Introgression Browser: High throughput whole-genome SNP visualization

Introgression Browser: High throughput whole-genome SNP visualization

Saulo Alves Aflitos, Gabino Sanchez-Perez, Dick de Ridder, Paul Fransz, Eric Schranz, Hans de Jong, Sander Peters
(Submitted on 21 Apr 2015)

Breeding by introgressive hybridization is a pivotal strategy to broaden the genetic basis of crops. Usually, the desired traits are monitored in consecutive crossing generations by marker-assisted selection, but their analyses fail in chromosome regions where crossover recombinants are rare or not viable. Here, we present the Introgression Browser (IBROWSER), a novel bioinformatics tool aimed at visualizing introgressions at nucleotide or SNP accuracy. The software selects homozygous SNPs from Variant Call Format (VCF) information and filters out heterozygous SNPs, Multi-Nucleotide Polymorphisms (MNPs) and insertion-deletions (InDels). For data analysis IBROWSER makes use of sliding windows, but if needed it can generate any desired fragmentation pattern through General Feature Format (GFF) information. In an example of tomato (Solanum lycopersicum) accessions we visualize SNP patterns and elucidate both position and boundaries of the introgressions. We also show that our tool is capable of identifying alien DNA in a panel of the closely related S. pimpinellifolium by examining phylogenetic relationships of the introgressed segments in tomato. In a third example, we demonstrate the power of the IBROWSER in a panel of 600 Arabidopsis accessions, detecting the boundaries of a SNP-free region around a polymorphic 1.17 Mbp inverted segment on the short arm of chromosome 4. The architecture and functionality of IBROWSER makes the software appropriate for a broad set of analyses including SNP mining, genome structure analysis, and pedigree analysis. Its functionality, together with the capability to process large data sets and efficient visualization of sequence variation, makes IBROWSER a valuable breeding tool.

The generalised quasispecies

The generalised quasispecies

Raphaël Cerf, Joseba Dalmau
(Submitted on 22 Apr 2015)

We study Eigen’s quasispecies model in the asymptotic regime where the length of the genotypes goes to infinity and the mutation probability goes to 0. We give several explicit formulas for the stationary solutions of the limiting system of differential equations.

Genetic Basis of Transcriptome Diversity in Drosophila melanogaster

Genetic Basis of Transcriptome Diversity in Drosophila melanogaster

Wen Huang , Mary Anna Carbone , Michael Magwire , Jason Peiffer , Richard Lyman , Eric Stone , Robert Anholt , Trudy Mackay

Understanding how DNA sequence variation is translated into variation for complex phenotypes has remained elusive, but is essential for predicting adaptive evolution, selecting agriculturally important animals and crops, and personalized medicine. Here, we quantified genome-wide variation in gene expression in the sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP). We found that a substantial fraction of the Drosophila transcriptome is genetically variable and organized into modules of genetically correlated transcripts, which provide functional context for newly identified transcribed regions. We identified regulatory variants for the mean and variance of gene expression, the latter of which could often be explained by an epistatic model. Expression quantitative trait loci for the mean, but not the variance, of gene expression were concentrated near genes. This comprehensive characterization of population scale diversity of transcriptomes and its genetic basis in the DGRP is critically important for a systems understanding of quantitative trait variation.

Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data

Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data

Nicolas Duforet-Frebourg, Guillaume Laval, Eric Bazin, Michael G.B. Blum
(Submitted on 8 Apr 2015)

Large-scale genomic data offers the perspective to decipher the genetic architecture of natural selection. To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis. We show that the common Fst index of genetic differentiation between populations can be viewed as a proportion of variance explained by the principal components. Looking at the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) after removal of recently admixed individuals resulting in 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3X). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and non-coding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). PCA-based statistics retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially in non-model species for which defining populations can be difficult. Genome scan based on PCA is implemented in the open-source and freely available PCAdapt software.

Efficient compression and analysis of large genetic variation datasets

Efficient compression and analysis of large genetic variation datasets
Ryan M Layer , Neil Kindlon , Konrad J Karczewski , Exome Aggregation Consortium ExAC , Aaron R Quinlan

The economy of human genome sequencing has catalyzed ambitious efforts to interrogate the genomes of large cohorts in search of deeper insight into the genetic basis of disease. This manuscript introduces Genotype Query Tools (GQT) as a new indexing strategy and powerful toolset that enables interactive analyses based on genotypes, phenotypes and sample relationships. Speed improvements are achieved by operating directly on a compressed index without decompression. GQT’s data compression ratios increase favorably with cohort size and therefore, by avoiding data inflation, relative analysis performance improves in kind. We demonstrate substantial query performance improvements over state-of-the-art tools using datasets from the 1000 Genomes Project (46 fold), the Exome Aggregation Consortium (443 fold), and simulated datasets of up to 100,000 genomes (218 fold). Moreover, our genotype indexing strategy complements existing formats and toolsets to provide a powerful framework for current and future analyses of massive genome datasets.

Efficient Privacy-Preserving String Search and an Application in Genomics

Efficient Privacy-Preserving String Search and an Application in Genomics
Kana Shimizu , Koji Nuida , Gunnar Rätsch

Motivation: Personal genomes carry inherent privacy risks and protecting privacy poses major social and technological challenges. We consider the case where a user searches for genetic information (e.g., an allele) on a server that stores a large genomic database and aims to receive allele-associated information. The user would like to keep the query and result private and the server the database. Approach: We propose a novel approach that combines efficient string data structures such as the Burrows-Wheeler transform with cryptographic techniques based on additive homomorphic encryption. We assume that the sequence data is searchable in efficient iterative query operations over a large indexed dictionary, for instance, from large genome collections and employing the (positional) Burrows-Wheeler transform. We use a technique called oblivious transfer that is based on additive homomorphic encryption to conceal the sequence query and the genomic region of interest in positional queries. Results: We designed and implemented an efficient algorithm for searching sequences of SNPs in large genome databases. During search, the user can only identify the longest match while the server does not learn which sequence of SNPs the user queries. In an experiment based on 2,184 aligned haploid genomes from the 1,000 Genomes Project, our algorithm was able to perform typical queries within ≈2 seconds and ≈20 seconds seconds for client and server side, respectively, on a laptop computer. The presented algorithm is at least one order of magnitude faster than an exhaustive baseline algorithm.

Relationship between LD Score and Haseman-Elston Regression

Relationship between LD Score and Haseman-Elston Regression
Brendan Bulik-Sullivan

Estimating SNP-heritability from summary statistics using LD Score regression provides a convenient alternative to standard variance component models, because LD Score regression is computationally very fast and does not require individual genotype data. However, the mathematical relationship between variance component methods and LD Score regression is not clear; in particular, it is not known in general how much of an increase in standard error one incurs by working with summary data instead of individual genotypes. In this paper, I show that in samples of unrelated individuals, LD Score regression with constrained intercept is essentially the same as Haseman-Elston (HE) regression, which is currently the state-of-the-art method for estimating SNP-heritability from ascertained case/control samples. Similar results hold for SNP-genetic correlation.