Longitudinal familial analysis of blood pressure involving parametric (co)variance functions-0

<p><b>Copyright information:</b></p><p>Taken from "Longitudinal familial analysis of blood pressure involving parametric (co)variance functions"</p><p>http://www.biomedcentral.com/1471-2156/4/s1/S87</p><p>BMC Genetics 2003;4(Suppl 1):S87-S87.</p><p>Published online 31 Dec 2003</p><p>PMCID:PMC1866527.</p><p></p

Linkage disequilibrium across two different single-nucleotide polymorphism genome scans-0

<p><b>Copyright information:</b></p><p>Taken from "Linkage disequilibrium across two different single-nucleotide polymorphism genome scans"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S86-S86.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866694.</p><p></p> SNP maps, as measured by (left) an

Linkage disequilibrium across two different single-nucleotide polymorphism genome scans-1

<p><b>Copyright information:</b></p><p>Taken from "Linkage disequilibrium across two different single-nucleotide polymorphism genome scans"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S86-S86.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866694.</p><p></p>3 trait. LOD scores were calculated using MIBDs constructed by removing SNP pairs that in founders showed LD as ≥ {0.2, 0.4, 0.6}. LOD scores calculated using uncorrected MIBDs are also shown

Results of linkage analysis based on microsatellite markers and of QTDT and QTLD linkage disequilibrium tests based on SNP genotypes

<p><b>Copyright information:</b></p><p>Taken from "The quantitative trait linkage disequilibrium test: a more powerful alternative to the quantitative transmission disequilibrium test for use in the absence of population stratification"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S91-S91.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866688.</p><p></p

Fast and Powerful Multiple Testing Inference in FamilyBased Heritability Studies

Poster submitted to the 2014 Organization for Human Brain Mapping (OHBM) conference in Hamburg, 8-12 June

Accelerated Permutation Inference for Pedigree Heritability Inference with Family-Based Neuroimaging

Poster submitted to the 2015 Organization for Human Brain Mapping (OHBM) in Hawaii, 14-18 June

Expected χstatistics for the measured genotype (MG), QTDT, and QTLD tests

<p><b>Copyright information:</b></p><p>Taken from "The quantitative trait linkage disequilibrium test: a more powerful alternative to the quantitative transmission disequilibrium test for use in the absence of population stratification"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S91-S91.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866688.</p><p></p

Genome-wide LOD scores for CHOL (red), HDL-C (green), SBP (blue), TG (orange), and BMI (pink)

<p><b>Copyright information:</b></p><p>Taken from "Phenotypic, genetic, and genome-wide structure in the metabolic syndrome"</p><p>http://www.biomedcentral.com/1471-2156/4/s1/S95</p><p>BMC Genetics 2003;4(Suppl 1):S95-S95.</p><p>Published online 31 Dec 2003</p><p>PMCID:PMC1866536.</p><p></p

DataSheet1_Metabolic syndrome traits exhibit genotype-by-environment interaction in relation to socioeconomic status in the Mexican American family heart study.PDF

Background: Socioeconomic Status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for metabolic disease. We analyzed data from the Mexican American Family Studies (MAFS) to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an essential determinant of variation in risk factors for metabolic syndrome (MS).Methods: We employed a maximum likelihood estimation of the decomposition of variance components to detect GxE interaction. After excluding individuals with diabetes and individuals on medication for diabetes, hypertension, or dyslipidemia, we analyzed 12 MS risk factors: fasting glucose (FG), fasting insulin (FI), 2-h glucose (2G), 2-h insulin (2I), body mass index (BMI), waist circumference (WC), leptin (LP), high-density lipoprotein-cholesterol (HDL-C), triglycerides (TG), total serum cholesterol (TSC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Our SES variable used a combined score of Duncan’s socioeconomic index and education years. Heterogeneity in the additive genetic variance across the SES continuum and a departure from unity in the genetic correlation coefficient were taken as evidence of GxE interaction. Hypothesis tests were conducted using standard likelihood ratio tests.Results: We found evidence of GxE for fasting glucose, 2-h glucose, 2-h insulin, BMI, and triglycerides. The genetic effects underlying the insulin/glucose metabolism component of MS are upregulated at the lower end of the SES spectrum. We also determined that the household variance for systolic blood pressure decreased with increasing SES.Conclusion: These results show a significant change in the GxE interaction underlying the major components of MS in response to changes in socioeconomic status. Further mRNA sequencing studies will identify genes and canonical gene pathways to support our molecular-level hypotheses.</p

Image1_Metabolic syndrome traits exhibit genotype-by-environment interaction in relation to socioeconomic status in the Mexican American family heart study.jpeg

Background: Socioeconomic Status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for metabolic disease. We analyzed data from the Mexican American Family Studies (MAFS) to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an essential determinant of variation in risk factors for metabolic syndrome (MS).Methods: We employed a maximum likelihood estimation of the decomposition of variance components to detect GxE interaction. After excluding individuals with diabetes and individuals on medication for diabetes, hypertension, or dyslipidemia, we analyzed 12 MS risk factors: fasting glucose (FG), fasting insulin (FI), 2-h glucose (2G), 2-h insulin (2I), body mass index (BMI), waist circumference (WC), leptin (LP), high-density lipoprotein-cholesterol (HDL-C), triglycerides (TG), total serum cholesterol (TSC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Our SES variable used a combined score of Duncan’s socioeconomic index and education years. Heterogeneity in the additive genetic variance across the SES continuum and a departure from unity in the genetic correlation coefficient were taken as evidence of GxE interaction. Hypothesis tests were conducted using standard likelihood ratio tests.Results: We found evidence of GxE for fasting glucose, 2-h glucose, 2-h insulin, BMI, and triglycerides. The genetic effects underlying the insulin/glucose metabolism component of MS are upregulated at the lower end of the SES spectrum. We also determined that the household variance for systolic blood pressure decreased with increasing SES.Conclusion: These results show a significant change in the GxE interaction underlying the major components of MS in response to changes in socioeconomic status. Further mRNA sequencing studies will identify genes and canonical gene pathways to support our molecular-level hypotheses.</p