Hepatic steatosis is known to be heritable, but its genetic basis is mostly uncharacterized. Steatosis is associated with metabolic and adiposity features; recent studies hypothesize that shared genetic effects between these traits could account for some of the unexplained heritability. This study aimed to quantify these genetic associations in a family-based sample of non-Hispanic white adults. 704 participants (18-95 years, 55.8% female) from the Fels Longitudinal Study with an MRI assessment of liver fat were included. Quantitative genetic analyses estimated the age- and sex-adjusted heritability of individual traits and the genetic correlations within trait pairs. Mean liver fat was 5.95% (SE = 0.23) and steatosis (liver fat >5.56%) was present in 29.8% of participants. Heritability (h²± SE) of steatosis was 0.72 ± 0.17 (p = 6.80e^-6). All other traits including liver enzymes, fasting glucose, HOMA-IR, visceral and subcutaneous adipose tissue (VAT, SAT), body mass index, body fat percent, waist circumference, lipids and blood pressure were also heritable. Significant genetic correlations were found between liver fat and all traits except aspartate aminotransferase (AST), and among most trait pairs. Highest genetic correlations were between liver fat and HOMA-IR (0.85 ± 0.08, p = 1.73e^-8), fasting glucose and ALT (0.89 ± 0.26, p = 6.68e^-5), and HOMA-IR with: waist circumference (0.81 ± 0.12, p = 3.76e^-6), body fat percent (0.78 ± 0.12 p = 2.42e^-5) and VAT (0.73 ± 0.07, p = 6.37e^-8). Common genes may exist between liver fat accumulation, metabolic features and adiposity phenotypes.

Objective: To examine the associations of metabolic health and obesity phenotypes with liver fat accumulation and hepatic steatosis in adults. Methods: We analyzed 676 non-Hispanic white adults (18–95 years; 55.8% female) from the Fels Longitudinal Study using a cross-sectional design. Participants were classified into metabolically healthy normal weight (MHNW), metabolically healthy obesity (MHO), metabolically unhealthy normal weight (MUNW), and metabolically unhealthy obesity (MUO) phenotypes. Metabolically unhealthy status was defined as the presence of ≥1 metabolic dysfunction, consistent with prior epidemiological definitions; secondary analyses using ≥2 were also performed. Obesity was defined using DXA-derived body fat percentage. Liver fat (%) was quantified using magnetic resonance imaging, and hepatic steatosis was defined as liver fat > 5.56%. Multivariable linear and probit regression models were used to evaluate associations, adjusting for demographic and lifestyle covariates; secondary models additionally examined dietary intake. Results: Mean liver fat was 5.95% (SE = 0.23), and steatosis was present in 29.8% of participants. Compared to MHNW individuals, liver fat was significantly higher in MHO (mean 3.77% vs. 2.67%), MUNW (4.63%), and MUO (8.47%) phenotypes. After covariate adjustment, liver fat was 33.8% (95% CI: 13.7–57.5%) higher in MHO, 28.1% (10.1–49.0%) higher in MUNW, and 113.0% (85.3–144.7%) higher in MUO relative to MHNW. Corresponding increases in steatosis probabilities were observed across phenotypes. No individual dietary component or dietary pattern was significantly associated with liver fat after adjustment. Conclusions: Metabolically healthy obesity was associated with higher liver fat and steatosis probability compared with metabolically healthy normal weight, with levels comparable to metabolically unhealthy normal weight individuals. These findings suggest that the absence of overt metabolic abnormalities does not necessarily indicate a metabolically benign state with respect to liver fat accumulation. Given the cross-sectional design, these results should be interpreted as associations rather than causal relationships.

The identification of causal genomic regions for liver fat accumulation in the context of metabolic dysfunction remains a challenging goal. This study aimed to identify potential endophenotypes for liver fat content and employ them in bivariate linkage searches for pleiotropic genetic regions where targeted association analysis is more likely to reveal significant variants. Multiple metabolic risk and adiposity distribution traits were assessed using the endophenotype ranking value. The top-ranked endophenotypes were then used in a bivariate linkage analysis, paired with liver fat content. Quantitative trait loci (QTLs) identified as significant or suggestive were targeted for measured genotype association analyses. The highest-ranked endophenotypes for liver fat accumulation were insulin resistance (IR), visceral adipose tissue (VAT), and high-density lipoprotein cholesterol (HDL-C). The univariate linkage analysis for liver fat content identified one significant QTL at chromosome 17p13.2 (Logarithm of odds score (LOD) = 2.90, p = 1.29 × 10⁻⁴). The bivariate linkage analysis pairing liver fat with IR and VAT improved the localization of two suggestive QTLs at 13q21.31 (LOD = 2.11, p = 9.03 × 10⁻⁴), and 6q21 (LOD = 2.35, p = 5.07 × 10⁻⁴), respectively. Targeted association analyses within the -1-LOD score regions of these QTLs revealed 17 marginally significant single nucleotide polymorphisms (SNPs) associated with liver fat content or its combination with the selected endophenotypes. The endophenotype-informed linkage analysis successfully identified regions suitable for the targeted association analysis of liver fat content, either alone or in combination with IR or VAT, leading to the discovery of marginally significant variants with potential for future functional studies.