Computational methods for gene allele prediction have been proposed to substitute dedicated and expensive assays with cheaper in-silico analyses that operate on routinely collected data, such as SNP genotypes. Most of these methods are tailored to the needs and characteristics of human genetic studies where they achieve good prediction accuracy. However, genomic analyses are becoming increasingly important in livestock species too. For livestock species generally the underlying—usually quite large and complex—pedigree is known and available; this information is not fully exploited by current allele prediction methods. In this paper, we propose a new gene allele prediction method based on a simple, but robust, combinatorial formulation for the problem of discovering haplotype-allele associations. The inherent uncertainty of the haplotype inference process is reduced by taking into account the inheritance of gene alleles across the population pedigree while genotypes are phased. The accuracy of the method has been extensively evaluated on a representative real-world livestock dataset under several scenarios and choices of parameters. The median error rate ranged from 0.0537 to 0.0896, with an average of 0.0678; this is 21% better than another state-of-the-art prediction algorithm that does not use the pedigree information. The experimental results support the validity of the proposed approach and, in particular, of the use of pedigree information in gene allele predictions.

Pirola, Y., DELLA VEDOVA, G., Bonizzoni, P., Stella, A., Biscarini, F. (2013). Haplotype-based prediction of gene alleles using pedigrees and SNP genotypes. In Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics - BCB'13 (pp.33-41). ACM [10.1145/2506583.2506592].

Haplotype-based prediction of gene alleles using pedigrees and SNP genotypes

PIROLA, YURI
Primo
;
DELLA VEDOVA, GIANLUCA;BONIZZONI, PAOLA;
2013

Abstract

Computational methods for gene allele prediction have been proposed to substitute dedicated and expensive assays with cheaper in-silico analyses that operate on routinely collected data, such as SNP genotypes. Most of these methods are tailored to the needs and characteristics of human genetic studies where they achieve good prediction accuracy. However, genomic analyses are becoming increasingly important in livestock species too. For livestock species generally the underlying—usually quite large and complex—pedigree is known and available; this information is not fully exploited by current allele prediction methods. In this paper, we propose a new gene allele prediction method based on a simple, but robust, combinatorial formulation for the problem of discovering haplotype-allele associations. The inherent uncertainty of the haplotype inference process is reduced by taking into account the inheritance of gene alleles across the population pedigree while genotypes are phased. The accuracy of the method has been extensively evaluated on a representative real-world livestock dataset under several scenarios and choices of parameters. The median error rate ranged from 0.0537 to 0.0896, with an average of 0.0678; this is 21% better than another state-of-the-art prediction algorithm that does not use the pedigree information. The experimental results support the validity of the proposed approach and, in particular, of the use of pedigree information in gene allele predictions.
No
paper
computational biology, genotypes, SNP, pedigree, haplotype, algorithms
English
International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
9781450324342
http://dl.acm.org/citation.cfm?doid=2506583.2506592
Pirola, Y., DELLA VEDOVA, G., Bonizzoni, P., Stella, A., Biscarini, F. (2013). Haplotype-based prediction of gene alleles using pedigrees and SNP genotypes. In Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics - BCB'13 (pp.33-41). ACM [10.1145/2506583.2506592].
Pirola, Y; DELLA VEDOVA, G; Bonizzoni, P; Stella, A; Biscarini, F
File in questo prodotto:
File Dimensione Formato  
conf-paper-13-acmbcb.pdf

Solo gestori archivio

Descrizione: Articolo principale
Dimensione 415.46 kB
Formato Adobe PDF
415.46 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/48671
Citazioni
  • Scopus 7
  • ???jsp.display-item.citation.isi??? ND
Social impact