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MultiSTAAR provides a general and flexible statistical framework for functionally informed multi-trait rare variant analysis of biobank-scale sequencing studies by jointly analyzing multiple traits and incorporating annotation information.

Genetic background, plasma metabolome and dietary data from 4,215 women and 1,490 men reveal 57 metabolites whose associations with dementia risk vary by APOE4 genotype and other risk variants.

Polygenic scores for body mass index and obesity constructed using data from 5 million people with different ancestries were associated with distinct weight gain trajectories from early childhood to adulthood.

Most studies of the genetics of the metabolome have been done in individuals of European descent. Here, the authors integrate genomics and metabolomics in Black individuals, highlighting the value of whole genome sequencing in diverse populations and linking circulating metabolites to human disease.

Using data from a single time point, passenger-approximated clonal expansion rate (PACER) estimates the fitness of common driver mutations that lead to clonal haematopoiesis and identifies TCL1A activation as a mediator of clonal expansion.

STAARpipeline is a comprehensive framework for flexible and scalable rare-variant association analysis using whole-genome sequencing data and annotation information.

Analysis of whole-genome sequences of 25,465 individuals of European ancestry shows that rare variants contribute substantially to the heritability of height and body mass index.

Genome-wide analyses identify 30 independent loci associated with obsessive–compulsive disorder, highlighting genetic overlap with other psychiatric disorders and implicating putative effector genes and cell types contributing to its etiology.

Allele-preferential transcription factor binding can influence pancreatic ductal adenocarcinoma risk loci function. Here, the authors show allele-specific JunB and JunD binding at chr1p36.33 and propose a role for KLHL17 in protein homeostasis by mitigating inflammation.

Precision nutrition requires accurate predictions of individual metabolic responses to diets. Here, authors show their deep-learning model, McMLP, outperforms existing methods in predicting metabolite responses to dietary interventions.

Although the common genetic variants contributing to blood lipid levels have been studied, the contribution of rare variants is less understood. Here, the authors perform a rare coding and noncoding variant association study of blood lipid levels using whole genome sequencing data.

Ke et al. investigated the association between trauma exposure and post-traumatic stress disorder symptoms and the gut microbiome and dietary pattern of a cohort of women enrolled in a longitudinal study in the United States.

Analyses of genome and exome sequencing data identify rare coding and structural variants associated with atrial fibrillation and highlight genetic links between atrial fibrillation and cardiomyopathies.

Here, the authors perform large trans-ancestry fine-mapping analyses identifying large numbers of association signals and putative target genes for colorectal cancer risk, advancing our understanding of the genetic and biological basis of this cancer.

Here, the authors introduce METRIC, a deep-learning method that corrects measurement errors in self-reported dietary nutrient profiles using assessed data and microbial composition, effectively enhancing dietary accuracy.

This study investigates the genetic similarities and differences between two subtypes of glaucoma (normal tension and high tension). Multi-ethnic meta-analysis reveals overlapping risk loci, with a lower effect magnitude in normal tension glaucoma. The authors also use their gene discovery approach to highlight possible neuro-protective drug targets for glaucoma.

A multi-ancestry meta-regression study analyses diverse genome-wide association studies and genome loci associated with tobacco and alcohol use.

Histopathological analysis is an essential tool in diagnosing colorectal cancer, but is limited in predicting prognosis and molecular profiles. Here, the authors designed a machine learning-based platform to predict multi-omics profiles and prognosis from pathology images.

MetaSTAAR enables functionally informed rare variant association analysis in biobank-scale cohorts using an efficient, sparse matrix approach for summary statistic storage.

Predicting the colonization of exogenous species in complex communities is a challenge in ecology. Here, the authors propose a data-driven approach to predict colonization outcomes and perform validation experiments in human gut microbial communities.

Wei Zheng and colleagues report the results of a large-scale genome-wide association study of colorectal cancer in East Asians. They identify six new susceptibility loci, including variants near TCF7L2 and TGFB1.

RMC-7977, a multi-selective RAS(ON) inhibitor, exhibits potent tumour-selective activity in multiple pre-clinical models of pancreatic ductal adenocarcinoma through a combination of pharmacology and oncogene dependence.

Using deep learning to identify the assembly rules of microbial communities from different habitats, the authors develop a framework to quantify and predict the community-specific keystoneness of each species in any microbiome sample.

Here, using whole-metagenome shotgun sequencing data from patients with COVID-19 and controls, the authors reconstruct 11,584 microbial metagenome-assembled genomes (MAGs) including 5,403 non-redundant MAGs, revealing microbiota and metabolic pathways associations with SARS-CoV-2 infection at strain-level resolution.

A multi-ancestry genome-wide association study meta-analysis, combined with transcriptome- and methylome-wide association analyses, identifies risk loci associated with colorectal cancer. Credible effector genes and their target tissues are also highlighted, showing that over a third probably act outside the colonic mucosa.

A global network of researchers was formed to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity; this paper reports 13 genome-wide significant loci and potentially actionable mechanisms in response to infection.

Here, Li et al. show that functional redundancy, which has not previously been quantified at the proteome level, can arise when different microbes play similar roles in the gut microbiome, revealing that proteomes are nested among gut microbes, favoring high functional redundancy.

Here, leveraging species-specific Type IIB restriction endonuclease digestion sites as reference instead of universal markers or whole microbial genomes, the authors introduce MAP2B, a metagenomic profiler, showing it can significantly remove false-positive identification and generate highly accurate taxonomic profiling results.

Computational models can help predict metabolic profiles of microbial communities such as human gut microbiomes or environmental microbiomes, but they lack generalizability and interpretability. To address this challenge, Wang et al. report a deep learning approach for metabolic profile prediction called mNODE that incorporates a neural network module with hidden layers described by ordinary differential equations.

State-of-the-art machine learning models in drug discovery fail to reliably predict the binding properties of poorly annotated proteins and small molecules. Here, the authors present AI-Bind, a machine learning pipeline to improve generalizability and interpretability of binding predictions.

A computational method for rapid and accurate gap-filling of metabolic networks without using phenotypic data is unavailable. Here, the authors address this problem by developing a deep learning based method that can predict missing reactions using topological features of the metabolic networks.

Whole genome sequences enable discovery of rare variants which may help to explain the heritability of common diseases. Here the authors find that ultra-rare variants explain ~50% of coronary artery disease (CAD) heritability and highlight several functional processes including cell type-specific regulatory mechanisms as key drivers of CAD genetic risk.

Protein–protein interactomes incorporating structural data predict the functional consequences of disease mutations.

The use of biomarkers to complement traditional dietary assessment methods can help strengthen findings from nutritional epidemiologic studies. This study identifies plasma proteomic signatures that reflect the response to healthy dietary patterns and are associated with reduced incidence of chronic disease and mortality.

Data on chemical concentrations in commonly recommended diets are sparse. This study estimates the association between adherence to three dietary patterns considered healthy and exposure to environmental chemicals in a multi-racial pregnancy cohort, underscoring the need for increased regulation and monitoring of environmental contaminants.

Small distinctive patterns or ‘motifs’ are more prevalent in real systems than they are in randomly generated networks. It now seems that these motifs emerge naturally according to a principle that favours interconnections biased towards stability.

Here, the authors develop a genome evolution model to investigate the origin of functional redundancy in the human microbiome by analyzing its genomic content network and illustrate potential ecological and evolutionary processes that may contribute to its resilience.

Most genetic studies have been done on European cohorts, which affects the efficacy of polygenic risk scores in non-European populations. Here, the authors demonstrate that a colorectal cancer PRS including Asian and European ancestries has improved performance over the European-centric PRS across racial and ethnic groups.

It is unclear how often genetic mosaicism of chromosome X arises. Here, the authors examine women with cancer and cancer-free controls and show that X chromosome mosaicism occurs more frequently than on autosomes, especially on the inactive X chromosome, but is not linked to non-haematologic cancer risk

Studying human diet may help us identify measures to treat or prevent chronic diseases. Here, the authors discover the nutritional redundancy phenomenon in human diet and demonstrate its association with cardiovascular disease and type 2 diabetes.

The inner centromere protein (INCENP) activates Aurora kinase B (AURKB) and Aurora kinase C. Here the authors provide insights into the activation mechanism of AURKB/C by determining the crystal structure of fully active phosphorylated human AURKC bound to the phosphorylated C-terminal IN-box section of human INCENP.

Here, the authors present a theoretical framework based on community ecology and network science to investigate the efficacy of fecal microbiota transplantation in conditions associated with a disrupted gut microbiota, using the recurrent Clostridioides difficile infection as a prototype disease.

Controlling microbial communities could help restore ecosystems and maintain healthy microbiota. Here, the authors introduce the notion of structural accessibility and develop a framework to identify minimal sets of driver species, manipulation of which could allow control of a microbial community.

Population structure enables emergence of cooperation among individuals, but the impact of the dynamic nature of real interaction networks is not understood. Here, the authors study the evolution of cooperation on temporal networks and find that temporality enhances the evolution of cooperation.

Understanding the decentralized self-organization in animal groups helps design swarm robotics, yet the underlying mechanism remains elusive. Xiao et al. analyze collective motions of three large bird-flocking datasets and translate their findings to guide evacuation of a swarm of miniature robots in confinement.