Use Of Whole-genome Sequence Data Research Articles

Use of whole-genome sequence data for fine mapping and genomic prediction of sea louse resistance in Atlantic salmon.

Sea louse (Lepeophtheirus salmonis) infestation of Atlantic salmon (Salmo salar) is a significant challenge in aquaculture. Over the years, this parasite has developed immunity to medicinal control compounds, and non-medicinal control methods have been proven to be stressful, hence the need to study the genomic architecture of salmon resistance to sea lice. Thus, this research used whole-genome sequence (WGS) data to study the genetic basis of the trait since most research using fewer SNPs did not identify significant quantitative trait loci. Mowi Genetics AS provided the genotype (50k SNPs) and phenotype data for this research after conducting a sea lice challenge test on 3,185 salmon smolts belonging to 191 full-sib families. The 50 k SNP genotype was imputed to WGS using the information from 197 closely related individuals with sequence data. The WGS and 50k SNPs of the challenged population were then used to estimate genetic parameters, perform a genome-wide association study (GWAS), predict genomic breeding values, and estimate its accuracy for host resistance to sea lice. The heritability of host resistance to sea lice was estimated to be 0.21 and 0.22, while the accuracy of genomic prediction was estimated to be 0.65 and 0.64 for array and WGS data, respectively. In addition, the association test using both array and WGS data did not identify any marker associated with sea lice resistance at the genome-wide level. We conclude that sea lice resistance is a polygenic trait that is moderately heritable. The genomic predictions using medium-density SNP genotyping array were equally good or better than those based on WGS data.

Prioritized imputed sequence variants from multi-population GWAS improve prediction accuracy for sea lice count in Atlantic salmon (Salmo salar)

Sea lice infestation is one of the major fish health problems that occurs during the grow-out phase in Atlantic salmon (Salmo salar) aquaculture. In this study, we integrated different genomic approaches, including whole-genome sequencing (WGS), genotype imputation and meta-analysis of genome-wide association studies (GWAS), to identify single-nucleotide polymorphisms (SNPs) associated with sea lice count in Atlantic salmon. Different sets of trait-associated SNPs were prioritized and compared against randomly chosen markers, based on the accuracy of genomic predictions for the trait. Lice count phenotypes and dense genotypes of five breeding populations challenged with sea lice were used. Genotype imputation was applied to increase SNP density of challenged animals to WGS level. The summary statistics from GWAS of each population were then combined in a meta-analysis to increase the sample size and improve the statistical power of associations. Eight different genotyping scenarios were considered for genomic prediction: 70K_array: 70 K standard genotyping panel; 70K_priori: 70 K SNPs with the highest p-values identified in the meta-analysis; 30K_priori: 30 K SNPs with the highest p-values identified in the meta-analysis; WGS: SNPs imputed to whole-genome sequencing level. The remaining four scenarios were the same SNP sets with a linkage disequilibrium (LD) pruning filter: 70K_array_LD; 70K_priori_LD; 30K_priori_LD and WGS_LD. Genomic prediction accuracy was evaluated using a five-fold cross-validation scheme in two different populations which were excluded from the meta-analysis to remove possible validation-reference bias. Results showed significant genetic variation for the number of sea lice in Atlantic salmon across populations, with heritabilities ranging from 0.06 to 0.24. The meta-analysis identified several SNPs associated with sea lice resistance, mainly in Ssa03 and Ssa09 chromosomes. Genomic prediction using the GWAS-based prioritized SNPs showed higher accuracy compared to the standard SNP array in most of scenarios; achieving up to 57% increase in accuracy. Accuracy of prioritized scenarios was higher for the 70K_priori than the 30K_priori. The use of WGS data in genomic prediction presented marginal or negative accuracy gain compared to the standard SNP array. The LD-pruning filter presented no benefits, reducing accuracy in most scenarios. Overall, our study demonstrated the potential of prioritized imputed sequence variants from multi-population GWAS meta-analysis to improve prediction accuracy for sea lice counts in Atlantic salmon. The findings suggest that incorporating WGS data and prioritized SNPs from GWAS meta-analysis can accelerate the genetic progress of selection for polygenic traits in salmon aquaculture.

ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data

BackgroundHigh oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneously staining regions (HSRs). These genome-level variants lead to the overexpression of the corresponding oncogenes, resulting in poor prognosis. Most existing detection methods identify ecDNA using whole genome sequencing (WGS) data. However, these techniques usually detect many false positive regions owing to chromosomal DNA interference.ResultsIn the present study, an algorithm called “ATACAmp” that can identify ecDNA/HSRs in tumor genomes using ATAC-seq data has been described. High chromatin accessibility, one of the characteristics of ecDNA, makes ATAC-seq naturally enriched in ecDNA and reduces chromosomal DNA interference. The algorithm was validated using ATAC-seq data from cell lines that have been experimentally determined to contain ecDNA regions. ATACAmp accurately identified the majority of validated ecDNA regions. AmpliconArchitect, the widely used ecDNA detecting tool, was used to detect ecDNA regions based on the WGS data of the same cell lines. Additionally, the Circle-finder software, another tool that utilizes ATAC-seq data, was assessed. The results showed that ATACAmp exhibited higher accuracy than AmpliconArchitect and Circle-finder. Moreover, ATACAmp supported the analysis of single-cell ATAC-seq data, which linked ecDNA to specific cells.ConclusionsATACAmp, written in Python, is freely available on GitHub under the MIT license: https://github.com/chsmiss/ATAC-amp. Using ATAC-seq data, ATACAmp offers a novel analytical approach that is distinct from the conventional use of WGS data. Thus, this method has the potential to reduce the cost and technical complexity associated ecDNA analysis.

Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs

BackgroundPork quality can directly affect customer purchase tendency and meat quality traits have become valuable in modern pork production. However, genetic improvement has been slow due to high phenotyping costs. In this study, whole genome sequence (WGS) data was used to evaluate the prediction accuracy of genomic best linear unbiased prediction (GBLUP) for meat quality in large-scale crossbred commercial pigs.ResultsWe produced WGS data (18,695,907 SNPs and 2,106,902 INDELs exceed quality control) from 1,469 sequenced Duroc × (Landrace × Yorkshire) pigs and developed a reference panel for meat quality including meat color score, marbling score, L* (lightness), a* (redness), and b* (yellowness) of genomic prediction. The prediction accuracy was defined as the Pearson correlation coefficient between adjusted phenotypes and genomic estimated breeding values in the validation population. Using different marker density panels derived from WGS data, accuracy differed substantially among meat quality traits, varied from 0.08 to 0.47. Results showed that MultiBLUP outperform GBLUP and yielded accuracy increases ranging from 17.39% to 75%. We optimized the marker density and found medium- and high-density marker panels are beneficial for the estimation of heritability for meat quality. Moreover, we conducted genotype imputation from 50K chip to WGS level in the same population and found average concordance rate to exceed 95% and r2 = 0.81.ConclusionsOverall, estimation of heritability for meat quality traits can benefit from the use of WGS data. This study showed the superiority of using WGS data to genetically improve pork quality in genomic prediction.

Genome-wide identification of copy neutral loss of heterozygosity reveals its possible association with spatial positioning of chromosomes.

Loss of heterozygosity (LOH) is a genetic alteration that results from the loss of one allele at a heterozygous locus. In particular, copy neutral LOH (CN-LOH) events are generated, for example, by mitotic homologous recombination after monoallelic defection or gene conversion, resulting in novel homozygous locus having two copies of the normal counterpart allele. This phenomenon can serve as a source of genome diversity and is associated with various diseases. To clarify the nature of the CN-LOH such as the frequency, genomic distribution and inheritance pattern, we made use of whole-genome sequencing data of the three-generation CEPH/Utah family cohort, with the pedigree consisting of grandparents, parents and offspring. We identified an average of 40.7 CN-LOH events per individual taking advantage of 285 healthy individuals from 33 families in the cohort. On average 65% of them were classified as gonosomal-mosaicism-associated CN-LOH, which exists in both germline and somatic cells. We also confirmed that the incidence of the CN-LOH has little to do with the parents' age and sex. Furthermore, through the analysis of the genomic region including the CN-LOH, we found that the chance of the occurrence of the CN-LOH tends to increase at the GC-rich locus and/or on the chromosome having a relatively close inter-homolog distance. We expect that these results provide significant insights into the association between genetic alteration and spatial position of chromosomes as well as the intrinsic genetic property of the CN-LOH.

The power of classic maize mutants: Driving forward our fundamental understanding of plants.

Since Mendel, maize has been a powerhouse of fundamental genetics research. From testing the Mendelian laws of inheritance, to the first genetic and cytogenetic maps, to the use of whole-genome sequencing data for crop improvement, maize is at the forefront of genetics advances. Underpinning much of this revolutionary work are the classic morphological mutants; the "freaks" that stood out in the field to even the untrained eye. Here we review some of these classic developmental mutants and their importance in the history of genetics, as well as their key role in our fundamental understanding of plant development.

Optimizing Sequencing Resources in Genotyped Livestock Populations Using Linear Programming.

Low-cost genome-wide single-nucleotide polymorphisms (SNPs) are routinely used in animal breeding programs. Compared to SNP arrays, the use of whole-genome sequence data generated by the next-generation sequencing technologies (NGS) has great potential in livestock populations. However, sequencing a large number of animals to exploit the full potential of whole-genome sequence data is not feasible. Thus, novel strategies are required for the allocation of sequencing resources in genotyped livestock populations such that the entire population can be imputed, maximizing the efficiency of whole genome sequencing budgets. We present two applications of linear programming for the efficient allocation of sequencing resources. The first application is to identify the minimum number of animals for sequencing subject to the criterion that each haplotype in the population is contained in at least one of the animals selected for sequencing. The second application is the selection of animals whose haplotypes include the largest possible proportion of common haplotypes present in the population, assuming a limited sequencing budget. Both applications are available in an open source program LPChoose. In both applications, LPChoose has similar or better performance than some other methods suggesting that linear programming methods offer great potential for the efficient allocation of sequencing resources. The utility of these methods can be increased through the development of improved heuristics.

In silico species identification and serotyping for Cronobacter isolates by use of whole-genome sequencing data

Cronobacter spp. are foodborne pathogens that can cause severe infections in neonates through contaminated powdered infant formula. Accurate and rapid pathogen identification and serotyping are crucial to limit the detrimental effects of bacterial infections, and to prevent outbreaks and sporadic infections. Conventional serotyping is tedious, laborious, and time-consuming; however, with whole-genome sequencing (WGS) becoming faster and cheaper, WGS has vast potential in routine typing and surveillance. Hence, in this study, we developed a publicly available tool, CroTrait (CronobacterTraits), for in silico species identification and O serotyping of Cronobacter isolates based on WGS data. CroTrait showed excellent performance in species identification and O serotyping when 810 genomes with known species identities and 276 genomes with known O serotype were tested. Moreover, CroTrait allows rapid prediction of new potential O serotypes. We identified 11 novel potential O serotypes of Cronobacter using CroTrait. Therefore, CroTrait is a convenient and promising tool for species identification and O serotyping of Cronobacter isolates.

Cost of whole genome sequencing for non-typhoidal Salmonella enterica.

BackgroundWhile whole genome sequencing (WGS) may be more expensive than traditional testing and polymerase chain reaction (PCR), simple cost comparisons ignore the potential for WGS to reduce the societal costs of non-typhoidal Salmonella enterica through public health action to prevent illness.MethodsWe determined how many cases the use of WGS data would need to prevent to be cost-equal to serotyping and MLVA, or culture independent testing based on PCR in Australia. We then examined the costs and cost-savings of current typing methods compared with WGS in outbreak scenarios.ResultsA median of 275 (90% CrI -55-775) or 1.9% (90% CrI -0.4%-5.4%) of notified serotyped Salmonella cases would need to be prevented for WGS to be cost-equal to current typing methods and 1,550 (90% CrI 820–2,725) or 9.6% of all notified Salmonella cases would need to be prevented to be cost-equal to PCR. WGS is likely to result in cost savings in prolonged outbreaks, where data can support earlier public health action.ConclusionsDespite currently having a higher cost per isolate, routine WGS of Salmonella was no more expensive than existing typing methods or PCR where >2% of illness was averted.

Outcome of Different Sequencing and Assembly Approaches on the Detection of Plasmids and Localization of Antimicrobial Resistance Genes in Commensal Escherichia coli.

Antimicrobial resistance (AMR) is a major threat to public health worldwide. Currently, AMR typing changes from phenotypic testing to whole-genome sequence (WGS)-based detection of resistance determinants for a better understanding of the isolate diversity and elements involved in gene transmission (e.g., plasmids, bacteriophages, transposons). However, the use of WGS data in monitoring purposes requires suitable techniques, standardized parameters and approved guidelines for reliable AMR gene detection and prediction of their association with mobile genetic elements (plasmids). In this study, different sequencing and assembly strategies were tested for their suitability in AMR monitoring in Escherichia coli in the routines of the German National Reference Laboratory for Antimicrobial Resistances. To assess the outcomes of the different approaches, results from in silico predictions were compared with conventional phenotypic- and genotypic-typing data. With the focus on (fluoro)quinolone-resistant E. coli, five qnrS-positive isolates with multiple extrachromosomal elements were subjected to WGS with NextSeq (Illumina), PacBio (Pacific BioSciences) and ONT (Oxford Nanopore) for in depth characterization of the qnrS1-carrying plasmids. Raw reads from short- and long-read sequencing were assembled individually by Unicycler or Flye or a combination of both (hybrid assembly). The generated contigs were subjected to bioinformatics analysis. Based on the generated data, assembly of long-read sequences are error prone and can yield in a loss of small plasmid genomes. In contrast, short-read sequencing was shown to be insufficient for the prediction of a linkage of AMR genes (e.g., qnrS1) to specific plasmid sequences. Furthermore, short-read sequencing failed to detect certain duplications and was unsuitable for genome finishing. Overall, the hybrid assembly led to the most comprehensive typing results, especially in predicting associations of AMR genes and mobile genetic elements. Thus, the use of different sequencing technologies and hybrid assemblies currently represents the best approach for reliable AMR typing and risk assessment.

On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

BackgroundWhole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision.MethodsThe Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits.ResultsThe prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits.ConclusionsAcross-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.

384 Genetic and Genomic Analysis in Livestock with Increasing Datasets

Abstract In animal breeding and genetics, statistical methods have been used for several decades to identify animals with the best genetic potential. One of the foundations for computing accurate estimated breeding values (EBV) is the amount of data that is used in the evaluation system — as the more data points one animal has, the more accurate its EBV is going to be. However, the animal breeding and genetics field periodically faces a big data paradox, where efficient methods have to be developed to handle the amount of data collected over time, given the computing capacity becomes the limiting factor. For instance, running genetic evaluations based on phenotypes and pedigree for a million animals was impossible in 1970. Methods and algorithms evolved to a point where using data for millions of animals was not a problem, until genomic information became available. After the development of single nucleotide polymorphism (SNP) chips for livestock in 2008, genomic information started being used in addition to phenotypes and pedigree to further improve accuracy of EBV. However, each animal is genotyped for around 50,000 SNP, which makes this data dense and difficult to work with. Over 790,000 Angus and 3.4 million Holstein cattle have been genotyped in the US as of April 2020. As the amount of new data considerably increases every week, most of the genomic evaluations are done on a weekly basis. Given that data have to be processed, the computation of EBV cannot take more than four days, which can be challenging depending on the model. In this talk we will discuss the challenges and solutions for successful genomic evaluations in large livestock populations. Finally, perspectives on the use of whole-genome sequence data and high-throughput phenotypes in genomic analysis will be summarized.

Use of whole-genome sequence data and novel genomic selection strategies to improve selection for age at puberty in tropically-adapted beef heifers

BackgroundIn tropically-adapted beef heifers, application of genomic prediction for age at puberty has been limited due to low prediction accuracies. Our aim was to investigate novel methods of pre-selecting whole-genome sequence (WGS) variants and alternative analysis methodologies; including genomic best linear unbiased prediction (GBLUP) with multiple genomic relationship matrices (MGRM) and Bayesian (BayesR) analyses, to determine if prediction accuracy for age at puberty can be improved.MethodsGenotypes and phenotypes were obtained from two research herds. In total, 868 Brahman and 960 Tropical Composite heifers were recorded in the first population and 3695 Brahman, Santa Gertrudis and Droughtmaster heifers were recorded in the second population. Genotypes were imputed to 23 million whole-genome sequence variants. Eight strategies were used to pre-select variants from genome-wide association study (GWAS) results using conditional or joint (COJO) analyses. Pre-selected variants were included in three models, GBLUP with a single genomic relationship matrix (SGRM), GBLUP MGRM and BayesR. Five-way cross-validation was used to test the effect of marker panel density (6 K, 50 K and 800 K), analysis model, and inclusion of pre-selected WGS variants on prediction accuracy.ResultsIn all tested scenarios, prediction accuracies for age at puberty were highest in BayesR analyses. The addition of pre-selected WGS variants had little effect on the accuracy of prediction when BayesR was used. The inclusion of WGS variants that were pre-selected using a meta-analysis with COJO analyses by chromosome, fitted in a MGRM model, had the highest prediction accuracies in the GBLUP analyses, regardless of marker density. When the low-density (6 K) panel was used, the prediction accuracy of GBLUP was equal (0.42) to that with the high-density panel when only six additional sequence variants (identified using meta-analysis COJO by chromosome) were included.ConclusionsWhile BayesR consistently outperforms other methods in terms of prediction accuracies, reasonable improvements in accuracy can be achieved when using GBLUP and low-density panels with the inclusion of a relatively small number of highly relevant WGS variants.

Igcaller: Reconstructing the Rearranged Immunoglobulin Gene in Lymphoid Neoplasms from Whole-Genome Sequencing Data

Introduction: Mature normal and tumor B cells express a unique rearranged immunoglobulin (IG) gene that can be used as a marker of the clonal expansion of the cell. Somatic hypermutation (SHM) in the V(D)J region of IG genes are acquired in the germinal center and are a surrogate imprint of the cell of origin of lymphoid neoplasms. In chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL), the identification of SHM distinguishes subtypes of tumors with different clinical and biological behavior. Although still less studied, cases carrying highly similar IG sequences (i.e. stereotyped IG), specific light chain (LC) rearrangements, and the presence of class switch recombination (CSR) of the constant region of the heavy chains further sub-classify patients into potentially distinct clinico-biological subgroups. The analysis of the rearranged IG gene is currently performed by specific Sanger sequencing (SSeq) or next-generation sequencing protocols. Whole-genome sequencing (WGS) of B-cell neoplasms should store the information to reconstruct the entire rearranged IG gene [heavy (IGH) and kappa or lambda (IGK, IGL) LC]. However, the high genomic complexity and homology of these regions have prevented the analysis of the rearranged IG genes in WGS using standard bioinformatics pipelines. Aim: To assess the use of WGS data to fully characterize the rearranged IG gene in B-cell neoplasms. Methods: We developed IgCaller, a fast, easy-to-run program that uses already aligned WGS data to dissect the rearranged IGH V(D)J genes, IGK and IGL VJ genes, and the presence of constant heavy chain CSR. IgCaller also determines the homology of the rearranged sequences compared to the patient's germ line or reference genome. We demonstrated the accuracy of IgCaller using WGS data of 331 B-cell neoplasms [240 CLL (152 cohort 1 (C1)-CLL; 88 independent cohort 2 (C2)-CLL), 61 MCL, and 30 multiple myeloma (MM)] and compared with SSeq of the IGH V(D)J and/or LC and isotype expression. Results: IgCaller identified a complete IGH productive rearrangement [V(D)J] in 133 (88%) C1-CLL, 80 (91%) C2-CLL, 61 (100%) MCL, and 21 (70%) MM. A partial (VJ) rearrangement was detected in 8 (5%) C1-CLL and 1 (3%) MM. Available SSeq of the V(D)J or at least V gene for 131 C1-CLL, 10 C2-CLL, and 60 MCL successfully characterized by WGS highlighted only one discordant V(D)J rearrangement. Small discrepancies (only J or V disagreement) were found when the J (n=4) or V (n=1) gene identified by SSeq based on homology (IMGT/V-QUEST tool) did not correspond to the rearranged gene detected by WGS, which was the second scoring gene in IMGT/V-QUEST suggesting that our non-homology WGS-based approach might be more accurate. Of note, IgCaller identified the presence of two distinct IGH subclones in 1 case. Next, the comparison of the percentage of homology of the rearranged sequence to the germ line in 131 C1-CLL and 60 MCL with complete V gene both by SSeq and WGS showed a high correlation and concordance in both cohorts [R>0.95, p<1e-30; Passing Bablok regression: 0.05+1*SSeq (CLL), -0.19+1*SSeq (MCL)]. Only 1 CLL patient was differentially classified as mutated or unmutated between SSeq (complete rearrangement) and WGS (partial rearrangement) using the cut off of 98%. A productive IGK or IGL was found in 147 (97%) C1-CLL, 85 (97%) C2-CLL, 61 (100%) MCL, and 26 (87%) MM. These results fully agreed with the LC expression observed by flow cytometry (FC). Of clinical relevance, a total of 26/232 (11%) CLL carried IGLV3-21. The presence of IGLV3-21 was associated with a shorter time to first treatment (TTFT) independently of the IGHV status (p=0.045). Finally, IgCaller identified the CSR matching the isotype expressed by FC analysis in 26/30 (87%) MM. One of the 4 discordant cases with IgM by WGS expressed only LC by FC. Overall, IgCaller could not identify the isotype switch in 3 (10%) MM expressing IgG or IgA. In CLL, CSR was observed in 47/240 (20%) cases. Noteworthy, the presence of CSR identified 24% of M-CLL patients with a shorter TTFT than non-switched M-CLL (p=0.004), and similar to that of unmutated IGHV cases (p=0.15). Conclusions: IgCaller successfully characterized the entire IG gene of >90% B-cell neoplasms studied. The complete characterization of the rearranged IG gene based on WGS data, when available, could facilitate the analysis of LC rearrangements and CSR, and replace the traditional SSeq of the IG loci both in research and clinical settings. Disclosures No relevant conflicts of interest to declare.

Using imputation-based whole-genome sequencing data to improve the accuracy of genomic prediction for combined populations in pigs

BackgroundFor genomic selection in populations with a small reference population, combining populations of the same breed or populations of related breeds is an effective way to increase the size of the reference population. However, genomic predictions based on single nucleotide polymorphism (SNP)-chip genotype data using combined populations with different genetic backgrounds or from different breeds have not shown a clear advantage over using within-population or within-breed predictions. The increasing availability of whole-genome sequencing (WGS) data provides new opportunities for combined population genomic prediction. Our objective was to investigate the accuracy of genomic prediction using imputation-based WGS data from combined populations in pigs. Using 80K SNP panel genotypes, WGS genotypes, or genotypes on WGS variants that were pruned based on linkage disequilibrium (LD), three methods [genomic best linear unbiased prediction (GBLUP), single-step (ss)GBLUP, and genomic feature (GF)BLUP] were implemented with different prior information to identify the best method to improve the accuracy of genomic prediction for combined populations in pigs.ResultsIn total, 2089 and 2043 individuals with production and reproduction phenotypes, respectively, from three Yorkshire populations with different genetic backgrounds were genotyped with the PorcineSNP80 panel. Imputation accuracy from 80K to WGS variants reached 92%. The results showed that use of the WGS data compared to the 80K SNP panel did not increase the accuracy of genomic prediction in a single population, but using WGS data with LD pruning and GFBLUP with prior information did yield higher accuracy than the 80K SNP panel. For the 80K SNP panel genotypes, using the combined population resulted in a slight improvement, no change, or even a slight decrease in accuracy in comparison with the single population for GBLUP and ssGBLUP, while accuracy increased by 1 to 2.4% when using WGS data. Notably, the GFBLUP method did not perform well for both the combined population and the single populations.ConclusionsThe use of WGS data was beneficial for combined population genomic prediction. Simply increasing the number of SNPs to the WGS level did not increase accuracy for a single population, while using pruned WGS data based on LD and GFBLUP with prior information could yield higher accuracy than the 80K SNP panel.

Linkage of whole genome sequencing with administrative health, and electronic medical record data for the study of autism spectrum disorder: Feasibility, Opportunities and Challenges

IntroductionAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) that presents with a high degree of heterogeneity (e.g., co-occurrence of other NDDs and other co-morbid conditions), contributing to differential health system needs. Genetics are known to play an important role in ASD and may be associated with different disease trajectories.
 Objectives and ApproachIn this proof of principle project, our objective is to link >2,200 children with a confirmed diagnosis of a NDD from the Province of Ontario Neurodevelopmental (POND) Study to administrative health data and electronic medical record (EMR) data in order to identify subgroups of ASD with unique health system trajectories. POND includes detailed phenotype and whole genome sequencing (WGS) data. Identified subgroups will be characterized based on clinical phenotype and genetics. To meet this goal, consideration of WGS-specific privacy and data issues is needed to implement processes which are above and beyond traditional requirements for analyzing individual-level administrative health data.
 ResultsLinkage of WGS data with administrative health data is an emerging area of research. As such it has presented a number of initial challenges for our study of ASD. Privacy concerns surrounding the use of WGS data and rare-variant analysis are of particular importance. Practical issues required the need for analysts with expertise in administrative data, EMR data and genetic analyses, and specialized software and sufficient processing power to analyze WGS data. Transdisciplinary discussions of the scope and significance of research questions addressed through this linkage were crucial. The identification of genetic determinants of phenotypes and trajectories in ASD could support targeted early interventions; EMR linkage may inform algorithms to identify ASD in broader populations. These approaches could improve both patient outcome and family experience.
 Conclusion/ImplicationsAs the cost of genetic sequencing decreases, WGS data will become part of the routine clinical management of patients. Linkage of WGS, EMR and administrative data has tremendous potential that has largely not been realized; including population-level ASD research to improve our ability to predict long-term outcomes associated with ASD.

Two-way mixed-effects methods for joint association analysis using both host and pathogen genomes

Infectious diseases are often affected by specific pairings of hosts and pathogens and therefore by both of their genomes. The integration of a pair of genomes into genome-wide association mapping can provide an exquisitely detailed view of the genetic landscape of complex traits. We present a statistical method, ATOMM (Analysis with a Two-Organism Mixed Model), that maps a trait of interest to a pair of genomes simultaneously; this method makes use of whole-genome sequence data for both host and pathogen organisms. ATOMM uses a two-way mixed-effect model to test for genetic associations and cross-species genetic interactions while accounting for sample structure including interactions between the genetic backgrounds of the two organisms. We demonstrate the applicability of ATOMM to a joint association study of quantitative disease resistance (QDR) in the Arabidopsis thaliana-Xanthomonas arboricola pathosystem. Our method uncovers a clear host-strain specificity in QDR and provides a powerful approach to identify genetic variants on both genomes that contribute to phenotypic variation.

An update on the genetics of hyperuricaemia and gout.

A central aspect of the pathogenesis of gout is elevated urate concentrations, which lead to the formation of monosodium urate crystals. The clinical features of gout result from an individual's immune response to these deposited crystals. Genome-wide association studies (GWAS) have confirmed the importance of urate excretion in the control of serum urate levels and the risk of gout and have identified the kidneys, the gut and the liver as sites of urate regulation. The genetic contribution to the progression from hyperuricaemia to gout remains relatively poorly understood, although genes encoding proteins that are involved in the NLRP3 (NOD-, LRR- and pyrin domain-containing 3) inflammasome pathway play a part. Genome-wide and targeted sequencing is beginning to identify uncommon population-specific variants that are associated with urate levels and gout. Mendelian randomization studies using urate-associated genetic variants as unconfounded surrogates for lifelong urate exposure have not supported claims that urate is causal for metabolic conditions that are comorbidities of hyperuricaemia and gout. Genetic studies have also identified genetic variants that predict responsiveness to therapies (for example, urate-lowering drugs) for treatment of hyperuricaemia. Future research should focus on large GWAS (that include asymptomatic hyperuricaemic individuals) and on increasing the use of whole-genome sequencing data to identify uncommon genetic variants with increased penetrance that might provide opportunities for clinical translation.

SNVPhyl: a single nucleotide variant phylogenomics pipeline for microbial genomic epidemiology.

The recent widespread application of whole-genome sequencing (WGS) for microbial disease investigations has spurred the development of new bioinformatics tools, including a notable proliferation of phylogenomics pipelines designed for infectious disease surveillance and outbreak investigation. Transitioning the use of WGS data out of the research laboratory and into the front lines of surveillance and outbreak response requires user-friendly, reproducible and scalable pipelines that have been well validated. Single Nucleotide Variant Phylogenomics (SNVPhyl) is a bioinformatics pipeline for identifying high-quality single-nucleotide variants (SNVs) and constructing a whole-genome phylogeny from a collection of WGS reads and a reference genome. Individual pipeline components are integrated into the Galaxy bioinformatics framework, enabling data analysis in a user-friendly, reproducible and scalable environment. We show that SNVPhyl can detect SNVs with high sensitivity and specificity, and identify and remove regions of high SNV density (indicative of recombination). SNVPhyl is able to correctly distinguish outbreak from non-outbreak isolates across a range of variant-calling settings, sequencing-coverage thresholds or in the presence of contamination. SNVPhyl is available as a Galaxy workflow, Docker and virtual machine images, and a Unix-based command-line application. SNVPhyl is released under the Apache 2.0 license and available at http://snvphyl.readthedocs.io/ or at https://github.com/phac-nml/snvphyl-galaxy.

Use of whole-genome sequencing data to analyze 23S rRNA-mediated azithromycin resistance

The whole-genome sequences of 24 isolates of Neisseria gonorrhoeae with elevated minimum inhibitory concentrations (MICs) to azithromycin (≥2.0 µg/mL) were analyzed against a modified sequence derived from the whole-genome sequence of N. gonorrhoeae FA1090 to determine, by signal ratio, the number of mutant copies of the 23S rRNA gene and the copy number effect on 50S ribosome-mediated azithromycin resistance. Isolates that were predicted to contain four mutated copies were accurately identified compared with the results of direct sequencing. Fewer than four mutated copies gave less accurate results but were consistent with elevated MICs.