Biology subjects

Pe'er, I.

Publications and source records attributed to Pe'er, I..

5 recordsLinked to original sources

bioRxiv2018

Re-identification of genomic data using long range familial searches

Consumer genomics databases reached the scale of millions of individuals. Recently, law enforcement investigators have started to exploit some of these databases to find distant familial relatives, which can lead to a complete re-identification. Here, we leveraged genomic data of 600,000 individuals tested with consumer genomics to investigate the power of such long-range familial searches. We project that half of the searches with European-descent individuals will result with a third cousin or closer match and will provide a search space small enough to permit re-identification using common demographic identifiers. Moreover, in the near future, virtually any European-descent US person could be implicated by this technique. We propose a potential mitigation strategy based on cryptographic signature that can resolve the issue and discuss policy implications to human subject research.

genomics↗

bioRxiv2018

Inference of population structure from ancient DNA

Methods for inferring population structure from genetic information traditionally assume samples are contemporary. Yet, the increasing availability of ancient DNA sequences begs revision of this paradigm. We present Dystruct (Dynamic Structure), a framework and toolbox for inference of shared ancestry from data that include ancient DNA. By explicitly modeling population history and genetic drift as a time-series, Dystruct more accurately and realistically discovers shared ancestry from ancient and contemporary samples. Formally, we use a normal approximation of drift, which allows a novel, efficient algorithm for optimizing model parameters using stochastic variational inference. We show that Dystruct outperforms the state of the art when individuals are sampled over time, as is common in ancient DNA datasets. We further demonstrate the utility of our method on a dataset of 92 ancient samples alongside 1941 modern ones genotyped at 222755 loci. Our model tends to present modern samples as the mixtures of ancestral populations they really are, rather than the artifactual converse of presenting ancestral samples as mixtures of contemporary groups.\n\nAvailabilityDystruct is implemented in C++, open-source, and available at https://github.com/tyjo/dystruct.

evolutionary biology↗

bioRxiv2017

High-depth whole genome sequencing of a large population-specific reference panel: Enhancing sensitivity, accuracy, and imputation

BackgroundWhile increasingly large reference panels for genome-wide imputation have been recently made available, the degree to which imputation accuracy can be enhanced by population-specific reference panels remains an open question. In the present study, we sequenced at full-depth ([≥]30x) a moderately large (n=738) cohort of samples drawn from the Ashkenazi Jewish population across two platforms (Illumina X Ten and Complete Genomics, Inc.). We developed and refined a series of quality control steps to optimize sensitivity, specificity, and comprehensiveness of variant calls in the reference panel, and then tested the accuracy of imputation against target cohorts drawn from the same population.\n\nResultsFor samples sequenced on the Illumina X Ten platform, quality thresholds were identified that permitted highly accurate calling of single nucleotide variants across 94% of the genome. The Complete Genomics, Inc. platform was more conservative (fewer variants called) compared to the Illumina platform, but also demonstrated relatively greater numbers of false positives that needed to be filtered. Quality control procedures also permitted detection of novel genome reads that are not mapped to current reference or alternate assemblies. After stringent quality control, the population-specific reference panel produced more accurate and comprehensive imputation results relative to publicly available, large cosmopolitan reference panels. The population-specific reference panel also permitted enhanced filtering of clinically irrelevant variants from personal genomes.\n\nConclusionsOur primary results demonstrate enhanced accuracy of a population-specific imputation panel relative to cosmopolitan panels, especially in the range of infrequent (<5% non-reference allele frequency) and rare (<1% non-reference allele frequency) variants that may be most critical to further progress in mapping of complex phenotypes.

genomics↗

bioRxiv2017

2-Way k-Means as a Model for Microbiome Samples

MotivationMicrobiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones, and therefore lie in-between them in cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project.\n\nContactwjj2106@columbia.edu

bioinformatics↗

bioRxiv2017

Statistical Correction of the Winner’s Curse Explains Replication Variability in Quantitative Trait Genome-Wide Association Studies

Genome-wide association studies (GWAS) have identified hundreds of SNPs responsible for variation in human quantitative traits. However, genome-wide-significant associations often fail to replicate across independent cohorts, in apparent inconsistency with their apparent strong effects in discovery cohorts. This limited success of replication raises pervasive questions about the utility of the GWAS field. We identify all 332 studies of quantitative traits from the NHGRI-EBI GWAS Database with attempted replication. We find that the majority of studies provide insufficient data to evaluate replication rates. The remaining papers replicate significantly worse than expected (p < 10-14), even when adjusting for regression-to-the-mean of effect size between discovery- and replication-cohorts termed the Winners Curse (p < 10-16). We show this is due in part to misreporting replication cohort-size as a maximum number, rather than per-locus one. In 39 studies accurately reporting per-locus cohort-size for attempted replication of 707 loci in samples with similar ancestry, replication rate matched expectation (predicted 458, observed 457, p = 0.94). In contrast, ancestry differences between replication and discovery (13 studies, 385 loci) cause the most highly-powered decile of loci to replicate worse than expected, due to difference in linkage disequilibrium.\n\nAuthor SummaryThe majority of associations between common genetic variation and human traits come from genome-wide association studies, which have analyzed millions of single-nucleotide polymorphisms in millions of samples. These kinds of studies pose serious statistical challenges to discovering new associations. Finite resources restrict the number of candidate associations that can brought forward into validation samples, introducing the need for a significance threshold. This threshold creates a phenomenon called the Winners Curse, in which candidate associations close to the discovery threshold are more likely to have biased overestimates of the variants true association in the sampled population. We survey all human quantitative trait association studies that validated at least one signal. We find the majority of these studies do not publish sufficient information to actually support their claims of replication. For studies that did, we computationally correct the Winners Curse and evaluate replication performance. While all variants combined replicate significantly less than expected, we find that the subset of studies that (1) perform both discovery and replication in samples of the same ancestry; and (2) report accurate per-variant sample sizes, replicate as expected. This study provides strong, rigorous evidence for the broad reliability of genome-wide association studies. We furthermore provide a model for more efficient selection of variants as candidates for replication, as selecting variants using cursed discovery data enriches for variants with little real evidence for trait association.

genomics↗