The associations between obesity and liver diseases are complex and diverse. To explore the causal relationships between obesity and liver diseases, we applied two-sample Mendelian randomisation (MR) and multivariable MR analysis. The data of exposures (BMI and WHRadjBMI) and outcomes (liver diseases and liver function biomarker) were obtained from the open genome-wide association study database. A two-sample MR study revealed that the genetically predicted BMI and WHRadjBMI were associated with non-alcoholic fatty liver disease, liver fibrosis and autoimmune hepatitis. Obesity was not associated with primary biliary cholangitis, liver failure, liver cell carcinoma, viral hepatitis and secondary malignant neoplasm of liver. A higher WHRadjBMI was associated with higher levels of biomarkers of lipid accumulation and metabolic disorders. These findings indicated independent causal roles of obesity in non-alcoholic fatty liver disease, liver fibrosis and impaired liver metabolic function rather than in viral or autoimmune liver disease.

Previous studies have indicated an association between vitamin D and thyroid- and parathyroid-related diseases. However, it remains unclear whether it is a cause of the disease, a side effect of treatment or a consequence of the disease. The Mendelian randomisation (MR) study strengthens the causal inference by controlling for non-heritable environmental confounders and reverse causation. In this study, a two-sample bidirectional MR analysis was conducted to investigate the causal relationship between serum vitamin D levels and thyroid- and parathyroid-related diseases. Inverse variance weighted, weighted median and MR-Egger methods were performed, the Cochran Q test was used to evaluate the heterogeneity and the MR-PRESSO and MR-Egger intercepts were utilised to assess the possibility of pleiotropy. The Bonferroni-corrected significance threshold was 0·0038. At the Bonferroni-corrected significance level, we found that vitamin D levels suggestively decreased the risk of benign parathyroid adenoma (OR = 0·244; 95 % CI 0·074, 0·802; P = 0·0202) in the MR analyses. In the reverse MR study, a genetically predicted risk of thyroid cancer suggestively increased the risk of elevated vitamin D (OR = 1·007; 95 % CI 1·010, 1·013; P = 0·0284), chronic thyroiditis significantly increased the risk of elevated vitamin D (OR = 1·007; 95 % CI 1·002, 1·011; P = 0·0030) and thyroid nodules was significantly decreased the vitamin D levels (OR = 0·991; 95 % CI 0·985, 0·997; P = 0·0034). The findings might be less susceptible to horizontal pleiotropy and heterogeneity (P > 0·05). This study from a gene perspective indicated that chronic thyroiditis and thyroid nodules may impact vitamin D levels, but the underlying mechanisms require further investigation.

Postpartum depression (PPD) appears at two peak periods: early-onset prior to 2 months after delivery and late-onset (2 months after delivery and beyond). The aim of our study is to evaluate the different genetic factors associated with early- and late-onset PPD. With the French multicenter interaction of gene and environment of depression during postpartum (IGEDEPP) cohort, we conducted a genome-wide association study (GWAS) on 234 women with early-onset PPD and 223 women with late-onset PPD, as well as 1,204 controls with no history of lifetime depression. We performed post-GWAS analyses: functional mapping and annotation of GWAS results using MAGMA thanks to Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA), expression quantitative trait loci (QTL) analyses, mapping using data from the PsychENCODE and GTEx, and polygenic risk score (PRS) analysis based on published GWAS. We found two new significant candidate loci for early-onset PPD, rs6436132 in PTPRN gene on chromosome 2 and rs184644645 in RAD18 on chromosome 14, respectively, and one region of interest with five significant associated SNPs in chromosome 20 for late-onset PPD. Variant rs6436132 is the most significant associated with early-onset PPD, and it is a QTL that significantly modifies the expression and splicing of the PTPRN gene in different brain tissues. We also found an enrichment of uterus tissue in the early expression of PPD genes. PRS analysis showed a genetic overlap between both early and late-onset PPD and major depressive disorder, but only early-onset PPD overlaps with bipolar disorder. Our study presents two GWAS separately, highlighting two candidate loci for early-onset PPD and one different region of interest for late-onset PPD. These results have important consequences in our understanding of these disorders, especially since our data reinforce the hormonal pathophysiological hypotheses for early-onset PPD.

We aimed to investigate the intricate interplay between genetic predisposition and lifestyle factors on stroke. We conducted a comprehensive genome-wide association study to identify the genetic variants linked to stroke in the participants who experienced a stroke event (cases; n 672) and those with no stroke history (non-stroke; n 58 029) in a large hospital-based cohort. Using generalised multifactor dimensionality reduction, we identified genetic variants with interactive effects and constructed polygenic risk scores (PRS) by summing up the risk alleles from the genetic variants. Food intake was measured with a validated semi-quantitative FFQ. No significant differences in stroke incidence were seen in demographic variables between the two groups. Among the metabolic indicators, only serum TAG levels were higher in males with stroke than those without stroke. The daily nutrient intake, dietary inflammation index, glycaemic index, dietary patterns, alcohol consumption, exercise and smoking did not display associations with the OR for stroke. The stroke-linked genetic variants were related to the IL-18 pathway. After accounting for covariates, the PRS derived from the 5-, 6- and 7-SNP models were positively associated with stroke chance with 2·5-, 2·9- and 2·8-fold. Furthermore, interactions between genetic predisposition and dietary components, including energy, carbohydrates, n-3 fatty acids and branched-chain amino acids (BCAA), that affected OR for stroke were observed. A high intake of energy, carbohydrates and BCAA and a low intake of n-3 fatty acids were positively associated with the chances of stroke occurrence. In conclusion, understanding the interaction between genetic variants and lifestyle factors can assist in developing stroke prevention and management strategies.

This study aimed to investigate the causal effect of dietary habits on COVID-19 susceptibility, hospitalisation and severity. We used data from a large-scale diet dataset and the COVID-19 Host Genetics Initiative to estimate causal relationships using Mendelian randomisation. The inverse variance weighted (IVW) method was used as the main analysis. For COVID-19 susceptibility, IVW estimates indicated that milk (OR: 0·82; 95 % CI (0·68, 0·98); P = 0·032), unsalted peanut (OR: 0·53; 95 % CI (0·35, 0·82); P = 0·004), beef (OR: 0·59; 95 % CI (0·41, 0·84); P = 0·004), pork (OR: 0·63; 95 % CI (0·42, 0·93); P = 0·022) and processed meat (OR: 0·76; 95 % CI (0·63, 0·92); P = 0·005) were causally associated with reduced COVID-19 susceptibility, while coffee (OR: 1·23; 95 % CI (1·04, 1·45); P = 0·017) and tea (OR: 1·17; 95 % CI (1·05, 1·31); P = 0·006) were causally associated with increased risk. For COVID-19 hospitalisation, beef (OR: 0·51; 95 % CI (0·26, 0·98); P = 0·042) showed negative correlations, while tea (OR: 1·54; 95 % CI (1·16, 2·04); P = 0·003), dried fruit (OR: 2·08; 95 % CI (1·37, 3·15); P = 0·001) and red wine (OR: 2·35; 95 % CI (1·29, 4·27); P = 0·005) showed positive correlations. For COVID-19 severity, coffee (OR: 2·16; 95 % CI (1·25, 3·76); P = 0·006), dried fruit (OR: 1·98; 95 % CI (1·16, 3·37); P = 0·012) and red wine (OR: 2·84; 95 % CI (1·21, 6·68); P = 0·017) showed an increased risk. These findings were confirmed to be robust through sensitivity analyses. Our findings established a causal relationship between dietary habits and COVID-19 susceptibility, hospitalisation and severity.

It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.

Recent genome-wide association studies (GWAS) on the dietary habits of the Japanese population have shown that an effect rs671 allele was inversely associated with fish consumption, whereas it was directly associated with coffee consumption. Although meat is a major source of protein and fat in the diet, whether genetic factors that influence meat-eating habits in healthy populations are unknown. This study aimed to conduct a GWAS to find genetic variations that affect meat consumption in a Japanese population. We analysed GWAS data using 14 076 participants from the Japan Multi-Institutional Collaborative Cohort (J-MICC) study. We used a semi-quantitative food frequency questionnaire to estimate food intake that was validated previously. Association of the imputed variants with total meat consumption per 1000 kcal energy was performed by linear regression analysis with adjustments for age, sex, and principal component analysis components 1–10. We found that no genetic variant, including rs671, was associated with meat consumption. The previously reported single nucleotide polymorphisms that were associated with meat consumption in samples of European ancestry could not be replicated in our J-MICC data. In conclusion, significant genetic factors that affect meat consumption were not observed in a Japanese population.

Differences in individual eating habits may be influenced by genetic factors, in addition to cultural, social or environmental factors. Previous studies suggested that genetic variants within sweet taste receptor genes family were associated with sweet taste perception and the intake of sweet foods. The aim of this study was to conduct a genome-wide association study (GWAS) to find genetic variations that affect confection consumption in a Japanese population. We analysed GWAS data on confection consumption using 14 073 participants from the Japan Multi-Institutional Collaborative Cohort study. We used a semi-quantitative FFQ to estimate food intake that was validated previously. Association of the imputed variants with confection consumption was performed by linear regression analysis with adjustments for age, sex, total energy intake and principal component analysis components 1–3. Furthermore, the analysis was repeated adjusting for alcohol intake (g/d) in addition to the above-described variables. We found 418 SNP located in 12q24 that were associated with confection consumption. SNP with the ten lowest P-values were located on nine genes including at the BRAP, ACAD10 and aldehyde dehydrogenase 2 regions on 12q24.12-13. After adjustment for alcohol intake, no variant was associated with confections intake with genome-wide significance. In conclusion, we found a significant number of SNP located on 12q24 genes that were associated with confections intake before adjustment for alcohol intake. However, all of them lost statistical significance after adjustment for alcohol intake.

Previous genetic studies on hair morphology focused on the overall morphology of the hair using data collected by self-report or researcher observation. Here, we present the first genome-wide association study (GWAS) of a micro-level quantitative measure of hair curvature. We compare these results to GWAS results obtained using a macro-level classification of observable hair curvature performed in the same sample of twins and siblings of European descent. Observational data were collected by trained observers, while quantitative data were acquired using an Optical Fibre Diameter Analyser (OFDA). The GWAS for both the observational and quantitative measures of hair curvature resulted in genome-wide significant signals at chromosome 1q21.3 close to the trichohyalin (TCHH) gene, previously shown to harbor variants associated with straight hair morphology in Europeans. All genetic variants reaching genome-wide significance for both GWAS (quantitative measure lead single-nucleotide polymorphism [SNP] rs12130862, p = 9.5 × 10–09; observational measure lead SNP rs11803731, p = 2.1 × 10–17) were in moderate to very high linkage disequilibrium (LD) with each other (minimum r2 = .45), indicating they represent the same genetic locus. Conditional analyses confirmed the presence of only one signal associated with each measure at this locus. Results from the quantitative measures reconfirmed the accuracy of observational measures.

I write this commentary as a part of a special issue published in this journal to celebrate Nick Martin’s contribution to the field of human genetics. In this commentary, I briefly describe the background of the Yang et al. (2010) study and show some of the unpublished details of this study, its contribution to tackling the missing heritability problem and Nick’s contribution to the work.

Nick Martin is a pioneer in recognizing the need for large sample size to study the complex, heterogeneous and polygenic disorders of common mental disorders. In the predigital era, questionnaires were mailed to thousands of twin pairs around Australia. Always quick to adopt new technology, Nick’s studies progressed to phone interviews and then online. Moreover, Nick was early to recognize the value of collecting DNA samples. As genotyping technologies improved over the years, these twin and family cohorts were used for linkage, candidate gene and genome-wide association studies. These cohorts have underpinned many analyses to disentangle the complex web of genetic and lifestyle factors associated with mental health. With characteristic foresight, Nick is chief investigator of our Australian Genetics of Depression Study, which has recruited 16,000 people with self-reported depression (plus DNA samples) over a time frame of a few months — analyses are currently ongoing. The mantra of sample size, sample size, sample size has guided Nick’s research over the last 30 years and continues to do so.

Psychiatric disorders as well as subcortical brain volumes are highly heritable. Large-scale genome-wide association studies (GWASs) for these traits have been performed. We investigated the genetic correlations between five psychiatric disorders and the seven subcortical brain volumes and the intracranial volume from large-scale GWASs by linkage disequilibrium score regression. We revealed weak overlaps between the genetic variants associated with psychiatric disorders and subcortical brain and intracranial volumes, such as in schizophrenia and the hippocampus and bipolar disorder and the accumbens. We confirmed shared aetiology and polygenic architecture across the psychiatric disorders and the specific subcortical brain and intracranial volume.