Covariate powered cross‐weighted multiple testing

Deciphering novel TCF4-driven mechanisms underlying a common triplet repeat expansion-mediated disease

Fuchs endothelial corneal dystrophy (FECD) is an age-related cause of vision loss, and the most common repeat expansion-mediated disease in humans characterised to date. Up to 80% of European FECD cases have been attributed to expansion of a non-coding CTG repeat element (termed CTG18.1) located within the ubiquitously expressed transcription factor encoding gene, TCF4. The non-coding nature of the repeat and the transcriptomic complexity of TCF4 have made it extremely challenging to experimentally decipher the molecular mechanisms underlying this disease. Here we comprehensively describe CTG18.1 expansion-driven molecular components of disease within primary patient-derived corneal endothelial cells (CECs), generated from a large cohort of individuals with CTG18.1-expanded (Exp+) and CTG 18.1-independent (Exp-) FECD. We employ long-read, short-read, and spatial transcriptomic techniques to interrogate expansion-specific transcriptomic biomarkers. Interrogation of long-read sequencing and alternative splicing analysis of short-read transcriptomic data together reveals the global extent of altered splicing occurring within Exp+ FECD, and unique transcripts associated with CTG18.1-expansions. Similarly, differential gene expression analysis highlights the total transcriptomic consequences of Exp+ FECD within CECs. Furthermore, differential exon usage, pathway enrichment and spatial transcriptomics reveal TCF4 isoform ratio skewing solely in Exp+ FECD with potential downstream functional consequences. Lastly, exome data from 134 Exp- FECD cases identified rare (minor allele frequency <0.005) and potentially deleterious (CADD>15) TCF4 variants in 7/134 FECD Exp- cases, suggesting that TCF4 variants independent of CTG18.1 may increase FECD risk. In summary, our study supports the hypothesis that at least two distinct pathogenic mechanisms, RNA toxicity and TCF4 isoform-specific dysregulation, both underpin the pathophysiology of FECD. We anticipate these data will inform and guide the development of translational interventions for this common triplet-repeat mediated disease.

The growth factor EPIREGULIN promotes basal progenitor cell proliferation in the developing neocortex

Neocortex expansion during evolution is linked to higher numbers of neurons, which are thought to result from increased proliferative capacity and neurogenic potential of basal progenitor cells during development. Here, we show that EREG, encoding the growth factor EPIREGULIN, is expressed in the human developing neocortex and in gorilla cerebral organoids, but not in the mouse neocortex. Addition of EPIREGULIN to the mouse neocortex increases proliferation of basal progenitor cells, whereas EREG ablation in human cortical organoids reduces proliferation in the subventricular zone. Treatment of cortical organoids with EPIREGULIN promotes a further increase in proliferation of gorilla but not of human basal progenitor cells. EPIREGULIN competes with the epidermal growth factor (EGF) to promote proliferation, and inhibition of the EGF receptor abrogates the EPIREGULIN-mediated increase in basal progenitor cells. Finally, we identify putative cis-regulatory elements that may contribute to the observed inter-species differences in EREG expression. Our findings suggest that species-specific regulation of EPIREGULIN expression may contribute to the increased neocortex size of primates by providing a tunable pro-proliferative signal to basal progenitor cells in the subventricular zone.

Overexpression of SLIM1 transcription factor accelerates vegetative development in Arabidopsis thaliana

The transcription factor Sulfur Limitation 1 (SLIM1) belongs to the plant-specific Ethylene Insenstive3-Like transcription factor family and is known to coordinate gene expression in response to sulfur deficiency. However, the roles of SLIM1 in nutrient-sufficient conditions have not been characterized. Employing constitutive SLIM1 overexpression (35S::SLIM1) and CRISPR/Cas9 mutant plants (slim1-cr), we identified several distinct phenotypes in nutrient-sufficient conditions in Arabidopsis thaliana. Overexpression of SLIM1 results in plants with approximately twofold greater rosette area throughout vegetative development. 35S::SLIM1 plants also bolt earlier and exhibit earlier downregulation of photosynthesis-associated genes and earlier upregulation of senescence-associated genes than Col-0 and slim1-cr plants. This suggests that overexpression of SLIM1 accelerates development in A. thaliana. Genome-wide differential gene expression analysis relative to Col-0 at three time points with slim1-cr and two 35S::SLIM1 lines allowed us to identify 1,731 genes regulated directly or indirectly by SLIM1 in vivo.

DNA methylation haplotype block signatures responding to Staphylococcus aureus subclinical mastitis and association with production and health traits

BACKGROUND

DNA methylation has been documented to play vital roles in diseases and biological processes. In bovine, little is known about the regulatory roles of DNA methylation alterations on production and health traits, including mastitis.

RESULTS

Here, we employed whole-genome DNA methylation sequencing to profile the DNA methylation patterns of milk somatic cells from sixteen cows with naturally occurring Staphylococcus aureus (S. aureus) subclinical mastitis and ten healthy control cows. We observed abundant DNA methylation alterations, including 3,356,456 differentially methylated cytosines and 153,783 differential methylation haplotype blocks (dMHBs). The DNA methylation in regulatory regions, including promoters, first exons and first introns, showed global significant negative correlations with gene expression status. We identified 6435 dMHBs located in the regulatory regions of differentially expressed genes and significantly correlated with their corresponding genes, revealing their potential effects on transcriptional activities. Genes harboring DNA methylation alterations were significantly enriched in multiple immune- and disease-related pathways, suggesting the involvement of DNA methylation in regulating host responses to S. aureus subclinical mastitis. In addition, we found nine discriminant signatures (differentiates cows with S. aureus subclinical mastitis from healthy cows) representing the majority of the DNA methylation variations related to S. aureus subclinical mastitis. Validation of seven dMHBs in 200 cows indicated significant associations with mammary gland health (SCC and SCS) and milk production performance (milk yield).

CONCLUSIONS

In conclusion, our findings revealed abundant DNA methylation alterations in milk somatic cells that may be involved in regulating mammary gland defense against S. aureus infection. Particularly noteworthy is the identification of seven dMHBs showing significant associations with mammary gland health, underscoring their potential as promising epigenetic biomarkers. Overall, our findings on DNA methylation alterations offer novel insights into the regulatory mechanisms of bovine subclinical mastitis, providing further avenues for the development of effective control measures.

Adjusting for gene-specific covariates to improve RNA-seq analysis

SUMMARY

This article suggests a novel positive false discovery rate (pFDR) controlling method for testing gene-specific hypotheses using a gene-specific covariate variable, such as gene length. We suppose the null probability depends on the covariate variable. In this context, we propose a rejection rule that accounts for heterogeneity among tests by using two distinct types of null probabilities. We establish a pFDR estimator for a given rejection rule by following Storey's q-value framework. A condition on a type 1 error posterior probability is provided that equivalently characterizes our rejection rule. We also present a suitable procedure for selecting a tuning parameter through cross-validation that maximizes the expected number of hypotheses declared significant. A simulation study demonstrates that our method is comparable to or better than existing methods across realistic scenarios. In data analysis, we find support for our method's premise that the null probability varies with a gene-specific covariate variable.

AVAILABILITY AND IMPLEMENTATION

The source code repository is publicly available at https://github.com/hsjeon1217/conditional_method.

Transfer Learning in Genome-Wide Association Studies with Knockoffs

This paper presents and compares alternative transfer learning methods that can increase the power of conditional testing via knockoffs by leveraging prior information in external data sets collected from different populations or measuring related outcomes. The relevance of this methodology is explored in particular within the context of genome-wide association studies, where it can be helpful to address the pressing need for principled ways to suitably account for, and efficiently learn from the genetic variation associated to diverse ancestries. Finally, we apply these methods to analyze several phenotypes in the UK Biobank data set, demonstrating that transfer learning helps knockoffs discover more associations in the data collected from minority populations, potentially opening the way to the development of more accurate polygenic risk scores.

Functional integration of a semi-synthetic azido-queuosine derivative into translation and a tRNA modification circuit

Substitution of the queuine nucleobase precursor preQ₁ by an azide-containing derivative (azido-propyl-preQ₁) led to incorporation of this clickable chemical entity into tRNA via transglycosylation in vitro as well as in vivo in Escherichia coli, Schizosaccharomyces pombe and human cells. The resulting semi-synthetic RNA modification, here termed Q-L1, was present in tRNAs on actively translating ribosomes, indicating functional integration into aminoacylation and recruitment to the ribosome. The azide moiety of Q-L1 facilitates analytics via click conjugation of a fluorescent dye, or of biotin for affinity purification. Combining the latter with RNAseq showed that TGT maintained its native tRNA substrate specificity in S. pombe cells. The semi-synthetic tRNA modification Q-L1 was also functional in tRNA maturation, in effectively replacing the natural queuosine in its stimulation of further modification of tRNA^Asp with 5-methylcytosine at position 38 by the tRNA methyltransferase Dnmt2 in S. pombe. This is the first demonstrated in vivo integration of a synthetic moiety into an RNA modification circuit, where one RNA modification stimulates another. In summary, the scarcity of queuosinylation sites in cellular RNA, makes our synthetic q/Q system a ‘minimally invasive’ system for placement of a non-natural, clickable nucleobase within the total cellular RNA.

Statistical analysis of spatially resolved transcriptomic data by incorporating multi-omics auxiliary information

Effective control of false discovery rate is key for multiplicity problems. Here, we consider incorporating informative covariates from external datasets in the multiple testing procedure to boost statistical power while maintaining false discovery rate control. In particular, we focus on the statistical analysis of innovative high-dimensional spatial transcriptomic data while incorporating external multi-omics data that provide distinct but complementary information to the detection of spatial expression patterns. We extend OrderShapeEM, an efficient covariate-assisted multiple testing procedure that incorporates one auxiliary study, to make it permissible to incorporate multiple external omics studies, to boost statistical power of spatial expression pattern detection. Specifically, we first use a recently proposed computationally efficient statistical analysis method, spatial pattern recognition via kernels, to produce the primary test statistics for spatial transcriptomic data. Afterwards, we construct the auxiliary covariate by combining information from multiple external omics studies, such as bulk and single-cell RNA-seq data using the Cauchy combination rule. Finally, we extend and implement the integrative analysis method OrderShapeEM on the primary p-values along with auxiliary data incorporating multi-omics information for efficient covariate-assisted spatial expression analysis. We conduct a series of realistic simulations to evaluate the performance of our method with known ground truth. Four case studies in mouse olfactory bulb, mouse cerebellum, human breast cancer and human heart tissues further demonstrate the substantial power gain of our method in detecting genes with spatial expression patterns compared to existing classic approaches that do not utilize any external information.

Recurrent Germline Variant in RAD21 Predisposes Children to Lymphoblastic Leukemia or Lymphoma

Somatic loss of function mutations in cohesin genes are frequently associated with various cancer types, while cohesin disruption in the germline causes cohesinopathies such as Cornelia-de-Lange syndrome (CdLS). Here, we present the discovery of a recurrent heterozygous RAD21 germline aberration at amino acid position 298 (p.P298S/A) identified in three children with lymphoblastic leukemia or lymphoma in a total dataset of 482 pediatric cancer patients. While RAD21 p.P298S/A did not disrupt the formation of the cohesin complex, it altered RAD21 gene expression, DNA damage response and primary patient fibroblasts showed increased G2/M arrest after irradiation and Mitomycin-C treatment. Subsequent single-cell RNA-sequencing analysis of healthy human bone marrow confirmed the upregulation of distinct cohesin gene patterns during hematopoiesis, highlighting the importance of RAD21 expression within proliferating B- and T-cells. Our clinical and functional data therefore suggest that RAD21 germline variants can predispose to childhood lymphoblastic leukemia or lymphoma without displaying a CdLS phenotype.

Transplanted human cones incorporate and function in a murine cone degeneration model

Once human photoreceptors die, they do not regenerate, thus, photoreceptor transplantation has emerged as a potential treatment approach for blinding diseases. Improvements in transplant organization, donor cell maturation, and synaptic connectivity to the host will be critical in advancing this technology for use in clinical practice. Unlike the unstructured grafts of prior cell-suspension transplantations into end-stage degeneration models, we describe the extensive incorporation of induced pluripotent stem cell (iPSC) retinal organoid–derived human photoreceptors into mice with cone dysfunction. This incorporative phenotype was validated in both cone-only as well as pan-photoreceptor transplantations. Rather than forming a glial barrier, Müller cells extended throughout the graft, even forming a series of adherens junctions between mouse and human cells, reminiscent of an outer limiting membrane. Donor-host interaction appeared to promote polarization as well as the development of morphological features critical for light detection, namely the formation of inner and well-stacked outer segments oriented toward the retinal pigment epithelium. Putative synapse formation and graft function were evident at both structural and electrophysiological levels. Overall, these results show that human photoreceptors interacted readily with a partially degenerated retina. Moreover, incorporation into the host retina appeared to be beneficial to graft maturation, polarization, and function.

Leveraging Gene-Level Prediction as Informative Covariate in Hypothesis Weighting Improves Power for Rare Variant Association Studies

Gene-based rare variant association studies (RVASs) have low power due to the infrequency of rare variants and the large multiple testing burden. To correct for multiple testing, traditional false discovery rate (FDR) procedures which depend solely on P-values are often used. Recently, Independent Hypothesis Weighting (IHW) was developed to improve the detection power while maintaining FDR control by leveraging prior information for each hypothesis. Here, we present a framework to increase power of gene-based RVASs by incorporating prior information using IHW. We first build supervised machine learning models to assign each gene a prediction score that measures its disease risk, using the input of multiple biological features, fed with high-confidence risk genes and local background genes selected near GWAS significant loci as the training set. Then we use the prediction scores as covariates to prioritize RVAS results via IHW. We demonstrate the effectiveness of this framework through applications to RVASs in schizophrenia and autism spectrum disorder. We found sizeable improvements in the number of significant associations compared to traditional FDR approaches, and independent evidence supporting the relevance of the genes identified by our framework but not traditional FDR, demonstrating the potential of our framework to improve power of gene-based RVASs.

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.