Assessing the genetic burden of familial hypercholesterolemia in a large middle eastern biobank

Abstract

Background: The genetic architecture underlying Familial Hypercholesterolemia (FH) in Middle Eastern Arabs is yet to be fully described, and approaches to assess this from population-wide biobanks are important for public health planning and personalized medicine.

Methods: We evaluate the pilot phase cohort (n = 6,140 adults) of the Qatar Biobank (QBB) for FH using the Dutch Lipid Clinic Network (DLCN) criteria, followed by an in-depth characterization of all genetic alleles in known dominant (LDLR, APOB, and PCSK9) and recessive (LDLRAP1, ABCG5, ABCG8, and LIPA) FH-causing genes derived from whole-genome sequencing (WGS). We also investigate the utility of a globally established 12-SNP polygenic risk score to predict FH individuals in this cohort with Arab ancestry.

Results: Using DLCN criteria, we identify eight (0.1%) 'definite', 41 (0.7%) 'probable' and 334 (5.4%) 'possible' FH individuals, estimating a prevalence of 'definite or probable' FH in the Qatari cohort of ~ 1:125. We identify ten previously known pathogenic single-nucleotide variants (SNVs) and 14 putatively novel SNVs, as well as one novel copy number variant in PCSK9. Further, despite the modest sample size, we identify one homozygote for a known pathogenic variant (ABCG8, p. Gly574Arg, global MAF = 4.49E-05) associated with Sitosterolemia 2. Finally, calculation of polygenic risk scores found that individuals with 'definite or probable' FH have a significantly higher LDL-C SNP score than 'unlikely' individuals (p = 0.0003), demonstrating its utility in Arab populations.

Conclusion: We design and implement a standardized approach to phenotyping a population biobank for FH risk followed by systematically identifying known variants and assessing putative novel variants contributing to FH burden in Qatar. Our results motivate similar studies in population-level biobanks - especially those with globally under-represented ancestries - and highlight the importance of genetic screening programs for early detection and management of individuals with high FH risk in health systems.

Keywords: Cholesterol; Dutch lipid Clinic Network; Dyslipidemias; LDL; LDLRAP1; Lipoproteins/Receptors; Middle East region.; Polygenic risk scores; Premature coronary artery disease; Sitosterolemia.

Fig. 1

Distribution of higher LDL-C levels (≥ 4 mmol/L) among QBB participants according to their age group

Fig. 2

Structural variant analysis of loci 1:54828792-55862308 showing gene duplication in PCSK9 gene. Next-generation sequencing (NGS)-based detection of a PCSK9 copy number variation (CNV) in a likely FH individual. The duplication is marked by an increase in average read depth within the interval of (Chr1:54,828,792-55,862,308, 1.03 Mb) and is supported by paired-end reads (red boxes connected by thin line) that map to either side of the affected allele, confirming the duplication breakpoints. Region affected by duplication covers all 12 exons of the PCSK9 gene, plus the rs11206510 probe 8,655 bases upstream of PCSK9.

Fig. 3

Mapping of key regions in the 3D structure of PCSK9 and LDLR. A) Structure of PCSK9 showing arrangement of functional domains, pro-domain (brown), catalytic domain (gray) and cystine and histidine-rich domain (CHRD, cyan). (B-D) Zoomed in view of the domains [(B) pro-domain, (C) catalytic domain (D) CHRD (also binding region for annexin A2)] with labels. E) Structure of a PCSK9-LDLR complex shown with an epidermal growth factor (EGF) domain composed of EGF_A (purple), EGF_B (forest), EGF_C (orange) and β-propeller regions (slate). F) Zoomed in view of the β-propeller with neighbor domains. In this figure, positions are mapped and colored as follows: novel mutations, blue spheres; known mutations, yellow; catalytic triad, magenta; substrate binding region, pink; LDLR (EGF_A) binding region in PCSK9, green; allosteric inhibitor site, red

Fig. 4

LDL-C SNP score for Dutch Lipid Clinic Network criteria. 12 SNP LDL-C SNP scores are represented as mean±SD. Compared to ‘unlikely’ FH group, both ‘definite or probable’ FH and ‘possible’ FH groups have significantly higher LDL-C scores (one-way ANOVA). However, there is no statistical difference observed between ‘definite or probable’ FH and ‘possible’ FH groups (p=0.17)