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Integrative analysis of functional genomic annotations and sequencing data to identify rare causal variants via hierarchical modeling.

ABSTRACT: Identifying the small number of rare causal variants contributing to disease has been a major focus of investigation in recent years, but represents a formidable statistical challenge due to the rare frequencies with which these variants are observed. In this commentary we draw attention to a formal statistical framework, namely hierarchical modeling, to combine functional genomic annotations with sequencing data with the objective of enhancing our ability to identify rare causal variants. Using simulations we show that in all configurations studied, the hierarchical modeling approach has superior discriminatory ability compared to a recently proposed aggregate measure of deleteriousness, the Combined Annotation-Dependent Depletion (CADD) score, supporting our premise that aggregate functional genomic measures can more accurately identify causal variants when used in conjunction with sequencing data through a hierarchical modeling approach.

SUBMITTER: Capanu M

PROVIDER: S-EPMC4424902 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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Integrative analysis of functional genomic annotations and sequencing data to identify rare causal variants via hierarchical modeling.

Capanu Marinela M Ionita-Laza Iuliana I

Frontiers in genetics 20150508

Identifying the small number of rare causal variants contributing to disease has been a major focus of investigation in recent years, but represents a formidable statistical challenge due to the rare frequencies with which these variants are observed. In this commentary we draw attention to a formal statistical framework, namely hierarchical modeling, to combine functional genomic annotations with sequencing data with the objective of enhancing our ability to identify rare causal variants. Using ...[more]

PMID: 26005447

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Project description:As DNA sequencing technology has markedly advanced in recent years(2), it has become increasingly evident that the amount of genetic variation between any two individuals is greater than previously thought(3). In contrast, array-based genotyping has failed to identify a significant contribution of common sequence variants to the phenotypic variability of common disease(4,5). Taken together, these observations have led to the evolution of the Common Disease / Rare Variant hypothesis suggesting that the majority of the "missing heritability" in common and complex phenotypes is instead due to an individual's personal profile of rare or private DNA variants(6-8). However, characterizing how rare variation impacts complex phenotypes requires the analysis of many affected individuals at many genomic loci, and is ideally compared to a similar survey in an unaffected cohort. Despite the sequencing power offered by today's platforms, a population-based survey of many genomic loci and the subsequent computational analysis required remains prohibitive for many investigators. To address this need, we have developed a pooled sequencing approach(1,9) and a novel software package(1) for highly accurate rare variant detection from the resulting data. The ability to pool genomes from entire populations of affected individuals and survey the degree of genetic variation at multiple targeted regions in a single sequencing library provides excellent cost and time savings to traditional single-sample sequencing methodology. With a mean sequencing coverage per allele of 25-fold, our custom algorithm, SPLINTER, uses an internal variant calling control strategy to call insertions, deletions and substitutions up to four base pairs in length with high sensitivity and specificity from pools of up to 1 mutant allele in 500 individuals. Here we describe the method for preparing the pooled sequencing library followed by step-by-step instructions on how to use the SPLINTER package for pooled sequencing analysis (http://www.ibridgenetwork.org/wustl/splinter). We show a comparison between pooled sequencing of 947 individuals, all of whom also underwent genome-wide array, at over 20kb of sequencing per person. Concordance between genotyping of tagged and novel variants called in the pooled sample were excellent. This method can be easily scaled up to any number of genomic loci and any number of individuals. By incorporating the internal positive and negative amplicon controls at ratios that mimic the population under study, the algorithm can be calibrated for optimal performance. This strategy can also be modified for use with hybridization capture or individual-specific barcodes and can be applied to the sequencing of naturally heterogeneous samples, such as tumor DNA.

Dataset Information

Integrative analysis of functional genomic annotations and sequencing data to identify rare causal variants via hierarchical modeling.

Publications

Integrative analysis of functional genomic annotations and sequencing data to identify rare causal variants via hierarchical modeling.

Similar Datasets

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