Genomic Analyses of Major SARS-CoV-2 Variants Predicting Multiple Regions of Pathogenic and Transmissive Importance.

The rapid evolution of SARS-CoV-2 has fueled its global proliferation since its discovery in 2019, with several notable variants having been responsible for increases in cases of coronavirus disease 2019 (COVID-19). Analyses of codon bias and usage in these variants between phylogenetic clades or lineages may grant insights into the evolution of SARS-CoV-2 and identify target codons indicative of evolutionary or mutative trends that may prove useful in tracking or defending oneself against emerging strains. We processed a cohort of 120 SARS-CoV-2 genome sequences through a statistical and bioinformatic pipeline to identify codons presenting evidence of selective pressure as well as codon coevolution. We report the identification of two codon sites in the orf8 and N genes demonstrating such evidence with real-world impacts on pathogenicity and transmissivity.

Influence of Admixture on Phenotypes.

Individuals of European descent have historically been the focus of genetic studies and possess relatively homogenous genomes. As a result, analytical methods have been developed and optimized with such genomes in mind. African-descent and Latino individuals generally possess genomes of greater architectural complexity due to mosaic genomic ancestry, which can extensively and intricately impact phenotypic expression. As such, genetic analyses of admixed individuals require that genetic admixture be quantified to accurately model the impact of genetic variation on phenotypic expression. In this overview, we explore how fundamental genetic concepts such as linkage disequilibrium and differential allele frequency interact with genetic admixture to uniquely influence phenotypes in admixed individuals. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

A higher burden of multiple sclerosis genetic risk confers an earlier onset.

BACKGROUND: Age at onset of multiple sclerosis (MS) is an objective, influential predictor of the evolution of MS independent of disease duration. OBJECTIVES: Determine the influence of MS genetic predisposition on age of onset. METHODS: We conducted a comprehensive investigation of MS risk variants and age at onset in 3495 non-Latinx white individuals, including for combinations of HLA-DRB1*15:01 alleles and quintiles of an unweighted genetic risk score (GRS) for 198 of 200 autosomal MS risk variants that reside outside the major histocompatibility complex. RESULTS: The mean age at onset was 32 years, 29% were male, and 46% were HLA-DRB1*15:01 carriers. For those with the greatest genetic risk burden (the highest GRS quintile with two HLA-DRB1*15:01 alleles) were on average 5 years younger at onset (p = 0.002) than those with the lowest genetic risk burden (the lowest GRS quintile with no HLA-DRB1*15:01 alleles). There was a strong inverse relationship between the MS genetic risk burden and age at onset of MS (p < 5 × 10-8). CONCLUSION: We demonstrate a significant gradient between elevated MS genetic risk burden and an earlier onset of MS, suggesting that a higher MS genetic risk burden accelerates onset of the disease.

Contribution of viral infection to risk for cancer in systemic lupus erythematosus and multiple sclerosis.

Patients with autoimmune disorders (AD) have altered cancer risks compared to the general population. Systemic lupus erythematosus and multiple sclerosis lead to a heightened risk for hematological malignancies and decreased risk for breast, ovarian, and prostate malignancies. Often patients with autoimmune disease have dysregulated antiviral immune responses, including against oncogenic viruses. To uncover the relationship between viral incidence and cancer risk in the context of autoimmune disease, we extracted electronic health records (EHR) from Vanderbilt University. ICD-9/10 codes and laboratory values were collected for hematological, lung, anal-vaginal, thyroid, hepatobiliary, bladder, prostate, and breast cancers; and viruses including Epstein Barr virus (EBV), Human papilloma virus (HPV), and Hepatitis A/B/C (Hep). Only viral infections that led to a physician visit or laboratory test were entered into the EMR; therefore, only clinically relevant cases were noted and considered positive in this study. The relationship between virus infection and cancer in an SLE cohort (SLE-cases n = 2,313, and SLE-controls n = 5,702) and an MS cohort (MS-case n = 7,277, MS-control n = 7,277) was examined by multilinear logistic regression. Viral infection was strongly associated with increased risk for cancer overall. SLE and MS patients were more susceptible to all viral infections. MS patients trended toward increased risk for cancers overall, while decreased risk for hormone-based cancers in SLE patients non-significantly reduced their risk for overall cancer. Both SLE and MS patients had increased clinically relevant EBV infection, which was associated with risk for hematological cancers. Preventing viral infections by vaccination may be especially helpful in controlling risk for cancer in SLE and MS patients.

Predicting self-reported depression after the onset of multiple sclerosis using genetic and non-genetic factors.

BACKGROUND: Persons with multiple sclerosis (PwMS) are disproportionately burdened by depression compared to the general population. While several factors associated with depression and depression severity in PwMS have been identified, a prediction model for depression risk has not been developed. In addition, it is unknown if depression-related genetic variants, including Apolipoprotein E (APOE), would be informative for predicting depression in PwMS. OBJECTIVE: To develop a depression prediction model for PwMS who did not have a history of depression prior MS onset. METHODS: The study population included 917 non-Hispanic white PwMS. An optimized multivariable Cox proportional hazards model for time to depression was generated using non-genetic variables, to which APOE and a depression-related genetic risk score were included. RESULTS: Having a mother who had a history of depression, having obstructive pulmonary disease, obesity and other physical disorders at MS onset, and affect-related symptoms at MS onset predicted depression risk (hazards ratios (HRs): 1.6-2.3). Genetic variables improved the prediction model's performance. APOE ε4/ε4 and ε2/x conferred increased (HR = 2.5, p = 0.026) and decreased (HR = 0.65, p = 0.046) depression risk, respectively. CONCLUSION: We present a prediction model aligned with The Precision Medicine Initiative, which integrates genetic and non-genetic predictors to inform depression risk stratification after MS onset.

Depression in multiple sclerosis patients associated with risk variant near NEGR1.

BACKGROUND: A substantial number of patients diagnosed with multiple sclerosis (MS) suffer from depression in addition to physical symptoms and disability. Recent evidence suggests a stronger relationship may exist between MS and depression than previously thought, in which a diagnosis of depression may be prodromic to the development of MS. METHODS: A genome-wide association study (GWAS) was performed to identify genetic variants associated with the development of depression in a cohort of MS patients. The control group (n = 1180) was composed of MS patients with no diagnoses of depression as determined by ICD-9 and ICD-10 billing codes present in the electronic health record (EHR). Separate analyses were performed for three different case groups: 1) MS patients having a depression diagnosis at any time (n = 182), 2) MS patients having a depression diagnosis one year pre-MS diagnosis (n = 27), and 3) MS patients having a depression diagnosis one year post-MS diagnosis (n = 130). Logistic regression analyses were also performed to test for associations between the development of depression and an APOE tagging variant, as APOE was previously linked to depressive affect in MS. An additional logistic regression analysis tested for associations between depression in MS patients and SNPs associated with depression in the general population. Pathway enrichment analyses were also conducted to identify pathways that link the two diseases. RESULTS: GWAS identified no novel associations between variants and a diagnosis of depression relative to a diagnosis of MS. One variant, rs1432639, associated with depression in the general population, was significantly associated with the development of depression post-MS diagnosis. The APOE-related SNPs were not associated with depression in this study population. An IGF1 pathway approached statistical significance in patients diagnosed with depression prior to a diagnosis of MS. CONCLUSION: rs1432639 and the IGF1 pathway provide evidence for a genetic link between MS and depression that warrants further research.

Multiple sclerosis risk factors contribute to onset heterogeneity.

BACKGROUND: The phenotypic presentation of multiple sclerosis (MS) may predict long-term outcomes and little is known about factors contributing to heterogeneity at MS onset. Given temporality, it is likely MS risk factors also influence presentation of the disease near onset. METHODS: Using a retrospective cross-sectional study of MS cases, we investigated: age of onset (AOO), number of impaired functional domains (NIFDs), time to second relapse (TT2R), and early relapse activity (ERA). Machine learning variable selection was applied to epidemiologic data for each outcome, followed by multivariable regression models. The models were further adjusted for HLA-DRB1*15:01 carrier status and a MS genetic risk score (GRS). The TT2R and ERA analyses were restricted to relapsing remitting MS cases. RESULTS: HLA-DRB1*15:01, GRS, and smoking were associated with earlier AOO. Cases who were male, obese, had lower education, or had primary progressive MS were older at onset. For NIFDs, those with relapsing remitting MS and of lower SES had increased NIFDs. Among relapsing remitting cases, those who were older at onset, obese, and had polyfocal presentation had shorter TT2R, while ERA was greater among those younger at onset and who were obese. CONCLUSION: Individual characteristics including age, genetic profiles, obesity, and smoking status contribute to heterogeneity in disease presentation and modulate early disease course evolution.

Common variation near IRF6 is associated with IFN-ß-induced liver injury in multiple sclerosis.

Multiple sclerosis (MS) is a disease of the central nervous system treated with disease-modifying therapies, including the biologic, interferon-ß (IFN-ß). Up to 60% of IFN-ß-exposed MS patients develop abnormal biochemical liver test results1,2, and 1 in 50 experiences drug-induced liver injury3. Since genomic variation contributes to other forms of drug-induced liver injury4,5, we aimed to identify biomarkers of IFN-ß-induced liver injury using a two-stage genome-wide association study. The rs2205986 variant, previously linked to differential expression of IRF6, surpassed genome-wide significance in the combined two-stage analysis (P = 2.3 × 10-8, odds ratio = 8.3, 95% confidence interval = 3.6-19.2). Analysis of an independent cohort of IFN-ß-treated MS patients identified via electronic medical records showed that rs2205986 was also associated with increased peak levels of aspartate aminotransferase (P = 7.6 × 10-5) and alkaline phosphatase (P = 4.9 × 10-4). We show that these findings may be applicable to predicting IFN-ß-induced liver injury, offering insight into its safer use.

Bovine CCL28 Mediates Chemotaxis via CCR10 and Demonstrates Direct Antimicrobial Activity against Mastitis Causing Bacteria.

In addition to the well characterized function of chemokines in mediating the homing and accumulation of leukocytes to tissues, some chemokines also exhibit potent antimicrobial activity. Little is known of the potential role of chemokines in bovine mammary gland health and disease. The chemokine CCL28 has previously been shown to play a key role in the homing and accumulation of IgA antibody secreting cells to the lactating murine mammary gland. CCL28 has also been shown to act as an antimicrobial peptide with activity demonstrated against a wide range of pathogens including bacteria, fungi and protozoans. Here we describe the cloning and function of bovine CCL28 and document the concentration of this chemokine in bovine milk. Bovine CCL28 was shown to mediate cellular chemotaxis via the CCR10 chemokine receptor and exhibited antimicrobial activity against a variety of bovine mastitis causing organisms. The concentration of bovine CCL28 in milk was found to be highly correlated with the lactation cycle. Highest concentrations of CCL28 were observed soon after parturition, with levels decreasing over time. These results suggest a potential role for CCL28 in the prevention/resolution of bovine mastitis.

The intelligent use and clinical benefits of electronic medical records in multiple sclerosis.

Electronic medical records (EMRs) are being quickly adopted in clinics around the world. This advancement can greatly enhance the clinical care of patients with multiple sclerosis (MS) by providing formats that allow easier review of medical documents and more structured avenues to store relevant information. MS clinicians should be involved with implementing and updating EMRs at their institutions to ensure EMR formats that benefit MS clinics. EMRs also provide opportunities for research studies of MS to access detailed, longitudinal data of MS disease course that would otherwise be difficult to collect.

Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis.

Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 1.0 × 10(-4)). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10(-8)), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.

Automated extraction of clinical traits of multiple sclerosis in electronic medical records.

OBJECTIVES: The clinical course of multiple sclerosis (MS) is highly variable, and research data collection is costly and time consuming. We evaluated natural language processing techniques applied to electronic medical records (EMR) to identify MS patients and the key clinical traits of their disease course. MATERIALS AND METHODS: We used four algorithms based on ICD-9 codes, text keywords, and medications to identify individuals with MS from a de-identified, research version of the EMR at Vanderbilt University. Using a training dataset of the records of 899 individuals, algorithms were constructed to identify and extract detailed information regarding the clinical course of MS from the text of the medical records, including clinical subtype, presence of oligoclonal bands, year of diagnosis, year and origin of first symptom, Expanded Disability Status Scale (EDSS) scores, timed 25-foot walk scores, and MS medications. Algorithms were evaluated on a test set validated by two independent reviewers. RESULTS: We identified 5789 individuals with MS. For all clinical traits extracted, precision was at least 87% and specificity was greater than 80%. Recall values for clinical subtype, EDSS scores, and timed 25-foot walk scores were greater than 80%. DISCUSSION AND CONCLUSION: This collection of clinical data represents one of the largest databases of detailed, clinical traits available for research on MS. This work demonstrates that detailed clinical information is recorded in the EMR and can be extracted for research purposes with high reliability.

Evaluating power and type 1 error in large pedigree analyses of binary traits.

Studying population isolates with large, complex pedigrees has many advantages for discovering genetic susceptibility loci; however, statistical analyses can be computationally challenging. Allelic association tests need to be corrected for relatedness among study participants, and linkage analyses require subdividing and simplifying the pedigree structures. We have extended GenomeSIMLA to simulate SNP data in complex pedigree structures based on an Amish pedigree to generate the same structure and distribution of sampled individuals. We evaluated type 1 error rates when no disease SNP was simulated and power when disease SNPs with recessive, additive, and dominant modes of inheritance and odds ratios of 1.1, 1.5, 2.0, and 5.0 were simulated. We generated subpedigrees with a maximum bit-size of 24 using PedCut and performed two-point and multipoint linkage using Merlin. We also ran MQLS on the subpedigrees and unified pedigree. We saw no inflation of type 1 error when running MQLS on either the whole pedigrees or the sub-pedigrees, and we saw low type 1 error for two-point and multipoint linkage. Power was reduced when running MQLS on the subpedigrees versus the whole pedigree, and power was low for two-point and multipoint linkage analyses of the subpedigrees. These data suggest that MQLS has appropriate type 1 error rates in our Amish pedigree structure, and while type 1 error does not seem to be affected when dividing the pedigree prior to linkage analysis, power to detect linkage is diminished when the pedigree is divided.