SEARCH · bioRxiv Science

Results for “genetics”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,495 records · Page 83Linked to original sources

bioRxiv2022

Analysis of Chromatin Data Supports a Role For CD14+ Monocytes/Macrophages in Mediating Genetic Risk for Juvenile Idiopathic Arthritis

IntroductionGenome wide association studies (GWAS) have identified multiple regions that confer genetic risk for the polyarticular/oligoarticular forms of juvenile idiopathic arthritis (JIA). However, genome-wide scans do not identify the cells impacted by genetic polymorphisms on the risk haplotypes or the genes impacted by those variants. We have shown that genetic variants driving JIA risk are likely to affect both innate and adaptive immune functions. We provide additional evidence that JIA risk variants impact innate immunity. Materials and MethodsWe queried publicly available H3K4me1/H3K27ac ChIP-seq data in CD14+ monocytes to determine whether the linkage disequilibrium (LD) blocks incorporating the SNPs that tag JIA risk loci showed enrichment for these epigenetic marks. We also queried monocyte/macrophage GROseq data, a functional readout of active enhancers. We defined the topologically associated domains (TADs) encompassing enhancers on the risk haplotypes and identified genes within those TADs expressed in monocytes. We performed ontology analyses of these genes to identify cellular processes that may be impacted by these variants. We also used whole blood RNAseq data from the Genotype-Tissue Expression (GTEx) data base to determine whether SNPs lying within monocyte GROseq peaks influence plausible target genes within the TADs encompassing the JIA risk haplotypes. ResultsThe LD blocks encompassing the JIA genetic risk regions were enriched for H3K4me1/H3K27ac ChIPseq peaks (p=0.00021 and p=0.022) when compared to genome background. Eleven and sixteen JIA were enriched for resting and activated macrophage GROseq peaks, respectively risk regions (p=0.04385 and p=0.00004). We identified 321 expressed genes within the TADs encompassing the JIA haplotypes in human monocytes. Ontological analysis of these genes showed enrichment for multiple immune functions. Finally, we found that SNPs lying within the GROseq peaks are strongly associated with expression levels of plausible target genes in human whole blood. ConclusionsThese findings support the idea that both innate and adaptive immunity are impacted by JIA genetic risk variants.

genetics↗

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

Autism-specific PTEN p.I135L mutation and an autism genetic background combine to dysregulate cortical neurogenesis

Alterations in cortical neurogenesis are implicated in neurodevelopmental disorders including autism spectrum disorders (ASDs). The contribution of genetic backgrounds, in additional to ASD risk genes, on cortical neurogenesis remain understudied. Here, using isogenic induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPCs) and cortical organoid models, we report that a heterozygous PTEN p.I135L mutation found in an ASD patient with macrocephaly activates PI3K/AKT and dysregulates cortical neurogenesis in an ASD genetic background-dependent fashion. Transcriptome analysis at both bulk and single cell level revealed PTEN p.I135L mutation and ASD genetic background affected genes involved in neurogenesis, neural development and synapse signaling. We also found that this PTEN p.I135L mutation led to overproduction of NPC subtypes as well as neuronal subtypes including both deep and upper layer neurons in its ASD background, but not when introduced into a control genetic background. These findings provide experimental evidence that both a PTEN p.I135L mutation and ASD genetic background contribute to cellular features consistent with ASD associated with macrocephaly.

genetics↗

bioRxiv2022

Joint Multi-Ancestry and Admixed GWAS Reveals the Complex Genetics behind Human Cranial Vault Shape

The cranial vault - the portion of the skull surrounding the brain and cerebellum - is highly variable, clinically relevant, and heritable, yet its genetic architecture remains poorly understood. Here, we conducted a joint multi-ancestry and admixed multivariate GWAS on 3D cranial vault shape extracted from magnetic resonance images of 6,772 children from the ABCD study cohort, identifying 30 genome-wide significant genetic loci and replicating 20 of these signals in 16,947 additional individuals of the UK Biobank. This joint multi-ancestry GWAS was enriched for genetic components of cranial vault shape shared across ancestral groups and yielded a greater discovery than a European-only GWAS. We present supporting evidence for parietal versus frontal bone localization for several of the identified genes based on expression patterns in E15.5 mice. Collectively, our GWAS loci were enriched for processes related to skeletal development and showed elevated activity in cranial neural crest cells, suggesting a role during early craniofacial development. Among the identified genes, were RUNX2 and several of its upstream and downstream actors, highlighting the prominent role of intramembranous ossification - which takes place at the cranial sutures - in influencing cranial vault shape. We found that mutations in many genes associated with craniosynostosis exert their pathogenicity by modulating the same pathways involved in normal cranial vault development. This was further demonstrated in a non-syndromic sagittal craniosynostosis case-parent trio dataset of 63 probands (n = 189), where our GWAS signals near BMP2, BBS9, and ZIC2 contributed significantly to disease risk. Moreover, we found strong evidence of overlap with genes influencing the morphology of the face and the brain, suggesting a common genetic architecture connecting these developmentally adjacent structures. Overall, our study provides a comprehensive overview of the genetics underlying normal cranial vault shape and its relevance for understanding modern human craniofacial diversity and the etiology of congenital malformations.

genetics↗

bioRxiv2022

Heritability of de novo germline mutation reveals a contribution from paternal but not maternal genetic factors

De novo mutations (DNMs) in the germline have long been identified as a key element in the causes of developmental and other genetic disorders. Previous attempts to investigate genetic factors affecting DNMs have suffered from a lack of statistical power, due to the difficulty of obtaining a sufficient number of parent-offspring trios. Thus, the rare disease cohort of the UKs 100k Genomes Project (100kGP), comprising more than 10,000 trios, represents an unprecedented opportunity to investigate the genetics of germline mutation. Here we estimate SNP heritability of DNM count in offspring, as a measure of the relative contribution of genetic factors to the variance of the trait, in a PCA-selected subset of the 100kGP cohort. We estimate separate SNP heritabilities for paternally and maternally transmitted mutations (based on parentally phased DNMs in offspring), computed using parental genetic variants at a range of minimum frequencies and a variety of methodologies. We estimate a heritability of 10-20% for paternal DNMs; by contrast, for maternal DNMs we find no significant evidence for non-zero heritability. We investigated the partitioning of heritability among genes with different expression profiles in different tissue or cell states, and found a relative heritability enrichment for genes expressed in gonadal tissues, particularly testis. Among germ cells in adult testes we observed relative enrichment of heritability in genes associated with the (undifferentiated) spermatogonial stem cell state.

genetics↗

bioRxiv2023

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data

Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this "simulation mis-specification" problem can be framed as a "domain adaptation" problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art deep-learning population genetic methods--SIA, which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. In the case of SIA, the domain adaptive framework also compensates for ARG inference error. Using the domain-adaptive SIA (dadaSIA) model, we estimate improved selection coefficients at selected loci in the 1000 Genomes CEU population. We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics. Author SummaryPopulation genetic simulation is a powerful tool in the study of evolution. A number of supervised machine learning methods have been developed that take advantage of inexpensive simulations as training data. Despite their outstanding performance in benchmarks, these models can fail when the simulated training data deviate from the real data. In this work, we employed domain adaptation techniques to address this "simulation mis-specification" problem by training the machine learning model jointly with simulated and real data. We performed extensive benchmark experiments to demonstrate the improvement of the domain-adaptive models over standard machine learning models in the presence of different types of mis-specification. In addition, we applied dadaSIA, a domain-adaptive selection inference model, to improve the estimates of selection coefficients at selected loci in a European population. The domain adaptation framework proposed in our work is widely applicable to models relying on synthetic training data and therefore opens the door to many more applications in population genetics.

genetics↗

bioRxiv2023

Medieval social landscape through the genetic history of Cambridgeshire before and after the Black Death

The extent of the devastation of the Black Death pandemic (1346-53) on European populations is known from documentary sources and its bacterial source illuminated by studies of ancient pathogen DNA. What has remained less understood is the effect of the pandemic on human mobility and genetic diversity at local scale in the context of the social stratification of medieval communities. Here we study 275 newly reported ancient genomes from later medieval and post-medieval Cambridgeshire, from individuals buried before, during, and after the Black Death. The majority of individuals examined had local genetic ancestries. Consistent with the function of the institutions, we found a lack of close relatives among the friars and the inmates of the hospital in contrast to their abundance in general urban and rural parish communities. Accounting for the genetic component for height accentuates the disparities between social groups in stature estimated from long bones, as a proxy for health and the quality of life. While we detect long-term shifts in local genetic ancestry in Cambridgeshire that either pre- or postdate the Black Death, we find no evidence of major changes in genetic ancestry nor, in contrast to recent claims, higher differentiation of immune loci between cohorts living before and after the Black Death.

genetics↗

bioRxiv2023

Split-intein Gal4 provides intersectional genetic labeling that is fully repressible by Gal80

The split-Gal4 system allows for intersectional genetic labeling of highly specific cell-types and tissues in Drosophila. However, the existing split-Gal4 system, unlike the standard Gal4 system, cannot be repressed by Gal80, and therefore cannot be controlled temporally. This lack of temporal control precludes split-Gal4 experiments in which a genetic manipulation must be restricted to specific timepoints. Here, we describe a new split-Gal4 system based on a self-excising split-intein, which drives transgene expression as strongly as the current split-Gal4 system and Gal4 reagents, yet which is fully repressible by Gal80. We demonstrate the potent inducibility of "split-intein Gal4" in vivo using both fluorescent reporters and via reversible tumor induction in the gut. Further, we show that our split-intein Gal4 can be extended to the drug-inducible GeneSwitch system, providing an independent method for intersectional labeling with inducible control. We also show that the split-intein Gal4 system can be used to generate highly cell-type specific genetic drivers based on in silico predictions generated by single cell RNAseq (scRNAseq) datasets, and we describe a new algorithm ("Two Against Background" or TAB) to predict cluster-specific gene pairs across multiple tissue-specific scRNA datasets. We provide a plasmid toolkit to efficiently create split-intein Gal4 drivers based on either CRISPR knock-ins to target genes or using enhancer fragments. Altogether, the split-intein Gal4 system allows for the creation of highly specific intersectional genetic drivers that are inducible/repressible. Significance statementThe split-Gal4 system allows Drosophila researchers to drive transgene expression with extraordinary cell type specificity. However, the existing split-Gal4 system cannot be controlled temporally, and therefore cannot be applied to many important areas of research. Here, we present a new split-Gal4 system based on a self-excising split-intein, which is fully controllable by Gal80, as well as a related drug-inducible split GeneSwitch system. This approach can both leverage and inform single-cell RNAseq datasets, and we introduce an algorithm to identify pairs of genes that precisely and narrowly mark a desired cell cluster. Our split-intein Gal4 system will be of value to the Drosophila research community, and allow for the creation of highly specific genetic drivers that are also inducible/repressible.

genetics↗

bioRxiv2023

Maintaining long-term genetic gain using a segment-stacking algorithm

Loss of genetic diversity in elite crop breeding pools can severely limit long-term genetic gains, and limit ability to make gains in new traits, like heat tolerance, that are becoming important as the climate changes. Here we investigate and propose potential breeding program applications of optimal haplotype selection (OHS), a selection method which retains useful diversity in the population. OHS selects sets of candidates containing, between them, haplotype segments with very high segment breeding values for the target trait. We compared the performance of OHS, the similar method optimal population value (OPV), truncation selection on genomic estimated breeding values (GEBVs), and optimal cross selection (OCS) in stochastic simulations of recurrent selection on founder wheat genotypes. After 100 generations of inter-crossing and selection, OCS and truncation selection had exhausted the genetic diversity, while considerable diversity remained in the OHS population. Gain under OHS in these simulations ultimately exceeded that from truncation selection or OCS. OHS achieved faster gains when the population size was small, with many progeny per cross. A promising hybrid strategy, involving a single cycle of OHS selection in the first generation followed by recurrent truncation selection, substantially improved long term gain compared with truncation selection, and performed similarly to OCS. The results of this study provide initial insights into where OHS could be incorporated into breeding programs. Core IdeasO_LIWe investigate potential uses for a haplotype-stacking strategy, optimal haplotype selection (OHS) C_LIO_LISeveral selection strategies were compared in stochastic simulations of recurrent selection in wheat C_LIO_LIOHS maintained more useful diversity than optimal cross selection or truncation-based genomic selection C_LIO_LIRates of gain from OHS are competitive in small populations C_LIO_LIOne generation of OHS in a truncation selection program can increase short and long term genetic gain C_LI Plain language summaryBreeders use selection strategies based on genetic and phenotypic information to choose parents that will improve agriculturally-relevant traits (eg. grain yield) in their progeny. Generally, this involves estimating breeding values (scores) for each candidate parent. This study investigated an alternative haplotype stacking approach called Optimal Haplotype Selection (OHS), which instead estimates breeding values for each unique genome segment in the population, then selects a group of parents who, between them, carry the haplotypes with the highest estimated breeding value at each chromosomal segment. In simulations, OHS gives improvements close to existing methods when populations are small, and outperforms them in the long term (100+ generations). Using just one generation of OHS boosts the performance of other methods in the short and long term. Breeders might consider adopting haplotype stacking in their programs, once techniques to do so are established.

genetics↗

bioRxiv2023

Detecting genetic effects on phenotype variability to capture gene-by-environment interactions: a systematic method comparison

Phenotypic variability has been widely observed across organisms and traits, including in humans. Both gene-gene and gene-environment interactions can lead to an increase in phenotypic variability. Therefore, detecting the underlying genetic variants, or variance Quantitative Trait Loci (vQTLs), can provide novel insights into complex traits. Established approaches to detect vQTLs apply different methodologies from variance-only approaches to mean-variance joint tests, but a comprehensive comparison of these methods is lacking. Here, we review available methods to detect vQTLs in humans, carry out a simulation study to assess their performance under different biological scenarios of gene-environment interactions, and apply the optimal approaches for vQTL identification to gene expression data. We find that the squared residual value linear model (SVLM) method is optimal when the interacting exposure is discrete, and both SVLM and the deviation regression model (DRM) perform well when the interacting exposure is continuous. Additionally, a larger sample size, smaller minor allele frequency, and more balanced sample distribution in different exposure categories increase power of SVLM and DRM. Our results highlight vQTL detection methods that perform optimally under realistic simulation settings and show that their relative performance depends on the type of exposure in the interaction model underlying the vQTL. Author SummaryGenetic background can influence organismal response to changes in the environment, including through effects of gene-environment (GxE) interactions. GxE interactions form a fundamental component of complex phenotypes and disease. The presence of GxE interactions leads to increases in phenotypic variability, where individuals of a certain genotype can display more divergent phenotypes. Therefore, identifying genetic variation that can alter phenotypic variance is an alternative approach to identifying genetic variants that underlie GxE effects. Here, we conduct a systematic assessment of methods that detect genetic variants linked to phenotypic variance under several GxE scenarios. We identify the most optimal approaches for different environmental exposure settings and apply these to validate previously detected GxE signals. We also estimate power of these methods to detect GxE effects. Our results help guide experimental design for future studies aiming to identify genetic variant impacts on phenotypic variance, and their result interpretation in context of gene-environment interactions in complex traits.

genetics↗

bioRxiv2023

Is a Picture Worth 1,000 SNPs? Effects of User-Submitted Photographs on Ancestry Estimates from Direct-to-Consumer Canine Genetic Tests

ObjectiveTo evaluate whether the breed ancestry predictions of direct-to-consumer (DTC) genetic tests for dogs are influenced by the user-provided photograph. AnimalsTwelve pet dogs considered purebred (i.e., registered with a breed organization) representing twelve different breeds. MethodsSix buccal swabs per dog were collected by the owners and submitted to six DTC genetic testing companies. The experimenters registered each sample with the company. For half of the dogs, the registration included a photograph of the DNA donor. For the other half of the dogs, photographs were swapped between dogs. Analysis of the DNA and breed ancestry prediction was conducted by each company. Each companys breed predictions were evaluated to assess whether the condition (i.e., matching versus shuffled photograph) affected the odds of identifying the DNA donors registered breed. A convolutional neural network was also used to predict breed based solely on the photograph as a positive control. ResultsFive of the six tests always produced results that included the registered breed. One test and the convolutional neural network were unlikely to identify the registered breed and frequently returned results that included the breed in the photograph. This result suggests that one test on the market is relying on the photograph more than the DNA sample. Additionally, differences in the predictions made across all tests underscore the challenge of identifying breed ancestry, even in purebred dogs. Clinical RelevanceVeterinarians are likely to encounter patients who have conducted DTC genetic testing and may find themselves in the position of explaining genetic test results that they did not order. This systematic comparison of tests on the market provides context for interpreting unexpected results from consumer-grade DTC genetic testing kits.

genetics↗

bioRxiv2023

An expression-directed linear mixed model (edLMM) discovering low-effect genetic variants

Detecting genetic variants with low effect sizes using a moderate sample size is difficult, hindering downstream efforts to learn pathology and estimating heritability. In this work, by utilizing informative weights learned from training genetically predicted gene expression models, we formed an alternative approach to estimate the polygenic term in a linear mixed model (LMM). Our LMM estimates the genetic background by incorporating their relevance to gene expression. Our protocol, expression-directed linear mixed model (edLMM), enables the discovery of subtle signals of low-effect variants using moderate sample size. By applying edLMM to cohorts of around 5,000 individuals with either binary (WTCCC) or quantitative (NFBC1966) traits, we demonstrated its power gain at the low-effect end of the genetic etiology spectrum. In aggregate, the additional low-effect variants detected by edLMM substantially improved estimation of missing heritability. edLMM moves precision medicine forward by accurately detecting the contribution of low-effect genetic variants to human diseases.

genetics↗

bioRxiv2023

Structure and symmetries of the genetic codes for vertebrate, ascidian and yeast mitochondria

Genetic codes assign sixty-four codons to twenty amino acids. These assignments are known to follow certain rules. One question long considered but still unresolved is if these rules are derived from an underlying structure in genetic codes. Knowledge of such structure could facilitate better understanding of the biochemical, physico-chemical and evolutionary causes of the observed codon assignments. Our first finding reveals a coherent and symmetric structure in the genetic codes of vertebrate, ascidian, and yeast mitochondria. This structure is derived from a "simple" code that assigns all four codons with the same second nucleotide to a single amino acid if the first nucleotide is C or G, and assigns pairs of codons with the same second nucleotide to a single amino acid if the first nucleotide is A or U, and their third nucleotides are both purines (A and G) or both pyrimidines (U and C). The translation mechanism for the three mitochondria reflects this structure, one tRNA decoding each group of two or four codons into an amino acid. Our second finding is that the mycoplasma/spiroplasma and standard genetic codes are obtained by small sequential modifications of the vertebrate mitochondrial code and retain almost all its symmetries. We use group theory to characterize the symmetries of the simple and mitochondrial codes, and speculate on the implications of the structure for detecting translation errors and the evolution of the genetic code.

genetics↗

bioRxiv2023

Comparative epigenetic and genetic spatial structure in Mediterranean mountain plants: a multispecific comparison

AO_SCPLOWBSTRACTC_SCPLOWEpigenetic information can be heritable, but also respond to key environmental variables in situ, endowing individuals with an additional capacity to adapt to environmental changes. Thus, it is likely that in sesile organisms such as plants, part of the spatial epigenetic variation found across individuals will reflect the environmental heterogeneity of populations. Analysing the departure of the spatial epigenetic structure from the baseline genetic variation can help in understanding the value of epigenetic regulation in species with different breath of optimal environmental conditions. We performed a multispecies study that considered seven pairs of congeneric plant species, each encompassing a narrow endemic with habitat specialization and a widespread species. In three populations per species we used AFLP and methylation-sensitive AFLP markers to characterise the spatial genetic and epigenetic structures. In contrast to widespread species, narrow endemics showed a significant and generalised lower epigenetic than genetic differentiation across species. Within most populations of narrow species, epigenetic variation was less spatially structured than the genetic variation. This pattern resulted from a lack of correlation between epigenetic and genetic information in populations of narrow endemics. We argue that the differences found between narrow endemics and widespread congeners reflect contrasting breaths of environmental requirements. We pose the hypothesis that in species with a narrow niche breath, epigenetic variation may be more similar across populations and among individuals within a population given the expected higher similarity in environmental requirements.

genetics↗

bioRxiv2023

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

SummaryAdmixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic study of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations. Availability and implementationAdmix-kit package is open-source and available at https://github.com/KangchengHou/admix-kit. Additionally, users can use the pipeline designed for admixed genotype simulation available at https://github.com/UW-GAC/admix-kit_workflow.

genetics↗

bioRxiv2023

Linking the genetic structure of neuroanatomical phenotypes with psychiatric disorders

There is increasing evidence of shared genetic factors between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven challenging, and new approaches are needed to infer potential genetic structure underlying those phenotypes. Here, we demonstrate how multivariate analyses can help reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches. We first conducted univariate and multivariate genome-wide association studies (GWAS) for eight MRI-derived brain volume phenotypes in 20K UK Biobank participants. We performed various enrichment analyses to assess whether and how univariate and multitrait approaches can distinguish disorder-associated and non-disorder-associated variants from six psychiatric disorders: bipolarity, attention-deficit/hyperactivity disorder (ADHD), autism, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Univariate MRI GWAS displayed only negligible genetic correlation with psychiatric disorders at all the levels we investigated. Multitrait GWAS identified multiple new associations and showed significant enrichment for variants related to both ADHD and schizophrenia. We further clustered top associated variants based on their MRI multitrait association using an optimized k-medoids approach and detected two clusters displaying not only enrichment for association with ADHD and schizophrenia, but also consistent direction of effects. Functional annotation analyses pointed to multiple potential mechanisms, suggesting in particular a role of neurotrophin pathways on both MRI and schizophrenia. Altogether our results show that multitrait association signature can be used to infer genetically-driven latent MRI variables associated with psychiatric disorders, opening paths for future biomarker development.

genetics↗

bioRxiv2023

Genetic Discovery Enabled by A Large Language Model

Artificial intelligence (AI) has been used in many areas of medicine, and recently large language models (LLMs) have shown potential utility for clinical applications. However, since we do not know if the use of LLMs can accelerate the pace of genetic discovery, we used data generated from mouse genetic models to investigate this possibility. We examined whether a recently developed specialized LLM (Med-PaLM 2) could analyze sets of candidate genes generated from analysis of murine models of biomedical traits. In response to free-text input, Med-PaLM 2 correctly identified the murine genes that contained experimentally verified causative genetic factors for six biomedical traits, which included susceptibility to diabetes and cataracts. Med-PaLM 2 was also able to analyze a list of genes with high impact alleles, which were identified by comparative analysis of murine genomic sequence data, and it identified a causative murine genetic factor for spontaneous hearing loss. Based upon this Med-PaLM 2 finding, a novel bigenic model for susceptibility to spontaneous hearing loss was developed. These results demonstrate Med-PaLM 2 can analyze gene-phenotype relationships and generate novel hypotheses, which can facilitate genetic discovery.

genetics↗

bioRxiv2023

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population

Genetically heterogenous cell lines from laboratory mice are promising tools for population-based screening as they offer power for genetic mapping, and potentially, predictive value for in vivo experimentation in genetically matched individuals. To explore this further, we derived a panel of fibroblast lines from a genetic reference population of laboratory mice (the Diversity Outbred, DO). We then used high-content imaging to capture hundreds of cell morphology traits in cells exposed to the oxidative stress-inducing arsenic metabolite monomethylarsonous acid (MMAIII). We employed dose-response modeling to capture latent parameters of response and we then used these parameters to identify several hundred cell morphology quantitative trait loci (cmQTL). Response cmQTL encompass genes with established associations with cellular responses to arsenic exposure, including Abcc4 and Txnrd1, as well as novel gene candidates like Xrcc2. Moreover, baseline trait cmQTL highlight the influence of natural variation on fundamental aspects of nuclear morphology. We show that the natural variants influencing response include both coding and non-coding variation, and that cmQTL haplotypes can be used to predict response in orthogonal cell lines. Our study sheds light on the major molecular initiating events of oxidative stress that are under genetic regulation, including the NRF2-mediated antioxidant response, cellular detoxification pathways, DNA damage repair response, and cell death trajectories.

genetics↗