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

Pirola, Yuri; Vedova, Gianluca Della; Bonizzoni, Paola; Stella, Alessandra; Biscarini, Filippo

doi:10.1145/2506583.2506592

Cited by 10 publications

(9 citation statements)

References 25 publications

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“…On average, a very low error rate for the prediction of haplotype carriers was estimated in this study under all models (average total test error rate ∼≤1 % ). Low error rates for allele prediction at the HLA -humans (∼0−5 % )- and casein -cattle (∼6 % )- loci were reported in previous studies [ 11 , 19 ]. SNP genotypes are expected to be good predictors for genomic sequences (haplotypes, gene alleles) and a high prediction accuracy can therefore be reasonably achieved.…”

Section: Discussionmentioning

confidence: 68%

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Predicting haplotype carriers from SNP genotypes in Bos taurus through linear discriminant analysis

et al. 2015

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BackgroundSNP (single nucleotide polymorphisms) genotype data are increasingly available in cattle populations and, among other things, can be used to predict carriers of specific haplotypes. It is therefore convenient to have a practical statistical method for the accurate classification of individuals into carriers and non-carriers. In this paper, we present a procedure combining variable selection (i.e. the selection of predictive SNPs) and linear discriminant analysis for the identification of carriers of a haplotype on BTA19 (Bos taurus autosome 19) known to be associated with reduced cow fertility. A population of 3645 Brown Swiss cows and bulls genotyped with the 54K SNP-chip was available for the analysis.ResultsThe overall error rate for the prediction of haplotype carriers was on average very low (∼≤1%). The error rate was found to depend on the number of SNPs in the model and their density around the region of the haplotype on BTA19. The minimum set of SNPs to still achieve accurate predictions was 5, with a total test error rate of 1.59.ConclusionsThe paper describes a procedure to accurately identify haplotype carriers from SNP genotypes in cattle populations. Very few misclassifications were observed, which indicates that this is a very reliable approach for potential applications in cattle breeding.

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Section: Discussionmentioning

confidence: 68%

“…Previous studies on the prediction of haplotypes or gene alleles using SNP data in cattle have been reported: for instance, Pirola et al [ 19 ] used SNP genotypes together with pedigree records to predict K-casein alleles in a robust combinatorial formulation of the problem.…”

Section: Introductionmentioning

confidence: 99%

Predicting haplotype carriers from SNP genotypes in Bos taurus through linear discriminant analysis

et al. 2015

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“…In addition to identifying genomic regions subject to natural selection, signatures of artificial selection have been identified applying statistical approaches to genomic data ( Stella et al, 2010 ; Randhawa et al, 2014 ). Other uses of genomic information with potentially high impacts on management include: the estimation of genome-based relationships and inbreeding coefficients, from single nucleotide polymorphisms (SNPs; e.g., Manichaikul et al, 2010 ; VanRaden et al, 2011a ), from runs of homozygosity (e.g., Purfield et al, 2012 ), or unifying different sources of information (e.g., Wang and Da, 2014 ); genetic approaches for breed-based product identification and traceability, for authentication and quality assurance ( Nicoloso et al, 2013 ); and the identification of recessive lethals or other specific mutations of interest ( VanRaden et al, 2011b ; Pirola et al, 2013 ). All of the above can be applied to small breeds at relatively low cost (e.g., using low-density or custom panels).…”

Section: Opportunitiesmentioning

confidence: 99%

Challenges and opportunities in genetic improvement of local livestock breeds

et al. 2015

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Sufficient genetic variation in livestock populations is necessary both for adaptation to future changes in climate and consumer demand, and for continual genetic improvement of economically important traits. Unfortunately, the current trend is for reduced genetic variation, both within and across breeds. The latter occurs primarily through the loss of small, local breeds. Inferior production is a key driver for loss of small breeds, as they are replaced by high-output international transboundary breeds. Selection to improve productivity of small local breeds is therefore critical for their long term survival. The objective of this paper is to review the technology options available for the genetic improvement of small local breeds and discuss their feasibility. Most technologies have been developed for the high-input breeds and consequently are more favorably applied in that context. Nevertheless, their application in local breeds is not precluded and can yield significant benefits, especially when multiple technologies are applied in close collaboration with farmers and breeders. Breeding strategies that require cooperation and centralized decision-making, such as optimal contribution selection, may in fact be more easily implemented in small breeds.

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“…Haplotype analysis is of fundamental importance for a variety of applications including agricultural research, medical diagnostics, and drug design (Pirola et al, 2013;Bonizzoni et al, 2003;Browning and Browning, 2008).…”

Section: Introductionmentioning

confidence: 99%

On the Minimum Error Correction Problem for Haplotype Assembly in Diploid and Polyploid Genomes

Bonizzoni

Dondi

Klau

et al. 2016

Journal of Computational Biology

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In diploid genomes, haplotype assembly is the computational problem of reconstructing the two parental copies, called haplotypes, of each chromosome starting from sequencing reads, called fragments, possibly affected by sequencing errors. Minimum error correction (MEC) is a prominent computational problem for haplotype assembly and, given a set of fragments, aims at reconstructing the two haplotypes by applying the minimum number of base corrections. MEC is computationally hard to solve, but some approximation-based or fixed-parameter approaches have been proved capable of obtaining accurate results on real data. In this work, we expand the current characterization of the computational complexity of MEC from the approximation and the fixed-parameter tractability point of view. In particular, we show that MEC is not approximable within a constant factor, whereas it is approximable within a logarithmic factor in the size of the input. Furthermore, we answer open questions on the fixed-parameter tractability for parameters of classical or practical interest: the total number of corrections and the fragment length. In addition, we present a direct 2-approximation algorithm for a variant of the problem that has also been applied in the framework of clustering data. Finally, since polyploid genomes, such as those of plants and fishes, are composed of more than two copies of the chromosomes, we introduce a novel formulation of MEC, namely the k-ploid MEC problem, that extends the traditional problem to deal with polyploid genomes. We show that the novel formulation is still both computationally hard and hard to approximate. Nonetheless, from the parameterized point of view, we prove that the problem is tractable for parameters of practical interest such as the number of haplotypes and the coverage, or the number of haplotypes and the fragment length.

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

Cited by 10 publications

References 25 publications

Predicting haplotype carriers from SNP genotypes in Bos taurus through linear discriminant analysis

Predicting haplotype carriers from SNP genotypes in Bos taurus through linear discriminant analysis

Challenges and opportunities in genetic improvement of local livestock breeds

On the Minimum Error Correction Problem for Haplotype Assembly in Diploid and Polyploid Genomes

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