Biomedical subjects

Steven J Schrodi

Publications and source records attributed to Steven J Schrodi.

6 recordsLinked to original sources

PubMed2006

Pairwise linkage disequilibrium under disease models.

Many genetic studies of disease association rely heavily on linkage disequilibrium (LD) patterns between pairs of markers to detect susceptibility markers. This is true of large-scale positional mapping approaches as well as haplotype construction, selection of tagging single-nucleotide polymorphisms and population genetic analyses. Whereas the distribution of different LD measures has been investigated for randomly selected chromosomes from populations undergoing a variety of demographic effects, little is known about LD within disease-affected samples, and how various disease models influence the difference in LD between patients and the general population. As whole-genome efforts are now underway to characterize and utilize LD patterns in randomly sampled individuals, knowledge about the extent that LD differs between patients and the general population becomes crucial. Such information will allow investigators to design improved mapping experiments and better understand haplotype information arising from such experiments. In this paper, we explore two-site LD measures in the context of single gene disease models. Analytic expressions are presented for infinite populations and properties of sampling densities are reported for different disease models. Interestingly, results indicate that 'underdominant', some dominant, recessive and 'protective' disease models generate weaker LD levels in patients compared to the general population, whereas other models produce stronger LD among affected individuals. Analytic results are also presented for the ratio of LD in patients to the LD in the general population as a function of recombination fraction using a Haldane model. In addition, we explore the impact of various allele frequency combinations on LD differences.

Genetic Predisposition to Disease↗

PubMed2006

Identification of two gene variants associated with risk of advanced fibrosis in patients with chronic hepatitis C.

BACKGROUND & AIMS: Previously identified clinical risk factors such as sex, alcohol consumption, and age at infection do not accurately predict which patients with chronic hepatitis C (CHC) will develop advanced fibrosis (bridging fibrosis and cirrhosis). The aim of this study was to identify genetic polymorphisms that can predict the risk of advanced fibrosis in patients with CHC. METHODS: A total of 916 subjects with CHC was enrolled from 2 centers. A gene-centric disease association study of 24,832 putative functional, single nucleotide polymorphisms (SNPs) was performed. Of the 1609 SNPs that were significantly associated (P </= .05) with advanced fibrosis in the discovery cohort (University of California San Francisco [UCSF], N = 433), the first batch of 100 SNPs were selected for validation in the replication cohort (Virginia Commonwealth University [VCU], N = 483). RESULTS: A missense SNP in the DEAD box polypeptide 5 (DDX5) gene was significantly associated with an increased risk of advanced fibrosis in both the UCSF and the VCU cohorts (OR, 1.8 and 2.2, respectively). Two diplotype groups, carrying the haplotypes composed of the DDX5 SNP and 2 neighboring POLG2 SNPs were also significantly associated with an increased risk of advanced fibrosis and had comparable or better risk estimates. In addition, a missense SNP in the carnitine palmitoyltransferase 1A (CPT1A) gene was associated with a decreased risk of advanced fibrosis in both the UCSF and the VCU cohorts (OR, 0.3 and 0.6, respectively). CONCLUSIONS: Subjects with CHC carrying DDX5 minor allele or DDX5-POLG2 haplotypes are at an increased risk of developing advanced fibrosis, whereas those carrying the CPT1A minor allele are at a decreased risk.

Adolescent↗

PubMed2005

A probabilistic approach to large-scale association scans: a semi-Bayesian method to detect disease-predisposing alleles.

Recent analytic and technological breakthroughs have set the stage for genome-wide linkage disequilibrium studies to map disease-susceptibility variants. This paper discusses a probabilistic methodology for making disease-mapping inferences in large-scale case-control genetic studies. The semi-Bayesian approach promoted compares the probability of the observed data under disease hypotheses to the probability of the data under a null hypothesis defined by data at all the markers interrogated in a large study. This method automatically adjusts for the effects of diffuse population stratification. It is claimed that this characterization of the evidence for or against disease models may facilitate more appropriate inductions for large-scale genetic studies. Results include (i) an analytic solution for the population stratification-adjusted Bayes' factor, (ii) the relationship between sample size and Bayes' factors, (iii) an extension to an approximate Bayes' factor calculated across closely-linked sites, and (iv) an extension across multiple studies. Although this paper deals exclusively with genetic studies, it is possible to generalize the approach to treat many different large-scale experiments including studies of gene expression and proteomics.

Journal Article↗

PubMed2005

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.

Arthritis, Rheumatoid↗

PubMed2004

A missense single-nucleotide polymorphism in a gene encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis.

Rheumatoid arthritis (RA) is the most common systemic autoimmune disease, affecting approximately 1% of the adult population worldwide, with an estimated heritability of 60%. To identify genes involved in RA susceptibility, we investigated the association between putative functional single-nucleotide polymorphisms (SNPs) and RA among white individuals by use of a case-control study design; a second sample was tested for replication. Here we report the association of RA susceptibility with the minor allele of a missense SNP in PTPN22 (discovery-study allelic P=6.6 x 10(-4); replication-study allelic P=5.6 x 10(-8)), which encodes a hematopoietic-specific protein tyrosine phosphatase also known as "Lyp." We show that the risk allele, which is present in approximately 17% of white individuals from the general population and in approximately 28% of white individuals with RA, disrupts the P1 proline-rich motif that is important for interaction with Csk, potentially altering these proteins' normal function as negative regulators of T-cell activation. The minor allele of this SNP recently was implicated in type 1 diabetes, suggesting that the variant phosphatase may increase overall reactivity of the immune system and may heighten an individual carrier's risk for autoimmune disease.

Arthritis, Rheumatoid↗

PubMed2004

Selecting tagging SNPs for association studies using power calculations from genotype data.

Recent studies have indicated that linkage disequilibrium (LD) between single nucleotide polymorphism (SNP) markers can be used to derive a reduced set of tagging SNPs (tSNPs) for genetic association studies. Previous strategies for identifying tSNPs have focused on LD measures or haplotype diversity, but the statistical power to detect disease-associated variants using tSNPs in genetic studies has not been fully characterized. We propose a new approach of selecting tSNPs based on determining the set of SNPs with the highest power to detect association. Two-locus genotype frequencies are used in the power calculations. To show utility, we applied this power method to a large number of SNPs that had been genotyped in Caucasian samples. We demonstrate that a significant reduction in genotyping efforts can be achieved although the reduction depends on genotypic relative risk, inheritance mode and the prevalence of disease in the human population. The tSNP sets identified by our method are remarkably robust to changes in the disease model when small relative risk and additive mode of inheritance are employed. We have also evaluated the ability of the method to detect unidentified SNPs. Our findings have important implications in applying tSNPs from different data sources in association studies.

Algorithms↗