Simon K. G. Forsberg

Natural CMT2 variation is associated with genome-wide methylation changes and temperature seasonality.

As Arabidopsis thaliana has colonized a wide range of habitats across the world it is an attractive model for studying the genetic mechanisms underlying environmental adaptation. Here, we used public data from two collections of A. thaliana accessions to associate genetic variability at individual loci with differences in climates at the sampling sites. We use a novel method to screen the genome for plastic alleles that tolerate a broader climate range than the major allele. This approach reduces confounding with population structure and increases power compared to standard genome-wide association methods. Sixteen novel loci were found, including an association between Chromomethylase 2 (CMT2) and temperature seasonality where the genome-wide CHH methylation was different for the group of accessions carrying the plastic allele. Cmt2 mutants were shown to be more tolerant to heat-stress, suggesting genetic regulation of epigenetic modifications as a likely mechanism underlying natural adaptation to variable temperatures, potentially through differential allelic plasticity to temperature-stress.

Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast

Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, the notion that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that contribute to quantitative trait variation in a large yeast intercross population. The additive variance explained by individual loci in a network is highly dependent on the allele frequencies of the interacting loci. Modeling of phenotypes for multilocus genotype classes in the epistatic networks is often improved by accounting for the interactions. We discuss the implications of these results for attempts to dissect genetic architectures and to predict individual phenotypes and long-term responses to selection.

Breed Differences in Natriuretic Peptides in Healthy Dogs

Background Measurement of plasma concentration of natriuretic peptides (NPs) is suggested to be of value in diagnosis of cardiac disease in dogs, but many factors other than cardiac status may influence their concentrations. Dog breed potentially is 1 such factor. Objective To investigate breed variation in plasma concentrations of pro‐atrial natriuretic peptide 31‐67 (proANP 31‐67) and N‐terminal B‐type natriuretic peptide (NT‐proBNP) in healthy dogs. Animals 535 healthy, privately owned dogs of 9 breeds were examined at 5 centers as part of the European Union (EU) LUPA project. Methods Absence of cardiovascular disease or other clinically relevant organ‐related or systemic disease was ensured by thorough clinical investigation. Plasma concentrations of proANP 31‐67 and NT‐proBNP were measured by commercially available ELISA assays. Results Overall significant breed differences were found in proANP 31‐67 (P < .0001) and NT‐proBNP (P < .0001) concentrations. Pair‐wise comparisons between breeds differed in approximately 50% of comparisons for proANP 31‐67 as well as NT‐proBNP concentrations, both when including all centers and within each center. Interquartile range was large for many breeds, especially for NT‐proBNP. Among included breeds, Labrador Retrievers and Newfoundlands had highest median NT‐proBNP concentrations with concentrations 3 times as high as those of Dachshunds. German Shepherds and Cavalier King Charles Spaniels had the highest median proANP 31‐67 concentrations, twice the median concentration in Doberman Pinschers. Conclusions and Clinical Importance Considerable interbreed variation in plasma NP concentrations was found in healthy dogs. Intrabreed variation was large in several breeds, especially for NT‐proBNP. Additional studies are needed to establish breed‐specific reference ranges.

Ensemble learning prediction of protein–protein interactions using proteins functional annotations

Protein-protein interactions are important for the majority of biological processes. A significant number of computational methods have been developed to predict protein-protein interactions using protein sequence, structural and genomic data. Vast experimental data is publicly available on the Internet, but it is scattered across numerous databases. This fact motivated us to create and evaluate new high-throughput datasets of interacting proteins. We extracted interaction data from DIP, MINT, BioGRID and IntAct databases. Then we constructed descriptive features for machine learning purposes based on data from Gene Ontology and DOMINE. Thereafter, four well-established machine learning methods: Support Vector Machine, Random Forest, Decision Tree and Naïve Bayes, were used on these datasets to build an Ensemble Learning method based on majority voting. In cross-validation experiment, sensitivity exceeded 80% and classification/prediction accuracy reached 90% for the Ensemble Learning method. We extended the experiment to a bigger and more realistic dataset maintaining sensitivity over 70%. These results confirmed that our datasets are suitable for performing PPI prediction and Ensemble Learning method is well suited for this task. Both the processed PPI datasets and the software are available at .

The Multi-allelic Genetic Architecture of a Variance-Heterogeneity Locus for Molybdenum Concentration in Leaves Acts as a Source of Unexplained Additive Genetic Variance

Genome-wide association (GWA) analyses have generally been used to detect individual loci contributing to the phenotypic diversity in a population by the effects of these loci on the trait mean. More rarely, loci have also been detected based on variance differences between genotypes. Several hypotheses have been proposed to explain the possible genetic mechanisms leading to such variance signals. However, little is known about what causes these signals, or whether this genetic variance-heterogeneity reflects mechanisms of importance in natural populations. Previously, we identified a variance-heterogeneity GWA (vGWA) signal for leaf molybdenum concentrations in Arabidopsis thaliana. Here, fine-mapping of this association reveals that the vGWA emerges from the effects of three independent genetic polymorphisms that all are in strong LD with the markers displaying the genetic variance-heterogeneity. By revealing the genetic architecture underlying this vGWA signal, we uncovered the molecular source of a significant amount of hidden additive genetic variation or “missing heritability”. Two of the three polymorphisms underlying the genetic variance-heterogeneity are promoter variants for Molybdate transporter 1 (MOT1), and the third a variant located ~25 kb downstream of this gene. A fourth independent association was also detected ~600 kb upstream of MOT1. Use of a T-DNA knockout allele highlights Copper Transporter 6; COPT6 (AT2G26975) as a strong candidate gene for this association. Our results show that an extended LD across a complex locus including multiple functional alleles can lead to a variance-heterogeneity between genotypes in natural populations. Further, they provide novel insights into the genetic regulation of ion homeostasis in A. thaliana, and empirically confirm that variance-heterogeneity based GWA methods are a valuable tool to detect novel associations of biological importance in natural populations.

The Shepherds' Tale: A Genome-Wide Study across 9 Dog Breeds Implicates Two Loci in the Regulation of Fructosamine Serum Concentration in Belgian Shepherds.

Diabetes mellitus is a serious health problem in both dogs and humans. Certain dog breeds show high prevalence of the disease, whereas other breeds are at low risk. Fructosamine and glycated haemoglobin (HbA1c) are two major biomarkers of glycaemia, where serum concentrations reflect glucose turnover over the past few weeks to months. In this study, we searched for genetic factors influencing variation in serum fructosamine concentration in healthy dogs using data from nine dog breeds. Considering all breeds together, we did not find any genome-wide significant associations to fructosamine serum concentration. However, by performing breed-specific analyses we revealed an association on chromosome 3 (pcorrected ≈ 1:68 × 10-6) in Belgian shepherd dogs of the Malinois subtype. The associated region and its close neighbourhood harbours interesting candidate genes such as LETM1 and GAPDH that are important in glucose metabolism and have previously been implicated in the aetiology of diabetes mellitus. To further explore the genetics of this breed specificity, we screened the genome for reduced heterozygosity stretches private to the Belgian shepherd breed. This revealed a region with reduced heterozygosity that shows a statistically significant interaction (p = 0.025) with the association region on chromosome 3. This region also harbours some interesting candidate genes and regulatory regions but the exact mechanisms underlying the interaction are still unknown. Nevertheless, this finding provides a plausible explanation for breed-specific genetic effects for complex traits in dogs. Shepherd breeds are at low risk of developing diabetes mellitus. The findings in Belgian shepherds could be connected to a protective mechanism against the disease. Further insight into the regulation of glucose metabolism could improve diagnostic and therapeutic methods for diabetes mellitus.

Genetic Regulation of Transcriptional Variation in Natural Arabidopsis thaliana Accessions

An increased knowledge of the genetic regulation of expression in Arabidopsis thaliana is likely to provide important insights about the basis of the plant’s extensive phenotypic variation. Here, we reanalyzed two publicly available datasets with genome-wide data on genetic and transcript variation in large collections of natural A. thaliana accessions. Transcripts from more than half of all genes were detected in the leaves of all accessions, and from nearly all annotated genes in at least one accession. Thousands of genes had high transcript levels in some accessions, but no transcripts at all in others, and this pattern was correlated with the genome-wide genotype. In total, 2669 eQTL were mapped in the largest population, and 717 of them were replicated in the other population. A total of 646 cis-eQTL-regulated genes that lacked detectable transcripts in some accessions was found, and for 159 of these we identified one, or several, common structural variants in the populations that were shown to be likely contributors to the lack of detectable RNA transcripts for these genes. This study thus provides new insights into the overall genetic regulation of global gene expression diversity in the leaf of natural A. thaliana accessions. Further, it also shows that strong cis-acting polymorphisms, many of which are likely to be structural variations, make important contributions to the transcriptional variation in the worldwide A. thaliana population.

On the relationship between epistasis and genetic variance heterogeneity

Epistasis and genetic variance heterogeneity are two non-additive genetic inheritance patterns that are often, but not always, related. Here we use theoretical examples and empirical results from earlier analyses of experimental data to illustrate the connection between the two. This includes an introduction to the relationship between epistatic gene action, statistical epistasis, and genetic variance heterogeneity, and a brief discussion about how genetic processes other than epistasis can also give rise to genetic variance heterogeneity.

Effect of Breed on Plasma Endothelin-1 Concentration, Plasma Renin Activity, and Serum Cortisol Concentration in Healthy Dogs

Background There are breed differences in several blood variables in healthy dogs. Objective Investigate breed variation in plasma endothelin‐1 (ET‐1) concentration, plasma renin activity, and serum cortisol concentration. Animals Five‐hundred and thirty‐one healthy dogs of 9 breeds examined at 5 centers (2–4 breeds/center). Methods Prospective observational study. Circulating concentrations of ET‐1 and cortisol, and renin activity, were measured using commercially available assays. Absence of organ‐related or systemic disease was ensured by thorough clinical investigations, including blood pressure measurement, echocardiography, ECG, blood and urine analysis. Results Median ET‐1 concentration was 1.29 (interquartile range [IQR], 0.97–1.82) pg/mL, median cortisol concentration 46.0 (IQR, 29.0–80.8) nmol/L, and median renin activity 0.73 (IQR, 0.48–1.10) ng/mL/h in all dogs. Overall, breed differences were found in ET‐1 and cortisol concentrations, and renin activity (P < .0001 for all). Pair‐wise comparisons between breeds differed in 67% of comparisons for ET‐1, 22% for cortisol, and 19% for renin activity, respectively. Within centers, breed differences were found at 5/5 centers for ET‐1, 4/5 centers for cortisol, and 2/5 centers for renin activity. Newfoundlands had highest median ET‐1 concentration, 3 times higher than Cavalier King Charles Spaniels, Doberman Pinschers, and Dachshunds. Median renin activity was highest in Dachshunds, twice the median value in Newfoundlands and Boxers. Median cortisol concentration was highest in Finnish Lapphunds, almost 3 times higher than in Boxers. Conclusions and Clinical Importance Breed variation might be important to take into consideration when interpreting test results in clinical studies.

cgmisc: enhanced genome-wide association analyses and visualization

Summary: High-throughput genotyping and sequencing technologies facilitate studies of complex genetic traits and provide new research opportunities. The increasing popularity of genome-wide association studies (GWAS) leads to the discovery of new associated loci and a better understanding of the genetic architecture underlying not only diseases, but also other monogenic and complex phenotypes. Several softwares are available for performing GWAS analyses, R environment being one of them. Results: We present cgmisc, an R package that enables enhanced data analysis and visualization of results from GWAS. The package contains several utilities and modules that complement and enhance the functionality of the existing software. It also provides several tools for advanced visualization of genomic data and utilizes the power of the R language to aid in preparation of publication-quality figures. Some of the package functions are specific for the domestic dog (Canis familiaris) data. Availability and implementation: The package is operating system-independent and is available from: https://github.com/cgmisc-team/cgmisc Contact: marcin.kierczak@imbim.uu.se Supplementary information: Supplementary data are available at Bioinformatics online.