maize, assessing the utility of drought proxy traits, and optimizing low-coverage whole-genome sequencing approaches.

genetics↗

bioRxiv2025

Characterization of a novel heat tolerance trait and subsequent haplotype block-based analysis to identify causal regions in Dutch Holstein cattle

Heat stress is a major environmental challenge affecting dairy cattle, leading to behavioral changes, production losses, and welfare concerns. As heat stress events intensify and become more frequent due to climate change, identifying heat tolerant animals is crucial for sustainable dairy production. This study develops a pipeline to quantify the population-wise impact of heat stress on a dairy cattle population and subsequently defines individual-based heat tolerance traits. Data from 677,318 Dutch Holstein cows, including 15.6 million mid-infrared spectra and 762 million records from automated milking systems, were analyzed. An iterative approach using kernel regression was employed to estimate the population-wise effects of heat stress. Results indicate that fat and protein percentages decrease approximately linearly with increasing temperature humidity index (THI) with an absolute reduction of 0.3% from THI = 30 to THI = 70. In contrast, milk yield remains stable until a THI of 60, after which production losses increase quadratically reaching 5.0% at a THI of 75. The phenotype of an animal is subsequently defined as the slope in a linear regression model of the residuals of the population-wise models against THI for milk yield and concentration of fat, protein, lactose, and specific fatty acids. Compared to reaction-norm models, individual records per cow are combined before model fitting, thereby reducing computation times and allowing more flexibility in the design of the model. Heritabilities for heat tolerance traits ranged from 0.05 to 0.12, and genetic variances indicate substantial potential for breeding as an improvement of the population by one genetic standard deviation would already offset 69% of the losses in fat percentage, 65% in protein percentage, and 11% in milk yield. Heat tolerance based on milk yield showed favorable correlations with most commercial traits, whereas heat tolerance based on fat and protein percentage showed negative correlations to health and resilience. A genome-wide association study using both SNPs and haplotype blocks from the software HaploBlocker identified potential QTLs across the genome, with particularly strong signals on BTA5, 14, and 20. These findings support the potential of breeding for heat tolerance but highlight the need for complementary management strategies to mitigate heat stress impacts. Interpretive summaryThis study introduced a novel, computationally efficient method to quantify the impact of heat stress in dairy cattle and define novel heat tolerance traits based on milk production data from automated milking systems. Our results indicate quadratically increasing losses in milk yield with increasing heat load. The identified heat tolerance traits show substantial genetic variance, moderate heritabilities, and favorable correlation to key production traits. These findings highlight the potential for incorporating heat tolerance into dairy breeding goals to mitigate climate change impacts, improve animal welfare, and enhance sustainable milk production.

genetics↗

bioRxiv2025

Optimization of wheat breeding programs using an evolutionary algorithm achieves enhanced genetic gain through strategic resource allocation

Plant breeding is a complex process that involves trade-offs among competing breeding objectives and limited resources. Despite the necessity for optimization of breeding program design, the inherent complexity can make optimization challenging. Most often, a predefined set of scenarios is compared or a single parameter of a breeding scheme is assessed in depth. In a previous study, we developed an optimization pipeline, utilizing stochastic simulations and an evolutionary algorithm, suitable for the joint optimization of multiple class and continuous parameters. Here, we assess the applicability of our framework to realistic plant breeding schemes. For this, a wheat line breeding scheme simulated using AlphaSimR and a wheat hybrid breeding scheme simulated using MoBPS were considered. Both schemes were further optimized using our optimization pipeline. When aiming to maximize genetic gain while maintaining a fixed budget, the breeding program design suggested by our optimization pipeline results in 32.6% higher genetic gain compared to a proposed baseline wheat line breeding program. When adjusting the breeding objective to put 20% of the weight on the maintenance of genetic diversity, genetic gain still increased by 4.5% while maintaining 9.1% higher genetic diversity compared to the baseline. Similarly, 4.6%/8.8% higher genetic gains for the male/female part of the hybrid breeding scheme compared to its baseline scenario were obtained. Results highlight the importance of optimizing breeding program design to improve breeding efficiency, with the suggested pipeline offering breeders a powerful framework to refine breeding designs, balance breeding goals, and enhance competitiveness, profitability, and sustainability. Core Ideas Evolutionary algorithm can improve breeding design by optimizing many breeding decisions simultaneously Our framework is compatible with various backend simulators, enabling broad application across platforms Optimized designs enhance genetic gain and diversity, demonstrating greater overall breeding program efficiency Genetic gain in wheat line program increased by over 30% compared to a baseline program without additional costs Plain Language Summary Modern plant breeding programs must make many complex decisions, such as how many plants to test or where to grow them while staying with tight budgets. These choices are often connected and involve trade-offs between goals like improving genetic gain, maintaining diversity, and reducing costs. In this study, we used an evolutionary algorithm framework to evaluate thousands of breeding program designs and identify the most effective ones. The optimized programs outperformed the respective baseline programs, increasing genetic gain by up to 32% and preserving 9% more genetic diversity - all without additional costs. This flexible framework can support a wide range of breeding decisions and adapt to different program types, providing breeders with a powerful tool to improve outcomes and use resources more efficiently.

genetics↗

bioRxiv2025

Optimization of a maize rapid cycle breeding scheme using the Modular Breeding Program Simulator (MoBPS)

In recent years, the turnover of plant breeding has substantially increased as the use of genomic information allows for earlier selection and the integration of controlled growing environments reduces time to reach a particular growing stage. However, high generation turnover and intensive selection of lines before own yield trials are performed come at the risk of a drastic reduction of genetic diversity paired with lower prediction accuracies. To this end, we investigate strategies to cope with these challenges in a maize rapid cycle breeding scheme using stochastic simulations using the software MoBPS. We find that genetic gains soon reach a plateau when only the original breeding material is phenotyped. Updating the training data set via additional phenotyping of crosses or doubled haploid lines ensures long-term progress with a gain of 6.80 / 6.95 genetic standard deviations for the performance as a cross / per se after 30 cycles of breeding compared to 3.40 / 4.28 without additional phenotyping. Adding genetic material with comparable genetic level and novel diversity from outside the breeding material led to a further increase to 9.34 / 7.89 genetic standard deviations. In particular, for the management of genetic diversity, further additions to the breeding scheme are analyzed to optimize the number of selected lines per cycle and to account for the relatedness of F2 plants in the selection using the software AlphaMate. Finding a balance between genetic gains and diversity is important for a given time frame. MoBPS provides a tool for the quantification of these effects and provides solutions specific to the respective breeding program.

genetics↗

bioRxiv2025

Strategies to improve selection compared to selection based on estimated breeding values

BackgroundSelection of individuals based on their estimated breeding values aims to maximize response to selection to the next generation in the additive model. However, when the aim is not only about short-term population-wide genetic gain but also the gain over multiple generations, an optimal strategy is not as clear-cut, as the maintenance of genetic diversity may become an important factor. This study provides an extended comparison of existing selection strategies in a unifying testing pipeline using the simulation software MoBPS. ResultsApplying a weighting factor on the estimated SNP effects based on the frequency of the beneficial allele resulted in an increase of the long-term genetic gain of 1.6% after 50 generations while reducing inbreeding rates by 16.2% compared to truncation selection based on estimated breeding values. However, this also resulted in short-term losses in genetic gain of 1.2% with the break-even point reached after 25 generations. In contrast, inclusion of the average kinship of an individual to top individuals of the population as an additional trait in the selection index with a weight of 17.5% resulted in no short-term losses and increased long-term genetic gains by 4.3% while reducing inbreeding by 15.8%, achieving very similar efficiency to the use of optimum genetic contribution selection. Combining multiple diversity management strategies, with weights for each strategy optimized using an evolutionary algorithm resulted in a breeding scheme with 5.1% increased genetic gain and 37.3% reduced inbreeding rates. The proposed strategy included the use of optimum genetic contribution, weighting of SNP effects based on allele frequency, average kinship as a trait in the selection index, avoiding matings between related individuals, and lowering the proportion of selected individuals. ConclusionsThe combination of selection strategies for the management of genetic diversity was shown to be far superior to the use of any singular method tested in this study. As an efficient use of methods for the management of genetic diversity and inbreeding does not necessarily lead to short-term losses in genetic gain and comes at no extra costs, it is critical for breeding companies to implement such strategies for long-term success.

genetics↗

bioRxiv2023

Accelerated computations for iterative-solver techniques in single-step BLUP models

In the last decade, a number of methods have been suggested to deal with large amounts of genetic data in genomic predictions. Yet, steadily growing population sizes and the suboptimal use of computational resources are pushing the practical application of these approaches to their limits. As an extension to the C/CUDA library miraculix, we have developed tailored solutions for the computation of genotype matrix multiplications which is a critical bottleneck in the empirical evaluation of many statistical models. We demonstrate the benefits of our solutions at the example of single-step models which make repeated use of this kind of multiplication. Targeting modern Nvidia(R) GPUs as well as a broad range of CPU architectures, our implementation significantly reduces the time required for the estimation of breeding values in large population sizes. miraculix is released under the Apache 2.0 license and is freely available at https://github.com/alexfreudenberg/miraculix.

bioinformatics↗

bioRxiv2023

Beyond Scenarios - Optimization of breeding program design (MoBPSopti)

In recent years, breeding programs have become increasingly larger and more structurally complex, with various highly interdependent parameters and contrasting breeding goals. Therefore, resource allocation in a breeding program has become more complex, and the derivation of an optimal breeding strategy has become more and more challenging. As a result, it is a common practice to reduce the optimization problem to a set of scenarios that are only changed in a few parameters and, in turn, can be deeply analyzed in detail. This paper aims to provide a framework for the numerical optimization of breeding programs beyond just comparing scenarios. For this, we first determine the space of potential breeding programs that is only limited by basic constraints like the budget and housing capacities. Subsequently, the goal is to identify the optimal breeding program by finding the parametrization that maximizes the target function, as a combination of the different breeding goals. To assess the value of the target function for a parametrization, we propose the use of stochastic simulations and the subsequent use of a kernel regression method to cope with the stochasticity of simulation outcomes. This procedure is performed iteratively to narrow down the most promising areas of the search space and perform more and more simulations in these areas of interest. The developed concept was applied to a dairy cattle program with a target function aiming at genetic gain and genetic diversity conservation limited by budget constraints.

genetics↗

bioRxiv2022

Imputation of Low-density Marker Chip Data in Plant Breeding: Evaluation of Methods Based on Sugar Beet

Low-density genotyping followed by imputation reduces genotyping costs while still providing high-density marker information. An increased marker density has the potential to improve the outcome of all applications that are based on genomic data. This study investigates techniques for 1k to 20k genomic marker imputation for plant breeding programs with sugar beet as an example crop, where these are realistic marker numbers for modern breeding applications. The generally accepted gold standard for imputation, Beagle 5.1, was compared to the recently developed software AlphaPlantImpute2 which is designed specifically for plant breeding. For Beagle 5.1 and AlphaPlantImpute2, the imputation strategy as well as the imputation parameters were optimized in this study. We found that the imputation accuracy of Beagle could be tremendously improved (0.22 to 0.67) by tuning parameters, mainly by lowering the values for the parameter for the effective population size and increasing the number of iterations performed. Separating the phasing and imputation steps also improved accuracies when optimized parameters were used (0.67 to 0.82). We also found that the imputation accuracy of Beagle decreased when more low-density lines were included for imputation. AlphaPlantImpute2 produced very high accuracies without optimization (0.89) and was generally less responsive to optimization. Overall, AlphaPlantImpute2 performed relatively better for imputation while Beagle was better for phasing. Combining both tools yielded the highest accuracies. SummaryGenotype marker information allows the prediction of an individuals breeding value without the need to observe its actual phenotype which can accelerate the breeding progress. The more markers are genotyped, the better the genomic prediction may be. However, analyzing many markers is costly, particularly in commercial breeding programs where thousands of new individuals are genotyped. A solution to obtain information for all markers, while spending comparatively little on genotyping, is to genotype only a small fraction of markers in most individuals. Together with high-density information on other individuals, the low-density individuals can be imputed to high-density. High-density individuals are typically parents or highly influential individuals. In this study, we compare the widely used software Beagle with the recently developed software AlphaPlantImpute2 on plant breeding data. To allow a fair comparison, we first optimized existing methods and developed new approaches. This was done to avoid comparing results of a less ideal version of one software to optimized settings of another software. After optimization, the software were evaluated in different scenarios with regards to genotyping errors, population types and number of markers based on simulated data. Simulated data were based on real marker data from a sugar beet population as input to mimic the population history of a commercial breeding population. AlphaPlantImpute2 performs well with default parameters, while much optimization with regards to parameters and strategy was needed to boost accuracies of Beagle. A pipeline is presented which uses Beagle for phasing and AlphaPlantImpute2 for imputation. This pipeline yielded the highest accuracies and shortest run time. Core IdeasBeagle is sensitive to parameter tuning Best imputation accuracies could be achieved by using a combination of Beagle and AlphaPlantImpute2 The population structure influence imputation accuracy

bioinformatics↗

bioRxiv2021

Comparison of breeding strategies for the creation of a synthetic pig line

Creating a new synthetic line by crossbreeding means complementary traits from pure breeds can be combined in the new population. Although diversity is generated during the crossbreeding stage, in this study, we analyze diversity management before selection starts. Using genomic and phenotypic data from animals belonging to the first generation (G0) of a new line, different simulations were run to evaluate diversity management during the first generations of a new line and to test the effects of starting selection at two alternative times, G3 and G4. Genetic diversity was characterized by allele frequency, inbreeding coefficients based on genomic and pedigree data, and expected heterozygosity. Breeding values were extracted at each generation to evaluate differences in starting selection at G3 or G4. All simulations were run for ten generations. A scenario with genomic data to manage diversity during the first generations of a new line was compared with a random and a selection scenario. As expected, loss of diversity was higher in the selection scenario, while the scenario with diversity control preserved diversity. We also combined the diversity management strategy with different selection scenarios involving different degrees of diversity control. Our simulation results show that a diversity management strategy combining genomic data with selection starting at G4 and a moderate degree of diversity control generates genetic progress and preserves diversity.

genetics↗

bioRxiv2021

Increasing calling accuracy, coverage, and read depth in sequence data by the use of haplotype blocks

High-throughput genotyping of large numbers of lines remains a key challenge in plant genetics, requiring geneticists and breeders to find a balance between data quality and the number of genotyped lines under a variety of different existing technologies when resources are limited. In this work, we are proposing a new imputation pipeline ("HBimpute") that can be used to generate high-quality genomic data from low read-depth whole-genome-sequence data. The key idea of the pipeline is the use of haplotype blocks from the software HaploBlocker to identify locally similar lines and merge their reads locally. The effectiveness of the pipeline is showcased on a dataset of 321 doubled haploid lines of a European maize landrace, which were sequenced with 0.5X read-depth. Overall imputing error rates are cut in half compared to the state-of-the-art software BEAGLE, while the average read-depth is increased to 83X, thus enabling the calling of structural variation. The usefulness of the obtained imputed data panel is further evaluated by comparing the performance in common breeding applications to that of genomic data from a 600k array. In particular for genome-wide association studies, the sequence data is shown to be performing slightly better. Furthermore, genomic prediction based on the overlapping markers from the array and sequence is leading to a slightly higher predictive ability for the imputed sequence data, thereby indicating that the data quality obtained from low read-depth sequencing is on par or even slightly higher than high-density array data. When including all markers for the sequence data, the predictive ability is slightly reduced indicating overall lower data quality in non-array markers. Author summaryHigh-throughput genotyping of large numbers of lines remains a key challenge in plant genetics and breeding. Cost, precision, and throughput must be balanced to achieve optimal efficiencies given available technologies and finite resources. Although genotyping arrays are still considered the gold standard in high-throughput quantitative genetics, recent advances in sequencing provide new opportunities for this. Both the quality and cost of genomic data generated based on sequencing are highly dependent on the used read depth. In this work, we are proposing a new imputation pipeline ("HBimpute") that uses haplotype blocks to detect individuals of the same genetic origin and subsequently uses all reads of those individuals in the variant calling. Thus, the obtained virtual read depth is artificially increased, leading to higher calling accuracy, coverage, and the ability to all copy number variation based on relatively cheap low-read depth sequencing data. Thus, our approach makes sequencing a cost-competitive alternative to genotyping arrays with the additional benefit of the potential use of structural variation.

genetics↗

bioRxiv2020

Bivariate genomic prediction of phenotypes by selecting epistatic interactions across years

The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this study, we investigated the ability of three models to improve prediction accuracy by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The results indicate that bivariate ERRBLUP is slightly superior to bivariate GBLUP in predication accuracy, while bivariate sERRBLUP has the highest prediction accuracy in most cases. The average relative increase in prediction accuracy from bivariate GBLUP to maximum bivariate sERRBLUP across eight phenotypic traits and studied dataset from 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot, were 7.61 and 3.47 percent, respectively. We further investigated the genomic correlation, phenotypic correlation and trait heritability as the factors affecting the bivariate models predication accuracy, with genetic correlation between growing seasons being the most important one. For all three considered model architectures results were far worse when using a univariate version of the model, e.g. with an average reduction in prediction accuracy of 0.23/0.14 for Kemater/Petkuser when using univariate GBLUP. Key MassageBivariate models based on selected subsets of pairwise SNP interactions can increase the prediction accuracy by utilizing phenotypic data across years under the assumption of high genomic correlation across years.

genetics↗

bioRxiv2020

Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments

We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested in six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate statistical settings within and across environments. Our results indicate that modeling all pairwise SNP interactions into the univariate/bivariate model (ERRBLUP) is not superior in predictive ability to the respective additive model (GBLUP). However, incorporating only a selected subset of interactions with the highest effect variances in univariate/bivariate sERRBLUP can increase predictive ability significantly compared to the univariate/bivariate GBLUP. Overall, bivariate models consistently outperform univariate models in predictive ability. Over all studied traits, locations, and landraces, the increase in prediction accuracy from univariate GBLUP to univariate sERRBLUP ranged from 5.9 to 112.4 percent, with an average increase of 47 percent. For bivariate models, the change ranged from -0.3 to +27.9 percent comparing the bivariate sERRBLUP to the bivariate GBLUP. The average increase across traits and locations was 11 percent. This considerable increase in predictive ability achieved by sERRBLUP may be of interest for "sparse testing" approaches in which only a subset of the lines/hybrids of interest is observed at each location. Key MessageThe prediction accuracy of genomic prediction of phenotypes can be increased by only including top ranked pairwise SNP interactions into the prediction models.

genetics↗

bioRxiv2020

Using local convolutional neural networks for genomic prediction

The prediction of breeding values and phenotypes is of central importance for both livestock and crop breeding. With increasing computational power and more and more data to potentially utilize, Machine Learning and especially Deep Learning have risen in popularity over the last few years. In this study, we are proposing the use of local convolutional neural networks for genomic prediction, as a region specific filter corresponds much better with our prior genetic knowledge of traits than traditional convolutional neural networks. Model performances are evaluated on a simulated maize data panel (n = 10,000) and real Arabidopsis data (n = 2,039) for a variety of traits with the local convolutional neural network outperforming both multi layer perceptrons and convolutional neural networks for basically all considered traits. Linear models like the genomic best linear unbiased prediction that are often used for genomic prediction are outperformed by up to 24%. Highest gains in predictive ability was obtained in cases of medium trait complexity with high heritability and large training populations. However, for small dataset with 100 or 250 individuals for the training of the models, the local convolutional neural network is performing slightly worse than the linear models. Nonetheless, this is still 15% better than a traditional convolutional neural network, indicating a better performance and robustness of our proposed model architecture for small training populations. In addition to the baseline model, various other architectures with different windows size and stride in the local convolutional layer, as well as different number of nodes in subsequent fully connected layers are compared against each other. Finally, the usefulness of Deep Learning and in particular local convolutional neural networks in practice is critically discussed, in regard to multi dimensional inputs and outputs, computing times and other potential hazards.

genetics↗

bioRxiv2019

How Array Design Affects SNP Ascertainment Bias

Single nucleotide polymorphisms (SNPs), genotyped with SNP arrays, have become a widely used marker type in population genetic analyses over the last 10 years. However, compared to whole genome re-sequencing data, arrays are known to lack a substantial proportion of globally rare variants and tend to be biased towards variants present in populations involved in the development process of the respective array. This affects population genetic estimators and is known as SNP ascertainment bias. We investigated factors contributing to ascertainment bias in array development by redesigning the Axiom Genome-Wide Chicken Array in silico and evaluating changes in allele frequency spectra and heterozygosity estimates in a stepwise manner. A sequential reduction of rare alleles during the development process was shown with main influencing factors being the identification of SNPs in a limited set of populations and a within-population selection of common SNPs when aiming for equidistant spacing. These effects were shown to be less severe with a larger discovery panel. Additionally, a generally massive overestimation of expected heterozygosity for the ascertained SNP sets was shown. This overestimation was 24% higher for populations involved in the discovery process than not involved populations in case of the original array. The same was observed after the SNP discovery step in the redesign. However, an unequal contribution of populations during the SNP selection can mask this effect but also adds uncertainty. Finally, we make suggestions for the design of specialized arrays for large scale projects where whole genome re-sequencing techniques are still too expensive.

genomics↗

bioRxiv2019

MoBPS - Modular Breeding Program Simulator

The R-package MoBPS provides a computationally efficient and flexible framework to simulate complex breeding programs and compare their economic and genetic impact. Simulations are performed on the base of individuals and haplotypes are calculated on-the-fly by only saving founder haplotypes, points of recombination and mutations. MoBPS utilizes a highly efficient implementation with bit-wise storage of data and matrix multiplications from the associated R-package miraculix allowing to handle large scale populations. The modular structure of MoBPS allows to combine rather coarse simulations, as needed to generate founder populations, with a very detailed modeling of todays complex breeding programs, making use of all available biotechnologies. MoBPS provides pre-implemented functions for common breeding practices such as optimum genetic contributions and single-step GBLUP but also allows the user to replace certain steps with personalized and/or self-written solutions.

genetics↗

bioRxiv2019

Improving imputation quality in BEAGLE for crop and livestock data

Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals.

genetics↗

bioRxiv2018

HaploBlocker: Creation of subgroup specific haplotype blocks and libraries

The concept of haplotype blocks has been shown to be useful in genetics. Fields of application range from the detection of regions under positive selection to statistical methods that make use of dimension reduction. We propose a novel approach (\"HaploBlocker\") for defining and inferring haplotype blocks that focuses on linkage instead of the commonly used population-wide measures of linkage disequilibrium (LD) which fail to identify segments shared by individuals in only a subset of the population. We define a haplotype block as a sequence of alleles that has a predefined minimum frequency in the population and only haplotypes with a similar sequence of alleles are considered to be carrying that block, effectively screening a dataset for group-wise identity-by-descent (IBD). Different to most other approaches these blocks are not restricted to shared start or end positions, but can overlap or even contain each other. From these haplotype blocks we construct a haplotype library that represents a large proportion of genetic variability of a population with a limited number of blocks. Our method is implemented in the associated R-package HaploBlocker and provides flexibility to not only optimize the structure of the obtained haplotype library for subsequent analyses (e.g., identification of shared segments between different populations), but is also able to handle datasets of different marker density and genetic diversity. By using haplotype blocks instead of SNPs, local epistatic interactions can be naturally modelled and the reduced number of parameter enables a wide variety of new methods for further genomic analyses. We illustrate our methodology with a dataset comprising 501 doubled haploid lines in a European maize landrace genotyped at 501124 SNPs. With the suggested approach, we identified 2851 haplotype blocks with an average length of 2633 SNPs (compared to 27.8 SNPs per block in HaploView) that together represent 94% of the dataset.\n\nAuthor summaryWhereas it is quite easy to identify segments of shared DNA between pairs of individuals, the problem becomes far more complex when analyzing a population. Especially for livestock and crop populations under strong selection one can observe long and possibly favourable segments that are segregating at high frequency. We propose here an adaptive and flexible approach to identify such segments (\"haplotype blocks\"). The main conceptual difference to other approaches is that we allow haplotype blocks to overlap so that patterns shared by a subset of the population can be mapped adequately. Afterwards, we select a set of those haplotype blocks that form a representation of the whole population (\"haplotype library\"). This haplotype library can be used similar to a SNP-dataset for subsequent genomic approaches with the advantage of a massive reduction of the number of parameters compared to standard haplotyping approaches. Since many breeding goals (e.g. grain yield, milk production) are known to be caused by complex interactions in genomic regions (or even the whole genome) using haplotype blocks instead of single base pairs provides a natural model for local interactions and enables the use of more complex models to incorporate distant interactions between genes, for instance.

genetics↗