Last updated: 2026-03-27

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Rmd	a2bc83b	WevertonGomesCosta	2025-08-29	Start workflowr project.

Kernel-Based Integration of Near-Infrared Spectroscopy and Genomic Data

Welcome. This website organizes the analytical workflows, intermediate products, and manuscript-oriented reports for the study “Kernel-Based Integration of Near-Infrared Spectroscopy and Genomic Data for Enhanced Predictive Performance”.

The project investigates whether combining genomic markers, near-infrared spectroscopy (NIRS), and weather covariates through kernel-based models can improve predictive performance for agronomic traits in multi-environment maize trials.

Central scientific question

The study evaluates whether heterogeneous biological information sources provide complementary predictive signal when modeled jointly. In practical terms, the repository compares:

genomic prediction (G) based on SNP-derived similarity;
phenomic prediction (P) based on NIRS-derived similarity;
environmental information (E/W) represented through fixed or kernel-based structures;
integrated multi-kernel models combining genomic, phenomic, and environmental components.

The main analytical focus is not only whether integration is possible, but whether it is stable, reproducible, and genuinely advantageous across cross-validation scenarios that reflect realistic breeding conditions.

Study scope

The analyses are based on maize multi-environment trials conducted in College Station, Texas, under two water regimes (well-watered and water-stressed) across two years (2011 and 2012). The repository integrates:

phenotypic records for agronomic traits;
genomic marker data;
whole-kernel NIRS spectra;
daily weather covariates used to derive environmental kernels.

The target traits currently emphasized in the study are:

Grain yield (GY)
500-kernel weight (KW)

Main analytical message

The original motivation of the project was to test whether integrating NIRS and genomic information could improve prediction beyond single-source models. The current version of the study supports a more selective conclusion:

genomic information provided the most robust predictive signal;
NIRS captured biologically relevant variation, but with lower stability across environments;
the environmental effect was strong and explained an important fraction of the observed variation;
adding weather kernels or explicit interaction kernels did not consistently improve predictive performance;
integrating G + P did not yield clear or systematic gains under the evaluated conditions.

This makes the repository useful not only as a positive integration example, but also as a reproducible case study on when multimodal data fusion does not deliver the expected predictive advantage.

Website contents

This site is organized around the main components of the analytical pipeline available in the repository:

About: project context, manuscript framing, and study overview.
Climate data: preprocessing and structuring of weather information used in environmental modeling.
Matrices: construction of genomic, phenomic, environmental, and interaction kernels.
Prediction analysis: model fitting and cross-validation using Bayesian multi-kernel regression.
Variance components: decomposition of explained variation across model structures.
Visualization: figures and summary plots used to interpret predictive ability and model behavior.
Additional analysis pages: exploratory and supporting reports used during the development of the study.

Reproducibility

The project is implemented in R and organized with workflowr for transparent reporting. Dependencies can be restored locally with renv.

install.packages("renv")
renv::restore()

The repository structure includes source analyses, data, helper code, intermediate outputs, and rendered website files.

Repository and contact

GitHub repository
WevertonGomesCosta/Integrating-nir-genomic-kernel

Project website
https://wevertongomescosta.github.io/Integrating-nir-genomic-kernel/

Author / Contact
Weverton Gomes da Costa
Departamento de Estatística, Universidade Federal de Viçosa (UFV)
Email: weverton.costa@ufv.br

R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=Portuguese_Brazil.utf8  LC_CTYPE=Portuguese_Brazil.utf8   
[3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C                      
[5] LC_TIME=Portuguese_Brazil.utf8    

time zone: America/Sao_Paulo
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] vctrs_0.7.1       cli_3.6.5         knitr_1.50        rlang_1.1.7      
 [5] xfun_0.54         stringi_1.8.7     otel_0.2.0        promises_1.5.0   
 [9] jsonlite_2.0.0    workflowr_1.7.2   glue_1.8.0        rprojroot_2.1.1  
[13] git2r_0.36.2      htmltools_0.5.9   httpuv_1.6.16     sass_0.4.10      
[17] rmarkdown_2.30    evaluate_1.0.5    jquerylib_0.1.4   tibble_3.3.1     
[21] fastmap_1.2.0     yaml_2.3.12       lifecycle_1.0.5   whisker_0.4.1    
[25] stringr_1.6.0     compiler_4.5.1    fs_1.6.6          Rcpp_1.1.1       
[29] pkgconfig_2.0.3   rstudioapi_0.17.1 later_1.4.4       digest_0.6.39    
[33] R6_2.6.1          pillar_1.11.1     magrittr_2.0.4    bslib_0.9.0      
[37] tools_4.5.1       cachem_1.1.0

Weverton Gomes da Costa, Pós-Doutorando, Departamento de Estatística - Universidade Federal de Viçosa, weverton.costa@ufv.br ↩︎

Kernel-Based Integration of Near-Infrared Spectroscopy and Genomic Data

Costa, W. G.1

2026-03-27

Kernel-Based Integration of Near-Infrared Spectroscopy and Genomic Data

Central scientific question

Study scope

Main analytical message

Website contents

Reproducibility

Repository and contact

Costa, W. G.¹