Candidate-gene approaches for studying complex genetic traits: practical considerations

Unraveling the Variability of Human Satiation: Implications for Precision Obesity Management

Satiation is the physiologic process that regulates meal size and termination, and it is quantified by the calories consumed to reach satiation. Given its role in energy intake, changes in satiation contribute to obesity's pathogenesis. Our study employed a protocolized approach to study the components of food intake regulation including a standardized breakfast, a gastric emptying study, appetite sensation testing, and a satiation measurement by an ad libitummeal test. These studies revealed that satiation is highly variable among individuals, and while baseline characteristics, anthropometrics, body composition and hormones, contribute to this variability, these factors do not fully account for it. To address this gap, we explored the role of a germline polygenic risk score, which demonstrated a robust association with satiation. Furthermore, we developed a machine-learning-assisted gene risk score to predict satiation and leveraged this prediction to anticipate responses to anti-obesity medications. Our findings underscore the significance of satiation, its inherent variability, and the potential of a genetic risk score to forecast it, ultimately allowing us to predict responses to different anti-obesity interventions.

Haplotype-based breeding: A new insight in crop improvement

Haplotype-based breeding (HBB) is one of the cutting-edge technologies in the realm of crop improvement due to the increasing availability of Single Nucleotide Polymorphisms identified by Next Generation Sequencing technologies. The complexity of the data can be decreased with fewer statistical tests and a lower probability of spurious associations by combining thousands of SNPs into a few hundred haplotype blocks. The presence of strong genomic regions in breeding lines of most crop species facilitates the use of haplotypes to improve the efficiency of genomic and marker-assisted selection. Haplotype-based breeding as a Genomic Assisted Breeding (GAB) approach harnesses the genome sequence data to pinpoint the allelic variation used to hasten the breeding cycle and circumvent the challenges associated with linkage drag. This review article demonstrates ways to identify candidate genes, superior haplotype identification, haplo-pheno analysis, and haplotype-based marker-assisted selection. The crop improvement strategies that utilize superior haplotypes will hasten the breeding progress to safeguard global food security.

Association of IL-4 polymorphism with severe periodontitis in a sample of Iraqi population

INTRODUCTION

Specific bacterial plaque and environmental factors cannot be considered the only cause of periodontitis. Still, several genetic factors affect the host response to the bacteria, like gene polymorphisms in anti-inflammatory cytokines. Several studies have reported that clones of T-helper 2 lymphocytes (TH2) are generated in response to dental plaque in periodontitis patients, while in healthy individuals, they are regulated by T-helper 1 (TH1) lymphocytes. Accordingly, such patients consistently produce more IL-4 (TH2) in response to bacterial stimulation, whereas healthy controls with intact periodontal tissues produce a significantly higher level of TH1.

Experimental validation and comprehensive analysis of m6A methylation regulators in intervertebral disc degeneration subpopulation classification

Intervertebral disc degeneration (IVDD) is one of the most prevalent causes of chronic low back pain. The role of m6A methylation modification in disc degeneration (IVDD) remains unclear. We investigated immune-related m6A methylation regulators as IVDD biomarkers through comprehensive analysis and experimental validation of m6A methylation regulators in disc degeneration. The training dataset was downloaded from the GEO database and analysed for differentially expressed m6A methylation regulators and immunological features, the differentially regulators were subsequently validated by a rat IVDD model and RT-qPCR. Further screening of key m6A methylation regulators based on machine learning and LASSO regression analysis. Thereafter, a predictive model based on key m6A methylation regulators was constructed for training sets, which was validated by validation set. IVDD patients were then clustered based on the expression of key m6A regulators, and the expression of key m6A regulators and immune infiltrates between clusters was investigated to determine immune markers in IVDD. Finally, we investigated the potential role of the immune marker in IVDD through enrichment analysis, protein-to-protein network analysis, and molecular prediction. By analysising of the training set, we revealed significant differences in gene expression of five methylation regulators including RBM15, YTHDC1, YTHDF3, HNRNPA2B1 and ALKBH5, while finding characteristic immune infiltration of differentially expressed genes, the result was validated by PCR. We then screen the differential m6A regulators in the training set and identified RBM15 and YTHDC1 as key m6A regulators. We then used RBM15 and YTHDC1 to construct a predictive model for IVDD and successfully validated it in the training set. Next, we clustered IVDD patients based on the expression of RBM15 and YTHDC1 and explored the immune infiltration characteristics between clusters as well as the expression of RBM15 and YTHDC1 in the clusters. YTHDC1 was finally identified as an immune biomarker for IVDD. We finally found that YTHDC1 may influence the immune microenvironment of IVDD through ABL1 and TXK. In summary, our results suggest that YTHDC1 is a potential biomarker for the development of IVDD and may provide new insights for the precise prevention and treatment of IVDD.

Deficits in emotion recognition and processing in children with high callous-unemotional traits: the role of the MAOA gene

Children with high Callous-Unemotional (CU) traits show deficits in recognizing and processing facial expressions. Alterations in emotion recognition have been linked to a higher synaptic concentration of monoaminergic neurotransmitters. The current study investigated the relationship between the MAOA-Low-activity alleles and the ability to recognize and process facial expressions in 97 male children (8-12 years old) diagnosed with disruptive behavior disorder. Participants completed a computerized emotion-recognition task while an eye-tracking system recorded the number (Fixation Count, FC) and length (Fixation Duration, FD) of fixations to the eye region of the emotional stimuli. Children with high CU traits exhibited lower scores in recognition of sadness and anger, and lower FC and FD for sadness and fear than children with low CU traits. Children carrying the MAOA-Low-activity alleles displayed lower FD for sadness, and FD and FC for fear than those carrying the MAOA-High-activity alleles. These genetic effects appeared even stronger in children with CU traits. Moderation analysis revealed that CU traits were associated with lower FC and FD for fear, and lower FD for sadness, probably due to the MAOA-Low-activity alleles. Our findings, although to be replicated, suggest MAOA-Low-activity alleles as potential genetic biomarkers to identify CU children in need of training focused on emotion processing.

An approach for searching genes in signaling pathways or gene-gene interaction networks related to Hypertension in the Mexican population

INTRODUCTION

The selection of single nucleotide polymorphisms (SNPs) to evaluate the genetic susceptibility in complex traits is often conducted in isolation, without considering the entire set of genes. Incorporating signaling pathways or gene-gene interaction search may provide a more comprehensive approach to selecting SNP candidates for further study.

OBJECTIVE

To propose a systematic procedure for identifying SNPs candidates with complex traits such as hypertension and blood pressure.

METHODS

Sequential stages to SNPs selection: 1) literature review to identify SNPs, following the PRISMA methodology, 2) identification and selection of signaling pathways and selection of gene-gene interaction networks using the STRING software, and 3) application of specific criteria for SNPs candidates, including: a) SNPs with minor allele frequency > 5% in the target population, b) SNPs located within genes involved in three or more signaling pathways, and c) SNPs that are not in linkage disequilibrium, with a D'or r2 value < 0.8.

RESULTS

Stage 1) A total of 44 publications were selected, providing information on 230 genes evaluated with blood pressure. Stage 2) Using the STRING software, we selected 7 signaling pathways with a false discovery rate < 0.0001 and strength ≥ 0.8; and we identified 16 genes belonging to gene-gene interaction networks, six of them share ≥ 3 signaling pathways. Stage 3) Finally, 7 SNPs were selected for genotyping in the Health Workers Cohort Study. We observed a positive association between SNPs with hypertension incidence in males (rs1130214, rs3807989) and females (rs5051, rs2493123).

CONCLUSION

Our methodological proposal may be a reliable way for selecting SNPs candidates to study complex traits.

DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge

The high-throughput genomic and proteomic scanning approaches allow investigators to measure the quantification of genome-wide genes (or gene products) for certain disease conditions, which plays an essential role in promoting the discovery of disease mechanisms. The high-throughput approaches often generate a large gene list of interest (GOIs), such as differentially expressed genes/proteins. However, researchers have to perform manual triage and validation to explore the most promising, biologically plausible linkages between the known disease genes and GOIs (disease signals) for further study. Here, to address this challenge, we proposed a network-based strategy DDK-Linker to facilitate the exploration of disease signals hidden in omics data by linking GOIs to disease knowns genes. Specifically, it reconstructed gene distances in the protein-protein interaction (PPI) network through six network methods (random walk with restart, Deepwalk, Node2Vec, LINE, HOPE, Laplacian) to discover disease signals in omics data that have shorter distances to disease genes. Furthermore, benefiting from the establishment of knowledge base we established, the abundant bioinformatics annotations were provided for each candidate disease signal. To assist in omics data interpretation and facilitate the usage, we have developed this strategy into an application that users can access through a website or download the R package. We believe DDK-Linker will accelerate the exploring of disease genes and drug targets in a variety of omics data, such as genomics, transcriptomics and proteomics data, and provide clues for complex disease mechanism and pharmacological research. DDK-Linker is freely accessible at http://ddklinker.ncpsb.org.cn/.

Genetic variation in the dopamine system is associated with mixed-strategy decision-making in patients with Parkinson's disease

Decision-making during mixed-strategy games requires flexibly adapting choice strategies in response to others' actions and dynamically tracking outcomes. Such decisions involve diverse cognitive processes, including reinforcement learning, which are affected by disruptions to the striatal dopamine system. We therefore investigated how genetic variation in dopamine function affected mixed-strategy decision-making in Parkinson's disease (PD), which involves striatal dopamine pathology. Sixty-six PD patients (ages 49-85, Hoehn and Yahr Stages 1-3) and 22 healthy controls (ages 54-75) competed in a mixed-strategy game where successful performance depended on minimizing choice biases (i.e., flexibly adapting choices trial by trial). Participants also completed a fixed-strategy task that was matched for sensory input, motor outputs and overall reward rate. Factor analyses were used to disentangle cognitive from motor aspects within both tasks. Using a within-subject, multi-centre design, patients were examined on and off dopaminergic therapy, and genetic variation was examined via a multilocus genetic profile score representing the additive effects of three single nucleotide polymorphisms (SNPs) that influence dopamine transmission: rs4680 (COMT Val158 Met), rs6277 (C957T) and rs907094 (encoding DARPP-32). PD and control participants displayed comparable mixed-strategy choice behaviour (overall); however, PD patients with genetic profile scores indicating higher dopamine transmission showed improved performance relative to those with low scores. Exploratory follow-up tests across individual SNPs revealed better performance in individuals with the C957T polymorphism, reflecting higher striatal D2/D3 receptor density. Importantly, genetic variation modulated cognitive aspects of performance, above and beyond motor function, suggesting that genetic variation in dopamine signalling may underlie individual differences in cognitive function in PD.

The Role of Genetics in Preterm Birth

Preterm birth (PTB), defined as the birth of a child before 37 completed weeks gestation, affects approximately 11% of live births and is the leading cause of death in children under 5 years. PTB is a complex disease with multiple risk factors including genetic variation. Much research has aimed to establish the biological mechanisms underlying PTB often through identification of genetic markers for PTB risk. The objective of this review is to present a comprehensive and updated summary of the published data relating to the field of PTB genetics. A literature search in PubMed was conducted and English studies related to PTB genetics were included. Genetic studies have identified genes within inflammatory, immunological, tissue remodeling, endocrine, metabolic, and vascular pathways that may be involved in PTB. However, a substantial proportion of published data have been largely inconclusive and multiple studies had limited power to detect associations. On the contrary, a few large hypothesis-free approaches have identified and replicated multiple novel variants associated with PTB in different cohorts. Overall, attempts to predict PTB using single "-omics" datasets including genomic, transcriptomic, and epigenomic biomarkers have been mostly unsuccessful and have failed to translate to the clinical setting. Integration of data from multiple "-omics" datasets has yielded the most promising results.

The Genetic Association with Athlete Status, Physical Performance, and Injury Risk in Soccer

The aim of this review was to critically appraise the literature concerning the genetic association with athlete status, physical performance, and injury risk in soccer. The objectives were to provide guidance on which genetic markers could potentially be used as part of future practice in soccer and to provide direction for future research in this area. The most compelling evidence identified six genetic polymorphisms to be associated with soccer athlete status (ACE I/D; ACTN3 rs1815739; AGT rs699; MCT1 rs1049434; NOS3 rs2070744; PPARA rs4253778), six with physical performance (ACTN3 rs1815739; AMPD1 rs17602729; BDNF rs6265; COL2A1 rs2070739; COL5A1 rs12722; NOS3 rs2070744), and seven with injury risk (ACTN3 rs1815739; CCL2 rs2857656; COL1A1 rs1800012; COL5A1 rs12722; EMILIN1 rs2289360; IL6 rs1800795; MMP3 rs679620). As well as replication by independent groups, large-scale genome-wide association studies are required to identify new genetic markers. Future research should also investigate the physiological mechanisms associating these polymorphisms with specific phenotypes. Further, researchers should investigate the above associations in female and non-Caucasian soccer players, as almost all published studies have recruited male participants of European ancestry. Only after robust, independently replicated genetic data have been generated, can genetic testing be considered an additional tool to potentially inform future practice in soccer.

Association between MC1R gene and coat color segregation in Shanxia long black pig and Lulai black pig

BACKGROUND

Coat color, as a distinct phenotypic characteristic of pigs, is often subject to preference and selection, such as in the breeding process of new breed. Shanxia long black pig was derived from an intercross between Berkshire boars and Licha black pig sows, and it was bred as a paternal strain with high-quality meat and black coat color. Although the coat color was black in the F1 generation of the intercross, it segregated in the subsequent generations. This study aims to decode the genetic basis of coat color segregation and develop a method to distinct black pigs from the spotted in Shanxia long black pig.

RESULTS

Only a QTL was mapped at the proximal end of chromosome 6, and MC1R gene was picked out as functional candidate gene. A total of 11 polymorphic loci were identified in MC1R gene, and only the c.67_68insCC variant was co-segregating with coat color. This locus isn't recognized by any restriction endonuclease, so it can't be genotyped by PCR-RFLP. The c.370G > A polymorphic locus was also significantly associated with coat color, and has been in tightly linkage disequilibrium with the c.67_68insCC. Furthermore, it is recognized by BspHI. Therefore, a PCR-RFLP method was set up to genotype this locus. Besides the 175 sequenced individuals, another more 1,391 pigs were genotyped with PCR-RFLP, and all of pigs with GG (one band) were black.

CONCLUSION

MC1R gene (c.67_68insCC) is the causative gene (mutation) for the coat color segregation, and the PCR-RFLP of c.370G > A could be used in the breeding program of Shanxia long black pig.

Multiomics-empowered Deep Phenotyping of Ulcerative Colitis Identifies Biomarker Signatures Reporting Functional Remission States

INTRODUCTION

Ulcerative colitis [UC] is a chronic disease with rising incidence and unclear aetiology. Deep molecular phenotyping by multiomics analyses may provide novel insights into disease processes and characteristic features of remission states.

METHODS

UC pathomechanisms were assessed by proteome profiling of human tissue specimens, obtained from five distinct colon locations for each of the 12 patients included in the study. Systemic disease-associated alterations were evaluated thanks to a cross-sectional setting of mass spectrometry-based multiomics analyses comprising proteins, metabolites, and eicosanoids of plasma obtained from UC patients during acute episodes and upon remission, in comparison with healthy controls.

RESULTS

Tissue proteome profiling indicated colitis-associated activation of neutrophils, macrophages, B and T cells, fibroblasts, endothelial cells and platelets, and hypoxic stress, and suggested a general downregulation of mitochondrial proteins accompanying the establishment of apparent wound healing-promoting activities including scar formation. Whereas pro-inflammatory proteins were apparently upregulated by immune cells, the colitis-associated epithelial cells, fibroblasts, endothelial cells, and platelets seemed to predominantly contribute anti-inflammatory and wound healing-promoting proteins. Blood plasma proteomics indicated chronic inflammation and platelet activation, whereas plasma metabolomics identified disease-associated deregulations of gut and gut microbiome-derived metabolites. Upon remission several, but not all, molecular candidate biomarker levels recovered back to normal.

CONCLUSION

The findings may indicate that microvascular damage and platelet deregulation hardly resolve upon remission, but apparently persist as disease-associated molecular signatures. This study presents local and systemic molecular alterations integrated in a model for UC pathomechanisms, potentially supporting the assessment of disease and remission states in UC patients.

Germline mechanisms of immunotherapy toxicities in the era of genome-wide association studies

Cancer immunotherapy has revolutionized the treatment of advanced cancers and is quickly becoming an option for early-stage disease. By reactivating the host immune system, immunotherapy harnesses patients' innate defenses to eradicate the tumor. By putatively similar mechanisms, immunotherapy can also substantially increase the risk of toxicities or immune-related adverse events (irAEs). Severe irAEs can lead to hospitalization, treatment discontinuation, lifelong immune complications, or even death. Many irAEs present with similar symptoms to heritable autoimmune diseases, suggesting that germline genetics may contribute to their onset. Recently, genome-wide association studies (GWAS) of irAEs have identified common germline associations and putative mechanisms, lending support to this hypothesis. A wide range of well-established GWAS methods can potentially be harnessed to understand the etiology of irAEs specifically and immunotherapy outcomes broadly. This review summarizes current findings regarding germline effects on immunotherapy outcomes and discusses opportunities and challenges for leveraging germline genetics to understand, predict, and treat irAEs.

A Pilot Genome-Wide Association Study Identifies Novel Markers of Metabolic Syndrome in Patients with Psoriasis

BACKGROUND

Recent studies have reported that psoriasis is associated with the development of metabolic syndrome. Genome-wide association studies have been used to discover gene variant markers that occur frequently in case group in relation to specific diseases.

OBJECTIVE

The aim of the present study was to investigate the variants of specific genes involved in metabolic syndrome associated with psoriasis.

METHODS

A total of 95 psoriasis patients were recruited and divided into two groups: one with metabolic syndrome (38 patients) and the other without (57 patients). After genotyping, imputation, and quality checking, the association between the several single nucleotide polymorphisms and metabolic syndrome in psoriasis was tested, followed by gene set enrichment analysis.

RESULTS

We found 76 gene polymorphisms that conferred an increased risk for metabolic syndrome in patients with psoriasis. Four single nucleotide polymorphisms (rs17154774 of FRMD4A, rs77498336 of GPR116, rs75949580 and rs187682251 of MAPK4) showed the strongest association between metabolic syndrome and psoriasis. The epidermal growth factor receptor protein was located at the center of the protein interactions for the gene polymorphisms.

CONCLUSION

This study identified several previously unknown polymorphisms associated with metabolic syndrome in psoriasis. These results highlight the potential for future genetic studies to elucidate the development, and ultimately prevent the onset, of metabolic syndrome in patients with psoriasis.

An Overview of Mapping Quantitative Trait Loci in Peanut (Arachis hypogaea L.)

Quantitative Trait Loci (QTL) mapping has been thoroughly used in peanut genetics and breeding in spite of the narrow genetic diversity and the segmental tetraploid nature of the cultivated species. QTL mapping is helpful for identifying the genomic regions that contribute to traits, for estimating the extent of variation and the genetic action (i.e., additive, dominant, or epistatic) underlying this variation, and for pinpointing genetic correlations between traits. The aim of this paper is to review the recently published studies on QTL mapping with a particular emphasis on mapping populations used as well as traits related to kernel quality. We found that several populations have been used for QTL mapping including interspecific populations developed from crosses between synthetic tetraploids and elite varieties. Those populations allowed the broadening of the genetic base of cultivated peanut and helped with the mapping of QTL and identifying beneficial wild alleles for economically important traits. Furthermore, only a few studies reported QTL related to kernel quality. The main quality traits for which QTL have been mapped include oil and protein content as well as fatty acid compositions. QTL for other agronomic traits have also been reported. Among the 1261 QTL reported in this review, and extracted from the most relevant studies on QTL mapping in peanut, 413 (~33%) were related to kernel quality showing the importance of quality in peanut genetics and breeding. Exploiting the QTL information could accelerate breeding to develop highly nutritious superior cultivars in the face of climate change.

Meta-Analysis of Rose Rosette Disease-Resistant Quantitative Trait Loci and a Search for Candidate Genes

Rose rosette disease (RRD), caused by the rose rosette emaravirus (RRV), is a major viral disease in roses (Rosa sp.) that threatens the rose industry. Recent studies have revealed quantitative trait loci (QTL) for reduced susceptibility to RRD in the linkage groups (LGs) 1, 5, 6, and 7 in tetraploid populations and the LGs 1, 3, 5, and 6 in diploid populations. In this study, we seek to better localize and understand the relationship between QTL identified in both diploid and tetraploid populations. We do so by remapping the populations found in these studies and performing a meta-analysis. This analysis reveals that the peaks and intervals for QTL using diploid and tetraploid populations co-localized on LG 1, suggesting that these are the same QTL. The same was seen on LG 3. Three meta-QTL were identified on LG 5, and two were discovered on LG 6. The meta-QTL on LG 1, MetaRRD1.1, had a confidence interval (CI) of 10.53 cM. On LG 3, MetaRRD3.1 had a CI of 5.94 cM. MetaRRD5.1 had a CI of 17.37 cM, MetaRRD5.2 had a CI of 4.33 cM, and MetaRRD5.3 had a CI of 21.95 cM. For LG 6, MetaRRD6.1 and MetaRRD6.2 had CIs of 9.81 and 8.81 cM, respectively. The analysis also led to the identification of potential disease resistance genes, with a primary interest in genes localized in meta-QTL intervals on LG 5 as this LG was found to explain the greatest proportion of phenotypic variance for RRD resistance. The results from this study may be used in the design of more robust marker-based selection tools to track and use a given QTL in a plant breeding context.

Collagen Gene Polymorphisms Previously Associated with Resistance to Soft-Tissue Injury Are More Common in Competitive Runners Than Nonathletes

ABSTRACT

Dines, HR, Nixon, J, Lockey, SJ, Herbert, AJ, Kipps, C, Pedlar, CR, Day, SH, Heffernan, SM, Antrobus, MR, Brazier, J, Erskine, RM, Stebbings, GK, Hall, ECR, and Williams, AG. Collagen gene polymorphisms previously associated with resistance to soft-tissue injury are more common in competitive runners than nonathletes. J Strength Cond Res 37(4): 799-805, 2023-Single-nucleotide polymorphisms (SNPs) of collagen genes have been associated with soft-tissue injury and running performance. However, their combined contribution to running performance is unknown. We investigated the association of 2 collagen gene SNPs with athlete status and performance in 1,429 Caucasian subjects, including 597 competitive runners (354 men and 243 women) and 832 nonathletes (490 men and 342 women). Genotyping for COL1A1 rs1800012 (C > A) and COL5A1 rs12722 (C > T) SNPs was performed by a real-time polymerase chain reaction. The numbers of "injury-resistant" alleles from each SNP, based on previous literature (rs1800012 A allele and rs12722 C allele), were combined as an injury-resistance score (RScore, 0-4; higher scores indicate injury resistance). Genotype frequencies, individually and combined as an RScore, were compared between cohorts and investigated for associations with performance using official race times. Runners had 1.34 times greater odds of being rs12722 CC homozygotes than nonathletes (19.7% vs. 15.5%, p = 0.020) with no difference in the rs1800012 genotype distribution ( p = 0.659). Fewer runners had an RScore 0 of (18.5% vs. 24.7%) and more had an RScore of 4 (0.6% vs. 0.3%) than nonathletes ( p < 0.001). Competitive performance was not associated with the COL1A1 genotype ( p = 0.933), COL5A1 genotype ( p = 0.613), or RScore ( p = 0.477). Although not associated directly with running performance among competitive runners, a higher combined frequency of injury-resistant COL1A1 rs1800012 A and COL5A1 rs12722 C alleles in competitive runners than nonathletes suggests these SNPs may be advantageous through a mechanism that supports, but does not directly enhance, running performance.

Genetic polymorphisms in immune- and inflammation-associated genes and their association with bovine mastitis resistance/susceptibility

Bovine mastitis, the inflammation of the mammary gland, is a contagious disease characterized by chemical and physical changes in milk and pathological changes in udder tissues. Depressed immunity and higher expression of inflammatory cytokines with an elevated milk somatic cell count can be observed during mastitis in dairy cattle. The use of somatic cell count (SCC) and somatic cell score (SCS) as correlated traits in the indirect selection of animals against mastitis resistance is in progress globally. Traditional breeding for mastitis resistance seems difficult because of the low heritability (0.10-0.16) of SCC/SCS and clinical mastitis. Thus, genetic-marker-selective breeding to improve host genetics has attracted considerable attention worldwide. Moreover, genomic selection has been found to be an effective and fast method of screening for dairy cattle that are genetically resistant and susceptible to mastitis at a very early age. The current review discusses and summarizes the candidate gene approach using polymorphisms in immune- and inflammation-linked genes (CD4, CD14, CD46, TRAPPC9, JAK2, Tf, Lf, TLRs, CXCL8, CXCR1, CXCR2, C4A, C5, MASP2, MBL1, MBL2, LBP, NCF1, NCF4, MASP2, A2M, and CLU, etc.) and their related signaling pathways (Staphylococcus aureus infection signaling, Toll-like receptor signaling, NF-kappa B signaling pathway, Cytokine-cytokine receptor, and Complement and coagulation cascades, etc.) associated with mastitis resistance and susceptibility phenotypic traits (IL-6, interferon-gamma (IFN-γ), IL17, IL8, SCS, and SCC) in dairy cattle.

Genome-wide association studies uncover genes associated with litter traits in the pig

Litter traits are critical economic variables in the pig industry as they represent a production indicator that can serve to determine sow fertility. In this study, a genome-wide association study on litter traits, including total number born (TNB), number born alive (NBA), litter birth weight (LBW), average birth weight (ABW), and piglet uniformity (PU), was carried out on two pig breeds (Yorkshire and Landrace). A total of 3 637 pigs of both breeds were genotyped using the GeneSeek GGP Porcine 50K SNP BeadChip. A mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) were employed in the genome-wide association studies for litter traits using combined data from the two pig breeds and data from each breed separately. Additionally, the heritability of traits was estimated using three methods-pedigree-based best linear unbiased prediction (PBLUP), genomic best linear unbiased prediction (GBLUP), and single-step best linear unbiased prediction (ssGBLUP)-and was found to lie between 0.065 and 0.1289, 0.0478 and 0.0938, 0.0793 and 0.0935, 0.1862 and 0.2163, and 0.0327 and 0.0419 for TNB, NBA, LBW, ABW, and PU, respectively. We also compared the genomic prediction accuracies and unbiasedness for litter traits of the three BLUP models. Our results indicated that the ssGBLUP method provided higher predictive accuracies and more rational unbiasedness compared with the PBLUP and GBLUP methodologies. Furthermore, based on their possible roles, eight candidate genes (INHBA, LEPR, HDHD2, CTNND2, RNF216, HMX1, PAPPA2, and NTN1) were identified as being linked with litter traits. In the middle of the test, these genes were found to be connected with pig metabolism and ovulation rate. Our results provide the insights into the genetic architecture of litter traits in pigs, and the potential single nucleotide polymorphisms (SNPs) and candidate genes identified may benefit economic profits in pig-breeding industry and contribute to improve litter traits.

Risky Early Family Environment and Genetic Associations with Adult Metabolic Dysregulation

Growing up in a family environment characterized by neglectful parenting, overt conflict, and unsupportive relationships is associated with poor health in adulthood. A risky early family environment may also be associated with obesity in adulthood, likely through the activation of the HPA axis. Likewise, the GABAergic (gamma-aminobutyric acid) T>C single nucleotide polymorphism in the 1519 nucleotide position of the GABAAα6 receptor subunit gene has been associated with a predisposition to a higher body mass index and a larger waist circumference. Participants (n = 213, Mage = 30.13 years, SD = 10.85; 57.7% men) from the Pittsburgh Cold Study 3 completed a demographic questionnaire, the Risky Families Questionnaire (RFQ) and had their height, weight, and waist circumference measured during a physical exam. Participant DNA was recovered from buccal swabs and genotyped for the various allelic types of the SNP according to published protocols. In secondary data analyses, we tested the hypothesis that early family environment and GABRA6 would be positively associated with body mass index and waist circumference. We also examined diurnal cortisol as a mechanism linking both early risky family environment and GABRA6 to metabolic outcomes. The findings provide evidence that a risky early family environment may exert more influence than genetic predisposition when determining the indices of metabolic health in adulthood.

Immuno-Modulatory Effects of Intervertebral Disc Cells

Low back pain is a highly prevalent, chronic, and costly medical condition predominantly triggered by intervertebral disc degeneration (IDD). IDD is often caused by structural and biochemical changes in intervertebral discs (IVD) that prompt a pathologic shift from an anabolic to catabolic state, affecting extracellular matrix (ECM) production, enzyme generation, cytokine and chemokine production, neurotrophic and angiogenic factor production. The IVD is an immune-privileged organ. However, during degeneration immune cells and inflammatory factors can infiltrate through defects in the cartilage endplate and annulus fibrosus fissures, further accelerating the catabolic environment. Remarkably, though, catabolic ECM disruption also occurs in the absence of immune cell infiltration, largely due to native disc cell production of catabolic enzymes and cytokines. An unbalanced metabolism could be induced by many different factors, including a harsh microenvironment, biomechanical cues, genetics, and infection. The complex, multifactorial nature of IDD brings the challenge of identifying key factors which initiate the degenerative cascade, eventually leading to back pain. These factors are often investigated through methods including animal models, 3D cell culture, bioreactors, and computational models. However, the crosstalk between the IVD, immune system, and shifted metabolism is frequently misconstrued, often with the assumption that the presence of cytokines and chemokines is synonymous to inflammation or an immune response, which is not true for the intact disc. Therefore, this review will tackle immunomodulatory and IVD cell roles in IDD, clarifying the differences between cellular involvements and implications for therapeutic development and assessing models used to explore inflammatory or catabolic IVD environments.

Articulating target-mining techniques to disinter Alzheimer's specific targets for drug repurposing

BACKGROUND AND OBJECTIVES

Alzheimer's Disease (AD), an extremely progressive neurodegenerative disorder is an amalgamation of numerous intricate pathological networks. This century old disease is still an unmet medical condition owing to the modest efficacy of existing therapeutic agents in antagonizing the multi-targeted pathological pathways underlying AD. Given the paucity in AD specific drugs, fabricating comprehensive research strategies to envision disease specific targets to channelize and expedite drug discovery are mandated. However, the dwindling approval rates and stringent regulatory constraints concerning the approval of a new chemical entity is daunting the pharmaceutical industries from effectuating de novo research. To bridge the existing gaps in AD drug research, a promising contemporary way out could be drug repurposing. This drug repurposing investigation is intended to envisage AD specific targets and create drug libraries pertinent to the shortlisted targets via a series of avant-garde bioinformatics and computational strategies.

METHODS

Transcriptomic analysis of three AD specific datasets viz., GSE122063, GSE15222 and GSE5281 revealed significant Differentially Expressed Genes (DEGs) and subsequent Protein-Protein Interactions (PPI) network analysis captured crucial AD targets. Later, homology model was constructed through I-TASSER for a shortlisted target protein which lacked X-ray crystallographic structure and the built protein model was validated by molecular dynamic simulations. Further, drug library was created for the shortlisted target based on structural and side effect similarity with respective standard drugs. Finally, molecular docking, binding energy calculations and molecular dynamics studies were carried out to unravel the interactions exhibited by drugs from the created library with amino acids in active binding pocket of RGS4.

RESULTS

SST and RGS4 were shortlisted as potentially significant AD specific targets, however, the less explored target RGS4 was considered for further sequential analysis. Homology model constructed for RGS4 displayed best quality when validated through Ramachandran plot and ERRAT plot. Subsequent docking and molecular dynamics studies showcased substantial affinity demonstrated by three drugs viz., Ziprasidone, Melfoquine and Metaxalone from the created drug libraries, towards RGS4.

CONCLUSION

This virtual analysis forecasted the repurposable potential of Ziprasidone, Melfoquine and Metaxalone against AD based on their affinity towards RGS4, a key AD-specific target.

COL1A1 and FGFR2 Single-Nucleotide Polymorphisms Found in Class II and Class III Skeletal Malocclusions in Javanese Population

OBJECTIVE

 The aim of this article is to analyze and compare the presence of single-nucleotide polymorphisms (SNPs) of COL1A1 and FGFR2 in class II and class III Javanese populations.

MATERIALS AND METHODS

 Cephalometric radiographs from total 63 patients of class II and III were analyzed. SNP analysis was performed based on both COL1A1 and FGFR2 sequences amplified from total DNA of patients' fresh blood. Principal component analysis was done to calculate the data and find the correlation of the cephalometric indicators influenced by each mutation. t-test and Mann-Whitney analysis were performed to check the significance of differences occurred in each studied parameter (p < 0.05).

RESULT

 There were three COL1A1 SNPs found in class II and only two in class III, while three FGFR2 SNPs found in both classes. Class II phenotype seemed to be strongly influenced by Y-axis and mandibular plane angle, while class III by lower gonial angle and mandibular plane angle.

CONCLUSION

 Based on this study, we suggest that rs2249492 of COL1A1 and rs2981582 of FGFR2 play important roles in class III, while rs2277632 of COL1A1 and rs2981582 of FGFR2 play important role in class II skeletal malocclusion in Javanese population.

The Effects of Common Genetic Variation in 96 Genes Involved in Thyroid Hormone Regulation on TSH and FT4 Concentrations

OBJECTIVE

While most of the variation in thyroid function is determined by genetic factors, single nucleotide polymorphisms (SNPs) identified via genome-wide association analyses have only explained ~5% to 9% of this variance so far. Most SNPs were in or nearby genes with no known role in thyroid hormone (TH) regulation. Therefore, we performed a large-scale candidate gene study investigating the effect of common genetic variation in established TH regulating genes on serum thyrotropin [thyroid-stimulating hormone (TSH)] and thyroxine (FT4) concentrations.

METHODS

SNPs in or within 10 kb of 96 TH regulating genes were included (30 031 TSH SNPs, and 29 962 FT4 SNPs). Associations were studied in 54 288 individuals from the ThyroidOmics Consortium. Linkage disequilibrium-based clumping was used to identify independently associated SNPs. SNP-based explained variances were calculated using SumHer software.

RESULTS

We identified 23 novel TSH-associated SNPs in predominantly hypothalamic-pituitary-thyroid axis genes and 25 novel FT4-associated SNPs in mainly peripheral metabolism and transport genes. Genome-wide SNP variation explained ~21% (SD 1.7) of the total variation in both TSH and FT4 concentrations, whereas SNPs in the 96 TH regulating genes explained 1.9% to 2.6% (SD 0.4).

CONCLUSION

Here we report the largest candidate gene analysis on thyroid function, resulting in a substantial increase in the number of genetic variants determining TSH and FT4 concentrations. Interestingly, these candidate gene SNPs explain only a minor part of the variation in TSH and FT4 concentrations, which substantiates the need for large genetic studies including common and rare variants to unravel novel, yet unknown, pathways in TH regulation.

Genome-wide association study of three litter traits in Yorkshire pigs

Litter traits are important in the development of production and economic profits of pigs. The object of this study was to perform genome-wide association studies (GWASs) for the total number born (TNB), number born alive (NBA) and number born healthy (NBH) traits in a Yorkshire population. By mixed model, we conducted GWASs with gcta software, and in total identified 3, 7 and 12 significant SNPs associated with TNB, NBA and NBH respectively: one on Sus scrofa chromosome (SSC) 2 (p = 7.03 × 10^-5 ), one on SSC4 (p = 8.53 × 10^-5 ), two on SSC 6 (p = 9.27 × 10^-5 to 9.64 × 10^-5 ), nine on SSC 8 (p = 1.53 × 10^-5 to 8.52 × 10^-5 ), four on SSC 14 (p = 2.17 × 10^-5 to 6.79 × 10^-5 ), and one on SSC 15 (p = 9.30 × 10^-5 ). We screened for flanking regions ±1 Mb nearby or within the significant SNPs, and found 203 candidate genes, including 53 for TNB, 71 for NBA and 112 for NBH. In conclusion, our findings show the significant SNPs with candidate genes for the TNB, NBA and NBH traits in Yorkshire pigs.

Identification of Candidate Genes Regulating Carcass Depth and Hind Leg Circumference in Simmental Beef Cattle Using Illumina Bovine Beadchip and Next-Generation Sequencing Analyses

Genome-wide association studies are a robust means of identifying candidate genes that regulate economically important traits in farm animals. The aim of this study is to identify single-nucleotide polymorphisms (SNPs) and candidate genes potentially related to carcass depth and hind leg circumference in Simmental beef cattle. We performed Illumina Bovine HD Beadchip (~670 k SNPs) and next-generation sequencing (~12 million imputed SNPs) analyses of data from 1252 beef cattle, to which we applied a linear mixed model. Using a statistical threshold (p = 0.05/number of SNPs identified) and adopting a false discovery rate (FDR), we identified many putative SNPs on different bovine chromosomes. We identified 12 candidate genes potentially annotated with the markers identified, including CDKAL1 and E2F3, related to myogenesis and skeletal muscle development. The identification of such genes in Simmental beef cattle will help breeders to understand and improve related traits, such as meat yield.

Construction of a Quantitative Genomic Map, Identification and Expression Analysis of Candidate Genes for Agronomic and Disease-Related Traits in Brassica napus

Rapeseed is the second most important oil crop in the world. Improving seed yield and seed oil content are the two main highlights of the research. Unfortunately, rapeseed development is frequently affected by different diseases. Extensive research has been made through many years to develop elite cultivars with high oil, high yield, and/or disease resistance. Quantitative trait locus (QTL) analysis has been one of the most important strategies in the genetic deciphering of agronomic characteristics. To comprehend the distribution of these QTLs and to uncover the key regions that could simultaneously control multiple traits, 4,555 QTLs that have been identified during the last 25 years were aligned in one unique map, and a quantitative genomic map which involved 128 traits from 79 populations developed in 12 countries was constructed. The present study revealed 517 regions of overlapping QTLs which harbored 2,744 candidate genes and might affect multiple traits, simultaneously. They could be selected to customize super-rapeseed cultivars. The gene ontology and the interaction network of those candidates revealed genes that highly interacted with the other genes and might have a strong influence on them. The expression and structure of these candidate genes were compared in eight rapeseed accessions and revealed genes of similar structures which were expressed differently. The present study enriches our knowledge of rapeseed genome characteristics and diversity, and it also provided indications for rapeseed molecular breeding improvement in the future.

Genomic and neuroimaging approaches to bipolar disorder

BACKGROUND

To date, besides genome-wide association studies, a variety of other genetic analyses (e.g. polygenic risk scores, whole-exome sequencing and whole-genome sequencing) have been conducted, and a large amount of data has been gathered for investigating the involvement of common, rare and very rare types of DNA sequence variants in bipolar disorder. Also, non-invasive neuroimaging methods can be used to quantify changes in brain structure and function in patients with bipolar disorder.

AIMS

To provide a comprehensive assessment of genetic findings associated with bipolar disorder, based on the evaluation of different genomic approaches and neuroimaging studies.

METHOD

We conducted a PubMed search of all relevant literatures from the beginning to the present, by querying related search strings.

RESULTS

ANK3, CACNA1C, SYNE1, ODZ4 and TRANK1 are five genes that have been replicated as key gene candidates in bipolar disorder pathophysiology, through the investigated studies. The percentage of phenotypic variance explained by the identified variants is small (approximately 4.7%). Bipolar disorder polygenic risk scores are associated with other psychiatric phenotypes. The ENIGMA-BD studies show a replicable pattern of lower cortical thickness, altered white matter integrity and smaller subcortical volumes in bipolar disorder.

CONCLUSIONS

The low amount of explained phenotypic variance highlights the need for further large-scale investigations, especially among non-European populations, to achieve a more complete understanding of the genetic architecture of bipolar disorder and the missing heritability. Combining neuroimaging data with genetic data in large-scale studies might help researchers acquire a better knowledge of the engaged brain regions in bipolar disorder.

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy

Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care.

Elucidation of Cellular Contributions to Heparin-Induced Thrombocytopenia Using Omic Approaches

Heparin-induced thrombocytopenia (HIT) is an unpredictable, complex, immune-mediated adverse drug reaction associated with a high mortality. Despite decades of research into HIT, fundamental knowledge gaps persist regarding HIT likely due to the complex and unusual nature of the HIT immune response. Such knowledge gaps include the identity of a HIT immunogen, the intrinsic roles of various cell types and their interactions, and the molecular basis that distinguishes pathogenic and non-pathogenic PF4/heparin antibodies. While a key feature of HIT, thrombocytopenia, implicates platelets as a seminal cell fragment in HIT pathogenesis, strong evidence exists for critical roles of multiple cell types. The rise in omic technologies over the last decade has resulted in a number of agnostic, whole system approaches for biological research that may be especially informative for complex phenotypes. Applying multi-omics techniques to HIT has the potential to bring new insights into HIT pathophysiology and identify biomarkers with clinical utility. In this review, we review the clinical, immunological, and molecular features of HIT with emphasis on key cell types and their roles. We then address the applicability of several omic techniques underutilized in HIT, which have the potential to fill knowledge gaps related to HIT biology.

Sex hormone-related polymorphisms in endometriosis and migraine: A narrative review

Some evidence indicates endometriosis and migraine have a common genetic predisposition in sex-hormone genes, which could have important implications for the treatment of these two heterogenous conditions. To date, the genes responsibility remains unknown. Based on the biological hypothesis that polymorphisms of genes involved in sex-hormone pathways may influence estrogen levels and phenotypes of both disorders, we did a literature search for candidate sex-hormone genes and genes involved in the metabolism of estradiol. The aim was to review the evidence for shared sex-hormone-related polymorphisms between endometriosis and migraine and provide an exhaustive overview of the current literature. We included case-control studies investigating associations between candidate sex-hormone-related genes and the disorders endometriosis and migraine, respectively. Results showed three overlapping sex-hormone-associated polymorphisms in estrogen receptor genes that are associated with both conditions. To confirm possible associations with other sex-hormone genes, larger studies are needed.

Single Nucleotide Polymorphisms in Close Proximity to the Fibroblast Growth Factor 21 (FGF21) Gene Found to Be Associated with Sugar Intake in a Swedish Population

Hereditary mechanisms are partially responsible for individual differences in sensitivity to and the preference for sweet taste. The primary aim of this study was to examine the associations between 10 genetic variants and the intake of total sugar, added sugar, and sugars with sweet taste (i.e., monosaccharides and sucrose) in a middle-aged Swedish population. Two single nucleotide polymorphisms (SNPs) within the Fibroblast grow factor 21 (FGF21) gene, seven top hits from a genome-wide association study (GWAS) on total sugar intake, and one SNP within the fat mass and obesity associated (FTO) gene (the only SNP reaching GWAS significance in a previous study), were explored in relation to various forms of sugar intake in 22,794 individuals from the Malmö Diet and Cancer Study, a population-based cohort for which data were collected between 1991-1996. Significant associations (p = 6.82 × 10^-7 - 1.53 × 10^-3) were observed between three SNPs (rs838145, rs838133, and rs8103840) in close relation to the FGF21 gene with high Linkage Disequilibrium, and all the studied sugar intakes. For the rs11642841 within the FTO gene, associations were found exclusively among participants with a body mass index ≥ 25 (p < 5 × 10^-3). None of the remaining SNPs studied were associated with sugar intake in our cohort. A further GWAS should be conducted to identify novel genetic variants associated with the intake of sugar.

The genetic association with injury risk in male academy soccer players depends on maturity status

It is currently unknown if injury risk is associated with genetic variation in academy soccer players (ASP). We investigated whether nine candidate single nucleotide polymorphisms were associated (individually and in combination) with injury in ASP at different stages of maturation. Saliva samples and one season's injury records were collected from 402 Caucasian male ASP from England, Spain, Uruguay, and Brazil, whose maturity status was defined as pre- or post-peak height velocity (PHV). Pre-PHV COL5A1 rs12722 CC homozygotes had relatively higher prevalence of any musculoskeletal soft tissue (22.4% vs. 3.0%, p = 0.018) and ligament (18.8% vs. 11.8%, p = 0.029) injury than T-allele carriers, while VEGFA rs2010963 CC homozygotes had greater risk of ligament/tendon injury than G-allele carriers. Post-PHV IL6 rs1800795 CC homozygotes had a relatively higher prevalence of any (67.6% vs. 40.6%, p = 0.003) and muscle (38.2% vs. 19.2%, p = 0.013) injuries than G-allele carriers. Relatively more post-PHV EMILIN1 rs2289360 CC homozygotes suffered any injury than CT and TT genotypes (56.4% vs. 40.3% and 32.8%, p = 0.007), while the "protective" EMILIN1 TT genotype was more frequent in post- than pre-PHV ASP (22.3 vs. 10.0%, p = 0.008). Regardless of maturity status, T-alleles of ACTN3 rs1815739 and EMILIN1 rs2289360 were associated with greater absence following ankle injury, while the MMP3 rs679620 T-allele and MYLK rs28497577 GT genotype were associated with greater absence following knee injury. The combination of injury-associated genotypes was greater in injured vs. non-injured ASP. This study is the first to demonstrate that a genetic association exists with injury prevalence in ASP, which differs according to maturity status.

Cortical thickness is differently associated with ALDH2 rs671 polymorphism according to level of amyloid deposition

Accumulating evidence indicates that amyloid-beta (Aβ) deposition and biogenic aldehyde accumulation contribute to the pathogenesis of neurodegenerative diseases. Human aldehyde dehydrogenase 2 (ALDH2) metabolizes biogenic aldehydes produced in the brain to prevent damage. However, r671G>A, a single nucleotide polymorphism of ALDH2, causes aldehyde accumulation and decreased ALDH2 activity. We aimed to investigate whether Aβ deposition and rs671 polymorphism have an interaction effect on cortical thickness (CTh). We grouped 179 participants in the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer's disease Study as follows: amyloid (–) [A(–)] and amyloid (+) [A(+)] groups based on the Aβ deposition degree; A-carrier (AC) and GG (GG) groups based on the presence/absence of the rs671 A allele; and their combinations, i.e., A(–)AC, A(–)GG, A(+)AC, and A(+)GG groups. A multiple regression analysis identified nine regions of interest. Compared with the A(–)GG group, the A(–)AC group showed thinner CTh in all regions. There were no significant differences between the A(+)AC and A(+)GG groups. We observed an interaction effect of amyloid deposition and rs671 polymorphism on CTh. The CTh in the A(–) group appeared to be strongly influenced by rs671 polymorphism, which could have contributed to cortical thinning and biogenic aldehyde accumulation in the AC group. Additionally, CTh in the A(+) group appeared to be strongly influenced by amyloid deposition.

Meta-analysis: Congruence of genomic and phenotypic differentiation across diverse natural study systems

Linking genotype to phenotype is a primary goal for understanding the genomic underpinnings of evolution. However, little work has explored whether patterns of linked genomic and phenotypic differentiation are congruent across natural study systems and traits. Here, we investigate such patterns with a meta-analysis of studies examining population-level differentiation at subsets of loci and traits putatively responding to divergent selection. We show that across the 31 studies (88 natural population-level comparisons) we examined, there was a moderate (R 2 = 0.39) relationship between genomic differentiation (F ST ) and phenotypic differentiation (P ST ) for loci and traits putatively under selection. This quantitative relationship between P ST and F ST for loci under selection in diverse taxa provides broad context and cross-system predictions for genomic and phenotypic adaptation by natural selection in natural populations. This context may eventually allow for more precise ideas of what constitutes "strong" differentiation, predictions about the effect size of loci, comparisons of taxa evolving in nonparallel ways, and more. On the other hand, links between P ST and F ST within studies were very weak, suggesting that much work remains in linking genomic differentiation to phenotypic differentiation at specific phenotypes. We suggest that linking genotypes to specific phenotypes can be improved by correlating genomic and phenotypic differentiation across a spectrum of diverging populations within a taxon and including wide coverage of both genomes and phenomes.

Understanding the fundamentals of microbial remediation with emphasize on metabolomics

The post-genomic tool metabolomics is a great advancement in science and technology which acquires novel strategies and pathways to analyze various biological compounds. Metabolomics aids in retrieving the qualitative and quantitative data from the various biological system. The current review is focused on the application of metabolomics in bioremediation and helps to focus on the xenobiotic compounds which are discharged into the environment and have long term impact. The microbial based biodegradation can be effectively used along with the combination of metabolomic approach for a better understanding of the breakdown of certain recalcitrant. Additionally, this review also discusses the candidate gene approach which helps to comprehend the functional analysis of microbial genes in response to different contaminants. Therefore, this review intends to discuss the metabolomics in bioremediation by studying the complete set of metabolites involved during the process of degradation and their interaction with the environment.

Identification of a Novel Genetic Marker for Risk of Degenerative Rotator Cuff Disease Surgery in the UK Biobank

BACKGROUND

While evidence indicates that familial predisposition influences the risk of developing degenerative rotator cuff disease (RCD), knowledge of specific genetic markers is limited. We conducted a genome-wide association study of RCD surgery using the UK Biobank, a prospective cohort of 500,000 people (40 to 69 years of age at enrollment) with genotype data.

METHODS

Cases with surgery for degenerative RCD were identified using linked hospital records. The cases were defined as an International Classification of Diseases, Tenth Revision (ICD-10) code of M75.1 determined by a trauma/orthopaedic specialist and surgery consistent with RCD treatment. Cases were excluded if a diagnosis of traumatic injury had been made during the same hospital visit. For each case, up to 5 controls matched by age, sex, and follow-up time were chosen from the UK Biobank. Analyses were limited to European-ancestry individuals who were not third-degree or closer relations. We used logistic regression to test for genetic association of 674,405 typed and >10 million imputed markers, after adjusting for age, sex, population principal components, and follow-up.

RESULTS

We identified 2,917 RCD surgery cases and 14,158 matched controls. We observed 1 genome-wide significant signal (p < 5 × 10-8) for a novel locus tagged by rs2237352 in the CREB5 gene on chromosome 7 (odds ratio [OR] = 1.17, 95% confidence interval [CI] = 1.11 to 1.24). The single-nucleotide polymorphism (SNP) rs2237352 was imputed with a high degree of confidence (info score = 0.9847) and is common, with a minor allele frequency of 47%. After expanding the control sample to include additional unmatched non-cases, rs2237352 and another SNP in the CREB5 gene, rs12700903, were genome-wide significant. We did not detect genome-wide significant signals at loci associated with RCD in previous studies.

CONCLUSIONS

We identified a novel association between a variant in the CREB5 gene and RCD surgery. Validation of this finding in studies with imaging data to confirm diagnoses will be an important next step.

CLINICAL RELEVANCE

Identification of genetic RCD susceptibility markers can guide understanding of biological processes in rotator cuff degeneration and help inform disease risk in the clinical setting.

LEVEL OF EVIDENCE

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Sources of Individual Differences in Pain

Pain is an immense clinical and societal challenge, and the key to understanding and treating it is variability. Robust interindividual differences are consistently observed in pain sensitivity, susceptibility to developing painful disorders, and response to analgesic manipulations. This review examines the causes of this variability, including both organismic and environmental sources. Chronic pain development is a textbook example of a gene-environment interaction, requiring both chance initiating events (e.g., trauma, infection) and more immutable risk factors. The focus is on genetic factors, since twin studies have determined that a plurality of the variance likely derives from inherited genetic variants, but sex, age, ethnicity, personality variables, and environmental factors are also considered.

Genetic information improves the prediction of major adverse cardiovascular events in the GENEMACOR population

The inclusion of a genetic risk score (GRS) can modify the risk prediction of coronary artery disease (CAD), providing an advantage over the use of traditional models. The predictive value of the genetic information on the recurrence of major adverse cardiovascular events (MACE) remains controversial. A total of 33 genetic variants previously associated with CAD were genotyped in 1587 CAD patients from the GENEMACOR study. Of these, 18 variants presented an hazard ratio >1, so they were selected to construct a weighted GRS (wGRS). MACE discrimination and reclassification were evaluated by C-Statistic, Net Reclassification Index and Integrated Discrimination Improvement methodologies. After the addition of wGRS to traditional predictors, the C-index increased from 0.566 to 0.572 (p=0.0003). Subsequently, adding wGRS to traditional plus clinical risk factors, this model slightly improved from 0.620 to 0.622 but with statistical significance (p=0.004). NRI showed that 17.9% of the cohort was better reclassified when the primary model was associated with wGRS. The Kaplan-Meier estimator showed that, at 15-year follow-up, the group with a higher number of risk alleles had a significantly higher MACE occurrence (p=0.011). In CAD patients, wGRS improved MACE risk prediction, discrimination and reclassification over the conventional factors, providing better cost-effective therapeutic strategies.

Genetic Determinants of Peripheral Artery Disease

Peripheral artery disease-atherosclerosis of the abdominal aorta and lower extremity vascular bed-is a complex disease with both environmental and genetic determinants. Unmitigated disease is associated with major functional decline and can lead to chronic limb-threatening ischemia, amputation, and increased mortality. Over the last 10 years, major advances have been made in identifying the genetic basis of this common, complex disease. In this review, we provide an overview of the primary types of genetic analyses performed for peripheral artery disease, including heritability and linkage studies, and more recently biobank-based genome-wide association studies. Looking forward, we highlight areas of future study including efforts to identify causal peripheral artery disease genes, rare variant and structural variant analyses using whole-exome and whole-genome sequencing data, and the need to include individuals of diverse genetic ancestries.

Mutations and polymorphisms in genes involved in the infections by covid 19: a review

Covid19 is the third most aggressive coronavirus that spreads rapidly and kills many people. It is a multigenic and multifactorial disease with many genetic and environmental determinants. The identification of these factors is key to better understanding the etiology of Covid-19 and it can also help predict the risk and prevent Covid-19 infection. Many predisposing factors have been described for this coronavirus such as advanced age, male gender, and geographic location. In addition to these elements, genetic factors have an important role in Covid19 infection. Interindividual variation in susceptibility to infection by Covid-19 has been associated with to the presence of genetic polymorphisms in many genes, especially in those that code for proteins implicated in the infection process. The present review gives a brief overview of different genes involved in the infection by SARS-CoV-2 and its association with disease severity. The results of our research showed that many different genes are associated with a higher risk for COVID-19, notably those coding for proteins involved in coronavirus-cell entry and fusion such as ACE2 (angiotensin I converting enzyme 2), TMPRSS2 (transmembrane protease, serine 2) and CD26.

Identification of a variant in NDP associated with X-linked retinal dysplasia in the English cocker spaniel dog

Purpose

Three related male English Cocker Spaniels (ECS) were reported to be congenitally blind. Examination of one of these revealed complete retinal detachment. A presumptive diagnosis of retinal dysplasia (RD) was provided and pedigree analysis was suggestive of an X-linked mode of inheritance. We sought to investigate the genetic basis of RD in this family of ECS.

Methods

Following whole genome sequencing (WGS) of the one remaining male RD-affected ECS, two distinct investigative approaches were employed: a candidate gene approach and a whole genome approach. In the candidate gene approach, COL9A2, COL9A3, NHEJ1, RS1 and NDP genes were investigated based on their known associations with RD and retinal detachment in dogs and humans. In the whole genome approach, affected WGS was compared with 814 unaffected canids to identify candidate variants, which were filtered based on appropriate segregation and predicted pathogenic effects followed by subsequent investigation of gene function. Candidate variants were tested for appropriate segregation in the ECS family and association with disease was assessed using samples from a total of 180 ECS.

Results

The same variant in NDP (c.653_654insC, p.Met114Hisfs*16) that was predicted to result in 15 aberrant amino acids before a premature stop in norrin protein, was identified independently by both approaches and was shown to segregate appropriately within the ECS family. Association of this variant with X-linked RD was significant (P = 0.0056).

Conclusions

For the first time, we report a variant associated with canine X-linked RD. NDP variants are already known to cause X-linked RD, along with other abnormalities, in human Norrie disease. Thus, the dog may serve as a useful large animal model for research.

Individual Differences in Intertemporal Choice

Intertemporal choice involves deciding between smaller, sooner and larger, later rewards. People tend to prefer smaller rewards that are available earlier to larger rewards available later, a phenomenon referred to as temporal or delay discounting. Despite its ubiquity in human and non-human animals, temporal discounting is subject to considerable individual differences. Here, we provide a critical narrative review of this literature and make suggestions for future work. We conclude that temporal discounting is associated with key socio-economic and health-related variables. Regarding personality, large-scale studies have found steeper temporal discounting to be associated with higher levels of self-reported impulsivity and extraversion; however, effect sizes are small. Temporal discounting correlates negatively with future-oriented cognitive styles and inhibitory control, again with small effect sizes. There are consistent associations between steeper temporal discounting and lower intelligence, with effect sizes exceeding those of personality or cognitive variables, although socio-demographic moderator variables may play a role. Neuroimaging evidence of brain structural and functional correlates is not yet consistent, neither with regard to areas nor directions of effects. Finally, following early candidate gene studies, recent Genome Wide Association Study (GWAS) approaches have revealed the molecular genetic architecture of temporal discounting to be more complex than initially thought. Overall, the study of individual differences in temporal discounting is a maturing field that has produced some replicable findings. Effect sizes are small-to-medium, necessitating future hypothesis-driven work that prioritizes large samples with adequate power calculations. More research is also needed regarding the neural origins of individual differences in temporal discounting as well as the mediating neural mechanisms of associations of temporal discounting with personality and cognitive variables.

GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants in an individual’s sample in order to find if any of these variants are linked to a particular trait. In the last two decades, GWAS have contributed to several new discoveries in the field of genetics. This research presents a novel methodology to which GWAS can be applied to. It is mainly based on two machine learning methodologies, genetic algorithms and support vector machines. The database employed for the study consisted of information about 370,750 single-nucleotide polymorphisms belonging to 1076 cases of colorectal cancer and 973 controls. Ten pathways with different degrees of relationship with the trait under study were tested. The results obtained showed how the proposed methodology is able to detect relevant pathways for a certain trait: in this case, colorectal cancer.

Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Rabbits

Simple Summary

Genotyping costs are still the major limitation for the uptake of genomic selection by the rabbit meat industry, as a large number of genetic markers are needed for improving the prediction of breeding values by genomic data. In this study, several genotyping strategies were examined through simulation scenarios to disentangle the best feasible options of implementing genomic selection in rabbit breeding programs. Most scenarios emphasized the genotyping of candidate animals with a low Single Nucleotide Polymorphism (SNP) density platform. Imputation accuracies were high for the scenarios with ancestors genotyped at high or medium SNP-densities. However, the scenario with male ancestors genotyped at high SNP-density and only dams genotyped at medium SNP-density showed the best economically feasible strategy, taking into account the trade-off among genotyping costs, the accuracy of breeding values and response to selection. The results confirmed that by combining the imputation technique with a mindful selection of the animals to be genotyped, it is possible to achieve better performance than Best Linear Unbiased Prediction (BLUP), reducing genotyping cost at the same time.

Abstract

Genomic selection uses genetic marker information to predict genomic breeding values (gEBVs), and can be a suitable tool for selecting low-hereditability traits such as litter size in rabbits. However, genotyping costs in rabbits are still too high to enable genomic prediction in selective breeding programs. One method for decreasing genotyping costs is the genotype imputation, where parents are genotyped at high SNP-density (HD) and the progeny are genotyped at lower SNP-density, followed by imputation to HD. The aim of this study was to disentangle the best imputation strategies with a trade-off between genotyping costs and the accuracy of breeding values for litter size. A selection process, mimicking a commercial breeding rabbit selection program for litter size, was simulated. Two different Quantitative Trait Nucleotide (QTN) models (QTN_5 and QTN_44) were generated 36 times each. From these simulations, seven different scenarios (S1–S7) and a further replicate of the third scenario (S3_A) were created. Scenarios consist of a different combination of genotyping strategies. In these scenarios, ancestors and progeny were genotyped with a mix of three different platforms, containing 200,000, 60,000, and 600 SNPs under a cost of EUR 100, 50 and 11 per animal, respectively. Imputation accuracy (IA) was measured as a Pearson’s correlation between true genotype and imputed genotype, whilst the accuracy of gEBVs was the correlation between true breeding value and the estimated one. The relationships between IA, the accuracy of gEBVs, genotyping costs, and response to selection were examined under each QTN model. QTN_44 presented better performance, according to the results of genomic prediction, but the same ranks between scenarios remained in both QTN models. The highest IA (0.99) and the accuracy of gEBVs (0.26; QTN_44, and 0.228; QTN_5) were observed in S1 where all ancestors were genotyped at HD and progeny at medium SNP-density (MD). Nevertheless, this was the most expensive scenario compared to the others in which the progenies were genotyped at low SNP-density (LD). Scenarios with low average costs presented low IA, particularly when female ancestors were genotyped at LD (S5) or non-genotyped (S7). The S3_A, imputing whole-genomes, had the lowest accuracy of gEBVs (0.09), even worse than Best Linear Unbiased Prediction (BLUP). The best trade-off between genotyping costs and the accuracy of gEBVs (0.234; QTN_44 and 0.199) was in S6, in which dams were genotyped with MD whilst grand-dams were non-genotyped. However, this relationship would depend mainly on the distribution of QTN and SNP across the genome, suggesting further studies on the characterization of the rabbit genome in the Spanish lines. In summary, genomic selection with genotype imputation is feasible in the rabbit industry, considering only genotyping strategies with suitable IA, accuracy of gEBVs, genotyping costs, and response to selection.

IL-1B(3954) polymorphism and red complex bacteria increase IL-1β (GCF) levels in periodontitis

OBJECTIVE

The aim of this study was to compare IL-1β levels in gingival crevicular fluid (GCF) from healthy and periodontitis sites of IL-1B(3954)-Single Nucleotide Polymorphism (SNP) positive and IL-1B(3954)-SNP negative periodontitis subjects in association with their bacterial profiles.

BACKGROUND

Susceptibility to periodontitis has been associated with several risk factors, including allelic variants at multiple gene loci. Variations in the IL-1 gene cluster have been linked with increased risk for periodontitis. IL-1B(3954)-SNP has been previously associated with increased levels of IL-1β in GCF or periodontal tissues in chronic periodontitis patients, as well as higher levels of specific periodontal pathogens. There is insufficient evidence to conclude if IL-1B gene polymorphisms affect the susceptibility to periodontitis by ultimately modulating the levels of IL-1β in GCF, the subgingival microbial profile or both.

MATERIALS AND METHODS

GCF, subgingival plaque, and buccal epithelial cells were collected from 32 individuals with periodontitis. GCF IL-1β levels were measured by an enzyme-linked immunosorbent assay (ELISA). Bacterial plaque samples were analyzed for 11 periodontal pathogens using polymerase chain reaction (PCR) analysis with specific primers for the 16SrRNA gene of each bacterium. IL-1B(3954)-SNP status was determined by identifying the carriers of the polymorphic T allele.

RESULTS

A significant association was shown between IL-1B(3954)-SNP and IL-1β GCF levels (amount and concentration). The concomitant presence of two or three red complex bacterial species was associated with increased IL-1β GCF levels in periodontitis sites (site-level analysis). The concurrent presence of all three red complex periodontal pathogens and IL-1B(3954)-SNP was associated with the highest IL-1β GCF levels in periodontitis sites.

CONCLUSIONS

Our results indicate an independent association of both IL-1B(3954)-SNP and red complex bacterial species with increased IL-1β levels in GCF of periodontitis sites. A better understanding of the interaction between genetics, bacteria, and inflammation is essential to develop more effective diagnostic, prognostic, and therapeutic tools for periodontitis.

Type 1 diabetes: genes associated with disease development

Type 1 diabetes (T1D) is the third most common autoimmune disease which develops due to genetic and environmental risk factors. Based on the World Health Organization (WHO) report from 2014 the number of people suffering from all types of diabetes ascended to 422 million, compared to 108 million in 1980. It was calculated that this number will double by the end of 2030. In 2015 American Diabetes Association (ADA) announced that 30.3 million Americans (that is 9.4% of the overall population) had diabetes of which only approximately 1.25 million had T1D. Nowadays, T1D represents roughly 10% of adult diabetes cases total. Multiple genetic abnormalities at different loci have been found to contribute to type 1 diabetes development. The analysis of genome-wide association studies (GWAS) of T1D has identified over 50 susceptible regions (and genes within these regions). Many of these regions are defined by single nucleotide polymorphisms (SNPs) but molecular mechanisms through which they increase or lower the risk of diabetes remain unknown. Genetic factors (in existence since birth) can be detected long before the emergence of immunological or clinical markers. Therefore, a comprehensive understanding of the multiple genetic factors underlying T1D is extremely important for further clinical trials and development of personalized medicine for diabetic patients. We present an overview of current studies and information about regions in the human genome associated with T1D. Moreover, we also put forward information about epigenetic modifications, non-coding RNAs and environmental factors involved in T1D development and onset.