Detection of rare genomic variants from pooled sequencing using SPLINTER

Pharmacogenomics, How to Deal with Different Types of Variants in Next Generation Sequencing Data in the Personalized Medicine Area

Pharmacogenomics (PGx) is the knowledge of diverse drug responses and effects in people, based on their genomic profiles. Such information is considered as one of the main directions to reach personalized medicine in future clinical practices. Since the start of applying next generation sequencing (NGS) methods in drug related clinical investigations, many common medicines found their genetic data for the related metabolizing/shipping proteins in the human body. Yet, the employing of technology is accompanied by big obtained data, which most of them have no clear guidelines for consideration in routine treatment decisions for patients. This review article talks about different types of NGS derived PGx variants in clinical studies and try to display the current and newly developed approaches to deal with pharmacogenetic data with/without clear guidelines for considering in clinical settings.

A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data

Detection of somatic mutations holds great potential in cancer treatment and has been a very active research field in the past few years, especially since the breakthrough of the next-generation sequencing technology. A collection of variant calling pipelines have been developed with different underlying models, filters, input data requirements, and targeted applications. This review aims to enumerate these unique features of the state-of-the-art variant callers, in the hope to provide a practical guide for selecting the appropriate pipeline for specific applications. We will focus on the detection of somatic single nucleotide variants, ranging from traditional variant callers based on whole genome or exome sequencing of paired tumor-normal samples to recent low-frequency variant callers designed for targeted sequencing protocols with unique molecular identifiers. The variant callers have been extensively benchmarked with inconsistent performances across these studies. We will review the reference materials, datasets, and performance metrics that have been used in the benchmarking studies. In the end, we will discuss emerging trends and future directions of the variant calling algorithms.

Pooled-DNA Sequencing for Elucidating New Genomic Risk Factors, Rare Variants Underlying Alzheimer's Disease

Analyses of genome-wide association studies (GWAS) for complex disorders usually identify common variants with a relatively small effect size that only explain a small proportion of phenotypic heritability. Several studies have suggested that a significant fraction of heritability may be explained by low-frequency (minor allele frequency (MAF) of 1-5 %) and rare-variants that are not contained in the commercial GWAS genotyping arrays (Schork et al., Curr Opin Genet Dev 19:212, 2009). Rare variants can also have relatively large effects on risk for developing human diseases or disease phenotype (Cruchaga et al., PLoS One 7:e31039, 2012). However, it is necessary to perform next-generation sequencing (NGS) studies in a large population (>4,000 samples) to detect a significant rare-variant association. Several NGS methods, such as custom capture sequencing and amplicon-based sequencing, are designed to screen a small proportion of the genome, but most of these methods are limited in the number of samples that can be multiplexed (i.e. most sequencing kits only provide 96 distinct index). Additionally, the sequencing library preparation for 4,000 samples remains expensive and thus conducting NGS studies with the aforementioned methods are not feasible for most research laboratories.The need for low-cost large scale rare-variant detection makes pooled-DNA sequencing an ideally efficient and cost-effective technique to identify rare variants in target regions by sequencing hundreds to thousands of samples. Our recent work has demonstrated that pooled-DNA sequencing can accurately detect rare variants in targeted regions in multiple DNA samples with high sensitivity and specificity (Jin et al., Alzheimers Res Ther 4:34, 2012). In these studies we used a well-established pooled-DNA sequencing approach and a computational package, SPLINTER (short indel prediction by large deviation inference and nonlinear true frequency estimation by recursion) (Vallania et al., Genome Res 20:1711, 2010), for accurate identification of rare variants in large DNA pools. Given an average sequencing coverage of 30× per haploid genome, SPLINTER can detect rare variants and short indels up to 4 base pairs (bp) with high sensitivity and specificity (up to 1 haploid allele in a pool as large as 500 individuals). Step-by-step instructions on how to conduct pooled-DNA sequencing experiments and data analyses are described in this chapter.

Fine-mapping an association of FSHR with preterm birth in a Finnish population

Preterm birth is a complex disorder defined by gestations of less than 37 weeks. While preterm birth is estimated to have a significant genetic component, relative few genes have been associated with preterm birth. Polymorphism in one such gene, follicle-stimulating hormone receptor (FSHR), has been associated with preterm birth in Finnish and African American mothers but not other populations. To refine the genetic association of FSHR with preterm birth we conducted a fine-mapping study at the FSHR locus in a Finnish cohort. We sequenced a total of 44 kb, including protein-coding and conserved non-coding regions, in 127 preterm and 135 term mothers. Overall, we identified 288 single nucleotide variants and 65 insertion/deletions of 1-2 bp across all subjects. While no common SNPs in protein-coding regions were associated with preterm birth, including one previously associated with timing of fertilization, multiple SNPs spanning the first and second intron showed the strongest associations. Analysis of the associated SNPs revealed that they form both a protective (OR = 0.50, 95% CI = 0.25-0.93) as well as a risk (OR = 1.89, 95% CI = 1.08-3.39) haplotype with independent effects. In these haplotypes, two SNPs, rs12052281 and rs72822025, were predicted to disrupt ZEB1 and ELF3 transcription factor binding sites, respectively. Our results show that multiple haplotypes at FSHR are associated with preterm birth and we discuss the frequency and structure of these haplotypes outside of the Finnish population as a potential explanation for the absence of FSHR associations in some populations.