Julie A. Douglas

A common variant associated with prostate cancer in European and African populations

With the increasing incidence of prostate cancer, identifying common genetic variants that confer risk of the disease is important. Here we report such a variant on chromosome 8q24, a region initially identified through a study of Icelandic families. Allele −8 of the microsatellite DG8S737 was associated with prostate cancer in three case-control series of European ancestry from Iceland, Sweden and the US. The estimated odds ratio (OR) of the allele is 1.62 (P = 2.7 × 10−11). About 19% of affected men and 13% of the general population carry at least one copy, yielding a population attributable risk (PAR) of ∼8%. The association was also replicated in an African American case-control group with a similar OR, in which 41% of affected individuals and 30% of the population are carriers. This leads to a greater estimated PAR (16%) that may contribute to higher incidence of prostate cancer in African American men than in men of European ancestry.

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. I. An autosomal genome scan for genes that predispose to type 2 diabetes

We performed a genome scan at an average resolution of 8 cM in 719 Finnish sib pairs with type 2 diabetes. Our strongest results are for chromosome 20, where we observe a weighted maximum LOD score (MLS) of 2.15 at map position 69.5 cM from pter and secondary weighted LOD-score peaks of 2.04 at 56.5 cM and 1.99 at 17.5 cM. Our next largest MLS is for chromosome 11 (MLS = 1.75 at 84.0 cM), followed by chromosomes 2 (MLS = 0.87 at 5.5 cM), 10 (MLS = 0.77 at 75.0 cM), and 6 (MLS = 0.61 at 112.5 cM), all under an additive model. When we condition on chromosome 2 at 8.5 cM, the MLS for chromosome 20 increases to 5.50 at 69.0 cM (P=.0014). An ordered-subsets analysis based on families with high or low diabetes-related quantitative traits yielded results that support the possible existence of disease-predisposing genes on chromosomes 6 and 10. Genomewide linkage-disequilibrium analysis using microsatellite marker data revealed strong evidence of association for D22S423 (P=.00007). Further analyses are being carried out to confirm and to refine the location of these putative diabetes-predisposing genes.

Experimentally-derived haplotypes substantially increase the efficiency of linkage disequilibrium studies

The study of complex genetic traits in humans is limited by the expense and difficulty of ascertaining populations of sufficient sample size to detect subtle genetic contributions to disease. Here we introduce an application of a somatic cell hybrid construction strategy called conversion that maximizes the genotypic information from each sampled individual. The approach permits direct observation of individual haplotypes, thereby eliminating the need for collecting and genotyping DNA from family members for haplotype-based analyses. We describe experimental data that validate the use of conversion as a whole-genome haplotyping tool and evaluate the theoretical efficiency of using conversion-derived haplotypes instead of conventional genotypes in the context of haplotype-frequency estimation. We show that, particularly when phenotyping is expensive, conversion-based haplotyping can be more efficient and cost-effective than standard genotyping.

A Multipoint Method for Detecting Genotyping Errors and Mutations in Sibling-Pair Linkage Data

The identification of genes contributing to complex diseases and quantitative traits requires genetic data of high fidelity, because undetected errors and mutations can profoundly affect linkage information. The recent emphasis on the use of the sibling-pair design eliminates or decreases the likelihood of detection of genotyping errors and marker mutations through apparent Mendelian incompatibilities or close double recombinants. In this article, we describe a hidden Markov method for detecting genotyping errors and mutations in multilocus linkage data. Specifically, we calculate the posterior probability of genotyping error or mutation for each sibling-pair-marker combination, conditional on all marker data and an assumed genotype-error rate. The method is designed for use with sibling-pair data when parental genotypes are unavailable. Through Monte Carlo simulation, we explore the effects of map density, marker-allele frequencies, marker position, and genotype-error rate on the accuracy of our error-detection method. In addition, we examine the impact of genotyping errors and error detection and correction on multipoint linkage information. We illustrate that even moderate error rates can result in substantial loss of linkage information, given efforts to fine-map a putative disease locus. Although simulations suggest that our method detects </=50% of genotyping errors, it generally flags those errors that have the largest impact on linkage results. For high-resolution genetic maps, removal of the errors identified by our method restores most or nearly all the lost linkage information and can be accomplished without generating false evidence for linkage by removing incorrectly identified errors.

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. II. An autosomal genome scan for diabetes-related quantitative-trait loci

Type 2 diabetes mellitus is a complex disorder encompassing multiple metabolic defects. We report results from an autosomal genome scan for type 2 diabetes-related quantitative traits in 580 Finnish families ascertained for an affected sibling pair and analyzed by the variance components-based quantitative-trait locus (QTL) linkage approach. We analyzed diabetic and nondiabetic subjects separately, because of the possible impact of disease on the traits of interest. In diabetic individuals, our strongest results were observed on chromosomes 3 (fasting C-peptide/glucose: maximum LOD score [MLS] = 3.13 at 53.0 cM) and 13 (body-mass index: MLS = 3.28 at 5.0 cM). In nondiabetic individuals, the strongest results were observed on chromosomes 10 (acute insulin response: MLS = 3.11 at 21.0 cM), 13 (2-h insulin: MLS = 2.86 at 65.5 cM), and 17 (fasting insulin/glucose ratio: MLS = 3.20 at 9.0 cM). In several cases, there was evidence for overlapping signals between diabetic and nondiabetic individuals; therefore we performed joint analyses. In these joint analyses, we observed strong signals for chromosomes 3 (body-mass index: MLS = 3.43 at 59.5 cM), 17 (empirical insulin-resistance index: MLS = 3.61 at 0.0 cM), and 19 (empirical insulin-resistance index: MLS = 2.80 at 74.5 cM). Integrating genome-scan results from the companion article by Ghosh et al., we identify several regions that may harbor susceptibility genes for type 2 diabetes in the Finnish population.

Probability of Detection of Genotyping Errors and Mutations as Inheritance Inconsistencies in Nuclear-Family Data

Gene-mapping studies routinely rely on checking for Mendelian transmission of marker alleles in a pedigree, as a means of screening for genotyping errors and mutations, with the implicit assumption that, if a pedigree is consistent with Mendels laws of inheritance, then there are no genotyping errors. However, the occurrence of inheritance inconsistencies alone is an inadequate measure of the number of genotyping errors, since the rate of occurrence depends on the number and relationships of genotyped pedigree members, the type of errors, and the distribution of marker-allele frequencies. In this article, we calculate the expected probability of detection of a genotyping error or mutation as an inheritance inconsistency in nuclear-family data, as a function of both the number of genotyped parents and offspring and the marker-allele frequency distribution. Through computer simulation, we explore the sensitivity of our analytic calculations to the underlying error model. Under a random-allele-error model, we find that detection rates are 51%-77% for multiallelic markers and 13%-75% for biallelic markers; detection rates are generally lower when the error occurs in a parent than in an offspring, unless a large number of offspring are genotyped. Errors are especially difficult to detect for biallelic markers with equally frequent alleles, even when both parents are genotyped; in this case, the maximum detection rate is 34% for four-person nuclear families. Error detection in families in which parents are not genotyped is limited, even with multiallelic markers. Given these results, we recommend that additional error checking (e.g., on the basis of multipoint analysis) be performed, beyond routine checking for Mendelian consistency. Furthermore, our results permit assessment of the plausibility of an observed number of inheritance inconsistencies for a family, allowing the detection of likely pedigree-rather than genotyping-errors in the early stages of a genome scan. Such early assessments are valuable in either the targeting of families for resampling or discontinued genotyping.

The genetic response to short-term interventions affecting cardiovascular function: Rationale and design of the Heredity and Phenotype Intervention (HAPI) Heart Study

BACKGROUND The etiology of cardiovascular disease (CVD) is multifactorial. Efforts to identify genes influencing CVD risk have met with limited success to date, likely because of the small effect sizes of common CVD risk alleles and the presence of gene by gene and gene by environment interactions. METHODS The HAPI Heart Study was initiated in 2002 to measure the cardiovascular response to 4 short-term interventions affecting cardiovascular risk factors and to identify the genetic and environmental determinants of these responses. The measurements included blood pressure responses to the cold pressor stress test and to a high salt diet, triglyceride excursion in response to a high-fat challenge, and response in platelet aggregation to aspirin therapy. RESULTS The interventions were carried out in 868 relatively healthy Amish adults from large families. The heritabilities of selected response traits for each intervention ranged from 8% to 38%, suggesting that some of the variation associated with response to each intervention can be attributed to the additive effects of genes. CONCLUSIONS Identifying these response genes may identify new mechanisms influencing CVD and may lead to individualized preventive strategies and improved early detection of high-risk individuals.

Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5×10−8) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B, SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23, TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease susceptibility loci.

Chromosome 17q12 variants contribute to risk of early-onset prostate cancer

In a recent genome-wide association study by Gudmundsson and colleagues, two prostate cancer susceptibility loci were identified on chromosome 17q. The first locus, at 17q12, was distinguished by two intronic single-nucleotide polymorphisms (SNPs) in the TCF2 gene (rs4430796 and rs7501939). The second locus was in a gene-poor region of 17q24, where the strongest evidence of association was for SNP rs1859962. To determine if these loci were also associated with hereditary prostate cancer, we genotyped them in a family-based association sample of 403 non-Hispanic white families, including 1,015 men with and without prostate cancer. SNPs rs4430796 and rs7501939, which were in strong linkage disequilibrium (r(2) = 0.68), showed the strongest evidence of prostate cancer association. Using a family-based association test, the A allele of SNP rs4430796 was overtransmitted to affected men (P = 0.006), with an odds ratio of 1.40 (95% confidence interval, 1.09-1.81) under an additive genetic model. Notably, rs4430796 was significantly associated with prostate cancer among men diagnosed at an early (<50 years) but not later age (P = 0.006 versus P = 0.118). Our results confirm the prostate cancer association with SNPs on chromosome 17q12 initially reported by Gudmundsson and colleagues. In addition, our results suggest that the increased risk associated with these SNPs is approximately doubled in individuals predisposed to develop early-onset disease. Importantly, these SNPs do not account for a significant portion of our prior prostate cancer linkage evidence on chromosome 17. Thus, there likely exist one or more additional independent prostate cancer susceptibility loci in this region.

Identifying Susceptibility Genes for Prostate Cancer—A Family-Based Association Study of Polymorphisms in CYP17, CYP19, CYP11A1, and LH-β

Polymorphisms in genes that code for enzymes or hormones involved in the synthesis and metabolism of androgens are compelling biological candidates for prostate cancer. Four such genes, CYP17, CYP19, CYP11A1, and LH-β, are involved in the synthesis and conversion of testosterone to dihydrotestosterone and estradiol. In a study of 715 men with and without prostate cancer from 266 familial and early-onset prostate cancer families, we examined the association between prostate cancer susceptibility and common single-nucleotide polymorphisms in each of these four candidate genes. Family-based association tests revealed a significant association between prostate cancer and a common single-nucleotide polymorphism in CYP17 (P = 0.004), with preferential transmission of the minor allele to unaffected men. Conditional logistic regression analysis of 461 discordant sibling pairs from these same families reaffirmed the association between the presence of the minor allele in CYP17 and prostate cancer risk (odds ratio, 0.51; 95% confidence interval, 0.28-0.92). These findings suggest that variation in or around CYP17 predicts susceptibility to prostate cancer. Family-based association tests may be especially valuable in studies of genetic variation and prostate cancer risk because this approach minimizes confounding due to population substructure, which is of particular concern for prostate cancer given the tremendous variation in the worldwide incidence of this disease.