A random model for mapping imprinted quantitative trait loci in a structured pedigree: an implication for mapping canine hip dysplasia

Modification of Experimental Design and Statistical Method for Mapping Imprinted QTLs Based on Immortalized F2 Population

Genomic imprinting is an epigenetic phenomenon, which plays important roles in the growth and development of animals and plants. Immortalized F₂ (imF₂) populations generated by random cross between recombinant inbred (RI) or doubled haploid (DH) lines have been proved to have significant advantages for mapping imprinted quantitative trait loci (iQTLs), and statistical methods for this purpose have been proposed. In this paper, we propose a special type of imF₂ population (R-imF₂) for iQTL mapping, which is developed by random reciprocal cross between RI/DH lines. We also propose two modified iQTL mapping methods: two-step point mapping (PM-2) and two-step composite point mapping (CPM-2). Simulation studies indicated that: (i) R-imF₂ cannot improve the results of iQTL mapping, but the experimental design can probably reduce the workload of population construction; (ii) PM-2 can increase the precision of estimating the position and effects of a single iQTL; and (iii) CPM-2 can precisely map not only iQTLs, but also non-imprinted QTLs. The modified experimental design and statistical methods will facilitate and promote the study of iQTL mapping.

Genetic Variability in Polish Lowland Sheepdogs Assessed by Pedigree and Genomic Data

Simple Summary

Dogs are an important part of society. The Polish Lowland Sheepdog (PON) is one of 353 of the world’s largest cynological organization listed dog breeds. Breeds with small population sizes, like the PONs, are often characterized by high inbreeding rates and thus an increased risk of congenital diseases. To examine the endangerment of the PONs, measures for genetic diversity and inbreeding were calculated for the German PON population. The study showed that the PONs had to be classified as a minimally endangered population according to threshold values specified by the European Association for Animal Production. However, the very recent trend showed a slight improvement.

Abstract

Genetic variability of Polish Lowland Sheepdog (PON) population was evaluated using both pedigree and genomic data. The analyzed pedigree encompassed 8628 PONs, including 153 individuals genotyped on the Illumina CanineHD BeadChip. Runs of homozygosity (ROH) were defined for homozygous stretches extending over 60 to 4300 kb. The inbreeding coefficients F_Ped based on pedigree data and F_ROH50 based on ROHs were at 0.18 and 0.31. The correlation between both was 0.41 but 0.52 when excluding animals with less than seven complete generations. The realized effective population size (Ne¯) was 22.2 with an increasing trend over years. Five PONs explained 79% of the genetic diversity of the reference population. The effective population size derived from linkage disequilibrium measured by r^² was 36. PANTHER analysis of genes in ROHs shared by ≥50% of the PONs revealed four highly over- or underrepresented biological processes. One among those is the 7.35 fold enriched “forelimb morphogenesis”. Candidate loci for hip dysplasia and patent ductus arteriosus were discovered in frequently shared ROHs. In conclusion, the inbreeding measures of the PONs were high and the genetic variability small compared to various dog breeds. Regarding Ne¯, PON population was minimally endangered according to the European Association for Animal Production.

Incorporating parent-of-origin effects in whole-genome prediction of complex traits

Background

Parent-of-origin effects are due to differential contributions of paternal and maternal lineages to offspring phenotypes. Such effects include, for example, maternal effects in several species. However, epigenetically induced parent-of-origin effects have recently attracted attention due to their potential impact on variation of complex traits. Given that prediction of genetic merit or phenotypic performance is of interest in the study of complex traits, it is relevant to consider parent-of-origin effects in such predictions. We built a whole-genome prediction model that incorporates parent-of-origin effects by considering parental allele substitution effects of single nucleotide polymorphisms and gametic relationships derived from a pedigree (the POE model). We used this model to predict body mass index in a mouse population, a trait that is presumably affected by parent-of-origin effects, and also compared the prediction performance to that of a standard additive model that ignores parent-of-origin effects (the ADD model). We also used simulated data to assess the predictive performance of the POE model under various circumstances, in which parent-of-origin effects were generated by mimicking an imprinting mechanism.

Results

The POE model did not predict better than the ADD model in the real data analysis, probably due to overfitting, since the POE model had far more parameters than the ADD model. However, when applied to simulated data, the POE model outperformed the ADD model when the contribution of parent-of-origin effects to phenotypic variation increased. The superiority of the POE model over the ADD model was up to 8 % on predictive correlation and 5 % on predictive mean squared error.

Conclusions

The simulation and the negative result obtained in the real data analysis indicated that, in order to gain benefit from the POE model in terms of prediction, a sizable contribution of parent-of-origin effects to variation is needed and such variation must be captured by the genetic markers fitted. Recent studies, however, suggest that most parent-of-origin effects stem from epigenetic regulation but not from a change in DNA sequence. Therefore, integrating epigenetic information with genetic markers may help to account for parent-of-origin effects in whole-genome prediction.

Mapping complex traits as a dynamic system

Despite increasing emphasis on the genetic study of quantitative traits, we are still far from being able to chart a clear picture of their genetic architecture, given an inherent complexity involved in trait formation. A competing theory for studying such complex traits has emerged by viewing their phenotypic formation as a "system" in which a high-dimensional group of interconnected components act and interact across different levels of biological organization from molecules through cells to whole organisms. This system is initiated by a machinery of DNA sequences that regulate a cascade of biochemical pathways to synthesize endophenotypes and further assemble these endophenotypes toward the end-point phenotype in virtue of various developmental changes. This review focuses on a conceptual framework for genetic mapping of complex traits by which to delineate the underlying components, interactions and mechanisms that govern the system according to biological principles and understand how these components function synergistically under the control of quantitative trait loci (QTLs) to comprise a unified whole. This framework is built by a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function, and provides a quantitative and testable platform for assessing the multiscale interplay between QTLs and development. The method will enable geneticists to shed light on the genetic complexity of any biological system and predict, alter or engineer its physiological and pathological states.

A unified framework integrating parent-of-origin effects for association study

Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting is related to several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we generalize the natural and orthogonal interactions (NOIA) framework to allow for estimation of both main allelic effects and POEs. We develop a statistical (Stat-POE) model that has the orthogonal estimates of parameters including the POEs. We conducted simulation studies for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

A model for family-based case-control studies of genetic imprinting and epistasis

Genetic imprinting, or called the parent-of-origin effect, has been recognized to play an important role in the formation and pathogenesis of human diseases. Although the epigenetic mechanisms that establish genetic imprinting have been a focus of many genetic studies, our knowledge about the number of imprinting genes and their chromosomal locations and interactions with other genes is still scarce, limiting precise inference of the genetic architecture of complex diseases. In this article, we present a statistical model for testing and estimating the effects of genetic imprinting on complex diseases using a commonly used case-control design with family structure. For each subject sampled from a case and control population, we not only genotype its own single nucleotide polymorphisms (SNPs) but also collect its parents' genotypes. By tracing the transmission pattern of SNP alleles from parental to offspring generation, the model allows the characterization of genetic imprinting effects based on Pearson tests of a 2 × 2 contingency table. The model is expanded to test the interactions between imprinting effects and additive, dominant and epistatic effects in a complex web of genetic interactions. Statistical properties of the model are investigated, and its practical usefulness is validated by a real data analysis. The model will provide a useful tool for genome-wide association studies aimed to elucidate the picture of genetic control over complex human diseases.

A computational framework for the inheritance pattern of genomic imprinting for complex traits

Genetic imprinting, by which the expression of a gene depends on the parental origin of its alleles, may be subjected to reprogramming through each generation. Currently, such reprogramming is limited to qualitative description only, lacking more precise quantitative estimation for its extent, pattern and mechanism. Here, we present a computational framework for analyzing the magnitude of genetic imprinting and its transgenerational inheritance mode. This quantitative model is based on the breeding scheme of reciprocal backcrosses between reciprocal F(1) hybrids and original inbred parents, in which the transmission of genetic imprinting across generations can be tracked. We define a series of quantitative genetic parameters that describe the extent and transmission mode of genetic imprinting and further estimate and test these parameters within a genetic mapping framework using a new powerful computational algorithm. The model and algorithm described will enable geneticists to identify and map imprinted quantitative trait loci and dictate a comprehensive atlas of developmental and epigenetic mechanisms related to genetic imprinting. We illustrate the new discovery of the role of genetic imprinting in regulating hyperoxic acute lung injury survival time using a mouse reciprocal backcross design.

A statistical design for testing transgenerational genomic imprinting in natural human populations

Genomic imprinting is a phenomenon in which the same allele is expressed differently, depending on its parental origin. Such a phenomenon, also called the parent-of-origin effect, has been recognized to play a pivotal role in embryological development and pathogenesis in many species. Here we propose a statistical design for detecting imprinted loci that control quantitative traits based on a random set of three-generation families from a natural population in humans. This design provides a pathway for characterizing the effects of imprinted genes on a complex trait or disease at different generations and testing transgenerational changes of imprinted effects. The design is integrated with population and cytogenetic principles of gene segregation and transmission from a previous generation to next. The implementation of the EM algorithm within the design framework leads to the estimation of genetic parameters that define imprinted effects. A simulation study is used to investigate the statistical properties of the model and validate its utilization. This new design, coupled with increasingly used genome-wide association studies, should have an immediate implication for studying the genetic architecture of complex traits in humans.

A model for transgenerational imprinting variation in complex traits

Despite the fact that genetic imprinting, i.e., differential expression of the same allele due to its different parental origins, plays a pivotal role in controlling complex traits or diseases, the origin, action and transmission mode of imprinted genes have still remained largely unexplored. We present a new strategy for studying these properties of genetic imprinting with a two-stage reciprocal F mating design, initiated with two contrasting inbred lines. This strategy maps quantitative trait loci that are imprinted (i.e., iQTLs) based on their segregation and transmission across different generations. By incorporating the allelic configuration of an iQTL genotype into a mixture model framework, this strategy provides a path to trace the parental origin of alleles from previous generations. The imprinting effects of iQTLs and their interactions with other traditionally defined genetic effects, expressed in different generations, are estimated and tested by implementing the EM algorithm. The strategy was used to map iQTLs responsible for survival time with four reciprocal F populations and test whether and how the detected iQTLs inherit their imprinting effects into the next generation. The new strategy will provide a tool for quantifying the role of imprinting effects in the creation and maintenance of phenotypic diversity and elucidating a comprehensive picture of the genetic architecture of complex traits and diseases.

Functional mapping of genotype-environment interactions for soybean growth by a semiparametric approach

BACKGROUND

Functional mapping is a powerful approach for mapping quantitative trait loci (QTLs) that control biological processes. Functional mapping incorporates mathematical aspects of growth and development into a general QTL mapping framework and has been recently integrated with composite interval mapping to build up a so-called composite functional mapping model, aimed to separate multiple linked QTLs on the same chromosomal region.

RESULTS

This article reports the principle of using composite functional mapping to estimate the effects of QTL-environment interactions on growth trajectories by parametrically modeling the tested QTL in a marker interval and nonparametrically modeling the markers outside the interval as co-factors. With this new model, we can characterize the dynamic patterns of the genetic effects of QTLs governing growth trajectories, estimate the global effects of the underlying QTLs during the course of growth and development, and test the differentiation in the shapes of QTL genotype-specific growth curves between different environments. By analyzing a real example from a soybean genome project, our model detects several QTLs that cause significant genotype-environment interactions for plant height growth processes.

CONCLUSIONS

The model provides a basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments for any organism.

Modeling genetic imprinting effects of DNA sequences with multilocus polymorphism data

Single nucleotide polymorphisms (SNPs) represent the most widespread type of DNA sequence variation in the human genome and they have recently emerged as valuable genetic markers for revealing the genetic architecture of complex traits in terms of nucleotide combination and sequence. Here, we extend an algorithmic model for the haplotype analysis of SNPs to estimate the effects of genetic imprinting expressed at the DNA sequence level. The model provides a general procedure for identifying the number and types of optimal DNA sequence variants that are expressed differently due to their parental origin. The model is used to analyze a genetic data set collected from a pain genetics project. We find that DNA haplotype GAC from three SNPs, OPRKG36T (with two alleles G and T), OPRKA843G (with alleles A and G), and OPRKC846T (with alleles C and T), at the kappa-opioid receptor, triggers a significant effect on pain sensitivity, but with expression significantly depending on the parent from which it is inherited (p = 0.008). With a tremendous advance in SNP identification and automated screening, the model founded on haplotype discovery and statistical inference may provide a useful tool for genetic analysis of any quantitative trait with complex inheritance.

Computing genetic imprinting expressed by haplotypes

Different expression of maternally and paternally inherited alleles at certain genes is called genetic imprinting. Despite its great importance in trait formation, development, and evolution, it remains unclear how genetic imprinting operates in a complex network of interactive genes located throughout the genome. Genetic mapping has proven to be a powerful tool that can estimate the distribution and effects of imprinted genes. While traditional mapping models attempt to detect imprinted quantitative trait loci based on a linkage map constructed from molecular markers, we have developed a statistical model for estimating the imprinting effects of haplotypes composed of multiple sequenced single-nucleotide polymorphisms. The new model provides a characterization of the difference in the effect of maternally and paternally derived haplotypes, which can be used as a tool for genetic association studies at the candidate gene or genome-wide level. The model was used to map imprinted haplotype effects on body mass index in a random sample from a natural human population, leading to the detection of significant imprinted effects at the haplotype level. The new model will be useful for characterizing the genetic architecture of complex quantitative traits at the nucleotide level.

A Statistical Variance Components Framework for Mapping Imprinted Quantitative Trait Locus in Experimental Crosses

Current methods for mapping imprinted quantitative trait locus (iQTL) with inbred line crosses assume fixed QTL effects. When an iQTL segregates in experimental line crosses, combining different line crosses with similar genetic background can improve the accuracy of iQTLs inference. In this article, we develop a general interval-based statistical variance components framework to map iQTLs underlying complex traits by combining different backcross line crosses. We propose a new iQTL variance partition method based on the nature of marker alleles shared identical-by-decent (IBD) in inbred lines. Maternal effect is adjusted when testing imprinting. Efficient estimation methods with the maximum likelihood and the restricted maximum likelihood are derived and compared. Statistical properties of the proposed mapping strategy are evaluated through extensive simulations under different sampling designs. An extension to multiple QTL analysis is given. The proposed method will greatly facilitate genetic dissection of imprinted complex traits in inbred line crosses.