Causal Single Nucleotide Polymorphisms Research Articles

The predictive value of TNF family for pulmonary tuberculosis: a pooled causal effect analysis of multiple datasets

ObjectiveDespite extensive research on the relationship between pulmonary tuberculosis (PTB) and inflammatory factors, more robust causal evidence has yet to emerge. Therefore, this study aims to screen for inflammatory proteins that may contribute to the susceptibility to PTB in different populations and to explain the diversity of inflammatory and immune mechanisms of PTB in different ethnicity.MethodsThe inverse variance weighted (IVW) model of a two-sample Mendelian Randomization (MR) study was employed to conduct causal analysis on data from a genome-wide association study (GWAS). This cohort consisting PTB GWAS datasets from two European and two East Asian populations, as well as 91 human inflammatory proteins collected from 14,824 participants. Colocalization analysis aimed to determine whether the input inflammatory protein and PTB shared the same causal single nucleotide polymorphisms (SNPs) variation within the fixed region, thereby enhancing the robustness of the MR Analysis. Meta-analyses were utilized to evaluate the combined causal effects among different datasets.ResultsIn this study, we observed a significant negative correlation between tumor necrosis factor-beta levels (The alternative we employ is Lymphotoxin-alpha, commonly referred to as LT) (P < 0.05) and tumor necrosis factor receptor superfamily member 9 levels (TNFRSF9) (P < 0.05). These two inflammatory proteins were crucial protective factors against PTB. Additionally, there was a significant positive correlation found between interleukin-20 receptor subunit alpha levels (IL20Ra) (P < 0.05), which may elevate the risk of PTB. Colocalization analysis revealed that there was no overlap in the causal variation between LT and PTB SNPs. A meta-analysis further confirmed the significant combined effect of LT, TNFRSF9, and IL20Ra in East Asian populations (P < 0.05).ConclusionsLevels of specific inflammatory proteins may play a crucial role in triggering an immune response to PTB. Altered levels of LT and TNFRSF9 have the potential to serve as predictive markers for PTB development, necessitating further clinical validation in real-world settings to ascertain the impact of these inflammatory proteins on PTB.

SNPs in microRNA seed region and impact of miR-375 in concurrent regulation of multiple lipid accumulation-related genes

Bovine intramuscular fat (IMF), commonly referred to as marbling, is regulated by lipid metabolism, which includes adipogenesis, lipogenesis, glycerolipid synthesis, and lipolysis. In recent years, breeding researchers have identified single nucleotide polymorphisms (SNPs) as useful marker-assisted selection tools for improving marbling scores in national breeding programs. These included causal SNPs that induce phenotypic variation. MicroRNAs (miRNAs) are small highly conserved non-coding RNA molecules that bind to multiple non-coding regions. They are involved in post-transcriptional regulation. Multiple miRNAs may regulate a given target. Previously, three SNPs in the GPAM 3ʹ UTR and four miRNAs were identified through in silico assays. The aim of this study is to verify the binding ability of the four miRNAs to the SNPs within the 3ʹUTR of GPAM, and to identify the regulatory function of miR-375 in the expression of genes related to lipid metabolism in mammalian adipocytes. It was verified that the four miRNAs bind to the GPAM 3ʹUTR, and identified that the miR-375 sequence is highly conserved. Furthermore, it was founded that miR-375 upregulated the GPAM gene, C/EBPα, PPARγ and lipid metabolism-related genes and promoted lipid droplet accumulation in 3T3-L1 cells. In conclusion, these results suggest that miR-375 is a multifunctional regulator of multiple lipid metabolism-related genes and may aid in obesity research as a biomarker.

Association between protein arginine N-methyltransferase 1 polymorphism and overt diabetic nephropathy: Role of asymmetric dimethylarginine in vascular tone

Backgroundω-NG,NG-asymmetric dimethylarginine (ADMA) regulates vascular tone and may participate in the pathogenesis of diabetic nephropathy (DN). ObjectiveTo investigate whether single-nucleotide polymorphisms (SNPs) around the protein arginine N-methyltransferase 1 gene (PRMT1) influence ADMA dynamics and DN incidence and severity. MethodsThis study utilized a hospital-based database containing 310 Japanese patients with type 2 diabetes mellitus (T2DM). The association of PRMT1-related tagged SNPs with DN stage distribution was examined using a dominant model of minor alleles. PRMT1 mRNA, serum ADMA, reactive hyperemia-peripheral arterial tonometry index (RHI), and brachial-ankle pulse wave velocity (baPWV) were compared between the genotype-based subgroups of causal SNP, and correlations between these variables were evaluated. ResultsThe composition of DN stages significantly differed between the GG and GA + AA subgroups of rs892151 (p = 0.026). In a propensity-matching cohort of rs892151, the GA + AA subgroup had an increased incidence of overt DN (odds ratio 2.92, 95 % confidence interval 1.12–7.62, p = 0.028), along with higher PRMT1 mRNA, serum ADMA levels, and baPWV than the GG subgroup (p < 0.001, p = 0.023 and 0.047, respectively). There were correlations between PRMT1 mRNA and serum ADMA levels, between serum ADMA levels and RHI, and between baPWV and urinary albumin excretion (r = 0.335, p < 0.001, r = -0.221, p = 0.029, and r = 0.254, p = 0.004, respectively). ConclusionsT2DM patients carrying the PRMT1-related variant rs892151 were susceptible to overt DN. ADMA-mediated endothelial dysfunction and arterial stiffness may be involved in the variant-related pathogenesis of overt DN.

Integrating leiomyoma genetics, epigenomics, and single-cell transcriptomics reveals causal genetic variants, genes, and cell types

Uterine fibroids (UF), that can disrupt normal uterine function and cause significant physical and psychological health problems, are observed in nearly 70% of women of reproductive age. Although heritable genetics is a significant risk factor, specific genetic variations and gene targets causally associated with UF are poorly understood. Here, we performed a meta-analysis on existing fibroid genome-wide association studies (GWAS) and integrated the identified risk loci and potentially causal single nucleotide polymorphisms (SNPs) with epigenomics, transcriptomics, 3D chromatin organization from diverse cell types as well as primary UF patient’s samples. This integrative analysis identifies 24 UF-associated risk loci that potentially target 394 genes, of which 168 are differentially expressed in UF tumors. Critically, integrating this data with single-cell gene expression data from UF patients reveales the causal cell types with aberrant expression of these target genes. Lastly, CRISPR-based epigenetic repression (dCas9-KRAB) or activation (dCas9-p300) in a UF disease-relevant cell type further refines and narrows down the potential gene targets. Our findings and the methodological approach indicate the effectiveness of integrating multi-omics data with locus-specific epigenetic editing approaches for identifying gene- and celt type-targets of disease-relevant risk loci.

Genetic susceptibility and causal pathway analysis of eye disorders coexisting in multiple sclerosis.

The comorbidity of optic neuritis with multiple sclerosis has been well recognized. However, the causal association between multiple sclerosis and optic neuritis, as well as other eye disorders, remains incompletely understood. To address these gaps, we investigated the genetically relationship between multiple sclerosis and eye disorders, and explored potential drugs. In order to elucidate the genetic susceptibility and causal links between multiple sclerosis and eye disorders, we performed two-sample Mendelian randomization analyses to examine the causality between multiple sclerosis and eye disorders. Additionally, causal single-nucleotide polymorphisms were annotated and searched for expression quantitative trait loci data. Pathway enrichment analysis was performed to identify the possible mechanisms responsible for the eye disorders coexisting with multiple sclerosis. Potential therapeutic chemicals were also explored using the Cytoscape. Mendelian randomization analysis revealed that multiple sclerosis increased the incidence of optic neuritis while reducing the likelihood of concurrent of cataract and macular degeneration. Gene Ontology enrichment analysis implicated that lymphocyte proliferation, activation and antigen processing as potential contributors to the pathogenesis of eye disorders coexisting with multiple sclerosis. Furthermore, pharmaceutical agents traditionally employed for allograft rejection exhibited promising therapeutic potential for the eye disorders coexisting with multiple sclerosis. Multiple sclerosis genetically contributes to the development of optic neuritis while mitigating the concurrent occurrence of cataract and macular degeneration. Further research is needed to validate these findings and explore additional mechanisms underlying the comorbidity of multiple sclerosis and eye disorders.

Mendelian randomization study highlights hypothyroidism as a causal determinant of alopecia areata.

Although observational studies have found an association between hypothyroidism and alopecia areata, the causality of this relationship remains unclear. This study aimed to investigate the genetic variants associated with hypothyroidism and their potential impact on the risk of developing alopecia areata. genome-wide association study summary statistics for hypothyroidism (30,155 cases and 379,986 controls) and alopecia areata (289 cases and 211,139 controls) were obtained from the IEU OpenGwas project. The inverse variance-weighted method was used as the primary analysis method to evaluate the causality between hypothyroidism and alopecia areata, supplemented by the weighted median, MR-Egger, simple mode and weighted mode. Furthermore, the function of causal SNPs was evaluated by gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction networks. Utilizing two-sample Mendelian randomization analysis, we found that the single-nucleotide polymorphisms (SNPs) of hypothyroidism (OR = 1.40, 95% CI: 1.12-1.75, p = 3.03×10-3) significantly increased the risk of alopecia areata ( 289 cases and 211,139 controls ). KEGG pathway analysis showed that the candidate genes were mainly enriched in virion-herpesvirus, Th1 and Th2 cell differentiation, Th17 cell differentiation, T-cell receptor signaling pathway, PD-L1/PD-1 checkpoint pathway in cancer and Toll-like receptor signaling pathway. Protein-protein interaction networks results showed that CTLA4, STAT4, IL2RA, TYK2, IRF7, SH2B3, BACH2, TLR3, NOD2, and FLT3. This study provided compelling genetic evidence supporting a causative association between hypothyroidism and alopecia areata, which could potentially inform the development of more efficacious treatment strategies for patients afflicted by alopecia areata.

The osteoporosis susceptibility SNP rs188303909 at 2q14.2 regulates EN1 expression by modulating DNA methylation and E2F6 binding.

EN1 encodes a homeodomain-containing transcription factor and is a determinant of bone density and fracture. Previous powerful genome-wide association studies (GWASs) have identified multiple single-nucleotide polymorphisms (SNPs) near EN1 at 2q14.2 locus for osteoporosis, but the causal SNPs and functional mechanisms underlying these associations are poorly understood. The target genes regulated by the transcription factor EN1 are also unclear. In this study, we identified rs188303909, a functional CpG-SNP, as a causal SNP for osteoporosis at 2q14.2 through the integration of functional and epigenomic analyses. Functional experiments demonstrated that unmethylated rs188303909 acted as a strong allele-specific distal enhancer to regulate EN1 expression by modifying the binding of transcription factor E2F6, but rs188303909 methylation attenuated the active effect of E2F6 on EN1 expression. Importantly, transcription factor EN1 could differentially bind osteoporosis GWAS lead SNPs rs4869739-T and rs4355801-G to upregulate CCDC170 and COLEC10 expression, thus promoting bone formation. Our study provided a mechanistic insight into expression regulation of the osteoporosis susceptibility gene EN1, which could be a potential therapeutic target for osteoporosis precision medicine. KEY MESSAGES: CpG-SNP rs188303909 is a causal SNP at the osteoporosis susceptibility locus 2q14.2. Rs188303909 distally regulatesEN1expression by modulating DNA methylation and E2F6 binding. EN1 upregulatesCCDC170 and COLEC10expression through osteoporosis GWAS lead SNPs rs4869739 and rs4355801.

Improving dementia phenotype prediction with functional mapping of genetic data in non‐European ancestries

AbstractBackgroundMany common genetic variants have been associated with dementia, but their ability to predict dementia prospectively is unknown, especially in non‐European ancestral populations. We sought to optimize dementia risk prediction models with machine learning in individuals of diverse genetic ancestries.MethodSamples were selected from the University of California Los Angeles Health System with their electronic health records (EHRs) and linked genetic data. Dementia phenotype was defined using diagnosis codes and mapped phecodes. Polygenic risk scores (PRSs) were built using summary statistics of different population‐based genome‐wide association studies (GWASs). Independent significant and functionally mapped single nucleotide polymorphisms (SNPs) were identified using the FUMA tool. Logistic regression models were built with 1) APOE status, 2) a single PRS, or 3) multiple PRSs. The model with age and sex only was built as the baseline model. We trained LASSO models to determine dementia risk SNPs from weighted SNP‐level genetic data. We compared the predictive performance of these models after five‐fold cross‐validation with area under precision‐recall curve (AUPRC) and area under receiver operating characteristic (AUC). Finally, identified risk SNPs were mapped to genes for biological interpretation.ResultThere were 975 individuals included in the final Hispanic Latinx American ancestry sample, of which 9.44% (N = 92) were dementia cases. The model with LASSO‐selected functional mapped SNPs from multiple neurodegenerative diseases GWASs outperformed all other genetic risk score models (AUPRC: 0.39, AUROC: 0.87), resulting in a 19.6% increase in AUPRC from the best PRS model, and a 26.9% increase from the baseline model. The best‐performing LASSO model included nine SNPs, mostly located in the 19q13, 4p15, and 17q21 regions. Results of mapped genes not only revealed well‐known risk genes of dementia but identified risk genes from other neurodegenerative diseases.ConclusionWe demonstrated that building genetic risk scores with trans‐ancestry GWAS and selecting potential causal SNPs can be beneficial for dementia prediction in non‐European ancestry populations. Further training using machine learning methods with functionally mapped SNP‐level genetic data showed significant improvement in predictive power compared to PRS models. Genetic risks of multiple neurodegenerative diseases may contribute to the predisposition to dementia.

Identification of genetic loci jointly influencing COVID-19 and coronary heart diseases

BackgroundComorbidities of coronavirus disease 2019 (COVID-19)/coronary heart disease (CHD) pose great threats to disease outcomes, yet little is known about their shared pathology. The study aimed to examine whether comorbidities of COVID-19/CHD involved shared genetic pathology, as well as to clarify the shared genetic variants predisposing risks common to COVID-19 severity and CHD risks.MethodsBy leveraging publicly available summary statistics, we assessed the genetically determined causality between COVID-19 and CHD with bidirectional Mendelian randomization. To further quantify the causality contributed by shared genetic variants, we interrogated their genetic correlation with the linkage disequilibrium score regression method. Bayesian colocalization analysis coupled with conditional/conjunctional false discovery rate analysis was applied to decipher the shared causal single nucleotide polymorphisms (SNPs).FindingsBriefly, we observed that the incident CHD risks post COVID-19 infection were partially determined by shared genetic variants. The shared genetic variants contributed to the causality at a proportion of 0.18 (95% CI 0.18–0.19) to 0.23 (95% CI 0.23–0.24). The SNP (rs10490770) located near LZTFL1 suggested direct causality (SNPs → COVID-19 → CHD), and SNPs in ABO (rs579459, rs495828), ILRUN(rs2744961), and CACFD1(rs4962153, rs3094379) may simultaneously influence COVID-19 severity and CHD risks.InterpretationFive SNPs located near LZTFL1 (rs10490770), ABO (rs579459, rs495828), ILRUN (rs2744961), and CACFD1 (rs4962153, rs3094379) may simultaneously influence their risks. The current study suggested that there may be shared mechanisms predisposing to both COVID-19 severity and CHD risks. Genetic predisposition to COVID-19 is a causal risk factor for CHD, supporting that reducing the COVID-19 infection risk or alleviating COVID-19 severity among those with specific genotypes might reduce their subsequent CHD adverse outcomes. Meanwhile, the shared genetic variants identified may be of clinical implications for identifying the target population who are more vulnerable to adverse CHD outcomes post COVID-19 and may also advance treatments of ‘Long COVID-19.’

Alternative SNP weighting for multi-step and single-step genomic BLUP in the presence of causative variants.

The accuracy of genetic selection in dairy can be increased by the adoption of new technologies, such as the inclusion of sequence data. In simulation studies, assigning different weights to causative single-nucleotide polymorphism (SNP) markers led to better predictions depending on the genomic prediction method used. However, it is still not clear how the weights should be calculated. Our objective was to evaluate the accuracy of a multi-step method (GBLUP) and single-step GBLUP with simulated data using regular SNP, causatives variants (QTN) and the combination of both. Additionally, we compared the accuracies of all previous scenarios using alternatives for SNP weighting. The data were simulated assuming a single trait with a heritability of 0.3. The effective population size (Ne) was approximately 200. The pedigree contained 440,000 animals, and approximately 16,800 individuals were genotyped. A total of 49,974 SNP markers were evenly placed throughout the genome, and 100, 1000 and 2000 causative QTN were simulated. Both GBLUP and ssGBLUP were used in this study. We evaluated quadratic and nonlinear SNP weights in addition to the unweighted G. The inclusion of QTN to panels led to significant accuracy gains. Nonlinear A was demonstrated to be superior to quadratic weighting and unweighted approaches; however, results from Nonlinear A were dependent on the equation parameters. The unweighted approach was more suitable for less polygenic scenarios. Finally, SNP weighting might help elucidate trait architecture features based on changes in the accuracy of genomic prediction.

Single‑nucleotide polymorphism rs6592645 confers asthma risk through regulating LRRC32 expression.

Asthma is a complex disease, often with evident genetic predisposition; for example, the single-nucleotide polymorphism (SNP) rs7130588 was significantly associated with asthma by genome-wide association study (GWAS). Analysis of 1000 Genomes Project data suggests that there is another SNP, rs6592645, in complete linkage disequilibrium with rs7130588 and should present the same signal in GWAS. However, the causal SNP and the mechanism for the association between rs7130588 and asthma remain to be elucidated. In the presents study, results from dual-luciferase assays indicated that the A/G alleles of rs7130588 failed to present significantly different reporter gene expression. By contrast, A allele of rs6592645 presented a significant increase in relative luciferase activity than G allele, thus suggesting that rs6592645 may be a causal SNP. Using chromosome conformation capture, the enhancer region containing rs6592645 was observed to interact with promoter region of leucine-rich repeat-containing 32 (LRRC32). Gene expression quantification suggested that LRRC32 expression is significantly increased in lung tissue of patients with asthma and is dependent on the genotype of this locus, thus verifying that LRRC32 may be involved in asthma onset and that rs6592645 can regulate LRRC32 expression. Through chromatin immunoprecipitation, transcription factor 3 (TCF3) was identified to bind to rs6592645 surrounding region and the interaction between TCF3 and rs6592645 surrounding region was investigated. Results from the present study may improve our understanding of the mechanism by which the genetic variation in this locus might influence asthma susceptibility.

A simulation study comparing advanced marker-assisted selection with genomic selection in tree breeding programs.

Advances in DNA sequencing technologies allow the sequencing of whole genomes of thousands of individuals and provide several million single nucleotide polymorphisms (SNPs) per individual. These data combined with precise and high-throughput phenotyping enable genome-wide association studies (GWAS) and the identification of SNPs underlying traits with complex genetic architectures. The identified causal SNPs and estimated allelic effects could then be used for advanced marker-assisted selection (MAS) in breeding programs. But could such MAS compete with the broadly used genomic selection (GS)? This question is of particular interest for the lengthy tree breeding strategies. Here, with our new software "SNPscan breeder," we simulated a simple tree breeding program and compared the impact of different selection criteria on genetic gain and inbreeding. Further, we assessed different genetic architectures and different levels of kinship among individuals of the breeding population. Interestingly, apart from progeny testing, GS using gBLUP performed best under almost all simulated scenarios. MAS based on GWAS results outperformed GS only if the allelic effects were estimated in large populations (ca. 10,000 individuals) of unrelated individuals. Notably, GWAS using 3,000 extreme phenotypes performed as good as the use of 10,000 phenotypes. GS increased inbreeding and thus reduced genetic diversity more strongly compared to progeny testing and GWAS-based selection. We discuss the practical implications for tree breeding programs. In conclusion, our analyses further support the potential of GS for forest tree breeding and improvement, although MAS may gain relevance with decreasing sequencing costs in the future.

BUZZ: an essential gene for postinitiation root hair growth and a mediator of root architecture in Brachypodium distachyon.

Here, we discover a player in root development. Recovered from a forward-genetic screen in Brachypodium distachyon, the buzz mutant initiates root hairs but they fail to elongate. In addition, buzz roots grow twice as fast as wild-type roots. Also, lateral roots show increased sensitivity to nitrate, whereas primary roots are less sensitive to nitrate. Using whole-genome resequencing, we identified the causal single nucleotide polymorphism as occurring in a conserved but previously uncharacterized cyclin-dependent kinase (CDK)-like gene. The buzz mutant phenotypes are rescued by the wild-type B. distachyon BUZZ coding sequence and by an apparent homolog in Arabidopsis thaliana. Moreover, T-DNA mutants in A. thaliana BUZZ have shorter root hairs. BUZZ mRNA localizes to epidermal cells and develops root hairs and, in the latter, partially colocalizes with the NRT1.1A nitrate transporter. Based on qPCR and RNA-Seq, buzz overexpresses ROOT HAIRLESS LIKE SIX-1 and -2 and misregulates genes related to hormone signaling, RNA processing, cytoskeletal, and cell wall organization, and to the assimilation of nitrate. Overall, these data demonstrate that BUZZ is required for tip growth after root hair initiation and root architectural responses to nitrate.

A meta-analysis of genome-wide association studies of childhood wheezing phenotypes identifies ANXA1 as a susceptibility locus for persistent wheezing.

Many genes associated with asthma explain only a fraction of its heritability. Most genome-wide association studies (GWASs) used a broad definition of 'doctor-diagnosed asthma', thereby diluting genetic signals by not considering asthma heterogeneity. The objective of our study was to identify genetic associates of childhood wheezing phenotypes. We conducted a novel multivariate GWAS meta-analysis of wheezing phenotypes jointly derived using unbiased analysis of data collected from birth to 18 years in 9568 individuals from five UK birth cohorts. Forty-four independent SNPs were associated with early-onset persistent, 25 with pre-school remitting, 33 with mid-childhood remitting, and 32 with late-onset wheeze. We identified a novel locus on chr9q21.13 (close to annexin 1 [ANXA1], p<6.7 × 10-9), associated exclusively with early-onset persistent wheeze. We identified rs75260654 as the most likely causative single nucleotide polymorphism (SNP) using Promoter Capture Hi-C loops, and then showed that the risk allele (T) confers a reduction in ANXA1 expression. Finally, in a murine model of house dust mite (HDM)-induced allergic airway disease, we demonstrated that anxa1 protein expression increased and anxa1 mRNA was significantly induced in lung tissue following HDM exposure. Using anxa1-/- deficient mice, we showed that loss of anxa1 results in heightened airway hyperreactivity and Th2 inflammation upon allergen challenge. Targeting this pathway in persistent disease may represent an exciting therapeutic prospect. UK Medical Research Council Programme Grant MR/S025340/1 and the Wellcome Trust Strategic Award (108818/15/Z) provided most of the funding for this study.

Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease-gene network.

A gene or variant has pleiotropic effects, and genetic variant identification across multiple phenotypes can provide a comprehensive understanding of biological pathways shared among different diseases or phenotypes. Discovery of genetic loci associated with multiple diseases can simultaneously support general interventions. Several meta-analyses have shown genetic associations with gastric cancer (GC); however, no study has identified associations with other phenotypes using this approach. Here, we applied disease network analysis and gene-based analysis (GBA) to examine genetic variants linked to GC and simultaneously associated with other phenotypes. We conducted a single-nucleotide polymorphism (SNP) level meta-analysis and GBA through a systematic genome-wide association study (GWAS) linked to GC, to integrate published results for the SNP variants and group them into major GC-associated genes. We then performed disease network and expression quantitative trait loci (eQTL) analyses to evaluate cross-phenotype associations and expression levels of GC-related genes. Seven genes (MTX1, GBAP1, MUC1, TRIM46, THBS3, PSCA, and ABO) were associated with GC as well as blood urea nitrogen (BUN), glomerular filtration rate (GFR), and uric acid (UA). In addition, 17 SNPs regulated the expression of genes located on 1q22, 24 SNPs regulated the expression of PSCA on 8q24.3, and rs7849820 regulated the expression of ABO on 9q34.2. Furthermore, rs1057941 and rs2294008 had the highest posterior causal probabilities of being a causal candidate SNP in 1q22, and 8q24.3, respectively. These findings identified seven GC-associated genes exhibiting a cross-association with GFR, BUN, and UA.

CandiHap: a haplotype analysis toolkit for natural variation study.

The online version contains supplementary material available at 10.1007/s11032-023-01366-4.

Cis- and Trans-variations of Stearoyl-CoA Desaturase Provide New Insights into the Mechanisms of Diverged Pattern of Phenotypic Plasticity for Temperature Adaptation in Two Congeneric Oyster Species.

The evolution of phenotypic plasticity plays an essential role in adaptive responses to climate change; however, its regulatory mechanisms in marine organisms which exhibit high phenotypic plasticity still remain poorly understood. The temperature-responsive trait oleic acid content and its major gene stearoyl-CoA desaturase (Scd) expression have diverged in two allopatric congeneric oyster species, cold-adapted Crassostrea gigas and warm-adapted Crassostrea angulata. In this study, genetic and molecular methods were used to characterize fatty acid desaturation and membrane fluidity regulated by oyster Scd. Sixteen causative single-nucleotide polymorphisms (SNPs) were identified in the promoter/cis-region of the Scd between wild C. gigas and C. angulata. Further functional experiments showed that an SNP (g.-333C [C. gigas allele] >T [C. angulata allele]) may influence Scd transcription by creating/disrupting the binding motif of the positive trans-factor Y-box factor in C. gigas/C. angulata, which mediates the higher/lower constitutive expression of Scd in C. gigas/C. angulata. Additionally, the positive trans-factor sterol-regulatory element-binding proteins (Srebp) were identified to specifically bind to the promoter of Scd in both species, and were downregulated during cold stress in C. gigas compared to upregulated in C. angulata. This partly explains the relatively lower environmental sensitivity (plasticity) of Scd in C. gigas. This study serves as an experimental case to reveal that both cis- and trans-variations shape the diverged pattern of phenotypic plasticity, which provides new insights into the formation of adaptive traits and the prediction of the adaptive potential of marine organisms to future climate change.

Multi-Omics Studies in Historically Excluded Populations: The Road to Equity.

Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.

Rs77283072 influences breast cancer susceptibility by regulating CDKN2A expression.

Breast cancer is the cancer type with the highest morbidity rates in women, and previous genome-wide association studies (GWASs) have suggested that the single nucleotide polymorphism (SNP) rs1011970 is significantly associated with this disease. An analysis of data from the 1000 Genomes Project demonstrated that there is an SNP, rs77283072, in almost complete linkage disequilibrium with rs1011970, which should therefore present the same signal in a GWAS. However, the actual causal SNP and its associated underlying mechanism have yet to be elucidated. Therefore, the present study evaluated the role of rs77283072 in terms of its association with breast cancer. A dual-luciferase assay was performed, which demonstrated that the two alleles of rs1011970 did not exhibit significantly different reporter gene activity. However, the A allele of rs77283072 exhibited a significant increase in relative luciferase activity compared with the G allele, which suggested that rs77283072 was the causal SNP for breast cancer. Chromosome conformation capture demonstrated that the enhancer containing rs77283072 interacted with the promoter of cyclin-dependent kinase inhibitor 2A (CDKN2A). Furthermore, expression quantitative trait locus analysis demonstrated that the expression of CDKN2A was dependent on the genotype of rs77283072. Taken together, the findings of the present study provided novel insights into the mechanism underlying how the genetic variation in this locus was able to influence breast cancer susceptibility and further the treatment for this disease.

How data science and AI-based technologies impact genomics.

Advancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phenome-wide association study (PheWAS) methods to identify associations between the genotype and phenotype. The associated findings have contributed to pharmacogenomics and improved clinical decision support at the point of care in many healthcare systems. However, the accumulation of genomic data from sequencing and clinical data from electronic health records (EHRs) poses significant challenges for data scientists. Following the rise of artificial intelligence (AI) technology such as machine learning and deep learning, an increasing number of GWAS/PheWAS studies have successfully leveraged this technology to overcome the aforementioned challenges. In this review, we focus on the application of data science and AI technology in three areas, including risk prediction and identification of causal single-nucleotide polymorphisms, EHR-based phenotyping and CRISPR guide RNA design. Additionally, we highlight a few emerging AI technologies, such as transfer learning and multi-view learning, which will or have started to benefit genomic studies.