Karl W Broman

University of Wisconsin–Madison, Madison, Wisconsin, United States

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Publications (161)879.66 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Current influenza vaccines primarily aim to induce neutralizing antibodies (NAbs). Modified vaccinia Ankara (MVA) is a safe and well-characterized vector for inducing both antibody and cellular immunity. We evaluated the immunogenicity and protective efficacy of MVA encoding influenza virus hemagglutinin (HA) and/or nucleoprotein (NP) in cynomolgus macaques. Animals were given 2 doses of MVA based vaccines 4 weeks apart, and were challenged with a 2009 pandemic H1N1 isolate (H1N1pdm) 8 weeks after the last vaccination. MVA-based vaccines encoding HA induced potent serum antibody responses against homologous H1 or H5 HAs, but did not stimulate strong T cell responses prior to challenge. However, animals that received MVA encoding influenza HA and/or NP had high frequencies of virus-specific CD4+ and CD8+ T cell responses within the first 7 days of H1N1pdm infection, while animals vaccinated with MVA encoding irrelevant antigens did not. We detected little or no H1N1pdm replication in animals that received vaccines encoding H1 (homologous) HA, while a vaccine encoding NP from an H5N1 isolate afforded no protection. Surprisingly, H1N1pdm viral shedding was reduced in animals vaccinated with MVA encoding HA and NP from an H5N1 isolate. This reduced shedding was associated with cross-reactive antibodies capable of mediating antibody-dependent cellular cytotoxicity (ADCC) effector functions. Our results suggest that ADCC may play a role in cross-protective immunity against influenza. Vaccines optimized to stimulate cross-reactive antibodies with ADCC function may provide an important measure of protection against emerging influenza viruses when NAbs are ineffective.
    Journal of Virology 09/2014; · 5.08 Impact Factor
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    ABSTRACT: The most important risk factor for human aneuploidy is increasing maternal age, but the basis of this association remains unknown. Indeed, one of the earliest models of the maternal-age effect-the "production-line model" proposed by Henderson and Edwards in 1968-remains one of the most-cited explanations. The model has two key components: (1) that the first oocytes to enter meiosis are the first ovulated and (2) that the first to enter meiosis have more recombination events (crossovers) than those that enter meiosis later in fetal life. Studies in rodents have demonstrated that the first oocytes to enter meiosis are indeed the first to be ovulated, but the association between the timing of meiotic entry and recombination levels has not been tested. We recently initiated molecular cytogenetic studies of second-trimester human fetal ovaries, allowing us to directly examine the number and distribution of crossover-associated proteins in prophase-stage oocytes. Our observations on over 8,000 oocytes from 191 ovarian samples demonstrate extraordinary variation in recombination within and among individuals but provide no evidence of a difference in recombination levels between oocytes entering meiosis early in fetal life and those entering late in fetal life. Thus, our data provide a direct test of the second tenet of the production-line model and suggest that it does not provide a plausible explanation for the human maternal-age effect, meaning that-45 years after its introduction-we can finally conclude that the production-line model is not the basis for the maternal-age effect on trisomy.
    The American Journal of Human Genetics 07/2014; 95(1):108-12. · 11.20 Impact Factor
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    ABSTRACT: Noise-induced hearing loss (NIHL) is a prevalent health risk. Inbred mouse strains 129S6/SvEvTac (129S6) and MOLF/EiJ (MOLF) show strong NIHL resistance (NR) relative to CBA/CaJ (CBACa). In this study, we developed quantitative trait locus (QTL) maps for NR. We generated F1 animals by intercrossing (129S6 × CBACa) and (MOLF × CBACa). In each intercross, NR was recessive. N2 animals were produced by backcrossing F1s to their respective parental strain. The 232 N2-129S6 and 225 N2-MOLF progenies were evaluated for NR using auditory brainstem response. In 129S6, five QTL were identified on chromosomes (Chr) 17, 18, 14, 11, and 4, referred to as loci nr1, nr2, nr3, nr4, and nr5, respectively. In MOLF, four QTL were found on Chr 4, 17, 6, and 12, referred to as nr7, nr8, nr9, and nr10, respectively. Given that NR QTL were discovered on Chr 4 and 17 in both the N2-129S6 and N2-MOLF cross, we generated two consomic strains by separately transferring 129S6-derived Chr 4 and 17 into an otherwise CBACa background and a double-consomic strain by crossing the two strains. Phenotypic analysis of the consomic strains indicated that whole 129S6 Chr 4 contributes strongly to mid-frequency NR, while whole 129S6 Chr 17 contributes markedly to high-frequency NR. Therefore, we anticipated that the double-consomic strain containing Chr 4 and 17 would demonstrate NR across the mid- and high-frequency range. However, whole 129S6 Chr 17 masks the expression of mid-frequency NR from whole 129S6 Chr 4. To further dissect NR on 129S6 Chr 4 and 17, CBACa.129S6 congenic strains were generated for each chromosome. Phenotypic analysis of the Chr 17 CBACa.129S6 congenic strains further defined the NR region on proximal Chr 17, uncovered another NR locus (nr6) on distal Chr 17, and revealed an epistatic interaction between proximal and distal 129S6 Chr 17.
    Journal of the Association for Research in Otolaryngology : JARO. 06/2014;
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    ABSTRACT: Most statistical methods for QTL mapping focus on a single phenotype. However, multiple phenotypes are commonly measured, and recent technological advances have greatly simplified the automated acquisition of numerous phenotypes, including function-valued phenotypes, such as growth measured over time. While there exist methods for QTL mapping with function-valued phenotypes, they are generally computationally intensive and focus on single-QTL models. We propose two simple, fast methods that maintain high power and precision and are amenable to extensions with multiple-QTL models using a penalized likelihood approach. After identifying multiple QTL by these approaches, we can view the function-valued QTL effects to provide a deeper understanding of the underlying processes. Our methods have been implemented as a package for R, funqtl.
    Genetics 06/2014; · 4.39 Impact Factor
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    ABSTRACT: Most statistical methods for QTL mapping focus on a single phenotype. However, multiple phenotypes are commonly measured, and recent technological advances have greatly simplified the automated acquisition of numerous phenotypes, including function-valued phenotypes, such as growth measured over time. While there exist methods for QTL mapping with function-valued phenotypes, they are generally computationally intensive and focus on single-QTL models. We propose two simple, fast methods that maintain high power and precision and are amenable to extensions with multiple-QTL models using a penalized likelihood approach. After identifying multiple QTL by these approaches, we can view the function-valued QTL effects to provide a deeper understanding of the underlying processes. Our methods have been implemented as a package for R, funqtl.
    02/2014;
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    ABSTRACT: In a mouse intercross with more than 500 animals and genome-wide gene expression data on six tissues, we identified a high proportion (18%) of sample mix-ups in the genotype data. Local expression quantitative trait loci (eQTL; genetic loci influencing gene expression) with extremely large effect were used to form a classifier to predict an individual's eQTL genotype based on expression data alone. By considering multiple eQTL and their related transcripts, we identified numerous individuals whose predicted eQTL genotypes (based on their expression data) did not match their observed genotypes, and then went on to identify other individuals whose genotypes did match the predicted eQTL genotypes. The concordance of predictions across six tissues indicated that the problem was due to mix-ups in the genotypes (though we further identified a small number of sample mix-ups in each of the six panels of gene expression microarrays). Consideration of the plate positions of the DNA samples indicated a number of off-by-one and off-by-two errors, likely the result of pipetting errors. Such sample mix-ups can be a problem in any genetic study, but eQTL data allow us to identify, and even correct, such problems. Our methods have been implemented in an R package, R/lineup.
    02/2014;
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    ABSTRACT: Segregation of chromosomes during the first meiotic division relies on crossovers established during prophase. Although crossovers are strictly regulated so that at least one occurs per chromosome, individual variation in crossover levels is not uncommon. In an analysis of different inbred strains of male mice, we identified among-strain variation in the number of foci for the crossover-associated protein MLH1. We report studies of strains with "low" (CAST/EiJ), "medium" (C3H/HeJ), and "high" (C57BL/6J) genome-wide MLH1 values to define factors responsible for this variation. We utilized immunofluorescence to analyze the number and distribution of proteins that function at different stages in the recombination pathway: RAD51 and DMC1, strand invasion proteins acting shortly after double-strand break (DSB) formation, MSH4, part of the complex stabilizing double Holliday junctions, and the Bloom helicase BLM, thought to have anti-crossover activity. For each protein, we identified strain-specific differences that mirrored the results for MLH1; i.e., CAST/EiJ mice had the lowest values, C3H/HeJ mice intermediate values, and C57BL/6J mice the highest values. This indicates that differences in the numbers of DSBs (as identified by RAD51 and DMC1) are translated into differences in the number of crossovers, suggesting that variation in crossover levels is established by the time of DSB formation. However, DSBs per se are unlikely to be the primary determinant, since allelic variation for the DSB-inducing locus Spo11 resulted in differences in the numbers of DSBs but not the number of MLH1 foci. Instead, chromatin conformation appears to be a more important contributor, since analysis of synaptonemal complex length and DNA loop size also identified consistent strain-specific differences; i.e., crossover frequency increased with synaptonemal complex length and was inversely related to chromatin loop size. This indicates a relationship between recombination and chromatin compaction that may develop as DSBs form or earlier during establishment of the meiotic axis.
    PLoS Genetics 01/2014; 10(1):e1004125. · 8.52 Impact Factor
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    ABSTRACT: Genetic mapping studies in the mouse and other model organisms are used to search for genes underlying complex phenotypes. Traditional genetic mapping studies that employ single-generation crosses have poor mapping resolution and limit discovery to loci that are polymorphic between the two parental strains. Multiparent outbreeding populations address these shortcomings by increasing the density of recombination events and introducing allelic variants from multiple founder strains. However, multiparent crosses present new analytical challenges and require specialized software to take full advantage of these benefits. Each animal in an outbreeding population is genetically unique and must be genotyped using a high-density marker set; regression models for mapping must accommodate multiple founder alleles, and complex breeding designs give rise to polygenic covariance among related animals that must be accounted for in mapping analysis. The Diversity Outbred (DO) mice combine the genetic diversity of eight founder strains in a multigenerational breeding design that has been maintained for >16 generations. The large population size and randomized mating ensure the long-term genetic stability of this population. We present a complete analytical pipeline for genetic mapping in DO mice, including algorithms for probabilistic reconstruction of founder haplotypes from genotyping array intensity data, and mapping methods that accommodate multiple founder haplotypes and account for relatedness among animals. Power analysis suggests that studies with as few as 200 DO mice can detect loci with large effects, but loci that account for <5% of trait variance may require a sample size of up to 1000 animals. The methods described here are implemented in the freely available R package DOQTL.
    G3 (Bethesda, Md.). 01/2014; 4(9):1623-33.
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    Karl W. Broman
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    ABSTRACT: R/qtl is an R package for mapping quantitative trait loci (genetic loci that contribute to variation in quantitative traits) in experimental crosses. Its development began in 2000. There have been 32 software releases since 2002. The latest release contains 35k lines of R code and 24k lines of C code, plus 15k lines of code for the manual pages. A key to its success is that it remains a central tool for the chief developer's own research work, and so its maintenance is of selfish importance.
    Journal of open research software. 09/2013; 2(1).
  • Stem Cells in Reproductive Medicine: Basic Science & Therapeutic Potential, Third edited by Carlos Simón, Antonio Pellicer, Renee Reijo Pera, 09/2013: chapter Meiotic Recombination in Human Oocytes: pages 63-75; Cambridge University Press., ISBN: 9781107034471
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    ABSTRACT: Automated image acquisition, a custom analysis algorithm, and a distributed computing resource were used to add time as a third dimension to a quantitative trait locus (QTL) map for plant root gravitropism, a model growth response to an environmental cue. Digital images of Arabidopsis thaliana seedling roots from two independently reared sets of 162 recombinant inbred lines (RILs) and one set of 92 near isogenic lines (NILs) derived from a Cape Verde Islands (Cvi) x Landsberg erecta (Ler) cross were collected automatically every two minutes for eight hours following induction of gravitropism by 90 degree reorientation of the sample. High Throughput Computing (HTC) was used to measure root tip angle in each of the 1.1 million images acquired and to perform statistical regression of tip angle against the genotype at each of the 234 (RIL) or 102 (NIL) DNA markers independently at each time point using a standard stepwise procedure. Time-dependent QTL were detected on chromosomes 1, 3, and 4 by this mapping method and by an approach developed to treat the phenotype time course as a function-valued trait. The QTL on chromosome 4 was earliest, appearing at 0.5 h and remaining significant for 5 h, while the QTL on chromosome 1 appeared at 3 h and thereafter remained significant. The Cvi allele generally had a negative effect of 2.6-4.0%. Heritability due to the QTL approached 25%. This study shows how computer vision and statistical genetic analysis by HTC can characterize the developmental timing of genetic architectures.
    Genetics 08/2013; · 4.39 Impact Factor
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    ABSTRACT: Loci controlling plasma lipid concentrations were identified by performing a quantitative trait locus (QTL) analysis on genotypes from 233 mice from a F2 cross between KK/HlJ and I/LnJ, two strains known to differ in their high-density lipoprotein (HDL) cholesterol levels. When fed a standard diet, HDL cholesterol concentration was affected by two significant loci, the Apoa2 locus on Chromosome (Chr) 1 and a novel locus on Chr X, along with one suggestive locus on Chr 6. Non-HDL concentration was also affected by loci on Chr 1 and X along with a suggestive locus on Chr 3. Additional loci that may be sex-specific were identified for HDL cholesterol on Chr 2, 3 and 4 and for non-HDL cholesterol on Chr 5, 7 and 14. Further investigation into the potential causative gene on Chr X for reduced HDL cholesterol levels revealed a novel, I/LnJ-specific non-synonymous polymorphism in Nsdhl, which codes for sterol-4-alpha-carboxylate 3-dehydrogenase in the cholesterol synthesis pathway. While many lipid QTL have been reported previously, these data suggest there are additional genes left to be identified that control lipid levels and that can provide new pharmaceutical targets.
    G3-Genes Genomes Genetics 08/2013; · 1.79 Impact Factor
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    ABSTRACT: The overall CD8 T cell response to human/simian immunodeficiency virus (HIV/SIV) targets a collection of discrete epitope specificities. Some of these epitope-specific CD8 T cells emerge in the weeks and months following infection and rapidly select for sequence variants, whereas other CD8 T cell responses develop during chronic infection and rarely select for sequence variants. In this study, we tested the hypothesis that acute CD8 T cell responses that do not rapidly select for escape variants are unable to control viral replication in vivo as well as those that do rapidly select for escape variants. We created a derivative of the live attenuated SIV (SIVmac239Δnef) in which we ablated five epitopes that elicit early CD8 T cell responses and rapidly accumulate sequence variants in SIVmac239-infected Mauritian cynomolgus macaques (MCMs) who are homozygous for the M3 major histocompatibility complex (MHC) haplotype. This live attenuated SIV variant was called m3KOΔnef. Viremia was significantly higher in M3 homozygous MCMs infected with m3KOΔnef when compared to either MHC-mismatched MCMs infected with m3KOΔnef or MCMs infected with SIVmac239Δnef. Three CD8 T cell responses, including two that do not rapidly select for escape variants, predominated during early m3KOΔnef infection in the M3 homozygous MCMs, but these animals were unable to control viral replication. These results provide evidence that acute CD8 T cell responses that have the potential to rapidly select for escape variants in the early phase of infection are needed to establish viral control in vivo.
    Journal of Virology 06/2013; · 5.08 Impact Factor
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    ABSTRACT: We have estimated the prevalence of FMR1 premutation and gray zone CGG repeat expansions in a population-based sample of 19,996 male and female adults in Wisconsin and compared the observed sex ratios of the prevalence of FMR1 CGG premutation and gray zone expansions to theoretical sex ratios. The female premutation prevalence was 1 in 148 and comparable to past research, but the male premutation prevalence of 1 in 290 is somewhat higher than most previous estimates. The female:male premutation prevalence ratio is in line with the theoretically predicted sex ratio. The prevalence of CGG repeats in the gray zone (45-54 repeats) was 1 in 33 females and 1 in 62 males. The prevalence of the "expanded" gray zone (defined here as 41-54 CGG repeats) was 1 in 14 females and 1 in 22 males, leading to a female:male ratio of 1.62 (95% confidence interval 1.39-1.90). This female:male ratio was significantly lower than the expected ratio of 2.0. We examined results from three previously published FMR1 prevalence studies and found similar female:male ratios for CGG repeats in this "expanded" gray zone range (pooled female:male ratio across all four studies 1.66, 95% confidence interval 1.51-1.82). Further research is needed to understand the apparent excess prevalence of males with CGG repeats in this range. © 2013 Wiley Periodicals, Inc.
    American Journal of Medical Genetics Part B Neuropsychiatric Genetics 06/2013; · 3.23 Impact Factor
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    ABSTRACT: Meiotic recombination is sexually dimorphic in most mammalian species, including humans, but the basis for the male:female differences remains unclear. In the present study, we used cytological methodology to directly compare recombination levels between human males and females, and to examine possible sex-specific differences in upstream events of double-strand break (DSB) formation and synaptic initiation. Specifically, we utilized the DNA mismatch repair protein MLH1 as a marker of recombination events, the RecA homologue RAD51 as a surrogate for DSBs, and the synaptonemal complex proteins SYCP3 and/or SYCP1 to examine synapsis between homologs. Consistent with linkage studies, genome-wide recombination levels were higher in females than in males, and the placement of exchanges varied between the sexes. Subsequent analyses of DSBs and synaptic initiation sites indicated similar male:female differences, providing strong evidence that sex-specific differences in recombination rates are established at or before the formation of meiotic DSBs. We then asked whether these differences might be linked to variation in the organization of the meiotic axis and/or axis-associated DNA and, indeed, we observed striking male:female differences in synaptonemal complex (SC) length and DNA loop size. Taken together, our observations suggest that sex specific differences in recombination in humans may derive from chromatin differences established prior to the onset of the recombination pathway.
    PLoS ONE 01/2013; 8(12):e85075. · 3.53 Impact Factor
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    ABSTRACT: Alleles derived from skin tumor-resistant Car-R mice provide resistance to both skin and lung tumorigenesis over the susceptibility of the SWR/J strain. In an effort to map tumor modifier loci affecting both tumor types, we carried out a genetic linkage analysis in backcross SWR/J x (SWR/J x Car-R) mice and identified a locus (Lsktm1) on chromosome 1 linked to both skin (LOD score = 3.93) and lung (LOD score = 8.74) tumorigenesis. Two genes, Igfbp5 and Igfbp2, residing in this locus and belonging to the insulin-like growth factor binding protein family were expressed at significantly greater levels in normal lung tissue from cancer-resistant Car-R mice than in cancer-susceptible SWR/J mice. Overexpression of the recombinant Igfbp5 and Igfbp2 genes in two lung cancer cell lines significantly inhibited clonogenicity (P < 0.0001). Collectively, we have identified a single polymorphic locus that affects skin and lung tumorigenesis and identify Igfbp5 and Igfbp2 as candidate modifier genes of lung tumorigenesis.
    G3-Genes Genomes Genetics 09/2012; 2(9):1041-6. · 1.79 Impact Factor
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    ABSTRACT: Despite advances in genetic mapping of quantitative traits and in phylogenetic comparative approaches, these two perspectives are rarely combined. The joint consideration of multiple crosses among related taxa (whether species or strains) not only allows more precise mapping of the genetic loci (called quantitative trait loci, QTL) that contribute to important quantitative traits, but also offers the opportunity to identify the origin of a QTL allele on the phylogenetic tree that relates the taxa. We describe a formal method for combining multiple crosses to infer the location of a QTL on a tree. We further discuss experimental design issues for such endeavors, such as how many crosses are required and which sets of crosses are best. Finally, we explore the method's performance in computer simulations, and we illustrate its use through application to a set of four mouse intercrosses among five inbred strains, with data on HDL cholesterol.
    Genetics 06/2012; 192(1):267-79. · 4.39 Impact Factor
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    ABSTRACT: Quantitative trait loci (QTL) hotspots (genomic locations affecting many traits) are a common feature in genetical genomics studies and are biologically interesting since they may harbor critical regulators. Therefore, statistical procedures to assess the significance of hotspots are of key importance. One approach, randomly allocating observed QTL across the genomic locations separately by trait, implicitly assumes all traits are uncorrelated. Recently, an empirical test for QTL hotspots was proposed on the basis of the number of traits that exceed a predetermined LOD value, such as the standard permutation LOD threshold. The permutation null distribution of the maximum number of traits across all genomic locations preserves the correlation structure among the phenotypes, avoiding the detection of spurious hotspots due to nongenetic correlation induced by uncontrolled environmental factors and unmeasured variables. However, by considering only the number of traits above a threshold, without accounting for the magnitude of the LOD scores, relevant information is lost. In particular, biologically interesting hotspots composed of a moderate to small number of traits with strong LOD scores may be neglected as nonsignificant. In this article we propose a quantile-based permutation approach that simultaneously accounts for the number and the LOD scores of traits within the hotspots. By considering a sliding scale of mapping thresholds, our method can assess the statistical significance of both small and large hotspots. Although the proposed approach can be applied to any type of heritable high-volume "omic" data set, we restrict our attention to expression (e)QTL analysis. We assess and compare the performances of these three methods in simulations and we illustrate how our approach can effectively assess the significance of moderate and small hotspots with strong LOD scores in a yeast expression data set.
    Genetics 06/2012; 191(4):1355-65. · 4.39 Impact Factor
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    ABSTRACT: Nonalchoholic fatty liver disease (NAFLD) is the most common cause of liver dysfunction and is associated with metabolic diseases, including obesity, insulin resistance, and type 2 diabetes. We mapped a quantitative trait locus (QTL) for NAFLD to chromosome 17 in a cross between C57BL/6 (B6) and BTBR mouse strains made genetically obese with the Lep(ob/ob) mutation. We identified Tsc2 as a gene underlying the chromosome 17 NAFLD QTL. Tsc2 functions as an inhibitor of mammalian target of rapamycin, which is involved in many physiological processes, including cell growth, proliferation, and metabolism. We found that Tsc2(+/-) mice have increased lipogenic gene expression in the liver in an insulin-dependent manner. The coding single nucleotide polymorphism between the B6 and BTBR strains leads to a change in the ability to inhibit the expression of lipogenic genes and de novo lipogenesis in AML12 cells and to promote the proliferation of Ins1 cells. This difference is due to a different affinity of binding to Tsc1, which affects the stability of Tsc2.
    The Journal of Lipid Research 05/2012; 53(8):1493-501. · 4.39 Impact Factor
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    ABSTRACT: Genetic dissection of complex, polygenic trait variation is a key goal of medical and evolutionary genetics. Attempts to identify genetic variants underlying complex traits have been plagued by low mapping resolution in traditional linkage studies, and an inability to identify variants that cumulatively explain the bulk of standing genetic variation in genome-wide association studies (GWAS). Thus, much of the heritability remains unexplained for most complex traits. Here we describe a novel, freely available resource for the Drosophila community consisting of two sets of recombinant inbred lines (RILs), each derived from an advanced generation cross between a different set of eight highly inbred, completely resequenced founders. The Drosophila Synthetic Population Resource (DSPR) has been designed to combine the high mapping resolution offered by multiple generations of recombination, with the high statistical power afforded by a linkage-based design. Here, we detail the properties of the mapping panel of >1600 genotyped RILs, and provide an empirical demonstration of the utility of the approach by genetically dissecting alcohol dehydrogenase (ADH) enzyme activity. We confirm that a large fraction of the variation in this classic quantitative trait is due to allelic variation at the Adh locus, and additionally identify several previously unknown modest-effect trans-acting QTL (quantitative trait loci). Using a unique property of multiparental linkage mapping designs, for each QTL we highlight a relatively small set of candidate causative variants for follow-up work. The DSPR represents an important step toward the ultimate goal of a complete understanding of the genetics of complex traits in the Drosophila model system.
    Genome Research 04/2012; 22(8):1558-66. · 14.40 Impact Factor

Publication Stats

7k Citations
879.66 Total Impact Points

Institutions

  • 2008–2014
    • University of Wisconsin–Madison
      • • Department of Biostatistics and Medical Informatics
      • • Department of Pathology and Laboratory Medicine
      Madison, Wisconsin, United States
  • 2011
    • University of North Carolina at Chapel Hill
      • Department of Genetics
      Chapel Hill, NC, United States
  • 2010
    • Washington State University
      • School of Molecular Biosciences
      Pullman, WA, United States
    • University of Missouri
      • Department of Veterinary Medicine and Surgery
      Columbia, MO, United States
    • University of Groningen
      • Groningen Bioinformatics Centre (GBIC)
      Groningen, Province of Groningen, Netherlands
  • 2009
    • University of Virginia
      • Department of Biomedical Engineering
      Charlottesville, VA, United States
  • 2007–2009
    • University of California, San Francisco
      • Department of Epidemiology and Biostatistics
      San Francisco, CA, United States
  • 2003–2008
    • Johns Hopkins Bloomberg School of Public Health
      • Department of Biostatistics
      Baltimore, Maryland, United States
  • 2000–2007
    • Johns Hopkins University
      • • Department of Biostatistics
      • • Department of Environmental Health Sciences
      • • Wilmer Eye Institute
      Baltimore, MD, United States
    • St. Mary's Hospital (WI, USA)
      Madison, Wisconsin, United States
    • Mayo Foundation for Medical Education and Research
      Rochester, Michigan, United States