Analysis of independent cohorts of outbred CFW mice reveals novel loci for behavioral and physiological traits and identifies factors determining reproducibility

Combining samples for genetic association is standard practice in human genetic analysis of complex traits, but is rarely undertaken in rodent genetics. Here, using 23 phenotypes and genotypes from two independent laboratories, we obtained a sample size of 3,076 commercially available outbred mice and identified 70 loci, more than double the number of loci identified in the component studies. Fine-mapping in the combined sample reduced the number of likely causal variants, with a median reduction in set size of 51%, and indicated novel gene associations, including Pnpo, Ttll6 and GM11545 with bone mineral density, and Psmb9 with weight. However replication at a nominal threshold of 0.05 between the two component studies was low, with less than a third of loci identified in one study replicated in the second. In addition to overestimates in the effect size in the discovery sample (Winner's Curse), we also found that heterogeneity between studies explained the poor replication, but the contribution of these two factors varied among traits. Leveraging these observations we integrated information about replication rates, study-specific heterogeneity, and Winner's Curse corrected estimates of power to assign variants to one of four confidence levels. Our approach addresses concerns about reproducibility, and demonstrates how to obtain robust results from mapping complex traits in any genome-wide association study

Fleece variation in alpaca (Vicugna pacos): a two-locus model for the Suri/Huacaya phenotype

Background: Genetic improvement of fibre-producing animal species has often induced transition from double coated to single coated fleece, accompanied by dramatic changes in skin follicles and hair composition, likely implying variation at multiple loci. Huacaya, the more common fleece phenotype in alpaca (Vicugna pacos), is characterized by a thick dense coat growing perpendicularly from the body, whereas the alternative rare and more prized single-coated Suri phenotype is distinguished by long silky fibre that grows parallel to the body and hangs in separate, distinctive pencil locks. A single-locus genetic model has been proposed for the Suri-Huacaya phenotype, where Huacaya is recessive. Results: Two reciprocal experimental test-crosses (Suri x Huacaya) were carried out, involving a total of 17 unrelated males and 149 unrelated females. An additional dataset of 587 offspring of Suri x Suri crosses was analyzed. Segregation ratios, population genotype frequencies, and/or recombination fraction under different genetic models were estimated by maximum likelihood. The single locus model for the Suri/Huacaya phenotype was rejected. In addition, we present two unexpected observations: 1) a large proportion (about 3/4) of the Suri animals are segregating (with at least one Huacaya offspring), even in breeding conditions where the Huacaya trait would have been almost eliminated; 2) a model with two different values of the segregation ratio fit the data significantly better than a model with a single parameter. Conclusions: The data support a genetic model in which two linked loci must simultaneously be homozygous for recessive alleles in order to produce the Huacaya phenotype. The estimated recombination rate between these loci was 0.099 (95% C. L. = 0.029-0.204). Our genetic analysis may be useful for other species whose breeding system produces mainly half-sib families

Identification of QTLs controlling gene expression networks defined a priori

BACKGROUND: Gene expression microarrays allow the quantification of transcript accumulation for many or all genes in a genome. This technology has been utilized for a range of investigations, from assessments of gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Current analysis techniques facilitate the statistical querying of individual genes to evaluate the significance of a change in response, also known as differential expression. Since genes are also known to respond as groups due to their membership in networks, effective approaches are needed to investigate transcriptome variation as related to gene network responses. RESULTS: We describe a statistical approach that is capable of assessing higher-order a priori defined gene network response, as measured by microarrays. This analysis detected significant network variation between two Arabidopsis thaliana accessions, Bay-0 and Shahdara. By extending this approach, we were able to identify eQTLs controlling network responses for 18 out of 20 a priori-defined gene networks in a recombinant inbred line population derived from accessions Bay-0 and Shahdara. CONCLUSION: This approach has the potential to be expanded to facilitate direct tests of the relationship between phenotypic trait and transcript genetic architecture. The use of a priori definitions for network eQTL identification has enormous potential for providing direction toward future eQTL analyses

Genome-wide association of multiple complex traits in outbred mice by ultra-low-coverage sequencing

Two bottlenecks impeding the genetic analysis of complex traits in rodents are access to mapping populations able to deliver gene-level mapping resolution and the need for population-specific genotyping arrays and haplotype reference panels. Here we combine low-coverage (0.15×) sequencing with a new method to impute the ancestral haplotype space in 1,887 commercially available outbred mice. We mapped 156 unique quantitative trait loci for 92 phenotypes at a 5% false discovery rate. Gene-level mapping resolution was achieved at about one-fifth of the loci, implicating Unc13c and Pgc1a at loci for the quality of sleep, Adarb2 for home cage activity, Rtkn2 for intensity of reaction to startle, Bmp2 for wound healing, Il15 and Id2 for several T cell measures and Prkca for bone mineral content. These findings have implications for diverse areas of mammalian biology and demonstrate how genome-wide association studies can be extended via low-coverage sequencing to species with highly recombinant outbred populations

Leucocyte subset-specific type 1 interferon signatures in SLE and other immune-mediated diseases.

OBJECTIVES: Type 1 interferons (IFN-1) are implicated in the pathogenesis of systemic lupus erythematosus (SLE), but most studies have only reported the effect of IFN-1 on mixed cell populations. We aimed to define modules of IFN-1-associated genes in purified leucocyte populations and use these as a basis for a detailed comparative analysis. METHODS: CD4+ and CD8+ T cells, monocytes and neutrophils were purified from patients with SLE, other immune-mediated diseases and healthy volunteers and gene expression then determined by microarray. Modules of IFN-1-associated genes were defined using weighted gene coexpression network analysis. The composition and expression of these modules was analysed. RESULTS: 1150 of 1288 IFN-1-associated genes were specific to myeloid subsets, compared with 11 genes unique to T cells. IFN-1 genes were more highly expressed in myeloid subsets compared with T cells. A subset of neutrophil samples from healthy volunteers (HV) and conditions not classically associated with IFN-1 signatures displayed increased IFN-1 gene expression, whereas upregulation of IFN-1-associated genes in T cells was restricted to SLE. CONCLUSIONS: Given the broad upregulation of IFN-1 genes in neutrophils including in some HV, investigators reporting IFN-1 signatures on the basis of whole blood samples should be cautious about interpreting this as evidence of bona fide IFN-1-mediated pathology. Instead, specific upregulation of IFN-1-associated genes in T cells may be a useful biomarker and a further mechanism by which elevated IFN-1 contributes to autoimmunity in SLE.SMF holds a Translational Medicine and Therapeutics PhD studentship from the Wellcome Trust and GlaxoSmithKline and has also received funding for this work from the Addenbrooke’s Charitable Trust. KGCS is the Khoo Oon Teik Professor of Nephrology, National University of Singapore. Singapore recruitment was supported by the Khoo Investigator Grant from the Duke-NUS Graduate Medical School, Singapore, and by National Medical Research Council of Singapore grants (NMRC/1164/2008 and IRG07nov089). This work was also supported by UK National Institute of Health Research Cambridge Biomedical Research Centre, the Lupus Research Institute (Distinguished Innovator Award, KGCS), the Medical Research Council UK (programme grant MR/L019027/1) and the Wellcome Trust (programme grant 083650/Z/07/Z and project grant 094227/Z/10/Z). The Cambridge Institute for Medical Research is in receipt of Wellcome Trust Strategic Award 079895.This is the final version of the article. It first appeared from BMJ Group via https://doi.org/10.1136/rmdopen-2015-00018

Quantitative trait loci mapping reveals candidate pathways regulating cell cycle duration in Plasmodium falciparum

<p>Abstract</p> <p>Background</p> <p>Elevated parasite biomass in the human red blood cells can lead to increased malaria morbidity. The genes and mechanisms regulating growth and development of <it>Plasmodium </it><it>falciparum </it>through its erythrocytic cycle are not well understood. We previously showed that strains HB3 and Dd2 diverge in their proliferation rates, and here use quantitative trait loci mapping in 34 progeny from a cross between these parent clones along with integrative bioinformatics to identify genetic loci and candidate genes that control divergences in cell cycle duration.</p> <p>Results</p> <p>Genetic mapping of cell cycle duration revealed a four-locus genetic model, including a major genetic effect on chromosome 12, which accounts for 75% of the inherited phenotype variation. These QTL span 165 genes, the majority of which have no predicted function based on homology. We present a method to systematically prioritize candidate genes using the extensive sequence and transcriptional information available for the parent lines. Putative functions were assigned to the prioritized genes based on protein interaction networks and expression eQTL from our earlier study. DNA metabolism or antigenic variation functional categories were enriched among our prioritized candidate genes. Genes were then analyzed to determine if they interact with cyclins or other proteins known to be involved in the regulation of cell cycle.</p> <p>Conclusions</p> <p>We show that the divergent proliferation rate between a drug resistant and drug sensitive parent clone is under genetic regulation and is segregating as a complex trait in 34 progeny. We map a major locus along with additional secondary effects, and use the wealth of genome data to identify key candidate genes. Of particular interest are a nucleosome assembly protein (PFL0185c), a Zinc finger transcription factor (PFL0465c) both on chromosome 12 and a ribosomal protein L7Ae-related on chromosome 4 (PFD0960c).</p

A Dynamic and Complex Network Regulates the Heterosis of Yield-Correlated Traits in Rapeseed (Brassica napus L.)

Although much research has been conducted, the genetic architecture of heterosis remains ambiguous. To unravel the genetic architecture of heterosis, a reconstructed F2 population was produced by random intercross among 202 lines of a double haploid population in rapeseed (Brassica napus L.). Both populations were planted in three environments and 15 yield-correlated traits were measured, and only seed yield and eight yield-correlated traits showed significant mid-parent heterosis, with the mean ranging from 8.7% (branch number) to 31.4% (seed yield). Hundreds of QTL and epistatic interactions were identified for the 15 yield-correlated traits, involving numerous variable loci with moderate effect, genome-wide distribution and obvious hotspots. All kinds of mode-of-inheritance of QTL (additive, A; partial-dominant, PD; full-dominant, D; over-dominant, OD) and epistatic interactions (additive × additive, AA; additive × dominant/dominant × additive, AD/DA; dominant × dominant, DD) were observed and epistasis, especially AA epistasis, seemed to be the major genetic basis of heterosis in rapeseed. Consistent with the low correlation between marker heterozygosity and mid-parent heterosis/hybrid performance, a considerable proportion of dominant and DD epistatic effects were negative, indicating heterozygosity was not always advantageous for heterosis/hybrid performance. The implications of our results on evolution and crop breeding are discussed

Systematic Detection of Epistatic Interactions Based on Allele Pair Frequencies

Epistatic genetic interactions are key for understanding the genetic contribution to complex traits. Epistasis is always defined with respect to some trait such as growth rate or fitness. Whereas most existing epistasis screens explicitly test for a trait, it is also possible to implicitly test for fitness traits by searching for the over- or under-representation of allele pairs in a given population. Such analysis of imbalanced allele pair frequencies of distant loci has not been exploited yet on a genome-wide scale, mostly due to statistical difficulties such as the multiple testing problem. We propose a new approach called Imbalanced Allele Pair frequencies (ImAP) for inferring epistatic interactions that is exclusively based on DNA sequence information. Our approach is based on genome-wide SNP data sampled from a population with known family structure. We make use of genotype information of parent-child trios and inspect 3×3 contingency tables for detecting pairs of alleles from different genomic positions that are over- or under-represented in the population. We also developed a simulation setup which mimics the pedigree structure by simultaneously assuming independence of the markers. When applied to mouse SNP data, our method detected 168 imbalanced allele pairs, which is substantially more than in simulations assuming no interactions. We could validate a significant number of the interactions with external data, and we found that interacting loci are enriched for genes involved in developmental processes

Metabolic compartmentalization in the human cortex and hippocampus: evidence for a cell- and region-specific localization of lactate dehydrogenase 5 and pyruvate dehydrogenase

BACKGROUND: For a long time now, glucose has been thought to be the main, if not the sole substrate for brain energy metabolism. Recent data nevertheless suggest that other molecules, such as monocarboxylates (lactate and pyruvate mainly) could be suitable substrates. Although monocarboxylates poorly cross the blood brain barrier (BBB), such substrates could replace glucose if produced locally.The two key enzymatiques systems required for the production of these monocarboxylates are lactate dehydrogenase (LDH; EC1.1.1.27) that catalyses the interconversion of lactate and pyruvate and the pyruvate dehydrogenase complex that irreversibly funnels pyruvate towards the mitochondrial TCA and oxydative phosphorylation. RESULTS: In this article, we show, with monoclonal antibodies applied to post-mortem human brain tissues, that the typically glycolytic isoenzyme of lactate dehydrogenase (LDH-5; also called LDHA or LDHM) is selectively present in astrocytes, and not in neurons, whereas pyruvate dehydrogenase (PDH) is mainly detected in neurons and barely in astrocytes. At the regional level, the distribution of the LDH-5 immunoreactive astrocytes is laminar and corresponds to regions of maximal 2-deoxyglucose uptake in the occipital cortex and hippocampus. In hippocampus, we observed that the distribution of the oxidative enzyme PDH was enriched in the neurons of the stratum pyramidale and stratum granulosum of CA1 through CA4, whereas the glycolytic enzyme LDH-5 was enriched in astrocytes of the stratum moleculare, the alveus and the white matter, revealing not only cellular, but also regional, selective distributions. The fact that LDH-5 immunoreactivity was high in astrocytes and occurred in regions where the highest uptake of 2-deoxyglucose was observed suggests that glucose uptake followed by lactate production may principally occur in these regions. CONCLUSION: These observations reveal a metabolic segregation, not only at the cellular but also at the regional level, that support the notion of metabolic compartmentalization between astrocytes and neurons, whereby lactate produced by astrocytes could be oxidized by neurons

Dissection of a QTL Hotspot on Mouse Distal Chromosome 1 that Modulates Neurobehavioral Phenotypes and Gene Expression

A remarkably diverse set of traits maps to a region on mouse distal chromosome 1 (Chr 1) that corresponds to human Chr 1q21–q23. This region is highly enriched in quantitative trait loci (QTLs) that control neural and behavioral phenotypes, including motor behavior, escape latency, emotionality, seizure susceptibility (Szs1), and responses to ethanol, caffeine, pentobarbital, and haloperidol. This region also controls the expression of a remarkably large number of genes, including genes that are associated with some of the classical traits that map to distal Chr 1 (e.g., seizure susceptibility). Here, we ask whether this QTL-rich region on Chr 1 (Qrr1) consists of a single master locus or a mixture of linked, but functionally unrelated, QTLs. To answer this question and to evaluate candidate genes, we generated and analyzed several gene expression, haplotype, and sequence datasets. We exploited six complementary mouse crosses, and combed through 18 expression datasets to determine class membership of genes modulated by Qrr1. Qrr1 can be broadly divided into a proximal part (Qrr1p) and a distal part (Qrr1d), each associated with the expression of distinct subsets of genes. Qrr1d controls RNA metabolism and protein synthesis, including the expression of ∼20 aminoacyl-tRNA synthetases. Qrr1d contains a tRNA cluster, and this is a functionally pertinent candidate for the tRNA synthetases. Rgs7 and Fmn2 are other strong candidates in Qrr1d. FMN2 protein has pronounced expression in neurons, including in the dendrites, and deletion of Fmn2 had a strong effect on the expression of few genes modulated by Qrr1d. Our analysis revealed a highly complex gene expression regulatory interval in Qrr1, composed of multiple loci modulating the expression of functionally cognate sets of genes