Combined sequence-based and genetic mapping analysis of complex traits in outbred rats

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doi:10.1038/ng.2644

Cited by 178 publications

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“…Since strain AB8, which was used to found both panels of RILs, is only present at an appreciable frequency at the Q4 locus in population pB, clarifying the relationship between the groups we identify in pA and in pB is difficult. It is plausible that the effect at Q4 is due to an allelic series, as we (Kislukhin et al 2013) and others (Baud et al 2013) have observed previously for QTL mapped in panels founded by multiple parental genotypes. However, given that Q4 maps to a region containing a family of detoxification genes (see below), it is perhaps more likely that multiple genes are collectively responsible for generating the signal at Q4.…”

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confidence: 80%

“…Furthermore, expression of a number of genes, including the P450 Cyp6g1, have been implicated in mediating increased survival time on nicotine (Passador-Gurgel et al 2007;Li et al 2012). However, the complete catalog of Drosophila genes has yet to be interrogated for any role in resistance to nicotine.A powerful method of identifying naturally segregating genetic variants contributing to complex trait variation is to use a large set of recombinant genotypes derived from an advanced generation intercross among several founding genomes (Churchill et al 2004;Macdonald and Long 2007;Kover et al 2009;Svenson et al 2012;Threadgill and Churchill 2012;Baud et al 2013). We have constructed the DSPR to allow such genetic dissection in D. melanogaster (King et al 2012a,b).…”

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confidence: 99%

“…A powerful method of identifying naturally segregating genetic variants contributing to complex trait variation is to use a large set of recombinant genotypes derived from an advanced generation intercross among several founding genomes (Churchill et al 2004;Macdonald and Long 2007;Kover et al 2009;Svenson et al 2012;Threadgill and Churchill 2012;Baud et al 2013). We have constructed the DSPR to allow such genetic dissection in D. melanogaster (King et al 2012a,b).…”

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Fine-Mapping Nicotine Resistance Loci inDrosophilaUsing a Multiparent Advanced Generation Inter-Cross Population

et al. 2014

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Animals in nature are frequently challenged by toxic compounds, from those that occur naturally in plants as a defense against herbivory, to pesticides used to protect crops. On exposure to such xenobiotic substances, animals mount a transcriptional response, generating detoxification enzymes and transporters that metabolize and remove the toxin. Genetic variation in this response can lead to variation in the susceptibility of different genotypes to the toxic effects of a given xenobiotic. Here we use Drosophila melanogaster to dissect the genetic basis of larval resistance to nicotine, a common plant defense chemical and widely used addictive drug in humans. We identified quantitative trait loci (QTL) for the trait using the DSPR (Drosophila Synthetic Population Resource), a panel of multiparental advanced intercross lines. Mapped QTL collectively explain 68.4% of the broad-sense heritability for nicotine resistance. The two largest-effect loci-contributing 50.3 and 8.5% to the genetic variation-map to short regions encompassing members of classic detoxification gene families. The largest QTL resides over a cluster of ten UDP-glucuronosyltransferase (UGT) genes, while the next largest QTL harbors a pair of cytochrome P450 genes. Using RNAseq we measured gene expression in a pair of DSPR founders predicted to harbor different alleles at both QTL and showed that Ugt86Dd, Cyp28d1, and Cyp28d2 had significantly higher expression in the founder carrying the allele conferring greater resistance. These genes are very strong candidates to harbor causative, regulatory polymorphisms that explain a large fraction of the genetic variation in larval nicotine resistance in the DSPR.A routine part of life for all organisms is avoiding, and if necessary metabolizing, toxic substances encountered in the environment. A common challenge for animals are those toxins produced by potential prey and plant hosts as chemical defenses against predation and herbivory (Glendinning 2002(Glendinning , 2007. Understanding how organisms overcome these defenses can give us insight into the evolution of host specialization, which can often involve an organism overcoming the defenses of a particular host, avoiding competition by making use of a resource toxic to other species (for example, Hungate et al. 2013). Many animals, especially insects, are also commonly exposed to chemical pesticides used to protect crop plants. As a consequence of this strong evolutionary pressure, there are a number of examples of insecticide resistance arising in natural populations (Crow 1957). Understanding the biology and molecular genetics underlying resistance to insecticides (Perry et al. 2011;Ffrench-Constant 2013) is valuable in the design of pest management strategies. In addition, humans are frequently exposed to an array of potentially harmful compounds, notably pharmaceuticals. Given the desire to achieve maximal drug efficacy while minimizing dosage and avoiding adverse drug responses, elaborating the mechanisms of drug metabolism, and the genet...

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Fine-Mapping Nicotine Resistance Loci inDrosophilaUsing a Multiparent Advanced Generation Inter-Cross Population

et al. 2014

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“…This result indicates that Tame-1 might harbor several genes influencing behavior through altered gene expression in the brain. QTL identified in F 2 crosses frequently fractionate into loci of more minor effects during fine mapping (Demarest et al 2001;Mozhui et al 2008) and contain multiple causative variants (Baud et al 2013;Flister et al 2013). An advanced intercross using large numbers of rats to generate more recombination events will be necessary to investigate whether variation at more than one gene underlies Tame-1 (Darvasi and Soller 1995).…”

Section: Discussionmentioning

confidence: 99%

Genetic Influences on Brain Gene Expression in Rats Selected for Tameness and Aggression

Heyne

Lautenschlaeger²,

Nelson

et al. 2014

Preprint

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Interindividual differences in many behaviors are partly due to genetic differences, but the identification of the genes and variants that influence behavior remains challenging. Here, we studied an F 2 intercross of two outbred lines of rats selected for tame and aggressive behavior toward humans for .64 generations. By using a mapping approach that is able to identify genetic loci segregating within the lines, we identified four times more loci influencing tameness and aggression than by an approach that assumes fixation of causative alleles, suggesting that many causative loci were not driven to fixation by the selection. We used RNA sequencing in 150 F 2 animals to identify hundreds of loci that influence brain gene expression. Several of these loci colocalize with tameness loci and may reflect the same genetic variants. Through analyses of correlations between allele effects on behavior and gene expression, differential expression between the tame and aggressive rat selection lines, and correlations between gene expression and tameness in F 2 animals, we identify the genes Gltscr2, Lgi4, Zfp40, and Slc17a7 as candidate contributors to the strikingly different behavior of the tame and aggressive animals. B EHAVIORAL differences among members of a species are in part due to genetic variation. The identification of the genes and variants that influence behavior remains challenging. In human genome-wide association studies of psychiatric and cognitive traits, the identified loci typically explain only a small fraction of the heritability, i.e., the additive genetic contribution to the trait (Watanabe et al. 2007;Hovatta and Barlow 2008;Deary et al. 2009;Otowa et al. 2009;Calboli et al. 2010;Terracciano et al. 2010;Flint and Munafo 2013;Rietveld et al. 2013;Sokolowska and Hovatta 2013). In experimental crosses in model species, especially between inbred lines, the identified loci (termed quantitative trait loci, QTL), often explain much more of the heritability (Lynch and Walsh 1998). However, in this design the spatial resolution is limited-the QTL are wide and contain many, sometimes hundreds of genes. With the exception of a handful of identified genes with quantitative effects on behavioral traits (Yalcin et al. 2004;Watanabe et al. 2007;McGrath et al. 2009;Bendesky et al. 2012;Goodson et al. 2012), gene identification is particularly difficult for behavioral QTL that often have modest effect sizes (Flint 2003;Willis-Owen and Flint 2006;Wright et al. 2006;Hovatta and Barlow 2008).The causal variants within a QTL can alter the protein sequence encoded by a gene or affect gene expression. Such Musunuru et al. 2010). A few eQTL studies have also recently been carried out to identify candidate genes for behavioral QTL (Hitzemann et al. 2004;De Jong et al. 2011;Saba et al. 2011;Kelly et al. 2012).Here, we studied two lines of rats (Rattus norvegicus) that, starting from one wild population, have been selected for increased tameness and increased aggression toward humans, respectively. These experimental...

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“…The HS rat colony (N:NIH-HS) was first established by the National Institutes of Health in 1984 to serve as a source of genetically segregating animals for both experimental and selection studies (36). Similar to the HS mouse, the HS rat has been used to map hundreds of traits to an average of 4.5 Mb, with several causal genes being identified (6). In addition to this large study, the N:NIH-HS rat colony has also been used to fine-map traits involved in fear (44) and diabetes (76,77).…”

Section: Heterogeneous Stocksmentioning

confidence: 99%

QTL mapping in outbred populations: successes and challenges

Woods

2014

Physiological Genomics

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Solberg Woods LC. QTL mapping in outbred populations: successes and challenges. Physiol Genomics 46: 81-90, 2014. First published December 10, 2013 doi:10.1152/physiolgenomics.00127.2013.-Quantitative trait locus (QTL) mapping in animal populations has been a successful strategy for identifying genomic regions that play a role in complex diseases and traits. When conducted in an F2 intercross or backcross population, the resulting QTL is frequently large, often encompassing 30 Mb or more and containing hundreds of genes. To narrow the locus and identify candidate genes, additional strategies are needed. Congenic strains have proven useful but work less well when there are multiple tightly linked loci, frequently resulting in loss of phenotype. As an alternative, we discuss the use of highly recombinant outbred models for directly fine-mapping QTL to only a few megabases. We discuss the use of several currently available models such as the advanced intercross (AI), heterogeneous stocks (HS), the diversity outbred (DO), and commercially available outbred stocks (CO). Once a QTL has been finemapped, founder sequence and expression QTL mapping can be used to identify candidate genes. In this regard, the large number of alleles found in outbred stocks can be leveraged to identify causative genes and variants. We end this review by discussing some important statistical considerations when analyzing outbred populations. Fine-resolution mapping in outbred models, coupled with full genome sequence, has already led to the identification of several underlying causative genes for many complex traits and diseases. These resources will likely lead to additional successes in the coming years.intercross; heterogeneous stock; diversity outbred; genetic mapping; high resolution mapping QUANTITATIVE TRAIT LOCUS (QTL) mapping in rodents has been a successful strategy for identifying regions of the genome that play a role in many diseases and traits. Initial QTL studies used F2 intercross or backcrosses, in which two strains that differ phenotypically and genetically are bred for two generations (see Fig. 1). With this strategy, F2 progeny are phenotyped and genotyped, and genetic loci linked to the trait of interest are identified. Although this method works extremely well for detecting QTL, it provides only a broad localization, with identified regions tending to be very large (30 -40 Mb) and often encompassing hundreds of genes. As a result, although F2 intercrosses and backcrosses have identified thousands of QTL over the past 30 yr, they have proven less useful for identifying the underlying causative genes and variants (see Ref. 30).In an attempt to narrow the QTL regions and identify causative genes, multiple methods have been developed (see Ref. 32). One popular method is to create a congenic strain in which consecutively smaller portions of the QTL from the disease strain are bred onto the genetic background of the disease resistant strain. This strategy has successfully narrowed numerous QTL and has the potential to lead ...

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Combined sequence-based and genetic mapping analysis of complex traits in outbred rats

Cited by 178 publications

References 48 publications

Fine-Mapping Nicotine Resistance Loci inDrosophilaUsing a Multiparent Advanced Generation Inter-Cross Population

Fine-Mapping Nicotine Resistance Loci inDrosophilaUsing a Multiparent Advanced Generation Inter-Cross Population

Genetic Influences on Brain Gene Expression in Rats Selected for Tameness and Aggression

QTL mapping in outbred populations: successes and challenges

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