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J S Witte

Publications and source records attributed to J S Witte.

At least 19 recordsLinked to original sources
PubMed2001

Model-free linkage analysis with covariates confirms linkage of prostate cancer to chromosomes 1 and 4.

As with many complex genetic diseases, genome scans for prostate cancer have given conflicting results, often failing to provide replication of previous findings. One factor contributing to the lack of consistency across studies is locus heterogeneity, which can weaken or even eliminate evidence for linkage that is present only in a subset of families. Currently, most analyses either fail to account for locus heterogeneity or attempt to account for it only by partitioning data sets into smaller and smaller portions. In the present study, we model locus heterogeneity among affected sib pairs with prostate cancer by including covariates in the linkage analysis that serve as surrogate measures of between-family linkage differences. The model is a modification of the Olson conditional logistic model for affected relative pairs. By including Gleason score, age at onset, male-to-male transmission, and/or number of affected first-degree family members as covariates, we detected linkage near three locations that were previously identified by linkage (1q24-25 [HPC1; LOD score 3.25, P=.00012], 1q42.2-43 [PCAP; LOD score 2.84, P=.0030], and 4q [LOD score 2.80, P=.00038]), near the androgen-receptor locus on Xq12-13 (AR; LOD score 3.06, P=.00053), and at five new locations (LOD score > 2.5). Without covariates, only a few weak-to-moderate linkage signals were found, none of which replicate findings of previous genome scans. We conclude that covariate-based linkage analysis greatly improves the likelihood that linked regions will be found by incorporation of information about heterogeneity within the sample.

Age of Onset↗
PubMed2001

Predicting quantitative trait levels by modeling SNP interaction.

Predicting phenotype from genotype is difficult when the phenotype is affected by a gene with numerous weakly penetrant alleles that differ only in the pattern of their single nucleotide polymorphisms (SNPs). While it is probable that SNP interactions affect phenotype, to our knowledge no one has determined the most effective way of evaluating whether SNPs interact and of modeling the interaction. Therefore, to explore this issue, we investigate here three methods of modeling SNP interaction using data from Genetic Analysis Workshop 12. Since major gene 5 (MG5) has sequence information and explains 37% of the variation in quantitative trait 5 (Q5), we focus on using SNPs within MG5 to predict Q5 among individuals who married into the pedigree. As a preliminary screening step, we reduced the number of SNPs from 269 to 34 based on their association with Q5. In our first models we assumed that SNPs affected Q5 in a simple additive manner. These models explained 34% and 15% of the variation in Q5 in women and men, respectively. Our second model was a linear model, which used individual SNPs and simple interaction terms as predictors. These models explained 36% and 16% of the variation in Q5 levels for women and men, respectively. Our last model was a "hit"-based model which was motivated by the hypothesis that disequilibrium between SNPs may reflect the fact that SNPs affect phenotype by acting in concert with other SNPs within their "disequilibrium set." Thus, the number of hits within the disequilibrium sets were used as predictors. These models explained 35% and 19% of the variation in Q5 for women and men, respectively. Our results suggest that phenotype can be predicted from complex patterns of weakly penetrant SNPs using relatively simple models. We concluded that SNP interaction either was not included in the simulation model, or had only a weak impact on Q5 levels.

Female↗
PubMed2001

Hierarchical modeling of the relation between sequence variants and a quantitative trait: addressing multiple comparison and population stratification issues.

When analyzing the relation between genetic sequence information and disease traits, false-positive associations can arise due to multiple comparisons and population stratification. In an attempt to address these issues, we incorporate into a conventional analytic model higher-level--or "prior"--models that use additional information to improve estimates while allowing for differing population structures. We apply this hierarchical model to simulated data from the Genetic Analysis Workshop 12. We focus on the effects of common candidate gene sequence variants on quantitative risk factor 5 (Q5) levels. In particular, we compare the regression coefficients (and 95% confidence intervals) obtained from conventional (one-stage) analyses versus the corresponding results from the hierarchical analyses. When examining either the marry-ins or all subjects in the general and isolate populations, the conventional model detected numerous sites in candidate genes 1-5 and 7 that had statistically significant regression coefficients (alpha level = 0.05). In contrast, our hierarchical model primarily only detected associations for variants in candidate gene 2, which is the casual gene for Q5.

Chromosome Mapping↗
PubMed2001

Impact of preadjusting a quantitative phenotype prior to sib-pair linkage analysis when gene x environment interaction exists.

The investigation of potential gene x environment (G x E) interactions is an important facet in the study of complex diseases. When G x E interaction exists, linkage analyses of the interacting gene must treat the environmental factor appropriately. Specifically, the common approach of regressing out an environmental factor prior to linkage analysis may be inappropriate if that factor has an interaction with the gene. This is explored here in the Genetic Analysis Workshop 12 simulated data set using the G x E interaction between major gene four (MG4) and environmental factor two (E2). The analysis shows that preadjusting the quantitative trait three (Q3) phenotype for the main effects of several environmental variables, including one (E2) that interacts with MG4, affects the results of a Haseman-Elston linkage analysis. In particular, the agreement in detecting linkage between preadjusting versus not preadjusting was only 78% and 66% using alpha levels of 0.05 and 0.10, respectively. For both approaches, incorporating an interaction term in the regression models enabled linkage to be detected where the evidence was either minimal or not present in an identical-by-descent main effects-only model. Furthermore, preadjustment for E2 did not appear to account for the major discrepancies between the approaches.

Chromosome Mapping↗
PubMed2000

Replication linkage study for prostate cancer susceptibility genes.

BACKGROUND: Since the publication of the first genome screen for prostate cancer (CaP) 5 years ago, over a dozen linkage studies have appeared. Most attention has been directed to chromosome 1, where two separate regions have been identified as harboring a prostate cancer susceptibility locus: HPC1 in the 1q24-25 interval and PCaP in the 1q42.2-43 interval. Linkage analysis of chromosome 16 has also provided evidence of harboring two loci predisposing to CaP. METHODS: We report on a replication linkage study of chromosomes 1 and 16 in 45 new and 4 expanded multiplex CaP families. Multipoint Z-scores were obtained for 30 highly polymorphic short-sequence tandem repeat markers spanning chromosome 1, and 22 markers spanning chromosome 16. RESULTS: The replication sample gave no evidence for a CaP susceptibility locus in the 1q24-25 interval and equivocal evidence for such a locus at 1q42.2-43. With respect to chromosome 16, positive Z-scores were obtained over a contiguous interval covering the entire p arm and the proximal half of the q arm. CONCLUSIONS: The linkage analysis of our replication sample does not support the existence of HPC1, and the evidence for the existence of PCaP remains equivocal. Evidence of a susceptibility locus on 16p remains strong, but the evidence for a susceptibility locus on 16q is weakened.

Chromosomes, Human, Pair 1↗
PubMed2000

Identification and fine mapping of a region showing a high frequency of allelic imbalance on chromosome 16q23.2 that corresponds to a prostate cancer susceptibility locus.

Linkage to a prostate cancer susceptibility locus was recently reported on chromosome 16q23. We now report a region exhibiting a high frequency of allelic imbalance (AI) corresponding to this locus in tumors from 51 men diagnosed with prostate cancer using the same linked markers. The highest frequency of AI was found at markers D16S3096 (45%) and D16S516 (53%) that map to chromosome 16q23.2. In addition, 19 of the 51 (37%) prostate tumors showed interstitial AI involving one or both of these markers. This result strongly suggests that a candidate prostate cancer tumor suppressor gene maps between markers D16S3096 and D16S516. We estimate that the distance between these markers is approximately 118 kb using a Stanford radiation hybrid panel. We observed a positive association with family history (P = 0.048) when comparing those men showing interstitial AI at markers D16S3096 and/or D16S516 with those without any imbalance at these two markers. Taken together, these data suggest that we have precisely localized a region of chromosome 16q23.2 that may harbor a prostate cancer tumor suppressor gene implicated in the development of non-familial and possibly familial forms of prostate cancer.

Aged↗
PubMed2000

Genomewide scan for prostate cancer-aggressiveness loci.

The aggressiveness of prostate cancer (PCa) varies widely: some tumors progress to invasive, potentially life-threatening disease, whereas others stay latent for the remainder of an individual's lifetime. The mechanisms resulting in this variability are not yet understood, but they are likely to involve both genetic and environmental influences. To investigate genetic factors, we conducted a genomewide linkage analysis of 513 brothers with PCa, using the Gleason score, which reflects tumor histology, as a quantitative measure of PCa aggressiveness. To our knowledge, this is the first time that a measure of PCa aggressiveness has been directly investigated as a quantitative trait in a genomewide scan. We employed a generalized multipoint Haseman-Elston linkage-analysis approach that regresses the mean-corrected cross product between the brothers' Gleason scores on the estimated proportion of alleles shared by brothers identical by descent at each marker location. Our results suggest that candidate regions on chromosomes 5q, 7q, and 19q give evidence for linkage to PCa-aggressiveness genes. In particular, the strongest signals detected in these regions were at the following markers (with corresponding P values): for chromosome 5q31-33, between markers D5S1480 and D5S820 (P=.0002); for chromosome 7q32, between markers D7S3061 and D7S1804 (P=.0007); and, for chromosome 19q12, at D19S433 (P=.0004). This indicates that one or more of these candidate regions may contain genes that influence the progression of PCa from latent to invasive disease. Identification of such genes would be extremely valuable for elucidation of the mechanism underlying PCa progression and for determination of treatment in men in whom this disease has been diagnosed.

Adult↗
PubMed2000

On the relative sample size required for multiple comparisons.

Multiple comparisons are commonly made in epidemiologic and genetic research. How to appropriately adjust for multiple comparisons remains a controversial issue. This note demonstrates, however, that large increases in the number of comparisons has a limited effect on the sample size required to maintain an experimentwise alpha-level. In particular, the relative sample size required increases only linearly with the logarithm of the number of comparisons made.

Models, Statistical↗
PubMed2000

Clinical trials in the genomic era: effects of protective genotypes on sample size and duration of trial.

It is well known that individuals can vary widely in their disease susceptibilities. One potential source of this variation is the genetic makeup of individuals, which can confer either protection or susceptibility to disease. Here we examine the effects of protective genotypes on the sample sizes and time required to detect differences between clinical trial arms. We show that including individuals with protective genotypes in a clinical trial can increase required sample sizes and trial duration. One can deal with this issue by pregenotyping subjects and selectively enrolling them based on their genotype. Thus we also calculate the number of individuals that must be recruited and pregenotyped to fulfill sample size requirements. The benefits of genotypically screening study subjects will depend on numerous factors, including ease of patient recruitment, cost of genotyping, long-term costs of study (or long-term cost per subject), and the strength of the protective effect. We present several examples that show the potential value of incorporating information about protective genotypes into a clinical trial.

Clinical Trials as Topic↗
PubMed2000

CYP3A activity in African American and European American men: population differences and functional effect of the CYP3A4*1B5'-promoter region polymorphism.

OBJECTIVE: Cytochrome P4503A (CYP3A) activity exhibits considerable interindividual variability. Possible differences in CYP3A activity were investigated in European American and African American men with the use of midazolam as an in vivo probe. METHODS: Midazolam was simultaneously administered intravenously (1 mg, [15N3]-labeled) and orally (2 mg, unlabeled in capsule form) to 15 young healthy European American men and a similar group of men of African American descent. Plasma concentration-time curves were measured. The subjects were subsequently genotyped with respect to the CYP3A4*B1 polymorphism (A-290G) in the 5'-promoter (nifedipine-specific element) region. RESULTS: The oral bioavailability of midazolam was about equally determined by intestinal and hepatic extraction with CYP3A activity at the former site exhibiting greater variability. Oral bioavailability was related to intestinal metabolism (r = 0.98), whereas hepatic CYP3A activity contributed little to the interindividual variability (r = 0.03). A lower systemic clearance (265+/-54 versus 310+/-56 mL/min; P = .04), but not oral clearance, was observed in African Americans. With one exception, the African Americans possessed a variant CYP3A4*1B allele (4 heterozygotes A/G and 10 homozygote G/G), whereas all of the European Americans were wild-type homozygotes (A/A). Hepatic CYP3A activity and the systemic clearance of midazolam were about 30% lower in G/G homozygotes than in A/A homozygotes (252+/-53 versus 310+/-54 mL/min; P = .02), and a gene-dose effect was present (P = .01). There was no genotype/phenotype relationship with respect to the oral clearance of midazolam. CONCLUSION: Comparison of CYP3A activity between populations is complicated by frequency distribution differences in the regulatory CYP3A4*1B polymorphism and lower hepatic CYP3A activity associated with the variant allele. However, this reduction is modest; therefore no major and clinically important difference in CYP3A activity is present between Americans of African or European descent.

Administration, Oral↗
PubMed2000

Linkage disequilibrium and allele-frequency distributions for 114 single-nucleotide polymorphisms in five populations.

Single-nucleotide polymorphisms (SNPs) may be extremely important for deciphering the impact of genetic variation on complex human diseases. The ultimate value of SNPs for linkage and association mapping studies depends in part on the distribution of SNP allele frequencies and intermarker linkage disequilibrium (LD) across populations. Limited information is available about these distributions on a genomewide scale, particularly for LD. Using 114 SNPs from 33 genes, we compared these distributions in five American populations (727 individuals) of African, European, Chinese, Hispanic, and Japanese descent. The allele frequencies were highly correlated across populations but differed by >20% for at least one pair of populations in 35% of SNPs. The correlation in LD was high for some pairs of populations but not for others (e.g., Chinese American or Japanese American vs. any other population). Regardless of population, average minor-allele frequencies were significantly higher for SNPs in noncoding regions (20%-25%) than for SNPs in coding regions (12%-16%). Interestingly, we found that intermarker LD may be strongest with pairs of SNPs in which both markers are nonconservative substitutions, compared to pairs of SNPs where at least one marker is a conservative substitution. These results suggest that population differences and marker location within the gene may be important factors in the selection of SNPs for use in the study of complex disease with linkage or association mapping methods.

Alleles↗
PubMed2000

A genome screen of multiplex sibships with prostate cancer.

Analysis of a genome screen of 504 brothers with prostate cancer (CaP) who were from 230 multiplex sibships identified five regions with nominally positive linkage signals, on chromosomes 2q, 12p, 15q, 16p, and 16q. The strongest signal in these data is found on chromosome 16q, between markers D16S515 and D16S3040, a region suspected to contain a tumor-suppressor gene. On the basis of findings from previous genome screens of families with CaP, three preplanned subanalyses were carried out, in the hope of increasing the subgroup homogeneity. Subgroups were formed by dividing the sibships into a group with a positive family history (FH+) that met criteria for "hereditary" CaP (n=111) versus those which did not meet the criteria (n=119) and by dividing the families into those with a mean onset age below the median (n=115) versus those with a mean onset age above the median (n=115). A separate subanalysis was carried out for families with a history of breast cancer (CaB+ [n=53]). Analyses of these subgroups revealed a number of potentially important differences in regions that were nonsignificant when all the families were analyzed together. In particular, the subgroup without a positive family history (FH-) had a signal in a region that is proximal to the putative site of the HPC1 locus on chromosome 1, whereas the late-age-at-onset group had a signal on 4q. The CaB+ subgroup revealed a strong linkage signal at 1p35.1.

Age of Onset↗
PubMed2000

Multilevel modeling in epidemiology with GLIMMIX.

Previous work has shown that multilevel modeling can be a valuable technique for epidemiologic analysis. The complexity of using this approach, however, continues to restrict its general application. A critical factor is the lack of flexible and appropriate software for multilevel modeling. SAS provides a macro, GLIMMIX, that can be used for multilevel modeling, but that is not sufficient for a complete epidemiologic analysis. We here provide additional code to obtain epidemiologic output from GLIMMIX, illustrated with new data on diet and breast cancer from the European Community Multicenter Study on Antioxidants, Myocardial Infarction, and Breast Cancer (EURAMIC). Our results give epidemiologists an easily used tool for fitting multilevel models.

Breast Neoplasms↗
PubMed2000

Testing drug response in the presence of genetic information: sampling issues for clinical trials.

Progress towards construction of a dense map of di-allelic markers across the human genome has generated considerable enthusiasm for pharmacogenomic applications. To date, however, nearly all of the effort on single nucleotide polymorphism (SNP) projects has been focused on marker identification and screening, not on how the SNP genotype data actually can be used in clinical trials to advance medical practice. Here, we explore how different properties of SNPs impact the size, scope and design of clinical trials using a simple trial design. We evaluate the clinical trial sampling requirements under different allele frequencies, gene action, gene effect size and number of markers in a genome screen. Power and sample size calculations suggest that allele frequency and type of gene action can have a dramatic impact on trial sample sizes, in that under some conditions the required sample sizes are too large to be applicable in a costly clinical trial setting. In other situations, however, pharmacogenomic clinical trials can yield significant sampling/cost savings over traditional trials. These properties are discussed with regard to the general usage of genetic information in clinical trial settings.

Clinical Trials as Topic↗
PubMed1999

Genetic mapping of complex traits.

Statistical genetic mapping methods are powerful tools for finding genes that contribute to complex human traits. Mapping methods combine knowledge of the biological mechanisms of inheritance and the randomness inherent in those mechanisms to locate, with increasing precision, trait genes on the human genome. We provide an overview of the two major classes of mapping methods, genetic linkage analysis and linkage disequilibrium analysis, and related concepts of genetic inheritance.

Chromosome Mapping↗
PubMed1999

Asymptotic bias and efficiency in case-control studies of candidate genes and gene-environment interactions: basic family designs.

Case-control designs that use population controls are compared with those that use controls selected from their relatives (i.e., siblings, cousins, or "pseudosibs" based on parental alleles) for estimating the effect of candidate genes and gene-environment interactions. The authors first evaluate the asymptotic bias in relative risk estimates resulting from using population controls when there is confounding due to population stratification. Using siblings or pseudosibs as controls completely addresses this issue, whereas cousins provide only partial protection from population stratification. Next, they show that the conventional conditional likelihood for matched case-control studies can give asymptotically biased effect estimates when applied to the pseudosib approach; the asymptotic bias is toward the null and disappears with disease rarity. They show how to reparameterize the pseudosib likelihood so this approach gives consistent effect estimates. They then show that the designs using population or pseudosib controls are generally the most efficient for estimating the main effect of a candidate gene, followed in efficiency by the design using cousins. Finally, they show that the design using sibling controls can be quite efficient when studying gene-environment interactions. In addition to asymptotic bias and efficiency issues, family-based designs might benefit from a higher motivation to participate among cases' relatives, but these designs have the disadvantage that many potential cases will be excluded from study by having no available controls.

Bias↗
PubMed1999

Likelihood-based approach to estimating twin concordance for dichotomous traits.

Genetic epidemiologists are well aware that the casewise and pairwise twin concordances are two different measures. In determining appropriate estimators for each of these measures, the method of ascertainment must be considered. Here, we derive expressions for the concordance estimators and their asymptotic variances appropriate to different twin ascertainment schemes using a likelihood framework, and apply these formulas to existing data. We emphasize the distinction between concordance measures (i.e., the parameters of interest) and the concordance estimators based on the number of pairs observed. Under random or complete ascertainment the casewise estimator is asymptotically unbiased for the casewise concordance, and the pairwise estimator is asymptotically unbiased for the pairwise concordance. Under incomplete ascertainment, the casewise estimator is biased for the casewise concordance, the pairwise estimator is biased for the pairwise concordance, but the probandwise estimator is asymptotically unbiased for the casewise concordance. One can extend the likelihood equations presented here to allow the concordance parameter of interest to depend on zygosity and, if measured, other factors such as cohabitation status and similarity for genetic markers, while concurrently allowing the disease prevalence to depend on measured covariates.

Humans↗