Genomic Evaluation Research Articles

Comprehensive genomic evaluation of advanced and recurrent breast cancer patients for tailored precision treatments

AimThe aim of this study was to investigate genetic alterations within breast cancer in the setting of recurrent or de novo stage IV disease.Patients and methods: This study included 22 patients with recurrent breast cancer (n = 19) and inoperable de novo stage IV breast cancer (n = 3). For next generation sequencing, FoundationOneCDx (F1CDx) (Foundation Medicine Inc., Cambridge, MA, USA) was performed in 21 patients and FoundationOneLiquid CDx was performed in 1 patient.ResultsMedian age was 62.9 years (range, 33.4–82.1). Pathological diagnoses of specimens included invasive ductal carcinoma (n = 19), invasive lobular carcinoma (n = 2), and invasive micropapillary carcinoma (n = 1). F1CDx detected a median of 4.5 variants (range, 1–11). The most commonly altered gene were PIK3CA (n = 9), followed by TP53 (n = 7), MYC (n = 4), PTEN (n = 3), and CDH1 (n = 3). For hormone receptor-positive patients with PIK3CA mutations, hormonal treatment plus a phosphoinositide 3-kinase inhibitor was recommended as the treatment of choice. Patients in the hormone receptor-negative and no human epidermal growth factor receptor 2 expression group had significantly higher tumor mutational burden than patients in the hormone receptor-positive group. A BRCA2 reversion mutation was revealed by F1CDx in a patient with a deleterious germline BRCA2 mutation during poly ADP ribose polymerase inhibitor treatment.ConclusionGuidance on tailored precision therapy with consideration of genomic mutations was possible for some patients with information provided by F1CDx. Clinicians should consider using F1CDx at turning points in the course of the disease.

Reviewing the definition of mortality in broiler chickens and its implications in genomic evaluations.

Mortality is an economically important trait usually handled as a discrete outcome from hatch time until selection in most broiler breeder programs. However, in other species, it has been shown that not only does the genetic component change over time, but also there are maternal genetic effects to be considered when mortality is recorded early in life. This study aimed to investigate alternative trait definitions of mortality with varying models and effects. Three years' worth of data were provided by Cobb-Vantress, Inc. and included 2 mortality traits. The first trait was binary, whether the bird died or not (OM), and the second trait was a categorical weekly mortality trait. After data cleaning, 6wk of data for the 2 given mortality traits were used to develop 5 additional trait definitions. The definitions were broiler mortality (BM), early and late mortality (EM & LM), and 2 traits with repeated records as cumulative or binary (CM and RM, respectively). Variance components were estimated using linear and threshold models to investigate whether either model had a benefit. Genomic breeding values were predicted using the BLUP90 software suite, and linear regression validation (LR) was used to compare trait definitions and models. Heritability estimates ranged from 0.01 (0.00) to 0.16 (0.01) under linear and 0.04 (0.01) to 0.21 (0.01) under threshold models, indicating genetic variability within the population across these trait definitions. The genetic correlation between EM and LM ranged from 0.48 to 0.81 across the different lines, indicating they have divergent genetic backgrounds and should be considered different traits. The LR accuracies showed that EM and LM used together in a 2-trait model have comparable accuracies to that of OM while giving a more precise picture of mortality. When including the maternal effect, the direct heritability considerably decreased for EM, indicating that the maternal effect plays an important role in early mortality. Therefore, a suitable approach would be a model with EM and LM while considering the maternal effect for EM. Single nucleotide polymorphism effects were estimated, and no individual SNP explained more than 1% of the additive genetic variance. Additionally, the SNP with the largest effect size and variance were inconsistent across trait definitions. Chicken mortality can be defined in different ways, and reviewing these definitions and models may benefit poultry breeding programs.

Genomic evaluation for breeding and genetic management in Cordia africana, a multipurpose tropical tree species

BackgroundPlanting tested forest reproductive material is crucial to ensure the increased resilience of intensively managed productive stands for timber and wood product markets under climate change scenarios. Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) analysis is a cost-effective option for using genomic tools to enhance the accuracy of predicted breeding values and genetic parameter estimation in forest tree species. Here, we tested the efficiency of ssGBLUP in a tropical multipurpose tree species, Cordia africana, by partial population genotyping. A total of 8070 trees from three breeding seedling orchards (BSOs) were phenotyped for height. We genotyped 6.1% of the phenotyped individuals with 4373 single nucleotide polymorphisms. The results of ssGBLUP were compared with pedigree-based best linear unbiased prediction (ABLUP) and genomic best linear unbiased prediction (GBLUP), based on genetic parameters, theoretical accuracy of breeding values, selection candidate ranking, genetic gain, and predictive accuracy and prediction bias.ResultsGenotyping a subset of the study population provided insights into the level of relatedness in BSOs, allowing better genetic management. Due to the inbreeding detected within the genotyped provenances, we estimated genetic parameters both with and without accounting for inbreeding. The ssGBLUP model showed improved performance in terms of additive genetic variance and theoretical breeding value accuracy. Similarly, ssGBLUP showed improved predictive accuracy and lower bias than the pedigree-based relationship matrix (ABLUP).ConclusionsThis study of C. africana, a species in decline due to deforestation and selective logging, revealed inbreeding depression. The provenance exhibiting the highest level of inbreeding had the poorest overall performance. The use of different relationship matrices and accounting for inbreeding did not substantially affect the ranking of candidate individuals. This is the first study of this approach in a tropical multipurpose tree species, and the analysed BSOs represent the primary effort to breed C. africana.

Genomic and Functional Evaluation of Two Lacticaseibacillus paracasei and Two Lactiplantibacillus plantarum Strains, Isolated from a Rearing Tank of Rotifers (Brachionus plicatilis), as Probiotics for Aquaculture.

Aquaculture plays a crucial role in meeting the increasing global demand for food and protein sources. However, its expansion is followed by increasing challenges, such as infectious disease outbreaks and antibiotic misuse. The present study focuses on the genetic and functional analyses of two Lacticaseibacillus paracasei (BF3 and RT4) and two Lactiplantibacillus plantarum (BF12 and WT12) strains isolated from a rotifer cultivation tank used for turbot larviculture. Whole-genome sequencing (WGS) and bioinformatics analyses confirmed their probiotic potential, the absence of transferable antibiotic resistance genes, and the absence of virulence and pathogenicity factors. Bacteriocin mining identified a gene cluster encoding six plantaricins, suggesting their role in the antimicrobial activity exerted by these strains. In vitro cell-free protein synthesis (IV-CFPS) analyses was used to evaluate the expression of the plantaricin genes. The in vitro-synthesized class IIb (two-peptide bacteriocins) plantaricin E/F (PlnE/F) exerted antimicrobial activity against three indicator microorganisms, including the well-known ichthyopathogen Lactococcus garvieae. Furthermore, MALDI-TOF MS on colonies detected the presence of a major peptide that matches the dimeric form of plantaricins E (PlnE) and F (PlnF). This study emphasizes the importance of genome sequencing and bioinformatic analysis for evaluating aquaculture probiotic candidates. Moreover, it provides valuable insights into their genetic features and antimicrobial mechanisms, paving the way for their application as probiotics in larviculture, which is a major bottleneck in aquaculture.

ГЕНОМНАЯ ОЦЕНКА ПРИЗНАКА КОНВЕРСИИ КОРМА СВИНЕЙ ПОРОДЫ ДЮРОК

Currently, the implementation of genomic evaluation in all types of farm animals and commercial aquaculture objects is an urgent and necessary goal of scientific work. This paper presents the results of genome-wide association studies on feed conversion based on two main BLUP methodologies – BLUP Animal Model and genomic BLUP. An example of genomic evaluation parameters cataloging by feed efficiency and feeding behavior characteristics in Duroc pigs for implementation and application in practical direction is given. Five significant genes were identified, for which test systems will be developed in the future.

Machine Learning Mediated Advanced Phage and Antimicrobial Therapy- A Futuristic Approach

The emergence of antimicrobial resistance (AMR) has overwhelmed the contemporary curatives and have turned into one of the major challenges in the biomedical sector. With increasing deaths being associated with AMR every year; early detection of pathogens and development of novel drugs and alternative therapies, have all become ad hoc in diagnosis, prognosis and patient survival. Bacteriophage therapy remains a viable strategy to counteract AMR, yet unduly restrained by phage resistance. Phage infection is a natural phenomenon and can be widely manipulated in vitro using advanced techniques including the CRISPR/Cas systems which renders phage therapy an upper hand in comparison to conventional drugs. Phage identification, host range detection, determination of phage-receptor binding efficiency, adsorption rate, phage genome analysis are crucial stages in phage selection and phage cocktail preparation and moreover pivotal in flourishing phage therapy. The ascent of translational research and omics has allowed the development of quick, reliable and precise strategies for phage-based diagnosis and treatment techniques. However, in vitro evaluation of AMR and phage factors as well as storing, processing and analyzing large laboratory data outputs are expensive, time-consuming and labor-intensive. Machine learning (ML) is a utilitarian strategy to organize, store, analyze data sets and more importantly allows prediction of certain features by recognizing patterns in the data sets. With the huge number of research been carried out around the globe and enormous data sets being published and stored in databases, ML can utilize the available data to perform and guide in developing alternative therapeutics. Several ML based tools have been developed to predict resistance in host, phage grouping for cocktail preparation, resistance and lysogenic genes detection, phage genomic evaluation and to understand phage-host interactions. ML also allows the in silico analysis of large samples (drug/phage) and reduces sample size for in vitro evaluation thereby reducing overall costs, time and labor. The present review summarizes the available ML algorithms and corresponding databases used in AMR and phage research. It also emphasizes the status quo of antimicrobial and phage resistance in the healthcare sector and analyses the role of ML in analyzing biological databases in order to predict possible phage/drug-host interaction patterns, phage susceptibility, suitability of phage strains for therapy and recommends the most efficient drug combinations and treatment strategies.

70 Possibility for genomic evaluation using single blastomeres derived from 8-cell stage IVP embryos in Japanese Black cows

70 Possibility for genomic evaluation using single blastomeres derived from 8-cell stage IVP embryos in Japanese Black cows

Genomic analysis of feed efficiency traits in beef cattle using random regression models.

Feed efficiency plays a major role in the overall profitability and sustainability of the beef cattle industry, as it is directly related to the reduction of the animal demand for input and methane emissions. Traditionally, the average daily feed intake and weight gain are used to calculate feed efficiency traits. However, feed efficiency traits can be analysed longitudinally using random regression models (RRMs), which allow fitting random genetic and environmental effects over time by considering the covariance pattern between the daily records. Therefore, the objectives of this study were to: (1) propose genomic evaluations for dry matter intake (DMI), body weight gain (BWG), residual feed intake (RFI) and residual weight gain (RWG) data collected during an 84-day feedlot test period via RRMs; (2) compare the goodness-of-fit of RRM using Legendre polynomials (LP) and B-spline functions; (3) evaluate the genetic parameters behaviour for feed efficiency traits and their implication for new selection strategies. The datasets were provided by the EMBRAPA-GENEPLUS beef cattle breeding program and included 2920 records for DMI, 2696 records for BWG and 4675 genotyped animals. Genetic parameters and genomic breeding values (GEBVs) were estimated by RRMs under ssGBLUP for Nellore cattle using orthogonal LPs and B-spline. Models were compared based on the deviance information criterion (DIC). The ranking of the average GEBV of each test week and the overall GEBV average were compared by the percentage of individuals in common and the Spearman correlation coefficient (top 1%, 5%, 10% and 100%). The highest goodness-of-fit was obtained with linear B-Spline function considering heterogeneous residual variance. The heritability estimates across the test period for DMI, BWG, RFI and RWG ranged from 0.06 to 0.21, 0.11 to 0.30, 0.03 to 0.26 and 0.07 to 0.27, respectively. DMI and RFI presented within-trait genetic correlations ranging from low to high magnitude across different performance test-day. In contrast, BWG and RWG presented negative genetic correlations between the first 3 weeks and the other days of performance tests. DMI and RFI presented a high-ranking similarity between the GEBV average of week eight and the overall GEBV average, with Spearman correlations and percentages of individuals selected in common ranging from 0.95 to 1.00 and 93 to 100, respectively. Week 11 presented the highest Spearman correlations (ranging from 0.94 to 0.98) and percentages of individuals selected in common (ranging from 85 to 94) of BWG and RWG with the average GEBV of the entire period of the test. In conclusion, the RRM using linear B-splines is a feasible alternative for the genomic evaluation of feed efficiency. Heritability estimates of DMI, RFI, BWG and RWG indicate enough additive genetic variance to achieve a moderate response to selection. A new selection strategy can be adopted by reducing the performance test to 56 days for DMI and RFI selection and 77 days for BWG and RWG selection.

A Single-Step Genome-Wide Association Study for Semen Traits of Egyptian Buffalo Bulls.

The present study aimed to contribute to the limited research on buffalo (Bubalus bubalis) semen traits by incorporating genomic data. A total of 8465 ejaculates were collected. The genotyping procedure was conducted using the Axiom® Buffalo Genotyping 90 K array designed by the Affymetrix Expert Design Program. After conducting a quality assessment, we utilized 67,282 SNPs genotyped in 192 animals. We identified several genomic loci explaining high genetic variance by employing single-step genomic evaluation. The aforementioned regions were located on buffalo chromosomes no. 3, 4, 6, 7, 14, 16, 20, 22, and the X-chromosome. The X-chromosome exhibited substantial influence, accounting for 4.18, 4.59, 5.16, 5.19, and 4.31% of the genomic variance for ejaculate volume, mass motility, livability, abnormality, and concentration, respectively. In the examined genomic regions, we identified five novel candidate genes linked to male fertility and spermatogenesis, four in the X-chromosome and one in chromosome no. 16. Additional extensive research with larger sample sizes and datasets is imperative to validate these findings and evaluate their applicability for genomic selection.

Comparison of first-tier whole-exome sequencing with a multi-step traditional approach for diagnosing paediatric outpatients: An Italian prospective study.

The recent guidelines suggest the use of genome-wide analyses, such as whole exome sequencing (WES), at the beginning of the diagnostic approach for cases with suspected genetic conditions. However, in many realities it still provides for the execution of a multi-step pathway, thus requiring several genetic tests to end the so-called 'diagnostic odyssey'. We reported the results of GENE Project (Genomic analysis Evaluation NEtwork): a multicentre prospective cohort study on 125 paediatric outpatients with a suspected genetic disease in which we performed first-tier trio-WES, including exome-based copy number variation analysis, in parallel to a 'traditional approach' of two/three sequential genetic tests. First-tier trio-WES detected a conclusive diagnosis in 41.6% of patients, way above what was found with routine genetic testing (25%), with a time-to-result of about 50 days. Notably, the study showed that 44% of WES-reached diagnoses would be missed with the traditional approach. The diagnostic rate (DR) of the two approaches varied in relation to the phenotypic class of referral and to the proportion of cases with a defined diagnostic suspect, proving the major difference for neurodevelopmental disorders. Moreover, trio-WES analysis detected variants in candidate genes of unknown significance (EPHA4, DTNA, SYNCRIP, NCOR1, TFDP1, SPRED3, EDA2R, PHF12, PPP1R12A, WDR91, CDC42BPG, CSNK1D, EIF3H, TMEM63B, RIPPLY3) in 19.4% of undiagnosed cases. Our findings represent real-practice evidence of how first-tier genome-wide sequencing tests significantly improve the DR for paediatric outpatients with a suspected underlying genetic aetiology, thereby allowing a time-saving setting of the correct management, follow-up and family planning.

Assessing the Performance of Ridge Regression Method-6 in Genomic Evaluation of Discrete Threshold Traits with Additive and Dominance Genetic Architecture

Assessing the Performance of Ridge Regression Method-6 in Genomic Evaluation of Discrete Threshold Traits with Additive and Dominance Genetic Architecture

Retrieving single-cells and cell clusters from cytology direct smear slides for genomic evaluation

Abstract Introduction/Objective The key role of cytopathologists is to generate diagnoses based on cytologically stained samples. However, diagnoses increasingly need genomic data in addition to morphologic information. By current methods, morphologically diagnostic samples may be inadequate to perform comprehensive ancillary testing. Therefore, there is a great need for a workflow that can reliably identify and retrieve target cells based on their morphological features. This workflow should also enable highly sensitive molecular profiling from individual cells or small clusters of cells, allowing accurate detection of genomic alterations like copy number variations (CNVs) and single nucleotide variations (SNVs). Methods/Case Report Previously, we established a workflow that combines the RareCyte (Seattle, WA) cell-retrieval platform with primary template-directed amplification (PTA) to generate whole genome libraries from Propidium iodide (PI) stained effusion tumor single-cells or cell clusters and detected copy number variation. In this study, we modified and expanded the assay to identify and retrieve malignant or suspicious cells from Diff-Quik direct smear slides from patient pleural effusion specimens. The samples were further subjected to PTA amplification and library preparation for future copy number variation (CNV) evaluation. In parallel, patient cells from PI-stained smear slides were also obtained and processed to serve as controls. Results (if a Case Study enter NA) Direct smear slides were made from patient pleural fluid (N=5). In contrast to the previous PI staining, which only highlighted nuclei under fluorescence microscopy, Diff-Quik staining allowed nuclear and cytoplasmic morphology evaluation to identify target cells via light microscope. The XYZ coordinates of each target cell or cluster were further documented in the RareCyte Imager software to facilitate cell retrieval. We have successfully collected 39 single-cell and 18 bulk-cell samples from 5 different cases. PTA amplification has been completed and DNA yields of amplified libraries are promising. Conclusion We demonstrated that our modified cell retrieval platform can reliably obtain both single-cell or cluster samples based on morphology from cytologic smear slides made from patient effusion specimens. These collected samples were shown to be compatible with downstream amplification and library preparation enabling CNV evaluation and future deep sequencing for diagnostic, prognostic, and predictive pathogenic mutation identification.

Interpace, Affirma, and Thyroseq Performance Characteristics in 341 Bethesda Category III/IV Thyroid Nodules

Abstract Introduction/Objective Multiple FDA-approved molecular testing platforms are used by our institution for evaluating genomic characteristics of thyroid nodules. We aim to correlate molecular results and final histopathology (HPE) of thyroid nodules with cytologic diagnosis of The Bethesda System (TBS) Category III or IV. Methods/Case Report We included 341 cases of thyroid nodules diagnosed as TBS-III or TBS-IV with subsequent molecular analysis performed on 1 of 5 platforms (Interpace ThyraMIR Thyroid miRNA Classifier, Afirma Genomic Sequencing Classifier, Thyroseq Genomic Classifier, Next-Generation Sequencing (NGS) Fusion Panel, or RosettaGX Reveal Thyroid miRNA Classifier). All findings reported on molecular analysis and all benign or malignant neoplasms diagnosed on final HPE were considered “significant findings.” Performance characteristics of all 5 molecular platforms were analyzed. Results (if a Case Study enter NA) Of 341 cases, 80.1% (n=273) were classified as TBS-III and 19.9% (n=68) were classified as TBS-IV. The overall concordance of molecular and final HPE when available was 60.9% (n=28). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) are respectively 75.0%, 60.0%, 75.0%, and 60.0% for Interpace; 65.0%, 60.0%, 85.0%, and 33.0% for Afirma; and 75.0%, 50.0%, 75.0%, and 50.0% for Thyroseq platforms. Of TBS-III cases, 66.3% (n=181), 25.6% (n=70), 4.8% (n=13), 1.5% (n=4), and 1.8% (n=5) underwent molecular testing via Interpace, Afirma, Thyroseq, NGS, and Rosetta platforms, respectively. Only 9.2% (n=25) of TBS-III cases had subsequent HPE. Concordance of molecular and final HPE for TBS-III cases was 52.0% (n=13) with a PPV of 75.0% and NPV of 40.0%. Concordances of molecular and final HPE according to molecular platform are: 57.1% (n=4) on Interpace, 53.3% (n=8) on Afirma, and 50.0% (n=1) on Thyroseq. Of TBS-IV cases, 52.9% (n=36), 36.8% (n=25), 5.9% (n=4), 2.9% (n=2), and 1.5% (n=1) underwent molecular testing via Interpace, Afirma, Thyroseq, NGS, and Rosetta platforms, respectively. Only 30.9% (n=21) of TBS-IV cases had subsequent HPE. Concordance of molecular and final HPE for TBS-IV cases was 71.4% (n=15) with a PPV of 86.0% and NPV of 43.0% Concordances of molecular and final HPE according to molecular platform are: 71.4% (n=5), 66.7% (n=6), and 75.0% (n=3) on Interpace, Afirma, and Thyroseq respectively. Conclusion Our study reports similar performance characteristics of 3 commonly used molecular platforms (Interpace, Afirma, and Thyroseq) for genomic evaluation of thyroid nodules.

Mitochondrial genome-based synthesis and timeline of Eurasian otter (Lutra lutra) phylogeography

ABSTRACT Eurasian otters (Lutra lutra) have a broad distribution across Eurasia, but biogeographic data outside of western Europe is limited to disconnected pockets. Based on current subspecies designations, Asia appears to harbour a large proportion of the species’ diversity, with 10 of 12 Eurasian otter subspecies found in Asia. Here we provide a range-wide synthesis of mitochondrial data, inferring a timeline and pattern of phylogeographic signals. Whole mitochondrial genomes of 27 Eurasian otters across 4 subspecies are presented from newly generated data (n = 6; 4 from Korea, 1 from Hong Kong and 2 from UK), assembled from the Sequence Read Archive (n = 4), and sourced from GenBank (n = 17). We then combined whole mitochondrial genome results with cytochrome b data to increase the sample size and contextualise our results with prior studies. We identified five distinct lineages that were discordant with current subspecies classification. Phylogenetic dating revealed that the earliest diverging lineage was the Japanese lineage, with remaining lineages diverging ≥0.08 million years ago. Mitochondrial diversity calculated by sample locations seemed mainly driven by the presence of multiple lineages. When grouping samples by lineage, genetic diversity was highest in Lineage 1 (primarily found in China and Laos), followed by Lineage 2 (primarily found in Korea), and lowest diversity identified in Lineage 3 (primarily found in Europe). Our findings highlight previously undetected lineage diversity within Eurasian otters, but also the need for further taxonomic and genomic evaluation of the species in Asia. The identified unique, distinct lineages of Eurasian otters also warrant urgent conservation attention.

Amoeba predation of Cryptococcus: A quantitative and population genomic evaluation of the accidental pathogen hypothesis.

The "Amoeboid Predator-Fungal Animal Virulence Hypothesis" posits that interactions with environmental phagocytes shape the evolution of virulence traits in fungal pathogens. In this hypothesis, selection to avoid predation by amoeba inadvertently selects for traits that contribute to fungal escape from phagocytic immune cells. Here, we investigate this hypothesis in the human fungal pathogens Cryptococcus neoformans and Cryptococcus deneoformans. Applying quantitative trait locus (QTL) mapping and comparative genomics, we discovered a cross-species QTL region that is responsible for variation in resistance to amoeba predation. In C. neoformans, this same QTL was found to have pleiotropic effects on melanization, an established virulence factor. Through fine mapping and population genomic comparisons, we identified the gene encoding the transcription factor Bzp4 that underlies this pleiotropic QTL and we show that decreased expression of this gene reduces melanization and increases susceptibility to amoeba predation. Despite the joint effects of BZP4 on amoeba resistance and melanin production, we find no relationship between BZP4 genotype and escape from macrophages or virulence in murine models of disease. Our findings provide new perspectives on how microbial ecology shapes the genetic architecture of fungal virulence, and suggests the need for more nuanced models for the evolution of pathogenesis that account for the complexities of both microbe-microbe and microbe-host interactions.

Identification and characterization of Glycolate oxidase gene family in garden lettuce (Lactuca sativa cv. ‘Salinas’) and its response under various biotic, abiotic, and developmental stresses

Glycolate oxidase (GLO) is an FMN-containing enzyme localized in peroxisomes and performs in various molecular and biochemical mechanisms. It is a key player in plant glycolate and glyoxylate accumulation pathways. The role of GLO in disease and stress resistance is well-documented in various plant species. Although studies have been conducted regarding the role of GLO genes from spinach on a microbial level, the direct response of GLO genes to various stresses in short-season and leafy plants like lettuce has not been published yet. The genome of Lactuca sativa cultivar ‘Salinas’ (v8) was used to identify GLO gene members in lettuce by performing various computational analysis. Dual synteny, protein–protein interactions, and targeted miRNA analyses were conducted to understand the function of GLO genes. The identified GLO genes showed further clustering into two groups i.e., glycolate oxidase (GOX) and hydroxyacid oxidase (HAOX). Genes were observed to be distributed unevenly on three chromosomes, and syntenic analysis revealed that segmental duplication was prevalent. Thus, it might be the main reason for GLO gene diversity in lettuce. Almost all LsGLO genes showed syntenic blocks in respective plant genomes under study. Protein–protein interactions of LsGLO genes revealed various functional enrichments, mainly photorespiration, and lactate oxidation, and among biological processes oxidative photosynthetic carbon pathway was highly significant. Results of in-depth analyses disclosed the interaction of GLO genes with other members of the glycolate pathway and the activity of GLO genes in various organs and developmental stages in lettuce. The extensive genome evaluation of GLO gene family in garden lettuce is believed to be a reference for cloning and studying functional analyses of GLO genes and characterizing other members of glycolate/glyoxylate biosynthesis pathway in various plant species.

PATH-08. LONGITUDINAL MOLECULAR EVOLUTION OF IDH-WILDTYPE GLIOBLASTOMA IN ADULTS

Abstract Glioblastoma, IDH-wildtype is a clinically and molecularly heterogeneous disease. To investigate how longitudinal molecular evolution drives tumor progression and treatment resistance, we performed comprehensive histopathologic, genomic, and epigenomic evaluation of paired initial and recurrent IDH-wildtype glioblastomas from 106 patients. The vast majority (96/106, 91%) demonstrated clonal genetic evolution and/or epigenetic subclass shifting between primary and recurrent tumors. This included oncogenic alterations initially present in a minute subclone that expanded to predominate at time of recurrence. 11% (12/106) developed hypermutation with a predominance of C > T transitions at time of recurrence secondary to the alkylating effects of temozolomide. In contrast, the remaining 9% (10/106) demonstrated nearly identical molecular signatures between the primary and recurrent tumors without additional acquired genetic drivers. We found that TERT promoter mutation and CDKN2A homozygous deletion were uniformly shared between initial and recurrent tumors, indicating that these are fundamental early events in gliomagenesis, whereas alterations involving EGFR, PDGFRA, PTEN, NF1, and TP53 were commonly among the events private to initial or recurrent tumors, indicating acquisition later during clonal evolution. Furthermore, we identified that 17% (18/106) of glioblastomas underwent sarcomatous transformation (i.e. “gliosarcoma”) at recurrence, which were highly enriched for NF1 inactivation and mesenchymal epigenetic subclass. Cell type deconvolution revealed an increased abundance of T-lymphocytes, tumor-associated macrophages, and neutrophils in NF1 inactivated glioblastomas. Together, our analyses demonstrate that most glioblastomas undergo longitudinal genomic and/or epigenomic evolution. While there is no predominant genetic event enriched in recurrent tumors, personalized genomic analysis at time of recurrence can reveal acquired treatment resistance mechanisms (e.g. EGFR variant switching, temozolomide-induced hypermutation, novel MGMT gene amplification) that may impact therapeutic decision making. As such, we believe that precision medicine treatment regimens for glioblastoma should be based on molecular studies performed on the most recent surgical specimen.

117 Combining Large Broiler Populations into a Single Genomic Evaluation: Dealing with Genetic Divergence

Abstract Combining populations that diverged at some point is a common practice, especially in poultry, where generation intervals are short and genetic evaluations should be available often. This study assessed whether combining large, divergent broiler populations into one genomic evaluation is feasible and the best strategy for doing that in single-step GBLUP. The pedigree for populations 1 and 2 included 397,954 and 411,780 animals, of which 154,911 and 168,429 were genotyped. Phenotypes for body weight (g), carcass yield (%), mortality (0-1), and feet health (0-6) were available for 397,954 animals in population 1 and 411,780 in population 2. A four-trait model was used in the analyses, and the genetic differences between the two populations were modeled in different ways: M1) included an extra fixed effect that accounted for population origin; M2) added origin-specific fixed effects, where the animal origin was concatenated with all fixed effects in the model. The two models were compared against a conventional model without accounting for the animal origin as a fixed effect (M0). Unknown parent groups (UPG) were fit to account for the genetic differences in M0, M1, and M2; therefore, they were set based origin population of the animal and sex. The models were evaluated based on accuracy, bias, and dispersion from the linear regression (LR) method and predictive ability (PA), where GEBV in the reduced data was compared against phenotypes adjusted for fixed effects. Validations were done separately in single and multi-populations to better assess the benefits of combining the two populations. Overall, the accuracy obtained by PA was less than by LR. No apparent accuracy, PA, bias, and dispersion differences were observed among the multi-population models across all traits. This was also true when comparing validation parameters in a multi-population against the ones from a single population. However, we observed that animals from one population were always superior to the other. A multi-trait model was applied, treating the same trait in each population differently. Genetic correlations between the same trait in different populations were moderate to low, suggesting that the two lines should possibly be evaluated separately. Defining whether populations should be combined in a single evaluation may go beyond validation parameters. Identifying if traits are the same across populations is crucial for the feasibility of combined evaluations.

125 Implementing a Genetic Evaluation Program for Acute Hepatopancreatic Necrosis Disease Resistance in Penaeus Vannamei (Pacific White Shrimp)

Abstract This study aims to evaluate the feasibility of a genomic evaluation program for Acute hepatopancreatic necrosis disease (AHPND) resistance in a commercial population of Penaeus vannamei. AHPND is caused by a strain of Vibrio parahaemolyticus (VpAHPND) producing a binary toxin. It is a World Organization for Animal Health (WOAH, Paris, France)-listed bacterial disease that has caused severe economic losses to the shrimp industry worldwide in recent years. The working hypothesis of this study is that the genetic variation in a commercial P. vannamei population allows an efficient genetic and genomic selection program. To investigate the genetic parameters for AHPND resistance, a controlled challenge experiment was performed at the Shrimp Improvement System biosafety laboratory in Miami, Florida, following an immersion challenge protocol using 4.95 × 107 CFU/ mL bacteria and Specific Pathogen Free (SPF) P. vannamei post larvae (n = 2,400) representing 40 selected families. The bioassay was terminated at 3 days post-challenge. Each family consisted of 60 animals, with one-half of the families (n = 20) representing a fast-growth (FG) line and the remaining one-half (n = 20) representing a slow-growth (SG) line. Survival was recorded as a binary of dead or alive at the end of the three days challenge experiment. Variance components were estimated under an animal threshold binary model using a Gibbs sampler. Estimation of genetic parameters was done within lines and by combining all data records. The average hazard risk ratio (HRR) of survival was 80% greater for the FG line (average mortality 40%) than the SG line (average mortality 20%, P < 0.05) based on survival analysis with a 31% mortality rate combining all data records. The posterior mean estimates of heritabilities were 0.14 (± 0.06) for FG, 0.16 (± 0.07) for SG, and 0.38 (± 0.08), combining all records. Estimated breeding values (EBVs) based on the animal threshold binary model ranged from -0.76 to 1.01 on the liability scale. Spearmans’ rank correlation coefficient between the average EBV of parents and the average mortality rates of corresponding families was 0.98. The results suggest that a breeding program for AHPND resistance is feasible. Moreover, the heritabilities suggest that both lines will respond to mass selection due to the presence of additive genetic variation for APHND-survivability. The next step of this project is to genotype animals from every family and implement a genomic selection program. Additionally, high throughput laboratory results and histopathology scores will be introduced in the genetic evaluation as correlated traits.

141 Exploring Alternative Approaches for the National Argentine Angus Cattle Genomic Evaluation Program

Abstract Beef cattle production is a relevant component of the Argentine economy. The Argentine Angus is the most common beef cattle breed, and this population has a large economic and cultural importance for the country. In 2019, a national genomic selection program was implemented in the Argentine Angus population. The objective of this study was to explore alternative approaches for the genomic selection program in Argentinean Angus so that it will better serve the stakeholders. For this study, a total of 9,944 phenotypes for weaning weight from Argentine Angus born in 2018 were used. All animals had pedigree information for at least 3 generations. Genomic information on 1,078 individuals for 150k SNPs was used. The genotyped individuals were connected to the population by pedigree. Weaning weight EPDs were calculated under an animal model considering contemporary group as fixed effects and additive direct and maternal genetic effects as random. Variance components were estimated using AIREMLF90, and EPDs were calculated using BLUPF90. The enhanced EPDs were the genomic estimated breeding values obtained by single-step GBLUP, by including the genomic information into the original model. Validation of the EPDs and enhanced EPDs were made by cross-validation, where a random sample of 70 genotyped individuals with more than 20 progeny was the validation population. The reference breeding values were the EPDs of the validation population with complete data. In a second analysis, the phenotype of the progeny of the validation population was removed. The correlation between the reduced and complete EPDs was the accuracy for this study, according to the LR method. The accuracy obtained by this method was overestimated due to potential relationships between animals in the training and validation population. However, the enhanced EPDs were 4% more accurate than the pedigree EPDs. The next steps of this research are exploring alternative approaches of the validation methodology, which will be accomplished by implementing a forward-in-time cross-validation. Such validation will be performed once more data is included in the model. Additionally, other models will be tested to identify the ideal combination of effects that will yield higher accuracies. Details regarding the construction of the genomic relationship matrix will be tested with the overarching goal of improving convergence without affecting accuracy. Moreover, a weighted single-step GBLUP approach will be tested to verify if prediction accuracy can improve even further. Finally, indirect prediction models will be tested so that the Argentine Angus Association can implement interim enhanced EPDs for young animals. In conclusion, including genomic data into this dataset yielded greater accuracy and such values are expected to improve even further after optimization.