Targeted screening of cis-regulatory variation in human haplotypes - PubMed (original) (raw)

doi: 10.1101/gr.084798.108. Epub 2008 Oct 29.

Bing Ge, Elin Grundberg, Rose Hoberman, Kevin C L Lam, Vonda Koka, Joana Dias, Scott Gurd, Nicolas W Martin, Hans Mallmin, Olof Nilsson, Eef Harmsen, Ken Dewar, Tony Kwan, Tomi Pastinen

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Targeted screening of cis-regulatory variation in human haplotypes

Dominique J Verlaan et al. Genome Res. 2009 Jan.

Abstract

Regulatory cis-acting variants account for a large proportion of gene expression variability in populations. Cis-acting differences can be specifically measured by comparing relative levels of allelic transcripts within a sample. Allelic expression (AE) mapping for cis-regulatory variant discovery has been hindered by the requirements of having informative or heterozygous single nucleotide polymorphisms (SNPs) within genes in order to assign the allelic origin of each transcript. In this study we have developed an approach to systematically screen for heritable cis-variants in common human haplotypes across >1,000 genes. In order to achieve the highest level of information per haplotype studied, we carried out allelic expression measurements by using both intronic and exonic SNPs in primary transcripts. We used a novel RNA pooling strategy in immortalized lymphoblastoid cell lines (LCLs) and primary human osteoblast cell lines (HObs) to allow for high-throughput AE. Screening hits from RNA pools were further validated by performing allelic expression mapping in individual samples. Our results indicate that >10% of expressed genes in human LCLs show genotype-linked AE. In addition, we have validated cis-acting variants in over 20 genes linked with common disease susceptibility in recent genome-wide studies. More generally, our results indicate that RNA pooling coupled with AE read-out by second generation sequencing or by other methods provides a high-throughput tool for cataloging the impact of common noncoding variants in the human genome.

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Figures

Figure 1.

Figure 1.

(A) Hypothetical Manhattan plot for a disease GWAS, demonstrating a strong association to SNPs on chromosome 14, highlighted in red block. (B) Genomic region demonstrating an LD block spanning the GWAS SNPs. Marker SNPs (m1SNP, m2SNP, m3SNP) in genes (A and B), found in strong LD with the GWAS SNP, are used to assay for AE (rSNP, regulatory SNP). (C) Normalized allele ratio (log10) graph demonstrating different scenarios. Strong allelic expression is found for Gene A using m1SNP that tests for the same haplotypes found for the GWAS SNP. Marker m2SNP shows borderline AE for the same gene (Gene A) and tests for a different pattern of haplotypes. No AE is found for Gene B using m3SNP as the gDNA, and cDNA ratios are not significantly different from each other.

Figure 2.

Figure 2.

Allelic imbalance association mapping results for GWAS genes. Vertical red lines correspond to –log10(_P_-value) of the AE assay for each SNP tested. The arrow points to the GWAS SNPs, indicated by the vertical blue lines. (A) GSDMB (asthma); (B) ORMDL3 (asthma); (C) IL23R (Crohn’s disease); (D) SORT1 (Dyslipidemia); (E) INSIG2 (Obesity); (F) TRAF1 (Rheumatoid arthritis).

Figure 3.

Figure 3.

(A) Comparison of expected and observed allele count (n = 278) in DNA pools derived from 55 CEU LCLs. (B) Average error with standard deviation (error bars) of allele frequencies derived from 454 counting as compared with known frequencies in the DNA pool. (C) Correlation of estimated fold difference (log10) in allele frequencies between cDNA and gDNA pools based on normalized sequencing (_y_-axis) and 454 allele counting (_x_-axis).

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