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Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.

ABSTRACT: The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and the number of variants tested per loci. Using this strategy, we interrogated known prostate cancer (PCa) risk-associated loci and demonstrated that 35% of them harbor SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.

SUBMITTER: Morova T

PROVIDER: S-EPMC9943666 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.

Morova Tunc T Ding Yi Y Huang Chia-Chi F CF Sar Funda F Schwarz Tommer T Giambartolomei Claudia C Baca Sylvan C SC Grishin Dennis D Hach Faraz F Gusev Alexander A Freedman Matthew L ML Pasaniuc Bogdan B Lack Nathan A NA

Nucleic acids research 20230201 3

The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding ...[more]

PMID: 36546757

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Project description:BackgroundLarge-scale genome-wide association studies have identified multiple genetic variants that are associated with elevated body mass index (BMI) or the risk of obesity in Caucasian or Asian populations. We examined whether these variants are individually associated with obesity in Chinese children, and also assessed their cumulative effects and predictive value for obesity risk in Chinese children.MethodsWe genotyped 40 single nucleotide polymorphisms (SNPs) and conducted association analyses for 32/40 SNPs with an estimated minor allele frequency >1% in 2 030 unrelated Chinese children, including 607 normal-weight, 718 overweight, and 705 obese individuals from two cross-sectional study groups. Logistic regression and linear regression under the additive model were used to examine associations, and the area under the receiver operating characteristic curve (AUCROC) was reported as prediction summary.ResultsWe identified obesity association for 6 SNPs near SEC16B, RBJ, CDKAL1, TFAP2B, MAP2K5 and FTO (odds ratios (ORs) ranged from 1.19 to 1.41, nominal two-sided P-values < 0.05). Association (Bonferroni corrected) of rs543874 near SEC16B and rs2241423 near MAP2K5 had presumably stronger effects on obesity in Chinese children than in Caucasian populations. Their risk alleles were also associated with BMI standard deviation score (BMI-SDS) variability. We demonstrated the cumulative effects of the 32 SNPs on obesity risk (per risk allele: OR = 1.06, 95 % CI: 1.03-1.11, P = 4.84 × 10(-4)) and BMI-SDS (β = 0.04, 95% CI: 0.02-0.06, P = 3.69 × 10(-7)). The difference in AUCROC for a model with covariates (age, age square, sex and study group) and the model including covariates and all 32 SNPs was 2.8% (P = 0.0002).ConclusionWhile six SNPs were individually associated with obesity in Chinese children, the 32 common variants identified by recent GWA studies had cumulative effects and resulted in a limited increase in the AUCROC predictive value for childhood obesity.

Dataset Information

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.

Publications

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.

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