Fish intake interacts with TM6SF2 gene variant to affect NAFLD risk: results of a case–control study

Purpose: Non-alcoholic fatty liver disease (NAFLD) is a complex disease, resulting from a variety of genetic and environmental factors. The aim of this case–control study was to evaluate the effect of selected genetic polymorphisms, nutrition aspects and their interaction on the risk of NAFLD. Methods: The sample consisted of 134 patients with NAFLD and 217 controls. Disease was diagnosed by liver ultrasound and volunteers were clinically and nutritionally assessed. Food groups were extracted from a 172 food-item FFQ questionnaire. Three genetic polymorphisms were assessed: PNPLA3 rs738409, TM6SF2 rs58542926 and GCKR rs780094. Results: We replicated the effect of previously reported risk factors for NAFLD, such as elevated liver enzymes, obesity and metabolic syndrome. Food groups rich in simple sugars, fat and especially saturated fat were positively associated with NAFLD risk, whereas food groups rich in polyunsaturated fatty acids were reversely associated with the possibility of developing the disease (p < 0.05). Only the PNPLA3 genetic variant was statistically significantly associated with the disease (padditive = 0.015). However, it was found that a one-portion increase in fish intake increased the risk of NAFLD in carriers of the risk allele of TM6SF2 rs58542926 polymorphism compared to non-carriers, after adjusting for age, gender, energy intake, pack-years, PAL, TM6SF2 genotype and fish consumption (ORdominant = 1.503, 95% CI 1.094–2.064). Conclusions: Fish intake exerts an additive effect on NAFLD risk for carriers of the TM6SF2 polymorphism. This novel finding provides further rationale on the need for personalized nutritional advice, based on the genetic background of NAFLD patients. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature

Evaluation of Plasma Trace Elements in Different Stages of Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is considered as the hepatic manifestation of metabolic syndrome. Its global prevalence is estimated between 25 and 45%, occurring mainly in overweight individuals with unhealthy dietary habits and low levels of physical activity. Many studies have investigated the association of trace elements with liver diseases, though not with NAFLD. In this work, we investigated trace element levels in plasma of patients and not-patients and their possible association with various stages of the disease. Inductively coupled plasma mass spectrometry (ICP-MS) was employed for the determination of As, Ba, Cd, Co, Cs, Cu, Fe, Rb, Sr, Tl, and Zn in the plasma of 189 free-living residents of Athens, Greece, either healthy or patients with mild, moderate, or severe NAFLD. The disease was diagnosed by abdominal ultrasound; blood samples were analyzed for total, HDL and LDL cholesterol, triglycerides, fasting glucose, fasting insulin, and liver enzymes, namely aspartate aminotransferase (AST), alanine transaminase (ALT), and γ-glutamyltransferase (Gamma-GT); insulin resistance was determined by the homeostatic model assessment (HOMA-IR). Zinc exhibited a statistically significant negative association with the severity of the disease, while cesium showed a statistically significant positive association. Moreover, thallium and iron were inversely associated with insulin levels. Trace element determination in plasma could be useful for establishing relationships with NAFLD status of patients. Further research is required for the verification and interpretation of these findings. © 2019, Springer Science+Business Media, LLC, part of Springer Nature

Gene-Environment Interactions of Circadian-Related Genes for Cardiometabolic Traits (vol 38, pg 1456, 2015)

Public Health and primary carePrevention, Population and Disease management (PrePoD

Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium

Clinical epidemiolog

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.Public Health and primary carePrevention, Population and Disease management (PrePoD

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.</p

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences

Molecular Epidemiolog

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes &lt;sup&gt;1&lt;/sup&gt; . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel &lt;sup&gt;2&lt;/sup&gt; ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries