Radiation and hybridization of the Little Devil poison frog (Oophaga sylvatica) in Ecuador

AbstractGeographic variation of color pattern in the South American poison frogs (Dendrobatidae) is an intriguing evolutionary phenomenon. These chemically defended anurans use bright aposematic colors to warn potential predators of their unpalatibility. However, aposematic signals are frequency-dependent and individuals deviating from a local model are at a higher risk of predation. The well-known examples of Batesian and Müllerian mimics, hymenopterans (wasps and bees) and Heliconius butterflies, both support the benefits of unique models with relatively high frequencies. However, extreme diversity in the aposematic signal has been documented in the poison frogs of the genus Dendrobates, especially in the Oophaga subgenus. Here we investigate the phylogenetic and genomic differentiations among populations of Oophaga sylvatica, which exhibit one of the highest phenotypic diversification among poison frogs. Using a combination of PCR amplicons (mitochondrial and nuclear markers) and genome wide markers from a double-digested RAD data set, we characterize 13 populations (12 monotypic and 1 polytypic) across the O. sylvatica distribution. These populations are mostly separated in two lineages distributed in the Northern and the Southern part of their range in Ecuador. We found relatively low genetic differentiation within each lineage, despite considerable phenotypic variation, and evidence suggesting ongoing gene flow and genetic admixture among some populations of the Northern lineage. Overall these data suggest that phenotypic diversification and novelty in aposematic coloration can arise in secondary contact zones even in systems where phenotypes are subject to strong stabilizing selection.

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A likelihood ratio approach for identifying three-quarter siblings in genetic databases

Heredity ◽

10.1038/s41437-020-00392-8 ◽

2021 ◽

Author(s):

Iván Galván-Femenía ◽

Carles Barceló-Vidal ◽

Lauro Sumoy ◽

Victor Moreno ◽

Rafael de Cid ◽

...

Keyword(s):

Likelihood Ratio ◽

Family Relationships ◽

Genetic Research ◽

Data Set ◽

Scientific Disciplines ◽

Genome Wide ◽

Standard Quality ◽

Control Procedures ◽

Identity By State ◽

Second Degree

AbstractThe detection of family relationships in genetic databases is of interest in various scientific disciplines such as genetic epidemiology, population and conservation genetics, forensic science, and genealogical research. Nowadays, screening genetic databases for related individuals forms an important aspect of standard quality control procedures. Relatedness research is usually based on an allele sharing analysis of identity by state (IBS) or identity by descent (IBD) alleles. Existing IBS/IBD methods mainly aim to identify first-degree relationships (parent–offspring or full siblings) and second degree (half-siblings, avuncular, or grandparent–grandchild) pairs. Little attention has been paid to the detection of in-between first and second-degree relationships such as three-quarter siblings (3/4S) who share fewer alleles than first-degree relationships but more alleles than second-degree relationships. With the progressively increasing sample sizes used in genetic research, it becomes more likely that such relationships are present in the database under study. In this paper, we extend existing likelihood ratio (LR) methodology to accurately infer the existence of 3/4S, distinguishing them from full siblings and second-degree relatives. We use bootstrap confidence intervals to express uncertainty in the LRs. Our proposal accounts for linkage disequilibrium (LD) by using marker pruning, and we validate our methodology with a pedigree-based simulation study accounting for both LD and recombination. An empirical genome-wide array data set from the GCAT Genomes for Life cohort project is used to illustrate the method.

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Eyes of Africa: The Genetics of Blindness: Study Design and Methodology

BMC Ophthalmology ◽

10.1186/s12886-021-02029-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Olusola Olawoye ◽

Chimdi Chuka-Okosa ◽

Onoja Akpa ◽

Tony Realini ◽

Michael Hauser ◽

...

Keyword(s):

Genome Wide Association Study ◽

Case Control Study ◽

African Ancestry ◽

Collaborative Study ◽

Data Set ◽

Human Heredity ◽

Genome Wide ◽

A Genome ◽

Multiplex Families ◽

Control Study

Abstract Background This report describes the design and methodology of the “Eyes of Africa: The Genetics of Blindness,” a collaborative study funded through the Human Heredity and Health in Africa (H3Africa) program of the National Institute of Health. Methods This is a case control study that is collecting a large well phenotyped data set among glaucoma patients and controls for a genome wide association study. (GWAS). Multiplex families segregating Mendelian forms of early-onset glaucoma will also be collected for exome sequencing. Discussion A total of 4500 cases/controls have been recruited into the study at the end of the 3rd funded year of the study. All these participants have been appropriately phenotyped and blood samples have been received from these participants. Recent GWAS of POAG in African individuals demonstrated genome-wide significant association with the APBB2 locus which is an association that is unique to individuals of African ancestry. This study will add to the existing knowledge and understanding of POAG in the African population.

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CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores

Genome Medicine ◽

10.1186/s13073-021-00835-9 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Philipp Rentzsch ◽

Max Schubach ◽

Jay Shendure ◽

Martin Kircher

Keyword(s):

Prediction Models ◽

Splice Variants ◽

Superior Performance ◽

Data Set ◽

Pathogenic Variants ◽

Genome Wide ◽

Donor And Acceptor ◽

Human Proteins ◽

Variant Effect ◽

Variant Effect Prediction

Abstract Background Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies. Methods It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants. Results We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance. Conclusions While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction.

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Open access resources for genome-wide association mapping in rice

Nature Communications ◽

10.1038/ncomms10532 ◽

2016 ◽

Vol 7 (1) ◽

Cited By ~ 169

Author(s):

Susan R. McCouch ◽

Mark H. Wright ◽

Chih-Wei Tung ◽

Lyza G. Maron ◽

Kenneth L. McNally ◽

...

Keyword(s):

Open Access ◽

Association Mapping ◽

High Density ◽

Genome Wide Association ◽

Minor Effect ◽

Data Set ◽

Genome Wide ◽

Analytical Strategies ◽

Biological Interpretation ◽

Rice Improvement

Abstract Increasing food production is essential to meet the demands of a growing human population, with its rising income levels and nutritional expectations. To address the demand, plant breeders seek new sources of genetic variation to enhance the productivity, sustainability and resilience of crop varieties. Here we launch a high-resolution, open-access research platform to facilitate genome-wide association mapping in rice, a staple food crop. The platform provides an immortal collection of diverse germplasm, a high-density single-nucleotide polymorphism data set tailored for gene discovery, well-documented analytical strategies, and a suite of bioinformatics resources to facilitate biological interpretation. Using grain length, we demonstrate the power and resolution of our new high-density rice array, the accompanying genotypic data set, and an expanded diversity panel for detecting major and minor effect QTLs and subpopulation-specific alleles, with immediate implications for rice improvement.

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A genome-wide scan for a simulated data set using two newly developed methods

Genetic Epidemiology ◽

10.1002/gepi.13701707101 ◽

1999 ◽

Vol 17 (S1) ◽

pp. S621-S626

Author(s):

Li Hsu ◽

Corinne Aragaki ◽

Filemon Quiaoit ◽

Xiangjing Wang ◽

Xiubin Xu ◽

...

Keyword(s):

Simulated Data ◽

Data Set ◽

Genome Wide ◽

A Genome ◽

Genome Wide Scan

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Stabilizing selection on individual pattern elements of aposematic signals

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2017.0926 ◽

2017 ◽

Vol 284 (1861) ◽

pp. 20170926 ◽

Cited By ~ 17

Author(s):

Anne E. Winters ◽

Naomi F. Green ◽

Nerida G. Wilson ◽

Martin J. How ◽

Mary J. Garson ◽

...

Keyword(s):

Pattern Analysis ◽

Marine Organism ◽

Warning Signal ◽

Colour Pattern ◽

Stabilizing Selection ◽

Warning Signals ◽

Fish Predator ◽

Selection For ◽

Aposematic Signals ◽

Behavioural Experiments

Warning signal variation is ubiquitous but paradoxical: low variability should aid recognition and learning by predators. However, spatial variability in the direction and strength of selection for individual elements of the warning signal may allow phenotypic variation for some components, but not others. Variation in selection may occur if predators only learn particular colour pattern components rather than the entire signal. Here, we used a nudibranch mollusc, Goniobranchus splendidus , which exhibits a conspicuous red spot/white body/yellow rim colour pattern, to test this hypothesis. We first demonstrated that secondary metabolites stored within the nudibranch were unpalatable to a marine organism. Using pattern analysis, we demonstrated that the yellow rim remained invariable within and between populations; however, red spots varied significantly in both colour and pattern. In behavioural experiments, a potential fish predator, Rhinecanthus aculeatus , used the presence of the yellow rims to recognize and avoid warning signals. Yellow rims remained stable in the presence of high genetic divergence among populations. We therefore suggest that how predators learn warning signals may cause stabilizing selection on individual colour pattern elements, and will thus have important implications on the evolution of warning signals.

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Genome-wide analyses reveal clustering in Cannabis cultivars: the ancient domestication trilogy of a panacea

10.7287/peerj.preprints.1553v2 ◽

2015 ◽

Cited By ~ 3

Author(s):

Philippe Henry

Keyword(s):

Genetic Variation ◽

Genetic Structure ◽

Open Access ◽

Nucleotide Polymorphisms ◽

Data Set ◽

Single Nucleotide ◽

Genome Wide ◽

Access Data ◽

Diagnostic Snps ◽

Shed Light

In the present research, I used an open access data set (Medicinal Genomics) consisting of nearly 200'000 genome-wide single nucleotide polymorphisms (SNPs) typed in 28 cannabis accessions to shed light on the plant's underlying genetic structure. Genome-wide loadings were used to sequentially cull less informative markers. The process involved reducing the number of SNPs to 100K, 10K, 1K, 100 until I identified a set of 42 highly informative SNPs that I present here. The two first principal components, encompass over 3/4 of the genetic variation present in the dataset (PCA1 = 48.6%, PCA2= 26.3%). This set of diagnostic SNPs is then used to identify clusters into which cannabis accession segregate. I identified three clear and consistent clusters; reflective of the ancient domestication trilogy of the genus Cannabis.

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Insights into the genetic basis of retinal detachment

Human Molecular Genetics ◽

10.1093/hmg/ddz294 ◽

2019 ◽

Vol 29 (4) ◽

pp. 689-702 ◽

Cited By ~ 2

Author(s):

Thibaud S Boutin ◽

David G Charteris ◽

Aman Chandra ◽

Susan Campbell ◽

Caroline Hayward ◽

...

Keyword(s):

Retinal Detachment ◽

Association Studies ◽

Genetic Correlations ◽

Self Report ◽

Cataract Operation ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Associations ◽

Data Set ◽

Genome Wide

Abstract Retinal detachment (RD) is a serious and common condition, but genetic studies to date have been hampered by the small size of the assembled cohorts. In the UK Biobank data set, where RD was ascertained by self-report or hospital records, genetic correlations between RD and high myopia or cataract operation were, respectively, 0.46 (SE = 0.08) and 0.44 (SE = 0.07). These correlations are consistent with known epidemiological associations. Through meta-analysis of genome-wide association studies using UK Biobank RD cases (N = 3 977) and two cohorts, each comprising ~1 000 clinically ascertained rhegmatogenous RD patients, we uncovered 11 genome-wide significant association signals. These are near or within ZC3H11B, BMP3, COL22A1, DLG5, PLCE1, EFEMP2, TYR, FAT3, TRIM29, COL2A1 and LOXL1. Replication in the 23andMe data set, where RD is self-reported by participants, firmly establishes six RD risk loci: FAT3, COL22A1, TYR, BMP3, ZC3H11B and PLCE1. Based on the genetic associations with eye traits described to date, the first two specifically impact risk of a RD, whereas the last four point to shared aetiologies with macular condition, myopia and glaucoma. Fine-mapping prioritized the lead common missense variant (TYR S192Y) as causal variant at the TYR locus and a small set of credible causal variants at the FAT3 locus. The larger study size presented here, enabled by resources linked to health records or self-report, provides novel insights into RD aetiology and underlying pathological pathways.

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Extreme Phenotype Approach Suggests Taste Transduction Pathway for Carotid Plaque in a Multi-Ethnic Cohort

Stroke ◽

10.1161/strokeaha.120.028979 ◽

2020 ◽

Vol 51 (9) ◽

pp. 2761-2769

Author(s):

Nicole D. Dueker ◽

Brett Doliner ◽

Hannah Gardener ◽

Chuanhui Dong ◽

Ashley Beecham ◽

...

Keyword(s):

Risk Factors ◽

Ethnic Groups ◽

Pathway Analysis ◽

Carotid Plaque ◽

Vascular Risk Factors ◽

Plaque Burden ◽

Vascular Risk ◽

Data Set ◽

Gene Set ◽

Genome Wide

Background and Purpose: Carotid plaque is a heritable trait and a strong predictor of vascular events. Several loci have been identified for carotid plaque, however, studies in minority populations are lacking. Within a multi-ethnic cohort, we have identified individuals with extreme total carotid plaque area (TCPA), that is, higher or lower TCPA than expected based on traditional vascular risk factors (age, sex, smoking, diabetes mellitus, hypertension, etc). We hypothesized that these individuals are enriched with genetic variants accounting for the plaque burden that cannot be explained by traditional vascular risk factors. Herein, we sought to identify the genetic basis for TCPA using the multi-ethnic cohort. Methods: Three hundred forty participants (170 from each extreme group) from 3 race/ethnic groups (53% Hispanic, 29% non-Hispanic Black, and 18% non-Hispanic White) were genotyped using a genome-wide single-nucleotide polymorphism (SNP) array and imputed using 1000Genome data. SNP-based analyses using logistic regression and gene-based analyses using VEGAS2 were performed within each race/ethnic group and then meta-analyzed. Genes with P <0.001 were included in an overrepresentation enrichment pathway analysis using WebGestalt. Promising findings were tested for association with ischemic stroke using the MEGASTROKE Consortium data set. Results: No SNP or gene reached genome-wide significance. In the pathway analysis, GO:0050913 (sensory perception of bitter taste) gene set was significantly enriched ( P =4.5×10 −6 , false discovery rate=0.04), which was confirmed in MEGASTROKE ( P =0.01). Within the GO:0050913 gene set, 3 genes were associated with extreme TCPA in our study ( P <0.001): TAS2R20 , TAS2R50 , and ITPR3 . In TAS2R50 , rs1376251 is the top SNP and has been associated with myocardial infarction by others. In ITPR3 , a SNP with high regulatory potential (rs3818527, RegulomeScore=1f), and ITPR3 itself were among the top SNP-based and gene-based results and showed consistent evidence for association in all ethnic groups ( P <0.05). Conclusions: Extreme TCPA analysis identified new candidate genes for carotid plaque in understudied populations.

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Genome-wide association study of agronomic traits in bread wheat reveals novel putative alleles for future breeding programs

BMC Plant Biology ◽

10.1186/s12870-019-2165-4 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 11

Author(s):

Yousef Rahimi ◽

Mohammad Reza Bihamta ◽

Alireza Taleei ◽

Hadi Alipour ◽

Pär K. Ingvarsson

Keyword(s):

Bread Wheat ◽

Genome Wide Association Study ◽

Agronomic Traits ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Wheat Varieties ◽

Data Set ◽

Protein Coding ◽

Genome Wide

Abstract Background Identification of loci for agronomic traits and characterization of their genetic architecture are crucial in marker-assisted selection (MAS). Genome-wide association studies (GWAS) have increasingly been used as potent tools in identifying marker-trait associations (MTAs). The introduction of new adaptive alleles in the diverse genetic backgrounds may help to improve grain yield of old or newly developed varieties of wheat to balance supply and demand throughout the world. Landraces collected from different climate zones can be an invaluable resource for such adaptive alleles. Results GWAS was performed using a collection of 298 Iranian bread wheat varieties and landraces to explore the genetic basis of agronomic traits during 2016–2018 cropping seasons under normal (well-watered) and stressed (rain-fed) conditions. A high-quality genotyping by sequencing (GBS) dataset was obtained using either all original single nucleotide polymorphism (SNP, 10938 SNPs) or with additional imputation (46,862 SNPs) based on W7984 reference genome. The results confirm that the B genome carries the highest number of significant marker pairs in both varieties (49,880, 27.37%) and landraces (55,086, 28.99%). The strongest linkage disequilibrium (LD) between pairs of markers was observed on chromosome 2D (0.296). LD decay was lower in the D genome, compared to the A and B genomes. Association mapping under two tested environments yielded a total of 313 and 394 significant (−log10P >3) MTAs for the original and imputed SNP data sets, respectively. Gene ontology results showed that 27 and 27.5% of MTAs of SNPs in the original set were located in protein-coding regions for well-watered and rain-fed conditions, respectively. While, for the imputed data set 22.6 and 16.6% of MTAs represented in protein-coding genes for the well-watered and rain-fed conditions, respectively. Conclusions Our finding suggests that Iranian bread wheat landraces harbor valuable alleles that are adaptive under drought stress conditions. MTAs located within coding genes can be utilized in genome-based breeding of new wheat varieties. Although imputation of missing data increased the number of MTAs, the fraction of these MTAs located in coding genes were decreased across the different sub-genomes.

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