Rare and Low Frequency Variant Stratification in the UK Population: Description and Impact on Association Tests

Although variations in allele frequencies at common SNPs have been extensively studied in different populations, little is known about the stratification of rare variants and its impact on association tests. In this paper, we used Affymetrix 500K genotype data from the WTCCC to investigate if variants in three different frequency categories (below 1%, between 1 and 5%, above 5%) show different stratification patterns in the UK population. We found that these patterns are indeed different. The top principal component extracted from the rare variant category shows poor correlations with any principal component or combination of principal components from the low frequency or common variant categories. These results could suggest that a suitable solution to avoid false positive association due to population stratification would involve adjusting for the respective PCs when testing for variants in different allele frequency categories. However, we found this was not the case both on type 2 diabetes data and on simulated data. Indeed, adjusting rare variant association tests on PCs derived from rare variants does no better to correct for population stratification than adjusting on PCs derived from more common variants. Mixed models perform slightly better for low frequency variants than PC based adjustments but less well for the rarest variants. These results call for the need of new methodological developments specifically devoted to address rare variant stratification issues in association tests.     

Rare variation at the TNFAIP3 locus and susceptibility to rheumatoid arthritis

Genome-wide association studies (GWAS) conducted using commercial single nucleotide polymorphisms (SNP) arrays have proven to be a powerful tool for the detection of common disease susceptibility variants. However, their utility for the detection of lower frequency variants is yet to be practically investigated. Here we describe the application of a rare variant collapsing method to a large genome-wide SNP dataset, the Wellcome Trust Case Control Consortium rheumatoid arthritis (RA) GWAS. We partitioned the data into gene-centric bins and collapsed genotypes of low frequency variants (defined here as MAF ≤0.05) into a single count coupled with univariate analysis. We then prioritised gene regions for further investigation in an independent cohort of 3,355 cases and 2,427 controls based on rare variant signal p value and prior evidence to support involvement in RA. A total of 14,536 gene bins were investigated in the primary analysis and signals mapping to the TNFAIP3 and chr17q24 loci were selected for further investigation. We detected replicating association to low frequency variants in the TNFAIP3 gene (combined p = 6.6 × 10^?6). Even though rare variants are not well-represented and can be difficult to genotype in GWAS, our study supports the application of low frequency variant collapsing methods to genome-wide SNP datasets as a means of exploiting data that are routinely ignored.     

An evaluation of different target enrichment methods in pooled sequencing designs for complex disease association studies

Pooled sequencing can be a cost-effective approach to disease variant discovery, but its applicability in association studies remains unclear. We compare sequence enrichment methods coupled to next-generation sequencing in non-indexed pools of 1, 2, 10, 20 and 50 individuals and assess their ability to discover variants and to estimate their allele frequencies. We find that pooled resequencing is most usefully applied as a variant discovery tool due to limitations in estimating allele frequency with high enough accuracy for association studies, and that in-solution hybrid-capture performs best among the enrichment methods examined regardless of pool size.     

NPY and MC4R signaling regulate thyroid hormone levels during fasting through both central and peripheral pathways

Fasting-induced suppression of the hypothalamic-pituitary-thyroid (HPT) axis is an adaptive response to decrease energy expenditure during food deprivation. Previous studies demonstrate that leptin communicates nutritional status to the HPT axis through thyrotropin-releasing hormone (TRH) in the paraventricular nucleus (PVN) of the hypothalamus. Leptin targets TRH neurons either directly or indirectly via the arcuate nucleus through pro-opiomelanocortin (POMC) and agouti-related peptide/neuropeptide Y (AgRP/NPY) neurons. To evaluate the role of these pathways in vivo, we developed double knockout mice that lack both the melanocortin 4 receptor (MC4R) and NPY. We show that NPY is required for fasting-induced suppression of Trh expression in the PVN. However, both MC4R and NPY are required for activation of hepatic pathways that metabolize T₄ during the fasting response. Thus, these signaling pathways play a key role in the communication of fasting signals to reduce thyroid hormone levels both centrally and through a peripheral hepatic circuit.     

Evaluation of Two Highly-Multiplexed Custom Panels for Massively Parallel Semiconductor Sequencing on Paraffin DNA

Background—Aim

Massively parallel sequencing (MPS) holds promise for expanding cancer translational research and diagnostics. As yet, it has been applied on paraffin DNA (FFPE) with commercially available highly multiplexed gene panels (100s of DNA targets), while custom panels of low multiplexing are used for re-sequencing. Here, we evaluated the performance of two highly multiplexed custom panels on FFPE DNA.

Methods

Two custom multiplex amplification panels (B, 373 amplicons; T, 286 amplicons) were coupled with semiconductor sequencing on DNA samples from FFPE breast tumors and matched peripheral blood samples (n samples: 316; n libraries: 332). The two panels shared 37% DNA targets (common or shifted amplicons). Panel performance was evaluated in paired sample groups and quartets of libraries, where possible.

Results

Amplicon read ratios yielded similar patterns per gene with the same panel in FFPE and blood samples; however, performance of common amplicons differed between panels (p<0.001). FFPE genotypes were compared for 1267 coding and non-coding variant replicates, 999 out of which (78.8%) were concordant in different paired sample combinations. Variant frequency was highly reproducible (Spearman’s rho 0.959). Repeatedly discordant variants were of high coverage / low frequency (p<0.001). Genotype concordance was (a) high, for intra-run duplicates with the same panel (mean±SD: 97.2±4.7, 95%CI: 94.8–99.7, p<0.001); (b) modest, when the same DNA was analyzed with different panels (mean±SD: 81.1±20.3, 95%CI: 66.1–95.1, p = 0.004); and (c) low, when different DNA samples from the same tumor were compared with the same panel (mean±SD: 59.9±24.0; 95%CI: 43.3–76.5; p = 0.282). Low coverage / low frequency variants were validated with Sanger sequencing even in samples with unfavourable DNA quality.

Conclusions

Custom MPS may yield novel information on genomic alterations, provided that data evaluation is adjusted to tumor tissue FFPE DNA. To this scope, eligibility of all amplicons along with variant coverage and frequency need to be assessed.