Biology subjects

Sabatti, C.

Publications and source records attributed to Sabatti, C..

17 recordsLinked to original sources

bioRxiv2025

Uncovering Heterogeneous Effects via Localized Feature Selection

Identifying features that interact to trigger disease, while accounting for heterogeneity across diverse populations, is essential for the development of precision and targeted medicine. Despite the availability of vast and complex health-related datasets, most existing works focus on identifying disease-associated features at the population level or within a few subpopulations, often overlooking individual-level heterogeneity within these groups. To address this limitation, we propose a novel framework that utilizes localized test statistics to identify disease-associated features tailored to individual profiles. Our method leverages the recently developed knockoffs methodology to control the noise level of the selection set so that the results are replicable. Moreover, it allows for the discovery of hidden heterogeneous effects within the data, as demonstrated in an application to single-cell RNA sequencing data for Alzheimers disease. By aggregating localized feature selection results, our framework also enables powerful population-level feature selection. Our framework provides a powerful tool for exploratory studies of precision medicine, offering the potential to generate novel hypotheses for confirmatory biological experiments.

bioinformatics↗

bioRxiv2025

It's a wrap: deriving distinct discoveries with FDR control after a GWAS pipeline

The standard analysis pipeline for genome-wide association studies (GWAS) is based on marginal tests of association. These are computationally convenient and portable, but the discoveries resulting from their rejections are not immediately interpretable, and require post-processing as "clumping" and "fine mapping." An interesting alternative is provided by conditional independence hypotheses: their rejections lead to the identification of distinct signals across the genome, accounting for measured confounders, and pointing to separate causal pathways. An obstacle to the wide adoption of this approach has been that it requires access to individual level data. Overcoming this barrier, recent work has shown how summary statistics resulting from the standard marginal GWAS analysis can be used as input of a procedure to test conditional independence hypotheses while controlling the false discovery rate. This secondary analysis requires sampling of synthetic negative controls (knockoffs) from a distribution determined by the linkage disequilibrium patterns in the genome of the population under study. In prior work, we have pre-computed this distribution for European genomes, starting from information derived from the UK Biobank. Thus, researchers working with GWAS in a European population can carry out a knockoff analysis with minimal computational costs, using the distributed routine GhostKnockoffGWAS. Here we introduce and release a new software (solveblock) that extends this capability to a much richer collection of studies. Given a set of genotyped samples, or a reference dataset, our pipeline efficiently estimates the high-dimensional correlation matrices that describe dependencies across the genome, making rather common sparsity assumptions. Taking this sample-specific estimate as input, the software identifies groups of genetic variants that are highly correlated, and uses them to define an appropriate resolution for conditional independence hypotheses. Finally, we compute the distribution for the exchangeable negative controls necessary to test these hypotheses. The output of solveblock can be passed directly to GhostKnockoffGWAS, allowing users to carry out the complete analysis in a two step procedure. Simulations, based on five UK Biobank sub-populations, illustrate the methods FDR control. The analysis of 26 phenotypes of varying polygenicity in British individuals, results in{approx} 19 additional discoveries, compared to standard marginal association testing. Our code, precompiled software, and processed files for these five sub-populations are openly shared.

genetics↗

bioRxiv2024

In silico identification of putative causal genetic variants

Understanding the causal genetic architecture of complex phenotypes will fuel future research into disease mechanisms and potential therapies. Here, we illustrate the power of a novel framework: it detects, starting from summary statistics, and across the entire genome, sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. The approach, implemented in open-source software, is also computationally efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, we show that the method can substantially outperform existing methods in false discovery rate control, statistical power and various fine-mapping criteria. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimers disease (AD), we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline. Massively parallel reporter assays and CRISPR-Cas9 experiments have confirmed the functionality of the putative causal variants our method points to. Finally, we retrospectively analyzed summary statistics from 67 large-scale GWAS for a variety of phenotypes. Results reveal the methods capacity to robustly discover additional loci for polygenic traits and pinpoint potential causal variants underpinning each locus beyond conventional GWAS pipeline, contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses.

genetics↗

bioRxiv2021

Summary statistics knockoff inference empowers identification of putative causal variants in genome-wide association studies

Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) analysis of 1,403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry, and (2) a meta-analysis for Alzheimers disease (AD) comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies. The UK Biobank analysis demonstrates superior performance of the proposed method compared to conventional GWAS in both statistical power (2.05-fold more discoveries) and localization of putative causal variants at each locus (46% less proxy variants due to linkage disequilibrium). The AD meta-analysis identified 55 risk loci (including 31 new loci) with ~70% of the proximal genes at these loci showing suggestive signal in downstream single-cell transcriptomic analyses. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.

genetics↗

bioRxiv2020

Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics

Systematic and extensive investigation of enzymes is needed to understand their extraordinary efficiency and meet current challenges in medicine and engineering. We present HT-MEK, a microfluidic platform for high-throughput expression, purification, and characterization of >1500 enzyme variants per experiment. For 1036 mutants of the alkaline phosphatase PafA, we performed >670,000 reactions to determine >5000 kinetic and physical constants for multiple substrates and inhibitors. These constants allowed us to uncover extensive kinetic partitioning to a misfolded state and isolate catalytic effects, revealing spatially contiguous "regions" of residues linked to particular aspects of function. These regions included active-site proximal residues but also extended to the enzyme surface, providing a map of underlying architecture that could not be derived from existing approaches. HT-MEK, using direct and coupled fluorescent assays, has future applications to a wide variety of problems ranging from understanding molecular mechanisms to medicine to engineering and design. One Sentence SummaryHT-MEK, a microfluidic platform for high-throughput, quantitative biochemistry, reveals enzyme architectures shaping function.

biochemistry↗

bioRxiv2020

Integrative Spatial Single-cell Analysis with Graph-based Feature Learning

We propose GLISS, a strategy to discover spatially-varying genes by integrating two data sources: (1) spatial gene expression data such as image-based fluorescence in situ hybridization techniques, and (2) dissociated whole-transcriptome single-cell RNA-sequencing (scRNA-seq) data. GLISS utilizes a graph-based association measure to select and link genes that are spatially-dependent in both data sources. GLISS can discover new spatial genes and recover cell locations in scRNA-seq data from landmark genes determined from SGE data. GLISS also offers a new dimension reduction technique to cluster the genes, while accounting for the inferred spatial structure of the cells. We demonstrate the utility of GLISS on simulated and real datasets, including datasets on the mouse olfactory bulb and breast cancer biopsies, and two spatial studies of the mammalian liver and intestine.

bioinformatics↗

bioRxiv2020

Controlling the false discovery rate in GWAS with population structure

We present a comprehensive statistical framework to analyze data from genome-wide association studies of polygenic traits, producing distinct and interpretable discoveries while controlling the false discovery rate. This approach leverages sophisticated multivariate models, correcting for linkage disequilibrium, and accounts for population structure and relatedness, adapting to the characteristics of the samples at hand. A key element is the recognition that the observed genotypes can be considered as a random sample from an appropriate model, encapsulating our knowledge of genetic inheritance and human populations. This allows us to generate imperfect copies (knockoffs) of these variables which serve as ideal negative controls; knockoffs are indistinguishable from the original genotypes in distribution, and independent from the phenotype. In sharp contrast with state-of-the-art methods, the validity of our inference in no way depends on assumptions about the unknown relation between genotypes and phenotype. We develop and leverage a model for the genotypes that accounts for arbitrary and unknown population structure, which may be due to diverse ancestries or familial relatedness. We build a pipeline that is robust to the most prominent possible confounders, facilitating the discovery of causal variants. Validity and effectiveness are demonstrated by extensive simulations with real data, as well as by the analysis of several phenotypes in the UK Biobank. Finally, fast software is made available for researchers to apply the proposed methodology to Biobank-scale data sets.

genomics↗

bioRxiv2020

Progenitor identification and SARS-CoV-2 infection in long-term human distal lung organoid cultures

The distal lung contains terminal bronchioles and alveoli that facilitate gas exchange and is affected by disorders including interstitial lung disease, cancer, and SARS-CoV-2-associated COVID-19 pneumonia. Investigations of these localized pathologies have been hindered by a lack of 3D in vitro human distal lung culture systems. Further, human distal lung stem cell identification has been impaired by quiescence, anatomic divergence from mouse and lack of lineage tracing and clonogenic culture. Here, we developed robust feeder-free, chemically-defined culture of distal human lung progenitors as organoids derived clonally from single adult human alveolar epithelial type II (AT2) or KRT5+ basal cells. AT2 organoids exhibited AT1 transdifferentiation potential, while basal cell organoids progressively developed lumens lined by differentiated club and ciliated cells. Organoids consisting solely of club cells were not observed. Upon single cell RNA-sequencing (scRNA-seq), alveolar organoids were composed of proliferative AT2 cells; however, basal organoid KRT5+ cells contained a distinct ITGA6+ITGB4+ mitotic population whose proliferation segregated to a TNFRSF12Ahi subfraction. Clonogenic organoid growth was markedly enriched within the TNFRSF12Ahi subset of FACS-purified ITGA6+ITGB4+ basal cells from human lung or derivative organoids. In vivo, TNFRSF12A+ cells comprised ~10% of KRT5+ basal cells and resided in clusters within terminal bronchioles. To model COVID-19 distal lung disease, we everted the polarity of basal and alveolar organoids to rapidly relocate differentiated club and ciliated cells from the organoid lumen to the exterior surface, thus displaying the SARS-CoV-2 receptor ACE2 on the outwardly-facing apical aspect. Accordingly, basal and AT2 "apical-out" organoids were infected by SARS-CoV-2, identifying club cells as a novel target population. This long-term, feeder-free organoid culture of human distal lung alveolar and basal stem cells, coupled with single cell analysis, identifies unsuspected basal cell functional heterogeneity and exemplifies progenitor identification within a slowly proliferating human tissue. Further, our studies establish a facile in vitro organoid model for human distal lung infectious diseases including COVID-19-associated pneumonia.

cell biology↗

bioRxiv2019

Distinct and shared contributions of diagnosis and symptom domains to cognitive performance in severe mental illness

BackgroundSevere mental illness (SMI) diagnoses display overlapping symptomatology and shared genetic risk, motivating trans-diagnostic investigations of disease-relevant quantitative measures. We analyzed relationships between neurocognitive performance, symptom domains, and diagnoses, in a large sample of SMI cases (ascertained agnostic to diagnosis) and healthy controls from a single, homogeneous population. Methods2,406 participants (1,689 cases, 717 controls; mean age 39 years, 64% female) were assessed for speed and accuracy using the Penn Computerized Neurocognitive Battery (CNB). Cases carried structured-interview based diagnoses of schizophrenia (SCZ, n=160), bipolar-I (BP-I, n=519), bipolar-II (BP-II, n=204) and major depressive disorder (MDD, n=806). Linear mixed models, using CNB tests as repeated measures, modeled neurocognition as a function of diagnosis, sex, and all interactions. Follow-up analyses, in cases, included symptom factor scores obtained from exploratory factor analysis of symptom data, as main effects. FindingsBP-I and SCZ displayed nearly identical impairments in accuracy and speed, across cognitive domains. BP-II and MDD performed similarly to controls, with subtle deficits in executive and social cognition. A three-factor model (psychosis, mania, and depression) best represented symptom data. Controlling for diagnosis, premorbid IQ, and disease severity, high lifetime psychosis scores were associated with reduced accuracy and speed across cognitive domains, while high depression scores were associated with increased social cognition accuracy. InterpretationTrans-diagnostic investigations demonstrated that neurocognitive function in SMI is characterized by two distinct profiles (BP-I/SCZ and BP-II/MDD), and is associated with specific symptom domains. These results suggest the utility of this design for elucidating SMI causes and trajectories.

neuroscience↗

bioRxiv2019

Multi-resolution localization of causal variants across the genome

We present KnockoffZoom, a flexible method for the genetic mapping of complex traits at multiple resolutions. KnockoffZoom localizes causal variants by testing the conditional associations of genetic segments of decreasing width while provably controlling the false discovery rate using artificial genotypes as negative controls. Our method is equally valid for quantitative and binary phenotypes, making no assumptions about their genetic architectures. Instead, we rely on well-established genetic models of linkage disequilibrium. We demonstrate that our method can detect more associations than mixed effects models and achieve fine-mapping precision, at comparable computational cost. Lastly, we apply KnockoffZoom to data from 350k subjects in the UK Biobank and report many new findings.

genomics↗

bioRxiv2019

Genetic dysregulation of gene expression and splicing during a ten-year period of human aging

Molecular and cellular changes are intrinsic to aging and age-related diseases. Prior cross-sectional studies have investigated the combined effects of age and genetics on gene expression and alternative splicing; however, there has been no long-term, longitudinal characterization of these molecular changes, especially in older age. We performed RNA sequencing in whole-blood from the same individuals from the PIVUS study at ages 70 and 80 to quantify how gene expression, alternative splicing, and their genetic regulation are altered during this 10-year period of advanced aging. We observe that individuals are more similar to their own expression profiles later in life than profiles of other individuals their own age; 93% of samples cluster with their own measurement at another age, and there is a strong correlation of genetic effects on expression between the two ages (median{rho} G = 0.96). Despite this, we identify 1,291 and 294 genes differentially expressed and alternatively spliced with age, as well as 529 genes with outlying individual trajectories of aging. Further, 7.8% and 9.6% of tested genes show a reduction in genetic associations with expression and alternative splicing in older age, with impacted genes enriched in DNA repair pathways. Together these findings indicate that, although gene expression and alternative splicing and their genetic regulation are mostly stable late in life, a small subset of genes is dynamic and is characterized by changes in expression and splicing and a reduction in genetic regulation.

genomics↗

bioRxiv2018

Exome sequencing identifies high impact trait-associated alleles enriched in Finns

As yet undiscovered rare variants are hypothesized to substantially influence an individuals risk for common diseases and traits, but sequencing studies aiming to identify such variants have generally been underpowered. In isolated populations that have expanded rapidly after a population bottleneck, deleterious alleles that passed through the bottleneck may be maintained at much higher frequencies than in other populations. In an exome sequencing study of nearly 20,000 cohort participants from northern and eastern Finnish populations that exemplify this phenomenon, most novel trait-associated deleterious variants displayed frequencies 10-173 times higher than in other European populations. These enriched alleles underlie 30 novel associations with 20 disease-related quantitative traits and demonstrate a geographical clustering equivalent to that of Mendelian disease mutations characteristic of the Finnish population. Sequencing studies in populations without this unique history would require hundreds of thousands to millions of participants for comparable power.

genomics↗

bioRxiv2018

Exploratory Gene Ontology Analysis with Interactive Visualization

The Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. At the same time, the sheer number of concepts (>30,000) and relationships (>70,000) presents a challenge: it can be difficult to draw a comprehensive picture of how certain concepts of interest might relate with the rest of the ontology structure. Here we present new visualization strategies to facilitate the exploration and use of the information in the GO. We rely on novel graphical display and software architecture that allow significant interaction. To illustrate the potential of our strategies, we provide examples from high-throughput genomic analyses, including chromatin immunoprecipitation experiments and genome-wide association studies. The scientist can also use our visualizations to identify gene sets that likely experience coordinated changes in their expression and use them to simulate biologically-grounded single cell RNA sequencing data, or conduct power studies for differential gene expression studies using our built-in pipeline. Our software and documentation are available at http://aegis.stanford.edu.

bioinformatics↗

bioRxiv2018

Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates

Current evidence from case/control studies indicates that genetic risk for psychiatric disorders derives primarily from numerous common variants, each with a small phenotypic impact. The literature describing apparent segregation of bipolar disorder (BP) in numerous multigenerational pedigrees suggests that, in such families, large-effect inherited variants might play a greater role. To evaluate this hypothesis, we conducted genetic analyses in 26 Colombian (CO) and Costa Rican (CR) pedigrees ascertained for BP1, the most severe and heritable form of BP. In these pedigrees, we performed microarray SNP genotyping of 856 individuals and high-coverage whole-genome sequencing of 454 individuals. Compared to their unaffected relatives, BP1 individuals had higher polygenic risk scores estimated from SNPs associated with BP discovered in independent genome-wide association studies, and also displayed a higher burden of rare deleterious single nucleotide variants (SNVs) and rare copy number variants (CNVs) in genes likely to be relevant to BP1. Parametric and non-parametric linkage analyses identified 15 BP1 linkage peaks, encompassing about 100 genes, although we observed no significant segregation pattern for any particular rare SNVs and CNVs. These results suggest that even in extended pedigrees, genetic risk for BP appears to derive mainly from small to moderate effect rare and common variants.

genetics↗

bioRxiv2018

Understanding the Hidden Complexity of Latin American Population Isolates

Most population isolates examined to date were founded from a single ancestral population. Consequently, there is limited knowledge about the demographic history of admixed population isolates. Here we investigate genomic diversity of recently admixed population isolates from Costa Rica and Colombia and compare their diversity to a benchmark population isolate, the Finnish. These Latin American isolates originated during the 16th century from admixture between a few hundred European males and Amerindian females, with a limited contribution from African founders. We examine whole genome sequence data from 449 individuals, ascertained as families to build mutigenerational pedigrees, with a mean sequencing depth of coverage of approximately 24X. We find that Latin American isolates have increased genetic diversity relative to the Finnish. However, there is an increase in the amount of identity by descent (IBD) segments in the Latin American isolates relative to the Finnish. The increase in IBD segments is likely a consequence of a very recent and severe population bottleneck during the founding of the admixed population isolates. Furthermore, the proportion of the genome that falls within a long run of homozygosity (ROH) in Costa Rican and Colombian individuals was significantly greater than that in the Finnish, suggesting more recent consanguinity in the Latin American isolates relative to that seen in the Finnish. Lastly, we found that recent consanguinity increased the number of deleterious variants found in the homozygous state, which is relevant if deleterious variants are recessive. Our study suggests there is no single genetic signature of a population isolate.

genomics↗

bioRxiv2017

Whole Genome Sequencing in Psychiatric Disorders: the WGSPD Consortium

As technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome, through pilot WGS projects, will be critical to determine which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The WGSPD consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.

genomics↗

bioRxiv2016

Genetic variation and gene expression across multiple tissues and developmental stages in a non-human primate

By analyzing multi-tissue gene expression and genome-wide genetic variation data in samples from a vervet monkey pedigree, we generated a transcriptome resource and produced the first catalogue of expression quantitative trait loci (eQTLs) in a non-human primate model. This catalogue contains more genome-wide significant eQTLs, per sample, than comparable human resources, and reveals sex and age-related expression patterns. Findings include a master regulatory locus that likely plays a role in immune function, and a locus regulating hippocampal long non-coding RNAs (lncRNAs), whose expression correlates with hippocampal volume. This resource will facilitate genetic investigation of quantitative traits, including brain and behavioral phenotypes relevant to neuropsychiatric disorders.

genetics↗