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Logistic Regression Approach (logistic + regression_approach)
Selected AbstractsLogistic regression approach to modelling the variability of recombination rateJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 1 2000By J. Szyda The objective of the paper is to quantify the relationship between recombination rate and factors such as ,sex of sperm', paternal halfsib family, and individual, using logistic regression modelling. The analysed data set consists of 2214 single bovine sperm cell samples. Haplotypes at each single sperm were determined at eleven marker loci forming eight intervals located on chromosomes 6, 23 and X/Y. The experimental design comprises six paternal halfsib families. Six logistic regression models are fitted to the data from each interval. Departure from commonly assumed homogenous recombination is detected for one marker interval on chromosome 23 , influence of individual and ,sex of sperm' by family interaction, and for both intervals mapped to sex chromosomes , influence of ,sex of sperm' and paternal halfsib family. [source] Perils and pitfalls of permutation tests for distinguishing the effects of neighbouring polymorphismsGENETIC EPIDEMIOLOGY, Issue 7 2006Joanna M. Biernacka Abstract In a small region several marker loci may be associated with a trait, either because they directly influence the trait or because they are in linkage disequilibrium (LD) with a causal variant. Having established a potentially causal effect at a primary variant, we may ask if any other variants in the region appear to further contribute to the trait, indicating that the additional variant is either causal or is in LD with another causal locus. Methods of approaching this problem using case-parent trio data include the stepwise conditional logistic regression approach described by Cordell and Clayton ([2002] Am. J. Hum. Genet. 70:124,141), and a constrained-permutation method recently proposed by Spijker et al. ([2005] Ann. Hum. Genet. 69:90,101). Through simulation we demonstrate that the procedure described by Spijker et al. [2005], as well as unconditional logistic regression with "affected family-based controls" (AFBACs), can lead to inflated type 1 errors in situations when haplotypes are not inferable for all trios, whereas the conditional logistic regression approach gives correct significance levels. We propose an alternative to the permutation method of Spijker et al. [2005], which does not rely on haplotyping, and results in correct type 1 errors and potentially high power when assumptions of random mating, Hardy-Weinberg Equilibrium, and multiplicative effects of disease alleles are satisfied. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc. [source] Habitat models of bird species' distribution: an aid to the management of coastal grazing marshesJOURNAL OF APPLIED ECOLOGY, Issue 5 2000T. P. Milsom 1.,Coastal grazing marshes comprise an important habitat for wetland biota but are threatened by agricultural intensification and conversion to arable farmland. In Britain, the Environmentally Sensitive Area (ESA) scheme addresses these problems by providing financial incentives to farmers to retain their grazing marshes, and to follow conservation management prescriptions. 2.,A modelling approach was used to aid the development of management prescriptions for ground-nesting birds in the North Kent Marshes ESA. This ESA contains the largest area of coastal grazing marsh remaining in England and Wales (c. 6500 ha) and supports nationally important breeding populations of lapwing Vanellus vanellus and redshank Tringa totanus. 3.,Counts of ground-nesting birds, and assessments of sward structure, surface topography and wetness, landscape structure and sources of human disturbance were made in 1995 and again in 1996, on 19 land-holdings with a combined area of c. 3000 ha. The land-holdings varied from nature reserves at one extreme to an intensive dairy farm at the other. 4.,Models of relationship between the presence or absence of ground-nesting birds and the grazing marsh habitat in each of c. 430 marshes were constructed using a generalized linear mixed modelling (GLMM) method. This is an extension to the conventional logistic regression approach, in which a random term is used to model differences in the proportion of marshes occupied on different land-holdings. 5.,The combined species models predicted that the probability of marshes being occupied by at least one ground-nesting species increased concomitantly with the complexity of the grass sward and surface topography but decreased in the presence of hedgerows, roads and power lines. 6.,Models were also prepared for each of the 10 most widespread species, including lapwing and redshank. Their composition differed between species. Variables describing the sward were included in models for five species: heterogeneity of sward height tended to be more important than mean sward height. Surface topography and wetness were important for waders and wildfowl but not for other species. Effects of boundaries, proximity to roads and power lines were included in some models and were negative in all cases. 7.,Binomial GLMMs are useful for investigating habitat factors that affect the distribution of birds at two nested spatial scales, in this case fields (marshes) grouped within farms. Models of the type presented in this paper provide a framework for targeting of conservation management prescriptions for ground-nesting birds at the field scale on the North Kent Marshes ESA and on lowland wet grassland elsewhere in Europe. [source] Prevalence of self-perceived allergic diseases and risk factors in Italian adolescentsPEDIATRIC ALLERGY AND IMMUNOLOGY, Issue 6 2009Sonia Brescianini The aim of the study was to assess the symptoms prevalence of allergic diseases in a population of 11,15 yr old schoolchildren, to evaluate the associations between asthma and other symptoms and identify risk factors for asthma, rhinitis and eczema syndromes. A sample of 481 students was studied using an International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. Prevalence of different kind of self-reported symptoms was calculated. Using a logistic regression approach, we tried to identify risk factors for three syndromes , rhinitis, eczema and asthma. The highest and the lowest prevalence rates of self-reported symptoms were recorded for rhinitis (43.6%) and for eczema (8.1%), respectively. The prevalence of asthma was 15.7%. Univariate analysis showed a mutual association between wheeze and rhinitis symptoms. Multivariate logistic regression model for eczema syndrome revealed female gender as a significant risk factor. The polytomic logistic multivariate regression revealed female gender and family history of allergy as significant risk factors for rhinitis syndrome only, and maternal smoking and familial allergy for rhinitis and asthma together. In particular, familial allergy yields a 400% higher chance of developing asthma and rhinitis together. The synergistic effect of familial allergy on rhinitis and asthma syndromes suggests the implementation of preventive measures in children with family history of these diseases. [source] | |||||||||||||