Lipoprotein subclasses in genetic studies: The Berkeley data set

Krauss, Ronald M.; Williams, Paul T.; Blanche, Patricia J.; Cavanaugh, Adelle; Holl, Laura Glines; Austin, Melissa A.

doi:10.1002/gepi.1370100632

Cited by 5 publications

(2 citation statements)

References 32 publications

(26 reference statements)

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“…The feasibility of the identification of small, dense LDL particles as independent predictors of coronary artery disease risk in population studies remains questioned. Few data are available on population‐based studies and even less on subjects living in the Mediterranean area, where a low rate of cardiovascular mortality occurs [13–20]. This cardiovascular protection could result from genetic reasons but a major role must be attributed to environmental factors and in particular to the typical diet particularly helpful in terms of health effects.…”

Section: Introductionmentioning

confidence: 99%

Low‐density‐lipoprotein peak particle size in a Mediterranean population

Rizzo

Barbagallo

Severino

et al. 2003

Eur J Clin Investigation

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In this population, which appears to be somewhat protected by premature coronary artery disease, a low prevalence of the LDL pattern B was found in both men and women, and plasma TG could have a key role in regulating the LDL peak particle size. The follow up, still ongoing, will provide useful information on the predictive role of LDL peak particle size on cardiovascular risk, at least in a low-risk population.

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Section: Introductionmentioning

confidence: 99%

Low‐density‐lipoprotein peak particle size in a Mediterranean population

Rizzo

Barbagallo

Severino

et al. 2003

Eur J Clin Investigation

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“…Each of these models was simulated in the single large pedigree described above. Additionally, each model was simulated in the pedigrees of the Berkeley Lipid Data Set, which was also distributed to participants of the 8th Genetic Analysis Workshop (Krauss et al 1993). This data set includes 420 subjects in 27 pedigrees with 5-120 members (mean 14.2 members).…”

Section: Simulation 2: the Effect Of H At The Marker Locusmentioning

confidence: 99%

Detection of Gene-Environment Interactions in Joint Segregation and Linkage Analysis

Gauderman

Faucett

1997

The American Journal of Human Genetics

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We compare approaches for analysis of gene-environment (G x E) interaction, using segregation and joint segregation and linkage analyses of a quantitative trait. Analyses of triglyceride levels in a single large pedigree demonstrate the two methods and show evidence for a significant interaction (P=.015 when segregation analysis is used; P=.006 when joint analysis is used) between a codominant major gene and body-mass index. Genotype-specific correlation coefficients, between triglyceride levels and body-mass index, estimated from the joint model are rAA=.72, rAa=.49, and raa=. 20. Several simulation studies indicate that joint segregation and linkage analysis leads to less-biased and more-efficient estimates of a G x E-interaction effect, compared with segregation analysis alone. Depending on the heterozygosity of the marker locus and its proximity to the trait locus, we found joint analysis to be as much as 70% more efficient than segregation analysis, for estimation of a G x E-interaction effect. Over a variety of parameter combinations, joint analysis also led to moderate (5%-10%) increases in power to detect the interaction. On the basis of these results, we suggest the use of combined segregation and linkage analysis for improved estimation of G x E-interaction effects when the underlying trait gene is unmeasured.

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