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Preferential access to genetic information from endogenous hominin ancient DNA and accurate quantitative SNP-typing via SPEX.

ABSTRACT: The analysis of targeted genetic loci from ancient, forensic and clinical samples is usually built upon polymerase chain reaction (PCR)-generated sequence data. However, many studies have shown that PCR amplification from poor-quality DNA templates can create sequence artefacts at significant levels. With hominin (human and other hominid) samples, the pervasive presence of highly PCR-amplifiable human DNA contaminants in the vast majority of samples can lead to the creation of recombinant hybrids and other non-authentic artefacts. The resulting PCR-generated sequences can then be difficult, if not impossible, to authenticate. In contrast, single primer extension (SPEX)-based approaches can genotype single nucleotide polymorphisms from ancient fragments of DNA as accurately as modern DNA. A single SPEX-type assay can amplify just one of the duplex DNA strands at target loci and generate a multi-fold depth-of-coverage, with non-authentic recombinant hybrids reduced to undetectable levels. Crucially, SPEX-type approaches can preferentially access genetic information from damaged and degraded endogenous ancient DNA templates over modern human DNA contaminants. The development of SPEX-type assays offers the potential for highly accurate, quantitative genotyping from ancient hominin samples.

SUBMITTER: Brotherton P

PROVIDER: S-EPMC2811011 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Preferential access to genetic information from endogenous hominin ancient DNA and accurate quantitative SNP-typing via SPEX.

Brotherton Paul P Sanchez Juan J JJ Cooper Alan A Endicott Phillip P

Nucleic acids research 20091027 2

The analysis of targeted genetic loci from ancient, forensic and clinical samples is usually built upon polymerase chain reaction (PCR)-generated sequence data. However, many studies have shown that PCR amplification from poor-quality DNA templates can create sequence artefacts at significant levels. With hominin (human and other hominid) samples, the pervasive presence of highly PCR-amplifiable human DNA contaminants in the vast majority of samples can lead to the creation of recombinant hybrid ...[more]

PMID: 19864251

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Project description:In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls-for example: deletions-and there is increasing evidence that gene presence-absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker-trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates.

Project description:BackgroundThe accurate determination of parent-progeny relationships within both in situ natural populations and ex situ genetic resource collections can greatly enhance plant breeding/domestication efforts and support plant genetic resource conservation strategies. Although a range of parentage analysis tools are available, none are designed to infer such relationships using genome-wide single nucleotide polymorphism (SNP) data in the complete absence of guiding information, such as generational groups, partial pedigrees, or genders. The R package ('apparent') developed and presented here addresses this gap.Results'apparent' adopts a novel strategy of parentage analysis based on a test of genetic identity between a theoretically expected progeny (EPij), whose genotypic state can be inferred at all homozygous loci for a pair of putative parents (i and j), and all potential offspring (POk), represented by the k individuals of a given germplasm collection. Using the Gower Dissimilarity metric (GD), genetic identity between EPij and POk is taken as evidence that individuals i and j are the true parents of offspring k. Significance of a given triad (parental pairij + offspringk) is evaluated relative to the distribution of all GDij|k values for the population. With no guiding information provided, 'apparent' correctly identified the parental pairs of 15 lines of known pedigree within a test population of 77 accessions of Actinidia arguta, a performance unmatched by five other commonly used parentage analysis tools. In the case of an inconclusive triad analysis due to the absence of one parent from the test population, 'apparent' can perform a subsequent dyad analysis to identify a likely single parent for a given offspring. Average dyad analysis accuracy was 73.3% in the complete absence of pedigree information but increased to 100% when minimal generational information (adults vs. progeny) was provided.ConclusionsThe 'apparent' R package is a fast and accurate parentage analysis tool that uses genome-wide SNP data to identify parent-progeny relationships within populations for which no a priori knowledge of family structure exists.

Dataset Information

Preferential access to genetic information from endogenous hominin ancient DNA and accurate quantitative SNP-typing via SPEX.

Publications

Preferential access to genetic information from endogenous hominin ancient DNA and accurate quantitative SNP-typing via SPEX.

Similar Datasets

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