Browsing by Person "Chang, Che-Wei"

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Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome–environment association studies
(2025) Chang, Che-Wei; Schmid, Karl
Genome–environment association (GEA) is an approach for identifying adaptive loci by combining genetic variation with environmental parameters, offering potential for improving crop resilience. However, its application to genebank accessions is limited by missing geographic origin data. To address this limitation, we explored the use of neural networks to predict the geographic origins of barley accessions and integrate imputed environmental data into GEA. Neural networks demonstrated high accuracy in cross-validation but occasionally produced ecologically implausible predictions as models solely considered geographical proximity. For example, some predicted origins were located within non-arable regions, such as the Mediterranean Sea. Using barley flowering time genes as benchmarks, GEA integrating imputed environmental data ( N=11,032) displayed partially concordant yet complementary detection of genomic regions near flowering time genes compared to regular GEA ( N=1,626), highlighting the potential of GEA with imputed data to complement regular GEA in uncovering novel adaptive loci. Also, contrary to our initial hypothesis anticipating a significant improvement in GEA performance by increasing sample size, our simulations yield unexpected insights. Our study suggests potential limitations in the sensitivity of GEA approaches to the considerable expansion in sample size achieved through predicting missing geographical data. Overall, our study provides insights into leveraging incomplete geographical origin data by integrating deep learning with GEA. Our findings indicate the need for further development of GEA approaches to optimize the use of imputed environmental data, such as incorporating regional GEA patterns instead of solely focusing on global associations between allele frequencies and environmental gradients across large-scale landscapes.
Exploring adaptive genetic variation in exotic barley germplasm with landscape genomics
(2025) Chang, Che-Wei; Schmid, Karl
Understanding genetic variation underlying local adaptation is essential for improving crop resilience to address challenges posed by climate change. Barley (Hordeum vulgare L. ssp. vulgare), one of the most important crops, is suitable for studying local adaptation due to its remarkable adaptability. This PhD dissertation investigated adaptive genetic variation in exotic barley germplasm, including wild barley (Hordeum vulgare ssp. spontaneum) and barley landraces, from diverse environments and explored strategies to improve the use of genebank accessions for harnessing valuable genetic variants. In the first study, local adaptation in wild barley populations from the Southern Lev- ant was explored using landscape genomics approaches, combining genomic data with the climatic and soil properties of geographical origins. Through redundancy analysis (RDA), we found spatial autocorrelation explained 45% of genomic variation, and environmental factors accounted for 15%. Adaptive signatures were identified in the pericentromeric regions by the population-genetics-based scans and genome- environment association (GEA) scans, but they mostly disappeared when the population structure was considered. Our findings overall highlighted the role of nonselective forces in shaping the genetic variation of wild barley even in divergent environments. The second study addressed challenges in passport data quality control for large- scale samples, such as germplasm collections in genebanks. The R package GGoutlieR was developed in this work to tackle the shortcomings of traditional manual data cleaning. It efficiently detects and visualizes samples with unusual geo-genetic patterns by characterizing geography-genotype associations with distance-based statis- tics via K-nearest neighbors and calculating empirical p-values accordingly. By stream- lining data cleaning and quality control, GGoutlieR supports more reliable landscape genomics studies, which is crucial for studying loci involved in local adaptation. The third study explored the use of neural networks to predict geographical origins for genebank accessions lacking passport data, enabling their integration into genome- environment association (GEA) analyses. Neural network models demonstrated high prediction accuracy in cross-validation. Incorporating imputed environmental data (N = 11,032) into GEA, using barley flowering time genes as benchmarks, revealed complementary detection of genomic regions near flowering time genes compared to regular GEA (N = 1,626). Furthermore, simulations of polygenic local adaptation in selfing species showed that GEA power is insensitive to large sample sizes. These findings suggest that GEA with imputed environmental data can be a complementary approach for uncovering novel adaptive loci that might remain undetected in conventional GEA, rather than improving the statistical power of GEA. Overall, this dissertation contributes to understanding the adaptive genetic variation in barley and expanding methodologies in landscape genomics, providing a direction for the future development of GEA approaches to better support allele mining for prebreeding to enhance crop resilience.
Physical geography, isolation by distance and environmental variables shape genomic variation of wild barley (Hordeum vulgare L. ssp. spontaneum) in the Southern Levant
(2022) Chang, Che-Wei; Fridman, Eyal; Mascher, Martin; Himmelbach, Axel; Schmid, Karl
Determining the extent of genetic variation that reflects local adaptation in crop-wild relatives is of interest for the purpose of identifying useful genetic diversity for plant breeding. We investigated the association of genomic variation with geographical and environmental factors in wild barley ( Hordeum vulgare L. ssp. spontaneum ) populations of the Southern Levant using genotyping by sequencing (GBS) of 244 accessions in the Barley 1K+ collection. The inference of population structure resulted in four genetic clusters that corresponded to eco-geographical habitats and a significant association between lower gene flow rates and geographical barriers, e.g. the Judaean Mountains and the Sea of Galilee. Redundancy analysis (RDA) revealed that spatial autocorrelation explained 45% and environmental variables explained 15% of total genomic variation. Only 4.5% of genomic variation was solely attributed to environmental variation if the component confounded with spatial autocorrelation was excluded. A synthetic environmental variable combining latitude, solar radiation, and accumulated precipitation explained the highest proportion of genomic variation (3.9%). When conditioned on population structure, soil water capacity was the most important environmental variable explaining 1.18% of genomic variation. Genome scans with outlier analysis and genome-environment association studies were conducted to identify adaptation signatures. RDA and outlier methods jointly detected selection signatures in the pericentromeric regions, which have reduced recombination, of the chromosomes 3H, 4H, and 5H. However, selection signatures mostly disappeared after correction for population structure. In conclusion, adaptation to the highly diverse environments of the Southern Levant over short geographical ranges had a limited effect on the genomic diversity of wild barley. This highlighted the importance of nonselective forces in genetic differentiation.