Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed).

BACKGROUND: Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred. RESULTS: We apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations. CONCLUSIONS: Our algorithms provide a biologically assumption free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

Weighted single step GWAS reveals genomic regions associated with economic traits in Murrah buffaloes.

The objective of the present study was to identify genomic regions influencing economic traits in Murrah buffaloes using weighted single step Genome Wide Association Analysis (WssGWAS). Data on 2000 animals, out of which 120 were genotyped using a double digest Restriction site Associated DNA (ddRAD) sequencing approach. The phenotypic data were collected from NDRI, India, on growth traits, viz., body weight at 6M (month), 12M, 18M and 24M, production traits like 305D (day) milk yield, lactation length (LL) and dry period (DP) and reproduction traits like age at first calving (AFC), calving interval (CI) and first service period (FSP). The biallelic genotypic data consisted of 49353 markers post-quality check. The heritability estimates were moderate to high, low to moderate, low for growth, production, reproduction traits, respectively. Important genomic regions explaining more than 0.5% of the total additive genetic variance explained by 30 adjacent SNPs were selected for further analysis of candidate genes. In this study, 105 genomic regions were associated with growth, 35 genomic regions with production and 42 window regions with reproduction traits. Different candidate genes were identified in these genomic regions, of which important are OSBPL8, NAP1L1 for growth, CNTNAP2 for production and ILDR2, TADA1 and POGK for reproduction traits.

'Integrating Ethics and Equity with Economics and Effectiveness for newborn screening in the genomic age: A qualitative study protocol of stakeholder perspectives.

BACKGROUND: Newborn bloodspot screening is a well-established population health initiative that detects serious, childhood-onset, treatable conditions to improve health outcomes. With genomic technologies advancing rapidly, many countries are actively discussing the introduction of genomic assays into newborn screening programs. While adding genomic testing to Australia's newborn screening program could improve outcomes for infants and families, it must be considered against potential harms, ethical, legal, equity and social implications, and economic and health system impacts. We must ask not only 'can' we use genomics to screen newborns?' but 'should we'?' and 'how much should health systems invest in genomic newborn screening?'. METHODS: This study will use qualitative methods to explore understanding, priorities, concerns and expectations of genomic newborn screening among parents/carers, health professionals/scientists, and health policy makers across Australia. In-depth, semi-structured interviews will be held with 30-40 parents/carers recruited via hospital and community settings, 15-20 health professionals/scientists, and 10-15 health policy makers. Data will be analysed using inductive content analysis. The Sydney Children's Hospital Network Human Research Ethics Committee approved this study protocol [2023/ETH02371]. The Standards for Reporting Qualitative Research will guide study planning, conduct and reporting. DISCUSSION: Few studies have engaged a diverse range of stakeholders to explore the implications of genomics in newborn screening in a culturally and genetically diverse population, nor in a health system underpinned by universal health care. As the first study within a multi-part research program, findings will be used to generate new knowledge on the risks and benefits and importance of ethical, legal, social and equity implications of genomic newborn screening from the perspective of key stakeholders. As such it will be the foundation on which child and family centered criteria can be developed to inform health technology assessments and drive efficient and effective policy decision-making on the implementation of genomics in newborn screening.

kalis: a modern implementation of the Li & Stephens model for local ancestry inference in R.

BACKGROUND: Approximating the recent phylogeny of N phased haplotypes at a set of variants along the genome is a core problem in modern population genomics and central to performing genome-wide screens for association, selection, introgression, and other signals. The Li & Stephens (LS) model provides a simple yet powerful hidden Markov model for inferring the recent ancestry at a given variant, represented as an N × N distance matrix based on posterior decodings. RESULTS: We provide a high-performance engine to make these posterior decodings readily accessible with minimal pre-processing via an easy to use package kalis, in the statistical programming language R. kalis enables investigators to rapidly resolve the ancestry at loci of interest and developers to build a range of variant-specific ancestral inference pipelines on top. kalis exploits both multi-core parallelism and modern CPU vector instruction sets to enable scaling to hundreds of thousands of genomes. CONCLUSIONS: The resulting distance matrices accessible via kalis enable local ancestry, selection, and association studies in modern large scale genomic datasets.

Genome-wide association studies for economically important traits in mink using copy number variation.

Copy number variations (CNVs) are structural variants consisting of duplications and deletions of DNA segments, which are known to play important roles in the genetics of complex traits in livestock species. However, CNV-based genome-wide association studies (GWAS) have remained unexplored in American mink. Therefore, the purpose of the current study was to investigate the association between CNVs and complex traits in American mink. A CNV-based GWAS was performed with the ParseCNV2 software program using deregressed estimated breeding values of 27 traits as pseudophenotypes, categorized into traits of growth and feed efficiency, reproduction, pelt quality, and Aleutian disease tests. The study identified a total of 10,137 CNVs (6968 duplications and 3169 deletions) using the Affymetrix Mink 70K single nucleotide polymorphism (SNP) array in 2986 American mink. The association analyses identified 250 CNV regions (CNVRs) associated with at least one of the studied traits. These CNVRs overlapped with a total of 320 potential candidate genes, and among them, several genes have been known to be related to the traits such as ARID1B, APPL1, TOX, and GPC5 (growth and feed efficiency traits); GRM1, RNASE10, WNT3, WNT3A, and WNT9B (reproduction traits); MYO10, and LIMS1 (pelt quality traits); and IFNGR2, APEX1, UBE3A, and STX11 (Aleutian disease tests). Overall, the results of the study provide potential candidate genes that may regulate economically important traits and therefore may be used as genetic markers in mink genomic breeding programs.

Cost-Effectiveness of Whole-Genome vs Whole-Exome Sequencing Among Children With Suspected Genetic Disorders.

Importance: The diagnosis of rare diseases and other genetic conditions can be daunting due to vague or poorly defined clinical features that are not recognized even by experienced clinicians. Next-generation sequencing technologies, such as whole-genome sequencing (WGS) and whole-exome sequencing (WES), have greatly enhanced the diagnosis of genetic diseases by expanding the ability to sequence a large part of the genome, rendering a cost-effectiveness comparison between them necessary. Objective: To assess the cost-effectiveness of WGS compared with WES and conventional testing in children with suspected genetic disorders. Design, Setting, and Participants: In this economic evaluation, a bayesian Markov model was implemented from January 1 to June 30, 2023. The model was developed using data from a cohort of 870 pediatric patients with suspected genetic disorders who were enrolled and underwent testing in the Ospedale Pediatrico Bambino Gesù, Rome, Italy, from January 1, 2015, to December 31, 2022. The robustness of the model was assessed through probabilistic sensitivity analysis and value of information analysis. Main Outcomes and Measures: Overall costs, number of definitive diagnoses, and incremental cost-effectiveness ratios per diagnosis were measured. The cost-effectiveness analyses involved 4 comparisons: first-tier WGS with standard of care; first-tier WGS with first-tier WES; first-tier WGS with second-tier WES; and first-tier WGS with second-tier WGS. Results: The ages of the 870 participants ranged from 0 to 18 years (539 [62%] girls). The results of the analysis suggested that adopting WGS as a first-tier strategy would be cost-effective compared with all other explored options. For all threshold levels above €29 800 (US $32 408) per diagnosis that were tested up to €50 000 (US $54 375) per diagnosis, first-line WGS vs second-line WES strategy (ie, 54.6%) had the highest probability of being cost-effective, followed by first-line vs second-line WGS (ie, 54.3%), first-line WGS vs the standard of care alternative (ie, 53.2%), and first-line WGS vs first-line WES (ie, 51.1%). Based on sensitivity analyses, these estimates remained robust to assumptions and parameter uncertainty. Conclusions and Relevance: The findings of this economic evaluation encourage the development of policy changes at various levels (ie, macro, meso, and micro) of international health systems to ensure an efficient adoption of WGS in clinical practice and its equitable access.

Assessment of runs of homozygosity, heterozygosity-rich regions and genomic inbreeding estimates in a subpopulation of Guzerá (Bos indicus) dual-purpose cattle.

For decades, inbreeding in cattle has been evaluated using pedigree information. Nowadays, inbreeding coefficients can be obtained using genomic information such as runs of homozygosity (ROH). The aims of this study were to quantify ROH and heterozygosity-rich regions (HRR) in a subpopulation of Guzerá dual-purpose cattle, to examine ROH and HRR islands, and to compare inbreeding coefficients obtained by ROH with alternative genomic inbreeding coefficients. A subpopulation of 1733 Guzerá animals genotyped for 50k SNPs was used to obtain the ROH and HRR segments. Inbreeding coefficients by ROH (FROH ), by genomic relationship matrix based on VanRaden's method 1 using reference allele frequency in the population (FGRM ), by genomic relationship matrix based on VanRaden's method 1 using allele frequency fixed in 0.5 (FGRM_0.5 ), and by the proportion of homozygous loci (FHOM ) were calculated. A total of 15,660 ROH were identified, and the chromosome with the highest number of ROH was BTA6. A total of 4843 HRRs were identified, and the chromosome with the highest number of HRRs was BTA23. No ROH and HRR islands were identified according to established criteria, but the regions closest to the definition of an island were examined from 64 to 67 Mb of BTA6, from 36 to 37 Mb of BTA2 and from 0.50 to 1.25 Mb of BTA23. The genes identified in ROH islands have previously been associated with dairy and beef traits, while genes identified on HRR islands have previously been associated with reproductive traits and disease resistance. FROH was equal to 0.095 ± 0.084, and its Spearman correlation with FGRM was low (0.44) and moderate-high with FHOM (0.79) and with FGRM_0.5 (0.80). The inbreeding coefficients determined by ROH were higher than other cattle breeds' and higher than pedigree-based inbreeding in the Guzerá breed obtained in previous studies. It is recommended that future studies investigate the effects of inbreeding determined by ROH on the traits under selection in the subpopulation studied.

A rapid and reference-free imputation method for low-cost genotyping platforms.

Most current genotype imputation methods are reference-based, which posed several challenges to users, such as high computational costs and reference panel inaccessibility. Thus, deep learning models are expected to create reference-free imputation methods performing with higher accuracy and shortening the running time. We proposed a imputation method using recurrent neural networks integrating with an additional discriminator network, namely GRUD. This method was applied to datasets from genotyping chips and Low-Pass Whole Genome Sequencing (LP-WGS) with the reference panels from The 1000 Genomes Project (1KGP) phase 3, the dataset of 4810 Singaporeans (SG10K), and The 1000 Vietnamese Genome Project (VN1K). Our model performed more accurately than other existing methods on multiple datasets, especially with common variants with large minor allele frequency, and shrank running time and memory usage. In summary, these results indicated that GRUD can be implemented in genomic analyses to improve the accuracy and running-time of genotype imputation.

The Expanding Dissemination and Distribution Patterns of Diverse CRISPR Plasmids by Addgene.

CRISPR-based technologies have rapidly enabled the democratization of genome editing in academic institutions through distribution by Addgene over the past decade. Recently, several distribution milestones have been reached, with a collection of >15,000 plasmids deposited by >1,000 laboratories spanning ∼40 countries now shipped 300,000 times to ∼5,000 organizations traversing ∼100 countries. Yet, both deposits of and requests for CRISPR plasmids continue to rise for this disruptive technology. Distribution patterns revealed robust demand for three distinct classes of CRISPR effectors, namely nucleases (e.g., Cas9 and Cas12), modulators (deactivated CRISPR nucleases fused to transcriptional regulators and epigenome modifiers), and chimeric effectors (Cas proteins fused to enzymes carrying out other activities such as deamination, reverse transcription, transposition, and integration). Yearly deposits over the past decade are requested in near-even proportions, reflecting continuous technological development and requests for novel constructs. Though it is unclear whether the slowing rate of requests is inherent to a pandemic operational lag or a transition from emerging to mature technology, it is noteworthy that the relative proportion of requests from plasmids deposited in the previous year remains stable, suggesting robust development of novel tools concurrent with continued adoption of editing, base editing, prime editing, and more. Predictably, most requested plasmids are designed for mammalian genome manipulation, presumably for medical research and human health pursuits, reflecting investments in therapeutic applications. Concurrently, requests for plant and microbial constructs are on the rise, especially in regions of the world more reliant on local agricultural inputs and focused on food and feed applications, illustrating continued diversification of genome editing applications.

Unbiased assessment of genome integrity and purging of adverse outcomes at the target locus upon editing of CD4+ T-cells for the treatment of Hyper IgM1.

Hyper IgM1 is an X-linked combined immunodeficiency caused by CD40LG mutations, potentially treatable with CD4+ T-cell gene editing with Cas9 and a "one-size-fits-most" corrective template. Contrary to established gene therapies, there is limited data on the genomic alterations following long-range gene editing, and no consensus on the relevant assays. We developed drop-off digital PCR assays for unbiased detection of large on-target deletions and found them at high frequency upon editing. Large deletions were also common upon editing different loci and cell types and using alternative Cas9 and template delivery methods. In CD40LG edited T cells, on-target deletions were counter-selected in culture and further purged by enrichment for edited cells using a selector coupled to gene correction. We then validated the sensitivity of optical genome mapping for unbiased detection of genome wide rearrangements and uncovered on-target trapping of one or more vector copies, which do not compromise functionality, upon editing using an integrase defective lentiviral donor template. No other recurring events were detected. Edited patient cells showed faithful reconstitution of CD40LG regulated expression and function with a satisfactory safety profile. Large deletions and donor template integrations should be anticipated and accounted for when designing and testing similar gene editing strategies.

Identification of errors in draft genome assemblies at single-nucleotide resolution for quality assessment and improvement.

Assembly of a high-quality genome is important for downstream comparative and functional genomic studies. However, most tools for genome assembly assessment only give qualitative reports, which do not pinpoint assembly errors at specific regions. Here, we develop a new reference-free tool, Clipping information for Revealing Assembly Quality (CRAQ), which maps raw reads back to assembled sequences to identify regional and structural assembly errors based on effective clipped alignment information. Error counts are transformed into corresponding assembly evaluation indexes to reflect the assembly quality at single-nucleotide resolution. Notably, CRAQ distinguishes assembly errors from heterozygous sites or structural differences between haplotypes. This tool can clearly indicate low-quality regions and potential structural error breakpoints; thus, it can identify misjoined regions that should be split for further scaffold building and improvement of the assembly. We have benchmarked CRAQ on multiple genomes assembled using different strategies, and demonstrated the misjoin correction for improving the constructed pseudomolecules.

Potential of low-density genotype imputation for cost-efficient genomic selection for resistance to Flavobacterium columnare in rainbow trout (Oncorhynchus mykiss).

BACKGROUND: Flavobacterium columnare is the pathogen agent of columnaris disease, a major emerging disease that affects rainbow trout aquaculture. Selective breeding using genomic selection has potential to achieve cumulative improvement of the host resistance. However, genomic selection is expensive partly because of the cost of genotyping large numbers of animals using high-density single nucleotide polymorphism (SNP) arrays. The objective of this study was to assess the efficiency of genomic selection for resistance to F. columnare using in silico low-density (LD) panels combined with imputation. After a natural outbreak of columnaris disease, 2874 challenged fish and 469 fish from the parental generation (n = 81 parents) were genotyped with 27,907 SNPs. The efficiency of genomic prediction using LD panels was assessed for 10 panels of different densities, which were created in silico using two sampling methods, random and equally spaced. All LD panels were also imputed to the full 28K HD panel using the parental generation as the reference population, and genomic predictions were re-evaluated. The potential of prioritizing SNPs that are associated with resistance to F. columnare was also tested for the six lower-density panels. RESULTS: The accuracies of both imputation and genomic predictions were similar with random and equally-spaced sampling of SNPs. Using LD panels of at least 3000 SNPs or lower-density panels (as low as 300 SNPs) combined with imputation resulted in accuracies that were comparable to those of the 28K HD panel and were 11% higher than the pedigree-based predictions. CONCLUSIONS: Compared to using the commercial HD panel, LD panels combined with imputation may provide a more affordable approach to genomic prediction of breeding values, which supports a more widespread adoption of genomic selection in aquaculture breeding programmes.

A genomic assessment of the marine-speciation paradox within the toothed whale superfamily Delphinoidea.

The impact of post-divergence gene flow in speciation has been documented across a range of taxa in recent years, and may have been especially widespread in highly mobile, wide-ranging marine species, such as cetaceans. Here, we studied individual genomes from nine species across the three families of the toothed whale superfamily Delphinoidea (Delphinidae, Phocoenidae and Monodontidae). To investigate the role of post-divergence gene flow in the speciation process, we used a multifaceted approach, including (i) phylogenomics, (ii) the distribution of shared derived alleles and (iii) demographic inference. We found the divergence of lineages within Delphinoidea did not follow a process of pure bifurcation, but was much more complex. Sliding-window phylogenomics reveal a high prevalence of discordant topologies within the superfamily, with further analyses indicating these discordances arose due to both incomplete lineage sorting and gene flow. D-statistics and f-branch analyses supported gene flow between members of Delphinoidea, with the vast majority of gene flow occurring as ancient interfamilial events. Demographic analyses provided evidence that introgressive gene flow has likely ceased between all species pairs tested, despite reports of contemporary interspecific hybrids. Our study provides the first steps towards resolving the large complexity of speciation within Delphinoidea; we reveal the prevalence of ancient interfamilial gene flow events prior to the diversification of each family, and suggest that contemporary hybridisation events may be disadvantageous, as hybrid individuals do not appear to contribute to the parental species' gene pools.

A method for partitioning trends in genetic mean and variance to understand breeding practices.

BACKGROUND: In breeding programmes, the observed genetic change is a sum of the contributions of different selection paths represented by groups of individuals. Quantifying these sources of genetic change is essential for identifying the key breeding actions and optimizing breeding programmes. However, it is difficult to disentangle the contribution of individual paths due to the inherent complexity of breeding programmes. Here we extend the previously developed method for partitioning genetic mean by paths of selection to work both with the mean and variance of breeding values. METHODS: First, we extended the partitioning method to quantify the contribution of different paths to genetic variance assuming that the breeding values are known. Second, we combined the partitioning method with the Markov Chain Monte Carlo approach to draw samples from the posterior distribution of breeding values and use these samples for computing the point and interval estimates of partitions for the genetic mean and variance. We implemented the method in the R package AlphaPart. We demonstrated the method with a simulated cattle breeding programme. RESULTS: We show how to quantify the contribution of different groups of individuals to genetic mean and variance and that the contributions of different selection paths to genetic variance are not necessarily independent. Finally, we observed that the partitioning method under the pedigree-based model has some limitations, which suggests the need for a genomic extension. CONCLUSIONS: We presented a partitioning method to quantify sources of change in genetic mean and variance in breeding programmes. The method can help breeders and researchers understand the dynamics in genetic mean and variance in a breeding programme. The developed method for partitioning genetic mean and variance is a powerful method for understanding how different selection paths interact within a breeding programme and how they can be optimised.

Primate genomes offer new view of human health and our past.

Sequencing efforts may also aid primate conservation.

How economic weights translate into genetic and phenotypic progress, and vice versa.

BACKGROUND: This paper highlights the relationships between economic weights, genetic progress, and phenotypic progress in genomic breeding programs that aim at generating genetic progress in complex, i.e., multi-trait, breeding objectives via a combination of estimated breeding values for different trait complexes. RESULTS: Based on classical selection index theory in combination with quantitative genetic models, we provide a methodological framework for calculating expected genetic and phenotypic progress for all components of a complex breeding objective. We further provide an approach to study the sensitivity of the system to modifications, e.g. to changes in the economic weights. We propose a novel approach to derive the covariance structure of the stochastic errors of estimated breeding values from the observed correlations of estimated breeding values. We define 'realized economic weights' as those weights that would coincide with the observed composition of the genetic trend and show, how they can be calculated. The suggested methodology is illustrated with an index that aims at achieving a breeding goal composed of six trait complexes, that was applied in German Holstein cattle breeding until 2021. CONCLUSIONS: Based on the presented results, the main conclusions are (i) the composition of the observed genetic progress matches the expectations well, with predictions being slightly better when the covariance of estimation errors is taken into account; (ii) the composition of the expected phenotypic trend deviates significantly from the expected genetic trend due to the differences in trait heritabilities; and (iii) the realized economic weights derived from the observed genetic trend deviate substantially from the predefined ones, in one case even with a reversed sign. Further results highlight the implications of the change to a modified breeding goal based on the example of a new index comprising eight, partly new, trait complexes, which is used since 2021 in the German Holstein breeding program. The proposed framework and the analytical tools and software provided will be useful to define more rational and generally accepted breeding objectives in the future.

Genome-Wide Assessment of Runs of Homozygosity by Whole-Genome Sequencing in Diverse Horse Breeds Worldwide.

In the genomes of diploid organisms, runs of homozygosity (ROH), consecutive segments of homozygosity, are extended. ROH can be applied to evaluate the inbreeding situation of individuals without pedigree data and to detect selective signatures via ROH islands. We sequenced and analyzed data derived from the whole-genome sequencing of 97 horses, investigated the distribution of genome-wide ROH patterns, and calculated ROH-based inbreeding coefficients for 16 representative horse varieties from around the world. Our findings indicated that both ancient and recent inbreeding occurrences had varying degrees of impact on various horse breeds. However, recent inbreeding events were uncommon, particularly among indigenous horse breeds. Consequently, the ROH-based genomic inbreeding coefficient could aid in monitoring the level of inbreeding. Using the Thoroughbred population as a case study, we discovered 24 ROH islands containing 72 candidate genes associated with artificial selection traits. We found that the candidate genes in Thoroughbreds were involved in neurotransmission (CHRNA6, PRKN, and GRM1), muscle development (ADAMTS15 and QKI), positive regulation of heart rate and heart contraction (HEY2 and TRDN), regulation of insulin secretion (CACNA1S, KCNMB2, and KCNMB3), and spermatogenesis (JAM3, PACRG, and SPATA6L). Our findings provide insight into horse breed characteristics and future breeding strategies.

A first annotated genome sequence for Haliotis midae with genomic insights into abalone evolution and traits of economic importance.

Haliotis midae or "perlemoen" is one of five abalone species endemic to South Africa, and being palatable, the only commercially important abalone species with a high international demand. The higher demand for this abalone species has resulted in the decrease of natural stocks due to overexploitation by capture fisheries and poaching. Facilitating aquaculture production of H. midae should assist in minimising the pressure on the wild populations. Here, the draft genome of H. midae has been sequenced, assembled, and annotated. The draft assembly resulted in a total length of 1.5 Gb, contig N50 of 0.238 Mb, scaffold N50 of 0. 238 Mb and GC level of 40%. Gene annotation, combining ab initio and evidence-based pipelines identified 52,280 genes with protein coding potential. The genes identified were used to predict orthologous genes shared among the four other abalone species (H. laevigata, H. rubra, H. discus hannai and H. rufescens) and 4702 orthologous genes were shared across the five species. Among the orthologous genes in abalones, single copy genes were further analysed for signatures of selection and several molecular regulatory proteins involved in developmental functions were found to be under positive selection in specific abalone lineages. Furthermore, whole genome SNP-based phylogenomic assessment was performed to confirm the evolutionary relationship among the considered abalone species with draft genomes, reaffirming that H. midae is closely related to the Australian Greenlip (H. laevigata) and Blacklip (H. rubra). The study assists in the understanding of genes related to various biological systems underscoring the evolution and development of abalones, with potential applications for genetic improvement of commercial stocks.

CrusTome: a transcriptome database resource for large-scale analyses across Crustacea.

Transcriptomes from nontraditional model organisms often harbor a wealth of unexplored data. Examining these data sets can lead to clarity and novel insights in traditional systems, as well as to discoveries across a multitude of fields. Despite significant advances in DNA sequencing technologies and in their adoption, access to genomic and transcriptomic resources for nontraditional model organisms remains limited. Crustaceans, for example, being among the most numerous, diverse, and widely distributed taxa on the planet, often serve as excellent systems to address ecological, evolutionary, and organismal questions. While they are ubiquitously present across environments, and of economic and food security importance, they remain severely underrepresented in publicly available sequence databases. Here, we present CrusTome, a multispecies, multitissue, transcriptome database of 201 assembled mRNA transcriptomes (189 crustaceans, 30 of which were previously unpublished, and 12 ecdysozoans for phylogenetic context) as an evolving and publicly available resource. This database is suitable for evolutionary, ecological, and functional studies that employ genomic/transcriptomic techniques and data sets. CrusTome is presented in BLAST and DIAMOND formats, providing robust data sets for sequence similarity searches, orthology assignments, phylogenetic inference, etc. and thus allowing for straightforward incorporation into existing custom pipelines for high-throughput analyses. In addition, to illustrate the use and potential of CrusTome, we conducted phylogenetic analyses elucidating the identity and evolution of the cryptochrome/photolyase family of proteins across crustaceans.

Automatic block-wise genotype-phenotype association detection based on hidden Markov model.

BACKGROUND: For detecting genotype-phenotype association from case-control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the phenotype. RESULTS: In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case-control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher's exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus Algorithm. Across all simulations, our method consistently outperformed the comparisons. CONCLUSIONS: With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case-control GWAS.