A simple method for combining genetic mapping data from multiple crosses and experimental designs

Genetic loci and metabolic states associated with murine epigenetic aging

Changes in DNA methylation (DNAm) are linked to aging. Here, we profile highly conserved CpGs in 339 predominantly female mice belonging to the BXD family for which we have deep longevity and genomic data. We use a 'pan-mammalian' microarray that provides a common platform for assaying the methylome across mammalian clades. We computed epigenetic clocks and tested associations with DNAm entropy, diet, weight, metabolic traits, and genetic variation. We describe the multifactorial variance of methylation at these CpGs and show that high-fat diet augments the age-related changes. Entropy increases with age. The progression to disorder, particularly at CpGs that gain methylation over time, was predictive of genotype-dependent life expectancy. The longer-lived BXD strains had comparatively lower entropy at a given age. We identified two genetic loci that modulate epigenetic age acceleration (EAA): one on chromosome (Chr) 11 that encompasses the Erbb2/Her2 oncogenic region, and the other on Chr19 that contains a cytochrome P450 cluster. Both loci harbor genes associated with EAA in humans, including STXBP4, NKX2-3, and CUTC. Transcriptome and proteome analyses revealed correlations with oxidation-reduction, metabolic, and immune response pathways. Our results highlight concordant loci for EAA in humans and mice, and demonstrate a tight coupling between the metabolic state and epigenetic aging.

Genetic Architecture of a Rice Nested Association Mapping Population

Describing the genetic diversity in the gene pool of crops will provide breeders with novel resources for varietal improvement. Nested Association Mapping (NAM) populations are uniquely suited for characterizing parental diversity through the shuffling and fixation of parental haplotypes. Here, we describe a set of 1879 rice NAM lines created through the selfing and single-seed descent of F1 hybrids derived from elite IR64 indica crossed with 10 diverse tropical japonica lines. Genotyping data indicated tropical japonica alleles were captured at every queried locus despite the presence of segregation distortion factors. Several distortion loci were mapped, both shared and unique, among the 10 populations. Using two-point and multi-point genetic map calculations, our datasets achieved the ∼1500 cM expected map size in rice. Finally, we highlighted the utility of the NAM lines for QTL mapping, including joint analysis across the 10 populations, by confirming known QTL locations for the trait days to heading.

A Genetic Screen Identifies Hypothalamic Fgf15 as a Regulator of Glucagon Secretion

The counterregulatory response to hypoglycemia, which restores normal blood glucose levels to ensure sufficient provision of glucose to the brain, is critical for survival. To discover underlying brain regulatory systems, we performed a genetic screen in recombinant inbred mice for quantitative trait loci (QTL) controlling glucagon secretion in response to neuroglucopenia. We identified a QTL on the distal part of chromosome 7 and combined this genetic information with transcriptomic analysis of hypothalami. This revealed Fgf15 as the strongest candidate to control the glucagon response. Fgf15 was expressed by neurons of the dorsomedial hypothalamus and the perifornical area. Intracerebroventricular injection of FGF19, the human ortholog of Fgf15, reduced activation by neuroglucopenia of dorsal vagal complex neurons, of the parasympathetic nerve, and lowered glucagon secretion. In contrast, silencing Fgf15 in the dorsomedial hypothalamus increased neuroglucopenia-induced glucagon secretion. These data identify hypothalamic Fgf15 as a regulator of glucagon secretion.

High-resolution genetic maps of Eucalyptus improve Eucalyptus grandis genome assembly

Genetic maps are key tools in genetic research as they constitute the framework for many applications, such as quantitative trait locus analysis, and support the assembly of genome sequences. The resequencing of the two parents of a cross between Eucalyptus urophylla and Eucalyptus grandis was used to design a single nucleotide polymorphism (SNP) array of 6000 markers evenly distributed along the E. grandis genome. The genotyping of 1025 offspring enabled the construction of two high-resolution genetic maps containing 1832 and 1773 markers with an average marker interval of 0.45 and 0.5 cM for E. grandis and E. urophylla, respectively. The comparison between genetic maps and the reference genome highlighted 85% of collinear regions. A total of 43 noncollinear regions and 13 nonsynthetic regions were detected and corrected in the new genome assembly. This improved version contains 4943 scaffolds totalling 691.3 Mb of which 88.6% were captured by the 11 chromosomes. The mapping data were also used to investigate the effect of population size and number of markers on linkage mapping accuracy. This study provides the most reliable linkage maps for Eucalyptus and version 2.0 of the E. grandis genome.

Location-Dependent Empirical Thresholds for Quantitative Trait Mapping

The Churchill-Doerge approach toward constructing empirical thresholds has received widespread use in the genetic mapping literature through the past 16 years. The method is valued for both its simplicity and its ability to preserve the genome-wide error rate at a prespecified level. However, the Churchill-Doerge method is not designed to maintain the local (comparison-wise) error rate at a constant level except in situations that are unlikely to occur in practice. In this article, we introduce the objective of preserving the local error rate at a constant level in the context of mapping quantitative trait loci in linkage populations. We derive a method that preserves the local error rate at a constant level, provide an application via simulation on a Hordeum vulgare population, and demonstrate evidence of the relationship between recombination and location bias. Furthermore, we indicate that this method is equivalent to the Churchill-Doerge method when several assumptions are satisfied.

Increasing association mapping power and resolution in mouse genetic studies through the use of meta-analysis for structured populations

Genetic studies in mouse models have played an integral role in the discovery of the mechanisms underlying many human diseases. The primary mode of discovery has been the application of linkage analysis to mouse crosses. This approach results in high power to identify regions that affect traits, but in low resolution, making it difficult to identify the precise genomic location harboring the causal variant. Recently, a panel of mice referred to as the hybrid mouse diversity panel (HMDP) has been developed to overcome this problem. However, power in this panel is limited by the availability of inbred strains. Previous studies have suggested combining results across multiple panels as a means to increase power, but the methods employed may not be well suited to structured populations, such as the HMDP. In this article, we introduce a meta-analysis-based method that may be used to combine HMDP studies with F2 cross studies to gain power, while increasing resolution. Due to the drastically different genetic structure of F2s and the HMDP, the best way to combine two studies for a given SNP depends on the strain distribution pattern in each study. We show that combining results, while accounting for these patterns, leads to increased power and resolution. Using our method to map bone mineral density, we find that two previously implicated loci are replicated with increased significance and that the size of the associated is decreased. We also map HDL cholesterol and show a dramatic increase in the significance of a previously identified result.

Identification of quantitative trait loci influencing skeletal architecture in mice: emergence of Cdh11 as a primary candidate gene regulating femoral morphology

Bone strength is influenced by many properties intrinsic to bone, including its mass, geometry, and mineralization. To further advance our understanding of the genetic basis of bone-strength-related traits, we used a large (n = 815), moderately (G(4) ) advanced intercross line (AIL) of mice derived from a high-runner selection line (HR) and the C57BL/6J inbred strain. In total, 16 quantitative trait loci (QTLs) were identified that affected areal bone mineral density (aBMD) and femoral length and width. Four significant (p < .05) and one suggestive (p < .10) QTLs were identified for three aBMD measurements: total body, vertebral, and femoral. A QTL on chromosome (Chr.) 3 influenced all three aBMD measures, whereas the other four QTLs were unique to a single measure. A total of 10 significant and one suggestive QTLs were identified for femoral length (FL) and two measures of femoral width, anteroposterior (AP) and mediolateral (ML). FL QTLs were distinct from loci affecting AP and ML width, and of the 7 AP QTLs, only three affected ML. A QTL on Chr. 8 that explained 7.1% and 4.0% of the variance in AP and ML, respectively, was mapped to a 6-Mb region harboring 12 protein-coding genes. The pattern of haplotype diversity across the QTL region and expression profiles of QTL genes suggested that of the 12, cadherin 11 (Cdh11) was most likely the causal gene. These findings, when combined with existing data from gene knockouts, identify Cdh11 as a strong candidate gene within which genetic variation may affect bone morphology.

Exercise, weight loss, and changes in body composition in mice: phenotypic relationships and genetic architecture

The regulation of body weight and composition is complex, simultaneously affected by genetic architecture, the environment, and their interactions. We sought to analyze the complex phenotypic relationships between voluntary exercise, food consumption, and changes in body weight and composition and simultaneously localize quantitative trait loci (QTL) controlling these traits. A large (n = 815) murine advanced intercross line (G(4)) was created from a reciprocal cross between a high-running line and the inbred strain C57BL/6J. Body weight and composition (% fat, % lean) were measured at 4, 6, and 8 wk of age. After measurements at 8 wk of age, mice were given access to running wheels, during which food consumption was quantified and after which body weight and composition were assessed to evaluate exercise-induced changes. Phenotypic correlations indicated that the relationship between exercise and overall change in weight and adiposity depended on body composition before the initiation of exercise. Interval mapping revealed QTL for body weight, % fat, and % lean at 4, 6, and 8 wk of age. Furthermore, QTL were observed for food consumption and changes in weight, % fat, and % lean in response to short-term exercise. Here we provide some clarity for the relationship between weight loss, reduction in adiposity, food consumption, and exercise. Simultaneously, we reinforce the genetic basis for body weight and composition with some independent loci controlling growth at different ages. Finally, we present unique QTL providing insight regarding variation in weight loss and reduction in adiposity in response to exercise.

Integrative analysis of low- and high-resolution eQTL

The study of expression quantitative trait loci (eQTL) is a powerful way of detecting transcriptional regulators at a genomic scale and for elucidating how natural genetic variation impacts gene expression. Power and genetic resolution are heavily affected by the study population: whereas recombinant inbred (RI) strains yield greater statistical power with low genetic resolution, using diverse inbred or outbred strains improves genetic resolution at the cost of lower power. In order to overcome the limitations of both individual approaches, we combine data from RI strains with genetically more diverse strains and analyze hippocampus eQTL data obtained from mouse RI strains (BXD) and from a panel of diverse inbred strains (Mouse Diversity Panel, MDP). We perform a systematic analysis of the consistency of eQTL independently obtained from these two populations and demonstrate that a significant fraction of eQTL can be replicated. Based on existing knowledge from pathway databases we assess different approaches for using the high-resolution MDP data for fine mapping BXD eQTL. Finally, we apply this framework to an eQTL hotspot on chromosome 1 (Qrr1), which has been implicated in a range of neurological traits. Here we present the first systematic examination of the consistency between eQTL obtained independently from the BXD and MDP populations. Our analysis of fine-mapping approaches is based on 'real life' data as opposed to simulated data and it allows us to propose a strategy for using MDP data to fine map BXD eQTL. Application of this framework to Qrr1 reveals that this eQTL hotspot is not caused by just one (or few) 'master regulators', but actually by a set of polymorphic genes specific to the central nervous system.

Combining QTL data for HDL cholesterol levels from two different species leads to smaller confidence intervals

Quantitative trait locus (QTL) analysis detects regions of a genome that are linked to a complex trait. Once a QTL is detected, the region is narrowed by positional cloning in the hope of determining the underlying candidate gene-methods used include creating congenic strains, comparative genomics and gene expression analysis. Combined cross analysis may also be used for species such as the mouse, if the QTL is detected in multiple crosses. This process involves the recoding of QTL data on a per-chromosome basis, with the genotype recoded on the basis of high- and low-allele status. The data are then combined and analyzed; a successful analysis results in a narrowed and more significant QTL. Using parallel methods, we show that it is possible to narrow a QTL by combining data from two different species, the rat and the mouse. We combined standardized high-density lipoprotein phenotype values and genotype data for the rat and mouse using information from one rat cross and two mouse crosses. We successfully combined data within homologous regions from rat Chr 6 onto mouse Chr 12, and from rat Chr 10 onto mouse Chr 11. The combinations and analyses resulted in QTL with smaller confidence intervals and increased logarithm of the odds ratio scores. The numbers of candidate genes encompassed by the QTL on mouse Chr 11 and 12 were reduced from 1343 to 761 genes and from 613 to 304 genes, respectively. This is the first time that QTL data from different species were successfully combined; this method promises to be a useful tool for narrowing QTL intervals.

A genome-wide map of human genetic interactions inferred from radiation hybrid genotypes

Using radiation hybrid genotyping data, 99% of all possible gene pairs across the mammalian genome were tested for interactions based on co-retention frequencies higher (attraction) or lower (repulsion) than chance. Gene interaction networks constructed from six independent data sets overlapped strongly. Combining the data sets resulted in a network of more than seven million interactions, almost all attractive. This network overlapped with protein-protein interaction networks on multiple measures and also confirmed the relationship between essentiality and centrality. In contrast to other biological networks, the radiation hybrid network did not show a scale-free distribution of connectivity but was Gaussian-like, suggesting a closer approach to saturation. The radiation hybrid (RH) network constitutes a platform for understanding the systems biology of the mammalian cell.

Genetic architecture of voluntary exercise in an advanced intercross line of mice

Exercise is essential for health, yet the amount, duration, and intensity that individuals engage in are strikingly variable, even under prescription. Our focus was to identify the locations and effects of quantitative trait loci (QTL) controlling genetic predisposition for exercise-related traits, utilizing a large advanced intercross line (AIL) of mice. This AIL (G(4)) population originated from a reciprocal cross between mice with genetic propensity for increased voluntary exercise [high-runner (HR) line, selectively bred for increased wheel running] and the inbred strain C57BL/6J. After adjusting for family structure, we detected 32 significant and 13 suggestive QTL representing both daily running traits (distance, duration, average speed, and maximum speed) and the mean of these traits on days 5 and 6 (the selection criteria for HR) of a 6-day test conducted at 8 wk of age, with many co-localizing to similar genomic regions. Additionally, seven significant and five suggestive QTL were observed for the slope and intercept of a linear regression across all 6 days of running, some representing a combination of the daily traits. We also observed two significant and two suggestive QTL for body mass before exercise. These results, from a well-defined animal model, reinforce a genetic basis for the predisposition to engage in voluntary exercise, dissect this predisposition into daily segments across a continuous time period, and present unique QTL that may provide insight into the initiation, continuation, and temporal pattern of voluntary activity in mammals.

Replication and narrowing of gene expression quantitative trait loci using inbred mice

Gene expression quantitative trait locus (eQTL) mapping has become a powerful tool in systems biology. While many authors have made important discoveries using this approach, one persistent challenge in eQTL studies is the selection of loci and genes that should receive further biological investigation. In this study we compared eQTL generated from gene expression profiling in the livers of two panels of mouse strains: 41 BXD recombinant inbred and 36 Mouse Diversity Panel (MDP) strains. Cis-eQTL, loci in which the transcript and its maximum QTL are colocated, have been shown to be more reproducible than trans-eQTL, which are not colocated with the transcript. We observed that between 9.9 and 12.1% of cis-eQTL and between 2.0 and 12.6% of trans-eQTL replicated between the two panels depending on the degree of statistical stringency. Notably, a significant eQTL hotspot on distal chromosome 12 observed in the BXD panel was reproduced in the MDP. Furthermore, the shorter linkage disequilibrium in the MDP strains allowed us to considerably narrow the locus and limit the number of candidate genes to a cluster of Serpin genes, which code for extracellular proteases. We conclude that this strategy has some utility in increasing confidence and resolution in eQTL mapping studies; however, due to the high false-positive rate in the MDP, eQTL mapping in inbred strains is best carried out in combination with an eQTL linkage study.

Genome Reshuffling for Advanced Intercross Permutation (GRAIP): simulation and permutation for advanced intercross population analysis

BACKGROUND

Advanced intercross lines (AIL) are segregating populations created using a multi-generation breeding protocol for fine mapping complex trait loci (QTL) in mice and other organisms. Applying QTL mapping methods for intercross and backcross populations, often followed by naïve permutation of individuals and phenotypes, does not account for the effect of AIL family structure in which final generations have been expanded and leads to inappropriately low significance thresholds. The critical problem with naïve mapping approaches in AIL populations is that the individual is not an exchangeable unit.

METHODOLOGY/PRINCIPAL FINDINGS

The effect of family structure has immediate implications for the optimal AIL creation (many crosses, few animals per cross, and population expansion before the final generation) and we discuss these and the utility of AIL populations for QTL fine mapping. We also describe Genome Reshuffling for Advanced Intercross Permutation, (GRAIP) a method for analyzing AIL data that accounts for family structure. GRAIP permutes a more interchangeable unit in the final generation crosses - the parental genome - and simulating regeneration of a permuted AIL population based on exchanged parental identities. GRAIP determines appropriate genome-wide significance thresholds and locus-specific P-values for AILs and other populations with similar family structures. We contrast GRAIP with naïve permutation using a large densely genotyped mouse AIL population (1333 individuals from 32 crosses). A naïve permutation using coat color as a model phenotype demonstrates high false-positive locus identification and uncertain significance levels, which are corrected using GRAIP. GRAIP also detects an established hippocampus weight locus and a new locus, Hipp9a.

CONCLUSIONS AND SIGNIFICANCE

GRAIP determines appropriate genome-wide significance thresholds and locus-specific P-values for AILs and other populations with similar family structures. The effect of family structure has immediate implications for the optimal AIL creation and we discuss these and the utility of AIL populations.