Single-Step Genomic Analysis Increases the Accuracy of Within-Family Selection in a Clonally Replicated Population of Pinus taeda L.

The 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. Study Implications Genomic markers can improve the reliability of breeding values, resulting in a more confident ranking of individuals within families. For genotyped individuals, the improvements were comparable to clonal testing. Breeding programs for species that are difficult to propagate clonally should consider genotyping to replace or supplement clonal testing as a means to improve within-family selection. For genomic prediction of breeding values without phenotypes (genomic selection), a robust genetic relationship between model training and validation sets is required. The single-step model allows genotyping a subset of the population and is a straightforward extension of well-established methods.

Analysis of metabolomic and genomic markers for diagnosing cardiovascular diseases

Cardiovascular disease is the leading cause of death in the population. Unfortunately, cardiovascular disease and its associated risks are often difficult to diagnose due to the many factors associated with age and other comorbidities that lead to significant uncertainty in diagnostic classification and therapeutic decision making. Therefore, there is a great need to find new biomarkers for more accurate diagnosis, risk assessment and treatment recommendations for both acute and chronic cardiovascular disease. This article presents an analysis of metabolomic and genomic markers used for the diagnosis of cardiovascular disease. The study of the metabolome in combination with the genome and proteome can provide important information about both the pathogenesis of cardiovascular disease and the ability to search for and identify new cardiovascular disease biomarkers. Along with the fundamental data on new cardiovascular disease biomarkers, there is an urgent need for further research confirming their great potential for practical health care.

Rare alternative second line injectable drug resistance markers identified by gene-wise genome wide association in M. tuberculosis with unexplained resistance

Point mutations in the rrs gene and eis promoter are known to confer resistance to second-line injectable drugs (SLIDs) amikacin (AMK), capreomycin (CAP), and kanamycin (KAN). While mutations in these canonical genes confer a majority of SLID-resistance, alternative mechanisms of resistance are not uncommon and threaten effective treatment decisions when using conventional molecular diagnostics. In total, 1184 clinical M. tuberculosis isolates from 7 countries were studied for genomic markers associated with phenotypic resistance. The markers rrs:A1401G and rrs:G1484T were associated with resistance to all three SLIDs, and three known markers in the eis promoter (eis:G-10A, eis:C-12T, and eis:C-14T) were similarly associated with kanamycin resistance (KAN-R). Among 325, 324, 270 AMK-R, CAP-R, and KAN-R isolates, 264 (81.2%), 250 (77.2%), and 249 (92.3%) harbored canonical mutations, respectively. Thirteen isolates harbored more than one canonical mutation. Canonical mutations did not account for 111 of the phenotypically resistant isolates. A gene-wise method identified three genes and promoters with mutations that, on aggregate, associated with unexplained resistance to at least one SLID. Our analysis associated whiB7 promoter mutations with KAN resistance, supporting clinical relevance for the previously demonstrated role of whiB7 overexpression in KAN resistance. We also provide evidence for the novel association of ppe51 (a gene previously associated with various antimicrobial compounds) with AMK resistance, and for the novel association of thrB with AMK and CAP resistance. The use of gene-wise association can provide additional insight, and therefore is recommended for identification of rare mechanisms of resistance when individual mutations carry insufficient statistical power.

Characterisation and Molecular Analysis of an Unusual Chimeric Methicillin Resistant Staphylococcus Aureus Strain and its Bacteriophages

In the context of microarray-based epidemiological typing of the clonal organism Staphylococcus aureus/MRSA, a strain was identified that did not belong to known clonal complexes. The molecular analysis by microarray-based typing yielded signals suggesting that it was a mosaic or hybrid strain of two lineages. To verify this result, the isolate was sequenced with both, short-read Illumina and long-read Nanopore technologies and analysed in detail. This supported the hypothesis that the genome of this strain, ST6610-MRSA-IVg comprised of segments originating from two different clonal complexes (CC). While the backbone of the strain’s genome, i.e., roughly 2 megabases, belongs to CC8, a continuous insert of 894 kb (approx. 30% of the genome) originated from CC140. Beside core genomic markers in the normal succession and orientation, this insert also included the mecA gene, coding for PbP2a and causing methicillin resistance, localised on an SCCmec IVg element. This particular SCCmec type was also previously observed in CC140 MRSA from African countries. A second conspicuous observation was the presence of the trimethoprim resistance gene dfrG within on a prophage that occupied an attachment site normally used by Panton-Valentine Leucocidin phages. This observation could indicate a role of large-scale chromosomal recombination in the evolution of S. aureus as well as a role of phages in the dissemination of antibiotic resistance genes.

A proximal LAVA method for genome-wide association and prediction of traits with mixed inheritance patterns

Background The genetic basis of phenotypic traits is highly variable and usually divided into mono-, oligo- and polygenic inheritance classes. Relatively few traits are known to be monogenic or oligogeneic. The majority of traits are considered to have a polygenic background. To what extent there are mixtures between these classes is unknown. The rapid advancement of genomic techniques makes it possible to directly map large amounts of genomic markers (GWAS) and predict unknown phenotypes (GWP). Most of the multi-marker methods for GWAS and GWP falls into one of two regularization frameworks. The first framework is based on $$\ell _1$$ ℓ 1 -norm regularization (e.g. the LASSO) and is suitable for mono- and oligogenic traits, whereas the second framework regularize with the $$\ell _2$$ ℓ 2 -norm (e.g. ridge regression; RR) and thereby is favourable for polygenic traits. A general framework for mixed inheritance is lacking. Results We have developed a proximal operator algorithm based on the recent LAVA regularization method that jointly performs $$\ell _1$$ ℓ 1 - and $$\ell _2$$ ℓ 2 -norm regularization. The algorithm is built on the alternating direction method of multipliers and proximal translation mapping (LAVA ADMM). When evaluated on the simulated QTLMAS2010 data, it is shown that the LAVA ADMM together with Bayesian optimization of the regularization parameters provides an efficient approach with lower test prediction mean-squared-error (65.89) than the LASSO (66.11), Ridge regression (83.41) and Elastic net (66.11). For the real pig data the test MSE of the LAVA ADMM is 0.850 compared to the LASSO, RR and EN with 0.875, 0.853 and 0.853, respectively. Conclusions This study presents the LAVA ADMM that is capable of joint modelling of monogenic major genetic effects and polygenic minor genetic effects which can be used for both genome-wide assoiciation and prediction purposes. The statistical evaluations based on both simulated and real pig data set shows that the LAVA ADMM has better prediction properies than the LASSO, RR and EN. Julia code for the LAVA ADMM is available at: https://github.com/patwa67/LAVAADMM.

Dimensional reduction of phenotypes from 53,000 mouse models reveals a diverse landscape of gene function

Animal models have long been used to study gene function and the impact of genetic mutations on phenotype. Through the research efforts of thousands of research groups, systematic curation of published literature, and high-throughput phenotyping screens, the collective body of knowledge for the mouse now covers the majority of protein-coding genes. We here collected data for over 53,000 mouse models with mutations in over 15,000 genomic markers and characterized by more than 254,000 annotations using more than 9,000 distinct ontology terms. We investigated dimensional reduction and embedding techniques as means to facilitate access to this diverse and high-dimensional information. Our analyses provide the first visual maps of the landscape of mouse phenotypic diversity. We also summarize some of the difficulties in producing and interpreting embeddings of sparse phenotypic data. In particular, we show that data preprocessing, filtering, and encoding have as much impact on the final embeddings as the process of dimensional reduction. Nonetheless, techniques developed in the context of dimensional reduction create opportunities for explorative analysis of this large pool of public data, including for searching for mouse models suited to study human diseases.