Epistatic effects on carcass composition and meat quality in pigs

The analysis of epistasis is not yet a routine, but it has been shown by few studies in livestock animals that interaction effects contribute with considerable proportions to the phenotypic variance. Therefore the objective of this study was to evaluate the importance of epistatic effects in the Bonn Duroc × Pietrain resource population (DuPi) for carcass composition and meat quality traits. This population was investigated so far for single quantitative trait loci (QTL) considering additive, dominance and imprinting effects. In the first approach, 585 F2 pigs of DuPi were used to perform a two dimensional QTL scan. All animals were genotyped using 125 genetic markers (microsatellites and SNP) spread across the 18 pig autosomes. Phenotypic information for 26 carcass composition and meat quality traits was available for all F2 animals. Linkage analysis was performed in a two-step procedure using a maximum likelihood approach implemented in the QxPak program. A number of 56 interacting QTL was observed for 19 different traits. These interacting QTL pairs explained up to 8% of the phenotypic variance. Based on these results a variety of networks among chromosomal regions throughout the porcine genome were identified. Moreover, considering interactions between loci allowed to detect several novel QTL and trait-specific relationships of loci within and across chromosomes. In a second step the causes of an epistatic QTL pair between Sus scrofa chomsosome (SSC) 8 and 15 influencing pH value 1 h post mortem in M. long. dorsi were investigated. Gene expression data was obtained from loin tissue of 74 F2 which were selected from 585 animals. Gene expression profiles, genotypes and phenotypes of these pigs were investigated jointly applying three alternative models. Method A considered the phenotypic differences in pH values between groups of pigs with extreme values. Method B was based on differences between the genotype combinations of relevant epistatic QTL pairs between SSC8 and SSC15. Finally, method C was a linear model comprising the epistatic QTL genotypes as fixed effects. Overall method A, B and C revealed 1182, 480 and 1823 differentially expressed or associated genes, respectively. By means of a functional analysis it was possible to set up networks which contained mainly interactions between genes located within the specific regions on SSC8 and SSC15 and allowed a meaningful biological discussion. Expression QTL (eQTL) analyses were performed for functional and positional transcripts in order to assume regulations patterns. This approach showed that combining phenotype, genotype and transcriptome data helped to uncover the involved molecules of observed epistasis. In conclusion, this study revealed the importance of epistasis for the expression of complex traits. Furthermore, it was possible to uncover potential biological causes of observed epistatic QTL pairs applying different statistical models as well as bioinformatic tools.Epistatische Effekte auf die Schlachtkörperzusammensetzung und Fleischqualität beim Schwein Epistasie wird bisher nur selten in Untersuchungen komplexer Merkmale berücksichtigt. Dabei wurde bereits in einer Vielzahl von Studien gezeigt, dass die zu beobachtenden Variationen von quantitativen Merkmalen nicht alleine durch additive Effekte erklärt werden können. Daher war das Ziel dieser Studie, die Bedeutung von epistatischen Effekten auf Schlachtkörper- und Fleischqualitätsmerkmale innerhalb der Bonner Duroc × Piétain Ressourcenpopulation (DuPi) zu untersuchen. Bisherige Studien in der DuPi Population berücksichtigten nur einfache Quantitative Trait Loci (QTL), die additive, Dominanz oder Imprintingeffekte beinhalteten. In der ersten Analyse wurden 585 Schweine der F2-Generation verwendet um epistatische QTL Paare zu identifizieren. Diese Tiere sind mit 125 genetischen Markern genotypisiert worden, die sich gleichmäßig über alle 18 Autosomen verteilten. Als phänotypische Informationen wurden 26 verschiedene Schlachtkörper- und Fleischqualitätsmerkmale erfasst. Die Koppelungsanalyse wurde in einer zweistufigen Prozedur innerhalb des Programms Qxpak, basierend auf einem Maximum Likelihood Ansatzes, durchgeführt. Insgesamt konnten 56 interagierende QTL für 19 verschiedene Merkmale beobachtet werden. Für Schlachtkörpermerkmale konnten 17 und für Fleischqualitätsmerkmale 39 epistatische QTL Paare identifiziert werden. Diese interagierenden QTL Paare erklärten bis zu 8% der phänotypischen Varianz. Auf Grundlage dieser Ergebnisse konnten verschiedene Netzwerkstrukturen zwischen den verschiedenen Chromosomensegmenten identifiziert werden. Die Berücksichtigung der Beziehung zwischen zwei Genorten ermöglichte es einige neue QTL zu identifizieren, sowie merkmalsbezogene Beziehungen innerhalb eines Chromosoms und zwischen Chromosomen zu charakterisieren. In einer zweiten Untersuchung wurde versucht, die biologischen Gründe des epistatischen QTL Paares zwischen den porcinen Chromosomen (SSC) 8 und 15 aufzuklären. Für die Analyse standen die Muskeltranskriptionsprofile von 74 ausgewählten F2 Tieren der DuPi Population zur Verfügung. Die Interaktion zwischen SSC8 und 15 war assoziiert mit früh post mortalem pH Wert im M. long. dorsi. Genexpressionsprofile, Genotypen und Phänotypen dieser Tiere wurden mit drei verschiedenen statistischen Ansätzen und Modellen untersucht. Methode A berücksichtigte phänotypische Unterschiede des pH Wertes zwischen zwei Tiergruppen mit extremen Werten, Methode B basierte auf den Unterschieden zwischen den Genotypgruppen des relevanten epistatischen QTL Paares und Methode C berücksichtigte die Genotypen des epistatischen QTL Paares als fixen Effekt innerhalb eines linearen Modells. Insgesamt ließen sich mit Methode A, B und C 1182 und 480 unterschiedlich exprimierte Gene sowie 1823 linear assoziierte Gene identifizieren. Durch funktionale Analysen war es möglich Netzwerke zu erstellen, die nur Gene beinhalteten, die innerhalb der epistatischen Regionen lagen. Die daraus erzielten Ergebnisse erlaubten eine biologisch sinnvolle Diskussion möglicher Kandidatengene der epistatischen Regionen. Des Weiteren wurden Expressions-QTL Analysen durchgeführt um eine Aussage über die Genregulation zu treffen. Schlussfolgernd konnte gezeigt werden, dass Epistasie eine bedeutende Rolle bei der Ausprägung von komplexen Merkmalen beim Schwein hat. Es war des Weiteren möglich biologische Ursachen beobachteter epistatischer Beziehungen mit Hilfe verschiedener statistischer Methoden zu identifizieren

Comparison of analyses of the QTLMAS XIII common dataset. II: QTL analysis

Background - Five participants of the QTL-MAS 2009 workshop applied QTL analyses to the workshop common data set which contained a time-related trait: cumulative yield. Underlying the trait were 18 QTLs for three parameters of a logistic growth curve that was used for simulating the trait. Methods - Different statistical models and methods were employed to detect QTLs and estimate position and effect sizes of QTLs. Here we compare the results with respect to the numbers of QTLs detected, estimated positions and percentage explained variance. Furthermore, limiting factors in the QTL detection are evaluated. Results - All QTLs for the asymptote and the scaling factor of the logistic curve were detected by at least one of the participants. Only one out of six of the QTLs for the inflection point was detected. None of the QTLs were detected by all participants. Dominant, epistatic and imprinted QTLs were reported while only additive QTLs were simulated. The power to map QTLs for the inflection point increased when more time points were added. Conclusions - For the detection of QTLs related to the asymptote and the scaling factor, there were no strong differences between the methods used here. Also, it did not matter much whether the time course data were analyzed per single time point or whether parameters of a growth curve were first estimated and then analyzed. In contrast, the power for detection of QTLs for the inflection point was very low and the frequency of time points appeared to be a limiting factor. This can be explained by a low accuracy in estimating the inflection point from a limited time range and a limited number of time points, and by the low correlation between the simulated values for this parameter and the phenotypic data available for the individual time point

KELVIN: A Software Package for Rigorous Measurement of Statistical Evidence in Human Genetics

This paper describes the software package KELVIN, which supports the PPL (posterior probability of linkage) framework for the measurement of statistical evidence in human (or more generally, diploid) genetic studies. In terms of scope, KELVIN supports two-point (trait-marker or marker-marker) and multipoint linkage analysis, based on either sex-averaged or sex-specific genetic maps, with an option to allow for imprinting; trait-marker linkage disequilibrium (LD), or association analysis, in case-control data, trio data, and/or multiplex family data, with options for joint linkage and trait-marker LD or conditional LD given linkage; dichotomous trait, quantitative trait and quantitative trait threshold models; and certain types of gene-gene interactions and covariate effects. Features and data (pedigree) structures can be freely mixed and matched within analyses. The statistical framework is specifically tailored to accumulate evidence in a mathematically rigorous way across multiple data sets or data subsets while allowing for multiple sources of heterogeneity, and KELVIN itself utilizes sophisticated software engineering to provide a powerful and robust platform for studying the genetics of complex disorders

Genetics of Type 2 Diabetes - Pitfalls and Possibilities

Type 2 diabetes (T2D) is a complex disease that is caused by a complex interplay between genetic, epigenetic and environmental factors. While the major environmental factors, diet and activity level, are well known, identification of the genetic factors has been a challenge. However, recent years have seen an explosion of genetic variants in risk and protection of T2D due to the technical development that has allowed genome-wide association studies and next-generation sequencing. Today, more than 120 variants have been convincingly replicated for association with T2D and many more with diabetes-related traits. Still, these variants only explain a small proportion of the total heritability of T2D. In this review, we address the possibilities to elucidate the genetic landscape of T2D as well as discuss pitfalls with current strategies to identify the elusive unknown heritability including the possibility that our definition of diabetes and its subgroups is imprecise and thereby makes the identification of genetic causes difficult.Peer reviewe

AZI23'UTR Is a New SLC6A3 Downregulator Associated with an Epistatic Protection Against Substance Use Disorders

Regulated activity of SLC6A3, which encodes the human dopamine transporter (DAT), contributes to diseases such as substance abuse disorders (SUDs); however, the exact transcription mechanism remains poorly understood. Here, we used a common genetic variant of the gene, intron 1 DNP1B sequence, as bait to screen and clone a new transcriptional activity, AZI23'UTR, for SLC6A3. AZI23'UTR is a 3' untranslated region (3'UTR) of the human 5-Azacytidine Induced 2 gene (AZI2) but appeared to be transcribed independently of AZI2. Found to be present in both human cell nuclei and dopamine neurons, this RNA was shown to downregulate promoter activity through a variant-dependent mechanism in vitro. Both reduced RNA density ratio of AZI23'UTR/AZI2 and increased DAT mRNA levels were found in ethanol-naive alcohol-preferring rats. Secondary analysis of dbGaP GWAS datasets (Genome-Wide Association Studies based on the database of Genotypes and Phenotypes) revealed significant interactions between regions upstream of AZI23'UTR and SLC6A3 in SUDs. Jointly, our data suggest that AZI23'UTR confers variant-dependent transcriptional regulation of SLC6A3, a potential risk factor for SUDs

Misinformation, Misrepresentation, and Misuse of Human Behavioral Genetics Research

Kaplan discusses the limitations of human behavioral genetics studies, highlighting the research limitations inherent in studying humans and the narrow policy and legal applicability of results arising from behavioral genetics studies