Background and objectives: Diabetic nephropathy (DN) is a multifactorial complication characterized by persistent proteinuria in susceptible individuals with type 1 and type 2 diabetes. Disease burden in people of Mexican-American descent is particularly high, but there are only a few studies that characterize genes for DN in this ethnic group. Two genes, carnosine dipeptidase 1 (CNDP1) and engulfment and cell motility 1 (ELMO1) previously showed association with DN in other ethnic groups. CNDP1 and ELMO1 were examined along with eight other genes that are less well characterized for DN in a new study of Mexican-Americans.Design, setting, participants, & measurements: The target sample was patients of Mexican-American ancestry collected from three centers: 455 patients with DN and 437 controls with long-term diabetes but no incident nephropathy. Forty-two, 227, and 401 single nucleotide polymorphisms (SNPs) in CNDP1, ELMO1, and the other eight genes, respectively, were examined.Results: No region in CNDP1 or ELMO1 showed significant P values. Of the other eight candidate genes, an association of DN with a SNP pair, rs2146098 and rs6659783, was found in hemicentin 1 (HMCN1) (unadjusted P ‫؍‬ 6

In case-control Single Nucleotide Polymorphism (SNP) data, the Allele frequency, Hardy Weinberg Disequilibrium (HWD) and Linkage Disequilibrium (LD) contrast tests are three distinct sources of information about genetic association. While all three tests are typically developed in a retrospective context, we show that prospective logistic regression models may be developed that correspond conceptually to the retrospective tests. This approach provides a flexible framework for conducting a systematic series of association analyses using unphased genotype data and any number of covariates. For a single stage study, two single-marker tests and four two-marker tests are discussed. The true association models are derived and they allow us to understand why a model with only a linear term will generally fit well for a SNP in weak LD with a causal SNP, whatever the disease model, but not for a SNP in high LD with a non-additive disease SNP. We investigate the power of the association tests using real LD parameters from chromosome 11 in the HapMap CEU population data. Among the single-marker tests, the allelic test has on average the most power in the case of an additive disease; but, for dominant, recessive and heterozygote disadvantage diseases, the genotypic test has the most power. Among the six two-marker tests, the Allelic-LD contrast test, which incorporates linear terms for two markers and their interaction term, provides the most reliable power overall for the cases studied. Therefore, our result supports incorporating an interaction term as well as linear terms in multi-marker tests.

Significantly lower hospital costs and in-hospital deaths with higher hospice discharges were observed in integrative palliative care compared to consultative palliative care, but these findings were diminished with the presence of advance directives.

BackgroundThe detection of copy number variants (CNVs) and the results of CNV-disease association studies rely on how CNVs are defined, and because array-based technologies can only infer CNVs, CNV-calling algorithms can produce vastly different findings. Several authors have noted the large-scale variability between CNV-detection methods, as well as the substantial false positive and false negative rates associated with those methods. In this study, we use variations of four common algorithms for CNV detection (PennCNV, QuantiSNP, HMMSeg, and cnvPartition) and two definitions of overlap (any overlap and an overlap of at least 40% of the smaller CNV) to illustrate the effects of varying algorithms and definitions of overlap on CNV discovery.Methodology and Principal FindingsWe used a 56 K Illumina genotyping array enriched for CNV regions to generate hybridization intensities and allele frequencies for 48 Caucasian schizophrenia cases and 48 age-, ethnicity-, and gender-matched control subjects. No algorithm found a difference in CNV burden between the two groups. However, the total number of CNVs called ranged from 102 to 3,765 across algorithms. The mean CNV size ranged from 46 kb to 787 kb, and the average number of CNVs per subject ranged from 1 to 39. The number of novel CNVs not previously reported in normal subjects ranged from 0 to 212.Conclusions and SignificanceMotivated by the availability of multiple publicly available genome-wide SNP arrays, investigators are conducting numerous analyses to identify putative additional CNVs in complex genetic disorders. However, the number of CNVs identified in array-based studies, and whether these CNVs are novel or valid, will depend on the algorithm(s) used. Thus, given the variety of methods used, there will be many false positives and false negatives. Both guidelines for the identification of CNVs inferred from high-density arrays and the establishment of a gold standard for validation of CNVs are needed.

As compared with the usual care group, a significantly lower probability of transition to a nursing home was observed in the ITD intervention group, but the probabilities of delirium between the ITD intervention and usual care groups did not differ significantly.