A genomewide screen in multiplex rheumatoid arthritis families suggests genetic overlap with other autoimmune diseases

Rheumatoid arthritis (RA) is an autoimmune/inflammatory disorder with a complex genetic component. We report the first major genomewide screen of multiplex families with RA gathered in the United States. The North American Rheumatoid Arthritis Consortium, using well-defined clinical criteria, has collected 257 families containing 301 affected sibling pairs with RA. A genome screen for allele sharing was performed, using 379 microsatellite markers. A nonparametric analysis using SIBPAL confirmed linkage of the HLA locus to RA (P < .00005), with lambdaHLA = 1.79. However, the analysis also revealed a number of non-HLA loci on chromosomes 1 (D1S235), 4 (D4S1647), 12 (D12S373), 16 (D16S403), and 17 (D17S1301), with evidence for linkage at a significance level of P<.005. Analysis of X-linked markers using the MLOD method from ASPEX also suggests linkage to the telomeric marker DXS6807. Stratifying the families into white or seropositive subgroups revealed some additional markers that showed improvement in significance over the full data set. Several of the regions that showed evidence for nominal significance (P < .05) in our data set had previously been implicated in RA (D16S516 and D17S1301) or in other diseases of an autoimmune nature, including systemic lupus erythematosus (D1S235), inflammatory bowel disease (D4S1647, D5S1462, and D16S516), multiple sclerosis (D12S1052), and ankylosing spondylitis (D16S516). Therefore, genes in the HLA complex play a major role in RA susceptibility, but several other regions also contribute significantly to overall genetic risk.     

Antibiotic susceptibility patterns and resistance genes of starter cultures and probiotic bacteria used in food

A survey of starter and probiotic cultures was carried out to determine the current antibiotic resistance situation in microbial food additives in Switzerland. Two hundred isolates from 90 different sources were typed by molecular and other methods to belong to the genera Lactobacillus (74 samples), Staphylococcus (33 samples), Bifidobacterium (6 samples), Pediococcus (5 samples), or were categorized as lactococci or streptococci (82 samples). They were screened for phenotypic resistances to 20 antibiotics by the disk diffusion method. Twenty-seven isolates exhibiting resistances that are not an intrinsic feature of the respective genera were further analyzed by microarray hybridization as a tool to trace back phenotypic resistances to specific genetic determinants. Their presence was finally verified by PCR amplification or Southern hybridization. These studies resulted in the detection of the tetracycline resistance gene tet(K) in 5 Staphylococcus isolates used as meat starter cultures, the tetracycline resistance gene tet(W) in the probiotic cultures Bifidobacterium lactis DSM 10140 and Lactobacillus reuteri SD 2112 (residing on a plasmid), and the lincosamide resistance gene lnu(A) (formerly linA) in L. reuteri SD 2112.     

PTPN22 genetic variation: evidence for multiple variants associated with rheumatoid arthritis

The minor allele of the R620W missense single-nucleotide polymorphism (SNP) (rs2476601) in the hematopoietic-specific protein tyrosine phosphatase gene, PTPN22, has been associated with multiple autoimmune diseases, including rheumatoid arthritis (RA). These genetic data, combined with biochemical evidence that this SNP affects PTPN22 function, suggest that this phosphatase is a key regulator of autoimmunity. To determine whether other genetic variants in PTPN22 contribute to the development of RA, we sequenced the coding regions of this gene in 48 white North American patients with RA and identified 15 previously unreported SNPs, including 2 coding SNPs in the catalytic domain. We then genotyped 37 SNPs in or near PTPN22 in 475 patients with RA and 475 individually matched controls (sample set 1) and selected a subset of markers for replication in an additional 661 patients with RA and 1,322 individually matched controls (sample set 2). Analyses of these results predict 10 common (frequency >1%) PTPN22 haplotypes in white North Americans. The sole haplotype found to carry the previously identified W620 risk allele was strongly associated with disease in both sample sets, whereas another haplotype, identical at all other SNPs but carrying the R620 allele, showed no association. R620W, however, does not fully explain the association between PTPN22 and RA, since significant differences between cases and controls persisted in both sample sets after the haplotype data were stratified by R620W. Additional analyses identified two SNPs on a single common haplotype that are associated with RA independent of R620W, suggesting that R620W and at least one additional variant in the PTPN22 gene region influence RA susceptibility.     

Multiplicity generates diversity in the retinoic acid signalling pathways.

Complexity in the retinoid signalling system arises from a combination of several forms of retinoic acid, multiple cytoplasmic binding proteins and nuclear receptors, and the existence of polymorphic retinoic acid response elements. Additional diversity appears to be generated by heterodimeric interactions between two families of nuclear retinoic acid receptors, and between nuclear retinoic acid receptors and other members of the nuclear receptor superfamily. Thus, a complex array of combinatorial effects is beginning to emerge which may account for the pleiotropic effects of retinoids.     

A Minimum Distance Estimation Approach to Estimate the Recombination Fraction from a Marker Locus in Robust Linkage Analysis for Quantitative Traits

OLSON and WIJSMAN (1993) have proposed a robust linkage analysis between quantitative traits and a marker locus using all relative pairs. We extend their work to estimate the recombination fraction using a two-step procedure. In the first step Generalised Estimating Equations are solved. After robust linkage analysis minimum distance estimation is applied in the second step. Our approach requires a single codominant marker locus only. The relevant parameters of the genetic model can also be estimated by this method in the presence of linkage. We illustrate our approach by simulations.     

Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis

Background  

Concerns are often raised about the accuracy of microarray technologies and the degree of cross-platform agreement, but there are yet no methods which can unambiguously evaluate precision and sensitivity for these technologies on a whole-array basis.