Biomedical subjects

Fredrick R Schumacher

Publications and source records attributed to Fredrick R Schumacher.

3 recordsLinked to original sources

PubMed2006

Vitamin D receptor genotypes/haplotypes and prostate cancer risk.

The vitamin D receptor (VDR) gene has been associated with prostate cancer, although previous results are somewhat equivocal. To further study this, we did a family-based case-control study (N = 918) of the association between prostate cancer and six common VDR variants: Cdx2, FokI, BsmI, ApaI, TaqI, and the poly-A microsatellite. Looking at each variant alone, only FokI and ApaI were associated with disease. The FokI FF genotype was inversely associated with prostate cancer among men with less advanced disease (i.e., Gleason score <7 and tumor stage <T2c), where the odds ratio OR was 0.56 [95% confidence interval (95% CI), 0.31-1.01; P = 0.05]. ApaI, carrying one or two copies of the A allele, exhibited a weak inverse association with disease (OR, 0.64; 95% CI, 0.39-1.03; P = 0.06); this association was strengthened in Caucasian men with more advanced disease (OR, 0.44; 95% CI, 0.21-0.93; P = 0.03). We observed inverse associations between disease and the four-locus FBAt haplotype (OR, 0.48; 95% CI, 0.30-0.76; P = 0.002) and the fbaT haplotype (OR, 0.60; 95% CI, 0.38-0.95; P = 0.03; i.e., in comparison with the FbaT haplotype). These were stronger among men with more advanced disease: for FBAt, the OR was 0.31 (95% CI, 0.16-0.61; P = 0.0008); for fbaT, the OR was 0.32 (95% CI, 0.16-0.64; P = 0.001). These observations support a role for VDR variants in prostate cancer risk but suggest that any potential causal variant(s) may reside on the haplotypes reported here. This would help explain the somewhat equivocal results for VDR genotype-level associations with prostate cancer.

Case-Control Studies↗

PubMed2005

Comparison of microsatellites, single-nucleotide polymorphisms (SNPs) and composite markers derived from SNPs in linkage analysis.

There is growing evidence that a map of dense single-nucleotide polymorphisms (SNPs) can outperform a map of sparse microsatellites for linkage analysis. There is also argument as to whether a clustered SNP map can outperform an evenly spaced SNP map. Using Genetic Analysis Workshop 14 simulated data, we compared for linkage analysis microsatellites, SNPs, and composite markers derived from SNPs. We encoded the composite markers in a two-step approach, in which the maximum identity length contrast method was employed to allow for recombination between loci. A SNP map 2.3 times as dense as a microsatellite map (approximately 2.9 cM compared to approximately 6.7 cM apart) provided slightly less information content (approximately 0.83 compared to approximately 0.89). Most inheritance information could be extracted when the SNPs were spaced < 1 cM apart. Comparing the linkage results on using SNPs or composite markers derived from them based on both 3 cM and 0.3 cM resolution maps, we showed that the inter-SNP distance should be kept small (< 1 cM), and that for multipoint linkage analysis the original markers and the derived composite markers had similar power; but for single point linkage analysis the resulting composite markers lead to more power. Considering all factors, such as information content, flexibility of analysis method, map errors, and genotyping errors, a map of clustered SNPs can be an efficient design for a genome-wide linkage scan.

Chromosome Mapping↗

PubMed2003

Comparison of missing data approaches in linkage analysis.

BACKGROUND: Observational cohort studies have been little used in linkage analyses due to their general lack of large, disease-specific pedigrees. Nevertheless, the longitudinal nature of such studies makes them potentially valuable for assessing the linkage between genotypes and temporal trends in phenotypes. The repeated phenotype measures in cohort studies (i.e., across time), however, can have extensive missing information. Existing methods for handling missing data in observational studies may decrease efficiency, introduce biases, and give spurious results. The impact of such methods when undertaking linkage analysis of cohort studies is unclear. Therefore, we compare here six methods of imputing missing repeated phenotypes on results from genome-wide linkage analyses of four quantitative traits from the Framingham Heart Study cohort. RESULTS: We found that simply deleting observations with missing values gave many more nominally statistically significant linkages than the other five approaches. Among the latter, those with similar underlying methodology (i.e., imputation- versus model-based) gave the most consistent results, although some discrepancies remained. CONCLUSION: Different methods for addressing missing values in linkage analyses of cohort studies can give substantially diverse results, and must be carefully considered to protect against biases and spurious findings.

Algorithms↗