363. JWAS version 2: leveraging biological information and highthroughput phenotypes into genomic prediction and association

Cheng, Hao; Fernando, Rohan L.; Garrick, Dorian J.; Zhao, Tianjing; Qu, Jiayi

doi:10.3920/978-90-8686-940-4_363

Cited by 17 publications

(28 citation statements)

References 11 publications

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“…According to a previous study (Fan et al, 2011 ), a posterior inclusion probability threshold of 0.10 was used for manually measured phenotypes, and 0.50 was used as a more conservative threshold for hyperspectral GWA. All Bayesian analyses were fit using 60,000 Markov chain Monte Carlo samples, 6000 burn‐ins, and a thinning rate of 60 implemented in JWAS (Cheng et al, 2018 ). Model convergence was assessed using trace plots of the posterior means of the parameters.…”

Section: Methodsmentioning

confidence: 99%

Genome‐wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth‐related traits in maize under plant growth‐promoting bacteria inoculation

et al. 2023

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Plant growth‐promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth‐related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome‐wide association analyses to examine maize growth‐related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386–1021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome‐wide association analysis as potential markers for growth‐related traits under PGPB inoculation. Eight SNPs were detected, which were commonly associated with manually measured and hyperspectral phenotypes. Different genomic regions were found for plant growth and hyperspectral phenotypes between with and without PGPB inoculation. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multiphenotype genome‐wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral‐based phenotyping for studying maize growth‐related traits in response to PGPB inoculation.

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Section: Methodsmentioning

confidence: 99%

Genome‐wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth‐related traits in maize under plant growth‐promoting bacteria inoculation

et al. 2023

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“…When fitting models to these simulated data, we included the intercept for each trait as the only fixed effect. We compared to a single-trait implementation of BayesC ( ) using the package ( Cheng, Fernando, et al 2018 ). The model specification of was similar to that used in the Genomic Prediction application, except: (1) no fixed factor loadings were included in

, (2)

the number of factors fitted in the model was

10

.…”

Section: Methodsmentioning

confidence: 99%

“…After creating the simulated data, we applied 3 methods to identify QTL and estimate their effects on the focal trait: (1) single-trait Genome-Wide Association Studies (GWAS) using GCTA ( Yang et al 2011 ) (ST-GCTA); (2) single-trait BayesC implemented in JWAS ( Cheng, Fernando, et al 2018 ) ( ); and (3) implemented in .…”

Section: Methodsmentioning

confidence: 99%

Mega-scale Bayesian regression methods for genome-wide prediction and association studies with thousands of traits

Runcie

Cheng

2022

Genetics

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Large-scale phenotype data are expected to increase the accuracy of genome-wide prediction and the power of genome-wide association analyses. However, genomic analyses of high-dimensional, highly correlated traits are challenging. We developed a method for implementing high-dimensional Bayesian multivariate regression to simultaneously analyze genetic variants underlying thousands of traits. As a demonstration, we implemented the BayesC prior in the R package MegaLMM. Applied to Genomic Prediction, MegaBayesC effectively integrated hyperspectral reflectance data from 620 hyperspectral wavelengths to improve the accuracy of genetic value prediction on grain yield in a wheat dataset. Applied to Genome-Wide Association Studies, we used simulations to show that MegaBayesC can accurately estimate the effect sizes of QTL across a range of genetic architectures and causes of correlations among traits. To apply MegaBayesC to a realistic scenario involving whole-genome marker data, we developed a two-stage procedure involving a preliminary step of candidate marker selection prior to multivariate regression. We then used MegaBayesC to identify genetic associations with flowering time in Arabidopsis thaliana, leveraging expression data from 20843 genes. MegaBayesC selected 15 SNPs as important for flowering time, with 13 located within 100Kb of known flowering-time related genes, a higher validation rate than achieved by a single-stage analysis using only the flowering time data itself. These results demonstrate that MegaBayesC can efficiently and effectively leverage high-dimensional phenotypes in genetic analyses.

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“…where G is a 2 x 2 covariance matrix for the effects of SNPs for parities k and l, following Cheng et al (2018); and e ij is the residual effect, with e ik e il ~MVN 0, R ( ), where R is a 2 x 2 covariance matrix of residual effects for a sow's POP phenotypes for parities k and l.…”

Section: Estimation Of Genetic Parametersmentioning

confidence: 99%

Identification of the genetic basis of sow pelvic organ prolapse

et al. 2023

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Introduction: Pelvic organ prolapse (POP) is one contributor to recent increases in sow mortality that have been observed in some populations and environments, leading to financial losses and welfare concerns.Methods: With inconsistent previous reports, the objective here was to investigate the role of genetics on susceptibility to POP, using data on 30,429 purebred sows, of which 14,186 were genotyped (25K), collected from 2012 to 2022 in two US multiplier farms with a high POP incidence of 7.1% among culled and dead sows and ranging from 2% to 4% of all sows present by parity. Given the low incidence of POP for parities 1 and >6, only data from parities 2 to 6 were retained for analyses. Genetic analyses were conducted both across parities, using cull data (culled for POP versus another reason), and by parity, using farrowing data. (culled for POP versus culled for another reason or not culled).Results and Discussion: Estimates of heritability from univariate logit models on the underlying scale were 0.35 ± 0.02 for the across-parity analysis and ranged from 0.41 ± 0.03 in parity 2 to 0.15 ± 0.07 in parity 6 for the by-parity analyses. Estimates of genetic correlations of POP between parities based on bivariate linear models indicated a similar genetic basis of POP across parities but less similar with increasing distance between parities. Genome wide association analyses revealed six 1 Mb windows that explained more than 1% of the genetic variance in the across-parity data. Most regions were confirmed in several by-parity analyses. Functional analyses of the identified genomic regions showed a potential role of several genes on chromosomes 1, 3, 7, 10, 12, and 14 in susceptibility to POP, including the Estrogen Receptor gene. Gene set enrichment analyses showed that genomic regions that explained more variation for POP were enriched for several terms from custom transcriptome and gene ontology libraries.Conclusion: The influence of genetics on susceptibility to POP in this population and environment was confirmed and several candidate genes and biological processes were identified that can be targeted to better understand and mitigate the incidence of POP.

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363. JWAS version 2: leveraging biological information and highthroughput phenotypes into genomic prediction and association

Cited by 17 publications

References 11 publications

Genome‐wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth‐related traits in maize under plant growth‐promoting bacteria inoculation

Genome‐wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth‐related traits in maize under plant growth‐promoting bacteria inoculation

Mega-scale Bayesian regression methods for genome-wide prediction and association studies with thousands of traits

Identification of the genetic basis of sow pelvic organ prolapse

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