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

Abstract

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 (r g ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|r g | ≈ 0.1-0.3) and positive genetic correlations with physical activity (r g ≈ 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 (r g ≈−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.

Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial

“…These associations are consistent with prior studies demonstrating that higher numbers of FTO obesity-related risk alleles were associated with more eating episodes per day, higher calories consumed at lunch, and stronger responses to food cues [ 14 , 34 , 35 ]. Other loci have established roles in anorexigenic and orexigenic signaling pathways ( BDNF ) [ 36 ], nutrient preference such as carbohydrate ( RARB ) [ 6 ] and fat ( ADH1B ) [ 37 ], and energy homeostasis ( MTCH2 ) [ 38 ]. Our findings, however, do not implicate specific genes or mechanisms, and the precise biological role of most BMI loci remain to be elucidated [ 11 ].…”

Effects of Short-Term Dietary Protein Restriction on Blood Amino Acid Levels in Young Men

“…Additionally, this AA signature is reminiscent of several studies that have shown higher blood levels of these amino acids as predictive of current or future type 2 diabetes risk in humans [19][20][21]. Given that dietary protein intake positively correlates with T2D risk [6,22], it is then possible that this signature is reflective of this association.…”

Attention‐deficit/hyperactivity disorder symptoms and dietary habits in adulthood: A large population‐based twin study in Sweden

“…Second, our results suggest the association between ADHD symptoms and different dietary habits is in part explained by shared genetic factors. Part of the genetic overlap may reflect genetic risk variants with general effects cutting across boundaries between neuropsychiatric traits and nutrition‐related or metabolic problems (Demontis et al, 2018; Meddens et al, 2018; Watson et al, 2019). Another more specific mechanism may involve the addictive potential of highly palatable foods (such as sweet, fatty, and salty foods).…”

“…ADHD is strongly influenced by genetic factors, with heritability estimates (ℎ 2 ) between 70 and 80% in both children and adults (Asherson & Gurling, 2012; S. V. Faraone et al, 2005; Larsson, Chang, D'Onofrio, & Lichtenstein, 2014), and there are strong genetic links between ADHD diagnoses and sub‐threshold variations in ADHD traits, thus supporting a dimensional model of ADHD (Larsson, Anckarsater, Rastam, Chang, & Lichtenstein, 2012; Taylor et al, 2019). Diet composition is also reported to be moderately heritable, with h 2 estimates between 27 and 70% (Hasselbalch, Heitmann, Kyvik, & Sorensen, 2008; Meddens et al, 2018; Wade, Milner, & Krondl, 1981). Emerging support for the hypothesis that these two traits may share some etiological factors has been provided by a recent genome‐wide association study of ADHD that reported significant genetic correlations between ADHD and several metabolic traits (genetic correlation, r g = .22–.30) (Demontis et al, 2018).…”