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Artículos de revistas sobre el tema "Genomic trait"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

Bohlouli, Mehdi, Sadegh Alijani, Ardashir Nejati Javaremi, Sven König, and Tong Yin. "Genomic prediction by considering genotype × environment interaction using different genomic architectures." Annals of Animal Science 17, no. 3 (2017): 683–701. http://dx.doi.org/10.1515/aoas-2016-0086.

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Abstract In this study, accuracies of genomic prediction across various scenarios were compared using single- trait and multiple-trait animal models to detect genotype × environment (G × E) interaction based on REML method. The simulated high and low linkage disequilibrium (HLD and LLD) genome consisted of 15,000 and 50,000 SNP chip applications with 300 and 600 QTLs controlling the trait of interest. The simulation was done to create the genetic correlations between the traits in 4 environments and heritabilities of the traits were 0.20, 0.25, 0.30 and 0.35 in environments 1, 2, 3 and 4, resp

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2

Lozada, Dennis, and Arron Carter. "Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat." Agronomy 10, no. 3 (2020): 368. http://dx.doi.org/10.3390/agronomy10030368.

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Examining the architecture of traits through genomics is necessary to gain a better understanding of the genetic loci affecting important traits to facilitate improvement. Genomewide association study (GWAS) and genomic selection (GS) were implemented for grain yield, heading date, and plant height to gain insights into the genetic complexity of phenotypic stability of traits in a diverse population of US Pacific Northwest winter wheat. Analysis of variance using the Additive Main Effect and Multiplicative Interaction (AMMI) approach revealed significant genotype and genotype by environment in

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3

Calus, M. P. L., D. P. Berry, G. Banos, Y. de Haas, and R. F. Veerkamp. "Genomic selection: the option for new robustness traits?" Advances in Animal Biosciences 4, no. 3 (2013): 618–25. http://dx.doi.org/10.1017/s2040470013000186.

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Genomic selection is rapidly becoming the state-of-the-art genetic selection methodology in dairy cattle breeding schemes around the world. The objective of this paper was to explore possibilities to apply genomic selection for traits related to dairy cow robustness. Deterministic simulations indicate that replacing progeny testing with genomic selection may favour genetic response for production traits at the expense of robustness traits, owing to a disproportional change in accuracies obtained across trait groups. Nevertheless, several options are available to improve the accuracy of genomic

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4

Sunagar, Ramesh, and Manoj Kumar Pandey. "Genomic Approaches for Enhancing Yield and Quality Traits in Mustard (Brassica spp.): A Review of Breeding Strategies." Journal of Advances in Biology & Biotechnology 27, no. 6 (2024): 174–85. http://dx.doi.org/10.9734/jabb/2024/v27i6877.

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Mustard, a vital oilseed crop, plays a significant role in global agriculture due to its versatile applications in food, feed, and biofuel industries. However, meeting the increasing demands for yield and quality traits poses a substantial challenge to mustard breeders. In response, genomic approaches have emerged as powerful tools to expedite mustard breeding programs by unraveling the genetic basis of key agronomic traits. This review provides a comprehensive overview of genomic strategies aimed at enhancing yield and quality traits in mustard. Beginning with an exploration of traditional br

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5

Srivastava, Swati, Bryan Irvine Lopez, Sara de las Heras-Saldana, et al. "Estimation of Genetic Parameters by Single-Trait and Multi-Trait Models for Carcass Traits in Hanwoo Cattle." Animals 9, no. 12 (2019): 1061. http://dx.doi.org/10.3390/ani9121061.

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Hanwoo breed is preferred in South Korea because of the high standards in marbling and the palatability of its meat. Numerous studies have been conducted and are ongoing to increase the meat production and quality in this beef population. The aim of this study was to estimate and compare genetic parameters for carcass traits using BLUPF90 software. Four models were constructed, single trait pedigree model (STPM), single-trait genomic model (STGM), multi-trait pedigree model (MTPM), and multi-trait genomic model (MTGM), using the pedigree, phenotype, and genomic information of 7991 Hanwoo cattl

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6

Li, Hongwei, Yining Wang, Michael Vinsky, Tiago Valente, John A. Basarab, and Changxi Li. "114 Accuracy of genomic predictions using single and multiple-trait machine learning methods in Canadian beef cattle population." Journal of Animal Science 102, Supplement_3 (2024): 33–34. http://dx.doi.org/10.1093/jas/skae234.037.

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Abstract The advancement in various machine learning (ML) methods provides tools to extract features from a large data set of complex traits and to predict the outcome of the target trait. In this study, we assessed the genomic prediction accuracy using single trait models based on kernel ridge regression (ST_KRR) and linear support vector regression (STLinearSVR) for female feed intake, feed efficiency and fertility traits of Canadian crossbreed beef cattle (n = 2,834 genotyped with 83,875 single nucleotide polymorphisms) and compared their prediction performances to a single trait genomic be

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7

Eduardo, Iban, Pere Arús, Antonio José Monforte, et al. "Estimating the Genetic Architecture of Fruit Quality Traits in Melon Using a Genomic Library of Near Isogenic Lines." Journal of the American Society for Horticultural Science 132, no. 1 (2007): 80–89. http://dx.doi.org/10.21273/jashs.132.1.80.

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A melon (Cucumis melo L.) genomic library of near-isogenic lines derived from the cross between the Spanish cultivar Piel de Sapo and the exotic accession PI 161375 has been evaluated for fruit quality traits in four different locations. Traits evaluated were fruit weight, soluble solids content, maximum fruit diameter, fruit length, fruit shape index, ovary shape index, external color, and flesh color. Among these traits, soluble solids content showed the highest genotype × environment interaction, whereas genotype × environment interactions for fruit shape and fruit weight were low. Heritabi

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8

Fragomeni, Breno, Zulma Vitezica, Justine Liu, et al. "209 Genomic selection for multiple maternal and growth traits in large white pigs using Single-Step GBLUP." Journal of Animal Science 97, Supplement_3 (2019): 42. http://dx.doi.org/10.1093/jas/skz258.084.

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Abstract The objective of this study was to implement a multi-trait genomic evaluation for maternal and growth traits in a swine population. Phenotypes for preweaning mortality, litter size, weaning weight, and average daily gain were available for 282K Large White pigs. The pedigree included 314k individuals, of which 35,731 were genotyped for 45K SNPs. Variance components were estimated in a multi-trait animal model without genomic information by AIREMLF90. Genomic breeding values were estimated using the genomic information by single-step GBLUP. The algorithm for proven and young (APY) was

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9

Huang, Mao, Antonio Cabrera, Amber Hoffstetter, et al. "Genomic selection for wheat traits and trait stability." Theoretical and Applied Genetics 129, no. 9 (2016): 1697–710. http://dx.doi.org/10.1007/s00122-016-2733-z.

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10

Shabannejad, Morteza, Mohammad-Reza Bihamta, Eslam Majidi-Hervan, Hadi Alipour, and Asa Ebrahimi. "A classic approach for determining genomic prediction accuracy under terminal drought stress and well-watered conditions in wheat landraces and cultivars." PLOS ONE 16, no. 3 (2021): e0247824. http://dx.doi.org/10.1371/journal.pone.0247824.

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The present study aimed to improve the accuracy of genomic prediction of 16 agronomic traits in a diverse bread wheat (Triticum aestivum L.) germplasm under terminal drought stress and well-watered conditions in semi-arid environments. An association panel including 87 bread wheat cultivars and 199 landraces from Iran bread wheat germplasm was planted under two irrigation systems in semi-arid climate zones. The whole association panel was genotyped with 9047 single nucleotide polymorphism markers using the genotyping-by-sequencing method. A number of 23 marker-trait associations were selected

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11

Espuela-Ortiz, Antonio, Elena Martin-Gonzalez, Paloma Poza-Guedes, Ruperto González-Pérez, and Esther Herrera-Luis. "Genomics of Treatable Traits in Asthma." Genes 14, no. 9 (2023): 1824. http://dx.doi.org/10.3390/genes14091824.

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The astounding number of genetic variants revealed in the 15 years of genome-wide association studies of asthma has not kept pace with the goals of translational genomics. Moving asthma diagnosis from a nonspecific umbrella term to specific phenotypes/endotypes and related traits may provide insights into features that may be prevented or alleviated by therapeutical intervention. This review provides an overview of the different asthma endotypes and phenotypes and the genomic findings from asthma studies using patient stratification strategies and asthma-related traits. Asthma genomic research

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12

Baes, Christine F., Filippo Miglior, Flavio S. Schenkel, et al. "166 Livestock Resiliency: Concepts and Approaches." Journal of Animal Science 99, Supplement_3 (2021): 89–90. http://dx.doi.org/10.1093/jas/skab235.159.

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Abstract Genetic improvement of health, welfare, efficiency, and fertility traits is challenging due to expensive and fuzzy phenotypes, the polygenic nature of traits, antagonistic genetic correlations to production traits and low heritabilities. Nevertheless, many organizations have introduced large-scale genetic evaluations for such traits in routine selection indexes. Medium and high-density arrays can be applied in genomic selection strategies to improve breeding value accuracy, and also in genome-wide association studies (GWAS) to identify causative mutations responsible for economically

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13

Li, Wenjie, Wenqiang Li, Zichen Song, et al. "Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits." Genes 15, no. 4 (2024): 454. http://dx.doi.org/10.3390/genes15040454.

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The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates an

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14

Zhang, Shiwei, Jie Kong, Jian Tan, et al. "Multi-Trait Genomic Prediction of Meat Yield in Pacific Whiteleg Shrimp (Penaeus vannamei)." Animals 15, no. 8 (2025): 1165. https://doi.org/10.3390/ani15081165.

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The meat yield (MY) is a key economic trait in Pacific whiteleg shrimp (Penaeus vannamei) breeding, necessitating accurate genomic prediction for efficient genetic improvement. In this study, we investigated single-trait (STGMs) and multi-trait genomic models (MTGMs) for predicting MY and related traits, using two cross-validation strategies reflecting different data-availability scenarios. A total of 899 individuals from 63 full-sibling families were phenotyped for MY, net meat weight (MW), body weight (BW), body length (BL), and abdominal segment length (AL). We estimated the genomic heritab

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15

Moeinizade, Saba, Aaron Kusmec, Guiping Hu, Lizhi Wang, and Patrick S. Schnable. "Multi-trait Genomic Selection Methods for Crop Improvement." Genetics 215, no. 4 (2020): 931–45. http://dx.doi.org/10.1534/genetics.120.303305.

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Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing nonlinear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier o

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16

Guo, Jia, Jahangir Khan, Sumit Pradhan, et al. "Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes." Genes 11, no. 11 (2020): 1270. http://dx.doi.org/10.3390/genes11111270.

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The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-

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17

REDDY, S. N. K., PUNEET WALIA, and SANJEET SINGH SANDAL. "DIGITAL REVOLUTION IN PLANT BREEDING: A COMPREHENSIVE REVIEW OF METHODOLOGIES, TOOLS, APPLICATIONS, AND FUTURE PERSPECTIVES." Asian Journal of Microbiology, Biotechnology & Environmental Sciences 25, no. 04 (2023): 629–32. http://dx.doi.org/10.53550/ajmbes.2023.v25i04.003.

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Digital breeding integrates modern technologies like genomics, bioinformatics, and data analytics into conventional plant breeding. It accelerates the breeding process, improves selection effectiveness, and enhances crop yield and quality. High-throughput genotyping and phenotyping technologies enable efficient analysis of large populations and rapid characterization of plant traits. Genomic selection and data analytics aid in predicting breeding values and analyzing extensive data for trait improvement. Digital breeding applications include accelerated breeding cycles, trait-based breeding, d

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18

Ma, Xiang, Ole F. Christensen, Hongding Gao, et al. "Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait." Heredity 126, no. 1 (2020): 206–17. http://dx.doi.org/10.1038/s41437-020-0339-3.

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AbstractRecords on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The

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19

Ofria, Charles, Wei Huang, and Eric Torng. "On the Gradual Evolution of Complexity and the Sudden Emergence of Complex Features." Artificial Life 14, no. 3 (2008): 255–63. http://dx.doi.org/10.1162/artl.2008.14.3.14302.

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Evolutionary theory explains the origin of complex organismal features through a combination of reusing and extending information from less-complex traits, and by needing to exploit only one of many unlikely pathways to a viable solution. While the appearance of a new trait may seem sudden, we show that the underlying information associated with each trait evolves gradually. We study this process using digital organisms, self-replicating computer programs that mutate and evolve novel traits, including complex logic operations. When a new complex trait first appears, its proper function immedia

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20

Reid, Kerry, Michael A. Bell, and Krishna R. Veeramah. "Threespine Stickleback: A Model System For Evolutionary Genomics." Annual Review of Genomics and Human Genetics 22, no. 1 (2021): 357–83. http://dx.doi.org/10.1146/annurev-genom-111720-081402.

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The repeated adaptation of oceanic threespine sticklebacks to fresh water has made it a premier organism to study parallel evolution. These small fish have multiple distinct ecotypes that display a wide range of diverse phenotypic traits. Ecotypes are easily crossed in the laboratory, and families are large and develop quickly enough for quantitative trait locus analyses, positioning the threespine stickleback as a versatile model organism to address a wide range of biological questions. Extensive genomic resources, including linkage maps, a high-quality reference genome, and developmental gen

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21

Negawo, Alemayehu Teressa, Meki Shehabu Muktar, Ricardo Alonso Sánchez Gutiérrez, Ermias Habte, Alice Muchugi, and Chris S. Jones. "A Genome-Wide Association Study of Biomass Yield and Feed Quality in Buffel Grass (Cenchrus ciliaris L.)." Agriculture 14, no. 2 (2024): 257. http://dx.doi.org/10.3390/agriculture14020257.

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The development of modern genomic tools has helped accelerate genetic gains in the breeding program of food crops. More recently, genomic resources have been developed for tropical forages, providing key resources for developing new climate-resilient high-yielding forage varieties. In this study, we present a genome-wide association study for biomass yield and feed quality traits in buffel grass (Cenchrus ciliaris L. aka Pennisetum ciliare L.). Genome-wide markers, generated using the DArTSeq platform and mapped onto the Setaria italica reference genome, were used for the genome-wide associati

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22

Sanjay, Patil Shital, and Satya Prakash. "Advancements of Breeding and Genomics in Wheat (Triticum aestivum L.): Enhancing Yield and Nutritional Value for Sustainable Agriculture." Journal of Advances in Biology & Biotechnology 27, no. 5 (2024): 863–75. http://dx.doi.org/10.9734/jabb/2024/v27i5848.

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Advancements in wheat breeding and genomics presently explores the genomic interventions driving focusing on quantitative trait loci (QTL) mapping, marker-assisted selection (MAS) and genomic selection (GS). QTL mapping emerges as a pivotal method for pinpointing markers linked with desirable traits, facilitating MAS. Furthermore, genomic selection (GS) holds immense potential for crop improvement. It also delves into the current landscape of MAS and explores various prospects of GS for wheat biofortification. Looking ahead, accelerated mapping studies combined with MAS and GS schemes are pois

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23

Stalder, Kenneth J. "296 Awardee Talk: The Genetics of Sow Longevity." Journal of Animal Science 98, Supplement_4 (2020): 28. http://dx.doi.org/10.1093/jas/skaa278.050.

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Abstract Sow longevity is a key productivity indicator trait that has real economic and welfare importance for commercial swine farms globally. The average parity at culling is 3.8 parities. Reports indicate that it takes 3 to 4 parities before a sow “pays for herself.” Research groups around the world have reported heritabilities estimates for sow longevity traits ranging from 0.05 to 0.35. Estimate differences result from the animal population under evaluation, the trait being evaluated, and the methodology employed to obtain the genetic parameter estimate. Because sow longevity is measured

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Tang, Xi, Shijun Xiao, Nengshui Ding, Zhiyan Zhang, and Lusheng Huang. "Comparative Study of Single-Trait and Multi-Trait Genomic Prediction Models." Animals 14, no. 20 (2024): 2961. http://dx.doi.org/10.3390/ani14202961.

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Conventional genomic selection models trait individually, neglecting complex trait interactions. Multi-trait models address this by considering genetic correlations, thus improving breeding value accuracy. Despite their theoretical benefits, quantifying these models’ breeding advantages across genetic backgrounds is essential. This study evaluates the benefits of multi-trait models under varying population sizes and three levels of genetic correlations (low, medium, high) using simulations based on 50 K chip data from 5000 individuals. In equal heritability scenarios, the multi-trait GBLUP mod

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25

O’Brien, Anna M., Chandra N. Jack, Maren L. Friesen, and Megan E. Frederickson. "Whose trait is it anyways? Coevolution of joint phenotypes and genetic architecture in mutualisms." Proceedings of the Royal Society B: Biological Sciences 288, no. 1942 (2021): 20202483. http://dx.doi.org/10.1098/rspb.2020.2483.

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Evolutionary biologists typically envision a trait’s genetic basis and fitness effects occurring within a single species. However, traits can be determined by and have fitness consequences for interacting species, thus evolving in multiple genomes. This is especially likely in mutualisms, where species exchange fitness benefits and can associate over long periods of time. Partners may experience evolutionary conflict over the value of a multi-genomic trait, but such conflicts may be ameliorated by mutualism’s positive fitness feedbacks. Here, we develop a simulation model of a host–microbe mut

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Goddardt, M. E. "Animal breeding in the (post-) genomic era." Animal Science 76, no. 3 (2003): 353–65. http://dx.doi.org/10.1017/s1357729800058586.

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AbstractOne of the benefits of the genomics revolution for animal production will be knowledge of genes that can be used to select more profitable livestock. Although it is possible to use genetic markers linked to genes of economic importance, tests for the genes themselves will be much more successful. Consequently finding genes of economic importance to livestock will be a major research aim for the future. Most traits of economic importance are quantitative traits affected by many genes. Mutations at many genes (e.g. 500) and at many positions within a gene (e.g. 1000 coding and non-coding

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Osterman, Johanna, Lucia Gutiérrez, Linda Öhlund, et al. "Comparison of Single-Trait and Multi-Trait GBLUP Models for Genomic Prediction in Red Clover." Agronomy 14, no. 10 (2024): 2445. http://dx.doi.org/10.3390/agronomy14102445.

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Red clover (Trifolium pratense) is a perennial forage legume wildly used in temperate regions, including northern Europe. Its breeders are under increasing pressure to obtain rapid genetic gains to meet the high demand for improved forage yield and quality. One solution to increase genetic gain by reducing time and increasing accuracy is genomic selection. Thus, efficient genomic prediction (GP) models need to be developed, which are unbiased to traits and harvest time points. This study aimed to develop and evaluate single-trait (ST) and multi-trait (MT) models that simultaneously target more

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Edwards, M. D., C. W. Stuber, and J. F. Wendel. "Molecular-Marker-Facilitated Investigations of Quantitative-Trait Loci in Maize. I. Numbers, Genomic Distribution and Types of Gene Action." Genetics 116, no. 1 (1987): 113–25. http://dx.doi.org/10.1093/genetics/116.1.113.

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ABSTRACT Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17–20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by al

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de Oliveira Celeri, Maurício, Weverton Gomes da Costa, Ana Carolina Campana Nascimento, et al. "Multivariate Adaptive Regression Splines Enhance Genomic Prediction of Non-Additive Traits." Agronomy 14, no. 10 (2024): 2234. http://dx.doi.org/10.3390/agronomy14102234.

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The present work used Multivariate Adaptive Regression Splines (MARS) for genomic prediction and to study the non-additive fraction present in a trait. To this end, 12 scenarios for an F2 population were simulated by combining three levels of broad-sense heritability (h2 = 0.3, 0.5, and 0.8) and four amounts of QTLs controlling the trait (8, 40, 80, and 120). All scenarios included non-additive effects due to dominance and additive–additive epistasis. The individuals’ genomic estimated breeding values (GEBV) were predicted via MARS and compared against the GBLUP method, whose models were addit

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ASHISH, G. VALA, TOMAR RUKAMSINGH, J. RATHOD PANKAJ, and RAJIVKUMAR. "Genomics Approaches for Enhancing Abiotic Stress Tolerance in Groundnut: A Pathway to Crop Improvement." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 1661–63. https://doi.org/10.5281/zenodo.7992784.

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Genomics approaches have revolutionized crop improvement by providing insights into the genetic basis of abiotic stress tolerance in groundnut (Arachis hypogaea L.). Abiotic stresses such as drought, heat, salinity, and nutrient deficiencies pose significant challenges to groundnut production and global food security. This review highlights the key genomics approaches employed in groundnut research to enhance abiotic stress tolerance. Transcriptomics, including RNA sequencing (RNA-Seq), has enabled the identification of stress-responsive genes and regulatory networks. Marker-assisted selection

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Villar-Hernández, Bartolo de Jesús, Paulino Pérez-Rodríguez, Paolo Vitale, et al. "Optimizing Genomic Parental Selection for Categorical and Continuous–Categorical Multi-Trait Mixtures." Genes 15, no. 8 (2024): 995. http://dx.doi.org/10.3390/genes15080995.

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This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous–categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for

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Smith, Timothy P. "226 Genomics in animal agriculture: current technologies and applications." Journal of Animal Science 97, Supplement_3 (2019): 55–56. http://dx.doi.org/10.1093/jas/skz258.113.

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Abstract The early impact of genomic research on animal agriculture was relatively modest, as it proved difficult to translate quantitative trait loci mapping to industrial application. Fortunately, developments in technology have facilitated the application of genomics to animal agriculture, which has led to more substantial impacts on many commercially produced animal species. A brief look back on the history of genomic research will be presented, followed by an overview of recent developments in genomic technologies. Examples of application of genomic research, focusing on beef cattle and c

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Mehrban, Hossein, Masoumeh Naserkheil, Deuk Hwan Lee, et al. "Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle." Genes 12, no. 2 (2021): 266. http://dx.doi.org/10.3390/genes12020266.

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The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternati

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Eteqadi, Bahareh, Seyed A. Rafat, Sadegh Alijani, Sven König, and Mehdi Bohlouli. "Genomic evaluation of binary traits in dairy cattle by considering genotype × environment interactions." Spanish Journal of Agricultural Research 20, no. 1 (2022): e0401-e0401. http://dx.doi.org/10.5424/sjar/2022201-17417.

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Aim of study: To assess genotype by environment (G×E) interaction via single- and multi-trait animal models for binary traits in dairy cattle. Area of study: University of Tabriz, Tabriz, Iran. Material and methods: Phenotypic and genomic data were simulated considering a binary trait in four environments as different correlated traits. Heritabilities of 0.05, 0.10, 0.15, and 0.20 were considered to mimic the genetic variation of the binary trait in different environments. Eight scenarios resulted from combining the number of QTLs (60 or 300), LD level (high or low), and incidence of the binar

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Lozada, Dennis N., and Arron H. Carter. "Genomic Selection in Winter Wheat Breeding Using a Recommender Approach." Genes 11, no. 7 (2020): 779. http://dx.doi.org/10.3390/genes11070779.

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Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored. Different GS scenarios for IBCF were evaluated for a diverse population of winter wheat lines adapted to the Pacific Northwest region of the US. Predictions across years through cross-validations resulted in improved predictive ability when there is a high correlation between environments. Using m

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Thudi, Mahendar, Pooran M. Gaur, Lakshmanan Krishnamurthy, et al. "Genomics-assisted breeding for drought tolerance in chickpea." Functional Plant Biology 41, no. 11 (2014): 1178. http://dx.doi.org/10.1071/fp13318.

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Terminal drought is one of the major constraints in chickpea (Cicer arietinum L.), causing more than 50% production losses. With the objective of accelerating genetic understanding and crop improvement through genomics-assisted breeding, a draft genome sequence has been assembled for the CDC Frontier variety. In this context, 544.73 Mb of sequence data were assembled, capturing of 73.8% of the genome in scaffolds. In addition, large-scale genomic resources including several thousand simple sequence repeats and several million single nucleotide polymorphisms, high-density diversity array techno

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Sarup, Pernille, Just Jensen, Tage Ostersen, Mark Henryon, and Peter Sørensen. "Increased prediction accuracy using a genomic feature model including prior information on quantitative trait locus regions in purebred Danish Duroc pigs." BMC Genetics 17, no. 1 (2016): 11. https://doi.org/10.1186/s12863-015-0322-9.

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<strong>Background: </strong>In animal breeding, genetic variance for complex traits is often estimated using linear mixed models that incorporate information from single nucleotide polymorphism (SNP) markers using a realized genomic relationship matrix. In such models, individual genetic markers are weighted equally and genomic variation is treated as a “black box.” This approach is useful for selecting animals with high genetic potential, but it does not generate or utilise knowledge of the biological mechanisms underlying trait variation. Here we propose a linear mixed-model approach that c

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Ramzan, Faisal, Mehmet Gültas, Hendrik Bertram, David Cavero, and Armin Otto Schmitt. "Combining Random Forests and a Signal Detection Method Leads to the Robust Detection of Genotype-Phenotype Associations." Genes 11, no. 8 (2020): 892. http://dx.doi.org/10.3390/genes11080892.

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Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype–phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect remain unnoticed in many GWAS

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Kassem, My Abdelmajid. "Harnessing Artificial Intelligence and Machine Learning for Identifying Quantitative Trait Loci (QTL) Associated with Seed Quality Traits in Crops." Plants 14, no. 11 (2025): 1727. https://doi.org/10.3390/plants14111727.

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Seed quality traits, such as seed size, oil and protein content, mineral accumulation, and morphological characteristics, are crucial for enhancing crop productivity, nutritional value, and marketability. Traditional quantitative trait loci (QTL) mapping methods, such as linkage analysis and genome-wide association studies (GWAS), have played fundamental role in identifying loci associated with these complex traits. However, these approaches often struggle with high-dimensional genomic data, polygenic inheritance, and genotype-by-environment (GXE) interactions. Recent advances in artificial in

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Wijma, Robert, Daniel J. Weigel, Natascha Vukasinovic, et al. "Genomic Prediction for Abortion in Lactating Holstein Dairy Cows." Animals 12, no. 16 (2022): 2079. http://dx.doi.org/10.3390/ani12162079.

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Abortion in dairy cattle causes great economic losses due to reduced animal health, increase in culling rates, reduction in calf production, and milk yield, among others. Although the etiology of abortions can be of various origins, previous research has shown a genetic component. The objectives of this study were to (1) describe the development of the genomic prediction for cow abortions in lactating Holstein dairy cattle based on producer-recorded data and ssGBLUP methodology and (2) evaluate the efficacy of genomic predictions for cow abortions in commercial herds of US Holstein cows using

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Peters, Sunday O., Kadir Kizilkaya, Dorian Garrick, and James Reecy. "PSX-9 Comparison of single- and multiple-trait genomic predictions of cattle carcass quality traits in multibreed and purebred populations." Journal of Animal Science 102, Supplement_3 (2024): 453–54. http://dx.doi.org/10.1093/jas/skae234.512.

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Abstract Estimation of genetic parameters for carcass traits and their use in the evaluation of carcass quality is of considerable importance in the genetic improvement of beef cattle. The objective of this study was to compare the predictive ability of single- and multi-trait genomic prediction models for the following carcass quality traits: hot carcass weight (HCW); fat thickness (FAT); rib eye area (REA); marbling score (MARB); and peak force (PEAKF) from multibreed and purebred populations. The purebred population included 1,068 purebred Angus animals from Iowa State University whereas th

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Yan, Chao, Ping Lin, Tao Lyu, et al. "Unraveling the Roles of Regulatory Genes during Domestication of Cultivated Camellia: Evidence and Insights from Comparative and Evolutionary Genomics." Genes 9, no. 10 (2018): 488. http://dx.doi.org/10.3390/genes9100488.

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With the increasing power of DNA sequencing, the genomics-based approach is becoming a promising resolution to dissect the molecular mechanism of domestication of complex traits in trees. Genus Camellia possesses rich resources with a substantial value for producing beverage, ornaments, edible oil and more. Currently, a vast number of genetic and genomic research studies in Camellia plants have emerged and provided an unprecedented opportunity to expedite the molecular breeding program. In this paper, we summarize the recent advances of gene expression and genomic resources in Camellia species

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Nannuru, Vinay Kumar Reddy, Jon Arne Dieseth, Curt A. McCartney, et al. "Assessing Single‐Trait and Multitrait Genomic Prediction Model Abilities Including Significant GWAS Markers for Fusarium Head Blight Disease Resistance in Wheat (Triticum aestivum)." Plant Breeding, December 2024. https://doi.org/10.1111/pbr.13245.

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ABSTRACTDisease resistance traits are complex and quantitative in nature. Breeders regularly evaluate multiple important traits across diverse environments to employ them in genomics‐assisted breeding. In this study, we evaluated the prospects of genomic prediction models by incorporating genome‐wide association study (GWAS) results into single‐trait and multitrait genomic prediction scenarios, using two distinct panels: the NMBU panel and the GRAMINOR panel. A standard genomic prediction model (Base) and the Base model with the addition of significant GWAS markers as fixed covariates (Base +

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Meuwissen, Theo, and Vinzent Boerner. "Multitrait genome-wide association best linear unbiased prediction of genetic values." Genetics Selection Evolution 57, no. 1 (2025). https://doi.org/10.1186/s12711-025-00964-4.

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Abstract Background The GWABLUP (Genome-Wide Association based Best Linear Unbiased Prediction) approach used GWA analysis results to differentially weigh the SNPs in genomic prediction, and was found to improve the reliabilities of genomic predictions. However, the proposed multitrait GWABLUP method assumed that the SNP weights were the same across the traits. Here we extended and validated the multitrait GWABLUP method towards using trait specific SNP weights. Results In a 3-trait dairy data set, multitrait GWAS estimates of SNP effects and their standard errors were translated into trait sp

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Beier, Sara, Johannes Werner, Thierry Bouvier, Nicolas Mouquet, and Cyrille Violle. "Trait-trait relationships and tradeoffs vary with genome size in prokaryotes." Frontiers in Microbiology 13 (October 21, 2022). http://dx.doi.org/10.3389/fmicb.2022.985216.

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We report genomic traits that have been associated with the life history of prokaryotes and highlight conflicting findings concerning earlier observed trait correlations and tradeoffs. In order to address possible explanations for these contradictions we examined trait–trait variations of 11 genomic traits from ~18,000 sequenced genomes. The studied trait–trait variations suggested: (i) the predominance of two resistance and resilience-related orthogonal axes and (ii) at least in free living species with large effective population sizes whose evolution is little affected by genetic drift an ov

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Farooq, Muhammad, Aalt D. J. van Dijk, Harm Nijveen, et al. "Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana." Frontiers in Genetics 11 (January 20, 2021). http://dx.doi.org/10.3389/fgene.2020.609117.

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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 informatio

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Lozada-Soto, Emmanuel André, Daniela Lourenco, Christian Maltecca, et al. "Genotyping and phenotyping strategies for genetic improvement of meat quality and carcass composition in swine." Genetics Selection Evolution 54, no. 1 (2022). http://dx.doi.org/10.1186/s12711-022-00736-4.

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Abstract Background Meat quality and composition traits have become valuable in modern pork production; however, genetic improvement has been slow due to high phenotyping costs. Combining genomic information with multi-trait indirect selection based on cheaper indicator traits is an alternative for continued cost-effective genetic improvement. Methods Data from an ongoing breeding program were used in this study. Phenotypic and genomic information was collected on three-way crossbred and purebred Duroc animals belonging to 28 half-sib families. We applied different methods to assess the value

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Skovbjerg, Cathrine Kiel, Deepti Angra, Tom Robertson-Shersby-Harvie, et al. "Genetic analysis of global faba bean diversity, agronomic traits and selection signatures." Theoretical and Applied Genetics 136, no. 5 (2023). http://dx.doi.org/10.1007/s00122-023-04360-8.

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Abstract Key message We identified marker-trait associations for key faba bean agronomic traits and genomic signatures of selection within a global germplasm collection. Abstract Faba bean (Vicia faba L.) is a high-protein grain legume crop with great potential for sustainable protein production. However, little is known about the genetics underlying trait diversity. In this study, we used 21,345 high-quality SNP markers to genetically characterize 2678 faba bean genotypes. We performed genome-wide association studies of key agronomic traits using a seven-parent-MAGIC population and detected 2

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Roychowdhury, Rajib, Arindam Ghatak, Manoj Kumar, Kajal Samantara, Wolfram Weckwerth, and Palak Chaturvedi. "Accelerating wheat improvement through trait characterization: advances and perspectives." Physiologia Plantarum 176, no. 5 (2024). http://dx.doi.org/10.1111/ppl.14544.

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AbstractWheat (Triticum spp.) is a primary dietary staple food for humanity. Many wheat genetic resources with variable genomes have a record of domestication history and are widespread throughout the world. To develop elite wheat varieties, agronomical and stress‐responsive trait characterization is foremost for evaluating existing germplasm to promote breeding. However, genomic complexity is one of the primary impediments to trait mining and characterization. Multiple reference genomes and cutting‐edge technologies like haplotype mapping, genomic selection, precise gene editing tools, high‐t

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Wang, Zezhao, Haoran Ma, Hongwei Li, et al. "Multi‐trait genomic predictions using GBLUP and Bayesian mixture prior model in beef cattle." Animal Research and One Health, August 6, 2023. http://dx.doi.org/10.1002/aro2.13.

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AbstractMultiple trait genomic selection incorporating correlated traits can improve the predictive ability of low‐heritability traits. In this study, we evaluated genomic prediction accuracy using multi‐trait BayesCπ method (MT‐BayesCπ), which allows for a broader range of mixture priors for important traits in beef cattle. We compared the prediction performance of MT‐BayesCπ with single‐trait genomic best linear unbiased prediction (ST‐GBLUP), multi‐trait GBLUP (MT‐GBLUP), and single‐trait BayeCπ (ST‐BayesCπ) methods. We found that ribeye area (REA) and ribeye weight (REWT) showed high herit

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