Population genomics expands the picture of inherited cholesterol risk.

Familial hypercholesterolemia is an inherited disorder associated with elevated LDL cholesterol and increased cardiovascular risk. Its genetic architecture can involve more than a single high-impact variant, making broad and diverse genomic studies especially valuable.

A cohort designed to capture genetic complexity

The research team analyzed 300 participants from the Mexican Familial Hypercholesterolemia Registry. The study combined whole-exome sequencing with short-read and long-read whole-genome sequencing, allowing the investigators to examine small sequence changes, larger structural alterations, and common polygenic contributions in the same cohort.

According to the peer-reviewed paper, pathogenic point mutations and structural variants across established familial-hypercholesterolemia genes—together with findings involving genes such as APOE, CREB3L3, and PLIN1—supported a molecular diagnosis in 67% of families studied.

Rare variants and polygenic risk can coexist

The analysis did not stop at rare, high-impact variants. Ancestry-adjusted polygenic risk scores also identified inherited liability related to coronary artery disease, blood pressure, lipid levels, and type 2 diabetes. The LDL-related polygenic signal appeared both in people carrying rare pathogenic variants and, more prominently, among some participants without a single rare variant that fully explained their diagnosis.

The researchers also described a whole-gene duplication and common non-coding variants near PDZK1 that contributed to the cohort’s genetic burden. Those findings were evaluated against data from the UK Biobank, adding an independent source of evidence.

Why population diversity matters in genomic medicine

Much of the historical genetics literature on familial hypercholesterolemia has focused on populations of European ancestry. Studying a Mexican cohort created an opportunity to identify ancestry-linked variation and structural changes that may be missed when genomic reference data are less representative.

The work illustrates a broader principle in precision medicine: clinically meaningful risk may reflect the combined influence of rare variants, structural variation, common genetic background, and ancestry. Integrating those layers can improve the scientific understanding of inherited disease and help close gaps in genomic knowledge across populations.

Dr. Faghihi’s contribution to the scientific record

Dr. Mohammad Ali Faghihi is listed among the study’s coauthors and was affiliated with Galatea Bio in the published record. The official paper should be consulted for the complete author list, affiliations, methods, limitations, and contribution details.

This feature summarizes the published research record and does not provide medical advice or assign individual author contributions beyond the information reported by the journal.