Alternative splicing QTLs in European and African populations using Altrans, a novel method for splice junction quantification

Halit Ongen, Emmanouil T Dermitzakis
doi: http://dx.doi.org/10.1101/014126

With the advent of RNA-sequencing technology we now have the power to detect different types of alternative splicing and how DNA variation affects splicing. However, given the short read lengths used in most population based RNA-sequencing experiments, quantifying transcripts accurately remains a challenge. Here we present a novel method, Altrans, for discovery of alternative splicing quantitative trait loci (asQTLs). To assess the performance of Altrans we compared it to Cufflinks, a well-established transcript quantification method. Simulations show that in the presence of transcripts absent from the annotation, Altrans performs better in quantifications than Cufflinks. We have applied Altrans and Cufflinks to the Geuvadis dataset, which comprises samples from European and African populations, and discovered (FDR = 1%) 1806 and 243 asQTLs with Altrans, and 1596 and 288 asQTLs with Cufflinks for Europeans and Africans, respectively. Although Cufflinks results replicated better across the two populations, this likely due to the increased sensitivity of Altrans in detecting harder to detect associations. We show that, by discovering a set of asQTLs in a smaller subset of European samples and replicating these in the remaining larger subset of Europeans, both methods achieve similar replication levels (94% and 98% replication in Altrans and Cufflinks, respectively). We find that method specific asQTLs are largely due to different types of alternative splicing events detected by each method. We overlapped the asQTLs with biochemically active regions of the genome and observed significant enrichments for many functional marks and variants in splicing regions, highlighting the biological relevance of the asQTLs identified. All together, we present a novel approach for discovering asQTLs that is a more direct assessment of splicing compared to other methods and is complementary to other transcript quantification methods.

Approximate statistical alignment by iterative sampling of substitution matrices

Joseph L. Herman, Adrienn Szabó, Instván Miklós, Jotun Hein
(Submitted on 19 Jan 2015)

We outline a procedure for jointly sampling substitution matrices and multiple sequence alignments, according to an approximate posterior distribution, using an MCMC-based algorithm. This procedure provides an efficient and simple method by which to generate alternative alignments according to their expected accuracy, and allows appropriate parameters for substitution matrices to be selected in an automated fashion. In the cases considered here, the sampled alignments with the highest likelihood have an accuracy consistently higher than alignments generated using the standard BLOSUM62 matrix.

Software for the analysis and visualization of deep mutational scanning data

Jesse D Bloom
doi: http://dx.doi.org/10.1101/013623

Background Deep mutational scanning is a technique to estimate the impacts of mutations on a gene by using deep sequencing to count mutations in a library of variants before and after imposing a functional selection. The impacts of mutations must be inferred from changes in their counts after selection. Results I describe a software package, dms_tools, to infer the impacts of mutations from deep mutational scanning data using a likelihood-based treatment of the mutation counts. I show that dms_tools yields more accurate inferences on simulated data than the widely used but statistically biased approach of calculating ratios of counts pre- and post-selection. Using dms_tools, one can infer the preference of each site for each amino acid given a single selection pressure, or assess the extent to which these preferences change under different selection pressures. The preferences and their changes can be intuitively visualized with sequence-logo-style plots created using an extension to weblogo. Conclusions dms_tools implements a statistically principled approach for the analysis and subsequent visualization of deep mutational scanning data.

Marker-based estimation of heritability in immortal populations

Willem Kruijer, Martin Boer, Marcos Malosetti, Padraic J. Flood, Bas Engel, Rik Kooke, Joost Keurentjes, Fred van Eeuwijk
(Submitted on 21 Dec 2014)

Heritability is a central parameter in quantitative genetics, both from an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within and between genotype variability. This approach estimates broad-sense heritability, and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here, is to use mixed models at individual plant or plot level. Using statistical arguments, simulations and real data we investigate the feasibility of both approaches, and how these affect genomic prediction with G-BLUP and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For GWAS on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.

Mapping bias overestimates reference allele frequencies at the HLA genes in the 1000 Genomes Project phase I data

Debora Yoshihara Caldeira Brandt, Vitor Rezende da Costa Aguiar, Bárbara Domingues Bitarello, Kelly Nunes, Jérôme Goudet, Diogo Meyer
doi: http://dx.doi.org/10.1101/013151

Next Generation Sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the Human Leukocyte Antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analises, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the SNPs reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, -DQB1 ). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1,092 1000G samples, and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect, and that allele frequencies are estimated with an error higher than ??0.1 at approximately 25% of the SNPs in HLA genes. We found a bias towards overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates, and discuss the outcomes of including those sites in different kinds of analyses. Since the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity.

FORGE : A tool to discover cell specific enrichments of GWAS associated SNPs in regulatory regions.

Ian Dunham, Eugene Kulesha, Valentina Iotchkova, Sandro Morganella, Ewan Birney
doi: http://dx.doi.org/10.1101/013045

Genome wide association studies provide an unbiased discovery mechanism for numerous human diseases. However, a frustration in the analysis of GWAS is that the majority of variants discovered do not directly alter protein-coding genes. We have developed a simple analysis approach that detects the tissue-specific regulatory component of a set of GWAS SNPs by identifying enrichment of overlap with DNase I hotspots from diverse tissue samples. Functional element Overlap analysis of the Results of GWAS Experiments (FORGE) is available as a web tool and as standalone software and provides tabular and graphical summaries of the enrichments. Conducting FORGE analysis on SNP sets for 260 phenotypes available from the GWAS catalogue reveals numerous overlap enrichments with tissue–specific components reflecting the known aetiology of the phenotypes as well as revealing other unforeseen tissue involvements that may lead to mechanistic insights for disease.

Using Bayesian multilevel whole-genome regression models for partial pooling of estimation sets in genomic prediction

Frank Technow, L. Radu Totir
doi: http://dx.doi.org/10.1101/012971

Estimation set size is an important determinant of genomic prediction accuracy. Plant breeding programs are characterized by a high degree of structuring, particularly into populations. This hampers establishment of large estimation sets for each population. Pooling populations increases estimation set size but ignores unique genetic characteristics of each. A possible solution is partial pooling with multilevel models, which allows estimating population specific marker effects while still leveraging information across populations. We developed a Bayesian multilevel whole-genome regression model and compared its performance to that of the popular BayesA model applied to each population separately (no pooling) and to the joined data set (complete pooling). As example we analyzed a wide array of traits from the nested association mapping maize population. There we show that for small population sizes (e.g., < 50), partial pooling increased prediction accuracy over no or complete pooling for populations represented in the estimation set. No pooling was superior however when populations were large. In another example data set of interconnected biparental maize populations either partial or complete pooling were superior, depending on the trait. A simulation showed that no pooling is superior when differences in genetic effects among populations are large and partial pooling when they are intermediate. With small differences, partial and complete pooling achieved equally high accuracy. For prediction of new populations, partial and complete pooling had very similar accuracy in all cases. We conclude that partial pooling with multilevel models can maximize the potential of pooling by making optimal use of information in pooled estimation sets.

Common binding by redundant group B Sox proteins is evolutionarily conserved in Drosophila

Sarah H Carl, Steven Russell
doi: http://dx.doi.org/10.1101/012872

Background: Group B Sox proteins are a highly conserved group of transcription factors that act extensively to coordinate nervous system development in higher metazoans while showing both co-expression and functional redundancy across a broad group of taxa. In Drosophila melanogaster, the two group B Sox proteins Dichaete and SoxNeuro show widespread common binding across the genome. While some instances of functional compensation have been observed in Drosophila, the function of common binding and the extent of its evolutionary conservation is not known. Results: We used DamID-seq to examine the genome-wide binding patterns of Dichaete and SoxNeuro in four species of Drosophila. Through a quantitative comparison of Dichaete binding, we evaluated the rate of binding site turnover across the genome as well as at specific functional sites. We also examined the presence of Sox motifs within binding intervals and the correlation between sequence conservation and binding conservation. To determine whether common binding between Dichaete and SoxNeuro is conserved, we performed a detailed analysis of the binding patterns of both factors in two species. Conclusion: We find that, while the regulatory networks driven by Dichaete and SoxNeuro are largely conserved across the drosophilids studied, binding site turnover is widespread and correlated with phylogenetic distance. Nonetheless, binding is preferentially conserved at known cis-regulatory modules and core, independently verified binding sites. We observed the strongest binding conservation at sites that are commonly bound by Dichaete and SoxNeuro, suggesting that these sites are functionally important. Our analysis provides insights into the evolution of group B Sox function, highlighting the specific conservation of shared binding sites and suggesting alternative sources of neofunctionalisation between paralogous family members.

SpeedSeq: Ultra-fast personal genome analysis and interpretation

Colby Chiang, Ryan M Layer, Gregory G Faust, Michael R Lindberg, David B Rose, Erik P Garrison, Gabor T Marth, Aaron R Quinlan, Ira M Hall
doi: http://dx.doi.org/10.1101/012179

Comprehensive interpretation of human genome sequencing data is a challenging bioinformatic problem that typically requires weeks of analysis, with extensive hands-on expert involvement. This informatics bottleneck inflates genome sequencing costs, poses a computational burden for large-scale projects, and impedes the adoption of time-critical clinical applications such as personalized cancer profiling and newborn disease diagnosis, where the actionable timeframe can measure in hours or days. We developed SpeedSeq, an open-source genome analysis platform that vastly reduces computing time. SpeedSeq accomplishes read alignment, duplicate removal, variant detection and functional annotation of a 50X human genome in <24 hours, even using one low-cost server. SpeedSeq offers competitive or superior performance to current methods for detecting germline and somatic single nucleotide variants (SNVs), indels, and structural variants (SVs) and includes novel functionality for SV genotyping, SV annotation, fusion gene detection, and rapid identification of actionable mutations. SpeedSeq will help bring timely genome analysis into the clinical realm.