Editorial: Genomic Ancestry and Biological Traits.
Ontology highlight
ABSTRACT:
SUBMITTER: Rodrigues-Soares F
PROVIDER: S-EPMC8450575 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Banana trees, citrus fruit trees, pome fruit trees, grapevines, mango trees, and stone fruit trees are major fruit trees cultured worldwide and correspond to nearly 90% of the global production of woody fruit trees. In light of the above, the present manuscript summarizes the viruses that infect the major fruit trees, including their taxonomy and morphology, and highlights selected viruses that significantly affect fruit production, including their genomic and biological features. The results showed that a total of 163 viruses, belonging to 45 genera classified into 23 families have been reported to infect the major woody fruit trees. It is clear that there is higher accumulation of viruses in grapevine (80/163) compared to the other fruit trees (each corresponding to less than 35/163), while only one virus species has been reported infecting mango. Most of the viruses (over 70%) infecting woody fruit trees are positive-sense single-stranded RNA (+ssRNA), and the remainder belong to the -ssRNA, ssRNA-RT, dsRNA, ssDNA and dsDNA-RT groups (each corresponding to less than 8%). Most of the viruses are icosahedral or isometric (79/163), and their diameter ranges from 16 to 80 nm with the majority being 25-30 nm. Cross-infection has occurred in a high frequency among pome and stone fruit trees, whereas no or little cross-infection has occurred among banana, citrus and grapevine. The viruses infecting woody fruit trees are mostly transmitted by vegetative propagation, grafting, and root grafting in orchards and are usually vectored by mealybug, soft scale, aphids, mites or thrips. These viruses cause adverse effects in their fruit tree hosts, inducing a wide range of symptoms and significant damage, such as reduced yield, quality, vigor and longevity.
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Project description:People in the Americas represent a diverse continuum of populations with varying degrees of admixture among African, European, and Amerindigenous ancestries. In the United States, populations with non-European ancestry remain understudied, and thus little is known about the genetic architecture of phenotypic variation in these populations. Using genotype data from the Hispanic Community Health Study/Study of Latinos, we find that Amerindigenous ancestry increased by an average of ~20% spanning 1940s-1990s in Mexican Americans. These patterns result from complex interactions between several population and cultural factors which shaped patterns of genetic variation and influenced the genetic architecture of complex traits in Mexican Americans. We show for height how polygenic risk scores based on summary statistics from a European-based genome-wide association study perform poorly in Mexican Americans. Our findings reveal temporal changes in population structure within Hispanics/Latinos that may influence biomedical traits, demonstrating a need to improve our understanding of admixed populations.
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Project description:The Kelpie is a breed developed in Australia for use as a livestock herding dog. It has been proposed that the development of the breed included gene flow from the Australian Dingo (Canis dingo), a canid species present on the Australian continent for around 4000 years. The Kelpie breed is split between working and conformation types that have readily recognizable differences in external morphology. We characterize known gene variants relating to external morphology in sequenced representatives of both Kelpie types (Australian Kelpie-conformation; Australian Working Kelpie-herding) and compare the variants present with those in sequenced Australian Dingoes, including 25 canids with locus-constrained data and one with a whole genome sequence. Variants assessed include identified coat color and ear morphology variants. We describe a new variant site in the transcribed region of methionine sulfoxide reductase 3 that may relate to ear phenotype. None of the morphology variants analyzed offer support for co-ancestry of the Kelpie breed with the Australian Dingo.
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Project description:ObjectivesEthnic disparities in hypertension prevalence are well documented, though the influence of genetic ancestry is unclear. The aim of this study was to evaluate associations of geographic genetic ancestry with hypertension and underlying blood pressure traits.MethodsWe tested genetically inferred ancestry proportions from five 1000 Genomes reference populations (GBR, PEL, YRI, CHB, and LWK) for association with four continuous blood pressure (BP) traits (SBP, DBP, PP, MAP) and the dichotomous outcomes hypertension and apparent treatment-resistant hypertension in 220 495 European American, 59 927 African American, and 21 273 Hispanic American individuals from the Million Veteran Program. Ethnicity stratified results were meta-analyzed to report effect estimates per 10% difference for a given ancestry proportion in all samples.ResultsPercentage GBR was negatively associated with BP (P = 2.13 × 10-19, 7.92 × 10-8, 4.41 × 10-11, and 3.57 × 10-13 for SBP, DBP, PP, and MAP, respectively; coefficient range -0.10 to -0.21 mmHg per 10% increase in ancestry proportion) and was protective against hypertension [P = 2.59 × 10-5, odds ratio (OR) = 0.98] relative to other ancestries. YRI percentage was positively associated with BP (P = 1.63 × 10-23, 1.94 × 10-26, 0.012, and 3.26 × 10-29 for SBP, DBP, PP, and MAP, respectively; coefficient range 0.06-0.32 mmHg per 10% increase in ancestry proportion) and was positively associated with hypertension risk (P = 3.10 × 10-11, OR = 1.04) and apparent treatment-resistant hypertension risk (P = 1.86 × 10-4, OR = 1.04) compared with other ancestries. Percentage PEL was inversely associated with DBP (P = 2.84 × 10-5, beta = -0.11 mmHg per 10% increase in ancestry proportion).ConclusionThese results demonstrate that risk for BP traits varies significantly by genetic ancestry. Our findings provide insight into the geographic origin of genetic factors underlying hypertension risk and establish that a portion of BP trait ethnic disparities are because of genetic differences between ancestries.
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Project description:Dense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants.We introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits.BayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species.
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Project description:Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ PSII ) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both Φ PSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.
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