Detection Of Major Genes Research Articles

Macrolide resistance pattern of staphylococci collected from hospitalized patients in Egypt

Macrolide resistance of staphylococci has risen dramatically in recent years generating a real challenge for their treatment as therapeutic options have become very limited. In this study, an antibiogram analysis of one hundred and fifty Staphylococcus sp. isolates collected from various clinical specimens, against erythromycin, azithromycin, spiramycin, and clindamycin was carried out. Out of the 150 collected Staphylococcus sp. isolates, 54 isolates (36%) showed resistance to two or more of the tested macrolides. Inducible macrolide, lincosamides and streptogramin type B resistance phenotype (iMLS) using D-test was identified in 15 of the resistant isolates (27.8%). Molecular detection of major genes coding for macrolide resistance, including erythromycin ribosomal methylase (ermA and ermC), and macrolide-streptogramin resistance gene (msrA) was carried out using PCR. It was found that 51.8, 37.1 and 11.1% of the resistant isolates carried one, two and three types of the resistance genes, respectively. However, ermC was the most frequently occurring gene (81.5%), followed by the msrA gene (42.6%), then the ermA gene (35.2%). In conclusion, the genotypic analysis revealed that the majority of the tested isolates harbored two or more macrolide resistance-coding genes where 36% displayed resistance to at least two of the most common macrolide antibiotics used in the treatment of such important pathogens particularly in patients exhibiting hypersensitivity to penicillins according to several international guidelines. Therefore, it is crucial to carry out more epidemiologic studies to clearly understand the problem of increasing macrolide resistance among Staphylococci and to implement new guidelines for the treatment of such important pathogens, particularly in Egypt.

Detection of Major Genes for Body Weight Traits at Birth and Six Weeks of Age in a Commercial Broiler Line using Bayesian Segregation Method

Detection of Major Genes for Body Weight Traits at Birth and Six Weeks of Age in a Commercial Broiler Line using Bayesian Segregation Method

Detection of candidate genes for growth and carcass traits using genome-wide association strategy in Chinese Simmental beef cattle

In Chinese beef cattle industry, there are more than 60 million livestock, nearly half of which are Chinese Simmental beef cattle or Simmental crossbreds. Over the past decades, numerous quantitative trait loci for economic traits in cattle have been identified, while few studies for growth and carcass traits have been reported in Simmental beef cattle. In the present study, we conducted genome-wide association study based on BovineHD BeadChip and identified 41, 15, 3, 22 and 16 single-nucleotide polymorphisms significantly associated with average daily gain, liveweight before slaughter, carcass weight, dressing percentage and pure meat percentage respectively. In total, 18 candidate genes were found for growth and carcass traits, and four haplotype blocks for growth and carcass traits were discovered. These findings will facilitate detection of major genes and genetic variants involved in growth and carcass traits of beef cattle in further studies.

Heritability of Dollar Spot Resistance in Creeping Bentgrass

ABSTRACT The dollar spot disease incited by Sclerotinia homoeocarpa is an important disease of creeping bentgrass (Agrostis stolonifera). Genetic resistance is an important control strategy and could reduce fungicide use. Despite recent research, the genetic mechanism of dollar spot resistance in turfgrasses is still not fully understood. The objectives of this study were to (i) determine narrow-sense heritability and predicted gain from selection for dollar spot resistance in creeping bentgrass and (ii) evaluate inheritance characteristics of dollar spot disease resistance. Inheritance characteristics such as the detection of major genes, heterosis, maternal effects, and combining ability were determined by evaluating the disease severity of progeny from crosses between resistant and susceptible bent-grass clones. Parental clones and progenies from crosses were established in a field trial in a randomized complete block design and inoculated with one isolate of S. homoeocarpa applied at a rate of 0.25 g m(-2) of prepared inoculum. Differences in progeny means between crosses were observed over both years. Progeny from resistant x resistant crosses had significantly less disease severity than resistant x susceptible and susceptible x susceptible crosses. High narrow-sense heritability estimates (0.79 [2002], 0.79 [2003]) and large mean squares for general combining ability support the idea that additive gene action plays a significant role in disease resistance and support previous research that dollar spot resistance is most likely quantitatively inherited.

Complex Segregation Analysis of Categorical Traits in Farm Animals: Comparison of Linear and Threshold Models

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

An indication of major genes affecting hip and elbow dysplasia in four Finnish dog populations.

The aim of the study was to assess the possible existence of major genes influencing hip and elbow dysplasia in four dog populations. A Bayesian segregation analysis was performed separately on each population. In total, 34 140 dogs were included in the data set. Data were analysed with both a polygenic and a mixed inheritance model. Polygenic models included fixed and random environmental effects and additive genetic effects. To apply mixed inheritance models, the effect of a major gene was added to the polygenic models. The major gene was modelled as an autosomal biallelic locus with Mendelian transmission probabilities. Gibbs sampling and a Monte Carlo Markov Chain algorithm were used. The goodness-of-fit of the different models were compared using the residual sum-of-squares. The existence of a major gene was considered likely for hip dysplasia in all the breeds and for elbow dysplasia in one breed. Several procedures were followed to exclude the possible false detection of major genes based on non-normality of data: permuted datasets were analysed, data-transformations were applied, and residuals were judged for normality. Allelic effects at the major gene locus showed nearly to complete dominance, with a recessive, unfavourable allele in both traits. Relatively high estimates of the frequencies of unfavourable alleles in each breed suggest that considerable genetic progress would be possible by selection against major genes. However, the major genes that are possibly affecting hip and elbow dysplasia in these populations will require further study.

Mapping of Quantitative Trait Loci Controlling Physico-chemical Properties of Rice Grains (Oryza sativa L.)

Backcross inbred lines (BILs) derived from a cross of Nipponbare (japonica) / Kasalath (indica) // Nipponbare were used to identify quantitative trait loci (QTLs) controlling physico-chemical properties of rice grains such as amylose content (AC), alkali spreading score (ASS) and gel consistency (GC) by composite interval mapping over a period of two years. A total of 4 QTLs for AC were detected; qAC-5 and qAC-6 showed significant effects (hereafter referred to as “significant”) in both years, and qAC-6 explained more than 80 % of the phenotypic variance and was located in the wx region on the short arm of chromosome 6. The other 2 QTLs for AC with small additive effects were detected and were significant only in one year. Three QTLs for ASS were identified; qASS-6a and qASS-6b were significant in both years, and qASS-6a corresponded to a major gene located in the alk region on chromosome 6, while qASS-3 on chromosome 3 was significant only in one year. Five QTLs for GC were detected and all were significant only in one year. The lock of detection of major gene(s) for GC may be due to the fact that none of the parents were differentiated in terms of GC. These results showed that AC and ASS were mainly controlled by known gene loci, i.e., wx and alk, respectively, with modification by minor genes.

Model-free linkage analysis: performance under real-world conditions.

The simulated data of the Genetic Analysis Workshop 12 problem set affords an ideal environment for testing real-world performance of model-free linkage analysis methods. To this end, we applied three different methods of model-free linkage analysis: mod scores [Hodge and Elston, Genet Epidemiol 11:329-42, 1994], maximized maximum lod score (MMLS) [Greenberg et al., Am J Hum Genet 63:870-9, 1998], and nonparametric linkage (NPL) scores [Whittemore and Halpern, Biometrics 50:118-27, 1994], as well as standard parametric linkage analysis to the detection of major gene 6 (MG6) using only the qualitative disease status data. Our results indicate that both mod scores and NPL scores perform well, even in the presence of an extremely complicated disease model. MMLS analysis did not perform well, except at the disease locus itself.

Genetic Predisposition to Clinical Tuberculosis: Bridging the Gap between Simple and Complex Inheritance

Tuberculosis, a chronic infectious disease caused by Mycobacterium tuberculosis, has reemerged as a leading public health problem. Approximately one-third of the world’s population is infected with M. tuberculosis, and a recent World Health Organization report estimated that, in 1998, there were 8 million new cases of clinical tuberculosis and 1.9 million deaths from the disease. Interestingly, not all individuals exposed to M. tuberculosis become infected. Moreover, progression toward clinical tuberculosis is far from an inevitable consequence of infection with M. tuberculosis, since only ∼10% of the vast number of infected individuals actually develop clinical disease (Bloom and Small 1998). Both M. tuberculosis infection and clinical tuberculosis result from complex interactions between the infectious agent, environmental factors, and the host.

Accuracy of detection of major genes segregating in outbred population by Gibbs sampling using phenotypic values of quantitative traits

Based on the segregation analysis with Gibbs sampling the accuracy to detect a major gene segregating in outbred populations was investigated by using only phenotypic values and pedigree information.The analyses were carried out with 50 simulated populations (replicates). When a major gene effect was large enough to make the phenotypic distribution deviate from normal, the additive allele effect and total major gene variance were correctly estimated. In populations with a low or moderate effect of the major gene, the additive allele effect was systematically underestimated. When no major gene was present, the additive allele effect had a tendency to be overestimated but the major gene variances turned out to be very close to zero as expected. Our results suggest that the major gene variances can be used to detect major genes segregating in a population.

Use of molecular markers and major genes in the genetic improvement of livestock

Recent developments in molecular biology and statistics have opened the possibility of identifying and using genomic variation and major genes for the genetic improvement of livestock. Information concerning the basis of these techniques and their applications to the genetic improvement of animals is reviewed. Main marker molecular marker systems in animals (RFPL and microsatellites), genome maps, methods for detecting marker major gene linkages and use of marker assisted selection, genetic fingerprinting and mixture models based on segregation analysis are analyzed. The characteristics where the application of marker assisted selection can be more effective are those that are expressed late in the life of the animal, or controlled by a few pairs of genes. The first example correspond to the longevity and carcass characteristics in meat producing animals, the second, to the resistance to certain diseases or defects of simple inheritance. The detection of major genes using mixture models with segregation analysis can direct the work of identification of DNA marker genotypes towards populations and characteristics with greater probability of detecting a major gene using molecular markers. The present trend indicates that molecular, pedigree and phenotypic information will be integrated in the future through mixture models of segregation analysis that might contain major gene effects through the markers, polygenic inheritance and uses powerful and flexible methods of estimation such as Gibbs Sampling.

Use of molecular markers and major genes in the genetic improvement of livestock

Recent developments in molecular biology and statistics have opened the possibility of identifying and using genomic variation and major genes for the genetic improvement of livestock. Information concerning the basis of these techniques and their applications to the genetic improvement of animals is reviewed. Main marker molecular marker systems in animals (RFPL and microsatellites), genome maps, methods for detecting marker major gene linkages and use of marker assisted selection, genetic fingerprinting and mixture models based on segregation analysis are analyzed. The characteristics where the application of marker assisted selection can be more effective are those that are expressed late in the life of the animal, or controlled by a few pairs of genes. The first example correspond to the longevity and carcass characteristics in meat producing animals, the second, to the resistance to certain diseases or defects of simple inheritance. The detection of major genes using mixture models with segregation analysis can direct the work of identification of DNA marker genotypes towards populations and characteristics with greater probability of detecting a major gene using molecular markers. The present trend indicates that molecular, pedigree and phenotypic information will be integrated in the future through mixture models of segregation analysis that might contain major gene effects through the markers, polygenic inheritance and uses powerful and flexible methods of estimation such as Gibbs Sampling.

Epidemiologic and Genetic Approaches in the Study of Gene-Environment Interaction: an Overview of Available Methods

The interest in conducting studies to examine geneenvironment interaction is increasing for most chronic and complex diseases such as cancer. This increased interest is mostly due to considerable advances in molecular genetic techniques. Gene-environment studies are motivated by different situations including: 1) The detection of major genes that do not have estimated lifetime risks that reach 100 percent (i.e., incomplete penetrance); the incomplete penetrance may result from the role of other factors in disease etiology, such as environmental factors, that may or may not interact with the genetic factors. In such a situation, the genetic factor would be considered as the major risk factor and the environmental exposure as a modifier. 2) The identification of gene-environment interaction as a common biologic process in pharmacogenetic studies of complex diseases; pharmacogenetics hypothesizes that hereditary factors that control the metabolism of carcinogens or other toxic substances may modulate risk of disease. Indeed, different genotypes (for example, GSTM1 null genotype versus nonnull) seem to respond differently to environmental risk factors. In this situation, the environmental factor would be considered as the major risk factor and the genetic factor as a modifier. 3) Inconsistent associations across studies between a disease and a suspected risk factor; one reason for the inconsistencies is that relevant risk factors may be difficult to detect, possibly due to heterogeneity in the studied populations. One source of heterogeneity could be unknown genetic susceptibility that predisposes to differential

Detection of major genes underlying several quantitative traits associated with a common disease using different ascertainment schemes

Detection of major genes underlying several quantitative traits associated with a common disease using different ascertainment schemes

Maximum likelihood analysis of rare binary traits under different modes of inheritance.

Maximum likelihood methodology was applied to determine the mode of inheritance of rare binary traits with data structures typical for swine populations. The genetic models considered included a monogenic, a digenic, a polygenic, and three mixed polygenic and major gene models. The main emphasis was on the detection of major genes acting on a polygenic background. Deterministic algorithms were employed to integrate and maximize likelihoods. A simulation study was conducted to evaluate model selection and parameter estimation. Three designs were simulated that differed in the number of sires/number of dams within sires (10/10, 30/30, 100/30). Major gene effects of at least one SD of the liability were detected with satisfactory power under the mixed model of inheritance, except for the smallest design. Parameter estimates were empirically unbiased with acceptable standard errors, except for the smallest design, and allowed to distinguish clearly between the genetic models. Distributions of the likelihood ratio statistic were evaluated empirically, because asymptotic theory did not hold. For each simulation model, the Average Information Criterion was computed for all models of analysis. The model with the smallest value was chosen as the best model and was equal to the true model in almost every case studied.

Power and sensitivity of some simple tests for detection of major genes in outbred populations

SummaryThree simple statistical tests (Bartlett test, log‐anova test, and the mixture test, a model that fits a mixture of normal distributions to data) for detecting major genes in hierarchical data structures in outbred livestock populations were investigated. The power of the tests was evaluated when the major gene had 2–3 alleles and in the presence of a skewed distribution of polygenic phenotypes or a non‐genetic mixture of normal distributions. The most powerful test, but also the most sensitive to skewness, was the mixture test. The Bartlett test was more powerful than the log‐anova test, but was sensitive to skewness of the data and to an underlying non‐genetic mixture of distributions. All tests were able to detect a dominant major gene with a difference of 1.5 phenotypic standard deviations between homozygotes from a data set of 5000 records if the frequency of the dominant allele was less than 0.9. The most suitable robust simple test for major gene detection under a hierarchical data structure is the log‐anova test.ZusammenfassungMächtigkeit und Sensitivität einiger einfacher Tests auf das Vorliegen von Hauptgenen in tier‐züchterischen PopulationenEs wurden drei einfache statistische Tests auf das Vorliegen von Hautgenen in hierarchisch strukturierten tierzüchterischen Datensätzen untersucht: der Bartlett‐Test, der Log‐anova‐Test und der Mixture‐Test, welcher die Daten an eine Mischung von Normalverteilungen anpaßt. Die Mächtigkeit dieser Tests wurde unter einem Hauptgenmodell mit zwei oder drei Allelen untersucht, sowie unter der Annahme einer schiefen Verteilung der polygenen Phänotypen und einer nicht‐genetischen Mischung von Normalverteilungen. Der mächtigste Test war der Mixture‐Test, welcher allerdings auch auf eine Schiefe der Daten am sensitivsten reagierte. Der Bartlett‐Test war mächtiger als der Log‐anova‐Test, reagierte jedoch sehr sensitiv auf Schiefe und eine nicht‐genetische Mischung von Verteilungen. Alle Tests waren in der Lage, ein dominantes Hauptgen mit einem Unterschied von 1,5 phänotypischen Standardabweichungen zwischen den homozygoten Genotypen in einem Datensatz mit 5 000 Individuen zu entdecken, wenn die Frequenz des dominanten Allels kleiner als 0,9 war. Der geeignetste robuste Test auf das Vorliegen von Hautgenen in hierarchisch strukturierten tierzüchterischen Datensätzen ist der Log‐anova‐Test.

Testing in Normal Mixture Models with Some Information on the Parameters

AbstractThe aim of this paper is to study the distribution of the likelihood ratio for testing whether or not one is sampling from a mixture of two distributions or from a single distribution. We study the case where some information is available on the variation range of the parameters of populations. First we study the simplest case in which the difference between the mean of the two populations is known. We show certain distortions between theoretical and simulation results. Secondly, we show how this distortion spreads to the situation where this difference belongs to an interval. Finally, we give an example concerning the detection of major genes in animal population.

Use of DNA fingerprints for the detection of major genes for quantitative traits in domestic species

The detection of marker loci linked to major genes or quantitative trait loci (QTL) of large effect in farm animal populations is of great potential value, both because it allows the easy manipulation of the major genes and because it provides a possible route to their ultimate isolation. At present the number of markers available is limited in farm animals. DNA fingerprints provide a promising source of informative marker loci and have the advantage that several loci can be detected on a single Southern hybridization. The disadvantage of DNA fingerprints is the difficulty in determining allelism of DNA fingerprint bands in different pedigrees and the fact that not all potentially resolvable loci can be resolved in a single pedigree. With probes capable of detecting 50 randomly distributed loci, about 50% of the genome of a typical domestic mammal might be expected to be closely linked to a marker (at a distance of 0.2 Morgans or less). If a proportion of DNA fingerprint loci prove to be clustered near chromosomal telomeres or elsewhere in the genome, coverage will be less. In order to detect linkage to a major gene, sires known or suspected to be heterozygous are used to produce large half-sibships, all animals in the pedigree are DNA fingerprinted and the phenotypes of the offspring are recorded. Where several heterozygous sires are available, sires can be selected in an attempt to maximize the number of marker loci resolved. The optimum number of sires needed to produce pedigrees will depend upon the size of the major gene, the number of DNA fingerprint probes available and the characteristics of the DNA fingerprints produced, but often one or two pedigrees will be optimum. Monte Carlo simulation was used to explore the power of detection of linkage between a major gene and a marker locus in a backcross. Maximum likelihood and analysis of variance of mean differences between marker genotypes were of similar power, but maximum likelihood provided reasonable estimates of the major gene effect and its linkage to the marker under some circumstances. One hundred offspring informative for the segregation of a marker would provide reasonable power for the detection of a gene causing a difference between the heterozygote and the homozygote of at least one within-sire, within-genotype standard deviation when linkage was very close (0.05 or less).(ABSTRACT TRUNCATED AT 400 WORDS)

Estimation of the Density of G Given Observations of Y=G+E

AbstractWe study the problem of estimating the density of a random variable G, given observations of a random variable Y = G + E. The random variable E is independent of G and its probability distribution function is considered as known. We build a family of estimators of the density of G using characteristic functions. We then derive a family of estimators of the density of Y based on the model for Y. The estimators are shown to be asymptotically unbiased and consistent. Simulations show that these estimators are better, as measured by integrated squared error, than the standard kernel estimators. Finally, we give an example of the use of this method for the detection of major genes in animal populations.

Statistical techniques for detection of major genes in animal breeding data

Statistical techniques for detection of major loci and for making inferences about major locus parameters such as genotypic frequencies, effects and gene action from field-collected data are presented. In field data, major genotypic effects are likely to be masked by a large number of environmental differences in addition to additive and nonadditive polygenic effects. A graphical technique and a procedure for discriminating among genetic hypotheses based on a mixed model accounting for all these factors are proposed. The methods are illustrated by using simulated data.