Identification of SNPs and INDELS in swine transcribed sequences using short oligonucleotide microarrays.
BACKGROUND
Genome-wide detection of single feature polymorphisms (SFP) in swine using transcriptome profiling of day 25 placental RNA by contrasting probe intensities from either Meishan or an occidental composite breed with Affymetrix porcine microarrays is presented. A linear mixed model analysis was used to identify significant breed-by-probe interactions.


RESULTS
Gene specific linear mixed models were fit to each of the log2 transformed probe intensities on these arrays, using fixed effects for breed, probe, breed-by-probe interaction, and a random effect for array. After surveying the day 25 placental transcriptome, 857 probes with a q-value < or = 0.05 and |fold change| > or = 2 for the breed-by-probe interaction were identified as candidates containing SFP. To address the quality of the bioinformatics approach, universal pyrosequencing assays were designed from Affymetrix exemplar sequences to independently assess polymorphisms within a subset of probes for validation. Additionally probes were randomly selected for sequencing to determine an unbiased confirmation rate. In most cases, the 25-mer probe sequence printed on the microarray diverged from Meishan, not occidental crosses. This analysis was used to define a set of highly reliable predicted SFPs according to their probability scores.


CONCLUSIONS
By applying a SFP detection method to two mammalian breeds for the first time, we detected transition and transversion single nucleotide polymorphisms, as well as insertions/deletions which can be used to rapidly develop markers for genetic mapping and association analysis in species where high density genotyping platforms are otherwise unavailable.SNPs and INDELS discovered by this approach have been publicly deposited in NCBI's SNP repository dbSNP. This method is an attractive bioinformatics tool for uncovering breed-by-probe interactions, for rapidly identifying expressed SNPs, for investigating potential functional correlations between gene expression and breed polymorphisms, and is robust enough to be used on any Affymetrix gene expression platform.
Bischoff
Steve R
SR
Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC 27606, USA. steve_bischoff@ncsu.edu
Tsai
Shengdar
S
Hardison
Nicholas E
NE
York
Abby M
AM
Freking
Brad A
BA
Nonneman
Dan
D
Rohrer
Gary
G
Piedrahita
Jorge A
JA
D000818:Animals; D001483:Base Sequence; D019295:Computational Biology; D004247:DNA / Q000235:genetics; D005260:Female; D005333:Fetus / Q000378:metabolism; D020869:Gene Expression Profiling; D054643:INDEL Mutation; D016014:Linear Models; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data; D010920:Placenta / Q000378:metabolism; D020641:Polymorphism, Single Nucleotide; D011247:Pregnancy; D012313:RNA / Q000235:genetics; D013045:Species Specificity; D013552:Swine / Q000235:genetics*
D012313:RNA; D004247:DNA