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Kim et al. Hepatoma Res 2020;6:85  I  http://dx.doi.org/10.20517/2394-5079.2020.96                                               Page 5 of 10

               pathways, such as inflammation, endothelial dysfunction, and oxidative stress [51,52] . Therefore, we explored
               the relationship between CVD and two of the most well-validated NAFLD-associated loci: PNPLA3 and
               TM6SF2.

               A meta-analysis study, including 60,801 coronary heart disease (CHD) cases and 123,504 controls,
               determined that the rs738409 variant in PNPLA3 showed a protective effect against CVD [OR: 0.92 (0.87-
                             [53]
               0.97), P = 0.002] . In another study, the G allele of rs738409 was inversely related to CHD in 576 patients
                                                               [54]
               who underwent elective coronary angiography (P = 0.02) . However, this trend of association between the
               rs738409 G risk allele and CHD has been inconsistent. Another study found no association between the G
                                                                     [55]
               risk allele of rs738409 and CHD [OR: 0.98 (0.95-1.02), P = 0.79] . Interestingly, in a study including 1,103
               premature CHD patients and 1,469 healthy controls, the presence of the G allele of rs738409 was associated
               with increased risk of premature CHD development among T2DM patients [OR 1.20 (1.011-1.421), P
                      [56]
               = 0.042] . In addition to CHD, the G risk allele of rs738409 was reportedly linked to a greater risk of
               increased thickness of the carotid artery intima-media in 162 patients with biopsy-proven NAFLD [OR:
               2.94 (1.12-7.70), P = 0.02], and this finding was validated in 267 patients with biopsy-proven or clinical
               NAFLD .
                      [57]
               The rs58542926 variant in TM6SF2, which is associated with fatty liver, is also reportedly protective against
                                                                                             [58]
               CVD by lowering serum lipid levels (total cholesterol, LDL-cholesterol, and triglycerides) . This means
               that the T allele of rs58542926 in TM6SF2 confers protection against CVD at the expense of a higher risk of
               NAFLD. A meta-analysis confirmed that the rs58542926 T allele is associated with a tendency of decreased
                                                     [53]
               CVD risk [OR: 0.951 (0.92-0.98), P = 0.005] . Moreover, in a smaller cross-sectional study, this allele was
                                                                             [29]
               related to a decreased risk of carotid artery plaques [OR: 0.49 (0.25-0.94)] .
               Despite these studies, the shared genetic causality between CVD and NAFLD remains unclear. The four
               genes that are most commonly reported to be associated with NAFLD - PNPLA3, TM6SF2, MBOAT7,
               and GCKR - have been analyzed by weighted fixed-effects statistical modeling, revealing no association
               of NAFLD with CVD [OR: 1.00 (0.99-1.91), P = 0.93] . These findings indicate that more complex
                                                                [59]
               relationships may exist between NAFLD and CVD. Exploring the roles of NAFLD-associated variants
               in CVD risk demonstrates that biologically relevant insights can be obtained by identifying individual
               pleiotropic loci that affect different outcomes. Clearly, more research is needed.


               APPLICATIONS FOR PRECISION MEDICINE
               There are several other genetic variants associated with NAFLD [Supplementary Table 1]. These
               associations with SNPs from GWAS are generally reported as P values and/or effect sizes. However, these
               metrics do not fully reflect the SNP’s ability to differentiate between the control and the phenotype of
               interest. To apply genetic information for disease prediction, it is important to not only focus on the
               statistical power of a variant but also on the measurement of area under the ROC curve, which summarizes
               the true and false positive rates for a binary outcome .
                                                            [60]

               Another emerging metric is the development of polygenic risk scores (PRSs), which reflect the risk
               accumulation based on multiple SNPs, and can be calculated as a weighted sum of the disease risk alleles
               carried by an individual . PRS use would be a sensible approach in the study of NAFLD, as both common
                                   [61]
                                                                                   [62]
               and rare SNPs are related to NAFLD risk, irrespective of clinical risk factors . However, there is little
               evidence of the clinical application of PRSs. Similarly, Krawczyk et al.  reported that the summed number
                                                                          [63]
               of risk alleles (0-5) for three genes (PNPLA3, TM6SF2, and MBOAT7) was significantly correlated with the
               individual risks of increased hepatic triglyceride content and elevated serum liver enzyme levels (AST and
               ALT). This study further suggests that the historical concept of genetic risk score (GRS) - which includes
               the contemporary concept of a continuous spectrum of NAFLD risk - could also be useful for predicting
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