Single Nucleotide Polymorphisms Model Research Articles

Novel EDGE encoding method enhances ability to identify genetic interactions.

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)-rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.

EpiGEN: an epistasis simulation pipeline.

Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes. EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen. Supplementary data are available at Bioinformatics online.

SNP Discovery Using Next Generation Transcriptomic Sequencing in Atlantic Herring (Clupea harengus)

The introduction of Next Generation Sequencing (NGS) has revolutionised population genetics, providing studies of non-model species with unprecedented genomic coverage, allowing evolutionary biologists to address questions previously far beyond the reach of available resources. Furthermore, the simple mutation model of Single Nucleotide Polymorphisms (SNPs) permits cost-effective high-throughput genotyping in thousands of individuals simultaneously. Genomic resources are scarce for the Atlantic herring (Clupea harengus), a small pelagic species that sustains high revenue fisheries. This paper details the development of 578 SNPs using a combined NGS and high-throughput genotyping approach. Eight individuals covering the species distribution in the eastern Atlantic were bar-coded and multiplexed into a single cDNA library and sequenced using the 454 GS FLX platform. SNP discovery was performed by de novo sequence clustering and contig assembly, followed by the mapping of reads against consensus contig sequences. Selection of candidate SNPs for genotyping was conducted using an in silico approach. SNP validation and genotyping were performed simultaneously using an Illumina 1,536 GoldenGate assay. Although the conversion rate of candidate SNPs in the genotyping assay cannot be predicted in advance, this approach has the potential to maximise cost and time efficiencies by avoiding expensive and time-consuming laboratory stages of SNP validation. Additionally, the in silico approach leads to lower ascertainment bias in the resulting SNP panel as marker selection is based only on the ability to design primers and the predicted presence of intron-exon boundaries. Consequently SNPs with a wider spectrum of minor allele frequencies (MAFs) will be genotyped in the final panel. The genomic resources presented here represent a valuable multi-purpose resource for developing informative marker panels for population discrimination, microarray development and for population genomic studies in the wild.

Re: Discriminatory Accuracy From Single-Nucleotide Polymorphisms in Models to Predict Breast Cancer Risk

The advent of genome-wide association studies to identify low-penetrance common susceptibility alleles heralds the possibility of incorporating panels of gene variants into existing risk prediction models and of assessing improvement in model performance. However, to date, the updated models have shown only modest improvements in discrimination. Gail (1) had previously shown that adding seven single-nucleotide polymorphisms (SNPs) identified from genome-wide association analyses to the original Breast Cancer Risk Assessment Tool had yielded only a modest improvement in area under the curve (AUC) from 0.607 to 0.632. Gail (2) now reports that inclusion of 11 SNPs exhibits an even smaller improvement in the AUC (0.637) than that of the BRACTplus 7 model. The receiver operating characteristics curve may not be sensitive to differences in probabilities between models and, therefore, may be insufficient to assess the impact of adding new predictors. A very large independent association of the new marker is required for a meaningful improvement in AUC, and a substantial gain in performance may not yield a substantial increase in AUC. One suggested statistic for comparing nested models is the net reclassification index that is useful when risk categories are defined, and there is a consensus as to clinically meaningful cut points (3). The net reclassification index quantifies overall improvement in model sensitivity and specificity. A net improvement in risk classification implies upward reclassification of case patients and downward reclassification of control subjects. We evaluated these metrics in our own internally validated risk prediction model for lung cancer that incorporated easily attainable epidemiological and clinical variables (4). In a genome-wide association analysis of 315 450 tagging SNPs in 1154 patients with lung cancer who were current and former smokers and were of European ancestry and 1137 frequency-matched control subjects (5), two SNPs, rs1051730 and rs8034191, that mapped to a region within 15q25.1 (which encompasses the nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5) were strongly associated with risk (odds ratio [OR] = 1.32, 95% confidence interval [CI] = 1.24 to 1.41, P = 3.15 × 10−18 for rs8034191; and OR = 1.32, 95% CI = 1.23 to 1.39, P = 7.00 × 10−18 for rs1051730). In a subsequent meta-analysis (6) involving the UK genome-wide association study, the International Agency for Research on Cancer genome-wide association study, and our Texas genome-wide association study, the strongest associations remained for SNPs mapping to 15q25.1 (ie, rs1051730, P = 2.83 × 10−19; and rs8034191, P = 4.03 × 10−19). There was also consistent evidence for a new disease locus at 5p15.33 (ie, rs401681, P = 4.40 × 10−6). This locus contains two known genes: TERT (human telomerase reverse transcriptase) gene and CLPTM1L (cleft lip and palate transmembrane 1-like) gene. We therefore added one SNP from the 15q25.1 locus (ie, rs1051730, which was used because it was in strong linkage disequilibrium with rs8034191) and two SNPs from the 5p15.33 region (ie, rs2736100 and rs401681) to the baseline model and assessed discrimination improvement. Our AUC for the baseline epidemiological–clinical model including 1016 case patients and 1111 control subjects was 0.661 (95% CI = 0.64 to 0.68). With addition of the three SNPs, the AUC showed modest, yet statistically significant, improvement to 0.673 (95% CI = 0.65 to 0.70, P = .01). We defined risk categories on the basis of the lower and upper quartiles of predicted risk from our baseline model as proposed by Bach et al. (7): low (predicted risk 50%). The resulting net reclassification indices were 0.152 (95% CI = 0.112 to 0.193) overall, 0.089 (95% CI = 0.048 to 0.130) for case patients, and 0.064 (95% CI = 0.023 to 0.105) for control subjects (all statistically significant at the 0.2% level), indicating that the SNPs modestly improved both sensitivity (9%) and specificity (6%). Although it could be argued that models providing a continuous score are more appropriate in the clinical setting, it is likely that a variety of additional summary measures evaluating model performance will be needed to assess these multigenic models.

Response: Re: Discriminatory Accuracy From Single-Nucleotide Polymorphisms in Models to Predict Breast Cancer Risk

Response: Re: Discriminatory Accuracy From Single-Nucleotide Polymorphisms in Models to Predict Breast Cancer Risk

Abstract CN07-03: Discriminatory accuracy from Single Nucleotide Polymorphisms in models of absolute breast cancer risk

Abstract CN07-03 One purpose for seeking single nucleotide polymorphisms (SNPs) that are associated with disease is to improve models for projecting individualized disease risk. Seven SNPs have recently been confirmed to be associated with breast cancer. I used published estimates of relative risks and allele frequencies to calculate how much these SNPs could improve discriminatory accuracy measured as the area under the receiver operating characteristic curve (AUC). A model with these seven SNPs (AUC = 0.574 ) and a hypothetical model with 14 such SNPs (AUC = 0.604) have less discriminatory accuracy than a model, the National Cancer Institute’s Breast Cancer Risk Assessment Tool (BCRAT), which is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations (AUC = 0.607). Adding the seven SNPs to BCRAT improved discriminatory accuracy to an AUC of 0.632, which was, however, less than the improvement from adding mammographic density. Thus, these seven SNPs improve the discriminatory accuracy of BCRAT only modestly. I used other criteria to compare BCRAT with a model that added the seven SNPs to BCRAT. These criteria included the proportion of cases in the portion of the population with the highest 90% of risk, the expected losses when using risk models to decide which women should be given a mammogram, and the expected losses when using a risk model to decide which women should take tamoxifen to prevent breast cancer. The seven SNPs provided very little improvement over BCRAT for any of these applications. Hundreds of SNPs would be required to achieve high discriminatory accuracy. Citation Information: Cancer Prev Res 2008;1(7 Suppl):CN07-03.

Discriminatory Accuracy From Single-Nucleotide Polymorphisms in Models to Predict Breast Cancer Risk

One purpose for seeking common alleles that are associated with disease is to use them to improve models for projecting individualized disease risk. Two genome-wide association studies and a study of candidate genes recently identified seven common single-nucleotide polymorphisms (SNPs) that were associated with breast cancer risk in independent samples. These seven SNPs were located in FGFR2, TNRC9 (now known as TOX3), MAP3K1, LSP1, CASP8, chromosomal region 8q, and chromosomal region 2q35. I used estimates of relative risks and allele frequencies from these studies to estimate how much these SNPs could improve discriminatory accuracy measured as the area under the receiver operating characteristic curve (AUC). A model with these seven SNPs (AUC = 0.574) and a hypothetical model with 14 such SNPs (AUC = 0.604) have less discriminatory accuracy than a model, the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT), that is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations (AUC = 0.607). Adding the seven SNPs to BCRAT improved discriminatory accuracy to an AUC of 0.632, which was, however, less than the improvement from adding mammographic density. Thus, these seven common alleles provide less discriminatory accuracy than BCRAT but have the potential to improve the discriminatory accuracy of BCRAT modestly. Experience to date and quantitative arguments indicate that a huge increase in the numbers of case patients with breast cancer and control subjects would be required in genome-wide association studies to find enough SNPs to achieve high discriminatory accuracy.