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Biases induced by using geography and environment to guide ex situ conservation

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Abstract

Ex situ germplasm collections seek to conserve maximum genetic diversity in a small number of samples. Geographic and environmental information have long been treated as surrogate measures of genetic diversity, proposed to be useful for increasing allelic diversity of collections. We examine the effect of maximizing geographic and environmental diversity on the retention of distinct haplotype blocks in germplasm subsets, using three species with extensive genomewide genotypic data. We show that maximizing diversity in the surrogate measures produces subsets with uneven representation of haplotypic diversity across the genome. Some regions are well-conserved, exhibiting high haplotypic diversity, while others are poorly-conserved and contain significantly less haplotypic diversity than would be obtained via random sampling. In two of three species, poorly-conserved genomic regions were enriched in regulatory genes which, as a class, contribute to phenotypic variation. The specific genes affected varied by species but, overall, haplotypic diversity was poorly-conserved at genes controlling ~ 10% of major molecular functions and biological processes. While this study was limited to three exemplar species, we find little evidence to support continued use of geographic or environmental surrogates for ex situ conservation activities attempting to capture maximum genomewide allelic diversity. Although geographic and environmental diversity have proven to be reliable predictors of allele frequency differences and ecotypic differentiation across species ranges, they appear to be poor predictors of allelic diversity per se, offering little opportunity to enrich collections for haplotypic diversity overall, and ample opportunity to bias the conservation of important functional genetic variation. We propose a bioinformatic bridge between haplotypic diversity and the potential phenotypic diversity residing in collections using the Gene Ontology.

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Acknowledgements

We thank Kelly Robbins for helpful comments on the manuscript. This research used resources provided by the SCINet project of the USDA Agricultural Research Service, ARS Project Number 0500-00093-001-00-D.

Funding

This study was supported through funds provided to the National Laboratory for Genetic Resources Preservation, Plant Preservation Research Unit by USDA-ARS National Program 301.

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Correspondence to Patrick A. Reeves.

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10592_2018_1098_MOESM1_ESM.pdf

Supplementary Figure 1. Schematic representation of quantization scheme to convert continuous geographical and environmental variables into categorical data upon which diversity maximization can be conducted. For simplicity, a hypothetical sample of 5 populations from coastal Italy is shown, with geographic data (latitude and longitude) as the target for quantization. Supplementary Figure 2. Biased representation of biological processes and molecular functions in well-conserved genomic regions. The length of bars represents the ratio of the observed frequency of a term to its expected frequency using the plant GOslim ontology. The X axis is scaled as log base 2 to display folddifference between observed and expected values fairly. Values above one indicate enrichment of GO terms in regions of the genome where haplotypic variation was elevated. The 10 most biased GO terms are shown. For each species, the top chart shows GO term representation bias in geographic subsets; bottom, environmental subsets. (PDF 113 KB)

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Reeves, P.A., Richards, C.M. Biases induced by using geography and environment to guide ex situ conservation. Conserv Genet 19, 1281–1293 (2018). https://doi.org/10.1007/s10592-018-1098-z

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