Rheumatoid arthritis (RA) is a disorder with important public health implications. It is possible that there are clinically distinctive subtypes of the disorder with different genetic etiologies. We used the data provided to the participants in the Genetic Analysis Workshop 15 to evaluate and describe clinically based subgroups and their genetic associations with single-nucleotide polymorphisms (SNPs) on chromosome 6, which harbors the HLA region. Detailed two- and three-SNP haplotype analyses were conducted in the HLA region. We used demographic, clinical self-report, and biomarker data from the entire sample (n = 8477) to identify and characterize the subgroups. We did not use the RA diagnosis itself in the identification of the subgroups. Nuclear families (715 families, 1998 individuals) were used to examine the genetic association with the HLA region. We found five distinct subgroups in the data. The first comprised unaffected family members. Cluster 2 was a mix of affected and unaffected in which patients endorsed symptoms not corroborated by physicians. Clusters 3 through 5 represented a severity continuum in RA. Cluster 5 was characterized by early onset severe disease. Cluster 2 showed no association on chromosome 6. Clusters 3 through 5 showed association with 17 SNPs on chromosome 6. In the HLA region, Cluster 3 showed single-, two-, and three-SNP association with the centromeric side of the region in an area of linkage disequilibrium. Cluster 5 showed both single- and two-SNP association with the telomeric side of the region in a second area of linkage disequilibrium. It will be important to replicate the subgroup structure and the association findings in an independent sample.
The purpose of this case study is to assess a Medicaid health maintenance organization quality initiative designed to screen new members for behavioral health treatment needs on enrollment. New members were screened by the health maintenance organization, which then informed the for-profit managed care organization responsible for the management of the mental health and substance abuse benefit of its findings. Other than the screening, there were no contractual expectations. The managed care organization was given only the names of individuals who “screened positive,” but was not required to act on the screening results. Twenty percent of newly enrolled Medicaid health maintenance organization members were screened, and 2.5% were identified to have behavioral health and substance abuse treatment needs. As Medicaid managed care is responsible for the health care of low-income beneficiaries who are inherently vulnerable, it is important to make every effort to evaluate the impact of a quality project meant to improve their treatment.
Objectives: To assess the cause of death for centenarians' offspring and controls. Design: Cross‐sectional study. Setting: Community‐based, nationwide sample. Participants: Family pedigree information was collected on 295 offspring of centenarians (from 106 families with a parent already enrolled in the nationwide New England Centenarian Study) and on 276 controls (from 82 control families) from 1997 to 2000. Controls were individuals whose parents were born in the same year as the centenarians but at least one of whom died at the average life expectancy. Measurements: Age at death and cause of death. Results: Centenarians' offspring had a 62% lower risk of all‐cause mortality ( P <.001), a 71% lower risk of cancer‐specific mortality ( P =.002), and an 85% lower risk of coronary heart disease–specific mortality ( P <.001). Significant differences were not found for other causes of death. However of those who died centenarian offsprings dead at a significantly younger age than controls. Conclusion: These findings suggest that centenarians' offspring have lower all‐cause mortality rates and cause‐specific mortality rates for cancer and coronary heart disease. These results suggest that mechanisms for survival to exceptional old age may go beyond the avoidance or delay of cardiovascular disease and also include the avoidance or delay of cancer. Moreover survival advantage of centenarian offsprings may not be due to factors related to childhood mortality. Ultimately, survival to exceptional old age may involve lower susceptibility to a broad range of age‐related diseases, perhaps secondary to inhibition of basic mechanisms of aging.
Monitoring risk is often an important component of therapy. Some compounds require liver test (LT) monitoring, with the frequency detailed in the product label. Compliance with these instructions is generally unknown. The goal of this short study was to describe LT compliance for compounds with monitoring recommended at 2-week intervals or more frequently in three US administrative claims databases. The sample was drawn from three US claims databases during the period 1 January 2015 through 30 June 2018. This study examined nine compounds and five types of LTs. We looked at compounds in a published list of drugs requiring LTs at 2-week intervals or more frequently. Descriptive statistics about the days between tests were reported, as were the number and proportion of tests associated with each drug that met the recommended frequency. Compliance was < 33% with four drugs (ketoconazole, succimer, pentamidine, and felbamate) and > 60% with five drugs (oxaliplatin, rifampin, tolcapone, albendazole, and azathioprine). Among drugs with more than 1000 drug eras observed (all but succimer and tolcapone), LT compliance was highest for oxaliplatin (75.3%) and lowest for pentamidine (20.6%), with little difference in overall compliance by type of test (range 41–46). Compliance with frequent LT monitoring differed for the drugs examined. Two strata were found: compliance > 60% (oxaliplatin, rifampin, tolcapone, albendazole, and azathioprine) and compliance 20–30% (ketoconazole, succimer, pentamidine, and felbamate). No drug reached 80% compliance.
The participants of Presentation Group 1 used the GAW13 data to derive new phenotypes, which were then analyzed for linkage and, in one case, for association to the genetic markers. Since the trait measurements ranged over longer time periods, the participants looked at the time dependence of particular traits in addition to the trait itself. The phenotypes analyzed with the Framingham data can be roughly divided into 1) body weight-related traits, which also include a type 2 diabetes progression trait, and 2) traits related to systolic blood pressure. Both trait classes are associated with metabolic syndrome. For traits related to body weight, linkage was consistently identified by at least two participating groups to genetic regions on chromosomes 4, 8, 11, and 18. For systolic blood pressure, or its derivatives, at least two groups obtained linkage for regions on chromosomes 4, 6, 8, 11, 14, 16, and 19. Five of the 13 participating groups focused on the simulated data. Due to the rather sparse grid of microsatellite markers, an association analysis for several traits was not successful. Linkage analysis of hypertension and body mass index using LODs and heterogeneity LODs (HLODs) had low power. For the glucose phenotype, a combination of random coefficient regression models and variance component linkage analysis turned out to be strikingly powerful in the identification of a trait locus simulated on chromosome 5. Haseman-Elston regression methods, applied to the same phenotype, had low power, but the above-mentioned chromosome 5 locus was not included in this analysis.
Abstract Both theoretical and applied studies have proven that the utility of single nucleotide polymorphism (SNP) markers in linkage analysis is more powerful and cost-effective than current microsatellite marker assays. Here we performed a whole-genome scan on 115 White, non-Hispanic families segregating for alcohol dependence, using one 10.3-cM microsatellite marker set and two SNP data sets (0.33-cM, 0.78-cM spacing). Two definitions of alcohol dependence (ALDX1 and ALDX2) were used. Our multipoint nonparametric linkage analysis found alcoholism was nominal linked to 12 genomic regions. The linkage peaks obtained by using the microsatellite marker set and the two SNP sets had a high degree of correspondence in general, but the microsatellite marker set was insufficient to detect some nominal linkage peaks. The presence of linkage disequilibrium between markers did not significantly affect the results. Across the entire genome, SNP datasets had a much higher average linkage information content (0.33 cM: 0.93, 0.78 cM: 0.91) than did microsatellite marker set (0.57). The linkage peaks obtained through two SNP datasets were very similar with some minor differences. We conclude that genome-wide linkage analysis by using approximately 5,000 SNP markers evenly distributed across the human genome is sufficient and might be more powerful than current 10-cM microsatellite marker assays.
We first conducted a genome-wide screen for association with discordant sibships using the multi allelic and diallelic SDT. Markers at D4S1628, D8S1109, D9S66 and D7S1797 showed multi-allelic association. Deleterious diallelic association was found for markers at D1S1613, D1S534, D3S2459, D7S1817, and D9S131. Protective association was found at markers D8S1109, D8S1136, and D9S66. We then incorporated these findings with previous linkage findings in the exploration of oligogenes and epistasis using recursive partitioning. We conclude that recursive partitioning can be a useful adjunct to traditional linkage and association analyses in the exploration of these effects.
