Biology subjects

Sayan Mukherjee

Publications and source records attributed to Sayan Mukherjee.

6 recordsLinked to original sources

bioRxiv2016

Phylogenetic factorization of compositional data

Marker gene sequencing of microbial communities has generated big datasets of microbial relative abundances varying across environmental conditions, sample sites and treatments. These data often come with putative phylogenies, providing unique opportunities to investigate how shared evolutionary history affects microbial abundance patterns. Here, we present a method to identify the phylogenetic factors driving patterns in microbial community composition. We use the method, \"phylofactorization\", to re-analyze datasets from human body and soil microbial communities, demonstrating how phylofactorization can be a dimensionality-reducing tool, an ordination-visualization tool, and also mass-produce inferences on the edges in the phylogeny in which meaningful differences arose.

Bioinformatics↗

bioRxiv2016

Differential Expression Analysis for RNAseq using Poisson Mixed Models

Identifying differentially expressed (DE) genes from RNA sequencing (RNAseq) studies is among the most common analyses in genomics. However, RNAseq DE analysis presents several statistical and computational challenges, including over-dispersed read counts and, in some settings, sample non-independence. Previous count-based methods rely on simple hierarchical Poisson models (e.g., negative binomial) to model independent over-dispersion, but do not account for sample non-independence due to relatedness, population structure and/or hidden confounders. Here, we present a Poisson mixed model with two random effects terms that account for both independent over-dispersion and sample non-independence. We also develop a scalable sampling-based inference algorithm using a latent variable representation of the Poisson distribution. With simulations, we show that our method properly controls for type I error and is generally more powerful than other widely used approaches, except in small samples (n<15) with other unfavorable properties (e.g., small effect sizes). We also apply our method to three real data sets that contain related individuals, population stratification, or hidden confounders. Our results show that our method increases power in all three data compared to other approaches, though the power gain is smallest in the smallest sample (n=6). Our method is implemented in MACAU, freely available at www.xzlab.org/software.html.

Genomics↗

bioRxiv2016

A phylogenetic transform enhances analysis of compositional microbiota data

High-throughput DNA sequencing technologies have revolutionized the study of microbial communities (microbiota) and have revealed their importance in both human health and disease. However, due to technical limitations, data from microbiota surveys reflect the relative abundance of bacterial taxa and not their absolute levels. It is well known that applying common statistical methods, such as correlation or hypothesis testing, to relative abundance data can lead to spurious results. Here, we introduce the PhILR transform, a data transform that utilizes microbial phylogenetic information. This transform enables off-the-shelf statistical tools to be applied to microbiota surveys free from artifacts usually associated with analysis of relative abundance data. Using environmental and human-associated microbial community datasets as benchmarks, we find that the PhILR transform significantly improves the performance of distance-based and machine learning-based statistics, boosting the accuracy of widely used algorithms on reference benchmarks by 90%. Because the PhILR transform relies on bacterial phylogenies, statistics applied in the PhILR coordinate system are also framed within an evolutionary perspective. Regression on PhILR transformed human microbiota data identified evolutionarily neighboring bacterial clades that may have differentiated to adapt to distinct body sites. Variance statistics showed that the degree of covariation of bacterial clades across human body sites tended to increase with phylogenetic relatedness between clades. These findings support the hypothesis that environmental selection, not competition between bacteria, plays a dominant role in structuring human-associated microbial communities.

Bioinformatics↗

bioRxiv2016

Detecting Epistasis in Genome-wide Association Studies with the Marginal EPIstasis Test

Epistasis, commonly defined as the interaction between multiple genes, is an important genetic component underlying phenotypic variation. Many statistical methods have been developed to model and identify epistatic interactions between genetic variants. However, because of the large combinatorial search space of interactions, most epistasis mapping methods face enormous computational challenges and often suffer from low statistical power due to multiple test correction. Here, we present a novel, alternative strategy for mapping epistasis: instead of directly identifying individual pairwise or higher-order interactions, we focus on mapping variants that have non-zero marginal epistatic effects -- the combined pairwise interaction effects between a given variant and all other variants. By testing marginal epistatic effects, we can identify candidate variants that are involved in epistasis without the need to identify the exact partners with which the variants interact, thus potentially alleviating much of the statistical and computational burden associated with standard epistatic mapping procedures. Our method is based on a variance component model, and relies on a recently developed variance component estimation method for efficient parameter inference and p-value computation. We refer to our method as the \"MArginal ePIstasis Test\", or MAPIT. With simulations, we show how MAPIT can be used to estimate and test marginal epistatic effects, produce calibrated test statistics under the null, and facilitate the detection of pairwise epistatic interactions. We further illustrate the benefits of MAPIT in a QTL mapping study by analyzing the gene expression data of over 400 individuals from the GEUVADIS consortium.\n\nAuthor SummaryEpistasis is an important genetic component that underlies phenotypic variation and is also a key mechanism that accounts for missing heritability. Identifying epistatic interactions in genetic association studies can help us better understand the genetic architecture of complex traits and diseases. However, the ability to identify epistatic interactions in practice faces important statistical and computational challenges. Standard statistical methods scan through all-pairs (or all high-orders) of interactions, and the large number of interaction combinations results in slow computation time and low statistical power. We propose an alternative mapping strategy and a new variance component method for identifying epistasis. Our method examines one variant at a time, and estimates and tests its marginal epistatic effect -- the combined pairwise interaction effects between a given variant and all other variants. By testing for marginal epistatic effects, we can identify variants that are involved in epistasis without the need of explicitly searching for interactions. Our method also relies on a recently developed variance component estimation method for efficient and robust parameter inference, and accurate p-value computation. We illustrate the benefits of our method using simulations and real data applications.

Genetics↗

bioRxiv2015

Efficient genome-wide sequencing and low coverage pedigree analysis from non-invasively collected samples

Research on the genetics of natural populations was revolutionized in the 1990s by methods for genotyping non-invasively collected samples. However, these methods have remained largely unchanged for the past 20 years and lag far behind the genomics era. To close this gap, here we report an optimized laboratory protocol for genome-wide capture of endogenous DNA from non-invasively collected samples, coupled with a novel computational approach to reconstruct pedigree links from the resulting low-coverage data. We validated both methods using fecal samples from 62 wild baboons, including 48 from an independently constructed extended pedigree. We enriched fecal-derived DNA samples up to 40-fold for endogenous baboon DNA, and reconstructed near-perfect pedigree relationships even with extremely low-coverage sequencing. We anticipate that these methods will be broadly applicable to the many research systems for which only non-invasive samples are available. The lab protocol and software (\"WHODAD\") are freely available at www.tung-lab.org/protocols and www.xzlab.org/software, respectively.

Genomics↗

bioRxiv2015

Fast principal components analysis reveals convergent evolution of ADH1B gene in Europe and East Asia

Searching for genetic variants with unusual differentiation between subpopulations is an established approach for identifying signals of natural selection. However, existing methods generally require discrete subpopulations. We introduce a method that infers selection using principal components (PCs) by identifying variants whose differentiation along top PCs is significantly greater than the null distribution of genetic drift. To enable the application of this method to large data sets, we developed the FastPCA software, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using the PC-based test for natural selection, we replicate previously known selected loci and identify three new genome-wide significant signals of selection, including selection in Europeans at the ADH1B gene. The coding variant rs1229984*T has previously been associated to a decreased risk of alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents. We also detect new selection signals at IGFBP3 and IGH, which have also previously been associated to human disease.

Bioinformatics↗