scholarly journals Comparing low-pass sequencing and genotyping for trait mapping in pharmacogenetics

2019 ◽  
Author(s):  
Kaja Wasik ◽  
Tomaz Berisa ◽  
Joseph K. Pickrell ◽  
Jeremiah H. Li ◽  
Dana J. Fraser ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Genotyping Array ◽  
Trait Mapping ◽  
Mean Imputation ◽  
Hla Genotypes ◽  
Low Pass ◽  
The Mean

AbstractLow pass sequencing has been proposed as a cost-effective alternative to genotyping arrays to identify genetic variants that influence multifactorial traits in humans. For common diseases this typically has required both large sample sizes and comprehensive variant discovery. Genotyping arrays are also routinely used to perform pharmacogenetic (PGx) experiments where sample sizes are likely to be significantly smaller, but clinically relevant effect sizes likely to be larger. To assess how low pass sequencing would compare to array based genotyping for PGx we compared a low-pass assay (in which 1× coverage or less of a target genome is sequenced) along with software for genotype imputation to standard approaches. We sequenced 79 individuals to 1× genome coverage and genotyped the same samples on the Affymetrix Axiom Biobank Precision Medicine Research Array (PMRA). We then down-sampled the sequencing data to 0.8×, 0.6×, and 0.4× coverage, and performed imputation. Both the genotype data and the sequencing data were further used to impute human leukocyte antigen (HLA) genotypes for all samples. We compared the sequencing data and the genotyping array data in terms of four metrics: overall concordance, concordance at single nucleotide polymorphisms in pharmacogenetics-related genes, concordance in imputed HLA genotypes, and imputation r2. Overall concordance between the two assays ranged from 98.2% (for 0.4× coverage sequencing) to 99.2% (for 1× coverage sequencing), with qualitatively similar numbers for the subsets of variants most important in pharmacogenetics. At common single nucleotide polymorphisms (SNPs), the mean imputation r2 from the genotyping array was 90%, which was comparable to the imputation r2 from 0.4× coverage sequencing, while the mean imputation r2 from 1× sequencing data was 96%. These results indicate that low-pass sequencing to a depth above 0.4× coverage attains higher power for trait mapping when compared to the PMRA.

scholarly journals Comparing low-pass sequencing and genotyping for trait mapping in pharmacogenetics

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kaja Wasik ◽  
Tomaz Berisa ◽  
Joseph K. Pickrell ◽  
Jeremiah H. Li ◽  
Dana J. Fraser ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Genotyping Array ◽  
Trait Mapping ◽  
Mean Imputation ◽  
Hla Genotypes ◽  
Low Pass ◽  
The Mean

Abstract Background Low pass sequencing has been proposed as a cost-effective alternative to genotyping arrays to identify genetic variants that influence multifactorial traits in humans. For common diseases this typically has required both large sample sizes and comprehensive variant discovery. Genotyping arrays are also routinely used to perform pharmacogenetic (PGx) experiments where sample sizes are likely to be significantly smaller, but clinically relevant effect sizes likely to be larger. Results To assess how low pass sequencing would compare to array based genotyping for PGx we compared a low-pass assay (in which 1x coverage or less of a target genome is sequenced) along with software for genotype imputation to standard approaches. We sequenced 79 individuals to 1x genome coverage and genotyped the same samples on the Affymetrix Axiom Biobank Precision Medicine Research Array (PMRA). We then down-sampled the sequencing data to 0.8x, 0.6x, and 0.4x coverage, and performed imputation. Both the genotype data and the sequencing data were further used to impute human leukocyte antigen (HLA) genotypes for all samples. We compared the sequencing data and the genotyping array data in terms of four metrics: overall concordance, concordance at single nucleotide polymorphisms in pharmacogenetics-related genes, concordance in imputed HLA genotypes, and imputation r2. Overall concordance between the two assays ranged from 98.2% (for 0.4x coverage sequencing) to 99.2% (for 1x coverage sequencing), with qualitatively similar numbers for the subsets of variants most important in pharmacogenetics. At common single nucleotide polymorphisms (SNPs), the mean imputation r2 from the genotyping array was 0.90, which was comparable to the imputation r2 from 0.4x coverage sequencing, while the mean imputation r2 from 1x sequencing data was 0.96. Conclusions These results indicate that low-pass sequencing to a depth above 0.4x coverage attains higher power for association studies when compared to the PMRA and should be considered as a competitive alternative to genotyping arrays for trait mapping in pharmacogenetics.

scholarly journals Analytical Validation of the Tag-It High-Throughput Microsphere-Based Universal Array Genotyping Platform: Application to the Multiplex Detection of a Panel of Thrombophilia-Associated Single-Nucleotide Polymorphisms

2004 ◽  
Vol 50 (11) ◽  
pp. 2028-2036 ◽  
Author(s):  
Susan Bortolin ◽  
Margot Black ◽  
Hemanshu Modi ◽  
Ihor Boszko ◽  
Daniel Kobler ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
High Throughput ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Direct Dna Sequencing ◽  
Array Platform ◽  
Genotyping Platform ◽  
Universal Array ◽  
Primer Sets

Abstract Background: We have developed a novel, microsphere-based universal array platform referred to as the Tag-It™ platform. This platform is suitable for high-throughput clinical genotyping applications and was used for multiplex analysis of a panel of thrombophilia-associated single-nucleotide polymorphisms (SNPs). Methods: Genomic DNA from 132 patients was amplified by multiplex PCR using 6 primer sets, followed by multiplex allele-specific primer extension using 12 universally tagged genotyping primers. The products were then sorted on the Tag-It array and detected by use of the Luminex xMAP™ system. Genotypes were also determined by sequencing. Results: Empirical validation of the universal array showed that the highest nonspecific signal was 3.7% of the specific signal. Patient genotypes showed 100% concordance with direct DNA sequencing data for 736 SNP determinations. Conclusions: The Tag-It microsphere-based universal array platform is a highly accurate, multiplexed, high-throughput SNP-detection platform.

scholarly journals Leveraging reads that span multiple single nucleotide polymorphisms for haplotype inference from sequencing data

2013 ◽  
Vol 29 (18) ◽  
pp. 2245-2252 ◽  
Author(s):  
W.-Y. Yang ◽  
F. Hormozdiari ◽  
Z. Wang ◽  
D. He ◽  
B. Pasaniuc ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Haplotype Inference ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Single Nucleotide

scholarly journals An Approach to Identify Individual Functional Single Nucleotide Polymorphisms and Isoform MicroRNAs

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ying Wang ◽  
Jidong Ru ◽  
Tao Jin ◽  
Ming Sun ◽  
Lizhu Jia ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Single Nucleotide Polymorphisms ◽  
Disease Risk ◽  
Software Tool ◽  
Mature Mirnas ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
1000 Genomes ◽  
Mirna Sequencing

MicroRNAs (miRNAs) and single nucleotide polymorphisms (SNPs) play important roles in disease risk and development, especially cancer. Importantly, when SNPs are located in pre-miRNAs, they affect their splicing mechanism and change the function of miRNAs. To improve disease risk assessment, we propose an approach and developed a software tool, IsomiR_Find, to identify disease/phenotype-related SNPs and isomiRs in individuals. Our approach is based on the individual’s samples, with SNP information extracted from the 1000 Genomes Project. SNPs were mapped to pre-miRNAs based on whole-genome coordinates and then SNP-pre-miRNA sequences were constructed. Moreover, we developed matpred2, a software tool to identify the four splicing sites of mature miRNAs. Using matpred2, we identified isomiRs and then verified them by searching within individual miRNA sequencing data. Our approach yielded biomarkers for biological experiments, mined functions of miRNAs and SNPs, improved disease risk assessment, and provided a way to achieve individualized precision medicine.

scholarly journals Association of single-nucleotide polymorphisms at the Delta locus with genotype by environment interaction for sensory bristle number in Drosophila melanogaster

2002 ◽  
Vol 79 (3) ◽  
pp. 211-218 ◽  
Author(s):  
GRETCHEN L. GEIGER-THORNSBERRY ◽  
TRUDY F. C. MACKAY
Keyword(s):  
Standard Deviation ◽  
Single Nucleotide Polymorphisms ◽  
Genotype By Environment Interaction ◽  
Stabilizing Selection ◽  
Environment Interaction ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genotype By Environment ◽  
Bristle Number ◽  
The Mean

The nature of forces maintaining variation for quantitative traits can only be assessed at the level of individual genes affecting variation in the traits. Identification of single-nucleotide polymorphisms (SNPs) associated with variation in Drosophila sensory bristle number at the Delta (Dl) locus provides us with the opportunity to test a model for the maintenance of variation in bristle number by genotype by environment interaction (GEI). Under this model, genetic variation is maintained at a locus under stabilizing selection if phenotypic values of heterozygotes are more stable than homozygotes across a range of environments, and the mean allelic effect is much smaller than the standard deviation of allelic effects across environments. Homozygotes and heterozygotes for two SNPs at Dl, one affecting sternopleural and the other abdominal bristle number, were reared in five different environments. There was significant GEI for both bristle traits. Neither condition of the model was satisfied for Dl SNPs exhibiting GEI for sternopleural bristle number. Heterozygotes for the abdominal bristle number SNPs were indeed the most stable genotype for two of the three environment pairs exhibiting GEI, but the mean genotypic effect was greater than the standard deviation of effects across environments. Therefore, this mechanism of GEI seems unlikely to be responsible for maintaining the common bristle number polymorphisms at Dl.

scholarly journals In silico detection of phylogenetic informative Y-chromosomal single nucleotide polymorphisms from whole genome sequencing data

2014 ◽  
Vol 35 (21-22) ◽  
pp. 3102-3110 ◽  
Author(s):  
Anneleen Van Geystelen ◽  
Tom Wenseleers ◽  
Ronny Decorte ◽  
Maarten J. L. Caspers ◽  
Maarten H. D. Larmuseau
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
In Silico ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Single Nucleotide
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