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Complex Genetic Traits (complex + genetic_trait)
Selected AbstractsIncorporating covariates in mapping heterogeneous traits: a hierarchical model using empirical Bayes estimationGENETIC EPIDEMIOLOGY, Issue 7 2007Swati Biswas Abstract Complex genetic traits are inherently heterogeneous, i.e., they may be caused by different genes, or non-genetic factors, in different individuals. So, for mapping genes responsible for these diseases using linkage analysis, heterogeneity must be accounted for in the model. Heterogeneity across different families can be modeled using a mixture distribution by letting each family have its own heterogeneity parameter denoting the probability that its disease-causing gene is linked to the marker map under consideration. A substantial gain in power is expected if covariates that can discriminate between the families of linked and unlinked types are incorporated in this modeling framework. To this end, we propose a hierarchical Bayesian model, in which the families are grouped according to various (categorized) levels of covariate(s). The heterogeneity parameters of families within each group are assigned a common prior, whose parameters are further assigned hyper-priors. The hyper-parameters are obtained by utilizing the empirical Bayes estimates. We also address related issues such as evaluating whether the covariate(s) under consideration are informative and grouping of families. We compare the proposed approach with one that does not utilize covariates and show that our approach leads to considerable gains in power to detect linkage and in precision of interval estimates through various simulation scenarios. An application to the asthma datasets of Genetic Analysis Workshop 12 also illustrates this gain in a real data analysis. Additionally, we compare the performances of microsatellite markers and single nucleotide polymorphisms for our approach and find that the latter clearly outperforms the former. Genet. Epidemiol. 2007. © 2007 Wiley-Liss, Inc. [source] Current recommendations for the molecular evaluation of newly diagnosed holoprosencephaly patients,,AMERICAN JOURNAL OF MEDICAL GENETICS, Issue 1 2010Daniel E. Pineda-Alvarez§ Abstract Holoprosencephaly (HPE) is the most common structural malformation of the developing forebrain in humans and is typically characterized by different degrees of hemispheric separation that are often accompanied by similarly variable degrees of craniofacial and midline anomalies. HPE is a classic example of a complex genetic trait with "pseudo"-autosomal dominant transmission showing incomplete penetrance and variable expressivity. Clinical suspicion of HPE is typically based upon compatible craniofacial findings, the presence of developmental delay or seizures, or specific endocrinological abnormalities, and is then followed up by confirmation with brain imaging. Once a clinical diagnosis is made, a thorough genetic evaluation is necessary. This usually includes analysis of chromosomes by high-resolution karyotyping, clinical assessment to rule-out well recognized syndromes that are associated with HPE (e.g., Pallister-Hall syndrome, Smith-Lemli-Opitz syndrome and others), and molecular studies of the most common HPE associated genes (e.g., SHH, ZIC2 and SIX3). In this review, we provide current step-by-step recommendations that are medically indicated for the genetic evaluation of patients with newly diagnosed HPE. Moreover, we provide a brief review of several available methods used in molecular diagnostics of HPE and describe the advantages and limitations of both currently available and future tests as they relate to high throughput screening, cost, and the results that they may provide. Published 2010 Wiley-Liss, Inc. [source] Evaluations of maximization procedures for estimating linkage parameters under heterogeneityGENETIC EPIDEMIOLOGY, Issue 3 2004Swati Biswas Abstract Locus heterogeneity is a major problem plaguing the mapping of disease genes responsible for complex genetic traits via linkage analysis. A common feature of several available methods to account for heterogeneity is that they involve maximizing a multidimensional likelihood to obtain maximum likelihood estimates. The high dimensionality of the likelihood surface may be due to multiple heterogeneity (mixing) parameters, linkage parameters, and/or regression coefficients corresponding to multiple covariates. Here, we focus on this nontrivial computational aspect of incorporating heterogeneity by considering several likelihood maximization procedures, including the expectation maximization (EM) algorithm and the stochastic expectation maximization (SEM) algorithm. The wide applicability of these procedures is demonstrated first through a general formulation of accounting for heterogeneity, and then by applying them to two specific formulations. Furthermore, our simulation studies as well as an application to the Genetic Analysis Workshop 12 asthma datasets show that, among other observations, SEM performs better than EM. As an aside, we illustrate a limitation of the popular admixture approach for incorporating heterogeneity, proved elsewhere. We also show how to obtain standard errors (SEs) for EM and SEM estimates, using methods available in the literature. These SEs can then be combined with the corresponding estimates to provide confidence intervals of the parameters. © 2004 Wiley-Liss, Inc. [source] A score for Bayesian genome screeningGENETIC EPIDEMIOLOGY, Issue 3 2003E. Warwick Daw Abstract Bayesian Monte Carlo Markov chain (MCMC) techniques have shown promise in dissecting complex genetic traits. The methods introduced by Heath ([1997], Am. J. Hum. Genet. 61:748,760), and implemented in the program Loki, have been able to localize genes for complex traits in both real and simulated data sets. Loki estimates the posterior probability of quantitative trait loci (QTL) at locations on a chromosome in an iterative MCMC process. Unfortunately, interpretation of the results and assessment of their significance have been difficult. Here, we introduce a score, the log of the posterior placement probability ratio (LOP), for assessing oligogenic QTL detection and localization. The LOP is the log of the posterior probability of linkage to the real chromosome divided by the posterior probability of linkage to an unlinked pseudochromosome, with marker informativeness similar to the marker data on the real chromosome. Since the LOP cannot be calculated exactly, we estimate it in simultaneous MCMC on both real and pseudochromosomes. We investigate empirically the distributional properties of the LOP in the presence and absence of trait genes. The LOP is not subject to trait model misspecification in the way a lod score may be, and we show that the LOP can detect linkage for loci of small effect when the lod score cannot. We show how, in the absence of linkage, an empirical distribution of the LOP may be estimated by simulation and used to provide an assessment of linkage detection significance. Genet Epidemiol 24:181,190, 2003. © 2003 Wiley-Liss, Inc. [source] A new endogenous retroviral sequence is expressed in skin of patients with psoriasisBRITISH JOURNAL OF DERMATOLOGY, Issue 1 2005J-P. Molès Summary Background, The origin of psoriasis, a chronic inflammatory skin disease involving keratinocyte proliferation, immune disturbances and complex inheritance, remains unknown. Human endogenous retroviruses (HERVs) are part of the normal human genome and their participation in the pathogenesis of various human diseases with complex genetic traits has been proposed. A possible role of HERVs in the induction of psoriasis was suggested many years ago. However, to date no study has searched for HERV expression in psoriasis. Objectives, To determine firstly, which HERV families are expressed in the psoriatic lesion and secondly, whether specific variants can be detected. Methods, HERV expression was analysed at the mRNA level after degenerated reverse transcription,polymerase chain reaction (RT,PCR) of retroviral pol sequences followed by sequencing. Screening for a specific variant was performed by RT,PCR on lesional and nonlesional psoriatic skin and compared with normal and atopic dermatitis skin. Results, We report the expression of three HERV families in psoriatic lesions, namely HERV-W, K and E. We then partially characterized a new endogenous retroviral variant, which was related to the ERV-9/HERV-W family. This sequence contains at least two open reading frames that could encode for a gag protein and a retroviral protease. The expression of this sequence was detected in 29 of 43 lesional psoriasis skin samples and rarely in normal (two of 21) or atopic dermatitis (three of 14) skin samples. Conclusions, In psoriatic lesions, HERV sequences of the W, K and E families are expressed and a new variant of the ERV-9/HERV-W family has been characterized. The possible role of HERV-related sequences in the pathogenesis of psoriasis is under investigation. [source] | ||||||||||