Biomedical subjects

Brad Love

Publications and source records attributed to Brad Love.

4 recordsLinked to original sources

PubMed2004

Comprehensive evaluation of the association between prostate cancer and genotypes/haplotypes in CYP17A1, CYP3A4, and SRD5A2.

Genes involved in the testosterone biosynthetic pathway - such as CYP17A1, CYP3A4, and SRD5A2 - represent strong candidates for affecting prostate cancer. Previous work has detected associations between individual variants in these three genes and prostate cancer risk and aggressiveness. To more comprehensively evaluate CYP17A1, CYP3A4, and SRD5A2, we undertook a two-phase study of the relationship between their genotypes/haplotypes and prostate cancer. Phase I of the study first searched for single-nucleotide polymorphisms (SNPs) in these genes by resequencing 24 individuals from the Coriell Polymorphism Discovery Resource, 92-110 men from prostate cancer case-control sibships, and by leveraging public databases. In all, 87 SNPs were discovered and genotyped in 276 men from case-control sibships. Those SNPs exhibiting preliminary case-control allele frequency differences, or distinguishing (ie, 'tagging') common haplotypes across the genes, were identified for further study (24 SNPs in total). In Phase II of the study, the 24 SNPs were genotyped in an additional 841 men from case-control sibships. Finally, associations between genotypes/haplotypes in CYP17A1, CYP3A4, and SRD5A2 and prostate cancer were evaluated in the total case-control sample of 1117 brothers from 506 sibships. Family-based analyses detected associations between prostate cancer risk or aggressiveness and a number of CYP3A4 SNPs (P-values between 0.006 and 0.05), a CYP3A4 haplotype (P-values 0.05 and 0.009 in nonstratified and stratified analysis, respectively), and two SRD5A2 SNPs in strong linkage disequilibrium (P=0.02). Undertaking a two-phase study comprising SNP discovery, haplotype tagging, and association analyses allowed us to more fully decipher the relation between CYP17A1, CYP3A4, and SRD5A2 and prostate cancer.

3-Oxo-5-alpha-Steroid 4-Dehydrogenase↗

PubMed2003

Combined histomorphometric and gene-expression profiling applied to toxicology.

We have developed a unique methodology for the combined analysis of histomorphometric and gene-expression profiles amenable to intensive data mining and multisample comparison for a comprehensive approach to toxicology. This hybrid technology, termed extensible morphometric relational gene-expression analysis (EMeRGE), is applied in a toxicological study of time-varied vehicle- and carbon-tetrachloride (CCl4)-treated rats, and demonstrates correlations between specific genes and tissue structures that can augment interpretation of biological observations and diagnosis.

Animals↗

PubMed2002

Semiparametric method for estimating paleodemographic profiles from age indicator data.

This paper addresses the problem of estimating an age-at-death distribution or paleodemographic profile from osteological data. It is demonstrated that the classical two-stage procedure whereby one first constructs estimates of age-at-death of individual skeletons and then uses these age estimates to obtain a paleodemographic profile is not a correct approach. This is a consequence of Bayes' theorem. Instead, we demonstrate a valid approach that proceeds from the opposite starting point: given skeletal age-at-death, one first estimates the probability of assigning the skeleton into a specific osteological age-indicator stage. We show that this leads to a statistically valid method for obtaining a paleodemographic profile, and moreover, that valid individual age estimation itself requires a demographic profile and therefore is done subsequent to its construction. Individual age estimation thus becomes the last rather than the first step in the estimation procedure. A central concept of our statistical approach is that of a weight function. A weight function is associated with each osteological age-indicator stage or category, and provides the probability that a specific age indicator stage is observed, given age-at-death of the individual. We recommend that weight functions be estimated nonparametrically from a reference data set. In their entirety, the weight functions characterize the relevant stochastic properties of a chosen age indicator. For actual estimation of the paleodemographic profile, a parametric age distribution in the target sample is assumed. The maximum likelihood method is used to identify the unknown parameters of this distribution. As some components are estimated nonparametrically, one then has a semiparametric model. We show how to obtain valid estimates of individual age-at-death, confidence regions, and goodness-of-fit tests. The methods are illustrated with both real and simulated data.

Adolescent↗

PubMed2002

A conditional density error model for the statistical analysis of microarray data.

MOTIVATION: In many microarray experiments, relatively few intra- and inter-array replicate measurements are made due to significant cost limitations and sample availability. Compounding this problem is a lack of robust statistical methods for analyzing gene expression data with limited experimental replicates. As a result, the interpretation of the results of these experiments are difficult with little understanding of the probability of type I and type II errors. RESULTS: The variability in a series of replicate microarray measurements was modelled using a combination of parametric and non-parametric methods. A 3-dimensional surface was created for the conditional distribution of the variability given the mean signal intensity in both the Cy3 and Cy5 channels. The results were used as the basis for developing statistical methods for analyzing gene expression data with limited experimental replicates. AVAILABILITY: The statistical analysis scripts are available upon request.

Computer Simulation↗