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Team Uses 'Meta-Meta-Analysis' of Cardiochip Data to ID 21 Genes Linked to Cholesterol Levels


An international effort using SNP genotyping chips to study the genetics of cholesterol and other blood lipids has turned up 21 new gene variants associated with lipid levels.

Members of the International IBC Lipid Genetics Consortium described the findings this month in the American Journal of Human Genetics.

The scientists believe that the variants may be potential drug targets for lipid-related cardiovascular disease and have already embarked on a follow-on study to establish which of the loci directly cause disease, according to one of the study's senior authors.

"We were able to identify rare mutations associated with early-life high cholesterol in the general population, although the rarity of these polymorphisms does not permit as yet any safe conclusions on their actual impact on the lipid profile," said Brendan Keating, a researcher at the Center for Applied Genomics at the Children's Hospital of Philadelphia.

CHOP was one of the four coordinating sites for the consortium, which included 180 researchers from dozens of institutions. The other centers were the Institute of Cardiovascular Sciences at University College London; the Academic Medical Center at the University of Amsterdam in the Netherlands; and the department of cardiology at University Medical Center Utrecht.

Keating described the consortium's effort as a "meta-meta-analysis" of datasets available from 32 studies undertaken in the US, the UK, the Netherlands, and other sites in North America and Europe. All of these previous studies had used the Cardiochip, an Illumina-manufactured custom array that contains 50,000 SNPs found across 2,000 genes associated with cardiovascular disease. Since its initial design in 2007, the Cardiochip has been used to study type 2 diabetes, height, and coronary heart disease, among other indications, and more than 210,000 individuals have been genotyped with it in 60 different studies (BAN 3/20/2012).

In the study described this month in AJHG, Keating and fellow researchers set out to explore whether loci associated with plasma-lipid phenotypes, such as high-density lipoprotein cholesterol, or HDL-C; low-density lipoprotein cholesterol, or LDL-C; total cholesterol, or TC; and triglycerides, or TG, could be identified by a dense gene-centric approach.

To do this, the consortium first examined existing Cardiochip data from more than 66,000 individuals from 32 previous studies. "As we had a considerable number of individual-level datasets and deep coverage in so many loci it was very attractive to perform secondary analyses such as conditional analyses to look for independent signals," Keating said.

Then the consortium sought independent replication in other studies covering more than 25,000 individuals, as well as in a previously reported study of 100,000 individuals. Ultimately, they identified four, six, ten, and four unreported SNPs in established lipid genes for HDL-C, LDL-C, TC, and TGs, respectively. They also identified several lipid-related SNPs in previously unreported genes: DGAT2, HCAR2, GPIHBP1, PPARG, and FTO for HDL-C; SOCS3, APOH, SPTY2D1, BRCA2, and VLDLR for LDL-C; SOCS3, UGT1A1, BRCA2, UBE3B, FCGR2A, CHUK, and INSIG2 for TC; and SERPINF2, C4B, GCK, GATA4, INSR, and LPAL2 for TGs.

Fotios Drenos, a senior research associate at UCL and a senior author on the paper, said that the hits on the FTO and BRCA2 genes in particular were "interesting," as FTO is associated with obesity, while BRCA2 is associated with various cancers, including breast and ovarian cancer.

"We were also able to identify rare mutations associated with early-life high cholesterol in the general population, although the rarity of these polymorphisms does not permit as yet any safe conclusions on their actual impact on the lipid profile," Drenos told BioArray News this week.

"Finally, our work clearly shows that the genetic architecture of the relevant loci is more complex than previously thought, with multiple SNPs able to provide more information on the effect of the gene than the top signal in each case," he added.

Keating credited the Cardiochip in part with the results of the project, stating that it has advantages over current generic whole-genome genotyping chips, such as Illumina's Human1M-Duo BeadChip or Affymetrix's SNP 6.0 Array.

"A major limitation and strength of this array is that it is mostly gene-centric, and it focused only on 2,000 prioritized loci," said Keating. "At the time of development, it had much deeper coverage in a number of known and putative lipid loci than the conventional [genome-wide association study] tools, and it took advantage of public resequencing efforts of prioritized [cardiovascular disease] candidate genes such as SeattleSNPs," he said. "This really helped to delineate some of the independent genetic signals within such loci … and such approaches will certainly make inroads in explaining portions of the missing genetic variance for lipid levels."

Phenotypes and Outcomes

While array technology powered the current study, Keating and co-authors said that organizing the project in itself allowed the researchers to make new discoveries.

"The major strength of these types of studies is bringing the phenotypes and investigators together," said Keating. "Genomic platforms are constantly getting better and cheaper but the phenotypes and outcomes, and long-term interaction amongst the investigators, is the most important component," he said.

Drenos said similarly in a statement that the study results "underscore how international sharing of resources and datasets paves the way for robust, continuing discoveries of new and unexpected information from human genetic studies."

As Keating noted, the consortium is following this published work with a project to identify which of the reported loci directly cause disease, and how this knowledge can help in the development of novel drugs. The consortium will also devote its pooled resources to identifying interactions among SNPs and biological markers of downstream cardiovascular disease.

"We have also embarked on Mendelian randomization studies using a spectrum of intermediate phenotype and outcome data in attempts to ascertain causality, and there are also gene-gene interaction studies in a number of traits," Keating said of the project. He noted that other projects that relied on the Cardiochip should be described in forthcoming publications.

"There are also a number of meta-analyses coming through shortly with very large numbers," said Keating. He said these projects include efforts focused on body mass index, blood pressure, waist-hip ratio, and biomarkers, as well as multiethnic studies, as over 25,000 samples have been typed using the Cardiochip in non-European samples.

UCL's Drenos noted that he is interested in expanding the lipids-focused project to non-European populations as well, saying that he plans to use information gained through the AJHG study to predict lipid levels between samples of the same or different ethnicity.