scholarly journals Bayesian decision theoretic design of two-founder experimental crosses given diallel data

2018 ◽  
Author(s):  
Gregory R. Keele ◽  
Paul L. Maurizio ◽  
Daniel Oreper ◽  
William Valdar
Keyword(s):  
Experimental Design ◽  
Qtl Mapping ◽  
Utility Function ◽  
Effect Size ◽  
Complex Traits ◽  
Model Organisms ◽  
Theoretic Approach ◽  
Bayesian Decision ◽  
Pilot Data ◽  
Experimental Crosses

ABSTRACTIn designing experimental crosses of inbred strains of model organisms, researchers must make a number of decisions. These include the selection of the appropriate strains, the cross design (eg. F2 intercross), and the number of progeny to collect (sample size). These decisions strongly influence the potential for a successful quantitative trait locus (QTL) mapping experiment; good design decisions will lead to efficient and effective science. Thus experimental design deserves careful consideration and planning. Experimental outcomes can be quantified through utility functions using a Bayesian decision theoretic approaches. For QTL mapping experiments, the power to map a QTL is an appealing utility function to maximize. Using any utility function to aid in experimental design will be dependent on assumptions, such as the QTL effect size in the case of power. Rather than arbitrarily selecting QTL effect size values, they can be estimated from pilot data using a Bayesian hierarchical model. The information in the pilot data can be propagated to the utility function, using Markov Chain Monte Carlo (MCMC) to sample from the posterior distribution. Key features of this approach include: 1) distributional summaries of utility, which are preferable to point estimates, and 2) a comprehensive search of the experimental space of crosses of inbred lines for well-designed experiments. We evaluate this Bayesian theoretic approach using diallel crosses as the pilot data. We present results from simulations as well as present examples from both Mendelian and complex traits in the founder strains of the mouse Collaborative Cross. All analyses were performed using our R package, DIDACT (Diallel-Informed Decision theoretic Approach for Crosses Tool), developed to perform Bayesian cross selection based on diallel pilot data.

scholarly journals Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

2021 ◽  
Author(s):  
Alex N. Nguyen Ba ◽  
Katherine R. Lawrence ◽  
Artur Rego-Costa ◽  
Shreyas Gopalakrishnan ◽  
Daniel Temko ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Large Scale ◽  
Genetic Basis ◽  
Association Studies ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Trait Locus

Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.Significance statementUnderstanding the genetic basis of important phenotypes is a central goal of genetics. However, the highly polygenic architectures of complex traits inferred by large-scale genome-wide association studies (GWAS) in humans stand in contrast to the results of quantitative trait locus (QTL) mapping studies in model organisms. Here, we use a barcoding approach to conduct QTL mapping in budding yeast at a scale two orders of magnitude larger than the previous state of the art. The resulting increase in power reveals the polygenic nature of complex traits in yeast, and offers insight into widespread patterns of pleiotropy and epistasis. Our data and analysis methods offer opportunities for future work in systems biology, and have implications for large-scale GWAS in human populations.

scholarly journals Analysis of genetic dominance in the UK Biobank

2021 ◽  
Author(s):  
Duncan S Palmer ◽  
Wei Zhou ◽  
Liam Abbott ◽  
Nik Baya ◽  
Claire Churchhouse ◽  
...  
Keyword(s):  
Complex Traits ◽  
Multiple Testing ◽  
Model Organisms ◽  
Systematic Evaluation ◽  
Hair Color ◽  
Phenotypic Variance ◽  
Additive Effects ◽  
Uk Biobank ◽  
Genome Wide ◽  
The Uk

In classical statistical genetic theory, a dominance effect is defined as the deviation from a purely additive genetic effect for a biallelic variant. Dominance effects are well documented in model organisms. However, evidence in humans is limited to a handful of traits, particularly those with strong single locus effects such as hair color. We carried out the largest systematic evaluation of dominance effects on phenotypic variance in the UK Biobank. We curated and tested over 1,000 phenotypes for dominance effects through GWAS scans, identifying 175 loci at genome-wide significance correcting for multiple testing (P < 4.7 × 10-11). Power to detect non-additive loci is much lower than power to detect additive effects for complex traits: based on the relative effect sizes at genome-wide significant additive loci, we estimate a factor of 20-30 increase in sample size will be necessary to capture clear evidence of dominance similar to those currently observed for additive effects. However, these localised dominance hits do not extend to a significant aggregate contribution to phenotypic variance genome-wide. By deriving a version of LD-score regression to detect dominance effects tagged by common variation genome-wide (minor allele frequency > 0.05), we found no strong evidence of a contribution to phenotypic variance when accounting for multiple testing. Across the 267 continuous and 793 binary traits the median contribution was 5.73 × 10-4, with unbiased point estimates ranging from -0.261 to 0.131. Finally, we introduce dominance fine-mapping to explore whether the more rapid decay of dominance LD can be leveraged to find causal variants. These results provide the most comprehensive assessment of dominance trait variation in humans to date.

scholarly journals Pengaruh Penerapan Model Pembelajaran Numbered Head Together (NHT) Terhadap Hasil Belajar Siswa

2018 ◽  
Vol 6 (3) ◽  
pp. 354
Author(s):  
Eka Trisianawati ◽  
Tomo Djudin ◽  
Yayuk Dwi Stianingsih
Keyword(s):  
Experimental Design ◽  
Student Learning ◽  
Learning Outcomes ◽  
Effect Size ◽  
Student Learning Outcomes ◽  
Learning Model ◽  
T Test ◽  
Average Score ◽  
Rectilinear Motion ◽  
Before And After

Penelitian ini bertujuan untuk menganalisis: 1) hasil belajar siswa sebelum dan setelah diterapkan model pembelajaran NHT, 2) perbedaan hasil belajar siswa sebelum dan setelah diterapkan model pembelajaran NHT, dan 3) besar efektivitas model pembelajaran NHT terhadap hasil belajar siswa dikelas X SMAN 1 Sungai Raya. Metode penelitian ialah metode eksperimen (Pre-Experimental Design) dengan One-Group Pretest-Posttest Design. Populasi penelitian adalah seluruh siswa kelas X IPA SMAN 1 Sungai Raya yang terdiri dari 5 kelas. Sampel penelitian ini ialah kelas X IPA5 sebagai kelas eksperimen, dengan menggunakan teknik purporsive sampling.  Instrumen untuk mengukur hasil belajar siswa menggunakan tes essay. Data dianalisis dengan menggunakan uji rata-rata, uji pair sample t-test, dan effect size.  Hasil penelitian menunjukkan bahwa: 1) terdapat peningkatan skor rata-rata hasil belajar siswa dari 45,0 menjadi 77,8, 2) Dari hasil analisis data menggunakan uji pair sample t-test dapat disimpulkan bahwa terdapat perbedaan yang signifikan hasil belajar siswa sebelum dan setelah diterapkan model pembelajaran NHT, dan 3) Nilai Effect Size sebesar 2,2 yang termasuk dalam kategori tinggi. Disimpulkan bahwa penerapan model pembelajaran NHT pada materi gerak lurus berubah beraturan berpengaruh terhadap hasil belajar siswa kelas X SMA Negeri 1 Sungai Raya. This study aims to analyze: 1) student learning outcomes before and after applying the NHT learning model, 2) differences in student learning outcomes before and after applying the NHT learning model, and 3) the effectiveness of NHT learning models on student learning outcomes in class X SMAN 1 Sungai Raya. The research method is the experimental method (Pre-Experimental Design) with One-Group Pretest-Posttest Design. The study population was all students of class X Science at Sungai Raya 1 State High School consisting of 5 classes. The sample of this study is class X IPA5 as an experimental class, using purposive sampling technique. Instrument to measure student learning outcomes using an essay test. Data were analyzed using an average test, pair sample t-test, and effect size. The results show that: 1) there is an increase in the average score of student learning outcomes from 45.0 to 77.8, 2) From the results of data analysis using pair sample t-test can be concluded that there are significant differences in student learning outcomes before and after applying the NHT learning model, 3) Effect Size values of 2.2 which is included in the high category. It was concluded that the application of NHT learning  model affecting the learning outcomes of class X students of  SMAN  1 Sungai Raya, of the subject matter rectilinear motion with constant acceleration.

scholarly journals An evaluation of the interpretability and predictive performance of the BayesR model for genomic prediction

2020 ◽  
Author(s):  
Fanny Mollandin ◽  
Andrea Rau ◽  
Pascal Croiseau
Keyword(s):  
Linkage Disequilibrium ◽  
Qtl Mapping ◽  
Effect Size ◽  
Genomic Prediction ◽  
Simulated Data ◽  
Predictive Performance ◽  
Real Data ◽  
Sliding Windows ◽  
Size Classes ◽  
The Impact

ABSTRACTTechnological advances and decreasing costs have led to the rise of increasingly dense genotyping data, making feasible the identification of potential causal markers. Custom genotyping chips, which combine medium-density genotypes with a custom genotype panel, can capitalize on these candidates to potentially yield improved accuracy and interpretability in genomic prediction. A particularly promising model to this end is BayesR, which divides markers into four effect size classes. BayesR has been shown to yield accurate predictions and promise for quantitative trait loci (QTL) mapping in real data applications, but an extensive benchmarking in simulated data is currently lacking. Based on a set of real genotypes, we generated simulated data under a variety of genetic architectures, phenotype heritabilities, and we evaluated the impact of excluding or including causal markers among the genotypes. We define several statistical criteria for QTL mapping, including several based on sliding windows to account for linkage disequilibrium. We compare and contrast these statistics and their ability to accurately prioritize known causal markers. Overall, we confirm the strong predictive performance for BayesR in moderately to highly heritable traits, particularly for 50k custom data. In cases of low heritability or weak linkage disequilibrium with the causal marker in 50k genotypes, QTL mapping is a challenge, regardless of the criterion used. BayesR is a promising approach to simultaneously obtain accurate predictions and interpretable classifications of SNPs into effect size classes. We illustrated the performance of BayesR in a variety of simulation scenarios, and compared the advantages and limitations of each.

scholarly journals Dimensions of Design Space: A Decision-Theoretic Approach to Optimal Research Design

2009 ◽  
Vol 29 (6) ◽  
pp. 643-660 ◽  
Author(s):  
Stefano Conti ◽  
Karl Claxton
Keyword(s):  
Design Space ◽  
Treatment Options ◽  
Simultaneous Estimation ◽  
Theoretic Approach ◽  
Bayesian Decision ◽  
Sample Sizes ◽  
Net Benefit ◽  
Clinical Endpoints ◽  
Optimal Sample ◽  
Different Types

Bayesian decision theory can be used not only to establish the optimal sample size and its allocation in a single clinical study but also to identify an optimal portfolio of research combining different types of study design. Within a single study, the highest societal payoff to proposed research is achieved when its sample sizes and allocation between available treatment options are chosen to maximize the expected net benefit of sampling (ENBS). Where a number of different types of study informing different parameters in the decision problem could be conducted, the simultaneous estimation of ENBS across all dimensions of the design space is required to identify the optimal sample sizes and allocations within such a research portfolio. This is illustrated through a simple example of a decision model of zanamivir for the treatment of influenza. The possible study designs include: 1) a single trial of all the parameters, 2) a clinical trial providing evidence only on clinical endpoints, 3) an epidemiological study of natural history of disease, and 4) a survey of quality of life. The possible combinations, samples sizes, and allocation between trial arms are evaluated over a range of cost-effectiveness thresholds. The computational challenges are addressed by implementing optimization algorithms to search the ENBS surface more efficiently over such large dimensions.

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