Widespread adoption of new varieties can be valuable, especially in
developing countries, which tend to lack access to improved agricultural
production technologies. However, as farmers adopt new varieties, in
situ population structure and genetic diversity of their seed
holdings can change drastically. Consequences of adoption are still poorly
understood due to a lack of crop genetic diversity assessments and
detailed surveys of farmers’ seed management practices. Common
bean (Phaseolus vulgaris) is an excellent model for these types of
studies, as it has a long history of cultivation among smallholder
farmers, exhibits eco-geographic patterns of diversity (e.g., Andean vs.
Mesoamerican gene-pools), and has been subjected to post-Columbian
dispersal and recent introduction of improved cultivars. The Hoima
district of western Uganda additionally provides an excellent social
setting for evaluating consequences of adoption because access to improved
varieties has varied across farmer groups in this production region. This
study establishes a baseline understanding of the common bean diversity
found among household producers in Uganda and compares the crop population
structure, diversity and consequences of adoption of household producers
with different adoption practices. Molecular diversity analysis, based on
4,955 single nucleotide polymorphism (SNP) markers, evaluated a total of
1,156 seed samples that included 196 household samples collected from
household producers in the Hoima district, nineteen breeder-selected
varieties used in participatory breeding activities that had taken place
prior to the study in the region, and a global bean germplasm collection.
Households that had participated in regional participatory breeding
efforts were more likely to adopt new varieties and, consequently,
diversify their seed stocks than those that had not participated. Of the
three farmer groups that participated in breeding efforts, households from
the farmer group with the longest history of bean production were more
likely to conserve “Seed Engufu”, a local “Calima”-type variety of the
Andean bean gene pool, and, at the same time, introduce rare Mesoamerican
gene pool varieties into household seed stocks. Plant
Material The collection included a set of 196 household samples
collected from producers in Hoima district, nineteen breeder-selected
varieties (Table 1) originating from either the CIAT-Kawanda,
Uganda, or CIAT-Cali, Colombia, germplasm collections. For comparison,
this study included a database of a world-wide reference germplasm
collection that consisted of 502 accessions of the Andean Diversity Panel
(Cichy et al., 2015), 363 accessions of the USDA core collection (McClean
et al., 2012; S. Kuzay, P. Hamilton-Conaty
and P. Gepts, unpubl. results), and 57 reference and commercial
cultivars. A subset of the breeder-selected samples included in the
analysis had been evaluated and made available to breeding
program-affiliated households through the CIAT-managed PVS trials from
2012 to 2013. Within the breeder-selected varieties, NABE 11, 15, 17 and
21 were bred locally by the national Ugandan bean program using CIAT-bred
lines while the remaining NABE lines were bred under CIAT-led programs.
The KAT lines had been bred locally by a Kenyan breeder at the Katumani
Research Station in the 1990’s. The 196 household seed stock samples were
collected from eight-two households between May and June of 2014, within
2-3 weeks of the first harvest since PVS trials were completed.
SNP-Based
Genotyping The Illumina Infinium “BeadChip BARCBean6K-3” (Song et al.,
2015) from the USDA National Institute of Food and Agriculture BeanCAP
Project (Grant number 2009-01929) was used to genotype the entire seed
collection. Single nucleotide polymorphism genotyping was conducted
courtesy of Dr. Perry Cregan, USDA-ARS, Soybean Genomics Improvement
Laboratory, BARC-West, Beltsville, MD, on the Illumina platform following
the Infinium HD Assay Ultra Protocol (Illumina, San Diego, CA). Sequencing
output of the BARCBean6K-3 BeanChip was evaluated using GenomeStudio
software. Clusterw2 software was used to align sequences and generate SNP
calls. In order to generate reliable SNP calls for household seed samples
and breeder-selected samples, cluster files were calibrated from the
default set of cluster files. 4,955 of the 5,398 single nucleotide
polymorphism (SNP) markers were then called using the new cluster files
with a Gencall score cutoff of 0.15, according to the GenomeStudio
Genotyping Module v1.8.4 (Illumina, San Diego, CA). SNP data of samples
from the global germplasm collection BARCBean6K-3 assay were filtered to
include only those having less than 5% missing data and 5% heterozygosity.
SNP data of the remaining samples were pruned to 1,870 markers to reduce
linkage disequilibrium. Pruning was performed in a moving window of 50
SNPs removing one of a pair of SNPs if the linkage disequilibrium was
higher than 0.6. The steps of filtering and pruning were performed in
PLINK (Purcell et al., 2007).