Biology subjects

Tyler, A. L.

Publications and source records attributed to Tyler, A. L..

12 recordsLinked to original sources

bioRxiv2026

Humanized Klotho haplotypes cause widespread transcriptomic changes in mouse brain

Klotho KL is an aging factor that has been associated with Alzheimers Disease (AD) risk. Two common alleles circulate in human populations: the major allele FC and the minor allele VS, which is defined by two SNPs that cause two amino acid substitutions (F352V and C370S) in KLs second exon. To investigate the possibility that human KL variants influence brain aging and cognition, we developed a novel mouse model with humanized KL alleles. We used RNA-Seq to measure the whole brain transcriptome in four-and 12-month-old male and female C57Bl/6J mice carrying either the FC or the VS KL allele. We found that FC and VS carriers had widespread differences in gene expression in the brain at 12 months old, but not at four months old. The largest differences were in genes annotated to mitochondrial, ribosomal, and synaptic functions. Differential exon usage analysis identified differential splicing of synaptic genes, further supporting a role for KL on neuronal function. A more focused analysis of differential expression identified variation in glutamate receptors and amyloid precursor (APP) processing in particular, thereby linking human KL haplotypes to biological processes integral to AD pathogenesis. These results provide evidence that the human FC and VS KL haplotypes affect the function of the KL protein product in a manner that has widespread effects on gene expression in the brain and supports the hypothesis that these haplotypes may influence AD risk and pathogenesis.

genetics↗

bioRxiv2025

Meta-analysis of genetic mapping studies in mice reveals candidate epilepsy modifier genes that are outside the current drug development landscape

ObjectiveDespite decades of development in anti-seizure medications, approximately 30% of individuals remain refractory to all treatments, and none of the existing therapies are disease-modifying. Identifying targets outside the current preclinical paradigm is critically important. This study aimed to characterize the landscape of current epilepsy treatments at the level of gene interaction networks and identify novel genetic modifiers of epilepsy as potential novel therapeutic targets. MethodsWe performed a functional network analysis to score genes based on their interactions with known epilepsy genes and integrated these functional scores with population-genetic data and drug tractability information. In parallel, we performed a meta-analysis of genome wide association studies of epilepsy-related phenotypes in genetically diverse mice using a large compendium of historical phenotyping data. Genes within mapped loci were prioritized based on functional rankings and genomic evolutionary rate profiling (GERP) was used to identify highly SNPs at evolutionarily constrained positions. ResultsFunctional network analyses of known epilepsy genes revealed a strong involvement of neurodevelopmental processes in epilepsy pathogenesis, which are not targeted by existing or emerging treatments. Meta-analysis of seizure traits in mice identified 118 non-overlapping loci harboring potential seizure phenotype modifiers. Using functional rankings, we prioritized 168 candidate genes within these loci and used GERP scores to filter down to 75 single-nucleotide polymorphisms as candidate variants within these genes. Among them, five genes--Ephb2, En2, Cadps2, Igsf21, and Cep170--contain regulatory variants in evolutionarily constrained sites. Four of these genes are validated as modifiers of neurological traits, including epilepsy susceptibility. SignificanceThis study prioritized epilepsy modifier genes that are strongly predicted to influence neurodevelopmental processes that are underrepresented among current therapeutic targets. Furthermore, the identified genes represent novel candidate modifiers with potential clinical relevance. Our systems-level analysis offers a novel view into the potential target landscape, pointing toward promising new directions for disease-modifying treatments. KEY POINTS- Systems-level analysis of human epilepsy genes reveals neurodevelopment, synaptic plasticity, and membrane excitability as key biological processes defining risk. - The current drug-development landscape does not target neurodevelopmental processes - Meta-analysis of mouse genome-wide association studies of epilepsy-related phenotypes reveal hundreds of loci with putative modifier genes. - Prioritization of candidate genes reveals high-quality candidates that are known to influence neurodevelopment and synaptic plasticity, and be druggable.

neuroscience↗

bioRxiv2024

Transcripts with high distal heritability mediate genetic effects on complex metabolic traits

Although many genes are subject to local regulation, recent evidence suggests that complex distal regulation may be more important in mediating phenotypic variability. To assess the role of distal gene regulation in complex traits, we combined multi-tissue transcriptomes with physiological outcomes to model diet-induced obesity and metabolic disease in a population of Diversity Outbred mice. Using a novel high-dimensional mediation analysis, we identified a composite transcriptome signature that summarized genetic effects on gene expression and explained 30% of the variation across all metabolic traits. The signature was heritable, interpretable in biological terms, and predicted obesity status from gene expression in an independently derived mouse cohort and multiple human studies. Transcripts contributing most strongly to this composite mediator frequently had complex, distal regulation distributed throughout the genome. These results suggest that trait-relevant variation in transcription is largely distally regulated, but is nonetheless identifiable, interpretable, and translatable across species.

genetics↗

bioRxiv2023

Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice.

Mycobacterium tuberculosis, the bacillus that causes tuberculosis (TB), infects 2 billion people across the globe, and results in 8-9 million new TB cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. We investigated the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using clinical indicators of disease, granuloma histopathological features, and immune response traits identified five new loci on mouse chromosomes 1, 2, 4, 16 and three previously identified loci on chromosomes 3 and 17. Quantitative trait loci (QTLs) on chromosomes 1, 16, and 17, associated with multiple correlated traits and had similar patterns of allele effects, suggesting these QTLs contain important genetic regulators of responses to M. tuberculosis. To narrow the list of candidate genes in QTLs, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks, generating functional scores. The scores were then used to rank candidates for each mapped trait in each locus, resulting in 11 candidates: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Importantly, all 11 candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling. Further, all candidates contain single nucleotide polymorphisms (SNPs), and some but not all SNPs were predicted to have deleterious consequences on protein functions. Multiple methods were used for validation including (i) a statistical method that showed Diversity Outbred mice carrying PWH/PhJ alleles on chromosome 17 QTL have shorter survival; (ii) quantification of S100A8 protein levels, confirming predicted allele effects; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and new functionally relevant gene candidates that may be major regulators of granuloma necrosis and acute inflammation in pulmonary TB.

genetics↗

bioRxiv2023

A multi-network approach to Alzheimers Disease gene prioritization complements cis-regulatory prioritization with molecular quantitative trait loci

Gene prioritization within mapped disease-risk loci from genome-wide association studies (GWAS) remains one of the central bioinformatic challenges of human genetics. This problem is abundantly clear in Alzheimers Disease (AD) which has several dozen risk loci, but no therapeutically effective drug target. Dominant strategies emphasize alignment between molecular quantitative trait loci (mQTLs) and disease risk loci, under the assumption that cis-regulatory drivers of gene expression or protein abundance mediate disease risk. However, mQTL data do not capture clinically relevant time points or they derive from bulk tissue. These limitations are particularly significant in complex diseases like AD where access to diseased tissue occurs only in end-stage disease, while genetically encoded risk events accumulate over a lifetime. Network-based functional predictions, where bioinformatic databases of gene interaction networks are used to learn disease-associated gene networks to prioritize genes, complement mQTL-based prioritization. The choice of input network, however, can have a profound impact on the output gene rankings, and the optimal tissue network may not be known a priori. Here, we develop a natural extension of the popular NetWAS approach to gene prioritization that allows us to combine information from multiple networks at once. We applied our multi-network (MNFP) approach to AD GWAS data to prioritize candidate genes and compared the results to baseline, single-network models. Finally, we applied the models to prioritize genes in recently mapped AD risk loci and compared our prioritizations to the state-of-the-art mQTL approach used to functionally prioritize genes within those loci. We observed a significant concordance between the top candidates prioritized by our MNFP method and those prioritized by the mQTL approach. Our results show that network-based functional predictions are a strong complement to mQTL-based approaches and are significant to the AD genetics community as they provide a strong functional rationale to mechanistically follow-up novel AD-risk candidates. Author SummaryRisk genes give us insight into the failing molecular mechanisms that drive disease phenotypes. However, these risk genes are several layers of complexity removed from the emergent phenotypes they are influencing, the p-value that denotes their risk status gives little insight into the functional implications of that risk, and it is not clear when that risk gene may be having its effect. Methods like network-based functional prediction start to address several of these limitations by contextualizing risk genes in their broader genetic neighborhood within disease-relevant tissues. For complex diseases like Alzheimers, there are many possible relevant tissues incorporating everything from individual brain cell types to whole lobes of the brain. The work in this paper expands upon the traditional network-based functional prediction approach by considering a genes connections in multiple relevant tissue networks to prioritize candidate genes. Unlike traditional genetic risk studies, this kind prioritization benefits the Alzheimers genetics community as it provides a strong functional rationale to mechanistically follow-up on novel gene candidates.

genetics↗

bioRxiv2022

Variation in histone configurations correlates with gene expression across nine inbred strains of mice

It is well established that epigenetic features, such as histone modifications and DNA methylation, are associated with variation in gene expression across cell types. Less well known is the extent to which epigenetic states vary across genetically diverse individuals, and whether such variation corresponds to inter-individual variation in gene expression. To investigate genetically driven variation in epigenetics, we conducted a survey of epigenetic modifications and gene expression in hepatocytes of nine inbred mouse strains. We surveyed four histone modifications (H3K4me1, H3K4me3, H3K27me3, and H3K27ac), and DNA methylation. We used ChromHMM to identify 14 chromatin states, each of which represented a distinct combination of the four histone modifications. We found that chromatin states varied widely across the nine strains and that epigenetic state was strongly correlated with local gene expression. We replicated this correspondence between chromatin state and gene expression in an independent population of Diversity Outbred mice in which we imputed local chromatin state. In contrast, we found that DNA methylation did not vary across the inbred strains and was not correlated with variation in gene expression in DO mice. This work suggests that chromatin state is highly influenced by local genotype and may be a primary mode through which expression quantitative trait loci (eQTLs) are mediated. Through examples, we demonstrate that naturally occurring chromatin state variation, in conjunction with gene expression, can aid in functional annotation of the mouse genome. Finally, we provide a data resource that documents variation in chromatin state in hepatocytes across genetically distinct mice.

genomics↗

bioRxiv2021

Genetic Analysis of Bphse: a Novel Gene Complementing Resistance to Bordetella pertussis-Induced Histamine Sensitization

AO_SCPLOWBSTRACTC_SCPLOWHistamine is a bioactive amine associated with a plethora of normal and pathophysiological processes, with the latter being dependent on both genetic and environmental factors including infectious agents. Previously, we showed in mice that susceptibility to Bordetella pertussis and pertussis toxin (PTX) induced histamine sensitization (Bphs) is controlled by histamine receptor H1 (Hrh1/HRH1) alleles. Bphs susceptible and resistant alleles (Bphss/Bphsr) encode for two-conserved protein haplotypes. Given the importance of HRH1 signaling in health and disease, we sequenced Hrh1 across an extended panel of laboratory and wild-derived inbred strains and phenotyped them for Bphs. Unexpectedly, eight strains homozygous for the Bphsr allele phenotyped as Bphss, suggesting the existence of a modifying locus segregating among the strains capable of complementing Bphsr. Genetic analyses mapped this modifier locus to mouse chromosome 6; designated Bphs-enhancer (Bphse), within a functional linkage disequilibrium domain encoding multiple loci controlling responsiveness to histamine (Bphs/Hrh1 and Histh1-4). Interval-specific single-nucleotide polymorphism (SNP) based association testing across 50 laboratory and wild-derived inbred mouse strains and functional prioritization analyses resulted in the identification of candidate genes for Bphse within a [~]5.5 Mb interval (Chr6:111.0-116.4 Mb), including Atg7, Plxnd1, Tmcc1, Mkrn2, Il17re, Pparg, Lhfpl4, Vgll4, Rho and Syn2. Taken together, these results demonstrate the power of combining network-based computational methods with the evolutionarily significant diversity of wild-derived inbred mice to identify novel genetic mechanisms controlling susceptibility and resistance to histamine shock.

genetics↗

bioRxiv2021

Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations

It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied, are the effects of kinship on genetic interaction test statistics. Here we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using a LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.

genetics↗

bioRxiv2019

Transcriptomic Stratification of Late-Onset Alzheimer’s Cases Reveals Novel Genetic Modifiers of Disease Pathology

Late-Onset Alzheimers disease (LOAD) is a common, complex genetic disorder well-known for its heterogeneous pathology. The genetic heterogeneity underlying common complex diseases poses a major challenge for targeted therapies and the identification of novel disease-associated variants. Case-control approaches are often limited to examining a specific outcome in a group of heterogenous patients with different clinical characteristics. Here, we developed a novel approach to define relevant transcriptomic endophenotypes and stratify decedents based on molecular profiles in three independent human LOAD cohorts. By integrating post-mortem brain gene co-expression data from 2114 human samples with LOAD, we developed a novel quantitative, composite phenotype that can better account for the heterogeneity in genetic architecture underlying the disease. We used iterative weighted gene co-expression network analysis (WGCNA) analysis to reduce data dimensionality and to isolate gene sets that are highly co-expressed within disease subtypes and represent specific molecular pathways. We then performed single variant association testing using whole genome-sequencing data for the novel composite phenotype in order to identify genetic loci that contribute to disease heterogeneity. Distinct LOAD subtypes were identified for all three study cohorts (two in ROSMAP, three in Mayo Clinic, two in Mount Sinai Brain Bank). Single variant association analysis identified a genome-wide significant variant in TMEM106B (p-value < 5x10-8, rs1990620G) in the ROSMAP cohort that confers protection from the inflammatory LOAD subtype. Taken together, our novel approach can be used to stratify LOAD into distinct molecular subtypes based on affected disease pathways.

systems biology↗

bioRxiv2019

Network-based functional prediction augments genetic association to predict candidate genes for histamine hypersensitivity in mice

Genetic mapping is a primary tool of genetics in model organisms; however, many quantitative trait loci (QTL) contain tens or hundreds of positional candidate genes. Prioritizing these genes for validation is often ad hoc and biased by previous findings. Here we present a technique for computationally prioritizing positional candidates based on computationally inferred gene function. Our method uses machine learning with functional genomic networks, whose links encode functional associations among genes, to identify network-based signatures of functional association to a trait of interest. We demonstrate the method by functionally ranking positional candidates in a large locus on mouse Chr 6 (45.9 Mb to 127.8 Mb) associated with histamine hypersensitivity (Hhs). Hhs is characterized by systemic vascular leakage and edema in response to histamine challenge, which can lead to multiple organ failure and death. Although Hhs risk is strongly influenced by genetics, little is known about its underlying molecular or genetic causes, due to genetic and physiological complexity of the trait. To dissect this complexity, we ranked genes in the Hhs locus by predicting functional association with multiple Hhs-related processes. We integrated these predictions with new single nucleotide polymorphism (SNP) association data derived from a survey of 23 inbred mouse strains and congenic mapping data. The top-ranked genes included Cxcl12, Ret, Cacna1c, and Cntn3, all of which had strong functional associations and were proximal to SNPs segregating with Hhs. These results demonstrate the power of network-based computational methods to nominate highly plausible quantitative trait genes even in highly challenging cases involving large QTLs and extreme trait complexity.

genetics↗

bioRxiv2019

Genetic interactions affect lung function in patients with systemic sclerosis

Scleroderma, or systemic sclerosis (SSc), is an autoimmune disease characterized by progressive fibrosis of the skin and internal organs. The most common cause of death in people with SSc is lung disease, but the pathogenesis of lung disease in SSc is insufficiently understood to devise specific treatment strategies. Developing targeted treatments requires not only the identification of molecular processes involved in SSc-associated lung disease, but also understanding of how these processes interact to drive pathology. One potentially powerful approach is to identify alleles that interact genetically to influence lung outcomes in patients with SSc. Analysis of interactions, rather than individual allele effects, has the potential to delineate molecular interactions that are important in SSc-related lung pathology. However, detecting genetic interactions, or epistasis, in human cohorts is challenging. Large numbers of variants with low minor allele frequencies, paired with heterogeneous disease presentation, reduce power to detect epistasis. Here we present an analysis that increases power to detect epistasis in human genome-wide association studies (GWAS). We tested for genetic interactions influencing lung function and autoantibody status in a cohort of 416 SSc patients. Using Matrix Epistasis to filter SNPs followed by the Combined Analysis of Pleiotropy and Epistasis (CAPE), we identified a network of interacting alleles influencing lung function in patients with SSc. In particular, we identified a three-gene network comprising WNT5A, RBMS3, and MSI2, which in combination influenced multiple pulmonary pathology measures. The associations of these genes with lung outcomes in SSc are novel and high-confidence. Furthermore, gene coexpression analysis suggested that the interactions we identified are tissue-specific, thus differentiating SSc-related pathogenic processes in lung from those in skin.\n\nAuthor summarySystemic sclerosis (SSc), or scleroderma, is a devastating autoimmune disease. Patients experience progressive fibrosis of their skin and internal organs, reduced quality of life, and increased risk of death. Lung disease associated with SSc is particularly dangerous and is currently the leading cause of death in SSc patients. There are no specific treatments for SSc or SSc-related lung disease, but promising work in the genetics of this disease has identified more than 200 genetic variants that influence SSc [1]. Piecing together how genetic variants interact with each other to influence disease may provide clues for targeted therapies. Here we present a novel analytical approach for identifying genetic interactions in a human disease cohort. In this approach we first filtered SNPs to those that are most likely to interact to influence the disease traits. We then applied the Combined Analysis of Pleiotropy and Epistasis (CAPE), which combines information across multiple traits to increase power to detect genetic interactions. Using this approach, we identified a three-gene network among MSI2, WNT5A, and RBMS3 that influenced autoantibody status and lung function in a cohort of 416 SSc patients. Gene expression data suggest that this interaction network is tissue- and disease-specific, and may thus provide a specific target for SSc therapy.

genetics↗

bioRxiv2017

Epistatic networks jointly influence phenotypes related to metabolic disease and gene expression in Diversity Outbred mice

Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. Multi-parent populations provide a resource for developing methods to understand these relationships. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Using weighted gene co-expression network analysis (WGCNA), we summarized transcript data into functional modules which we then used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects. We were also able to identify alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes.

genetics↗