Genome Association Analysis Research Articles

Identification of novel genetic variants associated with feline cardiomyopathy using targeted next-generation sequencing

Cardiomyopathies are the most common heritable heart diseases in cats and humans. This study aimed to identify novel genetic variants in cats with hypertrophic cardiomyopathy (HCM) and restrictive cardiomyopathy (RCM) using a targeted panel of genes associated with human cardiomyopathy. Cats were phenotyped for HCM/RCM by echocardiography ± necropsy. DNA was extracted from residual blood, and targeted next-generation sequencing was performed on two separate feline cohorts: an across-breed cohort (23 healthy cats and 21 HCM-affected pedigree or Domestic Shorthair cats), and a within-breed cohort of Birman pedigree cats (14 healthy, 8 HCM-affected, and 6 RCM-affected). Genome Analysis Toolkit was used for variant discovery. Genomic association analyses, including the covariates breed, age, and sex, were conducted to identify genetic variants of interest. We identified genetic variants associated with both HCM and RCM susceptibility in the sarcomeric genes ACTC1, ACTN2, MYH7, TNNT2 and the non-sarcomeric gene CSRP3 in the Birman pedigree cats. These findings suggest that, as proposed in humans, there is at least partial overlap in the genetic background between the HCM and RCM phenotypes in cats. These findings offer potential insights for comparative cardiac research and translational medicine.

Seascape Genomics of Elacatinus punctiulatus (Ginsburg, 1938): Understanding the Historical and Contemporary Drivers of Population Structure in the Gulf of California.

The genomic diversity and population structure of marine species represents a complex mosaic shaped by historical and contemporary environmental seascape features that maintain or alter it over time. The Gulf of California (GC) is an interior sea with a dynamic history during its formation and a contemporary environmental and oceanographic complexity; hence, it is a suitable system to test the effect of historical and contemporary factors on genomic diversity in marine species. We investigated the genomic seascape of the redhead goby (Elacatinus puncticulatus; Ginsburg, 1938), a cryptobenthic marine fish, to gain insights into the historical and contemporary drivers shaping its population structure in the GC. A total of 4802 SNPs markers were analysed, with 3775 loci classified as neutral markers and 27 as outlier markers potentially under selection. Both markers demonstrated population structure, with the neutral markers showing two main groups corresponding to northern and southern locatities. The outlier markers identified an additional genetic group emerging in the central area of the sampled localities. Genetic differentiation between the North and South regions and historical demographic simulations was consistent with ancient divergence (1.04 Mya) and secondary contact (0.15 Mya). The genomic environmental association analysis revealed a possible adaptive scenario linked to ocean temperature. The study highlights the importance of historical events, contemporary environmental factors, and oceanographic circulation in shaping the genetic structure of E. puncticulatus populations in the GC.

Advancing Genetic Engineering through AI: Sequencing and Editing Innovations

As an important part of modern biotechnology, genetic engineering is widely used in fields such as disease treatment, agricultural improvement, and environmental protection. Gene sequencing technology, especially next-generation sequencing technology, provides a powerful tool for studying biological genetic information. However, with the rapid growth of genomic data, how to efficiently and accurately analyze and apply this huge data has become a major challenge facing genetic engineering. In recent years, artificial intelligence (AI) technology, especially deep learning, has been widely used in the automated processing of large-scale data and has shown great potential in genetic engineering. AI technology not only shows advantages in gene editing optimization, genetic variation detection and genome association analysis, but also significantly improves the efficiency and accuracy of genetic data analysis. Although AI has brought many conveniences in genetic engineering, challenges such as technology transparency, data quality issues, and ethics and privacy protection still need to be solved. This article explores the application of artificial intelligence in genetic engineering sequencing and data analysis, analyzes how AI can improve the efficiency and accuracy of genetic data analysis, and discusses the potential contribution of AI in gene editing and precision medicine. As AI continues to develop, it is expected to play an increasingly important role in fields such as genomics, gene editing, and precision medicine, and provide more effective strategies for future disease treatment

Exploring the potential link between gut microbiota and chronic kidney disease in causality: A 2-sample Mendelian randomization study.

Increasing evidence indicates a significant correlation between gut microbiota (GM) and susceptibility to chronic kidney disease (CKD). However, causal relationship presence remains uncertain. Mendelian randomization (MR) was applied to evaluate potential causal relation from GM to CKD. Genomic association analysis aggregates publicly online databases, utilizing Genome-Wide Association Study (GWAS) database focused on GM and CKD. For examination of potential causal connection from GM to CKD, a 2-way, 2-sample Mendelian randomization (MR) method was applied. Sensitivity analyses were utilized to scrutinize for heterogeneity, horizontal pleiotropy, MR outcomes resilience. Result from inverse variance weighting (IVW) method revealed that 10 microbiotas such as Porphyromonadaceae (OR = 1.351, 95% CI: 1.114-1.638, P = .002), Dorea (OR = 1.236, 95% CI: 1.040-1.468, P = .016), Ruminococcus torques group (OR = 1.290, 95% CI: 1.035-1.608, P = .024) are potential CKD risk factors. Five microbiotas, including the Prevotellaceae (OR = 0.814, 95% CI: 0.719-0.922, P = .001) are potential CKD protective factors. Sensitivity analyses reveal no horizontal pleiotropy or heterogeneity. Additionally, reverse MR results unveiled potential relation between CKD and disorders in 3 microbiotas, including Senegalimassilia. According to the investigation, MR method was employed to delve into reciprocal causal connection from GM to CKD. Our findings identified 15 types of GM causally linked to CKD, as well as CKD demonstrating causal associations with 3 types of GM. Further exploration of these associated GM types is hopeful to raise novel insights, for CKD preventing and early monitoring.

Integrated single-cell and bulk RNA sequencing revealed an epigenetic signature predicts prognosis and tumor microenvironment colorectal cancer heterogeneity.

Colorectal cancer (CRC) prognosis prediction is currently a major challenge. Epigenetic regulation has been widely reported for its role in cancer development. To construct a robust prognostic signature, we used developed and validated across datasets. After constructing the signature, the prognostic value of the signature was evaluated in the TCGA cohort and six independent datasets (GSE17526, GSE17537, GSE33113, GSE37892, GSE39048 and GSE39582). The clinical, genomic and transcriptomic features related to the signature were identified. The correlations of the signature score with immune cell infiltration and cell-cell interactions were analyzed. The correlations between the signature score and the sensitivity to different drugs were also predicted. In the TCGA cohort, patients in the low-risk group according to the signature score had longer survival than those in the high-risk group, and this finding was validated in the validation datasets. The signature was a prognostic factor independent of age and sex and was correlated with stage and PD-1/PD-L1 expression. Area under the receiving operating characteristic curve was 0.72. Genomic association analyses revealed that samples from high-risk patients exhibited chromosomal instability. Transcriptomic analyses revealed that the signature score was significantly associated with multiple cellular pathways. Bulk RNA-seq and single-cell sequencing data revealed that the signature reflected differences in infiltrating immune cell-tumor cell interactions, especially for macrophages. The signature also predicted the putative drug sensitivity of CRC samples. The signature is a valuable biomarker for predicting CRC prognosis and reflects multiple features of CRC, especially macrophage infiltration in the microenvironment.

Whole Genome Linkage and Association Analyses Identify DLG Associated Protein-1 as a Novel Positional and Biological Candidate Gene for Muscle Strength: The Long Life Family Study.

Grip strength is a robust indicator of overall health, is moderately heritable, and predicts longevity in older adults. Using genome-wide linkage analysis, we identified a novel locus on chromosome 18p (mega-basepair region: 3.4-4.0) linked to grip strength in 3755 individuals from 582 families aged 64 ± 12 years (range 30-110 years; 55% women). There were 26 families that contributed to the linkage peak (cumulative logarithm of the odds [LOD] score = 10.94), with 6 families (119 individuals) accounting for most of the linkage signal (LOD = 6.4). In these 6 families, using whole genome sequencing data, we performed association analyses between the 7312 single nucleotide (SNVs) and insertion deletion (INDELs) variants in the linkage region and grip strength. Models were adjusted for age, age2, sex, height, field center, and population substructure. We found significant associations between genetic variants (8 SNVs and 4 INDELs, p < 5×10-5) in the Disks Large-associated Protein 1 (DLGAP1) gene and grip strength. Haplotypes constructed using these variants explained up to 98.1% of the LOD score. Finally, RNAseq data showed that these variants were significantly associated with the expression of nearby Myosin Light Chain 12A (MYL12A), Structural Maintenance of Chromosomes Flexible Hinge Domain Containing 1 (SMCHD1), Erythrocyte Membrane Protein Band 4.1 Like 3 (EPB41L3) genes (p < .0004). The DLGAP1 gene plays an important role in the postsynaptic density of neurons; thus, it is both a novel positional and biological candidate gene for follow-up studies aimed at uncovering genetic determinants of muscle strength.

OsCSD2 and OsCSD3 Enhance Seed Storability by Modulating Antioxidant Enzymes and Abscisic Acid in Rice.

Seed deterioration during storage poses a significant challenge to rice production, leading to a drastic decline in both edible quality and viability, thereby impacting overall crop yield. This study aimed to address this issue by further investigating candidate genes associated with two previously identified QTLs for seed storability through genome association analysis. Among the screened genes, two superoxide dismutase (SOD) genes, OsCSD2 (Copper/zinc Superoxide Dismutase 2) and OsCSD3, were selected for further study. The generation of overexpression and CRISPR/Cas9 mutant transgenic lines revealed that OsCSD2 and OsCSD3 play a positive regulatory role in enhancing rice seed storability. Subsequent exploration of the physiological mechanisms demonstrated that overexpression lines exhibited lower relative electrical conductivity, indicative of reduced cell membrane damage, while knockout lines displayed the opposite trend. Furthermore, the overexpression lines of OsCSD2 and OsCSD3 showed significant increases not only in SOD but also in CAT and POD activities, highlighting an augmented antioxidant system in the transgenic seeds. Additionally, hormone profiling indicated that ABA contributed to the improved seed storability observed in these lines. In summary, these findings provide valuable insights into the regulatory mechanisms of OsCSDs in rice storability, with potential applications for mitigating grain loss and enhancing global food security.

A Study of the Resistance of Hu Sheep Lambs to Escherichia coli F17 Based on Whole Genome Sequencing.

This study aims to analyze the whole genome sequencing of E. coli F17 in antagonistic and susceptible Hu sheep lambs. The objective is to investigate the critical mutation loci in sheep and understand the genetic mechanism of sheep resistance to E. coli F17 at the genome level. Antagonist and susceptible venous blood samples were collected from Hu sheep lambs for whole genome sequencing and whole genome association analysis. A total of 466 genes with significant SNPs (p < 1.0 × 10-3) were found. GO and KEGG enrichment analysis and protein interaction network analysis were performed on these genes, and preliminary investigations showed that SNPs on CTNNB1, CDH8, APOD, HCLS1, Tet2, MTSS1 and YAP1 genes may be associated with the antagonism and susceptibility of Hu sheep lambs to E. coli F17. There are still some shortcomings that have not been explored via in vivo and in vitro functional experiments of the candidate genes, which will be our next research work. This study provides genetic loci and candidate genes for resistance of Hu sheep lambs to E. coli F17 infection, and provides a genetic basis for breeding disease-resistant sheep.

Phenotypic and genomic characterisation of performance of tropically adapted chickens raised in smallholder farm conditions in Ethiopia.

In sub-Saharan Africa, 80% of poultry production is on smallholder village farms, where chickens are typically reared outdoors in free-ranging conditions. There is limited knowledge on chickens' phenotypic characteristics and genetics under these conditions. The present is a large-scale study set out to phenotypically characterise the performance of tropically adapted commercial chickens in typical smallholder farm conditions, and to examine the genetic profile of chicken phenotypes associated with growth, meat production, immunity, and survival. A total of 2,573 T451A dual-purpose Sasso chickens kept outdoors in emulated free-ranging conditions at the poultry facility of the International Livestock Research Institute in Addis Ababa, Ethiopia, were included in the study. The chickens were raised in five equally sized batches and were individually monitored and phenotyped from the age of 56days for 8weeks. Individual chicken data collected included weekly body weight, growth rate, body and breast meat weight at slaughter, Newcastle Disease Virus (NDV) titres and intestinal Immunoglobulin A (IgA) levels recorded at the beginning and the end of the period of study, and survival rate during the same period. Genotyping by sequencing was performed on all chickens using a low-coverage and imputation approach. Chicken phenotypes and genotypes were combined in genomic association analyses. We discovered that the chickens were phenotypically diverse, with extensive variance levels observed in all traits. Batch number and sex of the chicken significantly affected the studied phenotypes. Following quality assurance, genotypes consisted of 2.9 million Single Nucleotide Polymorphism markers that were used in the genomic analyses. Results revealed a largely polygenic mode of genetic control of all phenotypic traits. Nevertheless, 15 distinct markers were identified that were significantly associated with growth, carcass traits, NDV titres, IgA levels, and chicken survival. These markers were located in regions harbouring relevant annotated genes. Results suggest that performance of chickens raised under smallholder farm conditions is amenable to genetic improvement and may inform selective breeding programmes for enhanced chicken productivity in sub-Saharan Africa.

Genetic Research on Egg Production Performance in Poultry: From the Perspective of Whole Genome Association Analysis

Genetic Research on Egg Production Performance in Poultry: From the Perspective of Whole Genome Association Analysis

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e−06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

Genetic diversity and genome-wide association analysis of pine wood nematode populations in different regions of China.

Pine wilt disease (Bursaphelenchus xylophilus) was recently detected in Liaoning Province, which was previously considered an unfavourable area for B. xylophilus due to its low temperatures. This study aims to compare the reproductivity and genetic variations of B. xylophilus isolates from Liaoning Province and other parts of China to explore their phenotypic and genomic differences. The samples from Liaoning, Anhui, Hubei, Henan, Zhejiang and Jiangsu were isolated and purified to obtain the strains. The reproductivity of the strains was determined at 15 °C. The genetic structure was analyzed by using SNP molecular markers, and the whole genome association analysis was carried out by integrating SNP information and feculence traits. A reproductivity experiment showed that Liaoning isolates have higher reproductive ability at 15 °C. Subsequent SNP profiling and population differentiation analysis revealed obvious genetic differentiation of Liaoning isolates from other isolates. A genome-wide association study showed that SNPs closely related to low-temperature tolerance were mainly located in GPCR, Acyl-CoA, and Cpn10, which are responsible for adaptation to environmental factors, such as temperature change. Pine wood nematodes likely adapted to the climate in Liaoning and maintained a certain reproductive capacity at low temperature via variants of adaptation-related genes. This study provides a theoretical basis for elucidating the prevalence and diffusion status of B. xylophilus in China.

Genomic Association Analysis of Growth and Backfat Traits in Large White Pigs.

The pig industry is significantly influenced by complex traits such as growth rate and fat deposition, which have substantial implications for economic returns. Over the years, remarkable genetic advancements have been achieved through intense artificial selection to enhance these traits in pigs. In this study, we aimed to investigate the genetic factors that contribute to growth efficiency and lean meat percentages in Large White pigs. Specifically, we focused on analyzing two key traits: age at 100 kg live weight (AGE100) and backfat thickness at 100 kg (BF100), in three distinct Large White pig populations-500 Canadian, 295 Danish, and 1500 American Large White pigs. By employing population genomic techniques, we observed significant population stratification among these pig populations. Utilizing imputed whole-genome sequencing data, we conducted single population genome-wide association studies (GWAS) as well as a combined meta-analysis across the three populations to identify genetic markers associated with the aforementioned traits. Our analyses highlighted several candidate genes, such as CNTN1-which has been linked to weight loss in mice and is potentially influential for AGE100-and MC4R, which is associated with obesity and appetite and may impact both traits. Additionally, we identified other genes-namely, PDZRN4, LIPM, and ANKRD22-which play a partial role in fat growth. Our findings provide valuable insights into the genetic basis of these important traits in Large White pigs, which may inform breeding strategies for improved production efficiency and meat quality.

JustRNA: a database of plant long noncoding RNA expression profiles and functional network.

Long noncoding RNAs (lncRNAs) are regulatory RNAs involved in numerous biological processes. Many plant lncRNAs have been identified, but their regulatory mechanisms remain largely unknown. A resource that enables the investigation of lncRNA activity under various conditions is required because the co-expression between lncRNAs and protein-coding genes may reveal the effects of lncRNAs. This study developed JustRNA, an expression profiling resource for plant lncRNAs. The platform currently contains 1 088 565 lncRNA annotations for 80 plant species. In addition, it includes 3692 RNA-seq samples derived from 825 conditions in six model plants. Functional network reconstruction provides insight into the regulatory roles of lncRNAs. Genomic association analysis and microRNA target prediction can be employed to depict potential interactions with nearby genes and microRNAs, respectively. Subsequent co-expression analysis can be employed to strengthen confidence in the interactions among genes. Chromatin immunoprecipitation sequencing data of transcription factors and histone modifications were integrated into the JustRNA platform to identify the transcriptional regulation of lncRNAs in several plant species. The JustRNA platform provides researchers with valuable insight into the regulatory mechanisms of plant lncRNAs. JustRNA is a free platform that can be accessed at http://JustRNA.itps.ncku.edu.tw.

A Gγ protein regulates alkaline sensitivity in crops.

The use of alkaline salt lands for crop production is hindered by a scarcity of knowledge and breeding efforts for plant alkaline tolerance. Through genome association analysis of sorghum, a naturally high-alkaline-tolerant crop, we detected a major locus, Alkaline Tolerance 1 (AT1), specifically related to alkaline-salinity sensitivity. An at1 allele with a carboxyl-terminal truncation increased sensitivity, whereas knockout of AT1 increased tolerance to alkalinity in sorghum, millet, rice, and maize. AT1 encodes an atypical G protein γ subunit that affects the phosphorylation of aquaporins to modulate the distribution of hydrogen peroxide (H2O2). These processes appear to protect plants against oxidative stress by alkali. Designing knockouts of AT1 homologs or selecting its natural nonfunctional alleles could improve crop productivity in sodic lands.

The genomic and epigenetic footprint of local adaptation to variable climates in kiwifruit.

A full understanding of adaptive genetic variation at the genomic level will help address questions of how organisms adapt to diverse climates. Actinidia eriantha is a shade-tolerant species, widely distributed in the southern tropical region of China, occurring in spatially heterogeneous environments. In the present study we combined population genomic, epigenomic, and environmental association analyses to infer population genetic structure and positive selection across a climatic gradient, and to assess genomic offset to climatic change for A. eriantha. The population structure is strongly shaped by geography and influenced by restricted gene flow resulting from isolation by distance due to habitat fragmentation. In total, we identified 102 outlier loci and annotated 455 candidate genes associated with the genomic basis of climate adaptation, which were enriched in functional categories related to development processes and stress response; both temperature and precipitation are important factors driving adaptive variation. In addition to single-nucleotide polymorphisms (SNPs), a total of 27 single-methylation variants (SMVs) had significant correlation with at least one of four climatic variables and 16 SMVs were located in or adjacent to genes, several of which were predicted to be involved in plant response to abiotic or biotic stress. Gradient forest analysis indicated that the central/east populations were predicted to be at higher risk of future population maladaptation under climate change. Our results demonstrate that local climate factors impose strong selection pressures and lead to local adaptation. Such information adds to our understanding of adaptive mechanisms to variable climates revealed by both population genome and epigenome analysis.

A cervical cancer biorepository for pharmacogenomics research in Zimbabwe

BackgroundResearch infrastructures such as biorepositories are essential to facilitate genomics and its growing applications in health research and translational medicine in Africa. Using a cervical cancer cohort, this study describes the establishment of a biorepository consisting of biospecimens and matched phenotype data for use in genomic association analysis and pharmacogenomics research.MethodWomen aged > 18 years with a recent histologically confirmed cervical cancer diagnosis were recruited. A workflow pipeline was developed to collect, store, and analyse biospecimens comprising donor recruitment and informed consent, followed by data and biospecimen collection, nucleic acid extraction, storage of genomic DNA, genetic characterization, data integration, data analysis and data interpretation. The biospecimen and data storage infrastructure included shared -20 °C to -80 °C freezers, lockable cupboards, secured access-controlled laptop, password protected online data storage on OneDrive software. The biospecimen or data storage, transfer and sharing were compliant with the local and international biospecimen and data protection laws and policies, to ensure donor privacy, trust, and benefits for the wider community.ResultsThis initial establishment of the biorepository recruited 410 women with cervical cancer. The mean (± SD) age of the donors was 52 (± 12) years, comprising stage I (15%), stage II (44%), stage III (47%) and stage IV (6%) disease. The biorepository includes whole blood and corresponding genomic DNA from 311 (75.9%) donors, and tumour biospecimens and corresponding tumour DNA from 258 (62.9%) donors. Datasets included information on sociodemographic characteristics, lifestyle, family history, clinical information, and HPV genotype. Treatment response was followed up for 12 months, namely, treatment-induced toxicities, survival vs. mortality, and disease status, that is disease-free survival, progression or relapse, 12 months after therapy commencement.ConclusionThe current work highlights a framework for developing a cancer genomics cohort-based biorepository on a limited budget. Such a resource plays a central role in advancing genomics research towards the implementation of personalised management of cancer.

Advances in sequencing and key character analysis of mango (Mangifera indica L.).

Mango (Mangifera indica L.) is an important fruit crop in tropical and subtropical countries associated with many agronomic and horticultural problems, such as susceptibility to pathogens, including powdery mildew and anthracnose, poor yield and quality, and short shelf life. Conventional breeding techniques exhibit significant limitations in improving mango quality due to the characteristics of long ripening, self-incompatibility, and high genetic heterozygosity. In recent years, much emphasis has been placed on identification of key genes controlling a certain trait through genomic association analysis and directly breeding new varieties through transgene or genotype selection of offspring. This paper reviews the latest research progress on the genome and transcriptome sequencing of mango fruit. The rapid development of genome sequencing and bioinformatics provides effective strategies for identifying, labeling, cloning, and manipulating many genes related to economically important traits. Preliminary verification of the functions of mango genes has been conducted, including genes related to flowering regulation, fruit development, and polyphenol biosynthesis. Importantly, modern biotechnology can refine existing mango varieties to meet the market demand with high economic benefits.

Association of Human Leukocyte Antigen Variants with Progression of Monoclonal Gammopathy of Undetermined Significance to Multiple Myeloma

Background: Multiple Myeloma (MM), a neoplasm of terminally differentiated plasma cells that produce a monoclonal immunoglobulin, represents 10% of hematologic malignancies. Virtually all MM arises from monoclonal gammopathy of undetermined significance (MGUS), an asymptomatic premalignant phase, at a rate of progression of about 1% annually. However, the risk of progression is not uniform as some individuals with MGUS could have progression rates as high as 58% in 20 years. Novel predictive markers are critically needed to accurately identify MGUS patients at high-risk of malignant transformation, so that early intervention can be implemented to prevent end-organ damage associated with frank myeloma. Genome-wide association studies (GWAS) have repeatedly identified loci at the major histocompatibility complex (MHC) that are associated with the risk of many cancers including MM. However, the impact of specific MHC loci on the MGUS-to-MM progression risk has not been elucidated. Methods: We used the Illumina Infinium Global Screening Array to scan the buffy coat samples of 259 MGUS and 654 MM patients enrolled from the Medical College of Wisconsin (MCW) Tissue Bank. All MGUS and MM patients have been chart reviewed and confirmed diagnosis. After a standard quality control (QC), 249 MGUS and 619 MM patients with 11,882 genotyped single nucleotide polymorphisms (SNPs) in the MHC region (20 to 40 mb at chromosome 6) were used for analysis. The SNP2HLA v1.0.3 software and HLA*IMP:03 were applied to impute classical human leukocyte antigen (HLA) alleles at a four-digit resolution based on SNP genotyping. SNP2HLA uses Beagle to impute all missing SNPs, classical HLA alleles, and amino acid polymorphisms to the HapMap reference panel. HLA*IMP:03 uses the Michigan Imputation Server to impute phased haplotypes to a multi-ethnic panel of 10,561 individuals with SNP genotype data and lab-based typing of alleles at 11 HLA genes. A post-imputation QC was conducted to remove variants with low imputation quality score (R2 < 0.3). Logistic regression analysis was performed using PLINK, a whole genome association analysis toolset, which adjusted for age, race, sex, and top principal components. Results: As evidenced by the quantile-quantile (QQ) plot (Figure 1), there was no evidence for hidden substructure or cryptic relatedness in the GWAS (λ=1.02). The final set of imputed variants from both tools used in association analysis were of high quality (R2 ≥0.9). Although we did not observe any HLA variants showing associations with the progression of MGUS to MM at Bonferroni adjusted significance level for SNP2HLA (P = 0.05/5865 = 8.53 × 10-6) and for HLA*IMP:03 (P = 0.05/215 = 2.33 × 10-4), we found 4 classical HLA alleles and 30 HLA Amino Acids were associated with MGUS progression at a P-value of < 0.05 via association analysis with SNP2HLA. Association analysis for HLA*IMP:03 imputation data identified 8 classical HLA alleles were associated with MGUS progression at P value < 0.05. Among them, two classical HLA alleles were identified in both tools, they are HLA_B*08:01 (odds ratio, OR=0.38, 95% confidence interval, CI =0.19-0.77 and OR=0.43, 95% CI=0.21-0.86 for SNP2HLA and HLA*IMP:03, respectively) and HLA_C*07:01 (OR=0.61, 95% CI=0.41-0.89 and OR=0.63, 95% CI=0.41-0.98 for SNP2HLA and HLA*IMP:03, respectively) (Table 1). Conclusions: These results implicate that HLA alleles may contribute to the progression of MGUS to MM. Further studies are needed to validate our findings with larger sample sizes and explore the biological mechanisms. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Latent class analysis of behavior across dog breeds reveal underlying temperament profiles

Latent class analysis (LCA) is a type of modeling analysis approach that has been used to identify unobserved groups or subgroups within multivariate categorical data. LCA has been used for a wide array of psychological evaluations in humans, including the identification of depression subtypes or PTSD comorbidity patterns. However, it has never been used for the assessment of animal behavior. Our objective here is to identify behavioral profile-types of dogs using LCA. The LCA was performed on a C-BARQ behavioral questionnaire dataset from 57,454 participants representing over 350 pure breeds and mixed breed dogs. Two, three, and four class LCA models were developed using C-BARQ trait scores and environmental covariates. In our study, LCA is shown as an effective and flexible tool to classify behavioral assessments. By evaluating the traits that carry the strongest relevance, it was possible to define the basis of these grouping differences. Groupings can be ranked and used as levels for simplified comparisons of complex constructs, such as temperament, that could be further exploited in downstream applications such as genomic association analyses. We propose this approach will facilitate dissection of physiological and environmental factors associated with psychopathology in dogs, humans, and mammals in general.