Genetic architecture of circulating lipid levels

Demirkan, Ayşe; Amin, Najaf; Isaacs, Aaron; Järvelin, Marjo‐Riitta; Whitfield, John B.; Wichmann, Heinz‐Erich; Kyvik, Kirsten Ohm; Rudan, Igor; Gieger, Christian; Hicks, Andrew A.; Johansson, Åsa; Hottenga, Jouke‐Jan; Smith, Johannes J; Pedersen, Nancy L.; Willemsen, Gonneke; Mangino, Massimo; Hayward, Caroline; Uitterlinden, André G.; Hofman, Albert; Witteman, Jacqueline C. M.; Montgomery, Grant W.; Pietiläinen, Kirsi H.; Rantanen, Taina; Kaprio, Jaakko; Döring, Angela; Pramstaller, Peter P.; Gyllensten, Ulf; Geus, Eco J. C. de; Penninx, Brenda W. J. H.; Wilson, James F.; Rivadeneria, Fernando; Magnusson, Patrik K. E.; Boomsma, Dorret I.; Spector, Tim D.; Campbell, Harry; Hoehne, Birgit; Martin, Nicholas G.; Oostra, Ben A.; McCarthy, Mark I.; Peltonen-Palotie, Leena; Aulchenko, Yurii S.; Visscher, Peter M.; Ripatti, Samuli; Janssens, A. Cecile J.W.; Duijn, Cornelia M. van

doi:10.1038/ejhg.2011.21

Cited by 21 publications

(12 citation statements)

References 37 publications

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“…A recent study by Demirkan et al (2012) found that a polygenic score only explained 2.6% of the phenotypic variance in LDLc [23]. Demirkan et al suggested that this low figure might have been a consequence of the small size of the discovery sample on which their weighting scheme was based (i.e.…”

Section: Discussionmentioning

confidence: 99%

Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates

et al. 2013

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It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.

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Section: Discussionmentioning

confidence: 99%

Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates

et al. 2013

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“…For reasons that are not yet understood, the relationship between HDL and certain metabolic syndrome traits, including triglycerides and body fat, tends to be opposite between mice and humans, although this finding is also to some degree cross-, diet-, and/or perturbation-dependent. For example, while human HDL and triglyceride (TG) levels tend to be anti-correlated (26), (65), we observe a positive correlation of 0.31. This is concordant with results of other studies (e.g., (66) (67)) that have found positive HDL-TG correlations of varying strength.…”

Section: Resultsmentioning

confidence: 84%

“…Human studies have observed that HDL and triglyceride levels (TG) tend to be anti-correlated (26), and at loci that are associated with both HDL and TG the two associations tend to go in the opposite direction. As mentioned earlier, in mice HDL and TG tend to be positively correlated; in our study HDL and TG show a moderate positive correlation of 0.31.…”

Section: Discussionmentioning

confidence: 99%

“…Also, several recent genome-wide association studies (GWAS), each involving tens of thousands of individuals typed for hundreds of thousands of single nucleotide polymorphisms (SNPs) revealed several novel HDL loci and confirmed the roles of a dozen previously identified genes such as CETP, LIPG, LIPC, ABCA1, LCAT, LPL, and APOAI. However, altogether, these explained only about 5–10% of the variation in HDL levels (2, 3, 22–26). Since estimates of the heritability of HDL in human populations have ranged from 40%–70%, there clearly remains a great deal to be discovered.…”

Section: Introductionmentioning

confidence: 98%

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A systems genetic analysis of high density lipoprotein metabolism and network preservation across mouse models

Langfelder¹,

Castellani

Zhou

et al. 2012

Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biolog

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We report a systems genetics analysis of high density lipoproteins (HDL) levels in an F2 intercross between inbred strains CAST/EiJ and C57BL/6J. We previously showed that there are dramatic differences in HDL metabolism in a cross between these strains, and we now report co-expression network analysis of HDL that integrates global expression data from liver and adipose with relevant metabolic traits. Using data from a total of 293 F2 intercross mice, we constructed weighted gene co-expression networks and identified modules (subnetworks) associated with HDL and clinical traits. These were examined for genes implicated in HDL levels based on large human genome-wide associations studies (GWAS) and examined with respect to conservation between tissue and sexes in a total of 9 data sets. We identify genes that are consistently ranked high by association with HDL across the 9 data sets. We focus in particular on two genes, Wfdc2 and Hdac3, that are located in close proximity to HDL QTL peaks where causal testing indicates that they may affect HDL. Our results provide a rich resource for studies of complex metabolic interactions involving HDL.

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“…Many common variants have been found to be significantly associated with HDL-C in humans, but collectively they only explain a small proportion of the estimated heritability. A recent study used significant GWAS SNPs to perform polygenic scoring and found that the best model only explained ~4.75% of the variation in the HDL-C trait 10 . Some groups have begun to examine a more complex genetic architecture to explain the missing heritability and several gene-gene interactions have been identified 11–13 .…”

Section: Introductionmentioning

confidence: 99%

Athena: A Tool for Meta-Dimensional Analysis Applied to Genotypes and Gene Expression Data to Predict HDL Cholesterol Levels

et al. 2012

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Technology is driving the field of human genetics research with advances in techniques to generate high-throughput data that interrogate various levels of biological regulation. With this massive amount of data comes the important task of using powerful bioinformatics techniques to sift through the noise to find true signals that predict various human traits. A popular analytical method thus far has been the genome-wide association study (GWAS), which assesses the association of single nucleotide polymorphisms (SNPs) with the trait of interest. Unfortunately, GWAS has not been able to explain a substantial proportion of the estimated heritability for most complex traits. Due to the inherently complex nature of biology, this phenomenon could be a factor of the simplistic study design. A more powerful analysis may be a systems biology approach that integrates different types of data, or a meta-dimensional analysis. For this study we used the Analysis Tool for Heritable and Environmental Network Associations (ATHENA) to integrate high-throughput SNPs and gene expression variables (EVs) to predict high-density lipoprotein cholesterol (HDL-C) levels. We generated multivariable models that consisted of SNPs only, EVs only, and SNPs + EVs with testing r-squared values of 0.16, 0.11, and 0.18, respectively. Additionally, using just the SNPs and EVs from the best models, we generated a model with a testing r-squared of 0.32. A linear regression model with the same variables resulted in an adjusted r-squared of 0.23. With this systems biology approach, we were able to integrate different types of high-throughput data to generate meta-dimensional models that are predictive for the HDL-C in our data set. Additionally, our modeling method was able to capture more of the HDL-C variation than a linear regression model that included the same variables.

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Genetic architecture of circulating lipid levels

Cited by 21 publications

References 37 publications

Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates

Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates

A systems genetic analysis of high density lipoprotein metabolism and network preservation across mouse models

Athena: A Tool for Meta-Dimensional Analysis Applied to Genotypes and Gene Expression Data to Predict HDL Cholesterol Levels

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