Biology subjects

McGrath, J. J.

Publications and source records attributed to McGrath, J. J..

9 recordsLinked to original sources

bioRxiv2024

Mutability and hypermutation antagonize immunoglobulin codon optimality

The efficacy of polyclonal antibody responses is inherently linked to paratope diversity, as generated through V(D)J recombination and somatic hypermutation (SHM). These processes arose in early jawed vertebrates; however, little is known about how immunoglobulin diversity, mutability, and hypermutation have evolved in tandem with another more ubiquitous feature of protein-coding DNA - codon optimality. Here, we explore these relationships through analysis of germline IG genes, natural V(D)J repertoires, serum VH usage, and monoclonal antibody (mAb) expression, each through the lens of multiple optimality metrics. Strikingly, proteomic serum IgG sequencing showed that germline IGHV codon optimality positively correlated with VH representation after influenza vaccination, and in vitro, codon deoptimization of mAbs with synonymous amino acid sequences caused consistent expression loss. Germline V genes exhibit a range of codon optimality that is maintained by functionality, and inversely related to mutability. SHM caused a load-dependent deoptimization of IGH VDJ repertoires within human tonsils, bone marrow, and lymph nodes (including SARS-CoV-2-specific clones from mRNA vaccinees), influenza-infected mice, and zebrafish. Comparison of natural mutation profiles to true random suggests the presence of selective pressures that constrain deoptimization. These findings shed light on immunoglobulin evolution, providing unanticipated insights into the antagonistic relationship between variable region diversification, codon optimality, and antibody secretion; ultimately, the need for diversity takes precedence over that for the most efficient expression of the antibody repertoire.

immunology↗

bioRxiv2021

Accounting for age-of-onset and family history improves power in genome-wide association studies

Genome-wide association studies (GWAS) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age-of-onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), that jointly accounts for age-of-onset and sex, as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields large power gains over both LT-FH and genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data, and to mortality in the UK Biobank, finding 20 genome-wide significant associations with LT-FH++, compared to 10 for LT-FH and 8 for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

bioinformatics↗

bioRxiv2021

Development and validation of a mouse model of contemporary cannabis smoke exposure

Cannabis is widely used for both recreational and medicinal purposes. Inhalation of combusted cannabis smoke is the most common mode of drug consumption, exposing the lungs to the pharmacologically active ingredients, including tetrahydrocannabinol (THC) and cannabidiol (CBD). While the relationship between cannabis smoke exposure and compromised respiratory health has yet to be sufficiently defined, previous investigations suggest that cannabis smoke may dysregulate pulmonary immunity. Presently, there exists few pre-clinical animal models that have been extensively validated for contemporary cannabis smoke exposure. To address this need, we developed a mouse model with readouts of total particular matter, serum cannabinoid and carboxyhemoglobin levels, lung cellular responses, and immune mediator production. Using a commercially available smoke exposure system and a cannabis source material of documented THC/CBD composition, we exposed mice to a total particulate matter of 698.89 (SD = 66.09) {micro}g/L and demonstrate increases in serum cannabinoids and carboxyhemoglobin. We demonstrate that cannabis smoke modulates immune cell populations and mediators in both male and female BALB/c mice. This modulation is highlighted by increases in airway and lung tissue macrophage populations, including tissue-resident alveolar macrophages, monocyte-derived alveolar macrophages, and interstitial macrophage subpopulations. No changes in airway or lung tissue infiltration of neutrophils were observed. Immune mediator analysis indicated significant upregulation of MDC, TARC, and VEGF within the lung tissue of cannabis smoke exposed mice. This accessible and reproducible smoke exposure model provides a foundation to explore the impact of chronic cannabis exposures and/or co-exposures with pathogens of clinical relevance, such as influenza.

immunology↗

bioRxiv2020

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWAS). However, it is now common for researchers to have access to large individual-level data as well, such as the UK biobank data. To the best of our knowledge, it has not yet been explored how to best combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (Meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using twelve real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare Meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and Meta-PRS. We find that, when large individual-level data is available, the linear combination of PRSs (Meta-PRS) is both a simple alternative to Meta-GWAS and often more accurate.

genetics↗

bioRxiv2019

Genome-wide association study identifies 143 loci associated with 25 hydroxyvitamin D concentration.

Vitamin D deficiency is a candidate risk factor for a range of adverse health outcomes. In a genome-wide association study of 25 hydroxyvitamin D (25OHD) concentration in 417,580 Europeans we identified 143 independent loci in 112 1-Mb regions providing new insights into the physiology of vitamin D and implicating genes involved in (a) lipid and lipoprotein metabolism, (b) dermal tissue properties, and (c) the sulphonation and glucuronidation of 25OHD. Mendelian randomization models found no robust evidence that 25OHD concentration had causal effects on candidate phenotypes (e.g. BMI, psychiatric disorders), but many phenotypes had (direct or indirect) causal effects on 25OHD concentration, clarifying the relationship between 25OHD status and health.

genetics↗

bioRxiv2019

Efficient toolkit implementing best practices for principal component analysis of population genetic data

Principal Component Analysis (PCA) of genetic data is routinely used to infer ancestry and control for population structure in various genetic analyses. However, conducting PCA analyses can be complicated and has several potential pitfalls. These pitfalls include (1) capturing Linkage Disequilibrium (LD) structure instead of population structure, (2) projected PCs that suffer from shrinkage bias, (3) detecting sample outliers, and (4) uneven population sizes. In this work, we explore these potential issues when using PCA, and present efficient solutions to these. Following applications to the UK Biobank and the 1000 Genomes project datasets, we make recommendations for best practices and provide efficient and user-friendly implementations of the proposed solutions in R packages bigsnpr and bigutilsr. For example, we find that PC19 to PC40 in the UK Biobank capture complex LD structure rather than population structure. Using our automatic algorithm for removing long-range LD regions, we recover 16 PCs that capture population structure only. Therefore, we recommend using only 16-18 PCs from the UK Biobank to account for population structure confounding. We also show how to use PCA to restrict analyses to individuals of homogeneous ancestry. Finally, when projecting individual genotypes onto the PCA computed from the 1000 Genomes project data, we find a shrinkage bias that becomes large for PC5 and beyond. We then demonstrate how to obtain unbiased projections efficiently using bigsnpr. Overall, we believe this work would be of interest for anyone using PCA in their analyses of genetic data, as well as for other omics data.

genetics↗

bioRxiv2019

The Australian Genetics of Depression Study: Study Description and Sample Characteristics

ObjectivesDepression is the most common psychiatric disorder and the largest contributor to global disability. The Australian Genetics of Depression study was established to recruit a large cohort of individuals who have been diagnosed with depression, and to investigate genetic and environmental risk factors for depression and response to commonly prescribed antidepressants. This paper describes the recruitment and characteristics of the sample.\n\nMethodsParticipants completed an online questionnaire that consisted of a compulsory module that assessed self-reported psychiatric history, clinical depression using the Composite Interview Diagnostic Interview Short Form, and experiences of using commonly prescribed antidepressants. Further voluntary modules assessed a wide range of traits of relevance to psychopathology. Participants who reported they were willing to provide a DNA sample were sent a saliva kit in the mail.\n\nResultsA total of 20,689 participants, 75% of whom were female, enrolled in the study. The average age of participants was 43 years {+/-} 15 years. 15,807 participants (76% of the participant group) returned saliva kits. The overwhelming majority of participants reported being given a diagnosis of depression by a medical practitioner and 88% met the criteria for a depressive episode. Rates of comorbidity with other psychiatric disorders were high. Two-thirds of the sample reported having taken more than one type of antidepressant during treatment for their depression.\n\nConclusionsThis study was effective in recruiting a large community sample of people with a history of clinical depression, highlighting the willingness of Australians to engage with medical research. A combination of recruitment through health records and media as well as use of an online questionnaire made it feasible to recruit the large sample needed for investigating the genetics of common diseases. It will be a valuable resource for investigating risk factors for depression, treatment response to antidepressants and susceptibility to side effects.

genetics↗

bioRxiv2018

Spatial gene-by-environment mapping for schizophrenia reveals locale of upbringing effects beyond urban-rural differences

Identification of mechanisms underlying the incidence of psychiatric disorders has been hampered by the difficulty in discovering highly-predictive environmental risk factors. For example, prior efforts have failed to establish environmental effects predicting geospatial clustering of schizophrenia incidence beyond urban-rural differences. Here, we employ a novel statistical framework for decomposing the geospatial risk for schizophrenia based on locale of upbringing (place of residence, ages 0-7 years) and its synergistic effects with genetic liabilities (polygenic risk for schizophrenia). We use this statistical framework to analyze unprecedented geolocation and genotyping data in a case-cohort study of n=24,028 subjects, drawn from the 1.47 million Danish persons born between 1981 and 2005. Using this framework we estimate the effects of upbringing locale (E) and gene-by-locale interactions (GxE). After controlling for potential confounding variables, upbringing at high-risk locales increases the risk for schizophrenia on average by 122%, while GxE modulates genetic risk for schizophrenia on average by 78%. Within the boundaries of Copenhagen (the largest and most densely populated city of Denmark) specific locales vary substantially in their E and GxE effects, with hazard ratios ranging from 0.26 to 9.26 for E and from 0.20 to 5.95 for GxE. This study provides insight into the degree of geospatial clustering of schizophrenia risk, and our novel analytic procedure provides a framework for decomposing variation in geospatial risk into G, E, and GxE components.

epidemiology↗

bioRxiv2017

Paternal-age-related de novo mutations and risk for five disorders

BackgroundThere are well-established epidemiologic associations between advanced paternal age and increased offspring risk for several psychiatric and developmental disorders. These associations are commonly attributed to age-related de novo mutations. However, the actual magnitude of risk conferred by age-related de novo mutations in the male germline is unknown. Quantifying this risk would clarify the clinical and public health significance of delayed paternity.\n\nMethodsUsing results from large, parent-child trio whole-exome-sequencing studies, we estimated the relationship between paternal-age-related de novo single nucleotide variants (dnSNVs) and offspring risk for five disorders: autism spectrum disorders (ASD), congenital heart disease (CHD), neurodevelopmental disorders with epilepsy (EPI), intellectual disability (ID), and schizophrenia (SCZ). Using Danish national registry data, we then investigated the degree to which the epidemiologic association between each disorder and advanced paternal age was consistent with the estimated role of de novo mutations.\n\nResultsIncidence rate ratios comparing dnSNV-based risk to offspring of 45 versus 25-year-old fathers ranged from 1.05 (95% confidence interval 1.01-1.13) for SCZ to 1.29 (95% CI 1.13-1.68) for ID. Epidemiologic estimates of paternal age risk for CHD, ID and EPI were consistent with the dnSNV effect. However, epidemiologic effects for ASDs and SCZ significantly exceeded the risk that could be explained by dnSNVs alone (p<2e-4 for both comparisons).\n\nConclusionIncreasing dnSNVs due to advanced paternal age confer a small amount of offspring risk for psychiatric and developmental disorders. For ASD and SCZ, epidemiologic associations with delayed paternity largely reflect factors that cannot be assumed to increase with age.

genetics↗