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Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR .

Recent years have witnessed a surge in the development of innovative polygenic score (PGS) methods, driving their extensive application in disease prevention, monitoring, and treatment. However, the accuracy of genetic risk prediction remains moderate for most traits. Currently, most PGSs were built based on the summary statistics from the target trait, while many traits exhibit varied degrees of shared genetic architecture or pleiotropy. Appropriate leveraging of pleiotropy from correlated traits can potentially improve the performance of PGS of the target trait. In this study, we present PleioSDPR, a novel method that jointly models the genetic effects of complex traits and identifies conditions under which leveraging pleiotropy can improve polygenic risk prediction. PleioSDPR models the joint distribution of effect sizes across traits, allowing SNPs to be null for both traits, causal for only one trait, or causal for both traits, and it flexibly captures region-specific genetic correlations and unequal heritability across traits. Through extensive simulations and real data applications, we demonstrate that PleioSDPR improves prediction performance compared with several univariate and multivariate PGS methods, especially when there is no validation dataset. For example, by incorporating information from schizophrenia or leg fat-free mass, PleioSDPR effectively improves the prediction accuracy of bipolar disorder (14.5% accuracy gain) and hip circumference (14.6% accuracy gain), respectively. Moreover, our results indicate that traits with stronger genetic correlations to the target trait, greater heritability, and limited sample overlap contribute more substantially to enhancing prediction accuracy for the target trait. Overall, our study highlights the potential of PleioSDPR to enhance the accuracy of genetic risk prediction by effectively leveraging pleiotropy across traits and diseases. These findings contribute to a broader understanding of polygenic risk prediction and underscore the importance of incorporating pleiotropic information to improve the use of these predictions in disease prevention and treatment strategies.

Colorectal cancer (CRC) is among the most frequently occurring cancers worldwide. Baicalin is isolated from the roots of Scutellaria baicalensis and is its dominant flavonoid. Anticancer activity of baicalin has been evaluated in different types of cancers, especially in CRC. However, the molecular mechanisms underlying the contribution of baicalin to the treatment of CRC are still unknown. Here, we confirmed that baicalin can effectively induce and enhance apoptosis in HT-29 cells in a dose-dependent manner and suppress tumour growth in xenografted nude mice. We further performed a miRNA microarray analysis of baicalin-treated and untreated HT-29 cells. The results showed that a large number of oncomiRs, including miR-10a, miR-23a, miR-30c, miR-31, miR-151a and miR-205, were significantly suppressed in baicalin-treated HT-29 cells. Furthermore, our in vitro and in vivo studies showed that baicalin suppressed oncomiRs by reducing the expression of c-Myc. Taken together, our study shows a novel mechanism for anti-cancer action of baicalin, that it induces apoptosis in colon cancer cells and suppresses tumour growth by reducing the expression of c-Myc and oncomiRs.

We did a phase 2 trial of pembrolizumab in patients with non-small-cell lung cancer (NSCLC) or melanoma with untreated brain metastases to determine the activity of PD-1 blockade in the CNS. Interim results were previously published, and we now report an updated analysis of the full NSCLC cohort. This was an open-label, phase 2 study of patients from the Yale Cancer Center (CT, USA). Eligible patients were at least 18 years of age with stage IV NSCLC with at least one brain metastasis 5–20 mm in size, not previously treated or progressing after previous radiotherapy, no neurological symptoms or corticosteroid requirement, and Eastern Cooperative Oncology Group performance status less than two. Modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria was used to evaluate CNS disease; systemic disease was not required for participation. Patients were treated with pembrolizumab 10 mg/kg intravenously every 2 weeks. Patients were in two cohorts: cohort 1 was for those with PD-L1 expression of at least 1% and cohort 2 was patients with PD-L1 less than 1% or unevaluable. The primary endpoint was the proportion of patients achieving a brain metastasis response (partial response or complete response, according to mRECIST). All treated patients were analysed for response and safety endpoints. This study is closed to accrual and is registered with ClinicalTrials.gov, NCT02085070. Between March 31, 2014, and May 21, 2018, 42 patients were treated. Median follow-up was 8·3 months (IQR 4·5–26·2). 11 (29·7% [95% CI 15·9–47·0]) of 37 patients in cohort 1 had a brain metastasis response. There were no responses in cohort 2. Grade 3–4 adverse events related to treatment included two patients with pneumonitis, and one each with constitutional symptoms, colitis, adrenal insufficiency, hyperglycaemia, and hypokalaemia. Treatment-related serious adverse events occurred in six (14%) of 42 patients and were pneumonitis (n=2), acute kidney injury, colitis, hypokalaemia, and adrenal insufficiency (n=1 each). There were no treatment-related deaths. Pembrolizumab has activity in brain metastases from NSCLC with PD-L1 expression at least 1% and is safe in selected patients with untreated brain metastases. Further investigation of immunotherapy in patients with CNS disease from NSCLC is warranted. Merck and the Yale Cancer Center.

Genetic risk prediction for non-European populations is hindered by limited Genome-Wide Association Study (GWAS) sample sizes and small tuning datasets. We propose JointPRS, a data-adaptive framework that leverages genetic correlations across multiple populations using GWAS summary statistics. It achieves accurate predictions without individual-level tuning data and remains effective in the presence of a small tuning set thanks to its data-adaptive approach. Through extensive simulations and real data applications to 22 quantitative and four binary traits in five continental populations evaluated using the UK Biobank (UKBB) and All of Us (AoU), JointPRS consistently outperforms six state-of-the-art methods across three data scenarios: no tuning data, same-cohort tuning and testing, and cross-cohort tuning and testing. Notably, in the Admixed American population, JointPRS improves lipid trait prediction in AoU by 6.46%–172.00% compared to the other existing methods. Polygenic risk prediction in non-European populations is limited by small sample sizes and tuning sets. Here, the authors show that JointPRS improves cross-population prediction accuracy by leveraging genetic correlations without requiring individual-level data.

Cell-derived exosomes have been demonstrated to be efficient carriers of small RNAs to neighbouring or distant cells, highlighting the preponderance of exosomes as carriers for gene therapy over other artificial delivery tools. In the present study, we employed modified exosomes expressing the neuron-specific rabies viral glycoprotein (RVG) peptide on the membrane surface to deliver opioid receptor mu (MOR) siRNA into the brain to treat morphine addiction. We found that MOR siRNA could be efficiently packaged into RVG exosomes and was associated with argonaute 2 (AGO2) in exosomes. These exosomes efficiently and specifically delivered MOR siRNA into Neuro2A cells and the mouse brain. Functionally, siRNA-loaded RVG exosomes significantly reduced MOR mRNA and protein levels. Surprisingly, MOR siRNA delivered by the RVG exosomes strongly inhibited morphine relapse via the down-regulation of MOR expression levels. In conclusion, our results demonstrate that targeted RVG exosomes can efficiently transfer siRNA to the central nervous system and mediate the treatment of morphine relapse by down-regulating MOR expression levels. Our study provides a brand new strategy to treat drug relapse and diseases of the central nervous system.

To increase statistical power to identify genes associated with complex traits, a number of transcriptome-wide association study (TWAS) methods have been proposed using gene expression as a mediating trait linking genetic variations and diseases. These methods first predict expression levels based on inferred expression quantitative trait loci (eQTLs) and then identify expression-mediated genetic effects on diseases by associating phenotypes with predicted expression levels. The success of these methods critically depends on the identification of eQTLs, which may not be functional in the corresponding tissue, due to linkage disequilibrium (LD) and the correlation of gene expression between tissues. Here, we introduce a new method called T-GEN ( T ranscriptome-mediated identification of disease-associated G enes with E pigenetic a N notation) to identify disease-associated genes leveraging epigenetic information. Through prioritizing SNPs with tissue-specific epigenetic annotation, T-GEN can better identify SNPs that are both statistically predictive and biologically functional. We found that a significantly higher percentage (an increase of 18.7% to 47.2%) of eQTLs identified by T-GEN are inferred to be functional by ChromHMM and more are deleterious based on their Combined Annotation Dependent Depletion (CADD) scores. Applying T-GEN to 207 complex traits, we were able to identify more trait-associated genes (ranging from 7.7% to 102%) than those from existing methods. Among the identified genes associated with these traits, T-GEN can better identify genes with high (>0.99) pLI scores compared to other methods. When T-GEN was applied to late-onset Alzheimer’s disease, we identified 96 genes located at 15 loci, including two novel loci not implicated in previous GWAS. We further replicated 50 genes in an independent GWAS, including one of the two novel loci.

Brain anatomy plays a key role in complex behaviors and mental disorders that are sexually divergent. While our understanding of the sex differences in the brain anatomy remains relatively limited, particularly of the underlying genetic and molecular mechanisms that contribute to these differences. We performed the largest study of sex differences in brain volumes ( N  = 33,208) by examining sex differences both in the raw brain volumes and after controlling the whole brain volumes. Genetic correlation analysis revealed sex differences only in the left amygdala. We compared transcriptome differences between males and females using data from GTEx and characterized cell-type compositions using GTEx bulk amygdala RNA-seq data and LIBD amygdala single-cell reference profiles. We also constructed polygenic risk scores (PRS) to investigate sex-specific genetic correlations between left amygdala volume and mental disorders ( N  = 25,576~105,318) of Psychiatric Genomics Consortium and other traits of UKB ( N  = 347,996). Although there were pronounced sex differences in brain volumes, there was no difference in the heritability between sexes. There was a significant sex-specific genetic correlation between male and female left amygdala. We identified sex-differentiated genetic effects of PRSs for schizophrenia on left amygdala volume, as well as significant sex-differentiated genetic correlations between PRSs of left amygdala and six traits in UKB. We also found several sex-differentially expressed genes in the amygdala. These findings not only advanced the current knowledge of genetic basis of sex differences in brain anatomy, but also presented an important clue for future research on the mechanism of sex differences in mental disorders and targeted treatments.

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA’s results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement.

Although there are pronounced sex differences for psychiatric disorders, relatively little has been published on the heterogeneity of sex-specific genetic effects for these traits until very recently for adults. Much less is known about children because most psychiatric disorders will not manifest until later in life and existing studies for children on psychiatric traits such as cognitive functions are underpowered. We used results from publicly available genome-wide association studies for six psychiatric disorders and individual-level data from the Adolescent Brain Cognitive Development (ABCD) study and the UK Biobank (UKB) study to evaluate the associations between the predicted polygenic risk scores (PRS) of these six disorders and observed cognitive functions, behavioral and brain imaging traits. We further investigated the mediation effects of the brain structure and function, which showed heterogeneity between males and females on the correlation between genetic risk of schizophrenia and fluid intelligence. There was significant heterogeneity in genetic associations between the cognitive traits and psychiatric disorders between sexes. Specifically, the PRSs of schizophrenia of boys showed stronger correlation with eight of the ten cognitive functions in the ABCD data set; whereas the PRSs of autism of females showed a stronger correlation with fluid intelligence in the UKB data set. Besides cognitive traits, we also found significant sexual heterogeneity in genetic associations between psychiatric disorders and behavior and brain imaging. These results demonstrate the underlying early etiology of psychiatric disease and reveal a shared and unique genetic basis between the disorders and cognition traits involved in brain functions between the sexes.