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Abstract Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases. 

Abstract DNA inverted repeats (IRs) are widespread across many eukaryotic genomes. Their ability to form stable hairpin/cruciform secondary structures is causative in triggering chromosome instability leading to several human diseases. Distance and sequence divergence between IRs are inversely correlated with their ability to induce gross chromosomal rearrangements (GCRs) because of a lesser probability of secondary structure formation and chromosomal breakage. In this study, we demonstrate that structural parameters that normally constrain the instability of IRs are overcome when the repeats interact in single-stranded DNA (ssDNA). We established a system in budding yeast whereby >73 kb of ssDNA can be formed in cdc13-707fs mutants. We found that in ssDNA, 12 bp or 30 kb spaced Alu-IRs show similarly high levels of GCRs, while heterology only beyond 25% suppresses IR-induced instability. Mechanistically, rearrangements arise after cis-interaction of IRs leading to a DNA fold-back and the formation of a dicentric chromosome, which requires Rad52/Rad59 for IR annealing as well as Rad1-Rad10, Slx4, Msh2/Msh3 and Saw1 proteins for nonhomologous tail removal. Importantly, using structural characteristics rendering IRs permissive to DNA fold-back in yeast, we found that ssDNA regions mapped in cancer genomes contain a substantial number of potentially interacting and unstable IRs. 

Abstract High-resolution reconstruction of spatial chromosome organizations from chromatin contact maps is highly demanded, but is hindered by extensive pairwise constraints, substantial missing data, and limited resolution and cell-type availabilities. Here, we present FLAMINGO, a computational method that addresses these challenges by compressing inter-dependent Hi-C interactions to delineate the underlying low-rank structures in 3D space, based on the low-rank matrix completion technique. FLAMINGO successfully generates 5 kb- and 1 kb-resolution spatial conformations for all chromosomes in the human genome across multiple cell-types, the largest resources to date. Compared to other methods using various experimental metrics, FLAMINGO consistently demonstrates superior accuracy in recapitulating observed structures with raises in scalability by orders of magnitude. The reconstructed 3D structures efficiently facilitate discoveries of higher-order multi-way interactions, imply biological interpretations of long-range QTLs, reveal geometrical properties of chromatin, and provide high-resolution references to understand structural variabilities. Importantly, FLAMINGO achieves robust predictions against high rates of missing data and significantly boosts 3D structure resolutions. Moreover, FLAMINGO shows vigorous cross cell-type structure predictions that capture cell-type specific spatial configurations via integration of 1D epigenomic signals. FLAMINGO can be widely applied to large-scale chromatin contact maps and expand high-resolution spatial genome conformations for diverse cell-types. 

Abstract In women, excess androgen causes polycystic ovary syndrome (PCOS), a common fertility disorder with comorbid metabolic dysfunctions including diabetes, obesity, and nonalcoholic fatty liver disease. Using a PCOS mouse model, this study shows that chronic high androgen levels cause hepatic steatosis while hepatocyte-specific androgen receptor (AR)-knockout rescues this phenotype. Moreover, through RNA-sequencing and metabolomic studies, we have identified key metabolic genes and pathways affected by hyperandrogenism. Our studies reveal that a large number of metabolic genes are directly regulated by androgens through AR binding to androgen response element sequences on the promoter region of these genes. Interestingly, a number of circadian genes are also differentially regulated by androgens. In vivo and in vitro studies using a circadian reporter [Period2::Luciferase (Per2::LUC)] mouse model demonstrate that androgens can directly disrupt the hepatic timing system, which is a key regulator of liver metabolism. Consequently, studies show that androgens decrease H3K27me3, a gene silencing mark on the promoter of core clock genes, by inhibiting the expression of histone methyltransferase, Ezh2, while inducing the expression of the histone demethylase, JMJD3, which is responsible for adding and removing the H3K27me3 mark, respectively. Finally, we report that under hyperandrogenic conditions, some of the same circadian/metabolic genes that are upregulated in the mouse liver are also elevated in nonhuman primate livers. In summary, these studies not only provide an overall understanding of how hyperandrogenism associated with PCOS affects liver gene expression and metabolism but also offer insight into the underlying mechanisms leading to hepatic steatosis in PCOS. 

Deciphering the functional effects of noncoding genetic variants stands as a fundamental challenge in human genetics. Traditional approaches, such as Genome-Wide Association Studies (GWAS), Transcriptome-Wide Association Studies (TWAS), and Quantitative Trait Loci (QTL) studies, are constrained by obscured the underlying molecular-level mechanisms, making it challenging to unravel the genetic basis of complex traits. The advent of Next-Generation Sequencing (NGS) technologies has enabled context-specific genome-wide measurements, encompassing gene expression, chromatin accessibility, epigenetic marks, and transcription factor binding sites, to be obtained across diverse cell types and tissues, paving the way for decoding genetic variation effects directly from DNA sequences only. Thede novopredictions of functional effects are pivotal for enhancing our comprehension of transcriptional regulation and its disruptions caused by the plethora of noncoding genetic variants linked to human diseases and traits. This review provides a systematic overview of the state-of-the-art models and algorithms for genetic variant effect predictions, including traditional sequence-based models, Deep Learning models, and the cutting-edge Foundation Models. It delves into the ongoing challenges and prospective directions, presenting an in-depth perspective on contemporary developments in this domain. 

Hypoxic reprogramming of vasculature relies on genetic, epigenetic, and metabolic circuitry, but the control points are unknown. In pulmonary arterial hypertension (PAH), a disease driven by hypoxia inducible factor (HIF)–dependent vascular dysfunction, HIF-2α promoted expression of neighboring genes, long noncoding RNA (lncRNA) histone lysineN-methyltransferase 2E-antisense 1 (KMT2E-AS1) and histone lysine N-methyltransferase 2E (KMT2E).KMT2E-AS1stabilized KMT2E protein to increase epigenetic histone 3 lysine 4 trimethylation (H3K4me3), driving HIF-2α–dependent metabolic and pathogenic endothelial activity. This lncRNA axis also increased HIF-2α expression across epigenetic, transcriptional, and posttranscriptional contexts, thus promoting a positive feedback loop to further augment HIF-2α activity. We identified a genetic association between rs73184087, a single-nucleotide variant (SNV) within aKMT2Eintron, and disease risk in PAH discovery and replication patient cohorts and in a global meta-analysis. This SNV displayed allele (G)–specific association with HIF-2α, engaged in long-range chromatin interactions, and induced the lncRNA-KMT2E tandem in hypoxic (G/G) cells. In vivo,KMT2E-AS1deficiency protected against PAH in mice, as did pharmacologic inhibition of histone methylation in rats. Conversely, forced lncRNA expression promoted more severe PH. Thus, theKMT2E-AS1/KMT2E pair orchestrates across convergent multi-ome landscapes to mediate HIF-2α pathobiology and represents a key clinical target in pulmonary hypertension.