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Mining the Unknown: Assigning Function to Noncoding Single Nucleotide Polymorphisms.

ABSTRACT: One of the formative goals of genetics research is to understand how genetic variation leads to phenotypic differences and human disease. Genome-wide association studies (GWASs) bring us closer to this goal by linking variation with disease faster than ever before. Despite this, GWASs alone are unable to pinpoint disease-causing single nucleotide polymorphisms (SNPs). Noncoding SNPs, which represent the majority of GWAS SNPs, present a particular challenge. To address this challenge, an array of computational tools designed to prioritize and predict the function of noncoding GWAS SNPs have been developed. However, fewer than 40% of GWAS publications from 2015 utilized these tools. We discuss several leading methods for annotating noncoding variants and how they can be integrated into research pipelines in hopes that they will be broadly applied in future GWAS analyses.

SUBMITTER: Nishizaki SS

PROVIDER: S-EPMC5553318 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Mining the Unknown: Assigning Function to Noncoding Single Nucleotide Polymorphisms.

Nishizaki Sierra S SS Boyle Alan P AP

Trends in genetics : TIG 20161206 1

One of the formative goals of genetics research is to understand how genetic variation leads to phenotypic differences and human disease. Genome-wide association studies (GWASs) bring us closer to this goal by linking variation with disease faster than ever before. Despite this, GWASs alone are unable to pinpoint disease-causing single nucleotide polymorphisms (SNPs). Noncoding SNPs, which represent the majority of GWAS SNPs, present a particular challenge. To address this challenge, an array of ...[more]

PMID: 27939749

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Project description:BACKGROUND: Single nucleotide polymorphisms (SNPs), the most abundant variations in a genome, have been widely used in various studies. Detection and characterization of citrus haplotype-based expressed sequence tag (EST) SNPs will greatly facilitate further utilization of these gene-based resources. RESULTS: In this paper, haplotype-based SNPs were mined out of publicly available citrus expressed sequence tags (ESTs) from different citrus cultivars (genotypes) individually and collectively for comparison. There were a total of 567,297 ESTs belonging to 27 cultivars in varying numbers and consequentially yielding different numbers of haplotype-based quality SNPs. Sweet orange (SO) had the most (213,830) ESTs, generating 11,182 quality SNPs in 3,327 out of 4,228 usable contigs. Summed from all the individually mining results, a total of 25,417 quality SNPs were discovered - 15,010 (59.1%) were transitions (AG and CT), 9,114 (35.9%) were transversions (AC, GT, CG, and AT), and 1,293 (5.0%) were insertion/deletions (indels). A vast majority of SNP-containing contigs consisted of only 2 haplotypes, as expected, but the percentages of 2 haplotype contigs varied widely in these citrus cultivars. BLAST of the 25,417 25-mer SNP oligos to the Clementine reference genome scaffolds revealed 2,947 SNPs had "no hits found", 19,943 had 1 unique hit / alignment, 1,571 had one hit and 2+ alignments per hit, and 956 had 2+ hits and 1+ alignment per hit. Of the total 24,293 scaffold hits, 23,955 (98.6%) were on the main scaffolds 1 to 9, and only 338 were on 87 minor scaffolds. Most alignments had 100% (25/25) or 96% (24/25) nucleotide identities, accounting for 93% of all the alignments. Considering almost all the nucleotide discrepancies in the 24/25 alignments were at the SNP sites, it served well as in silico validation of these SNPs, in addition to and consistent with the rate (81%) validated by sequencing and SNaPshot assay. CONCLUSIONS: High-quality EST-SNPs from different citrus genotypes were detected, and compared to estimate the heterozygosity of each genome. All the SNP oligo sequences were aligned with the Clementine citrus genome to determine their distribution and uniqueness and for in silico validation, in addition to SNaPshot and sequencing validation of selected SNPs.

Project description:Single-nucleotide polymorphisms (SNPs) provide an abundant source of DNA polymorphisms in a number of eukaryotic species. Information on the frequency, nature, and distribution of SNPs in plant genomes is limited. Thus, our objectives were (1) to determine SNP frequency in coding and noncoding soybean (Glycine max L. Merr.) DNA sequence amplified from genomic DNA using PCR primers designed to complete genes, cDNAs, and random genomic sequence; (2) to characterize haplotype variation in these sequences; and (3) to provide initial estimates of linkage disequilibrium (LD) in soybean. Approximately 28.7 kbp of coding sequence, 37.9 kbp of noncoding perigenic DNA, and 9.7 kbp of random noncoding genomic DNA were sequenced in each of 25 diverse soybean genotypes. Over the >76 kbp, mean nucleotide diversity expressed as Watterson's theta was 0.00097. Nucleotide diversity was 0.00053 and 0.00111 in coding and in noncoding perigenic DNA, respectively, lower than estimates in the autogamous model species Arabidopsis thaliana. Haplotype analysis of SNP-containing fragments revealed a deficiency of haplotypes vs. the number that would be anticipated at linkage equilibrium. In 49 fragments with three or more SNPs, five haplotypes were present in one fragment while four or less were present in the remaining 48, thereby supporting the suggestion of relatively limited genetic variation in cultivated soybean. Squared allele-frequency correlations (r(2)) among haplotypes at 54 loci with two or more SNPs indicated low genome-wide LD. The low level of LD and the limited haplotype diversity suggested that the genome of any given soybean accession is a mosaic of three or four haplotypes. To facilitate SNP discovery and the development of a transcript map, subsets of four to six diverse genotypes, whose sequence analysis would permit the discovery of at least 75% of all SNPs present in the 25 genotypes as well as 90% of the common (frequency >0.10) SNPs, were identified.

Dataset Information

Mining the Unknown: Assigning Function to Noncoding Single Nucleotide Polymorphisms.

Publications

Mining the Unknown: Assigning Function to Noncoding Single Nucleotide Polymorphisms.

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