- Massachusetts General Hospital researchers derived and validated a new score for evaluating the combined effect of multiple genetic (polygenic) risk factors for extreme blood lipid levels
- Whole genome sequence analysis of 16k multi-ancestry individuals contributed new mutations to the list of previously known gene mutations regulating blood lipid levels
- High polygenic scores were observed with high LDL-C in up to 10-fold more cases than were monogenic mutations (depending upon ethnic ancestry)
Blood-lipid levels are a heritable risk factor for atherosclerotic cardiovascular disease. Conventional approaches to identifying these factors have included gene and exome (genome-wide exon sequence) mutation screens as well as single nucleotide polymorphism (SNP) association studies. Genome-wide studies previously focused primarily on participants of European ancestry. Despite the inherent technical and other limitations of these approaches, they have led to the discovery of several single gene (monogenic) factors associated with blood lipid levels.
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A team lead by Pradeep Natarajan, MD, MMSc, director or Preventive Cardiology and Sekar Kathiresan, MD, director of the Center for Genomic Medicine at Massachusetts General Hospital recently reported their findings from a whole-genome sequencing (WGS) study of 16,324 participants of European, African, East Asian and Hispanic ancestries in Nature. Their findings provide a genome-wide, trans-ethnic population-based assessment of mutations associated with abnormal blood-lipid levels.
The team first conducted a genetic association study using all mutations detected in at least one of the populations used in this study. Results showed 592, 697, 447 and 522 mutations in four known genes involved in, respectively, total cholesterol, low-density lipid cholesterol (LDL-C), high-density lipid-cholesterol (HDL-C) and triglycerides. They also identified five new regions of the genome possibly involved in regulating blood lipids. In addition, they identified a haplotype (a genetic marker consisting of a unique combination of sequence variants) around the LDLR gene in African Americans, which is associated with a 28 mg/dl lowering of LDL-C.
To improve the power of detecting genetic factors for blood lipid levels, the team focused on the most severe gene mutations, those predicted to abolish protein function. For each gene, all identified severe mutations were combined into an aggregate mutation set. These aggregate sets were analyzed for association with blood lipid levels in study participants who were carriers versus non-carriers. This analysis identified six genes already known to be involved in Mendelian forms of dyslipidemia.
Mutations in gene regions that don't encode protein (non-coding variants) were also analyzed as aggregates. However, these aggregates were constructed based on either a "sliding window," on proximity to a gene enhancer or promoter, or based on direct physical interaction with a gene, predicted from chromosome folding data. This analysis yielded only suggestive evidence of association between two gene loci and blood lipid levels. Aside from two additional non-coding variants with weak associations that didn't stand up to further scrutiny, no additional signals for non-coding variants were identified.
Another goal of the study was to understand the cumulative risk for an individual carrying multiple genetic (polygenic) risk factors. The study assessed both protein coding and non-coding mutations.
First, six known genes for elevated LDL-C were screened for mutations in patients with "extreme" LDL-C levels. Then, risk alleles were combined to test a new polygenic scoring method. The results showed that only 2% of participants of European ancestry carried a monogenic mutation and 23% had a high polygenic score, while 3% of African American participants carried a monogenic mutation and 13% had a high polygenic score.
This trans-ethnic, genome-wide mutation analysis added new gene mutations to one-third of previously known genes regulating blood lipids. The study also identified a genetic marker in African Americans involving LDLR and that is associated with a lower level of LDL-C. The team developed a polygenic scoring algorithm that may be useful for diagnosing severe hypercholesterolemia in cases where single gene mutations are not found.
Finally, although this study did not reveal convincing evidence of any non-coding variants associated with aberrant blood lipid levels, power calculations indicate a much larger sample size for a future analysis will be required to detect any such association.
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