Nef interferes with development of thymic T cell precursors: differential mechanisms in HIV and SIV

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A genetic algorithm based method for stringent haplotyping of family data

<p>Abstract</p> <p>Background</p> <p>The linkage phase, or haplotype, is an extra level of information that in addition to genotype and pedigree can be useful for reconstructing the inheritance pattern of the alleles in a pedigree, and computing for example Identity By Descent probabilities. If a haplotype is provided, the precision of estimated IBD probabilities increases, as long as the haplotype is estimated without errors. It is therefore important to only use haplotypes that are strongly supported by the available data for IBD estimation, to avoid introducing new errors due to erroneous linkage phases.</p> <p>Results</p> <p>We propose a genetic algorithm based method for haplotype estimation in family data that includes a stringency parameter. This allows the user to decide the error tolerance level when inferring parental origin of the alleles. This is a novel feature compared to existing methods for haplotype estimation. We show that using a high stringency produces haplotype data with few errors, whereas a low stringency provides haplotype estimates in most situations, but with an increased number of errors.</p> <p>Conclusion</p> <p>By including a stringency criterion in our haplotyping method, the user is able to maintain the error rate at a suitable level for the particular study; one can select anything from haplotyped data with very small proportion of errors and a higher proportion of non-inferred haplotypes, to data with phase estimates for every marker, when haplotype errors are tolerable. Giving this choice makes the method more flexible and useful in a wide range of applications as it is able to fulfil different requirements regarding the tolerance for haplotype errors, or uncertain marker-phases.</p

QTL linkage analysis of connected populations using ancestral marker and pedigree information

The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals’ genotype probability without significantly increasing computational demand

Dominance and parent-of-origin effects of coding and non-coding alleles at the acylCoA-diacylglycerol-acyltransferase (DGAT1) gene on milk production traits in German Holstein cows

<p>Abstract</p> <p>Background</p> <p>Substantial gene substitution effects on milk production traits have formerly been reported for alleles at the K232A and the promoter VNTR loci in the bovine acylCoA-diacylglycerol-acyltransferase 1 (<it>DGAT1</it>) gene by using data sets including sires with accumulated phenotypic observations of daughters (breeding values, daughter yield deviations). However, these data sets prevented analyses with respect to dominance or parent-of-origin effects, although an increasing number of reports in the literature outlined the relevance of non-additive gene effects on quantitative traits.</p> <p>Results</p> <p>Based on a data set comprising German Holstein cows with direct trait measurements, we first confirmed the previously reported association of <it>DGAT1 </it>promoter VNTR alleles with milk production traits. We detected a dominant mode of effects for the <it>DGAT1 </it>K232A and promoter VNTR alleles. Namely, the contrasts between the effects of heterozygous individuals at the <it>DGAT1 </it>loci differed significantly from the midpoint between the effects for the two homozygous genotypes for several milk production traits, thus indicating the presence of dominance. Furthermore, we identified differences in the magnitude of effects between paternally and maternally inherited <it>DGAT1 </it>promoter VNTR – K232A haplotypes indicating parent-of-origin effects on milk production traits.</p> <p>Conclusion</p> <p>Non-additive effects like those identified at the bovine <it>DGAT1 </it>locus have to be accounted for in more specific QTL detection models as well as in marker assisted selection schemes. The <it>DGAT1 </it>alleles in cattle will be a useful model for further investigations on the biological background of non-additive effects in mammals due to the magnitude and consistency of their effects on milk production traits.</p

Expression of B-RAF V600E in Type II Pneumocytes Causes Abnormalities in Alveolar Formation, Airspace Enlargement and Tumor Formation in Mice

Growth factor induced signaling cascades are key regulatory elements in tissue development, maintenance and regeneration. Perturbations of these cascades have severe consequences, leading to developmental disorders and neoplastic diseases. As a major function in signal transduction, activating mutations in RAF family kinases are the cause of human tumorigenesis, where B-RAF V600E has been identified as the prevalent mutant. In order to address the oncogenic function of B-RAF V600E, we have generated transgenic mice expressing the activated oncogene specifically in lung alveolar epithelial type II cells. Constitutive expression of B-RAF V600E caused abnormalities in alveolar epithelium formation that led to airspace enlargements. These lung lesions showed signs of tissue remodeling and were often associated with chronic inflammation and low incidence of lung tumors. The inflammatory cell infiltration did not precede the formation of the lung lesions but was rather accompanied with late tumor development. These data support a model where the continuous regenerative process initiated by oncogenic B-RAF-driven alveolar disruption provides a tumor-promoting environment associated with chronic inflammation

Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies

Testing one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants, which is a plausible scenario for many complex diseases. We show that simultaneous analysis of the entire set of SNPs from a genome-wide study to identify the subset that best predicts disease outcome is now feasible, thanks to developments in stochastic search methods. We used a Bayesian-inspired penalised maximum likelihood approach in which every SNP can be considered for additive, dominant, and recessive contributions to disease risk. Posterior mode estimates were obtained for regression coefficients that were each assigned a prior with a sharp mode at zero. A non-zero coefficient estimate was interpreted as corresponding to a significant SNP. We investigated two prior distributions and show that the normal-exponential-gamma prior leads to improved SNP selection in comparison with single-SNP tests. We also derived an explicit approximation for type-I error that avoids the need to use permutation procedures. As well as genome-wide analyses, our method is well-suited to fine mapping with very dense SNP sets obtained from re-sequencing and/or imputation. It can accommodate quantitative as well as case-control phenotypes, covariate adjustment, and can be extended to search for interactions. Here, we demonstrate the power and empirical type-I error of our approach using simulated case-control data sets of up to 500 K SNPs, a real genome-wide data set of 300 K SNPs, and a sequence-based dataset, each of which can be analysed in a few hours on a desktop workstation

Tex19.1 Promotes Spo11-Dependent Meiotic Recombination in Mouse Spermatocytes

Meiosis relies on the SPO11 endonuclease to generate the recombinogenic DNA double strand breaks (DSBs) required for homologous chromosome synapsis and segregation. The number of meiotic DSBs needs to be sufficient to allow chromosomes to search for and find their homologs, but not excessive to the point of causing genome instability. Here we report that the mammal-specific gene Tex19.1 promotes Spo11-dependent recombination in mouse spermatocytes. We show that the chromosome asynapsis previously reported in Tex19.1-/- spermatocytes is preceded by reduced numbers of recombination foci in leptotene and zygotene. Tex19.1 is required for normal levels of early Spo11-dependent recombination foci during leptotene, but not for upstream events such as MEI4 foci formation or accumulation of H3K4me3 at recombination hotspots. Furthermore, we show that mice carrying mutations in Ubr2, which encodes an E3 ubiquitin ligase that interacts with TEX19.1, phenocopy the Tex19.1-/- recombination defects. These data suggest that Tex19.1 and Ubr2 are required for mouse spermatocytes to accumulate sufficient Spo11-dependent recombination to ensure that the homology search is consistently successful, and reveal a hitherto unknown genetic pathway promoting meiotic recombination in mammals

A Novel Mouse Synaptonemal Complex Protein Is Essential for Loading of Central Element Proteins, Recombination, and Fertility

The synaptonemal complex (SC) is a proteinaceous, meiosis-specific structure that is highly conserved in evolution. During meiosis, the SC mediates synapsis of homologous chromosomes. It is essential for proper recombination and segregation of homologous chromosomes, and therefore for genome haploidization. Mutations in human SC genes can cause infertility. In order to gain a better understanding of the process of SC assembly in a model system that would be relevant for humans, we are investigating meiosis in mice. Here, we report on a newly identified component of the murine SC, which we named SYCE3. SYCE3 is strongly conserved among mammals and localizes to the central element (CE) of the SC. By generating a Syce3 knockout mouse, we found that SYCE3 is required for fertility in both sexes. Loss of SYCE3 blocks synapsis initiation and results in meiotic arrest. In the absence of SYCE3, initiation of meiotic recombination appears to be normal, but its progression is severely impaired resulting in complete absence of MLH1 foci, which are presumed markers of crossovers in wild-type meiocytes. In the process of SC assembly, SYCE3 is required downstream of transverse filament protein SYCP1, but upstream of the other previously described CE–specific proteins. We conclude that SYCE3 enables chromosome loading of the other CE–specific proteins, which in turn would promote synapsis between homologous chromosomes