Biomedical subjects

Eric Jorgenson

Publications and source records attributed to Eric Jorgenson.

11 recordsLinked to original sources

PubMed2007

Plasminogen activator inhibitor type-1 (PAI-1) polymorphism 4G/5G is associated with prostate cancer among men with a positive family history.

BACKGROUND: Variation in the expression of plasminogen activator inhibitor type-1 (PAI-1) is associated with many human diseases, including several types of cancer. In particular, tumor cell overexpression of PAI-1 has been found to inhibit prostate cancer tumor growth, angiogenesis, and metastasis in mouse models. Normal host cell expression of PAI-1 is influenced by the 4G/5G insertion/deletion polymorphism in the promoter region of the PAI-1 gene. To evaluate the effect of PAI-1 expression on cancer development, we examined the association of the 4G/5G polymorphism in a sibling-based case-control study of prostate cancer. METHODS: One thousand one hundred thirty seven subjects, 655 cases, and 482 sibling controls from 526 families, were recruited from the major medical institutions in the greater Cleveland, OH area and from the Henry Ford Health System, Detroit, MI. A Cox age-of-onset model with robust variance estimation was used to evaluate the association between the PAI-1 4G/5G polymorphism and prostate cancer. RESULTS: No association was observed between the PAI-1 4G/5G polymorphism and prostate cancer in the entire sample. We did, however, identify a statistically significant association between the PAI-1 4G/5G polymorphism and prostate cancer in subjects with a family history of this disease (OR = 1.28, 95% CI 1.02-1.61, P-value = 0.036). The PAI-1 5G/5G genotype, associated with lower PAI-1 expression, appears to drive this result as it was associated with an increased risk of prostate cancer and an earlier mean age of onset compared to those with the 4G/4G genotype (OR = 1.83, 95% CI 1.12-2.99) while the 4G/5G genotype group did not show a significant difference in prostate cancer risk compared to the 4G/4G genotype group (OR = 0.98, 95% CI 0.75-1.28). CONCLUSIONS: These observations suggest that the 4G/5G polymorphism in PAI-1 may explain some of the increased risk and earlier mean age of onset of prostate cancer due to a positive family history.

Age of Onset↗

PubMed2006

Racial admixture and its impact on BMI and blood pressure in African and Mexican Americans.

Admixed populations such as African Americans and Hispanic Americans present both challenges and opportunities in genetic epidemiologic research. Because of variation in admixture levels among individuals, case-control association studies may be subject to stratification bias. On the other hand, admixed populations also present special opportunities both for examining the role of genetic and environmental factors for observed racial/ethnic differences, and for possibly mapping alleles that contribute to such differences. Here we examined the distribution and relationship of individual admixture (IA) estimates with BMI and three measures of blood pressure in two admixed populations in the NHLBI Family Blood Pressure Program (FBPP): African Americans and Mexican Americans. For the African Americans, we observed modest but significant differences in average African IA among four recruitment sites. We observed a slight excess of African IA among hypertensives compared to normotensives, and a positive (non-significant) regression of African IA on blood pressure in untreated participants. Within Mexican Americans, we found no difference in average IA between hypertensives and normotensives, but a positive (marginally significant) regression of African IA on diastolic blood pressure. We also observed a significant positive regression of Caucasian IA (and negative regression of Native American IA) on BMI. Our results are suggestive of genetic differences between Africans and non-Africans that influence blood pressure, but such effects are likely to be modest compared to environmental ones. Excess obesity among Native Americans compared to whites is not consistent with a simple genetic explanation.

Adult↗

PubMed2006

Coverage and power in genomewide association studies.

The ability of genomewide association studies to decipher genetic traits is driven in part by how well the measured single-nucleotide polymorphisms "cover" the unmeasured causal variants. Estimates of coverage based on standard linkage-disequilibrium measures, such as the average maximum squared correlation coefficient (r2), can lead to inaccurate and inflated estimates of the power of genomewide association studies. In contrast, use of the "cumulative r2 adjusted power" measure presented here gives more-accurate estimates of power for genomewide association studies.

Chromosome Mapping↗

PubMed2006

Genome-wide linkage analysis of lipids in nondiabetic Chinese and Japanese from the SAPPHIRe family study.

BACKGROUND: Lipid levels are recognized as major risk factors for coronary heart disease (CHD). Discovery of major loci underlying quantitative lipid traits could help to elucidate the genetics of CHD. METHODS: We performed a genome-wide search for quantitative trait loci linked to lipid phenotypes in 1538 Chinese subjects (509 families) and 625 Japanese subjects (204 families) not taking lipid-lowering medications from the Stanford-Asian Pacific Program in Hypertension and Insulin Resistance (SAPPHIRe) study. The multipoint variance-components method was used to test for linkage between marker loci and each trait by maximum likelihood methods adjusted for effects of age, age(2), gender, body mass index (BMI), smoking, alcohol drinking, physical activity, and field center. RESULTS: The highest logarithm of odds (LOD) score detected was 3.22 for logarithmically transformed HDL-cholesterol on chromosome 12 at 113 cM in Chinese subjects. This score overlaps the positive findings for HDL reported in Mexican Americans (chromosome 12 at 96 cM). Although no strong evidence for linkage was found in Japanese subjects, some modest peaks (LOD score >==1.5) were found in several regions that have been reported in other published genome scans. For example, the Japanese SAPPHIRe peak for HDL (chromosome 1 at 167 cM; LOD = 1.54) was very close to the quantitative trait loci (QTL) for HDL reported in the scan of white American HyperGEN (chromosome 1 at 159.9 cM). CONCLUSIONS: Genome-wide scan for genes influencing lipid phenotypes was conducted and we found significant linkage of HDL to a locus on chromosome 12 in Chinese subjects and no linkage signal exceeding 1.68 was found in the Japanese subjects.

Adult↗

PubMed2006

A gene-centric approach to genome-wide association studies.

Genic variants are more likely to alter gene function and affect disease risk than those that occur outside genes. Variants in genes, however, might not be sufficiently covered by the existing approaches to genome-wide association studies. Our analysis of the HapMap ENCODE data indicates that this concern is valid, and that an alternative approach that focuses on genic variants provides a more complete coverage of functionally important regions and a greater genotyping efficiency. We therefore argue that resources should be developed to make gene-centric genome-wide association studies feasible.

Databases, Genetic↗

PubMed2005

Hierarchical modeling in association studies of multiple phenotypes.

The genetic study of disease-associated phenotypes has become common because such phenotypes are often easier to measure and in many cases are under greater genetic control than the complex disease itself. Some disease-associated phenotypes are rare, however, making it difficult to evaluate their effects due to small informative sample sizes. In addition, analyzing numerous phenotypes introduces the issue of multiple comparisons. To address these issues, we have developed a hierarchical model (HM) for multiple phenotypes that provides more accurate effect estimates with a lower false-positive rate. We evaluated the validity and power of HM in association studies of multiple phenotypes using randomly selected cases and controls from the simulated data set in the Genetic Analysis Workshop 14. In particular, we first analyzed the association between each of the 12 subclinical phenotypes and single-nucleotide polymorphisms within the known causal loci using a conventional logistic regression model (LRM). Then we added a second-stage model by regressing all of the logistic coefficients of the phenotypes obtained from LRM on a Z matrix that incorporates the clinical correlation of the phenotypes. Specially, the 12 phenotypes were grouped into 3 clusters: 1) communally shared emotions; 2) behavioral related; and 3) anxiety related. A semi-Bayes HM effect estimate for each phenotype was calculated and compared with those from LRM. We observed that using HM to evaluate the association between SNPs and multiple related phenotypes slightly increased power for detecting the true associations and also led to fewer false-positive results.

False Positive Reactions↗

PubMed2005

Case-control analyses: Geneopardy!

The case-control analyses group focused on a diverse set of questions, including study design, power, and analysis of case-control studies. Papers on study design examined various case-selection strategies, as well as ways to combine information from family- and population-based case-control data. The power of various approaches to case-control analyses was compared in several papers, while two papers chose to apply specific methods to analyzing the data. Two groups examined haplotype-related issues, including resolving un-phased haplotypes and comparing haplotype- and single-nucleotide polymorphism-based methods for analyzing data. Finally, one group examined the optimal number of markers for the genomic control approach to population stratification.

Alcoholism↗

PubMed2005

An autosomal genome-wide scan for loci linked to pre-diabetic phenotypes in nondiabetic Chinese subjects from the Stanford Asia-Pacific Program of Hypertension and Insulin Resistance Family Study.

Type 2 diabetes is a complex disease involving both genetic and environmental components. Abnormalities in insulin secretion and insulin action usually precede the development of type 2 diabetes and can serve as good quantitative measures for genetic mapping. We therefore undertook an autosomal genomic search to locate the quantitative trait locus (QTL) linked to these traits in 1,365 nondiabetic Chinese subjects from 411 nuclear families. Residuals of these log-transformed quantitative traits were analyzed in multipoint linkage analysis using a variance-components approach. The most significant QTL for fasting insulin, which coincides with the QTL for homeostasis model assessment of insulin resistance, was located at 37 cM on chromosome 20, with a maximum empirical logarithm of odds (LOD) score of 3.01 (empirical P = 0.00006) when adjusted for age, sex, BMI, antihypertensive medications, recruitment centers, and environmental factors. In the same region, a QTL for fasting glucose was identified at 51 cM, with an empirical LOD score of 2.03 (empirical P = 0.0012). There were other loci with maximum empirical LOD scores >or=1.29 located on chromosomes 1q, 2p, 5q, 7p, 9q, 10p, 14q, 18q, and 19q for different diabetes-related traits. These loci may harbor genes that regulate glucose homeostasis either independently or via interactions of the genes within these regions.

Asian People↗

PubMed2004

Ethnicity and human genetic linkage maps.

Human genetic linkage maps are based on rates of recombination across the genome. These rates in humans vary by the sex of the parent from whom alleles are inherited, by chromosomal position, and by genomic features, such as GC content and repeat density. We have examined--for the first time, to our knowledge--racial/ethnic differences in genetic maps of humans. We constructed genetic maps based on 353 microsatellite markers in four racial/ethnic groups: whites, African Americans, Mexican Americans, and East Asians (Chinese and Japanese). These maps were generated using 9,291 subjects from 2,900 nuclear families who participated in the National Heart, Lung, and Blood Institute-funded Family Blood Pressure Program, the largest sample used for map construction to date. Although the maps for the different groups are generally similar, we did find regional and genomewide differences across ethnic groups, including a longer genomewide map for African Americans than for other populations. Some of this variation was explained by genotyping artifacts--namely, null alleles (i.e., alleles with null phenotypes) at a number of loci--and by ethnic differences in null-allele frequencies. In particular, null alleles appear to be the likely explanation for the excess map length in African Americans. We also found that nonrandom missing data biases map results. However, we found regions on chromosome 8p and telomeric segments with significant ethnic differences and a suggestive interval on chromosome 12q that were not due to genotype artifacts. The difference on chromosome 8p is likely due to a polymorphic inversion in the region. The results of our investigation have implications for inferences of possible genetic influences on human recombination as well as for future linkage studies, especially those involving populations of nonwhite ethnicity.

Black or African American↗

PubMed2003

Positional cloning of the human quantitative trait locus underlying taste sensitivity to phenylthiocarbamide.

The ability to taste the substance phenylthiocarbamide (PTC) has been widely used for genetic and anthropological studies, but genetic studies have produced conflicting results and demonstrated complex inheritance for this trait. We have identified a small region on chromosome 7q that shows strong linkage disequilibrium between single-nucleotide polymorphism (SNP) markers and PTC taste sensitivity in unrelated subjects. This region contains a single gene that encodes a member of the TAS2R bitter taste receptor family. We identified three coding SNPs giving rise to five haplotypes in this gene worldwide. These haplotypes completely explain the bimodal distribution of PTC taste sensitivity, thus accounting for the inheritance of the classically defined taste insensitivity and for 55 to 85% of the variance in PTC sensitivity. Distinct phenotypes were associated with specific haplotypes, which demonstrates that this gene has a direct influence on PTC taste sensitivity and that sequence variants at different sites interact with each other within the encoded gene product.

Alleles↗

PubMed2002

A polymorphism in the beta1 adrenergic receptor is associated with resting heart rate.

Resting heart rate is significantly associated with cardiovascular morbidity and mortality. However, the extent to which resting heart rate is genetically determined is poorly understood, and no genes have been found that contribute to variation in resting heart rate. Because signaling through the beta1 adrenergic receptor is a key determinant of cardiac function, we tested whether polymorphisms in this receptor are associated with resting heart rate. A cohort of >1,000 individuals of Chinese and Japanese descent, from nuclear families, was genotyped for two polymorphisms, resulting in a serine/glycine substitution at amino acid 49 (Ser49Gly) and an arginine/glycine substitution at residue 389 (Arg389Gly), in the beta1 adrenergic receptor. For comparison, polymorphisms in the beta2 and beta3 adrenergic receptors were also evaluated. The Ser49Gly polymorphism was significantly associated (P=.0004) with resting heart rate, independent of other variables, such as body-mass index, age, sex, ethnicity, exercise, smoking, alcohol intake, hypertension status, and treatment with beta blockers. The data support an additive model in which individuals heterozygous for the Ser49Gly polymorphism had mean heart rates intermediate to those of either type of homozygote, with Ser homozygotes having the highest mean heart rate and with Gly homozygotes having the lowest. Neither the Arg389Gly polymorphism in the beta1 adrenergic receptor nor polymorphisms in the beta2 and beta3 adrenergic receptors were associated with resting heart rate. The heritability of heart rate was 39.7% +/- 7.1% (P<10-7).

Age Factors↗