👤 Tevz Gorjanc

Thyroid hormones regulate lipoprotein metabolism-primarily by up-regulating the LDL receptor. Whether TSH relates to LDL-C in hypercholesterolemic children, and whether this depends on familial hypercholesterolemia (FH) status or the underlying defective gene, is uncertain. We evaluated TSH-lipid associations in prepubertal children and tested effect modification by FH status and, within FH, by gene with a pathogenic variant (LDLR vs APOB). We performed a cross-sectional study of prepubertal children referred to the Slovenian national tertiary center through the universal FH screening program or cascade screening. Eligibility required concurrent TSH and fasting lipid measurement and completed genetic testing (pathogenic/likely pathogenic variants in LDLR/APOB/PCSK9 vs polygenic hypercholesterolemia). Among 738 children, 182 (24.7%) were FH-positive (LDLR 132; APOB 50). In the pooled cohort, TSH did not correlate with age or lipids (all p ≥ 0.050). After sex stratification, TSH correlated with triglycerides only in males (ρ = 0.156; p = 0.012). In FH-positive children, TSH correlated with total cholesterol, LDL-cholesterol, and ApoB (ρ ~ 0.184-0.207; all p < 0.050), with no associations in FH-negative children. Interaction testing confirmed effect modification by FH (TSH × FH β = 0.141 mmol/L per mIU/L, p = 0.023). Within FH-positive children, a positive TSH-LDL-C slope was seen in LDLR carriers (β = 0.237, p = 0.004) but not in APOB carriers (β = -0.065, p = 0.655). TSH was positively associated with LDL-C only in FH due to LDLR variants, not in APOB carriers. These findings suggest that genetic background may shape hormonal sensitivity, and that attention to thyroid status could be particularly relevant in LDLR-FH. Show less

Familial hypercholesterolemia (FH) is a genetic disorder characterized by elevated low-density lipoprotein cholesterol (LDL-C) levels from birth, significantly increasing the risk of premature cardiac events and mortality. In Pakistan, despite the potential burden of FH, comprehensive studies evaluating its genetic characteristics, cascade screening significance, and lipoprotein (a) [Lp(a)] levels remain scarce. Understanding these factors is crucial for effective diagnosis, risk assessment, and management of FH in the Pakistani population. After the identification of index case with clinical homozygous FH, characterized by high LDL-C and high Lp(a) levels together with a positive personal and family history of cardiovascular disease, a cascade screening of 66 relatives from a consanguineous family was performed. Blood samples were obtained from all subjects for biochemical and genetic analysis. Simon Broome criteria was applied on children for clinical FH diagnosis. Dutch Lipid Clinic Network scores were calculated for individuals aged ≥16years. Genetic screening was performed using next-generation sequencing to analyse all coding regions and exon-intron borders of the following genes: ALMS1, APOA1, APOB, APOA5, APOC2, APOC3, APOE, ABCA1, ABCG5, ABCG8, CREB3L3, GPIHBP1, LDLR, LDLRAP1, LIPA, LMF1, LPL, and PCSK9. The identified variants were confirmed using Sanger sequencing. Cascade screening identified seven homozygous and 25 heterozygous FH patients with pathogenic variant in the LDLR gene (NM₀₀₀₅₂₇.5: c.2416dupG: p. Val806GlyfsTer11). Additionally, heterozygous variants of uncertain significance were identified in 4 other subjects. This study underscores the high effectiveness of cascade screening in consanguineous families and societies that could lead to early detection and prevention. Show less

Genome-wide association studies (GWAS) aim at identifying genomic regions involved in phenotype expression, but identifying causative variants is difficult. Pig Combined Annotation Dependent Depletion (pCADD) scores provide a measure of the predicted consequences of genetic variants. Incorporating pCADD into the GWAS pipeline may help their identification. Our objective was to identify genomic regions associated with loin depth and muscle pH, and identify regions of interest for fine-mapping and further experimental work. Genotypes for ~ 40,000 single nucleotide morphisms (SNPs) were used to perform GWAS for these two traits, using de-regressed breeding values (dEBV) for 329,964 pigs from four commercial lines. Imputed sequence data was used to identify SNPs in strong ([Formula: see text] 0.80) linkage disequilibrium with lead GWAS SNPs with the highest pCADD scores. Fifteen distinct regions were associated with loin depth and one with loin pH at genome-wide significance. Regions on chromosomes 1, 2, 5, 7, and 16, explained between 0.06 and 3.55% of the additive genetic variance and were strongly associated with loin depth. Only a small part of the additive genetic variance in muscle pH was attributed to SNPs. The results of our pCADD analysis suggests that high-scoring pCADD variants are enriched for missense mutations. Two close but distinct regions on SSC1 were associated with loin depth, and pCADD identified the previously identified missense variant within the MC4R gene for one of the lines. For loin pH, pCADD identified a synonymous variant in the RNF25 gene (SSC15) as the most likely candidate for the muscle pH association. The missense mutation in the PRKAG3 gene known to affect glycogen content was not prioritised by pCADD for loin pH. For loin depth, we identified several strong candidate regions for further statistical fine-mapping that are supported in the literature, and two novel regions. For loin muscle pH, we identified one previously identified associated region. We found mixed evidence for the utility of pCADD as an extension of heuristic fine-mapping. The next step is to perform more sophisticated fine-mapping and expression quantitative trait loci (eQTL) analysis, and then interrogate candidate variants in vitro by perturbation-CRISPR assays. Show less

Backfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds. Data comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10 We found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN. Our results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis. Show less