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The Pacific whiteleg shrimp Penaeus (Litopenaeus) vannamei is a highly relevant species for the world's aquaculture development, for which an incomplete genome is available in public databases. In this work, PacBio long-reads from 14 publicly available genomic libraries (131.2 Gb) were mined to improve the reference genome assembly. The libraries were assembled, polished using Illumina short-reads, and scaffolded with P. vannamei, Feneropenaeus chinensis, and Penaeus monodon genomes. The reference-guided assembly, organized into 44 pseudo-chromosomes and 15,682 scaffolds, showed an improvement from previous reference genomes with a genome size of 2.055 Gb, N50 of 40.14 Mb, L50 of 21, and the longest scaffold of 65.79 Mb. Most orthologous genes (92.6%) of the Arthropoda_odb10 database were detected as "complete," and BRAKER predicted 21,816 gene models; from these, we detected 1,814 single-copy orthologues conserved across the genomic references for Marsupenaeus japonicus, F. chinensis, and P. monodon. Transcriptomic-assembly data aligned in more than 99% to the new reference-guided assembly. The collinearity analysis of the assembled pseudo-chromosomes against the P. vannamei and P. monodon reference genomes showed high conservation in different sets of pseudo-chromosomes. In addition, more than 21,000 publicly available genetic marker sequences were mapped to single-site positions. This new assembly represents a step forward to previously reported P. vannamei assemblies. It will be helpful as a reference genome for future studies on the evolutionary history of the species, the genetic architecture of physiological and sex-determination traits, and the analysis of the changes in genetic diversity and composition of cultivated stocks.

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Mental illnesses have a huge impact on individuals, families, and society, so there is a growing need for more efficient treatments. In this context, brain-computer interface (BCI) technology has the potential to revolutionize the options for neuropsychiatric therapies. However, the development of BCI-based therapies faces enormous challenges, such as power dissipation constraints, lack of credible feedback mechanisms, uncertainty of which brain areas and frequencies to target, and even which patients to treat. Some of these setbacks are due to the large gap in our understanding of brain function. In recent years, large-scale genomic analyses uncovered an unprecedented amount of information regarding the biology of the altered brain function observed across the psychopathology spectrum. We believe findings from genetic studies can be useful to refine BCI technology to develop novel treatment options for mental illnesses. Here, we assess the latest advancements in both fields, the possibilities that can be generated from their intersection, and the challenges that these research areas will need to address to ensure that translational efforts can lead to effective and reliable interventions. Specifically, starting from highlighting the overlap between mechanisms uncovered by large-scale genetic studies and the current targets of deep brain stimulation treatments, we describe the steps that could help to translate genomic discoveries into BCI targets. Because these two research areas have not been previously presented together, the present article can provide a novel perspective for scientists with different research backgrounds. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Biomedical Engineering.

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Uncovering causal relationships between exposures and outcomes can be difficult in observational studies because of the potential for confounding and reverse causation to produce biased estimates. Conversely, randomized controlled trials (RCTs) provide the strongest evidence for causality but they are not always feasible. Mendelian randomization (MR) is a method that aims to strengthen causal inference using genetic variants as proxies or instrumental variables (IVs) for exposures, to overcome the above-mentioned biases. Since allele segregation occurs at random from parents to offspring, and alleles for a trait assort independently from those for other traits, MR studies have frequently been compared to "natural" RCTs. In biological anthropology (BA) relationships between variables of interest are usually evaluated using observational data, often remaining descriptive, and other approaches to causal inference have seldom been implemented. Here, we propose the use of MR to investigate cause and effect relationships in BA studies and provide examples to show how that can be done across areas of BA relevance, such as adaptation to the environment, nutrition and life history theory. While we consider MR a useful addition to the biological anthropologist's toolbox, we advocate the adoption of a wide range of methods, affected by different types of biases, in order to better answer the important causal questions for the discipline.

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Amyotrophic lateral sclerosis (ALS) is a multi-system neurodegenerative disease that affects both upper and lower motor neurons, resulting from a combination of genetic, environmental, and lifestyle factors. Usually, the association between single-nucleotide polymorphisms (SNPs) and this disease is tested individually, which leads to the testing of multiple hypotheses. In addition, this classical approach does not support the detection of interaction-dependent SNPs. We applied a two-step procedure to select SNPs and pairwise interactions associated with ALS. SNP data from 276 ALS patients and 268 controls were analyzed by a two-step group LASSO in 2000 iterations. In the first step, we fitted a group LASSO model to a bootstrap sample and a random subset of predictors (25%) from the original data set aiming to screen for important SNPs and, in the second step, we fitted a hierarchical group LASSO model to evaluate pairwise interactions. An in silico analysis was performed on a set of variables, which were prioritized according to their bootstrap selection frequency. We identified seven SNPs (rs16984239, rs10459680, rs1436918, rs1037666, rs4552942, rs10773543, and rs2241493) and two pairwise interactions (rs16984239:rs2118657 and rs16984239:rs3172469) potentially involved in nervous system conservation and function. These results may contribute to the understanding of ALS pathogenesis, its diagnosis, and therapeutic strategy improvement.

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Genetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness cannot be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to complex datasets. When compared with two other popular population genetics methodologies (PLINK and KING), NAToRA shows the best combination of removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar effects on the allele frequency spectrum and Principal Component Analysis than PLINK and KING. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also release a genealogies simulator software used for different tests performed in this study.

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Brazil is among the largest producers and consumers of common bean (Phaseolus vulgaris L.) and can be considered a secondary center of diversity for the species. The aim of this study was to estimate the genetic diversity, population structure, and relationships among 288 common bean accessions in an American Diversity Panel (ADP) genotyped with 4,042 high-quality single nucleotide polymorphisms (SNPs). The results showed inter-gene pool hybridization (hybrids) between the two main gene pools (i.e., Mesoamerican and Andean), based on principal component analysis (PCA), discriminant analysis of principal components (DAPC), and STRUCTURE analysis. The genetic diversity parameters showed that the Mesoamerican group has higher values of diversity and allelic richness in comparison with the Andean group. Considering the optimal clusters (K), clustering was performed according to the type of grain (i.e., market group), the institution of origin, the period of release, and agronomic traits. A new subset was selected and named the Mesoamerican Diversity Panel (MDP), with 205 Mesoamerican accessions. Analysis of molecular variance (AMOVA) showed low genetic variance between the two panels (i.e., ADP and MDP) with the highest percentage of the limited variance among accessions in each group. The ADP showed occurrence of high genetic differentiation between populations (i.e., Mesoamerican and Andean) and introgression between gene pools in hybrids based on a set of diagnostic SNPs. The MDP showed better linkage disequilibrium (LD) decay. The availability of genetic variation from inter-gene pool hybridizations presents a potential opportunity for breeders towards the development of superior common bean cultivars.

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Duration of untreated psychosis (DUP) is associated with clinical outcomes in people with a diagnosis of first-episode psychosis (FEP), but factors associated with length of DUP are still poorly understood. Aiming to obtain insights into the possible biological impact on DUP, we report genetic analyses of a large multi-center phenotypically well-defined sample encompassing individuals with a diagnosis of FEP recruited from 6 countries spanning 17 research sites, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) study. Genetic propensity was measured using polygenic scores for schizophrenia (SZ-PGS), bipolar disorder (BD-PGS), major depressive disorder (MDD-PGS), and intelligence (IQ-PGS), which were calculated based on the results from the most recent genome-wide association meta-analyses. Following imputation for missing data and log transformation of DUP to handle skewedness, the association between DUP and polygenic scores (PGS), adjusting for important confounders, was investigated with multivariable linear regression models. The sample comprised 619 individuals with a diagnosis of FEP disorders with a median age at first contact of 29.0 years (interquartile range [IQR] = 22.0-38.0). The median length of DUP in the sample was 10.1 weeks (IQR = 3.8-30.8). One SD increases in SZ-PGS, BD-PGS, MDD-PGS or IQ-PGS were not significantly associated with the length of DUP. Our results suggest that genetic variation does not contribute to the DUP in patients with a diagnosis of FEP disorders.

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Angular leaf spot (ALS) is a disease that causes major yield losses in the common bean crop. Studies based on different isolates and populations have already been carried out to elucidate the genetic mechanisms of resistance to ALS. However, understanding of the interaction of this resistance with the reproductive stages of common bean is lacking. The aim of the present study was to identify ALS resistance loci at different plant growth stages (PGS) by association and linkage mapping approaches. An BC2F3 inter-gene pool cross population (AND 277 × IAC-Milênio - AM population) profiled with 1,091 SNPs from genotyping by sequencing (GBS) was used for linkage mapping, and a carioca diversity panel (CDP) genotyped by 5,398 SNPs from BeadChip assay technology was used for association mapping. Both populations were evaluated for ALS resistance at the V2 and V3 PGSs (controlled conditions) and R8 PGS (field conditions). Different QTL (quantitative trait loci) were detected for the three PGSs and both populations, showing a different quantitative profile of the disease at different plant growth stages. For the three PGS, multiple interval mapping (MIM) identified seven significant QTL, and the Genome-wide association study (GWAS) identified fourteen associate SNPs. Several loci validated regions of previous studies, and Phg-1, Phg-2, Phg-4, and Phg-5, among the 5 loci of greatest effects reported in the literature, were detected in the CDP. The AND 277 cultivar contained both the Phg-1 and the Phg-5 QTL, which is reported for the first time in the descendant cultivar CAL143 as ALS10.1UC. The novel QTL named ALS11.1AM was located at the beginning of chromosome Pv11. Gene annotation revealed several putative resistance genes involved in the ALS response at the three PGSs, and with the markers and loci identified, new specific molecular markers can be developed, representing a powerful tool for common bean crop improvement and for gain in ALS resistance.

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Genetic epidemiology studies have shown that most epilepsies involve some genetic cause. In addition, twin studies have helped strengthen the hypothesis that in most patients with epilepsy, a complex inheritance is involved. More recently, with the development of high-density single-nucleotide polymorphism (SNP) microarrays and next-generation sequencing (NGS) technologies, the discovery of genes related to the epilepsies has accelerated tremendously. Especially, the use of whole exome sequencing (WES) has had a considerable impact on the identification of rare genetic variants with large effect sizes, including inherited or de novo mutations in severe forms of childhood epilepsies. The identification of pathogenic variants in patients with these childhood epilepsies provides many benefits for patients and families, such as the confirmation of the genetic nature of the diseases. This process will allow for better genetic counseling, more accurate therapy decisions, and a significant positive emotional impact. However, to study the genetic component of the more common forms of epilepsy, the use of high-density SNP arrays in genome-wide association studies (GWAS) seems to be the strategy of choice. As such, researchers can identify loci containing genetic variants associated with the common forms of epilepsy. The knowledge generated over the past two decades about the effects of the mutations that cause the monogenic epilepsy is tremendous; however, the scientific community is just starting to apply this information in order to generate better target treatments.

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No large-scale genome-wide association studies (GWASs) of psychosis have been conducted in Mexico or Latin America to date. Schizophrenia and bipolar disorder in particular have been found to be highly heritable and genetically influenced. However, understanding of the biological basis of psychosis in Latin American populations is limited as previous genomic studies have almost exclusively relied on participants of Northern European ancestry. With the goal of expanding knowledge on the genomic basis of psychotic disorders within the Mexican population, the National Institute of Psychiatry Ramón de la Fuente Muñiz (INPRFM), the Harvard T.H. Chan School of Public Health, and the Broad Institute's Stanley Center for Psychiatric Research launched the Neuropsychiatric Genetics Research of Psychosis in Mexican Populations (NeuroMex) project to collect and analyze case-control psychosis samples from 5 states across Mexico. This article describes the planned sample collection and GWAS protocol for the NeuroMex study. The 4-year study will span from April 2018 to 2022 and aims to recruit 9,208 participants: 4,604 cases and 4,604 controls. Study sites across Mexico were selected to ensure collected samples capture the genomic diversity within the Mexican population. Blood samples and phenotypic data will be collected during the participant interview process and will contribute to the development of a local biobank in Mexico. DNA extraction will be done locally and genetic analysis will take place at the Broad Institute in Cambridge, MA. We will collect extensive phenotypic information using several clinical scales. All study materials including phenotypic instruments utilized are openly available in Spanish and English. The described study represents a long-term collaboration of a number of institutions from across Mexico and the Boston area, including clinical psychiatrists, clinical researchers, computational biologists, and managers at the 3 collaborating institutions. The development of relevant data management, quality assurance, and analysis plans are the primary considerations in this protocol article. Extensive management and analysis processes were developed for both the phenotypic and genetic data collected. Capacity building, partnerships, and training between and among the collaborating institutions are intrinsic components to this study and its long-term success.

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BACKGROUND: The band 9p21.3 contains an established genomic risk zone for cardiovascular disease (CVD). Since the initial 2007 Wellcome Trust Case Control Consortium study (WTCCC), the increased CVD risk associated with 9p21.3 has been confirmed by multiple studies in different continents. However, many years later there was still no confirmed report of a corresponding association of 9p21.3 with hypertension, a major CV risk factor, nor with blood pressure (BP). THEORY: In this contribution, we review the bipartite haplotype structure of the 9p21.3 risk locus: one block is devoid of protein-coding genes but contains the lead CVD risk SNPs, while the other block contains the first exon and regulatory DNA of the gene for the cell cycle inhibitor p15. We consider how findings from molecular biology offer possibilities of an involvement of p15 in hypertension etiology, with expression of the p15 gene modulated by genetic variation from within the 9p21.3 risk locus. RESULTS: We present original results from a Colombian study revealing moderate but persistent association signals for BP and hypertension within the classic 9p21.3 CVD risk locus. These SNPs are mostly confined to a 'hypertension island' that spans less than 60 kb and coincides with the p15 haplotype block. We find confirmation in data originating from much larger, recent European BP studies, albeit with opposite effect directions. CONCLUSION: Although more work will be needed to elucidate possible mechanisms, previous findings and new data prompt reconsidering the question of how variation in 9p21.3 might influence hypertension components of cardiovascular risk.

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Studying the genetics of adaptation to new environments in ecologically and industrially important tree species is currently a major research line in the fields of plant science and genetic improvement for tolerance to abiotic stress. Specifically, exploring the genomic basis of local adaptation is imperative for assessing the conditions under which trees will successfully adapt in situ to global climate change. However, this knowledge has scarcely been used in conservation and forest tree improvement because woody perennials face major research limitations such as their outcrossing reproductive systems, long juvenile phase, and huge genome sizes. Therefore, in this review we discuss predictive genomic approaches that promise increasing adaptive selection accuracy and shortening generation intervals. They may also assist the detection of novel allelic variants from tree germplasm, and disclose the genomic potential of adaptation to different environments. For instance, natural populations of tree species invite using tools from the population genomics field to study the signatures of local adaptation. Conventional genetic markers and whole genome sequencing both help identifying genes and markers that diverge between local populations more than expected under neutrality, and that exhibit unique signatures of diversity indicative of "selective sweeps." Ultimately, these efforts inform the conservation and breeding status capable of pivoting forest health, ecosystem services, and sustainable production. Key long-term perspectives include understanding how trees' phylogeographic history may affect the adaptive relevant genetic variation available for adaptation to environmental change. Encouraging "big data" approaches (machine learning-ML) capable of comprehensively merging heterogeneous genomic and ecological datasets is becoming imperative, too.

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The term spondyloarthritis (SpA) encompasses a group of chronic inflammatory diseases with common features in terms of clinical presentation and genetic predisposition. SpA is characterized by inflammation of the spine and peripheral joints, and is also be associated with extra-articular inflammatory manifestations such as psoriasis, uveitis, or inflammatory bowel disease (IBD). The etiology of SpA is not completely understood, but it is known to have a strong genetic component dominated by the human leukocyte antigen (HLA)-B27. In the last few years, our understanding of genetic susceptibility to SpA, particularly ankylosing spondylitis (AS), has greatly improved thanks to the findings derived from powered genome-wide association studies (GWAS) based on single nucleotide polymorphism (SNP) arrays. These studies have identified many candidate genes, therefore providing new potential directions in the exploration of disease mechanisms, especially with regard to the key role of the immune system in the pathogenesis of SpA. SpA is a complex disease where genetic variability, environmental factors, and random events interact to trigger pathological pathways. The aim of this review is to summarize current findings on the genetics of SpA, some of which might help to study new treatment approaches.

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The presence of intermuscular bones in fisheries products limits the consumption and commercialization potential of many fish species, including tambaqui (Colossoma macropomum). These bones have caused medical emergencies and are an undesirable characteristic for fish farming because their removal is labor-intensive during fish processing. Despite the difficulty in identifying genes related to the lack of intermuscular bone in diverse species of fish, the discovery of individuals lacking intermuscular bones in a Neotropical freshwater characiform fish has provided a unique opportunity to delve into the genetic mechanisms underlying the pathways of intermuscular bone formation. In this study, we carried out a GWAS among boneless and wt tambaqui populations to identify markers associated with a lack of intermuscular bone. After analyzing 11 416 SNPs in 360 individuals (12 boneless and 348 bony), we report 675 significant (Padj  < 0.003) associations for this trait. Of those, 13 associations were located near candidate genes related to the reduction of bone mass, promotion of bone formation, inhibition of bone resorption, central control of bone remodeling, bone mineralization and other related functions. To the best of our knowledge, for the first time, we have successfully identified genes related to a lack of intermuscular bones using GWAS in a non-model species.

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Bovine babesiosis is a tick-borne disease caused by intraerythrocytic protozoa and leads to substantial economic losses for the livestock industry throughout the world. Babesia bovis is considered the most pathogenic species, which causes bovine babesiosis in Brazil. Genomic data could be used to evaluate the viability of improving resistance against B. bovis infection level (IB) through genomic selection, and, for that, knowledge of genetic parameters is needed. Furthermore, genome-wide association studies (GWAS) could be conducted to provide a better understanding of the genetic basis of the host response to B. bovis infection. No previous work in quantitative genetics of B. bovis infection was found. Thus, the objective of this study was to estimate the genetic correlation between IB and tick count (TC), evaluate predictive ability and applicability of genomic selection, and perform GWAS in Hereford and Braford cattle. The single-step genomic best linear unbiased prediction method was used, which allows the estimation of both breeding values and marker effects. Standard phenotyping was conducted for both traits. IB quantifications from the blood of 1,858 animals were carried using quantitative PCR assays. For TC, one to three subsequent tick counts were performed by manually counting adult female ticks on one side of each animal's body that was naturally exposed to ticks. Animals were genotyped using the Illumina BovineSNP50 panel. The posterior mean of IB heritability, estimated by the Bayesian animal model in a bivariate analysis, was low (0.10), and the estimations of genetic correlation between IB and TC were also low (0.15). The cross-validation genomic prediction accuracy for IB ranged from 0.18 to 0.35 and from 0.29 to 0.32 using k-means and random clustering, respectively, suggesting that genomic predictions could be used as a tool to improve genetics for IB, especially if a larger training population is developed. The top 10 single nucleotide polymorphisms from the GWAS explained 5.04% of total genetic variance for IB, which were located on chromosomes 1, 2, 5, 6, 12, 17, 18, 16, 24, and 26. Some candidate genes participate in immunity system pathways indicating that those genes are involved in resistance to B. bovis in cattle. Although the genetic correlation between IB and TC was weak, some candidate genes for IB were also reported in tick infestation studies, and they were also involved in biological resistance processes. This study contributes to improving genetic knowledge regarding infection by B. bovis in cattle.

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Molecular evolution offers an insightful theory to interpret the genomic consequences of thermal adaptation to previous events of climate change beyond range shifts. However, disentangling often mixed footprints of selective and demographic processes from those due to lineage sorting, recombination rate variation, and genomic constrains is not trivial. Therefore, here we condense current and historical population genomic tools to study thermal adaptation and outline key developments (genomic prediction, machine learning) that might assist their utilization for improving forecasts of populations' responses to thermal variation. We start by summarizing how recent thermal-driven selective and demographic responses can be inferred by coalescent methods and in turn how quantitative genetic theory offers suitable multi-trait predictions over a few generations via the breeder's equation. We later assume that enough generations have passed as to display genomic signatures of divergent selection to thermal variation and describe how these footprints can be reconstructed using genome-wide association and selection scans or, alternatively, may be used for forward prediction over multiple generations under an infinitesimal genomic prediction model. Finally, we move deeper in time to comprehend the genomic consequences of thermal shifts at an evolutionary time scale by relying on phylogeographic approaches that allow for reticulate evolution and ecological parapatric speciation, and end by envisioning the potential of modern machine learning techniques to better inform long-term predictions. We conclude that foreseeing future thermal adaptive responses requires bridging the multiple spatial scales of historical and predictive environmental change research under modern cohesive approaches such as genomic prediction and machine learning frameworks.

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The successful implementation of personalized medicine will rely on the integration of information obtained at the level of populations with the specific biological, genetic, and clinical characteristics of an individual. However, because genome-wide association studies tend to focus on populations of European descent, there is a wide gap to bridge between Caucasian and non-Caucasian populations before personalized medicine can be fully implemented, and rheumatoid arthritis (RA) is not an exception. In this review, we discuss advances in our understanding of genetic determinants of RA risk among global populations, with a focus on the Latin American population. Geographically restricted genetic diversity may have important implications for health and disease that will remain unknown until genetic association studies have been extended to include Latin American and other currently under-represented ancestries. The next few years will witness many breakthroughs in personalized medicine, including applications for common diseases and risk stratification instruments for targeted prevention/intervention strategies. Not all of these applications may be extrapolated from the Caucasian experience to Latin American or other under-represented populations.

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The contribution of genetic ancestry on chronic obstructive pulmonary disease (COPD) predisposition remains unclear. To explore this relationship, we analyzed the associations between 754,159 single nucleotide polymorphisms (SNPs) and risk of COPD (n = 214 cases, 193 healthy controls) in Talca, Chile, considering the genetic ancestry and established risk factors. The proportion of Mapuche ancestry (PMA) was based on a panel of 45 Mapuche reference individuals. Five PRDM15 SNPs and two PPP1R12B SNPs were associate with COPD risk (p = 0.05 to 5×10-4) in those individuals with lower PMA. Based on linkage disequilibrium and sliding window analyses, an adjacent PRDM15 SNPs were associated with COPD risk in the lower PMA group (p = 10-3 to 3.77×10-8). Our study is the first to report an association between PPP1R12B and COPD risk, as well as effect modification between ethnicity and PRDM15 SNPs in determining COPD risk. Our results are biologically plausible given that PPP1R12B and PRDM15 are involved in immune dysfunction and autoimmunity, providing mechanistic evidence for COPD pathogenesis and highlighting the importance to conduct more genome wide association studies (GWAS) in admixed populations with Amerindian descent.

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In plant and animal breeding, genomic prediction models are established to select new lines based on genomic data, without the need for laborious phenotyping. Prediction models can be trained on recent or historic phenotypic data and increasingly available genotypic data. This enables the adoption of genomic selection also in under-used legume crops such as common bean. Beans are an important staple food in the tropics and mainly grown by smallholders under limiting environmental conditions such as drought or low soil fertility. Therefore, genotype-by-environment interactions (G × E) are an important consideration when developing new bean varieties. However, G × E are often not considered in genomic prediction models nor are these models implemented in current bean breeding programs. Here we show the prediction abilities of four agronomic traits in common bean under various environmental stresses based on twelve field trials. The dataset includes 481 elite breeding lines characterized by 5,820 SNP markers. Prediction abilities over all twelve trials ranged between 0.6 and 0.8 for yield and days to maturity, respectively, predicting new lines into new seasons. In all four evaluated traits, the prediction abilities reached about 50-80% of the maximum accuracies given by phenotypic correlations and heritability. Predictions under drought and low phosphorus stress were up to 10 and 20% improved when G × E were included in the model, respectively. Our results demonstrate the potential of genomic selection to increase the genetic gain in common bean breeding. Prediction abilities improved when more phenotypic data was available and G × E could be accounted for. Furthermore, the developed models allowed us to predict genotypic performance under different environmental stresses. This will be a key factor in the development of common bean varieties adapted to future challenging conditions.