Biology subjects

Erlich, Y.

Publications and source records attributed to Erlich, Y..

8 recordsLinked to original sources

bioRxiv2018

FactorialHMM: Fast and exact inference in factorial hidden Markov models

MotivationHidden Markov models (HMMs) are powerful tools for modeling processes along the genome. In a standard genomic HMM, observations are drawn, at each genomic position, from a distribution whose parameters depend on a hidden state; the hidden states evolve along the genome as a Markov chain. Often, the hidden state is the Cartesian product of multiple processes, each evolving independently along the genome. Inference in these so-called Factorial HMMs has a naive running time that scales as the square of the number of possible states, which by itself increases exponentially with the number of subchains; such a running time scaling is impractical for many applications. While faster algorithms exist, there is no available implementation suitable for developing bioinformatics applications.\n\nResultsWe developed FactorialHMM, a Python package for fast exact inference in Factorial HMMs. Our package allows simulating either directly from the model or from the posterior distribution of states given the observations. Additionally, we allow the inference of all key quantities related to HMMs: (1) the (Viterbi) sequence of states with the highest posterior probability; (2) the likelihood of the data; and (3) the posterior probability (given all observations) of the marginal and pairwise state probabilities. The running time and space requirement of all procedures is linearithmic in the number of possible states. Our package is highly modular, providing the user with maximal flexibility for developing downstream applications.\n\nAvailabilityhttps://github.com/regevs/factorialhmm

bioinformatics↗

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

SciLMM: Computing heritability with millions of individuals

The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present Sparse Cholesky factorIzation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records.\n\nAuthor SummaryThe advent of online genealogy services allows the assembly of population-scale family trees, spanning millions of individuals and centuries of human history. Such datasets enable answering genetic epidemiology questions on unprecedented scales. Here we present Sci-LMM, a pedigree analysis framework that combines techniques from animal and plant breeding research and from human genetics research for large-scale pedigree analysis. We apply Sci-LMM to analyze population-scale human genealogical records, spanning trillions of relationships. We have made both Sci-LMM and an anonymized dataset of millions of individuals freely available to download, making the analysis of population-scale human family trees widely accessible to the research community. Together, these resources allow researchers to investigate genetic and epidemiological questions on an unprecedented scale.

genetics↗

bioRxiv2017

Major flaws in "Identification of individuals by trait prediction using whole-genome"

Genetic privacy is an area of active research. While it is important to identify new risks, it is equally crucial to supply policymakers with accurate information based on scientific evidence. Recently, Lippert et al. (PNAS, 2017) investigated the status of genetic privacy using trait-predictions from whole genome sequencing. The authors sequenced a cohort of about 1000 individuals and collected a range of demographic, visible, and digital traits such as age, sex, height, face morphology, and a voice signature. They attempted to use the genetic features in order to predict those traits and re-identify the individuals from small pool using the trait predictions. Here, I report major flaws in the Lippert et al. manuscript. In short, the authors technique performs similarly to a simple baseline procedure, does not utilize the power of whole genome markers, uses technically wrong metrics, and finally does not really identify anyone.

genomics↗

bioRxiv2017

DNA.Land: A Digital Biobank Using A Massive Crowdsourcing Approach

Precision medicine necessitates large scale collections of genomes and phenomes. Despite decreases in the costs of genomic technologies, collecting these types of information at scale is still a daunting task that poses logistical challenges and requires consortium-scale resources. Here, we describe DNA.Land, a digital biobank to collect genome and phenomes with a fraction of the resources of traditional studies at the same scale. Our approach relies on crowd-sourcing data from the rapidly growing number of individuals that have access to their own genomic datasets through Direct-to-Consumer (DTC) companies. To recruit participants, we developed a series of automatic return-of-results features in DNA.Land that increase users engagement while stratifying human subject research protection. So far, DNA.Land has collected over 43,000 genomes in 20 months of operation, orders of magnitude higher than previous digital attempts by academic groups. We report lessons learned in running a digital biobank, our technical framework, and our approach regarding ethical, legal, and social implications.

bioinformatics↗

bioRxiv2017

Rapid DNA Re-Identification for Cell Line Authentication and Forensics

DNA re-identification is used for a broad range of applications, ranging from cell line authentication to crime scene sample identification. However, current re-identification schemes suffer from high latency. Here, we describe a rapid, inexpensive, and portable strategy to re-identify human DNA called MinION sketching. Using data from Oxford Nanopore Technologies sequencer, MinION sketching requires only 3min of sequencing and [~]91 random SNPs to identify a sample, enabling near real-time applications of DNA re-identification. This method capitalizes on the vastly growing availability of genomic reference data for individuals and cancer cell lines. Hands-on preparation of the samples can be reduced to <1 hour. This empowers the application of MinION sketching in research settings for routine cell line authentication or in forensics.\n\nSoftware is available at https://github.com/TeamErlich/personal-identification-pipeline

cell biology↗

bioRxiv2017

Quantitative analysis of population-scale family trees using millions of relatives

Family trees have vast applications in multiple fields from genetics to anthropology and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. Here, we collected 86 million profiles from publicly-available online data from genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of longevity by inspecting millions of relative pairs and to provide insights to population genetics theories on the dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data.\n\nOne Sentence SummaryUsing massive crowd-sourced genealogy data, we created a population-scale family tree resource for scientific studies.

genomics↗

bioRxiv2016

A framework to interpret short tandem repeat variation in humans

Identifying regions of the genome that are depleted of mutations can reveal potentially deleterious variants. Short tandem repeats (STRs), also known as microsatellites, are among the largest contributors of de novo mutations in humans and are implicated in a variety of human disorders. However, because of the challenges STRs pose to bioinformatics tools, per-locus studies of STR mutations have been limited to highly ascertained panels of several dozen loci. Here, we harnessed bioinformatics tools and a novel analytical framework to estimate mutation parameters for each STR in the human genome by correlating STR genotypes with local sequence heterozygosity. We applied our method to obtain robust estimates of the impact of local sequence features on mutation parameters and used this to create a framework for measuring constraint at STRs by comparing observed vs. expected mutation rates. Constraint scores identified known pathogenic variants with early onset effects. Our constraint metrics will provide a valuable tool for prioritizing pathogenic STRs in medical genetics studies.

genetics↗