Genome-wide linkage analysis of systolic blood pressure: a comparison of two approaches to phenotype definition

Genome-Wide Association Studies for Methane Production in Dairy Cattle

Genomic selection has been proposed for the mitigation of methane (CH₄) emissions by cattle because there is considerable variability in CH₄ emissions between individuals fed on the same diet. The genome-wide association study (GWAS) represents an important tool for the detection of candidate genes, haplotypes or single nucleotide polymorphisms (SNP) markers related to characteristics of economic interest. The present study included information for 280 cows in three dairy production systems in Mexico: 1) Dual Purpose (n = 100), 2) Specialized Tropical Dairy (n = 76), 3) Familiar Production System (n = 104). Concentrations of CH₄ in a breath of individual cows at the time of milking (MEIm) were estimated through a system of infrared sensors. After quality control analyses, 21,958 SNPs were included. Associations of markers were made using a linear regression model, corrected with principal component analyses. In total, 46 SNPs were identified as significant for CH₄ production. Several SNPs associated with CH₄ production were found at regions previously described for quantitative trait loci of composition characteristics of meat, milk fatty acids and characteristics related to feed intake. It was concluded that the SNPs identified could be used in genomic selection programs in developing countries and combined with other datasets for global selection.

Estimation of Inorganic Arsenic Exposure in Populations With Frequent Seafood Intake: Evidence From MESA and NHANES

The sum of urinary inorganic arsenic (iAs) and methylated arsenic (monomethylarsonate and dimethylarsinate (DMA)) species is the main biomarker of iAs exposure. Assessing iAs exposure, however, is difficult in populations with moderate-to-high seafood intakes. In the present study, we used subsamples from the Multi-Ethnic Study of Atherosclerosis (2000-2002) (n = 310) and the 2003-2006 National Health and Nutrition Examination Survey (n = 1,175). We calibrated urinary concentrations of non-seafood-derived iAs, DMA, and methylarsonate, as well as the sum of inorganic and methylated arsenic species, in the Multi-Ethnic Study of Atherosclerosis and of DMA in the National Health and Nutrition Examination Survey by regressing their original concentrations by arsenobetaine and extracting model residuals. To confirm that calibrated biomarkers reflected iAs exposure but not seafood intake, we compared urinary arsenic concentrations by levels of seafood and rice intakes. Self-reported seafood intakes, estimated n-3 polyunsaturated fatty acid levels, and measured n-3 polyunsaturated fatty acid levels were positively associated with the original urinary arsenic biomarkers. Using the calibrated arsenic biomarkers, we found a marked attenuation of the associations with self-reported seafood intake and estimated or measured n-3 fatty acids, whereas associations with self-reported rice intake remained similar. Our residual-based method provides estimates of iAs exposure and metabolism for each participant that no longer reflect seafood intake and can facilitate research about low-to-moderate levels of iAs exposure in populations with high seafood intakes.

Bias due to two-stage residual-outcome regression analysis in genetic association studies

Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided.

Phenotype definition in epilepsy

Phenotype definition consists of the use of epidemiologic, biological, molecular, or computational methods to systematically select features of a disorder that might result from distinct genetic influences. By carefully defining the target phenotype, or dividing the sample by phenotypic characteristics, we can hope to narrow the range of genes that influence risk for the trait in the study population, thereby increasing the likelihood of finding them. In this article, fundamental issues that arise in phenotyping in epilepsy and other disorders are reviewed, and factors complicating genotype-phenotype correlation are discussed. Methods of data collection, analysis, and interpretation are addressed, focusing on epidemiologic studies. With this foundation in place, the epilepsy subtypes and clinical features that appear to have a genetic basis are described, and the epidemiologic studies that have provided evidence for the heritability of these phenotypic characteristics, supporting their use in future genetic investigations, are reviewed. Finally, several molecular approaches to phenotype definition are discussed, in which the molecular defect, rather than the clinical phenotype, is used as a starting point.

Comparison of longitudinal variance components and regression-based approaches for linkage detection on chromosome 17 for systolic blood pressure

We compare two methods to detect genetic linkage by using serial observations of systolic blood pressure in pedigree data from the Framingham Heart Study focusing on chromosome 17. The first method is a variance components (VC) approach that incorporates longitudinal pedigree data, and the second method is a regression-based approach that summarizes all longitudinal measures in one single measure. No evidence of linkage was found either using the VC longitudinal approach or the regression-based approach, except when all time points were used from Cohorts 1 and 2 and only subjects aged 25 and 75 years were included.

Genetic analysis of phenotypes derived from longitudinal data: Presentation Group 1 of Genetic Analysis Workshop 13

The participants of Presentation Group 1 used the GAW13 data to derive new phenotypes, which were then analyzed for linkage and, in one case, for association to the genetic markers. Since the trait measurements ranged over longer time periods, the participants looked at the time dependence of particular traits in addition to the trait itself. The phenotypes analyzed with the Framingham data can be roughly divided into 1) body weight-related traits, which also include a type 2 diabetes progression trait, and 2) traits related to systolic blood pressure. Both trait classes are associated with metabolic syndrome. For traits related to body weight, linkage was consistently identified by at least two participating groups to genetic regions on chromosomes 4, 8, 11, and 18. For systolic blood pressure, or its derivatives, at least two groups obtained linkage for regions on chromosomes 4, 6, 8, 11, 14, 16, and 19. Five of the 13 participating groups focused on the simulated data. Due to the rather sparse grid of microsatellite markers, an association analysis for several traits was not successful. Linkage analysis of hypertension and body mass index using LODs and heterogeneity LODs (HLODs) had low power. For the glucose phenotype, a combination of random coefficient regression models and variance component linkage analysis turned out to be strikingly powerful in the identification of a trait locus simulated on chromosome 5. Haseman-Elston regression methods, applied to the same phenotype, had low power, but the above-mentioned chromosome 5 locus was not included in this analysis.