Dataset Information

Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs.

ABSTRACT:

Background

With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F_ST) to select the most informative SNPs for ancestry inference.

Results

Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2%?±?0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof.

Conclusions

The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.

SUBMITTER: Momeni J

PROVIDER: S-EPMC7860026 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs.

Momeni Jamal J Parejo Melanie M Nielsen Rasmus O RO Langa Jorge J Montes Iratxe I Papoutsis Laetitia L Farajzadeh Leila L Bendixen Christian C Căuia Eliza E Charrière Jean-Daniel JD Coffey Mary F MF Costa Cecilia C Dall'Olio Raffaele R De la Rúa Pilar P Drazic M Maja MM Filipi Janja J Galea Thomas T Golubovski Miroljub M Gregorc Ales A Grigoryan Karina K Hatjina Fani F Ilyasov Rustem R Ivanova Evgeniya E Janashia Irakli I Kandemir Irfan I Karatasou Aikaterini A Kekecoglu Meral M Kezic Nikola N Matray Enikö Sz ES Mifsud David D Moosbeckhofer Rudolf R Nikolenko Alexei G AG Papachristoforou Alexandros A Petrov Plamen P Pinto M Alice MA Poskryakov Aleksandr V AV Sharipov Aglyam Y AY Siceanu Adrian A Soysal M Ihsan MI Uzunov Aleksandar A Zammit-Mangion Marion M Vingborg Rikke R Bouga Maria M Kryger Per P Meixner Marina D MD Estonba Andone A

BMC genomics 20210203 1

<h4>Background</h4>With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two high ...[more]

PMID: 33535965

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Project description:BACKGROUND:The presence of population structure in a sample may confound the search for important genetic loci associated with disease. Our four samples in the Family Investigation of Nephropathy and Diabetes (FIND), European Americans, Mexican Americans, African Americans, and American Indians are part of a genome- wide association study in which population structure might be particularly important. We therefore decided to study in detail one component of this, individual genetic ancestry (IGA). From SNPs present on the Affymetrix 6.0 Human SNP array, we identified 3 sets of ancestry informative markers (AIMs), each maximized for the information in one the three contrasts among ancestral populations: Europeans (HAPMAP, CEU), Africans (HAPMAP, YRI and LWK), and Native Americans (full heritage Pima Indians). We estimate IGA and present an algorithm for their standard errors, compare IGA to principal components, emphasize the importance of balancing information in the ancestry informative markers (AIMs), and test the association of IGA with diabetic nephropathy in the combined sample. RESULTS:A fixed parental allele maximum likelihood algorithm was applied to the FIND to estimate IGA in four samples: 869 American Indians; 1385 African Americans; 1451 Mexican Americans; and 826 European Americans. When the information in the AIMs is unbalanced, the estimates are incorrect with large error. Individual genetic admixture is highly correlated with principle components for capturing population structure. It takes ~700 SNPs to reduce the average standard error of individual admixture below 0.01. When the samples are combined, the resulting population structure creates associations between IGA and diabetic nephropathy. CONCLUSIONS:The identified set of AIMs, which include American Indian parental allele frequencies, may be particularly useful for estimating genetic admixture in populations from the Americas. Failure to balance information in maximum likelihood, poly-ancestry models creates biased estimates of individual admixture with large error. This also occurs when estimating IGA using the Bayesian clustering method as implemented in the program STRUCTURE. Odds ratios for the associations of IGA with disease are consistent with what is known about the incidence and prevalence of diabetic nephropathy in these populations.

Dataset Information

Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs.

Background

Results

Conclusions

Publications

Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs.

Similar Datasets

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