SNP characteristics and validation success in genome wide association studies

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Single Nucleotide Polymorphism rs9277336 Controls the Nuclear Alpha Actinin 4-Human Leukocyte Antigen-DPA1 Axis and Pulmonary Endothelial Pathophenotypes in Pulmonary Arterial Hypertension

Background Pulmonary arterial hypertension (PAH) is a complex, fatal disease where disease severity has been associated with the single nucleotide polymorphism (SNP) rs2856830, located near the human leukocyte antigen DPA1 (HLA-DPA1) gene. We aimed to define the genetic architecture of functional variants associated with PAH disease severity by identifying allele-specific binding transcription factors and downstream targets that control endothelial pathophenotypes and PAH. Methods and Results Electrophoretic mobility shift assays of oligonucleotides containing SNP rs2856830 and 8 SNPs in linkage disequilibrium revealed functional SNPs via allele-imbalanced binding to human pulmonary arterial endothelial cell nuclear proteins. DNA pulldown proteomics identified SNP-binding proteins. SNP genotyping and clinical correlation analysis were performed in 84 patients with PAH at University of Pittsburgh Medical Center and in 679 patients with PAH in the All of Us database. SNP rs9277336 was identified as a functional SNP in linkage disequilibrium (r2>0.8) defined by rs2856830, and the minor allele was associated with decreased hospitalizations and improved cardiac output in patients with PAH, an index of disease severity. SNP pulldown proteomics showed allele-specific binding of nuclear ACTN4 (alpha actinin 4) protein to rs9277336 minor allele. Both ACTN4 and HLA-DPA1 were downregulated in pulmonary endothelium in human patients and rodent models of PAH. Via transcriptomic and phenotypic analyses, knockdown of HLA-DPA1 phenocopied knockdown of ACTN4, both similarly controlling cell structure pathways, immune pathways, and endothelial dysfunction. Conclusions We defined the pathogenic activity of functional SNP rs9277336, entailing the allele-specific binding of ACTN4 and controlling expression of the neighboring HLA-DPA1 gene. Through inflammatory or genetic means, downregulation of this ACTN4-HLA-DPA1 regulatory axis promotes endothelial pathophenotypes, providing a mechanistic explanation for the association between this SNP and PAH outcomes.

Using multi-tissue transcriptome-wide association study to identify candidate susceptibility genes for respiratory infectious diseases

Objective: We explore the candidate susceptibility genes for influenza A virus (IAV), measles, rubella, and mumps and their underlying biological mechanisms.

Methods: We downloaded the genome-wide association study summary data of four virus-specific immunoglobulin G (IgG) level data sets (anti-IAV IgG, anti-measles IgG, anti-rubella IgG, and anti-mumps virus IgG levels) and integrated them with reference models of three potential tissues from the Genotype-Tissue Expression (GTEx) project, namely, whole blood, lung, and transformed fibroblast cells, to identify genes whose expression is predicted to be associated with IAV, measles, mumps, and rubella.

Results: We identified 19 significant genes (ULK4, AC010132.11, SURF1, NIPAL2, TRAP1, TAF1C, AC000078.5, RP4-639F20.1, RMDN2, ATP1B3, SRSF12, RP11-477D19.2, TFB1M, XXyac-YX65C7_A.2, TAF1C, PCGF2, and BNIP1) associated with IAV at a Bonferroni-corrected threshold of p < 0.05; 14 significant genes (SOAT1, COLGALT2, AC021860.1, HCG11, METTL21B, MRPL10, GSTM4, PAQR6, RP11-617D20.1, SNX8, METTL21B, ANKRD27, CBWD2, and TSFM) associated with measles at a Bonferroni-corrected threshold of p < 0.05; 15 significant genes (MTOR, LAMC1, TRIM38, U91328.21, POLR2J, SCRN2, Smpd4, UBN1, CNTROB, SCRN2, HOXB-AS1, SLC14A1, AC007566.10, AC093668.2, and CPD) associated with mumps at a Bonferroni-corrected threshold of p < 0.05; and 13 significant genes (JAGN1, RRP12, RP11-452K12.7, CASP7, AP3S2, IL17RC, FAM86HP, AMACR, RRP12, PPP2R1B, C11orf1, DLAT, and TMEM117) associated with rubella at a Bonferroni-corrected threshold of p < 0.05.

Conclusions: We have identified several candidate genes for IAV, measles, mumps, and rubella in multiple tissues. Our research may further our understanding of the pathogenesis of infectious respiratory diseases.

Association between single nucleotide variants and severe chronic pain in older adult patients after lower extremity arthroplasty

BACKGROUND

Hip or knee osteoarthritis (OA) is one of the main causes of disability worldwide and occurs mostly in the older adults. Total hip or knee arthroplasty is the most effective method to treat OA. However, severe postsurgical pain leading to a poor prognosis. So, investigating the population genetics and genes related to severe chronic pain in older adult patients after lower extremity arthroplasty is helpful to improve the quality of treatment.

METHODS

We collected blood samples from elderly patients who underwent lower extremity arthroplasty from September 2020 to February 2021 at the Drum Tower Hospital Affiliated to Nanjing University Medical School. The enrolled patients provided measures of pain intensity using the numerical rating scale on the 90th day after surgery. Patients were divided into the case group (Group A) and the control group (Group B) including 10 patients respectively by the numerical rating scale. DNA was isolated from the blood samples of the two groups for whole-exome sequencing.

RESULTS

In total, 661 variants were identified in the 507 gene regions that were significantly different between both groups (P < 0.05), including CASP5, RASGEF1A, CYP4B1, etc. These genes are mainly involved in biological processes, including cell-cell adhesion, ECM-receptor interaction, metabolism, secretion of bioactive substances, ion binding and transport, regulation of DNA methylation, and chromatin assembly.

CONCLUSIONS

The current study shows some variants within genes are significantly associated with severe postsurgical chronic pain in older adult patients after lower extremity arthroplasty, indicating a genetic predisposition for chronic postsurgical pain. The study was registered according to ICMJE guidelines. The trial registration number is ChiCTR2000031655 and registration date is April 6th, 2020.

Identification of genetic biomarkers associated with pharmacokinetics and pharmacodynamics of apixaban in Chinese healthy volunteers

BACKGROUND

Apixaban is a superior direct oral anticoagulant exihibiting interindividual variability in concentration and response in the real world. The present study aimed to identify genetic biomarkers associated with pharmacokinetics (PK) and pharmacodynamics (PD) of apixaban in healthy Chinese subjects.

METHODS

This multicenter study included 181 healthy Chinese adults taking a single dose of 2.5 mg or 5 mg apixaban and assessed their PK and PD parameters. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed using the Affymetrix Axiom CBC_PMRA Array. Candidate gene association analysis and genome-wide association study were conducted to identify genes with a predictive value for PK and PD parameters of apixaban.

RESULTS

Several ABCG2 variants were associated with C_max and AUC_0-t of apixaban (p < 6.12 × 10^-5) and also presented significant differences of anti-Xa_3h activity and dPT_3h according to different ABCG2 genotypes (p < 0.05). Besides, ABLIM2 variants were found to be associated with PK characteristics and F13A1 and C3 variants were associated with PD characteristics of apixaban (p < 9.46 × 10^-8).

CONCLUSION

ABCG2 variants were found to be ideal genetic biomarkers for both PK and PD characteristics of apixaban. ABLIM2, F13A1 and C3 were identified as potential candidate genes associated with inter-individual variability of apixaban. This study was registered on ClinicalTrials.gov NCT03259399.

Identification of deleterious nsSNPs in human HGF gene: in silico approach

HGF is a protein that binds to the hepatocyte growth factor receptor to regulate cell growth, cell motility and morphogenesis in different cells and tissues. Several bioinformatics tools and in silico methods were used to identify most deleterious nsSNPs that might change the structure and function of HGF protein. The in silico tools such as SIFT, SNP&GO and PolyPhen2 were used to distinguish deleterious nsSNPs from neutral ones. Protein stability is analysed by I-Mutant, MUpro and iStable. The functional and structural effects are predicted by other tools like MutPred2, Maestro, DUET etc. Analysis of structure was performed by HOPE and Mutation3D. SWISS-MODEL. server, was used for wild type and mutant proteins 3-D Modelling. Gene-gene and protein-protein interaction were predicted by GeneMANIA and STRING, respectively. The wildtype HGF protein and these three variants were independently docked with their close interactor protein MET by the use of ClusPro. Our study suggested that out of 392 missense nsSNPs of the HGF gene, five nsSNPs (D358G, G648R, I550N, N175S and R220Q), are the most deleterious in HGF gene. Gene-gene interactions showed relation of HGF with other genes depicting its importance in several pathways and co-expressions. The protein-protein interacting network is composed of 11 nodes. Analysis of protein stability by different tools indicated that the five nsSNPS decreased the stability of the protein. Anyway these nsSNPs need a confirmation analysis by experimental investigation and GWAS studiesCommunicated by Ramaswamy H. Sarma.

Genome-wide association study to identify SNPs and candidate genes associated with body size traits in donkeys

The Yangyuan donkey is a domestic animal breed mainly distributed in the northwest region of Hebei Province. Donkey body shape is the most direct production index, can fully reflect the donkey's growth status, and is closely related to important economic traits. As one of the main breeding selection criteria, body size traits have been widely used to monitor animal growth and evaluate the selection response. Molecular markers genetically linked to body size traits have the potential to accelerate the breeding process of animals via marker-assisted selection. However, the molecular markers of body size in Yangyuan donkeys have yet to be explored. In this study, we performed a genome-wide association study to identify the genomic variations associated with body size traits in a population of 120 Yangyuan donkeys. We screened 16 single nucleotide polymorphisms that were significantly associated with body size traits. Some genes distributed around these significant SNPs were considered candidates for body size traits, including SMPD4, RPS6KA6, LPAR4, GLP2R, BRWD3, MAGT1, ZDHHC15, and CYSLTR1. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated that these genes were mainly involved in the P13K-Akt signaling pathway, Rap1 signaling pathway, regulation of actin cytoskeleton, calcium signaling pathway, phospholipase D signaling pathway, and neuroactive ligand-receptor interactions. Collectively, our study reported on a list of novel markers and candidate genes associated with body size traits in donkeys, providing useful information for functional gene studies and offering great potential for accelerating Yangyuan donkey breeding.

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies

The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and the number of variants tested per loci. Using this strategy, we interrogated known prostate cancer (PCa) risk-associated loci and demonstrated that 35% of them harbor SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.