Case Control Study Open Access
Copyright ©The Author(s) 2017. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Sep 18, 2017; 9(26): 1101-1107
Published online Sep 18, 2017. doi: 10.4254/wjh.v9.i26.1101
Regional differences in genetic susceptibility to non-alcoholic liver disease in two distinct Indian ethnicities
Govardhan Bale, Avanthi Urmila Steffie, Vishnubhotla Venkata Ravi Kanth, Mitnala Sasikala, Asian Healthcare Foundation, Hyderabad 500082, Telangana, India
Padaki Nagaraja Rao, Mithun Sharma, Duvvur Nageshwar Reddy, Asian Institute of Gastroenterology, Hyderabad 500082, India
ORCID number: Govardhan Bale (0000-0002-2027-1647); Avanthi Urmila Steffie (0000-0001-9086-4336); Vishnubhotla Venkata Ravi Kanth (0000-0001-6970-0169); Padaki Nagaraja Rao (0000-0003-2983-5768); Mithun Sharma (0000-0003-4497-9209); Mitnala Sasikala (0000-0002-3785-0530); Duvvur Nageshwar Reddy (0000-0001-7540-0496).
Author contributions: Bale G and Steffie AU performed research; Rao PN, Sharma M and Reddy DN recruited patients; Ravi Kanth VV, Sasikala M and Rao PN designed the research; Ravi Kanth VV monitored the study, performed statistical analyses, and drafted the manuscript.
Institutional review board statement: The study was reviewed and approved by Institutional review (Scientific) board (AIG/AHF IRB: 16/2014) of Asian Institute of Gastroenterology.
Informed consent statement: All study participants provided informed written consent prior to study enrollment.
Conflict-of-interest statement: None to declare.
Data sharing statement: No additional data available.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Correspondence to: Dr. Vishnubhotla Venkata Ravi Kanth, Group Leader-Genetics, Asian Healthcare Foundation, 6-3-661, Somajiguda, Hyderabad 500082, Telangana, India.
Telephone: +91-40-23378888 Fax: +91-40-23324255
Received: April 27, 2017
Peer-review started: April 28, 2017
First decision: May 23, 2017
Revised: June 29, 2017
Accepted: July 7, 2017
Article in press: July 10, 2017
Published online: September 18, 2017


To validate the association of variants in PNPLA3 (rs2281135) and TM6SF2 (rs58542926) genes with ultrasound detected non-alcoholic fatty liver disease (NAFLD).


A total of 503 individuals with and without fatty infiltration were recruited. Fatty infiltration was confirmed based on ultrasound findings. Anthropometric data and blood samples were collected from the study group. DNA was isolated from peripheral blood, quality and quantity was assessed by gel electrophoresis and spectrophotometer respectively. Genotyping of the variants in PNPLA3 and TM6SF2 genes was carried out by employing taqman probes (C_15875080_10 for PNPLA3 and C_8946351_10 for TM6SF2 SNP) on real time PCR (Stepone-Lifetechnologies). Genotype data was tested for deviations from Hardy-Weinberg equilibrium. χ2 test was used to analyze the statistical significance of the difference in genotype distribution of the studied variants in patients and controls and the strength of association was expressed as odds ratio (95%CI). A two-tailed P value of ≤ 0.05 was considered statistically significant.


The study group comprised of 503 individuals of which 256 had fatty infiltration and 247 without fatty infiltration and thus formed the patient and control groups respectively. As the patient group could be divided in to two distinct ethnicities (ancestral South Indians-ASI and North-East Indians-NEI), further recruitment of control cohort and association analyses was carried out based on ethnicities. Of the 256 with fatty infiltration 93 were ASI and 163 were NEI and of the 247 controls 138 were ASI and 109 were NEI. As expected, there were significant differences in the anthropometric and other clinical data between the control and the patient groups. However significant differences within the ethnicities were also noted. While rs2281135 in PNPLA3 gene was significantly associated (P = 0.03) with higher risk (odds 1.9, 95%CI: 1.5-3.14, P = 0.03) of NAFLD in NEI ethnicity, rs58542926 in TM6SF2 gene was significantly associated with NAFLD with a 2.7 fold higher risk (odds 2.7, 95%CI: 1.37-5.3, P = 0.0004) of the disease. There were significantly higher proportions of individuals with variants in both the genes in the patient group in both ASI (patients - 14/93 and controls - 7/138; P = 0.009) and NEI ethnicities (patients - 17/163 and controls - 7/109; P = 0.01).


Although the study identified distinct genetic susceptibility in the two ethnicities, transheterozygosity of the variants suggests higher risk of NAFLD in individuals with both the variants.

Key Words: Transmembrane 6 superfamily 2, Patatin-like phospholipase domain-containing protein 3, Fatty infiltration, Genetic susceptibility, Ethnicity, Non-alcoholic fatty liver disease, Cirrhosis, Single nucleotide polymorphism

Core tip: Non-alcoholic fatty liver disease has become the leading cause of liver damage contributing to considerable mortality. The spectrum spans from simple steatosis, through non alcoholic steatohepatitis, fibrosis, cirrhosis and finally to hepatocellular carcinoma. Genetic variants have now been recognized to contribute to a substantial extent to the onset of the disease. Reliable genetic markers that confer susceptibility to the disease have to be identified for better management of the disease. Identification of at risk individuals at a younger age by screening for genetic susceptibility will aid in better management by early interventions and lifestyle changes. This study identified regional differences and ethnicity based genetic susceptibility for non-alcoholic liver disease.


Non-alcoholic liver disease (NAFLD) describes a range of liver conditions beginning with fatty liver (accumulation of fat in the liver) that progresses to non-alcoholic steatohepatitis (NASH; fat accumulation along with inflammation and scarring) and cirrhosis (scar tissue replaces hepatic cells)[1], that may finally lead to hepatocellular carcinoma (HCC)[2]. While conditions up to NASH are reversible[3], progression beyond NASH to cirrhosis is irreversible[4]. Therefore it is very important to identify individuals with genetic susceptibility to fat accumulation at an early stage so that appropriate interventions can be planned to curtail/avoid progression to higher stages. Environmental factors including intake of calories[5], processed food[6] and sedentary lifestyles[7] have an impact on the predisposition of an individual to fatty liver and progression. Apart from environmental factors various studies have now confirmed the role of genetics in conferring susceptibility to the disease. Diseases with complex traits including NAFLD result from interactions between environment and polygenic genetic susceptibility made up of many independent modifiers[8]. Family aggregation, studies on twins and differences in susceptibility and progression suggest a significant heritable component to NAFLD that may be classified under “common disease-common variant” hypothesis[9].

The first Genome wide association study for NAFLD identified a SNP in PNPLA3 gene (rs738409; c.444 C > G, p.I148M). Carrier of the minor allele and 148M was associated with a twofold increase in HTGC (Hepatic triglyceride content)[10]. Subsequent to this, the SNP was replicated in almost all the ethnicities successfully[8]. Further, two exome wide association studies[11,12] carried out independently in African-American and Norwegian ethnicities identified that a variant rs58542926 (p.E167K) in TM6SF2 gene was associated with susceptibility to NAFLD, influencing total cholesterol levels and enhanced risk of myocardial infarction. Subsequently, functional studies identified TM6SF2 as a regulator of liver fat metabolism influencing secretion of triglycerides and lipid droplet content in the liver[13]. A recent review suggested that male sex, PNPLA3 I148M, TM6SF2 E167K and low birth weight as important predictors of adult NAFLD[14] reiterating the importance of variants in both PNPLA3 and TM6SF2 genes.

Our earlier pilot study[15] identified variants in PNPLA3 (rs738409), PARVB (rs2073080), SAMM50 (rs2143571) and PZP (rs6487679) genes to be associated with a higher risk of fatty infiltration in individuals of NEI ethnicity. In the present study we replicated variants namely rs58542926 in TM6SF2 and rs2281135 in PNPLA3 genes, identified earlier[12] to confer susceptibility to NAFLD in two distinct ethnicities. While one ethnicity belonged to South India, the other belonged to the North-Eastern region of the country. An earlier study on South Indians has reported that the genomic affinity is proportionate to caste rank-the upper castes being most similar to Europeans, while the lower castes are more similar to Asians[16]. However, the Northeast region’s population results from ancient and continuous flows of migrations from Indo-Gangetic India, Tibet, the Himalayas, present day Bangladesh and Myanmar[17].


A total of 503 individuals were recruited for the present study from the Hepatology clinics of Asian Institute of Gastroenterology. Although liver biopsy is considered to be the gold standard for identifying NAFLD, risk of complications, costs involved and ethical concerns limit its use, hence, patients with fatty infiltration were recruited based on ultrasound findings. Ethnicity, age and sex matched healthy subjects who volunteered to be part of the study were recruited as controls based on the sole criteria of the absence of liver fat on ultrasonography with normal liver function tests and negative for other viral indications. Written informed consent was obtained from individuals and the study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Institutional Review (Scientific) Board (AIG/AHF IRB: 16/2014). Demographic and anthropometric details (height, weight, BMI and waist circumference) were collected. Whole blood (3 mL) was collected in pre coated EDTA containers from the study group and stored at -20 °C until further analysis. Biochemical investigations like ALT, viral markers and lipid profiles were estimated as per standard methods.


DNA was isolated from blood using a commercial kit (Bioserve Biotechnologies, Hyderabad) following manufacturers protocol. DNA with high molecular weight on agarose gel and A260/280 ratios between 1.8-2.0 were included for genotyping analyses. All the samples were genotyped for SNPs namely rs2281135 in PNPLA3 and rs58542926 in TM6SF2 genes using Taqman single nucleotide genotyping assay (Life Technologies, United States) on the Realtime polymerase chain reaction (PCR) platform. PCR for genotyping consisted of 5 μL of 2 × Taqman genotyping master mix, 0.5 μL of 1 × assay mix (C_15875080_10 for PNPLA3 and C_8946351_10 for TM6SF2 SNP) and 4.5 μL consisting of 8-10 ng of DNA in a final volume of 10 μL. PCR was performed on Step One Realtime PCR (Life technologies, United States) with the following cycling conditions: 95 °C for 10 min, 95 °C for 15 s and 60 °C for 1 min with fluorescence read after each cycle for a total of 40 cycles. Genotyping calls were made using the allelic discrimination software (Life Technologies, United States) and only auto calls made by the software were considered for further analysis. A known heterozygous and homozygous variant sample was replicated across all the plates and these known genotypes were verified manually during analysis in all the plates.

Statistical analysis

Data was entered in to MS-EXCEL and edited for consistency. Continuous variables were expressed as mean (95%CI) and categorical variables as proportions. Patient characteristics were compared using Student’s t test for continuous variables and χ2 test for categorical variables. χ2 goodness-of-fit was used to confirm the agreement of the observed genotype frequencies with those of expected (Hardy-Weinberg equilibrium). χ2 test was used to analyze the statistical significance of the difference in genotypic distribution of the studied SNPs in patients and controls. The association of the studied SNPs with the disease and various clinical parameters was expressed as odds ratio (95%CI). For transheterozygoisty analysis chi-square test was applied to compare the number of variant carriers in both the genes between patients and controls. A two-tailed P value of ≤ 0.05 was considered statistically significant. The analyses were carried out using Med cal C package.


Although categorization of the study group based on the ultrasound findings yielded two groups, ethnicity was identified as a major confounder for further analysis. Samples were therefore sorted based on ancestry and classified in to Ancestral South Indians (ASI; n = 231; Controls-138 and patients-93) and North-East Indians (NEI; n = 272; controls-109 and patients-163). All the clinical characteristics as shown in Table 1 namely waist circumference, hip circumference, waist/hip ratio, BMI, ALT, AST, Triglycerides were significantly different between the cohorts from both the ethnicities. Further, there was significant difference in the HDL levels only in the NEI group but not in the ASI group.

Table 1 General characteristics of the study.
CharacteristicsAncestral South Indians
North-East Indians
ControlsPatientsP value1ControlsPatientsP value1
(n = 138) (mean ± SD)(n = 93) (mean ± SD)(n = 109) (mean ± SD)(n = 163) (mean ± SD)
Age (yr)34.2 ± 11.935.3 ± 8.00.4338.5 ± 12.736.5 ± 9.20.13
Gender male/female (n)95/4387/60.6472/37150/130.84
Waist circumference (cm)83.3 ± 9.494.7 ± 10.20.0181.1 ± 10.793.8 ± 10.10.01
Hip circumference (cm)93.0 ± 7.1100.5 ± 8.60.0191.2 ± 6.995.1 ± 8.50.01
Waist/hip ratio0.89 ± 0.060.95 ± ± 0.070.99 ± 0.120.01
BMI (kg/m2)23.2 ± 4.027.7 ± 4.10.0122.1 ± 3.525.7 ± 4.00.01
ALT (IU/L)19.8 ± 7.688.1 ± 49.50.0124.6 ± 7.9119.3 ± 68.30.01
AST (IU/L)21.2 ± 5.455.3 ± 25.60.0124.6 ± 6.972.3 ± 39.80.01
Triglycerides (mg/dL)134.8 ± 72.6169.7 ± 82.10.01131.4 ± 60.8180.3 ± 93.70.05
HDL (mg/dL)38.7 ± 8.536.3 ± 6.90.0947.9 ± 28.740.5 ± 13.80.02
Genotyping and association with clinical traits

While rs58542926 in TM6SF2 gene was significantly associated (P = 0.0004) with a 2.7 fold higher risk of fatty infiltration in ASI ethnicity, rs2281135 in PNPLA3 gene was associated with 1.9 fold higher risk in the NEI ethnicity (Table 2). rs58542926 in TM6SF2 gene was associated with higher ALT, AST levels in the ASI ethnicity and higher BMI in NEI ethnicity. rs2281135 in PNPLA3 gene was associated with ALT, AST levels in the NE ethnicity (Table 3).

Table 2 Genotype distribution of Tm6SF2 and PNPLA3 variants in the two ethnicities studies.
Ancestral South IndiansNorth-East Indians
TM6SF2 rs58542926
TM6SF2 rs58542926
Controls (n)Patients (n)Odds95%CIχ2P value1Controls (n)Patients (n)Odds95%CIχ2P value1
Wild (CC)110612.71.37-5.315.280.0004801101.510.86-2.662.290.31
Heterozygous (CT)18222244
Homozygous (TT)0726
PNPLA3 rs2281135
Wild (GG)79491.340.78-2.332.120.3463711.91.5-3.146.480.03
Heterozygous (GA)45353271
Homozygous (AA)46917
Table 3 Association of variants with clinical data.
Ancestral South Indians
North-East Indians
TM6SF2 rs58542926
< 22.9491220.030.98751858.240.01
> 22.911028494472
PNPLA3 rs2281135
< 22.9382320.590.744837100.0770.96
> 22.982528715814
TM6SF2 rs58542926
< 30891716.520.038652012.450.29
> 3056195108427
PNPLA3 rs2281135
< 30643941.30.525428410.270.005
> 3042335667119
TM6SF2 rs58542926
< 309018010.190.006682030.960.61
> 3055186105425
PNPLA3 rs2281135
< 30654032.890.23543075.550.06
> 3041326666916
TM6SF2 rs58542926
< 150541341.640.43582563.890.14
> 1503312160291
PNPLA3 rs2281135
< 150422580.290.86493282.490.28
> 15029143394011
TM6SF2 rs58542926
> 4026502.030.36492230.050.97
< 405718359273
PNPLA3 rs2281135
> 40201102.520.28392870.50.77
< 4046266715814
Transheterozygosity analysis

On transheterozygosity analysis (χ2 test), it was seen that there was a significant difference in individuals who carried variants in both the genes in the patient group as compared to control group in ASI ethnicity (P = 0.009), but not NEI ethnicity (P = 0.26) and increased the risk of the disease by 3 fold (OR = 3.11, 95%CI: 1.20-8.04) in the ASI ethnicity. Further, there were significantly higher proportion of individuals with variants in both the genes in the patient group in ASI (patients - 14/93 and controls - 7/138; Z proportion test P = 0.009) and NEI ethnicities (patients - 17/163 and controls - 7/109; Z proportion test P = 0.06).

Comparison of controls and patients within the ethnicities

There were significant differences in BMI (higher) AST, ALT and HDL levels (lower levels) in ASI controls as compared to NEI controls. While patients of ASI ethnicity had higher hip circumference, BMI and lower HDL levels patients of NEI ethnicity had higher waist-hip ratios, ALT and AST levels. Likewise, there were significant differences in hip circumference, BMI (higher levels in ASI as compared to NEI patients), waist-hip ratio, ALT, AST levels (higher levels in NEI patients as compared to ASI patients). It was also interesting to note that the HDL levels were significantly lower in the ASI patients (Table 4).

Table 4 Comparison of clinical data within the ethnicities.
ASI controls (n = 138) (mean ± SD)NEI controls (n = 109) (mean ± SD)P value1ASI patients (n = 93) (mean ± SD)NEI patients (n = 163) (mean ± SD)P value
Age (yr)34.2 ± 11.938.5 ± 12.70.00635.3 ± 8.036.5 ± 9.20.29
Gender male/female (n)95/4372/37-87/6150/13-
Waist circumference (cm)83.3 ± 9.481.1 ± 10.70.2794.7 ± 10.293.8 ± 10.10.54
Hip circumference (cm)93.0 ± 7.191.2 ± 6.90.23100.5 ± 8.695.1 ± 8.50.01
Waist/hip ratio0.89 ± 0.060.89 ± ± 0.130.99 ± 0.120.03
BMI (kg/m2)23.2 ± 4.022.1 ± 3.50.0227.7 ± 4.125.7 ± 4.00.003
ALT (IU/L)19.8 ± 7.624.6 ± 7.90.0188.1 ± 49.5119.3 ± 68.30.01
AST (IU/L)21.2 ± 5.424.6 ± 6.90.0155.3 ± 25.672.3 ± 39.80.01
Triglycerides (mg/dL)134.8 ± 72.6131.4 ± 60.80.79169.7 ± 82.1180.3 ± 93.70.44
HDL (mg/dL)38.7 ± 8.547.9 ± 28.70.0236.3 ± 6.940.5 ± 13.80.01

In a cohort of 503 individuals comprising individuals with and without NAFLD belonging to two distinct Indian ethnicities, we show here that rs58542926 in TM6SF2 in South Indian and rs2281135 in PNPLA3 in North-East Indian ethnicities confer higher susceptibility to ultrasound measured NAFLD. Further, there were a significant proportion of individuals with variants in both the genes in the patient group as compared to controls, in both the ethnicities, suggesting that although individually the variants may not confer susceptibility in the ethnicity, however carrying an additional variant might compound the risk of the disease. Our earlier pooled genetic association study in a predominantly North-East Indian ethnicity identified that rs738409 in PNPLA3 gene was associated with higher risk of NAFLD apart from variants in PARVB, SAMM50 and PZP genes[15].

The first Genome wide association study for NAFLD identified rs738409 in PNPLA3 gene conferring susceptibility to NAFLD[10]. Subsequent to this, the variant was found to be associated with the disease in various ethnicities across the world including our own and other studies from India[8,15,18]. PNPLA3 is a 481-residue protein, exhibiting lipase activity against triglycerides in hepatocytes and a missense variant (I148M; rs738409-C>G) results in loss of function promoting hepatic steatosis by limiting triglyceride hydrolysis[19]. Further, another variant (rs2281135) in PNPLA3 gene was identified, that conferred higher risk for NAFLD[11]. rs2281135 is an intronic variant and is known to be in tight linkage disequilibrium with rs738409 in ethnicities including African, Caucasian, Mexican Americans and East African (HapMap data). Apart from variants in PNPLA3, recent research has identified rs58542926 in TM6SF2 gene to be associated with NAFLD. Recombinant protein expression in cultured hepatocytes confirmed that 50% less Glu167Lys TM6SF2 protein was produced relative to wild-type TM6SF2[11]. Further a study identified that TM6SF2 regulates liver fat metabolism and influences triglyceride secretion and lipid droplet content[13]. There is compelling evidence by now that variants in PNPLA3 and TM6SF2 genes are associated with progressive fatty infiltration (steatosis and cirrhosis) and further have a higher risk of progressing to HCC. It is therefore very important to understand the genetic susceptibility an ethnicity carries, so that appropriate lifestyle interventions can be planned to minimize the risk of progression, more so in the absence of reversing the genetic defect.

The intronic SNP (rs2281135) in PNPLA3 gene was associated with a higher risk of fatty infiltration only in NEI ethnicity but not ASI. In an earlier study with a predominant NEI ethnicity we identified that rs738409 in PNPLA3 conferring a higher susceptibility to fatty infiltration. It is known in literature that rs2281135, an intronic variant and rs738409 a functional variant are in tight LD in ethnicities including African, Caucasian, Mexican Americans and East African (HapMap data).

Although the general characteristics between patients and controls were significantly different as expected, it was interesting to note ethnicity based differences in the patient cohorts that could be predictive of higher susceptibility to NAFLD. While, higher hip circumference, BMI, and lower HDL levels could be predictive of a higher risk for NAFLD in the SI ethnicity, higher Waist-Hip ratio could be predictive in NE ethnicity. Further, higher BMI and lower HDL levels were seen in the controls of SI ethnicity and higher AST and ALT levels were seen in the controls of NE ethnicity suggesting cohort based differences and cutoffs in the clinical characteristics. Further, interestingly there were higher ALT and AST levels in the NEI ethnicity as compare to ASI ethnicity both between control and patient cohorts suggesting a higher necroinflammatory state in the patients of NEI ethnicity. Earlier genome wide studies have ascribed higher levels to genetic predisposition apart from other influencing factors including demographic such as age, sex, ethnicity, anthropometric features (waist circumference, BMI) and diurnal variation[20].

The genotype data in general did not deviate from Hardy-Weinberg equilibrium. However, it was interesting to note that there was a significant difference (P = 0.02) in the observed and expected genotype frequencies from the patient cohort of ASI ethnicity. Although the samples were represented in sufficient numbers, genotypes visually checked and manually re-scored, non-random mating and population structure excluded, the deviation persisted suggesting that the variant may contribute to disease risk in this ethnicity.

The genotyping data from this study suggests that while TM6SF2 variant was significantly associated with susceptibility to fatty infiltration in the ASI ethnicity, PNPLA3 variant was associated in the NEI ethnicity. However, it was interesting to see that there were a higher proportion of individuals in the patient group who were transheterozygous for PNPLA3 and TM6SF2 variants as compared to the control group suggesting that although there might be individual susceptibility in the two ethnicities, it is important to genotype the individuals for both the variants as there might be additive risk in the presence of the other risk allele. A recent study from Chinese ethnicity corroborated the same[21].

In conclusion, our study has identified distinct genetic susceptibility for ultrasound detected NAFLD in the two ethnicities. However, it is suggested that both the variants have to be genotyped for assessing the risk of the disease, as transheterozygosity of the studied variants seems to confer a higher risk in the population.


Non-alcoholic fatty liver disease (NAFLD) with an incidence of 25%-30% is an epidemic that is on the rise globally. There are significant differences in the prevalence, severity and outcome of the disease in various ethnicities that suggests a genetic background to it. Approximately 26%-35% of NAFLD may be contributed by genetic susceptibility according to a study. Therefore it is important to identify genetic susceptibility an individual carries for better management of the disease.

Research frontiers

Understanding and identifying ethnicity based variants that confer higher risk of disease will aid in imparting lifestyle and nutrient based recommendations to an individual with fatty infiltration for better management of the disease.

Innovations and breakthroughs

The authors have identified distinct genetic susceptibility for NALFD in the two ethnicities that were studied. However, it was interesting to note that transheterozygosity of both the variants conferred a higher risk of the disease irrespective of ethnicity.


Individuals can be screened for these variants to assess their risk of developing NAFLD. Further, life style based modifications can be suggested to delay the onset/progression of the disease.


NAFLD describes a range of liver conditions that begins with accumulation of fat in the liver (fatty liver) and progresses to fat accumulation along with inflammation and scarring non-alcoholic steatohepatitis, hepatic cells replaced by scar tissue (cirrhosis) finally leading to hepatocellular carcinoma.


The present work deals with a human study in which genetic susceptibility to NAFLD in two Indian ethnicities is evaluated. This study constitutes an interesting work as the identification of population at risk is always desirable.


Manuscript source: Unsolicited manuscript

Specialty type: Gastroenterology and hepatology

Country of origin: India

Peer-review report classification

Grade A (Excellent): 0

Grade B (Very good): 0

Grade C (Good): C, C, C, C

Grade D (Fair): 0

Grade E (Poor): 0

P- Reviewer: Bellanti F, Han ZG, Pan WS, Ramos S S- Editor: Ji FF L- Editor: A E- Editor: Li D

1.  Sharma M, Mitnala S, Vishnubhotla RK, Mukherjee R, Reddy DN, Rao PN. The Riddle of Nonalcoholic Fatty Liver Disease: Progression From Nonalcoholic Fatty Liver to Nonalcoholic Steatohepatitis. J Clin Exp Hepatol. 2015;5:147-158.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 87]  [Cited by in F6Publishing: 88]  [Article Influence: 11.0]  [Reference Citation Analysis (0)]
2.  Sanyal AJ, Yoon SK, Lencioni R. The etiology of hepatocellular carcinoma and consequences for treatment. Oncologist. 2010;15 Suppl 4:14-22.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 325]  [Cited by in F6Publishing: 354]  [Article Influence: 29.5]  [Reference Citation Analysis (0)]
3.  Glass LM, Dickson RC, Anderson JC, Suriawinata AA, Putra J, Berk BS, Toor A. Total body weight loss of ≥ 10 % is associated with improved hepatic fibrosis in patients with nonalcoholic steatohepatitis. Dig Dis Sci. 2015;60:1024-1030.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 100]  [Cited by in F6Publishing: 91]  [Article Influence: 11.4]  [Reference Citation Analysis (0)]
4.  Dowman JK, Tomlinson JW, Newsome PN. Pathogenesis of non-alcoholic fatty liver disease. QJM. 2010;103:71-83.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 472]  [Cited by in F6Publishing: 472]  [Article Influence: 36.3]  [Reference Citation Analysis (2)]
5.  Sullivan S. Implications of diet on nonalcoholic fatty liver disease. Curr Opin Gastroenterol. 2010;26:160-164.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 44]  [Cited by in F6Publishing: 46]  [Article Influence: 3.5]  [Reference Citation Analysis (0)]
6.  Longato L. Non-alcoholic fatty liver disease (NAFLD): a tale of fat and sugar? Fibrogenesis Tissue Repair. 2013;6:14.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 24]  [Cited by in F6Publishing: 25]  [Article Influence: 2.5]  [Reference Citation Analysis (0)]
7.  Whitsett M, VanWagner LB. Physical activity as a treatment of non-alcoholic fatty liver disease: A systematic review. World J Hepatol. 2015;7:2041-2052.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 48]  [Cited by in F6Publishing: 44]  [Article Influence: 5.5]  [Reference Citation Analysis (0)]
8.  Ravi Kanth VV, Sasikala M, Sharma M, Rao PN, Reddy DN. Genetics of non-alcoholic fatty liver disease: From susceptibility and nutrient interactions to management. World J Hepatol. 2016;8:827-837.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 18]  [Cited by in F6Publishing: 15]  [Article Influence: 2.1]  [Reference Citation Analysis (0)]
9.  Anstee QM, Day CP. The genetics of NAFLD. Nat Rev Gastroenterol Hepatol. 2013;10:645-655.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 235]  [Cited by in F6Publishing: 233]  [Article Influence: 23.3]  [Reference Citation Analysis (0)]
10.  Romeo S, Kozlitina J, Xing C, Pertsemlidis A, Cox D, Pennacchio LA, Boerwinkle E, Cohen JC, Hobbs HH. Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease. Nat Genet. 2008;40:1461-1465.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2233]  [Cited by in F6Publishing: 2211]  [Article Influence: 147.4]  [Reference Citation Analysis (0)]
11.  Kozlitina J, Smagris E, Stender S, Nordestgaard BG, Zhou HH, Tybjærg-Hansen A, Vogt TF, Hobbs HH, Cohen JC. Exome-wide association study identifies a TM6SF2 variant that confers susceptibility to nonalcoholic fatty liver disease. Nat Genet. 2014;46:352-356.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 724]  [Cited by in F6Publishing: 750]  [Article Influence: 83.3]  [Reference Citation Analysis (0)]
12.  Holmen OL, Zhang H, Fan Y, Hovelson DH, Schmidt EM, Zhou W, Guo Y, Zhang J, Langhammer A, Løchen ML. Systematic evaluation of coding variation identifies a candidate causal variant in TM6SF2 influencing total cholesterol and myocardial infarction risk. Nat Genet. 2014;46:345-351.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 214]  [Cited by in F6Publishing: 223]  [Article Influence: 24.8]  [Reference Citation Analysis (0)]
13.  Mahdessian H, Taxiarchis A, Popov S, Silveira A, Franco-Cereceda A, Hamsten A, Eriksson P, van’t Hooft F. TM6SF2 is a regulator of liver fat metabolism influencing triglyceride secretion and hepatic lipid droplet content. Proc Natl Acad Sci USA. 2014;111:8913-8918.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 217]  [Cited by in F6Publishing: 230]  [Article Influence: 4.4]  [Reference Citation Analysis (0)]
14.  Valenti L, Romeo S. Destined to develop NAFLD? The predictors of fatty liver from birth to adulthood. J Hepatol. 2016;65:668-670.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 14]  [Cited by in F6Publishing: 14]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
15.  Kanth VV, Sasikala M, Rao PN, Steffie Avanthi U, Rao KR, Nageshwar Reddy D. Pooled genetic analysis in ultrasound measured non-alcoholic fatty liver disease in Indian subjects: A pilot study. World J Hepatol. 2014;6:435-442.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 23]  [Cited by in F6Publishing: 23]  [Article Influence: 2.6]  [Reference Citation Analysis (0)]
16.  Bamshad M, Kivisild T, Watkins WS, Dixon ME, Ricker CE, Rao BB, Naidu JM, Prasad BV, Reddy PG, Rasanayagam A. Genetic evidence on the origins of Indian caste populations. Genome Res. 2001;11:994-1004.  [PubMed]  [DOI]  [Cited in This Article: ]
17.  Rai N, Chaubey G, Tamang R, Pathak AK, Singh VK, Karmin M, Singh M, Rani DS, Anugula S, Yadav BK. The phylogeography of Y-chromosome haplogroup h1a1a-m82 reveals the likely Indian origin of the European Romani populations. PLoS One. 2012;7:e48477.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 25]  [Cited by in F6Publishing: 26]  [Article Influence: 2.4]  [Reference Citation Analysis (0)]
18.  Bhatt SP, Nigam P, Misra A, Guleria R, Pandey RM, Pasha MA. Genetic variation in the patatin-like phospholipase domain-containing protein-3 (PNPLA-3) gene in Asian Indians with nonalcoholic fatty liver disease. Metab Syndr Relat Disord. 2013;11:329-335.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 33]  [Cited by in F6Publishing: 36]  [Article Influence: 3.6]  [Reference Citation Analysis (0)]
19.  Baclig MO, Lozano-Kühne JP, Mapua CA, Gopez-Cervantes J, Natividad FF; St Luke’s Liver Diseases Study Group. Genetic variation I148M in patatin-like phospholipase 3 gene and risk of non-alcoholic fatty liver disease among Filipinos. Int J Clin Exp Med. 2014;7:2129-2136.  [PubMed]  [DOI]  [Cited in This Article: ]
20.  Sookoian S, Pirola CJ. Liver enzymes, metabolomics and genome-wide association studies: from systems biology to the personalized medicine. World J Gastroenterol. 2015;21:711-725.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 150]  [Cited by in F6Publishing: 137]  [Article Influence: 17.1]  [Reference Citation Analysis (0)]
21.  Wang X, Liu Z, Wang K, Wang Z, Sun X, Zhong L, Deng G, Song G, Sun B, Peng Z. Additive Effects of the Risk Alleles of PNPLA3 and TM6SF2 on Non-alcoholic Fatty Liver Disease (NAFLD) in a Chinese Population. Front Genet. 2016;7:140.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 29]  [Cited by in F6Publishing: 40]  [Article Influence: 5.7]  [Reference Citation Analysis (0)]