Principles of Statistical Genomics
Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle to the development of the field. Although many books have been published recently in bioin...
| Κύριος συγγραφέας: | |
|---|---|
| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
New York, NY :
Springer New York : Imprint: Springer,
2013.
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| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- Genetic Linkage Map
- Map Functions
- Physical map and genetic map
- Derivation of map functions
- Haldane map function
- Kosambi map function
- Recombination Fraction
- Mating designs
- Maximum likelihood estimation of recombination fraction
- Standard error and significance test
- Fisher’s scoring algorithm for estimating
- EM algorithm for estimating
- Genetic Map Construction
- Criteria of optimality
- Search algorithms
- Exhaustive search
- Heuristic search
- Simulated annealing
- Branch and bound
- Bootstrap confidence of a map
- Multipoint Analysis of Mendelian Loci
- Joint distribution of multiple locus genotype
- BC design
- F2 design
- Four-way cross design
- Incomplete genotype information
- Partially informative genotype
- BC and F2 are special cases of FW
- Dominance and missing markers
- Conditional probability of a missing marker genotype
- Joint estimation of recombination fractions
- Multipoint analysis for m markers
- Map construction with unknown recombination fractions
- Basic Concepts of Quantitative Genetics
- Gene frequency and genotype frequency
- Genetic effects and genetic variance
- Average effect of allelic substitution
- Genetic variance components
- Heritability
- An F2 family is in Hardy-Weinberg equilibrium
- Major Gene Detection
- Estimation of major gene effect
- BC design
- F2 design
- Hypothesis tests
- BC design
- F2 design
- Scale of the genotype indicator variable
- Statistical power
- Type I error and statistical power
- Wald-test statistic
- Size of a major gene
- Relationship between W-test and Z-test
- Extension to dominance effect
- Segregation Analysis
- Gaussian mixture distribution
- EM algorithm
- Closed form solution
- EM steps
- Derivation of the EM algorithm
- Proof of the EM algorithm
- Hypothesis tests
- Variances of estimated parameters
- Estimation of the mixing proportions
- Genome Scanning for Quantitative Trait Loci
- The mouse data
- Genome scanning
- Missing genotypes
- Test statistics
- Bonferroni correction
- Permutation test
- Piepho’s approximate critical value
- Theoretical consideration
- Interval Mapping
- Least squares method
- Weighted least squares
- Fisher scoring
- Maximum likelihood method
- EM algorithm
- Variance-covariance matrix of ˆθ
- Hypothesis test
- Remarks on the four methods of interval mapping
- Interval Mapping for Ordinal Traits
- Generalized linear model
- ML under homogeneous variance
- ML under heterogeneous variance
- ML under mixture distribution
- ML via the EM algorithm
- Logistic analysis
- Example
- Mapping Segregation Distortion Loci
- Probabilistic model
- The EM Algorithm
- Hypothesis test
- Variance matrix of the estimated parameters
- Selection coefficient and dominance
- Liability model
- EM algorithm
- Variance matrix of estimated parameters
- Hypothesis test
- Mapping QTL under segregation distortion
- Joint likelihood function
- EM algorithm
- Variance-covariance matrix of estimated parameters
- Hypothesis tests
- Example
- QTL Mapping in Other Populations
- Recombinant inbred lines
- Double haploids
- Four-way crosses
- Full-sib family
- F2 population derived from outbreds
- Example
- Random Model Approach to QTL Mapping
- Identity-by-descent (IBD)
- Random effect genetic model
- Sib-pair regression.- Maximum likelihood estimation
- EM algorithm
- EM algorithm under singular value decomposition
- Multiple siblings
- Estimating the IBD value for a marker
- Multipoint method for estimating the IBD value
- Genome scanning and hypothesis tests
- Multiple QTL model
- Complex pedigree analysis
- Mapping QTL for Multiple Traits
- Multivariate model
- EM algorithm for parameter estimation
- Hypothesis tests
- Variance matrix of estimated parameters
- Derivation of the EM algorithm
- Example
- Bayesian Multiple QTL Mapping
- Bayesian regression analysis
- Markov chain Monte Carlo
- Mapping multiple QTL
- Multiple QTL model
- Prior, likelihood and posterior
- Summary of the MCMC process
- Post MCMC analysis
- Alternative methods of Bayesian mapping
- Reversible jump MCMC
- Stochastic search variable selection
- Lasso and Bayesian Lasso
- Example: Arabidopsis data
- Empirical Bayesian QTL Mapping
- Classical mixed model
- Simultaneous updating for matrix G
- Coordinate descent method
- Block coordinate descent method
- Bayesian estimates of QTL effects
- Hierarchical mixed model
- Inverse chi-square prior
- Exponential prior
- Dealing with sparse models
- Infinitesimal model for whole genome sequence data
- Data trimming
- Concept of continuous genome
- Example: Simulated data
- Microarray Differential Expression Analysis
- Data preparation
- Data transformation
- Data normalization
- F-test and t-test
- Type I error and false discovery rate
- Selection of differentially expressed genes
- Permutation test
- Selecting genes by controlling FDR
- Problems of the previous methods
- Regularized t-test
- General linear model
- Fixed model approach
- Random model approach
- Hierarchical Clustering of Microarray Data
- Distance matrix
- UPGMA
- Neighbor joining
- Principle of neighbor joining
- Computational algorithm
- Other methods
- Bootstrap confidence
- Model-Based Clustering of Microarray Data
- Cluster analysis with the K-means method
- Cluster analysis under Gaussian mixture
- Multivariate Gaussian distribution
- Mixture distribution
- The EM algorithm
- Supervised cluster analysis
- Semi-supervised cluster analysis
- Inferring the number of clusters
- Microarray experiments with replications
- Gene Specific Analysis of Variances
- General linear model
- The SEM algorithm
- Hypothesis testing
- Factor Analysis of Microarray Data
- Background of factor analysis
- Linear model of latent factors
- EM algorithm
- Number of factors
- Cluster analysis
- Differential expression analysis
- MCMC algorithm
- Classification of Tissue Samples Using Microarrays
- Logistic regression
- Penalized logistic regression
- The coordinate descent algorithm
- Cross validation
- Prediction of disease outcome
- Multiple category classification
- Time-Course Microarray Data Analysis
- Gene expression profiles
- Orthogonal polynomial
- B-spline
- Mixed effect model
- Mixture mixed model
- EM algorithm
- Best linear unbiased prediction
- SEM algorithm
- Monte Carlo sampling
- SEM steps
- Quantitative Trait Associated Microarray Data Analysis
- Linear association
- Linear model
- Cluster analysis
- Three-cluster analysis
- Differential expres
- SEM algorithm
- MCMC algorithm
- Joint analysis of all markers
- Multiple eQTL model
- SEM algorithm
- MCMC algorithm
- Hierarchical evolutionary stochastic search (HESS).
