Intestinal Blastocystis is linked to healthier diets and more favorable cardiometabolic outcomes in 56,989 individuals from 32 countries.

Diet impacts human health, influencing body adiposity and the risk of developing cardiometabolic diseases. The gut microbiome is a key player in the diet-health axis, but while its bacterial fraction is widely studied, the role of micro-eukaryotes, including Blastocystis, is underexplored. We performed a global-scale analysis on 56,989 metagenomes and showed that human Blastocystis exhibits distinct prevalence patterns linked to geography, lifestyle, and dietary habits. Blastocystis presence defined a specific bacterial signature and was positively associated with more favorable cardiometabolic profiles and negatively with obesity (p < 1e-16) and disorders linked to altered gut ecology (p < 1e-8). In a diet intervention study involving 1,124 individuals, improvements in dietary quality were linked to weight loss and increases in Blastocystis prevalence (p = 0.003) and abundance (p < 1e-7). Our findings suggest a potentially beneficial role for Blastocystis, which may help explain personalized host responses to diet and downstream disease etiopathogenesis.

Early Alzheimer's disease pathology in human cortex involves transient cell states.

Cellular perturbations underlying Alzheimer's disease (AD) are primarily studied in human postmortem samples and model organisms. Here, we generated a single-nucleus atlas from a rare cohort of cortical biopsies from living individuals with varying degrees of AD pathology. We next performed a systematic cross-disease and cross-species integrative analysis to identify a set of cell states that are specific to early AD pathology. These changes-which we refer to as the early cortical amyloid response-were prominent in neurons, wherein we identified a transitional hyperactive state preceding the loss of excitatory neurons, which we confirmed by acute slice physiology on independent biopsy specimens. Microglia overexpressing neuroinflammatory-related processes also expanded as AD pathology increased. Finally, both oligodendrocytes and pyramidal neurons upregulated genes associated with ß-amyloid production and processing during this early hyperactive phase. Our integrative analysis provides an organizing framework for targeting circuit dysfunction, neuroinflammation, and amyloid production early in AD pathogenesis.

Limb development genes underlie variation in human fingerprint patterns.

Fingerprints are of long-standing practical and cultural interest, but little is known about the mechanisms that underlie their variation. Using genome-wide scans in Han Chinese cohorts, we identified 18 loci associated with fingerprint type across the digits, including a genetic basis for the long-recognized "pattern-block" correlations among the middle three digits. In particular, we identified a variant near EVI1 that alters regulatory activity and established a role for EVI1 in dermatoglyph patterning in mice. Dynamic EVI1 expression during human development supports its role in shaping the limbs and digits, rather than influencing skin patterning directly. Trans-ethnic meta-analysis identified 43 fingerprint-associated loci, with nearby genes being strongly enriched for general limb development pathways. We also found that fingerprint patterns were genetically correlated with hand proportions. Taken together, these findings support the key role of limb development genes in influencing the outcome of fingerprint patterning.

Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition.

Checkpoint inhibitors (CPIs) augment adaptive immunity. Systematic pan-tumor analyses may reveal the relative importance of tumor-cell-intrinsic and microenvironmental features underpinning CPI sensitization. Here, we collated whole-exome and transcriptomic data for >1,000 CPI-treated patients across seven tumor types, utilizing standardized bioinformatics workflows and clinical outcome criteria to validate multivariable predictors of CPI sensitization. Clonal tumor mutation burden (TMB) was the strongest predictor of CPI response, followed by total TMB and CXCL9 expression. Subclonal TMB, somatic copy alteration burden, and histocompatibility leukocyte antigen (HLA) evolutionary divergence failed to attain pan-cancer significance. Dinucleotide variants were identified as a source of immunogenic epitopes associated with radical amino acid substitutions and enhanced peptide hydrophobicity/immunogenicity. Copy-number analysis revealed two additional determinants of CPI outcome supported by prior functional evidence: 9q34 (TRAF2) loss associated with response and CCND1 amplification associated with resistance. Finally, single-cell RNA sequencing (RNA-seq) of clonal neoantigen-reactive CD8 tumor-infiltrating lymphocytes (TILs), combined with bulk RNA-seq analysis of CPI-responding tumors, identified CCR5 and CXCL13 as T-cell-intrinsic markers of CPI sensitivity.

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.

Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.

The body-wide human microbiome plays a role in health, but its full diversity remains uncharacterized, particularly outside of the gut and in international populations. We leveraged 9,428 metagenomes to reconstruct 154,723 microbial genomes (45% of high quality) spanning body sites, ages, countries, and lifestyles. We recapitulated 4,930 species-level genome bins (SGBs), 77% without genomes in public repositories (unknown SGBs [uSGBs]). uSGBs are prevalent (in 93% of well-assembled samples), expand underrepresented phyla, and are enriched in non-Westernized populations (40% of the total SGBs). We annotated 2.85 M genes in SGBs, many associated with conditions including infant development (94,000) or Westernization (106,000). SGBs and uSGBs permit deeper microbiome analyses and increase the average mappability of metagenomic reads from 67.76% to 87.51% in the gut (median 94.26%) and 65.14% to 82.34% in the mouth. We thus identify thousands of microbial genomes from yet-to-be-named species, expand the pangenomes of human-associated microbes, and allow better exploitation of metagenomic technologies.

Meta-analysis and public policy: Reconciling the evidence on deworming.

The WHO recommends mass drug administration (MDA) for intestinal worm infections in areas with over 20% infection prevalence. Recent Cochrane meta-analyses endorse treatment of infected individuals but recommend against MDA. We conducted a theory-agnostic random-effects meta-analysis of the effect of multiple-dose MDA and a cost-effectiveness analysis. We estimate significant effects of MDA on child weight (0.15 kg, 95% CI: 0.07, 0.24; P < 0.001), mid-upper arm circumference (0.20 cm, 95% CI: 0.03, 0.37; P = 0.02), and height (0.09 cm, 95% CI: 0.01, 0.16; P = 0.02) when prevalence is over 20% but not on Hb (0.06 g/dL, 95% CI: -0.01, 0.14; P = 0.1). These results suggest that MDA is a cost-effective intervention, particularly in the settings where it is recommended by the WHO.

White Americans' preference for Black people in advertising has increased in the past 66 y: A meta-analysis.

This study investigates Black and White consumers' preferences for Black versus White people in United States advertising contexts over 66 y, from 1956 until 2022, a time in which the United States has experienced significant ethno-racial diversification. Examining Black and White consumers' reactions to visual advertising over more than half a century offers a unique and dynamic view of interracial preferences. Mass advertising reaches an audience of billions and can shape people's attitudes and behavior, emphasizing the relevance of clarifying the influence of race in advertising, how it has evolved over time, and how it may contribute to mitigating discrimination based on racial perceptions. A meta-analysis of extant experiments into the relationship between the depicted endorser's race (i.e., the model in a visual ad) and the reaction of Black and White viewers pertains to 332 effect sizes from 62 studies reported in 52 scientific papers, comprising 10,186 Black and White participants. Our results are anchored in a conceptual framework, including a comprehensive set of perceiver (viewer), target (endorser), social/societal context, and publication characteristics. Without accounting for temporal dynamics, the results indicate ingroup favoritism, such that White viewers prefer White models and Black viewers prefer Black models. But by controlling for the publication year, it is possible to observe a time-dependent trend: Historically, White consumers preferred endorsers of the same race, but this preference has significantly shifted toward Black endorsers in recent years. In contrast, the level of Black consumers' reactions to endorsers of the same race remains largely unchanged over time.

Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes.

Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana. A GRN inference approach was used to determine the: i. influential transcription factors (TFs) in each organ and, ii. most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, an important food and biofuel trait. Of the top 20 predicted TFs, eight are known regulators of seed oil content, e.g., WRI1, LEC1, FUS3. Importantly, we validated our prediction of MybS2, TGA4, SPL12, AGL18, and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MybS2 and show that it induces purple acid phosphatase family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to find unique regulators of any trait-of-interest.

Deforestation impacts soil biodiversity and ecosystem services worldwide.

Deforestation poses a global threat to biodiversity and its capacity to deliver ecosystem services. Yet, the impacts of deforestation on soil biodiversity and its associated ecosystem services remain virtually unknown. We generated a global dataset including 696 paired-site observations to investigate how native forest conversion to other land uses affects soil properties, biodiversity, and functions associated with the delivery of multiple ecosystem services. The conversion of native forests to plantations, grasslands, and croplands resulted in higher bacterial diversity and more homogeneous fungal communities dominated by pathogens and with a lower abundance of symbionts. Such conversions also resulted in significant reductions in carbon storage, nutrient cycling, and soil functional rates related to organic matter decomposition. Responses of the microbial community to deforestation, including bacterial and fungal diversity and fungal guilds, were predominantly regulated by changes in soil pH and total phosphorus. Moreover, we found that soil fungal diversity and functioning in warmer and wetter native forests is especially vulnerable to deforestation. Our work highlights that the loss of native forests to managed ecosystems poses a major global threat to the biodiversity and functioning of soils and their capacity to deliver ecosystem services.

Polygenic risk prediction: why and when out-of-sample prediction R2 can exceed SNP-based heritability.

In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.

Loci for insulin processing and secretion provide insight into type 2 diabetes risk.

Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.

Genome-wide association study of esophageal squamous cell cancer identifies shared and distinct risk variants in African and Chinese populations.

Esophageal squamous cell carcinoma (ESCC) has a high disease burden in sub-Saharan Africa and has a very poor prognosis. Genome-wide association studies (GWASs) of ESCC in predominantly East Asian populations indicate a substantial genetic contribution to its etiology, but no genome-wide studies have been done in populations of African ancestry. Here, we report a GWAS in 1,686 African individuals with ESCC and 3,217 population-matched control individuals to investigate its genetic etiology. We identified a genome-wide-significant risk locus on chromosome 9 upstream of FAM120A (rs12379660, p = 4.58 × 10-8, odds ratio = 1.28, 95% confidence interval = 1.22-1.34), as well as a potential African-specific risk locus on chromosome 2 (rs142741123, p = 5.49 × 10-8) within MYO1B. FAM120A is a component of oxidative stress-induced survival signals, and the associated variants at the FAM120A locus co-localized with highly significant cis-eQTLs in FAM120AOS in both esophageal mucosa and esophageal muscularis tissue. A trans-ethnic meta-analysis was then performed with the African ESCC study and a Chinese ESCC study in a combined total of 3,699 ESCC-affected individuals and 5,918 control individuals, which identified three genome-wide-significant loci on chromosome 9 at FAM120A (rs12379660, pmeta = 9.36 × 10-10), chromosome 10 at PLCE1 (rs7099485, pmeta = 1.48 × 10-8), and chromosome 22 at CHEK2 (rs1033667, pmeta = 1.47 × 10-9). This indicates the existence of both shared and distinct genetic risk loci for ESCC in African and Asian populations. Our GWAS of ESCC conducted in a population of African ancestry indicates a substantial genetic contribution to ESCC risk in Africa.

A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes.

Genome-wide association studies (GWASs) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWASs) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify candidate genes, we performed a GWAS analysis in a breast cancer dataset from UK Biobank (UKB) and combined the results with the GWAS results of the Breast Cancer Association Consortium (BCAC) by a meta-analysis. Using the summary statistics from the meta-analysis, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 11 tissues that are potentially relevant to breast cancer from the Genotype-Tissue Expression (GTEx) data. In the GWAS analysis, we identified eight loci distinct from those reported previously. In the TWAS analysis, we identified 309 genes at 108 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold. Of these, 17 genes were located in eight regions that were at least 1 Mb away from published GWAS hits. The remaining TWAS-significant genes were located in 100 known genomic loci from previous GWASs of breast cancer. We found that 21 genes located in known GWAS loci remained statistically significant after conditioning on previous GWAS index variants. Our study provides insights into breast cancer genetics through mapping candidate target genes in a large proportion of known GWAS loci and discovering multiple new loci.

Telomere length is not a useful tool for chronological age estimation in animals.

Telomeres are short repetitive DNA sequences capping the ends of chromosomes. Telomere shortening occurs during cell division and may be accelerated by oxidative damage or ameliorated by telomere maintenance mechanisms. Consequently, telomere length changes with age, which was recently confirmed in a large meta-analysis across vertebrates. However, based on the correlation between telomere length and age, it was concluded that telomere length can be used as a tool for chronological age estimation in animals. Correlation should not be confused with predictability, and the current data and studies suggest that telomeres cannot be used to reliably predict individual chronological age. There are biological reasons for why there is large individual variation in telomere dynamics, which is mainly due to high susceptibility to a wide range of environmental, but also genetic factors, rendering telomeres unfeasible as a tool for age estimation. The use of telomeres for chronological age estimation is largely a misguided effort, but its occasional reappearance in the literature raises concerns that it will mislead resources in wildlife conservation.

Meta-analytic relations between personality and cognitive ability.

Cognitive ability and personality are fundamental domains of human psychology. Despite a century of vast research, most ability-personality relations remain unestablished. Using contemporary hierarchical personality and cognitive abilities frameworks, we meta-analyze unexamined links between personality traits and cognitive abilities and offer large-scale evidence of their relations. This research quantitatively summarizes 60,690 relations between 79 personality and 97 cognitive ability constructs in 3,543 meta-analyses based on data from millions of individuals. Sets of novel relations are illuminated by distinguishing hierarchical personality and ability constructs (e.g., factors, aspects, facets). The links between personality traits and cognitive abilities are not limited to openness and its components. Some aspects and facets of neuroticism, extraversion, and conscientiousness are also considerably related to primary as well as specific abilities. Overall, the results provide an encyclopedic quantification of what is currently known about personality-ability relations, identify previously unrecognized trait pairings, and reveal knowledge gaps. The meta-analytic findings are visualized in an interactive webtool. The database of coded studies and relations is offered to the scientific community to further advance research, understanding, and applications.

Similar photosynthetic but different yield responses of C3 and C4 crops to elevated O3.

The deleterious effects of ozone (O3) pollution on crop physiology, yield, and productivity are widely acknowledged. It has also been assumed that C4 crops with a carbon concentrating mechanism and greater water use efficiency are less sensitive to O3 pollution than C3 crops. This assumption has not been widely tested. Therefore, we compiled 46 journal articles and unpublished datasets that reported leaf photosynthetic and biochemical traits, plant biomass, and yield in five C3 crops (chickpea, rice, snap bean, soybean, and wheat) and four C4 crops (sorghum, maize, Miscanthus × giganteus, and switchgrass) grown under ambient and elevated O3 concentration ([O3]) in the field at free-air O3 concentration enrichment (O3-FACE) facilities over the past 20 y. When normalized by O3 exposure, C3 and C4 crops showed a similar response of leaf photosynthesis, but the reduction in chlorophyll content, fluorescence, and yield was greater in C3 crops compared with C4 crops. Additionally, inbred and hybrid lines of rice and maize showed different sensitivities to O3 exposure. This study quantitatively demonstrates that C4 crops respond less to elevated [O3] than C3 crops. This understanding could help maintain cropland productivity in an increasingly polluted atmosphere.

The effect of climate change on avian offspring production: A global meta-analysis.

Climate change affects timing of reproduction in many bird species, but few studies have investigated its influence on annual reproductive output. Here, we assess changes in the annual production of young by female breeders in 201 populations of 104 bird species (N = 745,962 clutches) covering all continents between 1970 and 2019. Overall, average offspring production has declined in recent decades, but considerable differences were found among species and populations. A total of 56.7% of populations showed a declining trend in offspring production (significant in 17.4%), whereas 43.3% exhibited an increase (significant in 10.4%). The results show that climatic changes affect offspring production through compounded effects on ecological and life history traits of species. Migratory and larger-bodied species experienced reduced offspring production with increasing temperatures during the chick-rearing period, whereas smaller-bodied, sedentary species tended to produce more offspring. Likewise, multi-brooded species showed increased breeding success with increasing temperatures, whereas rising temperatures were unrelated to reproductive success in single-brooded species. Our study suggests that rapid declines in size of bird populations reported by many studies from different parts of the world are driven only to a small degree by changes in the production of young.

Bayes factor functions for reporting outcomes of hypothesis tests.

Bayes factors represent a useful alternative to P-values for reporting outcomes of hypothesis tests by providing direct measures of the relative support that data provide to competing hypotheses. Unfortunately, the competing hypotheses have to be specified, and the calculation of Bayes factors in high-dimensional settings can be difficult. To address these problems, we define Bayes factor functions (BFFs) directly from common test statistics. BFFs depend on a single noncentrality parameter that can be expressed as a function of standardized effects, and plots of BFFs versus effect size provide informative summaries of hypothesis tests that can be easily aggregated across studies. Such summaries eliminate the need for arbitrary P-value thresholds to define "statistical significance." Because BFFs are defined using nonlocal alternative prior densities, they provide more rapid accumulation of evidence in favor of true null hypotheses without sacrificing efficiency in supporting true alternative hypotheses. BFFs can be expressed in closed form and can be computed easily from z, t, χ2, and F statistics.

Field interventions for climate change mitigation behaviors: A second-order meta-analysis.

Behavioral change is essential to mitigate climate change. To advance current knowledge, we synthesize research on interventions aiming to promote climate change mitigation behaviors in field settings. In a preregistered second-order meta-analysis, we assess the overall effect of 10 meta-analyses, incorporating a total of 430 primary studies. In addition, we assess subgroup analyses for six types of interventions, five behaviors, and three publication bias adjustments. Results showed that climate change mitigation interventions were generally effective (dunadjusted = 0.31, 95% CI [0.30, 0.32]). A follow-up analysis using only unique primary studies, adjusted for publication bias, provides a more conservative overall estimate (d = 0.18, 95% CI [0.13, 0.24]). This translates into a mean treatment effect of 7 percentage points. Furthermore, in a subsample of adequately powered large-scale interventions (n > 9,000, k = 32), the effect was adjusted downward to approximately 2 percentage points. This discrepancy might be because large-scale interventions often target nonvoluntary participants by less direct techniques (e.g., "home energy reports") while small-scale interventions often target voluntary participants by more direct techniques (e.g., face-to-face interactions). Subgroup analyses showed that interventions based on social comparisons or financial incentives were the most effective, while education or feedback was the least effective. These results provide a comprehensive state-of-the-art summary of climate change mitigation interventions, guiding both future research and practice.