Human genetics of metabolic dysfunction-associated steatotic liver disease: from variants to cause to precision treatment

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by increased hepatic steatosis with cardiometabolic disease and is a leading cause of advanced liver disease. We review here the genetic basis of MASLD. The genetic variants most consistently associated with hepatic steatosis implicate genes involved in lipoprotein input or output, glucose metabolism, adiposity/fat distribution, insulin resistance, or mitochondrial/ER biology. The distinct mechanisms by which these variants promote hepatic steatosis result in distinct effects on cardiometabolic disease that may be best suited to precision medicine. Recent work on gene-environment interactions has shown that genetic risk is not fixed and may be exacerbated or attenuated by modifiable (diet, exercise, alcohol intake) and nonmodifiable environmental risk factors. Some steatosis-associated variants, notably those in patatin-like phospholipase domain-containing 3 (PNPLA3) and transmembrane 6 superfamily member 2 (TM6SF2), are associated with risk of developing adverse liver-related outcomes and provide information beyond clinical risk stratification tools, especially in individuals at intermediate to high risk for disease. Future work to better characterize disease heterogeneity by combining genetics with clinical risk factors to holistically predict risk and develop therapies based on genetic risk is required.

Figure 1. PRSs.

(A) Sample distribution of risk alleles, which when combined and weighted by effect size, can contribute to calculation of a continuous PRS. (B) Sample PRS plotted versus the percentage of individuals with cirrhosis to show how this score can identify some individuals with high risk of developing the disease.

Figure 2. Gene-environment interactions.

(A) Schematic of gene-environment interactions. In this hypothetical example, the prevalence of hepatic steatosis (y axis) in individuals with low (red) vs. high (blue) environmental risk increases in a dose-dependent manner based on genetic risk (x axis). However, the effect of environmental risk is much greater in those with low genetic risk (absolute difference 10%) versus high genetic risk (absolute difference 30%), indicating a gene-environment interaction. (B) Summary of reported gene-environment interactions for hepatic steatosis severity or liver-related complications in MASLD. The leftmost column lists genes whose variants are known to interact with environmental risk. The top row displays categories of environmental risk factors that interact with genetic risk. Environmental risk factors in red indicate that higher levels of the risk factor confer greater risk of liver disease in those with higher genetic risk, whereas risk factors in blue indicate that higher levels of the risk factor confer lower risk in those with higher genetic risk. Checkmarks show where there is evidence for interactions between specific genes or the polygenic risk score with categories of environmental factors.

Figure 3. Risk gene subgroups associated with PheWAS-identified phenotypes.

Panel (A) illustrates the subgroups of risk genes in the context of intracellular and systemic functions linked to their gene products. Panel (B) summarizes the phenotype effects (top row) associated with each risk gene subgroup (leftmost column) that were identified in previously reported PRSs based on human outcomes. Effect sizes for continuous traits are reported as for β values on rank-based inverse normally transformed traits, and as log odds ratio for dichotomous traits. PRSs with significant positive associations are shown as red up arrows, those with significant negative associations are shown as blue down arrows, and those with no significant association (P > 0.05), as hyphens. Effect sizes for continuous traits are reported as β values on rank-based inverse normally transformed traits, and as log(odds ratio) for dichotomous traits. One, two, three, or four arrows indicate absolute value of effect size of <0.04, 0.04-<0.08, 0.08-<0.16, or ≥0.16, respectively. Epidemiologically-expected associations are shown at the bottom and the arrows are agnostic to effect size. VLDL, very low-density lipoproteins; TRIG, triglycerides; WHRadjBMI, waist-hip ratio adjusted for BMI; DM, diabetes mellitus; HTN, hypertension. Figure adapted from ref. with permission from Springer Nature, which retains the rights to the reference image. MTTP effect on steatosis based on meta-analysis with additional cohorts beyond UK Biobank.