Genomic analysis of diet composition finds novel loci and associations with health and lifestyle.

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10-8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10-5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1-0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈-0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction.

Metabolomics Reveals the Effects of Nitrogen/Phosphorus/Potassium (NPK) Fertilizer Levels on Cucumber Fruit Raised in Different Nutrient Soils.

Fertilizers are widely used to improve the quality of fruits and vegetables. However, the overuse of fertilizers has become an issue because it causes environmental problems and negatively affects productivity and fruit quality. In this study, we examined the effects of nitrogen, phosphorus, and potassium (NPK) fertilizer levels on the metabolism of cucumber fruit in low- and high-nutrient soils using mass-spectrometry-based metabolomics approaches. Cucumber metabolite content was notably different depending on the initial soil nutrient status. Most amino acids and phenylpropanoids were abundant in the cucumbers raised in low-nutrient soil, whereas organic acids, some amino acids (aspartate, glutamate, and ornithine), and carbohydrates were comparatively higher in fruits from high-nutrient soil. The fertilizer supply resulted in an alteration in the metabolite profile, while no change in fruit yield was observed in either low- or high-nutrient soils. Fertilizer treatment perturbed the metabolite contents in cucumbers from low-nutrient soil. In contrast, treatment with higher concentrations of fertilizer in high-nutrient soil increased phenylpropanoid content in the cucumbers, while most metabolites decreased. In conclusion, fertilization levels should be carefully determined, considering culture conditions such as the original soil status, to increase product yield and fruit quality and avoid environmental problems.

Mendelian imputation of parental genotypes improves estimates of direct genetic effects.

Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.