The design and analysis of binary variable traits in common garden genetic experiments of highly fecund species to assess heritability

Sarah W Davies , Samuel Scarpino , Thanapat Pongwarin , James Scott , Mikhail V Matz
doi: http://dx.doi.org/10.1101/018044

Many biologically important traits are binomially distributed, with their key phenotypes being presence or absence. Despite their prevalence, estimating the heritability of binomial traits presents both experimental and statistical challenges. Here we develop both an empirical and computational methodology for estimating the narrow-sense heritability of binary traits for highly fecund species. Our experimental approach controls for undesirable culturing effects, while minimizing culture numbers, increasing feasibility in the field. Our statistical approach accounts for known issues with model-selection by using a permutation test to calculate significance values and includes both fitting and power calculation methods. We illustrate our methodology by estimating the narrow-sense heritability for larval settlement, a key life-history trait, in the reef-building coral Orbicella faveolata. The experimental, statistical and computational methods, along with all of the data from this study, were deployed in the R package multiDimBio.

A pooling-based approach to mapping genetic variants associated with DNA methylation

Irene Miriam Kaplow , Julia L MacIsaac , Sarah M Mah , Lisa M McEwen , Michael S Kobor , Hunter B Fraser
doi: http://dx.doi.org/10.1101/013649

DNA methylation is an epigenetic modification that plays a key role in gene regulation. Previous studies have investigated its genetic basis by mapping genetic variants that are associated with DNA methylation at specific sites, but these have been limited to microarrays that cover less than 2% of the genome and cannot account for allele-specific methylation (ASM). Other studies have performed whole-genome bisulfite sequencing on a few individuals, but these lack statistical power to identify variants associated with DNA methylation. We present a novel approach in which bisulfite-treated DNA from many individuals is sequenced together in a single pool, resulting in a truly genome-wide map of DNA methylation. Compared to methods that do not account for ASM, our approach increases statistical power to detect associations while sharply reducing cost, effort, and experimental variability. As a proof of concept, we generated deep sequencing data from a pool of 60 human cell lines; we evaluated almost twice as many CpGs as the largest microarray studies and identified over 2,000 genetic variants associated with DNA methylation. We found that these variants are highly enriched for associations with chromatin accessibility and CTCF binding but are less likely to be associated with traits indirectly linked to DNA, such as gene expression and disease phenotypes. In summary, our approach allows genome-wide mapping of genetic variants associated with DNA methylation in any tissue of any species, without the need for individual-level genotype or methylation data.

A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data

Qiongshi Lu , Yiming Hu , Jiehuan Sun , Yuwei Cheng , Kei-Hoi Cheung , Hongyu Zhao
doi: http://dx.doi.org/10.1101/018093

Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu

Natural selection defines the cellular complexity

Han Chen , Xionglei He
doi: http://dx.doi.org/10.1101/018069

Current biology is perplexed by the lack of a theoretical framework for understanding the organization principles of the molecular system within a cell. Here we first studied growth rate, one of the seemingly most complex cellular traits, using functional data of yeast single-gene deletion mutants. We observed nearly one thousand expression informative genes (EIGs) whose expression levels are linearly correlated to the trait within an unprecedentedly large functional space. A simple model considering six EIG-formed protein modules revealed a variety of novel mechanistic insights, and also explained ~50% of the variance of cell growth rates measured by Bar-seq technique for over 400 yeast mutants (Pearson’s R = 0.69), a performance comparable to the microarray-based (R = 0.77) or colony-size-based (R = 0.66) experimental approach. We then applied the same strategy to 501 morphological traits of the yeast and achieved successes in most fitness-coupled traits each with hundreds of trait-specific EIGs. Surprisingly, there is no any EIG found for most fitness-uncoupled traits, indicating that they are controlled by super-complex epistases that allow no simple expression-trait correlation. Thus, EIGs are recruited exclusively by natural selection, which builds a rather simple functional architecture for fitness-coupled traits, and the endless complexity of a cell lies primarily in its fitness-uncoupled features.