SummaryRare genetic variation is considered a potential source of heritability in individuals with sporadic Alzheimer disease and related dementias (ADRD). The Variant-set test for association using annotation information (STAAR) framework leverages multiple functional annotations of genetic variants and combines association statistics from multiple variant aggregation-based methods, including burden, sequence kernel association test (SKAT), and aggregated Cauchy association test (ACAT-V), into a single measure of significance. Using whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP), we comprehensively examined the association of rare genetic variation with ADRD in 23,454 individuals (37% individuals affected by ADRD) and with cognitively healthy elder status in 13,292 individuals (13% cognitively healthy elders) from diverse populations via the STAAR framework. We identified several genes significantly associated with ADRD or cognitively healthy status. However, our analysis revealed several limitations within the STAAR framework incorporating ultra-rare variants with dichotomous outcomes. To enhance the robustness of the framework, we proposed several computational refinements, including creating a burden of ultra-rare variants and employing more precise annotations to match the expected mechanism. After implementing the proposed modifications, the association with ADRD for ZNF200 was no longer statistically significant (α = 1 × 10−7), while TBX19, PLXNB2, CARD11, and LINC01880 remained significantly associated with cognitively healthy status. We identified and addressed the computational limitations in the STAAR framework that could lead to potential spurious results for ultra-rare variant aggregates with an extremely low cumulative minor-allele count. Our proposed refinements produced more robust results for associations with rare variants in the context of dichotomous outcomes.

SummaryTo advance understanding of cellular heterogeneity in disease from single-cell sequencing data, we introduce residual principal-component analysis (ResidPCA), a robust method for identifying cell states that explicitly models cell-type heterogeneity. In simulations, ResidPCA achieved more than 4-fold higher accuracy than conventional PCA and over 3-fold higher accuracy than non-negative matrix factorization (NMF)-based methods in detecting states expressed across multiple cell types. Applied to single-cell RNA sequencing of light-stimulated mouse visual cortex cells, ResidPCA captured stimulus-driven variability with an accuracy more than 5-fold higher than NMF-based approaches. In single-nucleus datasets from an Alzheimer disease cohort, ResidPCA identified 44 chromatin accessibility-based states from single-nucleus ATAC-seq (snATAC-seq) and 42 transcriptional states from single-nucleus RNA-seq. Thirty snATAC-seq states were significantly enriched for Alzheimer disease heritability, often more so than established cell types such as microglia. The snATAC-seq state most significantly enriched for heritability further elucidates a recently implicated neuron-oligodendrocyte-microglial mechanistic axis, linking early amyloid production in neurons and oligodendrocytes with later microglial activation and immune response. These results highlight the ability of ResidPCA to uncover previously hidden biological variation in single-cell data and reveal disease-relevant cell states.

SummaryIn studies of individuals of primarily European genetic ancestry, common and low-frequency variants and rare coding variants have been found to be associated with the risk of bipolar disorder (BD) and schizophrenia (SZ). However, less is known for individuals of other genetic ancestries or the role of rare non-coding variants in BD and SZ risk. We performed whole-genome sequencing (∼27X) of African American individuals: 1,598 with BD, 3,295 with SZ, and 2,651 unaffected controls (InPSYght study). We increased power by incorporating 14,812 jointly called psychiatrically unscreened ancestry-matched controls from the Trans-Omics for Precision Medicine (TOPMed) Program for a total of 17,463 controls (∼37X). To identify variants and sets of variants associated with BD and/or SZ, we performed single-variant tests, gene-based tests for singleton protein truncating variants, and rare and low-frequency variant annotation-based tests with conservation and universal chromatin states and sliding windows. We found suggestive evidence of the association of BD with single variants on chromosome 18 and of lower BD risk associated with rare and low-frequency variants on chromosome 11 in a region with multiple BD genome-wide association study loci, using a sliding window approach. We also found that chromatin and conservation state tests can be used to detect differential calling of variants in controls sequenced at different centers and to assess the effectiveness of sequencing metric covariate adjustments. Our findings reinforce the need for continued whole-genome sequencing in additional samples of African American individuals and more comprehensive functional annotation of non-coding variants.

SummaryWe developed an imputation panel for Alzheimer disease (AD) and related dementias (ADRD) using 15,958 whole-genome sequencing (WGS) samples from the Alzheimer Disease Sequencing Project. Recognizing the importance of associations between structural variants (SVs) and AD and their underrepresentation in existing public reference panels, our panel uniquely integrates single-nucleotide variants (SNVs), short insertions or deletions (indels), and SVs. This panel enhances the imputation of rare variants underlying disease susceptibility onto genotype array data, offering a cost-effective alternative to WGS while significantly augmenting statistical power. Notably, we discovered 10 rare indels nominally significantly associated with AD that are absent in the TOPMed-r2 panel and identified one genome-wide significant (p < 5 × 10−8) and three suggestive significant (p < 1 × 10−5) AD-associated SVs. These findings provide the other insights into AD genetics and underscore the critical role of imputation panels in advancing our understanding of complex diseases like ADRD.

SummaryMCM8 and MCM9 are newly proposed cancer predisposition genes, linked to polyposis and early-onset cancer, in addition to their previously established association with hypogonadism. Given the uncertain range of phenotypic manifestations and unclear cancer risk estimates, this study aimed to delineate the molecular and clinical characteristics of biallelic germline MCM8/MCM9 variant carriers. We found significant enrichment of biallelic MCM9 variants in individuals with colonic polyps (odds ratio [OR] 6.51, 95% confidence interval [CI] 1.24–34.11, p = 0.03), rectal polyps (OR 8.40, 95% CI 1.28–55.35, p = 0.03), and gastric cancer (OR 27.03, 95% CI 2.93–248.5; p = 0.004) in data from the 100000 Genomes Project, compared to controls. No similar enrichment was found for biallelic MCM8 variants or in the 200000 UK Biobank. Likewise, in our case series, which included 26 MCM8 and 28 MCM9 variant carriers, we documented polyposis, gastric cancer, and early-onset colorectal cancer (CRC) in MCM9 carriers but not in MCM8 carriers. Moreover, our case series indicates that beyond hypogonadism, biallelic MCM8 and MCM9 variants are associated with early-onset germ cell tumors (occurring before age 15). Tumors from MCM8/MCM9 variant carriers predominantly displayed clock-like mutational processes, without evidence of DNA repair deficiency-associated signatures. Collectively, our data indicate that biallelic MCM9 variants are associated with polyposis, gastric cancer, and early-onset CRC, while both biallelic MCM8 and MCM9 variants are linked to hypogonadism and the early development of germ cell tumors. These findings underscore the importance of including MCM8/MCM9 in diagnostic gene panels for certain clinical contexts and suggest that biallelic carriers may benefit from cancer surveillance.

SummaryA significant barrier to the treatment of neurodevelopmental disorders (NDDs) is a limited understanding of disease mechanisms. Heterozygous missense variants in PPP2R5D cause Houge-Janssens syndrome 1, a rare NDD characterized by macrocephaly, developmental delay, intellectual disability, seizures, autism spectrum disorder, and early-onset Parkinson disease. This study investigated the impact of pathogenic PPP2R5D variants on neuronal development and evaluated allele-specific knockdown as a potential therapeutic strategy. Induced pluripotent stem cells derived from individuals carrying the E198K and E420K variants, along with CRISPR-corrected isogenic controls, were differentiated into neural progenitors and cortical glutamatergic neurons. Patient-derived neural progenitors were hyper-proliferative, and glutamatergic neurons differentiated from these cells exhibited increased neurite outgrowth. Notably, neuronal overgrowth phenotypes were not observed in neurons lacking PPP2R5D, suggesting the disorder does not result from loss of function. RNA sequencing (RNA-seq) of glutamatergic neurons derived from patient lines compared to their isogenic controls revealed disruptions in pathways critical for neuronal development, synaptic signaling, and axon guidance. To target pathogenic transcripts, antisense oligonucleotides (ASOs) were designed to selectively knock down the E198K allele, the most common disease-causing missense variant. The most effective ASOs reversed neurite outgrowth defects in patient-derived neurons. These findings uncover molecular mechanisms underlying PPP2R5D-related NDDs and support allele-specific knockdown as a potential therapeutic approach.

SummaryMendelian randomization (MR) is a widely used method for assessing causal relationships between risk factors and outcomes using genetic variants as instrumental variables (IVs). While traditional MR assumes uni-directional causality, bi-directional MR aims to identify the true causal direction. In uni-directional MR, invalid IVs due to pleiotropy can violate assumptions and introduce biases. In bi-directional MR, traditional MR can be performed separately for each direction, but the presence of invalid IVs poses even greater challenges. We introduce a new bi-directional MR method incorporating stepwise selection (Bidir-SW) designed to address these challenges. Our approach leverages public genome-wide association study (GWAS) datasets for two traits and uses model selection criteria to identify invalid IVs iteratively by stepwise selection. This method accounts for potential bi-directional causality in the presence of common invalid IVs for both directions, even if only GWAS summary statistics are provided. Through simulation studies, we demonstrate that our method outperforms traditional MR techniques, such as MR-Egger and inverse-variance weighted (IVW), with uncorrelated SNPs. We also provide simulations to compare our approach with existing transcriptome-wide association study (TWAS) to show its effectiveness. Finally, we apply the proposed method to genetic traits such as CRP levels and BMI to explore possible bi-directional relationships among these traits. We also used the proposed method to discover causal protein biomarkers. Our findings suggest that the Bidir-SW approach is a powerful tool for bi-directional MR or TWAS, which can provide a valuable framework for future genetic epidemiology studies.