Dataset Information

Not all summary statistics are made equal: Evidence from extracting summaries across time.

ABSTRACT: Over the past 15 years, a number of behavioral studies have shown that the human visual system can extract the average value of a set of items along a variety of feature dimensions, often with great facility and accuracy. These efficient representations of sets of items are commonly referred to as summary representations, but very little is known about whether their computation constitutes a single unitary process or if it involves different mechanisms in different domains. Here, we asked participants to report the average value of a set of items presented serially over time in four different feature dimensions. We then measured the contribution of different parts of the information stream to the reported summaries. We found that this temporal weighting profile differs greatly across domains. Specifically, summaries of mean object location (Experiment 1) were influenced approximately 2.5 times more by earlier items than by later items. Summaries of mean object size (Experiment 1), mean facial expression (Experiment 2), and mean motion direction (Experiment 3), however, were more influenced by later items. These primacy and recency effects show that summary representations computed across time do not incorporate all items equally. Furthermore, our results support the hypothesis that summary representations operate differently in different feature domains, and may be subserved by distinct mechanisms.

SUBMITTER: Hubert-Wallander B

PROVIDER: S-EPMC4463776 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Not all summary statistics are made equal: Evidence from extracting summaries across time.

Hubert-Wallander Bjorn B Boynton Geoffrey M GM

Journal of vision 20150101 4

Over the past 15 years, a number of behavioral studies have shown that the human visual system can extract the average value of a set of items along a variety of feature dimensions, often with great facility and accuracy. These efficient representations of sets of items are commonly referred to as summary representations, but very little is known about whether their computation constitutes a single unitary process or if it involves different mechanisms in different domains. Here, we asked partic ...[more]

PMID: 26053144

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Project description:Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available.In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1-5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case-control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of [Formula: see text] association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses.Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/.bpasaniuc@mednet.ucla.edu or aprice@hsph.harvard.eduSupplementary materials are available at Bioinformatics online.

Project description:BackgroundColocalization is a statistical method used in genetics to determine whether the same variant is causal for multiple phenotypes, for example, complex traits and gene expression. It provides stronger mechanistic evidence than shared significance, which can be produced through separate causal variants in linkage disequilibrium. Current colocalization methods require full summary statistics for both traits, limiting their use with the majority of reported GWAS associations (e.g. GWAS Catalog). We propose a new approximation to the popular coloc method that can be applied when limited summary statistics are available. Our method (POint EstiMation of Colocalization, POEMColoc) imputes missing summary statistics for one or both traits using LD structure in a reference panel, and performs colocalization using the imputed summary statistics.ResultsWe evaluate the performance of POEMColoc using real (UK Biobank phenotypes and GTEx eQTL) and simulated datasets. We show good correlation between posterior probabilities of colocalization computed from imputed and observed datasets and similar accuracy in simulation. We evaluate scenarios that might reduce performance and show that multiple independent causal variants in a region and imputation from a limited subset of typed variants have a larger effect while mismatched ancestry in the reference panel has a modest effect. Further, we find that POEMColoc is a better approximation of coloc when the imputed association statistics are from a well powered study (e.g., relatively larger sample size or effect size). Applying POEMColoc to estimate colocalization of GWAS Catalog entries and GTEx eQTL, we find evidence for colocalization of 150,000 trait-gene-tissue triplets.ConclusionsWe find that colocalization analysis performed with full summary statistics can be closely approximated when only the summary statistics of the top SNP are available for one or both traits. When applied to the full GWAS Catalog and GTEx eQTL, we find that colocalized trait-gene pairs are enriched in tissues relevant to disease etiology and for matches to approved drug mechanisms. POEMColoc R package is available at https://github.com/AbbVie-ComputationalGenomics/POEMColoc .

Dataset Information

Not all summary statistics are made equal: Evidence from extracting summaries across time.

Publications

Not all summary statistics are made equal: Evidence from extracting summaries across time.

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