Many linkage studies are performed in inbred populations, either small isolated populations or large populations with a long tradition of marriages between relatives. In such populations, there exist very complex genealogies with unknown loops. Therefore, the true inbreeding coefficient of an individual is often unknown. Good estimators of the inbreeding coefficient (f) are important, since it has been shown that underestimation of f may lead to false linkage conclusions. When an individual is genotyped for markers spanning the whole genome, it should be possible to use this genomic information to estimate that individual's f. To do so, we propose a maximum-likelihood method that takes marker dependencies into account through a hidden Markov model. This methodology also allows us to infer the full probability distribution of the identity-by-descent (IBD) status of the two alleles of an individual at each marker along the genome (posterior IBD probabilities) and provides a variance for the estimates. We simulate a full genome scan mimicking the true autosomal genome for (1) a first-cousin pedigree and (2) a quadruple-second-cousin pedigree. In both cases, we find that our method accurately estimates f for different marker maps. We also find that the proportion of genome IBD in an individual with a given genealogy is very variable. The approach is illustrated with data from a study of demyelinating autosomal recessive Charcot-Marie-Tooth disease.

Objective. The shared epitope hypothesis was formulated to explain the involvement of HLA-DRB1 in rheumatoid arthritis (RA). However, several studies, which considered only the HLA-DRB1 alleles shown to be associated with RA risk, rejected this hypothesis. In this report, we propose that a different classification of HLA-DRB1 alleles be considered, based on the amino acid sequence at position 70-74.Methods. The fit of both HLA-DRB1 classifications was tested in 2 groups of RA patients. All subjects were recruited through the European Consortium on Rheumatoid Arthritis Families, and included 100 patients with isolated RA and 132 patients with at least 1 affected sibling.Results. The new classification produced risk estimates that fit all of the observed data, i.e., the

Recent reports have challenged the notion that retroviruses and retroviral vectors integrate randomly into the host genome. These reports pointed to a strong bias toward integration in and near gene coding regions and, for gammaretroviral vectors, around transcription start sites. Here, we report the results obtained from a large-scale mapping of 572 retroviral integration sites (RISs) isolated from cells of 9 patients with X-linked SCID (SCID-X1) treated with a retrovirus-based gene therapy protocol. Our data showed that two-thirds of insertions occurred in or very near to genes, of which more than half were highly expressed in CD34 + progenitor cells. Strikingly, one-fourth of all integrations were clustered as common integration sites (CISs). The highly significant incidence of CISs in circulating T cells and the nature of their locations indicate that insertion in many gene loci has an influence on cell engraftment, survival, and proliferation. Beyond the observed cases of insertional mutagenesis in 3 patients, these data help to elucidate the relationship between vector insertion and long-term in vivo selection of transduced cells in human patients with SCID-X1.

Crossover (CO) is a key process for the accurate segregation of homologous chromosomes during the first meiotic division. In most eukaryotes, meiotic recombination is not homogeneous along the chromosomes, suggesting a tight control of the location of recombination events. We genotyped 71 single nucleotide polymorphisms (SNPs) covering the entire chromosome 4 of Arabidopsis thaliana on 702 F2 plants, representing 1404 meioses and allowing the detection of 1171 COs, to study CO localization in a higher plant. The genetic recombination rates varied along the chromosome from 0 cM/Mb near the centromere to 20 cM/Mb on the short arm next to the NOR region, with a chromosome average of 4.6 cM/Mb. Principal component analysis showed that CO rates negatively correlate with the G+C content (P =3x10(-4)), in contrast to that reported in other eukaryotes. COs also significantly correlate with the density of single repeats and the CpG ratio, but not with genes, pseudogenes, transposable elements, or dispersed repeats. Chromosome 4 has, on average, 1.6 COs per meiosis, and these COs are subjected to interference. A detailed analysis of several regions having high CO rates revealed "hot spots" of meiotic recombination contained in small fragments of a few kilobases. Both the intensity and the density of these hot spots explain the variation of CO rates along the chromosome