Ronald M. Nelson

A century after Fisher: time for a new paradigm in quantitative genetics

Quantitative genetics traces its roots back through more than a century of theory, largely formed in the absence of directly observable genotype data, and has remained essentially unchanged for decades. By contrast, molecular genetics arose from direct observations and is currently undergoing rapid changes, making the amount of available data ever greater. Thus, the two disciplines are disparate both in their origins and their current states, yet they address the same fundamental question: how does the genotype affect the phenotype? The rapidly accumulating genomic data necessitate sophisticated analysis, but many of the current tools are adaptations of methods designed during the early days of quantitative genetics. We argue here that the present analysis paradigm in quantitative genetics is at its limits in regards to unraveling complex traits and it is necessary to re-evaluate the direction that genetic research is taking for the field to realize its full potential.

Genetic influences on brain gene expression in rats selected for tameness and aggression

Interindividual differences in many behaviors are partly due to genetic differences, but the identification of the genes and variants that influence behavior remains challenging. Here, we studied an F2 intercross of two outbred lines of rats selected for tame and aggressive behavior toward humans for >64 generations. By using a mapping approach that is able to identify genetic loci segregating within the lines, we identified four times more loci influencing tameness and aggression than by an approach that assumes fixation of causative alleles, suggesting that many causative loci were not driven to fixation by the selection. We used RNA sequencing in 150 F2 animals to identify hundreds of loci that influence brain gene expression. Several of these loci colocalize with tameness loci and may reflect the same genetic variants. Through analyses of correlations between allele effects on behavior and gene expression, differential expression between the tame and aggressive rat selection lines, and correlations between gene expression and tameness in F2 animals, we identify the genes Gltscr2, Lgi4, Zfp40, and Slc17a7 as candidate contributors to the strikingly different behavior of the tame and aggressive animals.

Variance Heterogeneity in Saccharomyces cerevisiae Expression Data: Trans-Regulation and Epistasis

Here, we describe the results from the first variance heterogeneity Genome Wide Association Study (VGWAS) on yeast expression data. Using this forward genetics approach, we show that the genetic regulation of gene-expression in the budding yeast, Saccharomyces cerevisiae, includes mechanisms that can lead to variance heterogeneity in the expression between genotypes. Additionally, we performed a mean effect association study (GWAS). Comparing the mean and variance heterogeneity analyses, we find that the mean expression level is under genetic regulation from a larger absolute number of loci but that a higher proportion of the variance controlling loci were trans-regulated. Both mean and variance regulating loci cluster in regulatory hotspots that affect a large number of phenotypes; a single variance-controlling locus, mapping close to DIA2, was found to be involved in more than 10% of the significant associations. It has been suggested in the literature that variance-heterogeneity between the genotypes might be due to genetic interactions. We therefore screened the multi-locus genotype-phenotype maps for several traits where multiple associations were found, for indications of epistasis. Several examples of two and three locus genetic interactions were found to involve variance-controlling loci, with reports from the literature corroborating the functional connections between the loci. By using a new analytical approach to re-analyze a powerful existing dataset, we are thus able to both provide novel insights to the genetic mechanisms involved in the regulation of gene-expression in budding yeast and experimentally validate epistasis as an important mechanism underlying genetic variance-heterogeneity between genotypes.

Genetics of Interactive Behavior in Silver Foxes (Vulpes vulpes)

Individuals involved in a social interaction exhibit different behavioral traits that, in combination, form the individual’s behavioral responses. Selectively bred strains of silver foxes (Vulpes vulpes) demonstrate markedly different behaviors in their response to humans. To identify the genetic basis of these behavioral differences we constructed a large F2 population including 537 individuals by cross-breeding tame and aggressive fox strains. 98 fox behavioral traits were recorded during social interaction with a human experimenter in a standard four-step test. Patterns of fox behaviors during the test were evaluated using principal component (PC) analysis. Genetic mapping identified eight unique significant and suggestive QTL. Mapping results for the PC phenotypes from different test steps showed little overlap suggesting that different QTL are involved in regulation of behaviors exhibited in different behavioral contexts. Many individual behavioral traits mapped to the same genomic regions as PC phenotypes. This provides additional information about specific behaviors regulated by these loci. Further, three pairs of epistatic loci were also identified for PC phenotypes suggesting more complex genetic architecture of the behavioral differences between the two strains than what has previously been observed.

Selection on variance-controlling genes: adaptability or stability.

Simulations on a model system where a variance‐controlling master locus scales the effects of a set of effector loci show that selection affects the variance‐controlling locus more strongly than the effector loci, and that the direction of selection is dependent on the frequency of environmental changes.

qtl.outbred: Interfacing outbred line cross data with the R/qtl mapping software.

Backgroundqtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools. It is built as an umbrella package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/qtl.FindingsUsing qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL.Conclusionqtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets. This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.

MAPfastR : quantitative trait loci mapping in outbred line crosses

MAPfastR is a software package developed to analyze quantitative trait loci data from inbred and outbred line-crosses. The package includes a number of modules for fast and accurate quantitative trait loci analyses. It has been developed in the R language for fast and comprehensive analyses of large datasets. MAPfastR is freely available at: http://www.computationalgenetics.se/?page_id=7

Mo1802 Intestinal Dysbiosis in Collagenous Colitis

Background The etiology and pathogenesis of collagenous colitis (CC) are incompletely known. Similar to other inflammatory bowel diseases (IBD), an aberrant immune response to various unidentified luminal factors, particularly the intestinal microbiota, seems to play an important role. Aim We aimed to compare the microbiotic profile between patients with CC, ulcerative colitis (UC), Crohns disease (CD) and healthy controls (HC). Methods Fecal samples were collected from patients at the out-patient clinic; UC (n=32), CD (n=32) and CC (n=29). Healthy controls (n=29) were matched by sex and age (+/-5 years) to the CC group. DNA was extracted through the Arrow Stool DNA cartridge using the Arrow Instrument (NorDiag). The hypervariable V3-V4 regions of the 16S rRNA genes were amplified by PCR and analyzed using high-throughput 454 pyrosequencing (Roche). The sequence data was filtered from bad quality sequences, denoised using AmpliconNoise and taxonomically classified using the SILVA database. Alpha diversity was assessed by Shannons diversity index and beta diversity by a Bonferroni corrected ANOSIM analysis, using Bray Curtis metrics as distance index. Differences in operative taxonomic units (OTUs) between groups of patients were analyzed by Wilcoxons test, False discovery rate was applied to adjust for multiple comparisons. Results The dataset contained 349 963 sequences with an average of 2 869 sequences/sample. Three samples (2 CC and 1 UC) were excluded due to low number of sequences. There was no difference in Shannons diversity index between patients with CC and HC (p=0.54). Analysis of the beta diversity using ANOSIM showed that the CC group segregated from the HC with increasing taxonomic resolution, reaching significance at the highest taxonomic resolution when operative taxonomic unit data (OTU) were compared (p=0.03). Wilcoxons test was further used to identify which OTUs that contributed to this difference. We found that several OTUs belonging to the Ruminococcaceae family were underrepresented in the CC group compared with the controls. Similarly, the beta diversity of patients with CD differed significantly from the HC and segregated already at a phylum level (p=0.007). In addition, the CD cohort was characterized by a lower Shannons diversity index (p<0.001). The beta diversity of patients with UC did not significantly differ from the HC (p=0.08 p=1.0), but the UC cohort was characterized by a lower alpha diversity (p=0.02). Conclusion The fecal microbiotic profile of CC differs from HC and is characterized by a lower abundance of OTUs belonging to the Ruminococcaceae family. Intriguingly, underrepresentation of the Ruminococcaceae family has previously been associated with CD indicating that the microbiotic profile of CC share features with the profile of CD. 1.Morgan XC, Et. al. Genome Biol. 2012; 13(9): R79

Higher Order Interactions: Detection of Epistasis Using Machine Learning and Evolutionary Computation

Higher order interactions are known to affect many different phenotypic traits. The advent of large-scale genotyping has, however, shown that finding interactions is not a trivial task. Classical genome-wide association studies (GWAS) are a useful starting point for unraveling the genetic architecture of a phenotypic trait. However, to move beyond the additive model we need new analysis tools specifically developed to deal with high-dimensional genotypic data. Here we show that evolutionary algorithms are a useful tool in high-dimensional analyses designed to identify gene-gene interactions in current large-scale genotypic data.

Characterization of genetic diversity and gene mapping in two Swedish local chicken breeds

The aim of this paper is to study genetic diversity in the two Swedish local chicken breeds Bohuslän-Dals svarthöna and Hedemorahöna. The now living birds of both of these breeds (about 500 for Bohuslän-Dals svarthöna and 2600 for Hedemorahöna) originate from small relicts of earlier larger populations. An additional aim was to make an attempt to map loci associated with a trait that are segregating in both these breeds. The 60k SNP chip was used to genotype 12 Bohuslän-Dals svarthöna and 22 Hedemorahöna. The mean inbreeding coefficient was considerably larger in the samples from Hedemorahöna than in the samples from Bohuslän-Dals svarthöna. Also the proportion of homozygous SNPs in individuals was larger in Hedemorahöna. In contrast, on the breed level, the number of segregating SNPs were much larger in Hedemorahöna than in Bohuslän-Dals svarthöna. A multidimensional scaling plot shows that the two breeds form clusters well-separated from each other. Both these breeds segregate for the dermal hyperpigmentation phenotype. In Bohuslän-Dals svarthöna most animals have dark skin, but some individuals with lighter skin exists (most easily detected by their red comb). An earlier study of the Fm locus showed that this breed has the same complex rearrangement involving the EDN3 gene as Silkie chicken and two other studied Asian breeds. In the breed Hedemorahöna, most individuals have normal skin pigmentation (and red comb), but there are some birds with darker skin and dark comb. In this study the involvement of the EDN3 gene is confirmed also in Hedemorahöna. In addition we identify a region on chromosome 21 that is significantly associated with the trait.