Whole Genome Regulatory Variant Evaluation for Transcription Factor Binding

Haoyang Zeng , Tatsunori Hashimoto , Daniel D. Kang , David K. Gifford
doi: http://dx.doi.org/10.1101/017392

Contemporary approaches to predict single nucleotide polymorphisms (SNPs) that alter transcription factor binding rely upon the sequence affinity of a transcription factor as represented by its canonical motif. WAVE (Whole-genome regulAtory Variants Evaluation) is a novel method for predicting more general regulatory variants that affect transcription factor binding, including those that fall outside of the canonical motif. WAVE learns a k-mer based generative model of transcription factor binding from ChIP-seq data and scores variants using its generative binding model. The k-mers learned by WAVE capture more sequence feature in transcription factor binding than a motif-based approach alone, including both a transcription factor’s canonical motif as well as associated co-factor motifs. WAVE significantly outperforms motif-based methods in predicting SNPs associated with allele-specific binding.

XWAS: a software toolset for genetic data analysis and association studies of the X chromosome

Feng Gao , Diana Chang , Arjun Biddanda , Li Ma , Yingjie Guo , Zilu Zhou , Alon Keinan
doi: http://dx.doi.org/10.1101/009795

XWAS is a new software for the analysis of the X chromosome in association studies and similar studies. The X chromosome plays an important role in human disease, especially those with sexually dimorphic characteristics. Special attention needs to be given to its analysis due to the unique inheritance pattern, leading to analytical complications that have resulted in the majority of genome-wide association studies (GWAS) either not considering X or mishandling it with GWAS toolsets that have been designed for non-sex chromosomes.. Hence, XWAS fills the need for tools that are specially designed for analysis of X. Following extensive, stringent, and X-specific quality control, XWAS offers an array of statistical tests of association, including: (1) the standard test between a SNP (single nucleotide polymorphism) and disease risk, including after first stratifying individuals by sex, (2) a test for a differential effect of a SNP on disease between males and females, (3) motivated by X-inactivation, a test for higher variance of a trait in heterozygous females as compared to homozygous females, and (4) for all tests, a version that allows for combining evidence across all SNPs in a whole gene. We applied the toolset analysis pipeline to 16 GWAS datasets of immune-related disorders and to 7 risk factors of coronary artery disease, and discovered several new X-linked genetic associations. XWAS will provide the tools and incentive for others to incorporate the X chromosome into GWAS, hence enabling discoveries of novel loci implicated in many diseases and in their sexual dimorphism.

Network analysis of genome-wide selective constraint reveals a gene network active in early fetal brain intolerant of mutation

Jinmyung Choi , Parisa Shooshtari , Kaitlin E Samocha , Mark J Daly , Chris Cotsapas
doi: http://dx.doi.org/10.1101/017277
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Abstract

Using robust, integrated analysis of multiple genomic datasets, we show that genes depleted for non-synonymous de novo mutations form a subnetwork of 72 members under strong selective constraint. We further show this subnetwork is preferentially expressed in the early development of the human hippocampus and is enriched for genes mutated in neurological, but not other, Mendelian disorders. We thus conclude that carefully orchestrated developmental processes are under strong constraint in early brain development, and perturbations caused by mutation have adverse outcomes subject to strong purifying selection. Our findings demonstrate that selective forces can act on groups of genes involved in the same process, supporting the notion that adaptation can act coordinately on multiple genes. Our approach provides a statistically robust, interpretable way to identify the tissues and developmental times where groups of disease genes are active. Our findings highlight the importance of considering the interactions between genes when analyzing genome-wide sequence data.

RiboDiff: Detecting Changes of Translation Efficiency from Ribosome Footprints

Yi Zhong , Theofanis Karaletsos , Philipp Drewe , Vipin Thankam T Sreedharan , Kamini Singh , Hans-Guido Wendel , Gunnar Rätsch
doi: http://dx.doi.org/10.1101/017111

Motivation: Deep sequencing based ribosome footprint profiling can provide novel insights into the regulatory mechanisms of protein translation. However, the observed ribosome profile is fundamentally confounded by transcriptional activity. In order to decipher principles of translation regulation, tools that can reliably detect changes in translation efficiency in case-control studies are needed. Results: We present a statistical framework and analysis tool, RiboDiff, to detect genes with changes in translation efficiency across experimental treatments. RiboDiff uses generalized linear models to estimate the over-dispersion of RNA-Seq and ribosome profiling measurements separately, and performs a statistical test for differential translation efficiency using both mRNA abundance and ribosome occupancy. Availability: Source code and documentation are available at http://github.com/ratschlab/ribodiff. Supplementary Material can be found at http://bioweb.me/ribo.