Biology subjects

Lappalainen, T.

Publications and source records attributed to Lappalainen, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Isoform-level resolution in single-cell CRISPR screens reveals hidden functional consequences of gene perturbation

Single-cell CRISPR screens have enabled systematic investigation of gene function, but studies have largely focused on gene-level effects, overlooking transcriptional complexity and isoform usage. Methods capable of capturing splicing and isoform usage have emerged, including long-read sequencing and alternative library preparation strategies, but their suitability for large-scale perturbation screens remains unevaluated. We compare two library preparation methods (10x Genomics and Parse Biosciences) across Illumina short-read, Oxford Nanopore, and PacBio long-read sequencing, applying CRISPRi to silence three genes with distinct regulatory roles (DDX6, GEMIN5, GFI1B) in K562 cells. While short-read methods detected some splicing events, only long-read sequencing consistently captured isoform-level changes. Although Parse provided even transcript coverage, we observed strong intronic read enrichment, limiting its utility for splicing analysis. The primary constraint of long-read approaches was sequencing depth: [~]21 million reads are needed for 80% saturation of splicing events in a single perturbation. Notably, GEMIN5 knockdown produced only modest differential expression but the most extensive splicing changes, an effect invisible to gene-level analysis, underscoring the value of isoform-level screens. We provide a practical framework for isoform-level analysis in single-cell CRISPR screens, identifying current capabilities and limitations. As perturbation studies scale, long-read sequencing will be essential for comprehensive functional interpretation, capturing biology missed by gene-level analysis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=49 SRC="FIGDIR/small/737410v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@14bb963org.highwire.dtl.DTLVardef@783205org.highwire.dtl.DTLVardef@11c0233org.highwire.dtl.DTLVardef@1d468fe_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2026

Current challenges in GWAS integration and fine-mapping for variant interpretation

Over the past two decades, genome-wide association studies (GWAS) have identified thousands of trait- and disease-associated loci. However, the mechanistic understanding of these loci remains incomplete, which limits our ability to understand gene regulation and cellular programs underlying complex traits, predict disease risk, and develop therapeutics targeted to root causes. Here, we describe the current challenges for using GWAS to prioritize variants for functional follow-up experiments. These challenges span multiple domains, including limitations in data sharing and harmonization, limitations of statistical and functional fine-mapping, and the ambiguity in the added value of emerging deep learning frameworks for variant effect prediction as a complementary approach alongside traditional statistical genetics methods. We analyze these variant prioritization methods and suggest a multi-modal approach for resolving GWAS loci to a focused set of high-confidence variants for functional exploration. Fully realizing the potential of GWAS will require harmonized summary statistics and broader sharing of in-sample linkage disequilibrium (LD) data to enable robust and scalable causal variant prioritization.

genetics↗

bioRxiv2026

Identification of target genes and regulatory networks for bone mineral density GWAS loci through systematic targeting and inhibition

Osteoporosis is characterized by low bone mineral density (BMD) and elevated fracture risk. Most BMD-associated GWAS variants lie in noncoding regions, complicating efforts to identify causal genes and mechanisms. To overcome this variant-to-function challenge, we previously developed STING-seq, a framework integrating biobank-scale GWAS with single-cell CRISPR inhibition (CRISPRi) screening to directly connect noncoding cis-regulatory elements (CREs) to their target genes. Applied to human fetal osteoblast (hFOB) cells across osteogenic differentiation, STING-seq linked 76 CREs to 75 target genes at BMD loci. Arrayed CRISPRi and activation validated key CRE-gene relationships, including long-range enhancers regulating CXCL12 (over 500 kb apart). We further uncovered trans-regulatory networks and characterized the DAP3-YY1AP1 bidirectional promoter, demonstrating a role for DAP3 in mineralization via mitochondrial pathways. Together, these findings provide mechanistic insight into how noncoding GWAS variants shape osteoblast activity and highlight the genes and pathways that mediate genetic effects on BMD.

genomics↗

bioRxiv2025

CRISPRi perturbation screens and eQTLs provide complementary and distinct insights into GWAS target genes

Most genetic variants associated with human traits and diseases lie in noncoding regions of the genome 1, and a key challenge is determining which genes they affect 2,3. A common approach has been to leverage associations between natural genetic variation and gene expression to identify expression quantitative trait loci (eQTLs) in the population 4,5. At the same time, a novel approach uses pooled CRISPR interference (CRISPRi) perturbations of noncoding loci with single-cell transcriptome sequencing 6,7. Here, we systematically harmonized and compared the results from these approaches across hundreds of genomic regions associated with blood cell traits. We find that while the two approaches sometimes identify the same target genes, there are considerable differences that affect biological inferences made from the data. CRISPRi preferentially maps highly proximal, constraint-enriched genes, whereas eQTLs recover multiple, often distal targets. By benchmarking against 1,075 gold-standard CRE-gene pairs linked to blood traits, we show that the two approaches identify largely distinct targets; when combined, they achieve a balance between accuracy and completeness of gene discovery. Our results offer guidance for improved design of CRISPRi and eQTL studies and highlight their joint potential as a powerful toolkit for interpreting disease-associated loci.

genomics↗

bioRxiv2025

Spatial transcriptomics and genetically implicated genes identify putative causal tissue structures for complex traits

Spatially resolved transcriptomics is transforming our understanding of cellular and molecular diversity of tissues. Here, to identify tissue structures that are enriched for putatively causal disease processes, we integrated 31 human and mouse spatial datasets from 8 organs with genes that are genetically implicated in 32 human diseases. Applying our novel approach STEAM, we identified both known and novel roles of tissue structures in human diseases and identified spatially clustered disease gene subsets. For example, in the brain we observed enrichment of neuropsychiatric disease in cortical layers, and immune and barrier dysfunction in Alzheimers disease, with integration with single-cell data highlighting the complementary insights from the two data types. Spatial coexpression of drug target genes with genetically implicated genes in enriched tissue structures showed potential for spatially informed drug repurposing. Altogether, we show vast potential for integration of genetic discoveries with growing spatial datasets to understand human disease biology.

genetics↗

bioRxiv2024

Exploring penetrance of clinically relevant variants in over 800,000 humans from the Genome Aggregation Database

Incomplete penetrance, or absence of disease phenotype in an individual with a disease-associated variant, is a major challenge in variant interpretation. Studying individuals with apparent incomplete penetrance can shed light on underlying drivers of altered phenotype penetrance. Here, we investigate clinically relevant variants from ClinVar in 807,162 individuals from the Genome Aggregation Database (gnomAD), demonstrating improved representation in gnomAD version 4. We then conduct a comprehensive case-by-case assessment of 734 predicted loss of function variants (pLoF) in 77 genes associated with severe, early-onset, highly penetrant haploinsufficient disease. We identified explanations for the presumed lack of disease manifestation in 701 of the variants (95%). Individuals with unexplained lack of disease manifestation in this set of disorders rarely occur, underscoring the need and power of deep case-by-case assessment presented here to minimize false assignments of disease risk, particularly in unaffected individuals with higher rates of secondary properties that result in rescue.

genomics↗

bioRxiv2024

Recalibrating differential gene expression by genetic dosage variance prioritizes functionally relevant genes

Differential expression (DE) analysis is a widely used method for identifying genes that are functionally relevant for an observed phenotype or biological response. However, typical DE analysis includes selection of genes based on a threshold of fold change in expression under the implicit assumption that all genes are equally sensitive to dosage changes of their transcripts. This tends to favor highly variable genes over more constrained genes where even small changes in expression may be biologically relevant. To address this limitation, we have developed a method to recalibrate each genes differential expression fold change based on genetic expression variance observed in the human population. The newly established metric ranks statistically differentially expressed genes not by nominal change of expression, but by relative change in comparison to natural dosage variation for each gene. We apply our method to RNA sequencing datasets from rare disease and in-vitro stimulus response experiments. Compared to the standard approach, our method adjusts the bias in discovery towards highly variable genes, and enriches for pathways and biological processes related to metabolic and regulatory activity, indicating a prioritization of functionally relevant driver genes. With that, our method provides a novel view on DE and contributes towards bridging the existing gap between statistical and biological significance. We believe that this approach will simplify the identification of disease causing genes and enhance the discovery of therapeutic targets.

genomics↗

bioRxiv2024

Non-linear transcriptional responses to gradual modulation of transcription factor dosage

Genomic loci associated with common traits and diseases are typically non-coding and likely impact gene expression, sometimes coinciding with rare loss-of-function variants in the target gene. However, our understanding of how gradual changes in gene dosage affect molecular, cellular, and organismal traits is currently limited. To address this gap, we induced gradual changes in gene expression of four genes using CRISPR activation and inactivation. Downstream transcriptional consequences of dosage modulation of three master trans-regulators associated with blood cell traits (GFI1B, NFE2, and MYB) were examined using targeted single-cell multimodal sequencing. We showed that guide tiling around the TSS is the most effective way to modulate cis gene expression across a wide range of fold-changes, with further effects from chromatin accessibility and histone marks that differ between the inhibition and activation systems. Our single-cell data allowed us to precisely detect subtle to large gene expression changes in dozens of trans genes, revealing that many responses to dosage changes of these three TFs are nonlinear, including non-monotonic behaviours, even when constraining the fold-changes of the master regulators to a copy number gain or loss. We found that the dosage properties are linked to gene constraint and that some of these nonlinear responses are enriched for disease and GWAS genes. Overall, our study provides a straightforward and scalable method to precisely modulate gene expression and gain insights into its downstream consequences at high resolution.

genomics↗

bioRxiv2024

Specifying cellular context of transcription factor regulons for exploring context-specific gene regulation programs

Understanding the role of transcription and transcription factors in cellular identity and disease, such as cancer and autoimmunity, is essential. However, comprehensive data resources for cell line-specific transcription factor-to-target gene annotations are currently limited. To address this, we developed a straightforward method to define regulons that capture the cell-specific aspects of TF binding and transcript expression levels. By integrating cellular transcriptome and transcription factor binding data, we generated regulons for four common cell lines comprising both proximal and distal cell line-specific regulatory events. Through systematic benchmarking involving transcription factor knockout experiments, we demonstrated performance on par with state-of-the-art methods, with our method being easily applicable to other cell types of interest. We present case studies using three cancer single-cell datasets to showcase the utility of these cell-type-specific regulons in exploring transcriptional dysregulation. In summary, this study provides a valuable tool and a resource for systematically exploring cell line-specific transcriptional regulations, emphasizing the utility of network analysis in deciphering disease mechanisms.

bioinformatics↗

bioRxiv2023

Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits

Inference of directed biological networks is an important but notoriously challenging problem. We introduce inverse sparse regression (inspre), an approach to learning causal networks that leverages large-scale intervention-response data. Applied to 788 genes from the genome-wide perturb-seq dataset, inspre helps elucidate the network architecture of blood traits.

genetics↗

bioRxiv2023

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Bulk tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, while context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell type proportions, we demonstrate that cell type iQTLs could be considered as proxies for cell type-specific QTL effects. The interpretation of age iQTLs, however, warrants caution as the moderation effect of age on the genotype and molecular phenotype association may be mediated by changes in cell type composition. Finally, we show that cell type iQTLs contribute to cell type-specific enrichment of diseases that, in combination with additional functional data, may guide future functional studies. Overall, this study highlights iQTLs to gain insights into the context-specificity of regulatory effects.

genetics↗

bioRxiv2023

Genetic control of mRNA splicing as a potential mechanism for incomplete penetrance of rare coding variants

Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-seq data in GTEx v8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased WGS data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants.

genetics↗

bioRxiv2022

The impact of genetically controlled splicing on exon inclusion and protein structure

Common variants affecting mRNA splicing are typically identified though splicing quantitative trait locus (sQTL) mapping and have been shown to be enriched for GWAS signals by a similar degree to eQTLs. However, the specific splicing changes induced by these variants have been difficult to characterize, making it more complicated to analyze the effect size and direction of sQTLs, and to determine downstream splicing effects on protein structure. In this study, we catalogue sQTLs using exon percent spliced in (PSI) scores as a quantitative phenotype. PSI is an interpretable metric for identifying exon skipping events and has some advantages over other methods for quantifying splicing from short read RNA sequencing. In our set of sQTL variants, we find evidence of selective effects based on splicing effect size and effect direction, as well as exon symmetry. Additionally, we utilize AlphaFold2 to predict changes in protein structure associated with sQTLs overlapping GWAS traits, highlighting a potential new use-case for this technology for interpreting genetic effects on traits and disorders.

genomics↗

bioRxiv2022

Decoding the Variant-to-Function Relationship for LIPA, a Risk Locus for CAD

Translating human genomic discoveries into mechanistic insights requires linking genetic variations to candidate genes and their causal functional phenotypes. Genome-wide association studies have consistently identified LIPA as a risk locus for coronary artery disease (CAD), with previous expression quantitative trait loci (eQTL) analyses prioritizing LIPA as a candidate causal gene. However, functional studies elucidating the causal variants, regulatory mechanisms, target cell types, and their causal impact on atherosclerosis have been lacking. To address this gap, we applied functional genomics and experimental mouse models to establish the variant-to-function relationship at the LIPA locus. Our findings show that CAD risk alleles in the LIPA locus increase LIPA expression and enzyme activity specifically in monocytes/macrophages by enhancing PU.1 binding to an intronic enhancer region that interacts with the LIPA promoter. In myeloid Lipa-overexpressing mice, we observed larger atherosclerotic lesions accompanied by altered macrophage function, including increased macrophage accumulation due to enhanced monocyte recruitment, reduced neutral lipid accumulation, and upregulation of integrin and extracellular matrix pathways. Our work establishes a direct causal link between LIPA risk alleles and increased monocyte/macrophage LIPA that exacerbates atherosclerosis, bridging human functional genomic evidence to the mechanistic understanding of CAD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=58 SRC="FIGDIR/small/516293v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@11ce287org.highwire.dtl.DTLVardef@15c201aorg.highwire.dtl.DTLVardef@13f90c9org.highwire.dtl.DTLVardef@3aa3be_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2022

Uncovering Cross-Cohort Molecular Features with Multi-Omics Integration Analysis

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method. It was initially designed to extract latent features shared between two assays by finding the linear combinations of features - referred to as canonical vectors (CVs) - within each assay that achieve maximal across-assay correlation. Sparse multiple CCA (SMCCA), a widely-used derivative of CCA, allows more than two assays but can result in non-orthogonal CVs when applied to high-dimensional data. Here, we incorporated a variation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs. Applying our SMCCA-GS method to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS), we identified strong associations between blood cell counts and protein abundance. This finding suggests that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA, similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We further developed Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits. Author SummaryComprehensive understanding of human complex traits may benefit from incorporation of molecular features from multiple biological layers such as genome, epigenome, transcriptome, proteome, and metabolome. CCA is a correlation-based method for multi-omics data which reduces the dimension of each omic assay to several orthogonal components - commonly referred to as canonical vectors (CVs). The widely-used SMCCA method allows effective dimension reduction and integration of multi-omics data, but suffers from potentially highly correlated CVs when applied to high-dimensional omics data. Here, we improve the statistical independence among the CVs by adopting a variation of the GS algorithm. We applied our SMCCA-GS method to proteomic and methylomic data from two cohort studies, MESA and JHS. Our results reveal a pronounced effect of blood cell counts on protein abundance, strongly suggesting blood cell composition adjustment in protein-based association studies may be necessary. Finally, we present SSMCCA which allows supervised CCA analysis for the association between one phenotype of interest and more than two assays. We anticipate that SMCCA-GS would help reveal meaningful system-level factors from biological processes involving features from multiple assays; and SSMCCA would further empower interrogation of these factors for phenotypic traits related to health and diseases.

genomics↗

bioRxiv2022

Identifying Genetic Regulatory Variants that Affect Transcription Factor Activity

Assessing the functional impact of genetic variants across the human genome is essential for understanding the molecular mechanisms underlying complex traits and disease risk. Genetic variation that causes changes in gene expression can analyzed through parallel genotyping and functional genomics assays across sets of individuals. Trans-acting variants are of particular interest, but more challenging to identify than cis-acting variants. Here, to map variants that impact the expression of many genes simultaneously through a shared transcription factor (TF), we use an approach in which the protein-level regulatory activity of the TF is inferred from genome-wide expression data and then genetically mapped as a quantitative trait. To analyze RNA-seq profiles from the Genotype Tissue Expression (GTEx) project, we developed a generalized linear model (GLM) to estimate TF activity levels in an individual-specific manner. A key feature is that we fit a beta-binomial GLM at the level of pairs of neighboring genes in order to control for variation in local chromatin structure along the genome and other confounding effects. As a predictor in our model we use differential gene expression signatures from TF perturbation experiments. We estimated genotype-specific activities for 55 TFs across 49 tissues and performed genome-wide association analysis on the virtual TF activity trait. This revealed hundreds of TF activity quantitative trait loci, or aQTLs. Altogether, the set of tools we introduce here highlights the potential of genetic association studies for cellular endophenotypes based on a network-based multi-omic approach.

genomics↗

bioRxiv2022

Genetic Regulators of Sputum Mucin Concentration and Their Associations with COPD Phenotypes

Hyper-secretion and/or hyper-concentration of mucus is a defining feature of multiple obstructive lung diseases, including chronic obstructive pulmonary disease (COPD). Mucus itself is composed of a mixture of water, ions, salt and proteins, of which the gel-forming mucins, MUC5AC and MUC5B, are the most abundant. Recent studies have linked the concentrations of these proteins in sputum to COPD phenotypes, including chronic bronchitis (CB) and acute exacerbations (AE). We sought to determine whether common genetic variants influence sputum mucin concentrations and whether these variants are also associated with COPD phenotypes, specifically CB and AE. We performed a GWAS to identify quantitative trait loci for sputum mucin protein concentration (pQTL) in the Sub-Populations and InteRmediate Outcome Measures in COPD Study (SPIROMICS, n=708 for total mucin, n=215 for MUC5AC, MUC5B). Subsequently, we tested for associations of mucin pQTL with CB and AE using regression modeling (n=822-1300). Replication analysis was conducted using data from COPDGene (n =5740) and by examining results from the UK Biobank. We identified one genome-wide significant pQTL for MUC5AC (rs75401036) and two for MUC5B (rs140324259, rs10001928). The strongest association for MUC5B, with rs140324259 on chromosome 11, explained 14% of variation in sputum MUC5B. Despite being associated with lower MUC5B, the C allele of rs140324259 conferred increased risk of CB (odds ratio (OR) = 1.42; 95% confidence interval (CI): 1.10-1.80) as well as AE ascertained over three years of follow up (OR=1.41; 95% CI: 1.02-1.94). Associations between rs140324259 and CB or AE did not replicate in COPDGene. However, in the UK Biobank, rs140324259 was associated with phenotypes that define CB, namely chronic mucus production and cough, again with the C allele conferring increased risk. We conclude that sputum MUC5AC and MUC5B concentrations are associated with common genetic variants, and the top locus for MUC5B may influence COPD phenotypes, in particular CB. Author SummaryChronic obstructive pulmonary disease (COPD) is characterized by presence of emphysema and/or chronic bronchitis. Excessive mucus production is a defining phenotype of chronic bronchitis, and is associated with several important features of COPD, including exacerbations and loss of lung function. Recent studies have demonstrated that the amount of mucus produced in COPD patients is an important marker of disease state. We investigated whether common genetic variants are associated with the concentration of two key proteins in mucus, MUC5AC and MUC5B, and whether the variants we identified are also associated with COPD outcomes. We identified multiple genetic variants that were associated with MUC5AC or MUC5B concentration. The strongest association we detected, for MUC5B on chromosome 11, was also associated with features of COPD, including chronic bronchitis and acute exacerbations, in one COPD study population but not another. Results from a much larger study, the UK Biobank, indicate that this variant is associated with chronic mucus production and chronic cough, which are key features of chronic bronchitis. Thus, we conclude that the concentration of key proteins in mucus are influenced by genetic variation, and that a variant on chromosome 11 that affects MUC5B may in turn alter COPD outcomes.

genetics↗

bioRxiv2022

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis (MESA) which included several hundred individuals with whole genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, ten years apart. We evaluated each multi-omic phenotypes ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62x and rare frameshift variants 216x as frequently as controls, compared to 13x to 27x for expression or protein effects alone. We developed a Bayesian hierarchical model to prioritize specific rare variants underlying multi-omic signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimers disease.

genomics↗