A comprehensive understanding of gene-diet interactions is necessary to establish proper dietary guidelines to prevent and manage cardio-cerebrovascular disease (CCD). We investigated the role of genetic variants associated with dyslipidaemia (DL) and their interactions with macro-nutrients for cardiovascular disease using a large-scale genome-wide association study of Korean adults. A total of 58,701 participants from a Korean genome and epidemiology study were included. Their dietary intake was assessed using a food frequency questionnaire. Dyslipidaemia was defined as total cholesterol (TCHL) ≥ 240 mg/dL, high-density lipoprotein (HDL) < 40 mg/dL, low-density lipoprotein (LDL) ≥ 160 mg/dL, triglycerides (TG) ≥ 200 mg/dL, or dyslipidaemia history. Their nutrient intake was classified as follows: protein intake: high ≥ 30%, 30% > moderate ≥ 20%, and 20% > low in daily total energy intake (TEI); carbohydrate intake: high ≥ 60%, 60% > moderate ≥ 50%, and 50% > low; fat intake: high ≥ 40%, 40% > moderate ≥ 30%, and 30% > low. Odds ratios and 95% confidence intervals were calculated after adjusting for age; sex; body mass index (BMI); exercise status; smoking status; alcohol intake; principal component 1 (PC1); principal component 2 (PC2); and intake of carbohydrates, fats, and proteins. This analysis included 20,596 patients with dyslipidaemia and 1027 CCD patients. We found that rs2070895 related to LIPC was associated with HDL-cholesterol. Patients with the minor allele (A) in rs2070895 had a lower risk of CCD than those carrying the reference allele (G) (odds ratio [OR] = 0.8956, p-value = 1.78 × 10−2). Furthermore, individuals consuming protein below 20% TEI with the LIPC reference allele had a higher risk of CCD than those with the minor allele (interaction p-value 6.12 × 10−3). Our findings suggest that the interactions of specific polymorphisms associated with dyslipidaemia and nutrients intake can influence CCD.

Facial skin characteristics are complex traits determined by genetic and environmental factors. Because genetic factors continuously influenced facial skin characteristics, identifying associations between genetic variants [single-nucleotide polymorphisms (SNPs)] and facial skin characteristics may clarify genetic contributions. We previously reported a genome-wide association study (GWAS) for five skin phenotypes (wrinkles, pigmentation, moisture content, oil content, and sensitivity) conducted in 1079 subjects. In this study, face measurements and genomic data were generated for 261 samples, and significant SNPs described in previous papers were verified. We conducted a GWAS to identify additional genetic markers using the combined population of the previous study and current study samples. We identified 6 novel significant loci and 21 suggestive loci in the combined study with p-values < 5.0 × 10−8 (wrinkles: 4 SNPs; moisture content: 148 SNPs; pigmentation: 6 SNPs; sensitivity: 18 SNPs). Identifying SNPs using molecular genetic functional analysis is considered necessary for studying the mechanisms through which these genes affect the skin. We confirmed that of 23 previously identified SNPs, none were replicated. SNPs that could not be verified in a combined study may have been accidentally identified in an existing GWAS, or the samples added to this study may not have been a sufficient sample number to confirm those SNPs. The results of this study require validation in other independent population groups or larger samples. Although this study requires further research, it has the potential to contribute to the development of cosmetic-related genetic research in the future.

Childhood to adolescence is an accelerated growth period, and genetic features can influence differences of individual growth patterns. In this study, we examined the genetic basis of early age facial growth (EAFG) patterns. Facial shape phenotypes were defined using facial landmark distances, identifying five growth patterns: continued-decrease, decrease-to-increase, constant, increase-to-decrease, and continued-increase. We conducted genome-wide association studies (GWAS) for 10 horizontal and 11 vertical phenotypes. The most significant association for horizontal phenotypes was rs610831 (TRIM29; β = 0.92, p-value = 1.9 × 10−9) and for vertical phenotypes was rs6898746 (ZSWIM6; β = 0.1103, p-value = 2.5 × 10−8). It is highly correlated with genes already reported for facial growth. This study is the first to classify and characterize facial growth patterns and related genetic polymorphisms.