Biology subjects

Noah Zaitlen

Publications and source records attributed to Noah Zaitlen.

11 recordsLinked to original sources

bioRxiv2016

Genome-wide methylation data mirror ancestry information

Genetic data are known to harbor information about human demographics, and genotyping data are commonly used for capturing ancestry information by leveraging genome-wide differences between populations. In contrast, it is not clear to what extent population structure is captured by whole-genome DNA methylation data. We demonstrate, using three large cohort 450K methylation array data sets, that ancestry information signal is mirrored in genome-wide DNA methylation data, and that it can be further isolated more effectively by leveraging the correlation structure of CpGs with cis-located SNPs. Based on these insights, we propose a method, EPISTRUCTURE, for the inference of ancestry from methylation data, without the need for genotype data. EPISTRUCTURE can be used to infer ancestry information of individuals based on their methylation data in the absence of corresponding genetic data. Although genetic data are often collected in epigenetic studies of large cohorts, these are typically not made publicly available, making the application of EPISTRUCTURE especially useful for anyone working on public data. Implementation of EPISTRUCTURE is available in GLINT, our recently released toolset for DNA methylation analysis at: http://glint-epigenetics.readthedocs.io.

Genetics↗

bioRxiv2016

Playing Musical Chairs in Big Data to Reveal Variables Associations

Testing for associations in big data faces the problem of multiple comparisons, with true signals buried inside the noise of all associations queried. This is particularly true in genetic association studies where a substantial proportion of the variation of human phenotypes is driven by numerous genetic variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. While successful, this approach does not leverage the environmental and genetic factors shared between the multiple phenotypes collected in contemporary cohorts. Here we develop a method that improves the power of detecting associations when a large number of correlated variables have been measured on the same samples. Our analyses over real and simulated data provide direct support that large sets of correlated variables can be leveraged to achieve dramatic increases in statistical power equivalent to a two or even three folds increase in sample size.

Genetics↗

bioRxiv2016

The Effects of Migration and Assortative Mating on Admixture Linkage Disequilibrium

1Statistical models in medical and population genetics typically assume that individuals assort randomly in a population. While this simplifies model complexity, it contradicts an increasing body of evidence of non-random mating in human populations. Specifically, it has been shown that assortative mating is significantly affected by genomic ancestry. In this work we examine the effects of ancestry-assortative mating on the linkage disequilibrium between local ancestry tracks of individuals in an admixed population. To accomplish this, we develop an extension to the Wright-Fisher model that allows for ancestry based assortative mating. We show that ancestry-assortment perturbs the distribution of local ancestry linkage disequilibrium (LAD) and the variance of ancestry in a population as a function of the number of generations since admixture. This assortment effect can induce errors in demographic inference of admixed populations when methods assume random mating. We derive closed form formulae for LAD under an assortative-mating model with and without migration. We observe that LAD depends on the correlation of global ancestry of couples in each generation, the migration rate of each of the ancestral populations, the initial proportions of ancestral populations, and the number of generations since admixture. We also present the first evidence of ancestry-assortment in African Americans and examine LAD in simulated and real admixed population data of African Americans. We find that demographic inference under the assumption of random mating significantly underestimates the number of generations since admixture, and that accounting for assortative mating using the patterns of LAD results in estimates that more closely agrees with the historical narrative.

Genetics↗

bioRxiv2016

Dumpster diving in RNA-sequencing to find the source of every last read

High throughput RNA sequencing technologies have provided invaluable research opportunities across distinct scientific domains by producing quantitative readouts of the transcriptional activity of both entire cellular populations and single cells. The majority of RNA-Seq analyses begin by mapping each experimentally produced sequence (i.e., read) to a set of annotated reference sequences for the organism of interest. For both biological and technical reasons, a significant fraction of reads remains unmapped. In this work, we develop Read Origin Protocol (ROP) to discover the source of all reads originating from complex RNA molecules, recombinant T and B cell receptors, and microbial communities. We applied ROP to 8,641 samples across 630 individuals from 54 tissues. A fraction of RNA-Seq data (n=86) was obtained in-house; the remaining data was obtained from the Genotype-Tissue Expression (GTEx v6) project. To generalize the reported number of accounted reads, we also performed ROP analysis on thousands of different, randomly selected, and publicly available RNA-Seq samples in the Sequence Read Archive (SRA). Our approach can account for 99.9% of 1 trillion reads of various read length across the merged dataset (n=10641). Using in-house RNA-Seq data, we show that immune profiles of asthmatic individuals are significantly different from the profiles of control individuals, with decreased average per sample T and B cell receptor diversity. We also show that immune diversity is inversely correlated with microbial load. Our results demonstrate the potential of ROP to exploit unmapped reads in order to better understand the functional mechanisms underlying connections between the immune system, microbiome, human gene expression, and disease etiology. ROP is freely available at https://github.com/smangul1/rop and currently supports human and mouse RNA-Seq reads.

Genomics↗

bioRxiv2016

Local joint testing improves power and identifies missing heritability in association studies

There is mounting evidence that complex human phenotypes are highly polygenic, with many loci harboring multiple causal variants, yet most genetic association studies examine each SNP in isolation. While this has lead to the discovery of thousands of disease associations, discovered variants account for only a small fraction of disease heritability. Alternative multi-SNP methods have been proposed, but issues such as multiple testing correction, sensitivity to genotyping error, and optimization for the underlying genetic architectures remain. Here we describe a local joint testing procedure, complete with multiple testing correction, that leverages a genetic phenomenon we call linkage masking wherein linkage disequilibrium between SNPs hides their signal under standard association methods. We show that local joint testing on the original Wellcome Trust Case Control Consortium dataset leads to the discovery of 29% more associated loci that were later found in followup studies containing thousands of additional individuals. These loci double the heritability explained by genome-wide significant associations in the WTCCC dataset, implicating linkage masking as a novel source of missing heritability. Furthermore, we show that local joint testing in a cis-eQTL study of the gEUVADIS dataset increases the number of genes discovered by 10.7% over marginal analyses. Our multiple hypothesis correction and joint testing framework are available in a python software package called jester, available at github.com/brielin/Jester.

Genetics↗

bioRxiv2016

Differential methylation between ethnic sub-groups reflects the effect of genetic ancestry and environmental exposures

In clinical practice and biomedical research populations are often divided categorically into distinct racial/ethnic groups. In reality, these categories, which are based on social rather than biological constructs, comprise diverse groups with highly heterogeneous histories, cultures, traditions, religions, social and environmental exposures and ancestral backgrounds. Their use is thus widely debated and genetic ancestry has been suggested as a complement or alternative to this categorization. However, few studies have examined the relative contributions of racial/ethnic identity, genetic ancestry, and environmental exposures on well-established and fundamental biological processes. We examined the associations between ethnicity, ancestry, and environmental exposures and DNA methylation. We typed over 450,000 CpG sites in primary whole blood of 573 individuals of diverse Hispanic descent who also had high-density genotype data. We found that both self-identified ethnicity and genetically determined ancestry were significantly associated with methylation levels at a large number of CpG sites (916 and 194, respectively). Among loci differentially methylated between ethnic groups, a median of 75.7% (IQR 45.8% to 92%) of the variance in methylation associated with ethnicity could be accounted for by shared genomic ancestry accounts. We also found significant enrichment (p = 4.2 x 10-64) of ethnicity-associated sites amongst loci previously associated with environmental and social exposures, particularly maternal smoking during pregnancy. Our study suggests that although differential methylation between ethnic groups can be partially explained by the shared genetic ancestry, a significant effect of ethnicity is likely due to environmental, social, or cultural factors, which differ between ethnic groups.\n\nOne Sentence SummaryIn order to better understand the role of ethnic self-identification and genetically determined ancestry in biomedical outcomes, we explore their relative contributions to variation in methylation, a fundamental biological process.\n\nSources of FundingThis research was supported in part by the Sandler Family Foundation, the American Asthma Foundation, National Institutes of Health (P60 MD006902, R01 HL117004, R21ES24844, U54MD009523, R01 ES015794, R01 HL088133, M01 RR000083, R01 HL078885, R01 HL104608, U19 AI077439, M01 RR00188, U01 HG009080, and R01 HL135156), ARRA grant RC2 HL101651, and TRDRP 24RT-0025; EGB was supported in part through grants from the Flight Attendant Medical Research Institute (FAMRI), and NIH (K23 HL004464); NZ was supported in part by an NIH career development award from the NHLBI (K25HL121295). JMG was supported in part by NIH Training Grant T32 (T32GM007546) and career development awards from the NHLBI (K23HL111636) and NCATS (KL2TR000143) as well as the Hewett Fellowship; N.T. was supported in part by an institutional training grant from the NIGMS (T32-GM007546) and career development awards from the NHLBI (K12-HL119997 and K23-HL125551), Parker B. Francis Fellowship Program, and the American Thoracic Society; CRG was supported in part by NIH Training Grant T32 (GM007175) and the UCSF Chancellors Research Fellowship and Dissertation Year Fellowship; RK was supported with a career development award from the NHLBI (K23HL093023); HJF was supported in part by the GCRC (RR00188); PCA was supported in part by the Ernest S. Bazley Grant; MAS was supported in part by 1R01HL128439-01. This publication was supported by various institutes within the National Institutes of Health. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Genetics↗

bioRxiv2016

Transethnic genetic correlation estimates from summary statistics

The increasing number of genetic association studies conducted in multiple populations provides unprecedented opportunity to study how the genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population genetics. Here we develop a method for estimating the transethnic genetic correlation: the correlation of causal variant effect sizes at SNPs common in populations. We take advantage of the entire spectrum of SNP associations and use only summary-level GWAS data. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We apply our method to gene expression, rheumatoid arthritis, and type-two diabetes data and overwhelmingly find that the genetic correlation is significantly less than 1. Our method is implemented in a python package called popcorn.

Genetics↗

bioRxiv2016

An Ancestry Based Approach for Detecting Interactions

IBackgroundEpistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.\n\nResultsIn this work, we develop a new statistical approach to address these issues that leverages genetic ancestry in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals respectively, identifying nine interactions that were significant at p < 5x10-8, we show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low p-values (p < 1.8x10-6).\n\nConclusionWe show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.

Genetics↗

bioRxiv2015

A novel test for detecting gene-gene interactions in trio studies

Epistasis plays a significant role in the genetic architecture of many complex phenotypes in model organisms. To date, there have been very few interactions replicated in human studies due in part to the multiple hypothesis burden implicit in genome-wide tests of epistasis. Therefore, it is of paramount importance to develop the most powerful tests possible for detecting interactions. In this work we develop a new gene-gene interaction test for use in trio studies called the trio correlation (TC) test. The TC test computes the expected joint distribution of marker pairs in offspring conditional on parental genotypes. This distribution is then incorporated into a standard one degree of freedom correlation test of interaction. We show via extensive simulations that our test substantially outperforms existing tests of interaction in trio studies. The gain in power under standard models of phenotype is large, with previous tests requiring more than twice the number of trios to obtain the power of our test. We also demonstrate a bias in a previous trio interaction test and identify its origin. We conclude that the TC test shows improved power to identify interactions in existing, as well as emerging, trio association studies. The method is publicly available at www.github.com/BrunildaBalliu/TrioEpi.

Genetics↗

bioRxiv2015

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk, and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves LD-pruning markers and applying a P-value threshold to association statistics, but this discards information and may reduce predictive accuracy. We introduce a new method, LDpred, which infers the posterior mean causal effect size of each marker using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the pruning/thresholding approach, particularly at large sample sizes. Accordingly, prediction R2 increased from 20.1% to 25.3% in a large schizophrenia data set and from 9.8% to 12.0% in a large multiple sclerosis data set. A similar relative improvement in accuracy was observed for three additional large disease data sets and when predicting in non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Bioinformatics↗

bioRxiv2014

Mixed Model with Correction for Case-Control Ascertainment Increases Association Power

We introduce a Liability Threshold Mixed Linear Model (LTMLM) association statistic for ascertained case-control studies that increases power vs. existing mixed model methods, with a well-controlled false-positive rate. Recent work has shown that existing mixed model methods suffer a loss in power under case-control ascertainment, but no solution has been proposed. Here, we solve this problem using a chi-square score statistic computed from posterior mean liabilities (PML) under the liability threshold model. Each individuals PML is conditional not only on that individuals case-control status, but also on every individuals case-control status and on the genetic relationship matrix obtained from the data. The PML are estimated using a multivariate Gibbs sampler, with the liability-scale phenotypic covariance matrix based on the genetic relationship matrix (GRM) and a heritability parameter estimated via Haseman-Elston regression on case-control phenotypes followed by transformation to liability scale. In simulations of unrelated individuals, the LTMLM statistic was correctly calibrated and achieved higher power than existing mixed model methods in all scenarios tested, with the magnitude of the improvement depending on sample size and severity of case-control ascertainment. In a WTCCC2 multiple sclerosis data set with >10,000 samples, LTMLM was correctly calibrated and attained a 4.1% improvement (P = 0.007) in chi-square statistics (vs. existing mixed model methods) at 75 known associated SNPs, consistent with simulations. Larger increases in power are expected at larger sample sizes. In conclusion, an increase in power over existing mixed model methods is available for ascertained case-control studies of diseases with low prevalence.

Genetics↗