Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from ...human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (expression quantitative trait loci eQTLs) and concentration of metabolites (metabolic quantitative trait loci mQTLs) and under histone-modification marks in several tissues were discovered from multiomics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants, and for each set, we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent (r > 0.94) between bulls and cows. Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone-modification marks, and selection signatures. From these results,we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In additional 7,551 cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution, and trait heritability to rank variants, and the publicly available FAETH data provide a set of biological priors for cattle genomic selection worldwide.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
► Production of high-yield strains is an overriding objective for rice breeding. ► In this review, we focused on genes involved in rice crop yield. ► We list crop-related genes isolated by QTL-based ...and mutant-based approaches.
Breeding of high-yielding rice is crucial for meeting the food demand of the increasing world population. New technologies have facilitated identification of genes involved in quantitative traits, and many genes underpinning quantitative trait loci involved in rice crop yield have been isolated. Meanwhile, various kinds of mutants have been intensively studied, leading to characterization of many genes related to yield traits. A combination of quantitative trait locus analysis and studies of such mutants has made it possible to compile a list of genes available for breeding rice with higher yield.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Quantitative disease resistance (QDR) has been observed within many crop plants but is not as well understood as qualitative (monogenic) disease resistance and has not been used as extensively in ...breeding. Mapping quantitative trait loci (QTLs) is a powerful tool for genetic dissection of QDR. DNA markers tightly linked to quantitative resistance loci (QRLs) controlling QDR can be used for marker-assisted selection (MAS) to incorporate these valuable traits. QDR confers a reduction, rather than lack, of disease and has diverse biological and molecular bases as revealed by cloning of QRLs and identification of the candidate gene(s) underlying QRLs. Increasing our biological knowledge of QDR and QRLs will enhance understanding of how QDR differs from qualitative resistance and provide the necessary information to better deploy these resources in breeding. Application of MAS for QRLs in breeding for QDR to diverse pathogens is illustrated by examples from wheat, barley, common bean, tomato, and pepper. Strategies for optimum deployment of QRLs require research to understand effects of QDR on pathogen populations over time.
Grain yield in rice is a complex trait multiplicatively determined by its three component traits: number of panicles, number of grains per panicle, and grain weight; all of which are typical ...quantitative traits. The developments in genome mapping, sequencing, and functional genomic research have provided powerful tools for investigating the genetic and molecular bases of these quantitative traits. Dissection of the genetic bases of the yield traits based on molecular marker linkage maps resolved hundreds of quantitative trait loci (QTLs) for these traits. Mutant analyses and map-based cloning of QTLs have identified a large number of genes required for the basic processes underlying the initiation and development of tillers and panicles, as well as genes controlling numbers and sizes of grains and panicles. Molecular characterization of these genes has greatly advanced the mechanistic understanding of the regulation of these rice yield traits. These findings have significant implications in crop genetic improvement.
Prenatal fish intake is a key source of omega-3 polyunsaturated fatty acids needed for brain development, yet intake is generally low, and studies addressing associations with autism spectrum ...disorder (ASD) and related traits are lacking.
To examine associations of prenatal fish intake and omega-3 supplement use with both autism diagnosis and broader autism-related traits.
Participants were drawn from 32 cohorts in the Environmental influences on Child Health Outcomes (ECHO) Cohort Consortium. Children were born between 1999 and 2019 and part of ongoing follow-up with data available for analysis by August 2022. Exposures included self-reported maternal fish intake and omega-3/fish oil supplement use during pregnancy. Outcome measures included parent report of clinician-diagnosed ASD and parent-reported autism-related traits measured by the Social Responsiveness Scale (SRS)-Second Edition (n=3939 and n=3609 for fish intake analyses, respectively; n=4537 and n=3925 for supplement intake analyses, respectively).
In adjusted regression models, relative to no fish intake, fish intake during pregnancy was associated with reduced odds of autism diagnosis (OR=0.84, 95% CI 0.77 to 0.92), and a modest reduction in raw total SRS scores (b=-1.69, 95% CI -3.3 to -0.08). Estimates were similar across categories of fish consumption from “any” or “less than once per week” to “more than twice per week.” For omega-3 supplement use, relative to no use, no significant associations with autism diagnosis were identified, whereas a modest relation with SRS score was suggested (β=1.98, 95% CI 0.33-3.64).
These results extend prior work by suggesting that prenatal fish intake, but not omega-3 supplement use, may be associated with lower likelihood of both autism diagnosis and related traits. Given the low fish intake in the U.S. general population and the rising autism prevalence, these findings suggest the need for better public health messaging regarding guidelines on fish intake for pregnant individuals.
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CMK, GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
QTL mapping methods for complex traits are challenged by new developments in marker technology, phenotyping platforms, and breeding methods. In meeting these challenges, QTL mapping approaches will ...need to also acknowledge the central roles of QTL by environment interactions (QEI) and QTL by trait interactions in the expression of complex traits like yield. This paper presents an overview of mixed model QTL methodology that is suitable for many types of populations and that allows predictive modeling of QEI, both for environmental and developmental gradients. Attention is also given to multi-trait QTL models which are essential to interpret the genetic basis of trait correlations. Biophysical (crop growth) model simulations are proposed as a complement to statistical QTL mapping for the interpretation of the nature of QEI and to investigate better methods for the dissection of complex traits into component traits and their genetic controls.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Biological invasions are ‘natural’ experiments that can improve our understanding of contemporary evolution. We evaluate evidence for population differentiation, natural selection and adaptive ...evolution of invading plants and animals at two nested spatial scales: (i) among introduced populations (ii) between native and introduced genotypes. Evolution during invasion is frequently inferred, but rarely confirmed as adaptive. In common garden studies, quantitative trait differentiation is only marginally lower (~3.5%) among introduced relative to native populations, despite genetic bottlenecks and shorter timescales (i.e. millennia vs. decades). However, differentiation between genotypes from the native vs. introduced range is less clear and confounded by nonrandom geographic sampling; simulations suggest this causes a high false‐positive discovery rate (>50%) in geographically structured populations. Selection differentials (¦s¦) are stronger in introduced than in native species, although selection gradients (¦β¦) are not, consistent with introduced species experiencing weaker genetic constraints. This could facilitate rapid adaptation, but evidence is limited. For example, rapid phenotypic evolution often manifests as geographical clines, but simulations demonstrate that nonadaptive trait clines can evolve frequently during colonization (~two‐thirds of simulations). Additionally, QST‐FST studies may often misrepresent the strength and form of natural selection acting during invasion. Instead, classic approaches in evolutionary ecology (e.g. selection analysis, reciprocal transplant, artificial selection) are necessary to determine the frequency of adaptive evolution during invasion and its influence on establishment, spread and impact of invasive species. These studies are rare but crucial for managing biological invasions in the context of global change.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from ...a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components. Models for capturing different forms of interaction, e.g., chromosome-specific, are presented. Implementations can be carried out using software for likelihood-based or Bayesian inference.
We summarize, in this review, the evidence that genomic balance influences gene expression, quantitative traits, dosage compensation, aneuploid syndromes, population dynamics of copy number variants ...and differential evolutionary fate of genes after partial or whole-genome duplication. Gene balance effects are hypothesized to result from stoichiometric differences among members of macromolecular complexes, the interactome, and signaling pathways. The implications of gene balance are discussed.
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The genomic prediction of phenotypes and breeding values in animals and plants has developed rapidly into its own research field. Results of genomic prediction studies are often difficult to compare ...because data simulation varies, real or simulated data are not fully described, and not all relevant results are reported. In addition, some new methods have been compared only in limited genetic architectures, leading to potentially misleading conclusions. In this article we review simulation procedures, discuss validation and reporting of results, and apply benchmark procedures for a variety of genomic prediction methods in simulated and real example data. Plant and animal breeding programs are being transformed by the use of genomic data, which are becoming widely available and cost-effective to predict genetic merit. A large number of genomic prediction studies have been published using both simulated and real data. The relative novelty of this area of research has made the development of scientific conventions difficult with regard to description of the real data, simulation of genomes, validation and reporting of results, and forward in time methods. In this review article we discuss the generation of simulated genotype and phenotype data, using approaches such as the coalescent and forward in time simulation. We outline ways to validate simulated data and genomic prediction results, including cross-validation. The accuracy and bias of genomic prediction are highlighted as performance indicators that should be reported. We suggest that a measure of relatedness between the reference and validation individuals be reported, as its impact on the accuracy of genomic prediction is substantial. A large number of methods were compared in example simulated and real (pine and wheat) data sets, all of which are publicly available. In our limited simulations, most methods performed similarly in traits with a large number of quantitative trait loci (QTL), whereas in traits with fewer QTL variable selection did have some advantages. In the real data sets examined here all methods had very similar accuracies. We conclude that no single method can serve as a benchmark for genomic prediction. We recommend comparing accuracy and bias of new methods to results from genomic best linear prediction and a variable selection approach (e.g., BayesB), because, together, these methods are appropriate for a range of genetic architectures. An accompanying article in this issue provides a comprehensive review of genomic prediction methods and discusses a selection of topics related to application of genomic prediction in plants and animals.
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