Jeffrey P. Spence

Transcriptome-wide association studies (TWAS) are widely used to identify genes involved in complex traits and to infer the direction of gene effects on traits. However, despite their popularity, it remains unclear how accurately TWAS recover the true direction of a gene’s effect on a trait. Here we estimate the false sign rate of TWAS for plasma proteins, leveraging the expectation that increased gene expression should generally increase protein expression. We then extend this framework to complex traits, where loss-of-function burden tests provide the expected direction of effect. In both analyses, we observe high discordance with expectations, with TWAS showing a false sign rate of 23% for plasma proteins and 33% for complex traits. We propose that much of the sign discordance arises when expression quantitative trait loci in non-trait-relevant tissues tag haplotypes associated with traits in genome-wide association studies via distinct, tightly linked regulatory variants, yielding spurious TWAS associations with the correct genes but with unreliable direction of effect. These findings show that TWAS-based direction-of-effect estimates should be interpreted with caution and raise concerns about the reliability of TWAS more broadly.

Despite the ubiquity of wild boar populations across Eurasia, their origins, routes of dispersal, and phenotypic evolution remain uncertain. Here, using 745 genomes representing populations across Eurasia, we demonstrate that wild boar expanded from South Asia during the Middle Pleistocene. This expansion was preceded by a Pliocene introgression (~3 to 5 million years ago) in South Asia from an archaic Suinae lineage that introduced a ~43 megabase X-chromosomal haplotype into the ancestral wild boar population. This haplotype includes loci associated with increased body size and adaptation to cold climates. During domestication, this region was selected at least three times independently to develop modern large-bodied breeds. Our findings reveal a long-lasting evolutionary legacy of this ancient introgression, which provided genetic material that was later shaped by natural and artificial selection.

Genetic diversity within species underpins evolutionary adaptation and has recently been included as a target for protection in the United Nations’ Global Biodiversity Framework (GBF). Yet, we lack mathematical tools to estimate past genetic diversity loss across species—or predict future losses—based on demographic proxies used in conversation policy. To fill this gap, we developed a spatiotemporal framework to predict the dynamics of genetic diversity under realistic habitat change scenarios, calibrated with population-scale genomic data from 29 plant and animal species. To estimate how much genetic diversity has already been lost, we analyzed habitat area and population size losses for 4,611 species from the last five decades, using data from the Living Planet Index, the Red List, and new GBF indicators. We estimate that species have already lost 1 to 13% of π genetic diversity. Furthermore, we predict that genetic diversity losses lag behind population and habitat area declines, such that an average of 6 to 45% of genetic diversity will be lost under different scenarios even if population sizes or habitats do not decline further. Our results highlight that safeguarding existing habitats is insufficient to maintain the genetic health of species, and that genetic monitoring from proxy indicators will only detect major genetic diversity losses after it is already too late.

Genome-wide association studies (GWAS) have identified hundreds of significant loci for psychiatric disorders, yet the strength of these associations remains modest compared to other human complex traits with similar numbers of hits. Whether this pattern reflects statistical artifacts or real biological differences — and, if the latter, what underlies it — remains unclear. In addition to psychiatric disorders, we find that other traits with functional enrichment in the central nervous system (CNS), whether binary or quantitative, also share similar genetic architectures, characterized by GWAS hits of limited statistical significance and generally higher allele frequencies. To robustly compare traits that differ in GWAS statistical power, we demonstrate how binarizing a quantitative trait reduces power. This loss of power can be replicated by a matched “effective sample size” on the liability scale. After matching “effective sample sizes”, we show that CNS-enriched traits have large mutational target sizes, with contributing variants and genes experiencing stronger selection than those for other traits. Our findings reveal heterogeneity among diseases and provide insights into traits that more effectively capture fitness-relevant processes. More broadly, our results suggest that the genetic architectures of complex traits are shaped by the tissues through which these traits are mediated.

Epigenetic regulation orchestrates mammalian transcription, but functional links between them remain elusive. To tackle this problem, we use epigenomic and transcriptomic data from 13 ENCODE cell types to train machine learning models to predict gene expression from histone post-translational modifications (PTMs), achieving transcriptome-wide correlations of ∼0.70−0.79 for most cell types. Our models recapitulate known associations between histone PTMs and expression patterns, including predicting that acetylation of histone subunit H3 lysine residue 27 (H3K27ac) near the transcription start site (TSS) significantly increases expression levels. To validate this prediction experimentally and investigate how natural vs. engineered deposition of H3K27ac might differentially affect expression, we apply the synthetic dCas9-p300 histone acetyltransferase system to 8 genes in the HEK293T cell line and to 5 genes in the K562 cell line. Further, to facilitate model building, we perform MNase-seq to map genome-wide nucleosome occupancy levels in HEK293T. We observe that our models perform well in accurately ranking relative fold-changes among genes in response to the dCas9-p300 system; however, their ability to rank fold-changes within individual genes is noticeably diminished compared to predicting expression across cell types from their native epigenetic signatures. Our findings highlight the need for more comprehensive genome-scale epigenome editing datasets, better understanding of the actual modifications made by epigenome editing tools, and improved causal models that transfer better from endogenous cellular measurements to perturbation experiments. Together, these improvements would facilitate the ability to understand and predictably control the dynamic human epigenome with consequences for human health.

Transcriptome-wide association studies (TWAS) are widely used to identify genes involved in complex traits and to infer the direction of gene effects on traits. However, despite their popularity, it remains unclear how accurately TWAS recover the true direction of a gene's effect on a trait. Here, we estimate the false sign rate (FSR) of TWAS for plasma proteins, leveraging the expectation that increased gene expression should generally increase protein expression. We then extend this framework to complex traits, where loss-of-function burden tests provide the expected direction-of-effect. In both analyses, we observe high discordance with expectations, with TWAS showing an FSR of 23% for plasma proteins and 33% for complex traits. While colocalization-based filtering reduced the FSR, substantial discordance remained, and with substantial loss of recall. However, when we restricted gene-direction assignments for plasma proteins to using only relevant tissues in combination with colocalization-based filtering, the FSR dropped to 11%, and to just 5% if we excluded brain-specific proteins. We propose that much of the sign discordance arises when eQTLs in non-trait-relevant tissues tag GWAS-associated haplotypes via distinct, tightly-linked regulatory variants, yielding spurious TWAS associations with the correct genes but with unreliable direction-of-effect. These findings show that TWAS-based direction-of-effect estimates should be interpreted with caution and raise concerns about the reliability of TWAS more broadly.

Genetic association studies provide a unique tool for identifying candidate causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits. Here we propose approaches to bridge this gap by combining quantitative estimates of gene–trait relationships from loss-of-function burden tests¹ with gene-regulatory connections inferred from Perturb-seq experiments² in relevant cell types. By combining these two forms of data, we aim to build causal graphs in which the directional associations of genes with a trait can be explained by their regulatory effects on biological programs or direct effects on the trait³. As a proof of concept, we constructed a causal graph of the gene-regulatory hierarchy that jointly controls three partially co-regulated blood traits. We propose that perturbation studies in trait-relevant cell types, coupled with gene-level effect sizes for traits, can bridge the gap between genetic association and biological mechanism.