Use Of Genomic Markers Research Articles

Use of Milk Infrared Spectral Data as Environmental Covariates in Genomic Prediction Models for Production Traits in Canadian Holstein.

Simple SummaryGenomic selection models aim at predicting the performance of individuals with the use of genomic markers. In animal breeding, prediction models are seldomly tested for their ability to predict new individuals’ performance under different environmental conditions, despite the changes in management and diet that the industry undergoes. In this study, we propose a method to use milk infrared spectra as descriptors of environmental variation among herds. These descriptors can be incorporated in genomic prediction models similarly to how genomic markers are included. The inclusion of environmental descriptors is shown to improve the predictive ability for new genotypes under new environmental conditions.The purpose of this study was to provide a procedure for the inclusion of milk spectral information into genomic prediction models. Spectral data were considered a set of covariates, in addition to genomic covariates. Milk yield and somatic cell score were used as traits to investigate. A cross-validation was employed, making a distinction for predicting new individuals’ performance under known environments, known individuals’ performance under new environments, and new individuals’ performance under new environments. We found an advantage of including spectral data as environmental covariates when the genomic predictions had to be extrapolated to new environments. This was valid for both observed and, even more, unobserved families (genotypes). Overall, prediction accuracy was larger for milk yield than somatic cell score. Fourier-transformed infrared spectral data can be used as a source of information for the calculation of the ‘environmental coordinates’ of a given farm in a given time, extrapolating predictions to new environments. This procedure could serve as an example of integration of genomic and phenomic data. This could help using spectral data for traits that present poor predictability at the phenotypic level, such as disease incidence and behavior traits. The strength of the model is the ability to couple genomic with high-throughput phenomic information.

Single-Step Genomic Analysis Increases the Accuracy of Within-Family Selection in a Clonally Replicated Population ofPinus taedaL.

AbstractThe use of genomic markers in forest tree breeding is expected to improve the response to selection, especially within family. To evaluate the potential improvements from genotyping, we analyzed a large Pinus taeda L. clonal population (1,831 cloned individuals) tested in multiple environments. Of the total, 723 clones from five full-sib families were genotyped using 10,337 single-nucleotide polymorphism markers. Single-step models with genomic and pedigree-based relationships produced similar heritability estimates. Breeding value predictions were greatly improved with inclusion of genomic relationships, even when clonal replication was abundant. The improvement was limited to genotyped individuals and attributable to accounting for the Mendelian sampling effect. Reducing clonal replication by omitting data indicated that genotyping improved breeding values similar to clonal replication. Genomic selection predictive ability (masking phenotypes) was greater for stem straightness (0.68) than for growth traits (0.41 to 0.44). Predictive ability for a new full-sibling family was poorer than when full-sibling relationships were present between model training and validation sets. Species that are difficult to propagate clonally can use genotyping to improve within-family selection. Clonal testing combined with genotyping can produce breeding value accuracies adequate to graft selections directly into deployment orchards without progeny testing.

In vitro Bactericidal Activities of Combination Antibiotic Therapies Against Carbapenem-Resistant Klebsiella pneumoniae With Different Carbapenemases and Sequence Types.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is becoming increasingly problematic due to the limited effectiveness of new antimicrobials or other factors such as treatment cost. Thus, combination therapy remains a suitable treatment option. We aimed to evaluate the in vitro bactericidal activity of various antibiotic combinations against CRKP with different carbapenemase genotypes and sequence types (STs). Thirty-seven CRKP with various STs and carbapenemases were exposed to 11 antibiotic combinations (polymyxin B or tigecycline in combination with β-lactams including aztreonam, cefepime, piperacillin/tazobactam, doripenem, meropenem, and polymyxin B with tigecycline) in static time-kill studies (TKS) using clinically achievable concentrations. Out of the 407 isolate-combination pairs, only 146 (35.8%) were bactericidal (≥3 log10CFU/mL decrease from initial inoculum). Polymyxin B in combination with doripenem, meropenem, or cefepime was the most active, each demonstrating bactericidal activity in 27, 24, and 24 out of 37 isolates, respectively. Tigecycline in combination with β-lactams was rarely bactericidal. Aside from the lower frequency of bactericidal activity in the dual-carbapenemase producers, there was no apparent difference in combination activity among the strains with other carbapenemase types. In addition, bactericidal combinations were varied even in strains with similar STs, carbapenemases, and other genomic characteristics. Our findings demonstrate that the bactericidal activity of antibiotic combinations is highly strain-specific likely owing to the complex interplay of carbapenem-resistance mechanisms, i.e., carbapenemase genotype alone cannot predict in vitro bactericidal activity. The availability of WGS information can help rationalize the activity of certain combinations. Further studies should explore the use of genomic markers with phenotypic information to predict combination activity.

Genomic sequencing confirms absence of introgression despite past hybridisation between a critically endangered bird and its common congener

Genetic swamping resulting from interspecific hybridisation can increase extinction risk for threatened species. The development of high-throughput and reduced-representation genomic sequencing and analyses to generate large numbers of high resolution genomic markers has the potential to reveal introgression previously undetected using small numbers of genetic markers. However, few studies to date have implemented genomic tools to assess the extent of interspecific hybridisation in threatened species. Here we investigate the utility of genome-wide single nucleotide polymorphisms (SNPs) to detect introgression resulting from past interspecific hybridisation in one of the world’s rarest birds. Anthropogenic impacts have resulted in hybridisation and subsequent backcrossing of the critically endangered Aotearoa New Zealand endemic kakī (black stilts; Himantopus novaezelandiae) with the non-threatened self-introduced congeneric poaka (Aotearoa New Zealand population of pied stilts, Himantopus himantopus leucocephalus), yet genetic analyses with a limited set of microsatellite markers revealed no evidence of introgression of poaka genetic material in kakī, excluding one individual. We use genomic data for ~63% of the wild adult kakī population to reassess the extent of introgression resulting from hybridisation between kakī and poaka. Consistent with previous genetic analyses, we detected no introgression from poaka into kakī. These collective results indicate that, for kakī, existing microsatellite markers provide a robust, cost-effective approach to detect cryptic hybrids. Further, for well-differentiated species, the use of genomic markers may not be required to detect admixed individuals.

Opportunities and Challenges for Improving the Productivity of Swamp Buffaloes in Southeastern Asia.

The swamp buffalo is a domesticated animal commonly found in Southeast Asia. It is a highly valued agricultural animal for smallholders, but the production of this species has unfortunately declined in recent decades due to rising farm mechanization. While swamp buffalo still plays a role in farmland cultivation, this species’ purposes has shifted from draft power to meat, milk, and hide production. The current status of swamp buffaloes in Southeast Asia is still understudied compared to its counterparts such as the riverine buffaloes and cattle. This review discusses the background of swamp buffalo, with an emphasis on recent work on this species in Southeast Asia, and associated genetics and genomics work such as cytogenetic studies, phylogeny, domestication and migration, genetic sequences and resources. Recent challenges to realize the potential of this species in the agriculture industry are also discussed. Limited genetic resource for swamp buffalo has called for more genomics work to be done on this species including decoding its genome. As the economy progresses and farm mechanization increases, research and development for swamp buffaloes are focused on enhancing its productivity through understanding the genetics of agriculturally important traits. The use of genomic markers is a powerful tool to efficiently utilize the potential of this animal for food security and animal conservation. Understanding its genetics and retaining and maximizing its adaptability to harsher environments are a strategic move for food security in poorer nations in Southeast Asia in the face of climate change.

297 GWAS for complex models accounting for populations structure with GBLUP and ssGBLUP

Abstract Genomic Best Linear Unbiased Prediction (GBLUP) is the method of choice for incorporating genomic information into the genetic evaluation of livestock species. Furthermore, single step GBLUP (ssGBLUP) is adopted by many breeders’ associations and private entities managing large scale breeding programs. While prediction of breeding values remains the primary use of genomic markers in animal breeding, a secondary interest focuses on performing genome-wide association studies (GWAS). The goal of GWAS is to uncover genomic regions that harbor variants that explain a large proportion of the phenotypic variance, and thus become candidates for discovering and studying causative variants. Several methods have been proposed and successfully applied for embedding GWAS into genomic prediction models. Most methods commonly avoid formal hypothesis testing and resort to estimation of SNP effects, relying on visual inspection of graphical outputs to determine candidate regions. However, with the advent of high throughput phenomics and transcriptomics, a more formal testing approach with automatic discovery thresholds is more appealing. In this work we present the methodological details of a method for performing formal hypothesis testing for GWAS in GBLUP models. First, we present the method and its equivalencies and differences with other GWAS methods. Moreover, we demonstrate through simulation analyses that the proposed method controls type I error rate at the nominal level. Second, we demonstrate two possible computational implementations based on mixed model equations for ssGBLUP and based on the generalized least square equations (GLS). We show that ssGBLUP can deal with datasets with extremely large number of animals and markers and with multiple traits. GLS implementations are well suited for dealing with smaller number of animals with tens of thousands of phenotypes. Third, we show several useful extensions, such as: testing multiple markers at once, testing pleiotropic effects and testing association of social genetic effects.

Molecular Typing of Invasive Staphylococcus aureus from the Emerging Infections Program (EIP) Using Whole-Genome Sequencing

Background: The CDC has performed surveillance for invasive Staphylococcus aureus (iSA) infections through the Emerging Infections Program (EIP) since 2004. SCCmec and spa typing for clonal complex (CC) assignment and genomic markers have been used to characterize isolates. In 2019, whole-genome sequencing (WGS) of isolates began, allowing for high-resolution assessment of genomic diversity. Here, we evaluate the reliability of SCCmec typing, spa typing, and CC assignment using WGS data compared to traditional methods to ensure that backwards compatibility is maintained. Methods:S. aureus isolates were obtained from a convenience sample of iSA cases reported through the EIP surveillance system. Overall, 78 iSA isolates with diverse spa repeat patterns, CCs, SCCmec types, and antimicrobial susceptibility profiles were sequenced (MiSeq, Illumina). Real-time PCR and Sanger sequencing were used as the SCCmec and spa typing reference methods, respectively. spa-MLST mapping (Ridom SpaServer) served as the reference method for CC assignment. WGS assembly and multilocus sequence typing (MLST) were performed using the CDC QuAISAR-H pipeline. WGS-based MLST CCs were assigned using eBURST and SCCmec types using SCCmecFinder. spa types were assigned from WGS assemblies using BioNumerics. For isolate subtyping, previously published and validated canonical single-nucleotide polymorphisms (canSNPs) as well as the presence of the Panton-Valentine leukocidin (PVL) toxin and arginine catabolic mobile element (ACME) virulence factor were assessed for all genome assemblies. Results: All isolates were assigned WGS-based spa types, which were 100% concordant (78 of 78) with Sanger-based spa typing. SCCmecFinder assigned 91% of isolates (71 of 78) SCCmec types, which were 100% concordant with reference method results. Also, 7 isolates had multiple cassettes predicted or an incomplete SCCmec region assembly. Using WGS data, 96% (75 of 78) of isolates were assigned CCs; 3 isolates had unknown sequence types that were single-locus variants of established sequence types. Overall, 70 isolates had CCs assigned by the reference method; 100% (70 of 70) concordance was observed with WGS-based CCs. Analysis of canSNPs placed 42% (33 of 78) of isolates into CC8, with 17 (52%) of these isolates classified as USA300. PVL and ACME were not accurate markers for inferring the USA300 subtype as 24% (4 of 17) of isolates did not contain these markers. Conclusions:S. aureus CCs, SCCmec, and spa types can be reliably determined using WGS. Incorporation of canSNP analysis represents a more efficient method for CC8 assignment than the use of genomic markers alone. WGS allows for the replacement of multiple typing methods for increased laboratory efficiency, while maintaining backward compatibility with historical typing nomenclature.Funding: NoneDisclosures: None

Porcine seminal protein-I and II mRNA expression in boar spermatozoa is significantly correlated with fertility

In recent years, genomic and proteomic biomarkers have been identified for the diagnosis of male fertility to overcome the limitations of conventional semen analysis. Owing to the limited genes available so far, the single gene approach is commonly adopted for analyzing the phenotype of interest. However, the single-gene approach is less effective than multiple-gene strategies for diagnosing a desirable phenotype. Herein, we investigate the ability of two fertility-related genomic markers (porcine seminal protein (PSP)-I and PSP-II) in spermatozoa to predict boar litter size in addition to conventional semen parameters. First, we examined different semen parameters (motility, motion kinematics, and capacitation status) and gene expression in high- and low-litter size boar spermatozoa. Then, we evaluated the correlation of these parameters with the fertility of 21 Yorkshire boars. Finally, we investigated the efficacy of single/combined markers to predict male fertility using a comprehensive statistical model. Our result showed that there were no significant differences in sperm motility, motion kinematics, or capacitation status, however, the mRNA expression of PSP-I and PSP-II in spermatozoa was significantly different in high- and low-litter size boars. In the individual screening test, the expression of both genes was negatively correlated with boar fertility (r = 0-0.578 and −0.456, respectively), whereas only hyperactivation (HYP) showed a positive correlation (r = 0.444) among the tested semen parameters. As single markers, PSP-I and PSP-II have a better diagnostic power to predict boar fertility, regardless of HYP, in quality assessment analyses. In addition, when these markers were combined, the positive predictive value, negative predictive value, and overall test effectiveness for fertility detection were improved. Surprisingly, when PSP-I and PSP-II were considered together, the deviation of the predicted average litter size between high- and low-litter size boars was 1.77. Based on the findings, we suggest that the use of genomic markers in spermatozoa rather than commonly analyzed semen parameters may be more accurate for evaluating male fertility. Moreover, using a combination of markers could increase the overall accuracy of (in)fertility predictions, and thus, could be considered for field application.

Genetic Characterization of Indubrasil Cattle Breed Population.

The Indubrasil breed was developed in the Brazilian region called Triângulo Mineiro as a result of a cross between zebu cattle. Initially, it was used as a terminal cross and currently it represents approximately 4.45% of all the Brazilian zebu cattle. Studies were conducted to estimate genetic parameters in the Indubrasil using pedigree information, however, until now, no study has been developed using large-scale genomic markers in this breed. Pedigree information are widely used to investigate population parameters; however, they can neglect some estimates when compared to the use of genomic markers. Therefore, the objective of this study was to investigate the population structure and the genetic diversity of Indubrasil cattle using a high-density Single Nucleotide Polymorphism (SNP) panel (Illumina BovineHD BeadChip 700k). Levels of genomic homozygosity were evaluated using three different approaches: Runs of homozygosity (FROH), % of homozygosis (FSNP), and inbreeding coefficient (Fx). Further, Runs of Homozygosity (ROH) segments conserved among the animals were investigated to identify possible regions associated with the breed characteristics. Our results indicate that even the Indubrasil breed having a small effective population size, the levels of homozygosity (FROH = 0.046) are still small. This was possibly caused by the cross conducted among different breeds for its development. It suggests no immediate risks associated with loss of genetic variation. This information might be used in breeding programs, for the breed conservation and for the expansion of the Indubrasil breed.

Pharmacogenetics in cardiovascular diseases: State of the art and implementation-recommendations of the French National Network of Pharmacogenetics (RNPGx)

The use of genomic markers to predict drug response and effectiveness has the potential to improve healthcare by increasing drug efficacy and minimizing adverse effects. Polymorphisms associated with inter-individual variability in drug metabolism, transport, or pharmacodynamics of major cardiovascular drugs have been identified. These include single nucleotide polymorphisms (SNP) affecting clinical outcomes in patients receiving antiplatelet agents, oral anticoagulants and statins. Based on clinical evidence supporting genetic testing in the management of cardiovascular diseases using these drug classes, this short review presents clinical guidance regarding current pharmacogenetics implementation in routine medical practice.

Use of genomic markers to risk stratify men with prostate cancer

New genomic biomarkers are being developed to refine the risk for men with prostate cancer, but for healthcare professionals to believe in them, they must be both valid and of clinical utility. John Davis reviews three of these biomarkers, providing advice on how to interpret results and make decisions.

Genomic markers for decision making: what is preventing us from using markers?

The advent of novel genomic technologies that enable the evaluation of genomic alterations on a genome-wide scale has significantly altered the field of genomic marker research in solid tumors. Researchers have moved away from the traditional model of identifying a particular genomic alteration and evaluating the association between this finding and a clinical outcome measure to a new approach involving the identification and measurement of multiple genomic markers simultaneously within clinical studies. This in turn has presented additional challenges in considering the use of genomic markers in oncology, such as clinical study design, reproducibility and interpretation and reporting of results. This Review will explore these challenges, focusing on microarray-based gene-expression profiling, and highlights some common failings in study design that have impacted on the use of putative genomic markers in the clinic. Despite these rapid technological advances there is still a paucity of genomic markers in routine clinical use at present. A rational and focused approach to the evaluation and validation of genomic markers is needed, whereby analytically validated markers are investigated in clinical studies that are adequately powered and have pre-defined patient populations and study endpoints. Furthermore, novel adaptive clinical trial designs, incorporating putative genomic markers into prospective clinical trials, will enable the evaluation of these markers in a rigorous and timely fashion. Such approaches have the potential to facilitate the implementation of such markers into routine clinical practice and consequently enable the rational and tailored use of cancer therapies for individual patients.

Blood gene expression signatures predict exposure levels

To respond to potential adverse exposures properly, health care providers need accurate indicators of exposure levels. The indicators are particularly important in the case of acetaminophen (APAP) intoxication, the leading cause of liver failure in the U.S. We hypothesized that gene expression patterns derived from blood cells would provide useful indicators of acute exposure levels. To test this hypothesis, we used a blood gene expression data set from rats exposed to APAP to train classifiers in two prediction algorithms and to extract patterns for prediction using a profiling algorithm. Prediction accuracy was tested on a blinded, independent rat blood test data set and ranged from 88.9% to 95.8%. Genomic markers outperformed predictions based on traditional clinical parameters. The expression profiles of the predictor genes from the patterns extracted from the blood exhibited remarkable (97% accuracy) transtissue APAP exposure prediction when liver gene expression data were used as a test set. Analysis of human samples revealed separation of APAP-intoxicated patients from control individuals based on blood expression levels of human orthologs of the rat discriminatory genes. The major biological signal in the discriminating genes was activation of an inflammatory response after exposure to toxic doses of APAP. These results support the hypothesis that gene expression data from peripheral blood cells can provide valuable information about exposure levels, well before liver damage is detected by classical parameters. It also supports the potential use of genomic markers in the blood as surrogates for clinical markers of potential acute liver damage.

Integrating genomics and neuromarkers for the era of brain-related personalized medicine.

The harsh reality is that many medical treatments do not work as expected in a significant percentage of patients, and occasionally there are serious side effects. A new paradigm of personalized medicine is emerging, which proactively tailors treatment to each individual's biological and psychological profile. The first proof-of-concept phase of personalized medicine has now been achieved. However, it has thus far focused on the use of genomic markers and on disorders of the body. The complexity of the brain is likely to require a shift from a single genetic marker focus to a more integrated approach in which additional brain-related information (neuromarkers) is taken into account. Codevelopment of genomic neuromarkers with new compounds in a personalized medicine approach will lead to increased drug R&D and treatment benefits. The emerging genomic neuromarker potential has begun to be incorporated into the template for the next version of the Diagnostic and Statistical Manual (DSM-V). The statistical power of large subject numbers in databases in general (and standardized databases in particular) provides an ideal source for elucidating the best genomic-neuromarker profiles (explaining most of the main-effects variance), which will empower a brain-related personalized medicine into mainstream clinical practice.

Heart failure

The use of genomic markers to predict drug response and effectiveness has the potential to improve healthcare by increasing drug efficacy and minimizing adverse effects. Polymorphisms associated with inter-individual variability in drug metabolism, transport, or pharmacodynamics of major cardiovascular drugs have been identified. These include single nucleotide polymorphisms (SNP) affecting clinical outcomes in patients receiving antiplatelet agents, oral anticoagulants and statins. Based on clinical evidence supporting genetic testing in the management of cardiovascular diseases using these drug classes, this short review presents clinical guidance regarding current pharmacogenetics implementation in routine medical practice.