scholarly journals Linkage analysis in a full-sib family of an outbreeding plant species: overview and consequences for applications

1997 ◽  
Vol 70 (3) ◽  
pp. 237-250 ◽  
Author(s):  
C. MALIEPAARD ◽  
J. JANSEN ◽  
J. W. VAN OOIJEN
Keyword(s):  
Molecular Markers ◽  
Maximum Likelihood ◽  
Linkage Analysis ◽  
Plant Species ◽  
Recombination Frequency ◽  
Maximum Likelihood Estimators ◽  
Lod Score ◽  
Linkage Phase ◽  
Map Construction ◽  
Sib Families

Linkage analysis and map construction using molecular markers is far more complicated in full-sib families of outbreeding plant species than in progenies derived from homozygous parents. Markers may vary in the number of segregating alleles. One or both parents may be heterozygous, markers may be dominant or codominant and usually the linkage phases of marker pairs are unknown. Because of these differences, marker pairs provide different amounts of information for the estimation of recombination frequencies and the linkage phases of the markers in the two parents, and usually these have to be estimated simultaneously. In this paper we present a complete overview of all possible configurations of marker pairs segregating in full-sib families. Maximum likelihood estimators for the recombination frequency and LOD score formulas are presented for all cases. Statistical properties of the estimators are studied analytically and by simulation. Specific problems of dominant markers, in particular with respect to the probability of detecting linkage, the probability of obtaining zero estimates, and the ability to distinguish linkage phase combinations, and consequences for mapping studies in outbred progenies are discussed.

scholarly journals Construction of a Genetic Linkage Map in Tetraploid Species Using Molecular Markers

Genetics ◽  
2001 ◽  
Vol 157 (3) ◽  
pp. 1369-1385 ◽  
Author(s):  
Z W Luo ◽  
C A Hackett ◽  
J E Bradshaw ◽  
J W McNicol ◽  
D Milbourne
Keyword(s):  
Molecular Markers ◽  
Linkage Map ◽  
Recombination Frequency ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Lod Score ◽  
Data Set ◽  
Independent Segregation ◽  
The Em Algorithm ◽  
Small Set

Abstract This article presents methodology for the construction of a linkage map in an autotetraploid species, using either codominant or dominant molecular markers scored on two parents and their full-sib progeny. The steps of the analysis are as follows: identification of parental genotypes from the parental and offspring phenotypes; testing for independent segregation of markers; partition of markers into linkage groups using cluster analysis; maximum-likelihood estimation of the phase, recombination frequency, and LOD score for all pairs of markers in the same linkage group using the EM algorithm; ordering the markers and estimating distances between them; and reconstructing their linkage phases. The information from different marker configurations about the recombination frequency is examined and found to vary considerably, depending on the number of different alleles, the number of alleles shared by the parents, and the phase of the markers. The methods are applied to a simulated data set and to a small set of SSR and AFLP markers scored in a full-sib population of tetraploid potato.

Pooled DNA for linkage analysis: practical and statistical considerations

Genome ◽  
1994 ◽  
Vol 37 (6) ◽  
pp. 999-1004 ◽  
Author(s):  
N. A. Tinker ◽  
D. E. Mather ◽  
M. G. Fortin
Keyword(s):  
Molecular Markers ◽  
Maximum Likelihood ◽  
Linkage Analysis ◽  
High Probability ◽  
Dna Analysis ◽  
Recombination Fraction ◽  
Likelihood Estimator ◽  
Pooled Dna ◽  
Estimator Bias ◽  
Expected Values

In an F2 population, the alleles at two loci with a recombination fraction r < 0.5 are in linkage disequilibrium. If r is small, then a pool of DNA from k diploid individuals that are fixed at one locus has a relatively high probability (P = (1 − r)2k) of containing only the coupled allele at the second locus. Based on this principle, several methods have been described to detect linkage (using one or two pools) or to estimate r (using a group of n pools). This report compares maximum likelihood and approximate estimators of r for use in pooled-DNA analysis and illustrates the use of this analysis for dominant markers. Expected values and expected mean squares for estimators of r were computed for varying levels of r, k, and n. Both estimators were biased, but the bias and variability were slightly smaller for the maximum-likelihood estimator. Bias was not severe except when k was large relative to r and (or) n. Methods for optimizing k are discussed relative to several criteria: minimizing variance, minimizing bias, minimizing the probability that linkage cannot be detected, and minimizing the number of samples that must be screened.Key words: pooled DNA, linkage analysis, molecular markers, RAPD, genetic mapping.

scholarly journals Estimation of recombination frequency in bi-parental genetic populations

2012 ◽  
Vol 94 (3) ◽  
pp. 163-177 ◽  
Author(s):  
ZIQI SUN ◽  
HUIHUI LI ◽  
LUYAN ZHANG ◽  
JIANKANG WANG
Keyword(s):  
Linkage Analysis ◽  
Recombination Frequency ◽  
Frequency Standard ◽  
Accurate Estimation ◽  
Linkage Relationship ◽  
Lod Score ◽  
Dominant Markers ◽  
Ideal Situation ◽  
Theoretical Population ◽  
Significant Linkage

SummaryLinkage analysis plays an important role in genetic studies. In linkage analysis, accurate estimation of recombination frequency is essential. Many bi-parental populations have been used, and determining an appropriate population is of great importance in precise recombination frequency. In this study, we investigated the estimation efficiency of recombination frequency in 12 bi-parental populations. The criteria that we used for comparison were LOD score in testing linkage relationship, deviation between estimated and real recombination frequency, standard error (SE) of estimates and the least theoretical population size (PS) required to observe at least one recombinant and to declare the statistically significant linkage relationship. Theoretical and simulation results indicated that larger PS and smaller recombination frequency resulted in higher LOD score and smaller deviation. Lower LOD score, higher deviation and higher SE for estimating the recombination frequency in the advanced backcrossing and selfing populations are larger than those in backcross and F2 populations, respectively. For advanced backcrossing and selfing populations, larger populations were needed in order to observe at least one recombinant and to declare significant linkage. In comparison, in F2 and F3 populations higher LOD score, lower deviation and SE were observed for co-dominant markers. A much larger population was needed to observe at least one recombinant and to detect loose linkage for dominant and recessive markers. Therefore, advanced backcrossing and selfing populations had lower precision in estimating the recombination frequency. F2 and F3 populations together with co-dominant markers represent the ideal situation for linkage analysis and linkage map construction.

Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

2018 ◽  
pp. 397
Author(s):  
Nadia Hashim Al-Noor ◽  
Shurooq A.K. Al-Sultany
Keyword(s):  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Mean Squared Error ◽  
Maximum Likelihood Estimators ◽  
Reliability Function ◽  
Fuzzy Data ◽  
Monte Carlo Simulation Study ◽  
Weibull Parameters ◽  
Squared Error ◽  
Newton Raphson

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

scholarly journals Respiratory syncytial virus B sequence analysis reveals a novel early genotype

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Juan C. Muñoz-Escalante ◽  
Andreu Comas-García ◽  
Sofía Bernal-Silva ◽  
Daniel E. Noyola
Keyword(s):  
Molecular Markers ◽  
Respiratory Syncytial Virus ◽  
Maximum Likelihood ◽  
Respiratory Infections ◽  
Phylogenetic Analyses ◽  
Detection Methods ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Individual Gene ◽  
Syncytial Virus

AbstractRespiratory syncytial virus (RSV) is a major cause of respiratory infections and is classified in two main groups, RSV-A and RSV-B, with multiple genotypes within each of them. For RSV-B, more than 30 genotypes have been described, without consensus on their definition. The lack of genotype assignation criteria has a direct impact on viral evolution understanding, development of viral detection methods as well as vaccines design. Here we analyzed the totality of complete RSV-B G gene ectodomain sequences published in GenBank until September 2018 (n = 2190) including 478 complete genome sequences using maximum likelihood and Bayesian phylogenetic analyses, as well as intergenotypic and intragenotypic distance matrices, in order to generate a systematic genotype assignation. Individual RSV-B genes were also assessed using maximum likelihood phylogenetic analyses and multiple sequence alignments were used to identify molecular markers associated to specific genotypes. Analyses of the complete G gene ectodomain region, sequences clustering patterns, and the presence of molecular markers of each individual gene indicate that the 37 previously described genotypes can be classified into fifteen distinct genotypes: BA, BA-C, BA-CC, CB1-THB, GB1-GB4, GB6, JAB1-NZB2, SAB1, SAB2, SAB4, URU2 and a novel early circulating genotype characterized in the present study and designated GB0.

Pedigree Data Analysis With Crossover Interference

Genetics ◽  
2003 ◽  
Vol 164 (4) ◽  
pp. 1561-1566
Author(s):  
Sharon Browning
Keyword(s):  
Data Analysis ◽  
Linkage Analysis ◽  
Importance Sampling ◽  
Genetic Data ◽  
Genotype Data ◽  
Pedigree Data ◽  
Chi Square ◽  
Crossover Interference ◽  
Map Construction ◽  
Interference Models

AbstractWe propose a new method for calculating probabilities for pedigree genetic data that incorporates crossover interference using the chi-square models. Applications include relationship inference, genetic map construction, and linkage analysis. The method is based on importance sampling of unobserved inheritance patterns conditional on the observed genotype data and takes advantage of fast algorithms for no-interference models while using reweighting to allow for interference. We show that the method is effective for arbitrarily many markers with small pedigrees.

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