An atlas of genetic scores to predict multi-omic traits.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

The genetic landscape of neuro-related proteins in human plasma.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioural traits and the disease aetiology of neuropsychiatric disorders. Here the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,000 individuals for 184 neuro-related proteins in human plasma. The analysis identified 125 cis-regulatory protein quantitative trait loci (cis-pQTL) and 164 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. At the cis-pQTL, multiple proteins shared a genetic basis with human behavioural traits such as alcohol and food intake, smoking and educational attainment, as well as neurological conditions and psychiatric disorders such as pain, neuroticism and schizophrenia. Integrating with established drug information, the causal inference analysis validated 52 out of 66 matched combinations of protein targets and diseases or side effects with available drugs while suggesting hundreds of repurposing and new therapeutic targets.

Genetic mechanisms of 184 neuro-related proteins in human plasma.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the disease etiology of neuropsychiatric disorders and other complex traits and diseases. Here, the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,500 individuals for 184 neuro-reiated proteins in human plasma. The analysis identified 117 cis-regulatory protein quantitative trait loci (cis-pQTL) and 166 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. Mendelian randomization analyses revealed multiple proteins showing potential causal effects on neuro-reiated traits as well as complex diseases such as hypertension, high cholesterol, immune-related disorders, and psychiatric disorders. Integrating with established drug information, we validated 13 combinations of protein targets and diseases or side effects with available drugs, while suggesting hundreds of re-purposing and new therapeutic targets for diseases and comorbidities. This consortium effort provides a large-scale proteogenomic resource for biomedical research.

Unraveling Neuro-Proteogenomic Landscape and Therapeutic Implications for Human Behaviors and Psychiatric Disorders.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioral traits and the disease etiology of neuropsychiatric disorders. Here, the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,500 individuals for 184 neuro-related proteins in human plasma. The analysis identified 117 cis-regulatory protein quantitative trait loci (cis-pQTL) and 166 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. Mendelian randomization analyses revealed multiple proteins showing potential causal effects on neuro-related traits such as sleeping, smoking, feelings, alcohol intake, mental health, and psychiatric disorders. Integrating with established drug information, we validated 13 out of 13 matched combinations of protein targets and diseases or side effects with available drugs, while suggesting hundreds of re-purposing and new therapeutic targets. This consortium effort provides a large-scale proteogenomic resource for biomedical research on human behaviors and other neuro-related phenotypes.

Large-scale GWAS of food liking reveals genetic determinants and genetic correlations with distinct neurophysiological traits.

We present the results of a GWAS of food liking conducted on 161,625 participants from the UK-Biobank. Liking was assessed over 139 specific foods using a 9-point scale. Genetic correlations coupled with structural equation modelling identified a multi-level hierarchical map of food-liking with three main dimensions: "Highly-palatable", "Acquired" and "Low-caloric". The Highly-palatable dimension is genetically uncorrelated from the other two, suggesting that independent processes underlie liking high reward foods. This is confirmed by genetic correlations with MRI brain traits which show with distinct associations. Comparison with the corresponding food consumption traits shows a high genetic correlation, while liking exhibits twice the heritability. GWAS analysis identified 1,401 significant food-liking associations which showed substantial agreement in the direction of effects with 11 independent cohorts. In conclusion, we created a comprehensive map of the genetic determinants and associated neurophysiological factors of food-liking.

Genome-wide Association Study of Liking for Several Types of Physical Activity in the UK Biobank and Two Replication Cohorts.

INTRODUCTION: A lack of physical activity (PA) is one of the most pressing health issues today. Our individual propensity for PA is influenced by genetic factors. Stated liking of different PA types may help capture additional and informative dimensions of PA behavior genetics. METHODS: In over 157,000 individuals from the UK Biobank, we performed genome-wide association studies of five items assessing the liking of different PA types, plus an additional derived trait of overall PA-liking. We attempted to replicate significant associations in the Netherlands Twin Register (NTR) and TwinsUK. Additionally, polygenic scores (PGS) were trained in the UK Biobank for each PA-liking item and for self-reported PA behavior, and tested for association with PA in the NTR. RESULTS: We identified a total of 19 unique significant loci across all five PA-liking items and the overall PA-liking trait, and these showed strong directional consistency in the replication cohorts. Four of these loci were previously identified for PA behavior, including CADM2 , which was associated with three PA-liking items. The PA-liking items were genetically correlated with self-reported ( rg = 0.38-0.80) and accelerometer ( rg = 0.26-0.49) PA measures, and with a wide range of health-related traits. Each PA-liking PGS significantly predicted the same PA-liking item in NTR. The PGS of liking for going to the gym predicted PA behavior in the NTR ( r2 = 0.40%) nearly as well as a PGS based on self-reported PA behavior ( r2 = 0.42%). Combining the two PGS into a single model increased the r2 to 0.59%, suggesting that PA-liking captures distinct and relevant dimensions of PA behavior. CONCLUSIONS: We have identified the first loci associated with PA-liking and extended our understanding of the genetic basis of PA behavior.

MNK Inhibition Sensitizes KRAS-Mutant Colorectal Cancer to mTORC1 Inhibition by Reducing eIF4E Phosphorylation and c-MYC Expression.

KRAS-mutant colorectal cancers are resistant to therapeutics, presenting a significant problem for ∼40% of cases. Rapalogs, which inhibit mTORC1 and thus protein synthesis, are significantly less potent in KRAS-mutant colorectal cancer. Using Kras-mutant mouse models and mouse- and patient-derived organoids, we demonstrate that KRAS with G12D mutation fundamentally rewires translation to increase both bulk and mRNA-specific translation initiation. This occurs via the MNK/eIF4E pathway culminating in sustained expression of c-MYC. By genetic and small-molecule targeting of this pathway, we acutely sensitize KRASG12D models to rapamycin via suppression of c-MYC. We show that 45% of colorectal cancers have high signaling through mTORC1 and the MNKs, with this signature correlating with a 3.5-year shorter cancer-specific survival in a subset of patients. This work provides a c-MYC-dependent cotargeting strategy with remarkable potency in multiple Kras-mutant mouse models and metastatic human organoids and identifies a patient population that may benefit from its clinical application. SIGNIFICANCE: KRAS mutation and elevated c-MYC are widespread in many tumors but remain predominantly untargetable. We find that mutant KRAS modulates translation, culminating in increased expression of c-MYC. We describe an effective strategy targeting mTORC1 and MNK in KRAS-mutant mouse and human models, pathways that are also commonly co-upregulated in colorectal cancer.This article is highlighted in the In This Issue feature, p. 995.

Variants associated with HHIP expression have sex-differential effects on lung function.

Background: Lung function is highly heritable and differs between the sexes throughout life. However, little is known about sex-differential genetic effects on lung function. We aimed to conduct the first genome-wide genotype-by-sex interaction study on lung function to identify genetic effects that differ between males and females. Methods: We tested for interactions between 7,745,864 variants and sex on spirometry-based measures of lung function in UK Biobank (N=303,612), and sought replication in 75,696 independent individuals from the SpiroMeta consortium. Results: Five independent single-nucleotide polymorphisms (SNPs) showed genome-wide significant (P<5x10 -8) interactions with sex on lung function, and 21 showed suggestive interactions (P<1x10 -6). The strongest signal, from rs7697189 (chr4:145436894) on forced expiratory volume in 1 second (FEV 1) (P=3.15x10 -15), was replicated (P=0.016) in SpiroMeta. The C allele increased FEV 1 more in males (untransformed FEV 1 ß=0.028 [SE 0.0022] litres) than females (ß=0.009 [SE 0.0014] litres), and this effect was not accounted for by differential effects on height, smoking or pubertal age. rs7697189 resides upstream of the hedgehog-interacting protein ( HHIP) gene and was previously associated with lung function and HHIP lung expression. We found HHIP expression was significantly different between the sexes (P=6.90x10 -6), but we could not detect sex differential effects of rs7697189 on expression. Conclusions: We identified a novel genotype-by-sex interaction at a putative enhancer region upstream of the HHIP gene. Establishing the mechanism by which HHIP SNPs have different effects on lung function in males and females will be important for our understanding of lung health and diseases in both sexes.

Pro-inflammatory fatty acid profile and colorectal cancer risk: A Mendelian randomisation analysis.

BACKGROUND: While dietary fat has been established as a risk factor for colorectal cancer (CRC), associations between fatty acids (FAs) and CRC have been inconsistent. Using Mendelian randomisation (MR), we sought to evaluate associations between polyunsaturated (PUFA), monounsaturated (MUFA) and saturated FAs (SFAs) and CRC risk. METHODS: We analysed genotype data on 9254 CRC cases and 18,386 controls of European ancestry. Externally weighted polygenic risk scores were generated and used to evaluate associations with CRC per one standard deviation increase in genetically defined plasma FA levels. RESULTS: Risk reduction was observed for oleic and palmitoleic MUFAs (OROA = 0.77, 95% CI: 0.65-0.92, P = 3.9 × 10-3; ORPOA = 0.36, 95% CI: 0.15-0.84, P = 0.018). PUFAs linoleic and arachidonic acid had negative and positive associations with CRC respectively (ORLA = 0.95, 95% CI: 0.93-0.98, P = 3.7 × 10-4; ORAA = 1.05, 95% CI: 1.02-1.07, P = 1.7 × 10-4). The SFA stearic acid was associated with increased CRC risk (ORSA = 1.17, 95% CI: 1.01-1.35, P = 0.041). CONCLUSION: Results from our analysis are broadly consistent with a pro-inflammatory FA profile having a detrimental effect in terms of CRC risk.