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How to Detect Fitness-Related Genes

How to Detect Fitness-Related Genes. Approach 1: Detection by linkage to genetic markers. Detection is possible wherever a marker locus (with alleles M and m) is located near a QTL, or quantitative trait locus (with alleles Q and q)

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How to Detect Fitness-Related Genes

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  1. How to Detect Fitness-Related Genes

  2. Approach 1:Detection by linkage to genetic markers • Detection is possible wherever a marker locus (with alleles M and m) is located near a QTL, or quantitative trait locus (with alleles Q and q) • Both loci must be heterozygous in an individual to detect linkage in its progeny

  3. Segregation within the Family • The individual is mated (maybe many times) • Its progeny are screened for many markers and quantitative traits (e.g., tarsus length, number of offspring, milk yield, growth rate, …) • A test of linkage exists wherever it is heterozygous and its mate is homozygous • Hence, there will be many tests per family • The tests available will differ among families

  4. Are the marker and QTL loci linked? • Progeny of heterozygote are divided into two groups on the basis of which marker allele they inherited • Compare the performance (= quantitative trait, fitness) of the two groups, often by t-test • Most tests give insignificant results • A few tests show significance, implying that the marker and QTL are linked

  5. Mapping the trait on a chromosome • If there are multiple markers of known position on the chromosome, you can use maximum likelihood test to map QTL to intervals between markers • Termed composite interval mapping • Ex: Birth weight in pigs on chromosome 4 • Note threshold for significance, often 6

  6. Genome scan for QTLs • If you know the genetic map of the species and test segregating markers across the genome, you can perform a genome scan for QTLs for the trait • Ex: Genome scan for birth weight in pigs - only chromosome 4 has a major gene

  7. A linked marker is not itself a QTL • You’ll need to do some heavy-duty molecular genetics work to actually find the QTL • Rounds of positional cloning will yield more and more tightly linked markers till you find the one gene • Then, try and infer what it does • Lots of work!

  8. What are the fitness-related genes? • We’ve always screened selectively “neutral” maker loci • In some cases, we have noted direct effects of genetic variation on fitness (e.g., LDH in mummichog) • Identifying fitness-conferring loci is the Holy Grail of population and evolutionary genetics • Identifying QTLs linked with fitness is a key step towards identifying the fitness-conferring loci themselves

  9. QTL mapping for classical fitness-related traits in Drosophila melanogaster • Wayne et al. (2001. Genet. Res. 77:107-116) • Genome scan for four traits in a panel of 98 recombinant inbred lines (RIL): • Reproductive success (RS) • Ovariole number • Body size • Early fecundity • 76 informative markers, average spacing 3.2 cM • Composite interval mapping

  10. QTLs for fitness in Drosophila melanogaster • QTLs for female RS mapped to tip of X chromosome and to chromosome 2 • QTLs for ovariole number mapped to two regions on chromosome 3 • These regions had been found to harbor QTL for longevity • No QTLs found: • Male reproductive success • Body size in either sex • Early female fecundity

  11. QTL mapping of GxE interaction for fitnessin Drosophila melanogaster • Fry et al. (1998. Genet. Res: 71: 133-141) mapped QTLs for RS in 98 RIL in three environments: • Standard medium, 25oC • Ethanol-supplemented medium, 25oC • Standard medium, 18oC • Highly significant GxE interaction • Interactions arose because QTLS had stronger effects at one temperature than another; no evidence for QTLs with opposite effects in different environments • Suggests that antagonistic pleiotropy is an uncommon form of GxE, but much more study is needed

  12. QTLs detected were sex- or environment-specific • Differences in genetic architecture for male and female RS is perhaps not surprising • Genetic architecture for female RS distinct from that for ovariole number - inferences on genetic correlations of fitness traits • Genetic variation for RS very sensitive to environment - even different environments within very standardized experiments • Suggests that environmental heterogeneity contributes to maintenance of variation in life history traits

  13. Putting this into perspective... • RILs in laboratory, a simplified experimental system • Nature is a more complex system: • segregating genome • high allelic diversity • uncontrolled environmental fluctuation • open population • Even in simplified system, 300,000 individuals scored • QTL screening for all fitness loci in natural populations is not feasible • What, then, is the utility of such findings to population and evolutionary genetics? • Once fitness-related loci identified and dynamics inferred using simplified experimental designs, we can make inferences about significance of findings to fitness in nature

  14. Relating this to managed organisms... • A number of QTLs have been detected in fish: • Upper thermal tolerance (UTT) in rainbow trout • Spawning time in rainbow trout • Cold tolerance in tilapia • Etc. • Nothing yet in wildlife species, even these are aquaculture species • In each case, QTLs were detected in backcrosses or F2 intercrosses of species, inbred lines, or divergently selected lines • Can the findings be generalized?

  15. QTL detection in outbred fish populations • Danzmann/Ferguson group had shown linkage of markers at three loci with UTT in divergently selected lines of rainbow trout • Perry et al. (2001. Heredity 86:333-341) sought to determine whether markers associated with UTT in divergently selected lines showed same association in two outbred stocks • Two-way diallel crosses among commercial strains, third generation exposed to thermal challenge • Analyses for QTLs performed separately for each second generation parent

  16. QTLs for UTT in outbred rainbow trout • Alleles at Ssa20.19NUIG for sire 92-32-1 explained 7.5% of variance for UTT (that’s a lot) • GLM incorporating phenotypic, allelic, and pedigree information for all four sires were associated with UTT in grand-progeny • The marker Ssa20.19NUIG was previously reported to have strongest association with UTT, though amount of variance explained and overall UTT were less than in backcross families • Results exemplify use of results from inbred or divergently selected family material to screen for QTLs in outbred populations

  17. What experimental designs are appropriate for mapping fitness-related traits in outbred populations of aquatic organisms? • First, screenings in hybrid, inbred, or divergently selected lines under laboratory conditions: • Model species: medaka, zebrafish • Mapped aquaculture species: rainbow trout, tilapia, other species?) • Test for linkages in outbred populations in common garden experiments • With high fecundity and existing maps, aquatic species may prove attractive • Extend inferences to other species • Tough for mammals and birds – too few progeny

  18. Approach 2: Functional genomics, i.e., study of gene expression

  19. Rapid screening of gene expression • Microarray– thousands of gene transcripts immobilized on a solid surface • Expose your organism to a stimulus. Collect mRNA and screen a microarray for the species • The intensity of gene expression can be quantified… • A technical breakthrough in study of gene expression

  20. Case Study:How diet regulates gene expression in tilapia S. Craig1, E. McLean1, E. Hallerman1, and J. Craig2 1 Virginia Polytechnic Institute and State University Blacksburg, VA 24061-0321, USA 2 Virginia Bioinformatics InstituteBlacksburg, VA 24061-0477, USA

  21. Diet affects gene expression in intestine • Diet triggers signaling cascades in lumen in mammals • Does that also occur in fishes? • Could we develop diets that affect gene expression and confer benefits to production? • No studies of impact of diet on gene expression profile in fishes • Objective: To determine differential effects of diets on gene expression in tilapia

  22. Methods • Fish husbandry: • 20 tilapia (initial weight 35g) stocked into each of 8 tanks • Two replicates per treatment, 40 fish per treatment • Diets hand-fed at 3% BW for 9 weeks • Experimental diets:

  23. Methods • Sample collection: • 5 fish sampled arbitrarily per treatment • 10-cm section of intestine excised, starting 5 cm below pyloric sphincter • Tissue placed in mortar containing LN2 and crushed using per-cooled pestle • RNA isolation: • Total RNA isolated using RNeasy kit (Qiagen), ethanol precipitation • Quality and amount of mRNA assessed using Bioanalyzer agarose gel system (Agilent Technologies) • Total RNA used to synthesize biotin-labeled cRNA (Enzo Diagnostics)

  24. Methods • Microarray preparation and screening: • cRNA spiked with internal controls • Hybridized overnight to zebrafish gene chip (Affymetrix) • N = 8 chips total • Chips washed and stained with streptavidin-phycoerythrin • Fluorescence quantified using gene chip scanner (Affymetrix)

  25. Methods • Data pre-processing: • Probe-level data (22 spots per gene) pre-processed using RMA method: (Bioconductor, v. 1.5) • To adjust background • To perform within- and between-chip normalization • Filtering: • Uninformative genes eliminated from dataset (Genespring, v. 7.2): • Genes with signals very near background • Genes that did not change expression values across treatments • Data analysis: • Quality-checked dataset was 1, 210 genes • Individual gene chips examined for reproducibility (Genespring, v. 7.2) • Two dimensional scatter-plot • Hierarchical clustering

  26. Results • Husbandry: • No mortalities during study • Fish grew an average of 270% from initial weight

  27. LP LL HP HL The various diets elicited differences in gene expression profiles… A “heat diagram”

  28. Ubiquitin-related 7% Phosphatase 7% RNA binding Transport 16% 7% Chromatin 3% Receptor Transcription 3% 17% Signal Transduction Cell cycle 17% 17% Structural protein 3% Extracellular matrix protein 3% Functional gene categories affected by high protein diet • Most up-regulated genes were of unknown function • At left, genes of known or inferred function represented ~25% of all high protein- induced, up-regulated genes • Duplicate 33% of all high lipid-induced, up-regulated genes

  29. Genes exhibiting >1.5-fold increases in expression for the high protein diet • Gene Inc. Role • SPOP 4.4 Mediates protein-protein interactions • CyclinA2 3.8 Cell cycle regulation • CyclinB1 3.5 Cell cycle regulation • EGFR pathway substrate 8-related protein 1 3.3 Regulates apical morphogenesis in intestinal cells • ATP synthase subunit b-like 3.1 Mitochondrial metabolism, intracellular signal transduction • ODC antizyme 2 3.0 Regulates enzyme in polyamine synthesis • TFIID 70 kDa subunit 3.0 Factor regulating many cellular processes • Smarca4 2.9 Chromatin remodeling • E2F3 2.7 Transcription factor, cell cycle and apoptosis • NFAT 2.6 Transcription factor, regulates cell differentiation • Hoxb9a 2.4 Establishes major structural landmarks in gut • SDH cyt B small subunit 2.3 Membrane-bound protein, energy metabolism • Hoxb1a 2.4 Establishes major structural landmarks in gut • CLC7 2.2 Anion channel activated by rise in [Ca++] • Dystrobrevin b 2.1 Glycoprotein expressed in mesoderm • GABA (A) receptor-associated protein like 2.2 Modulates enteric motility and mucosal function, may regulate hormonal and paracrine signaling • Keratin4 1.8 Large constituent of high-shedding cells • Claudin6 1.5 Membrane protein, role in tight membrane function of intestinal barrier

  30. Transport 4% Membrane 2% Signal Transduction 10% Structure Apoptosis 2% Kinesin 8% 2% Transcription Factors 15% Cell cycle 6% Signaling Chromatin 10% 2% Growth factors Kinase Receptors Energy 4% 6% 15% 10% Decarboxylase 2% Carboxylase 2% Functional gene categories affected by high lipid diet • Up-regulated pathways included energy, signal transduction, and cellular communications pathways • Key up-regulated genes included apolipoprotein receptor E and apoptosis regulator birc5b

  31. Genes exhibiting >1.5-fold increases in expression for the high lipid diet Gene Inc. Role arf1 7.0x G-protein, activates phospholipase D aporE 4.3x Involved in lipid metabolism mbd2 4.1x Methyl-binding domain, directly impacted by nutrition, esp. vitamins B6 and riboflavin bambi 3.7x Inhibits TGF-b signaling PI3KC 2.8x Involved in inositol lipid metabolism esr1 2.8x Estrogen receptor, regulates DNA transcription RFP2 2.6x Involved in protein degradation FSC2 2.6x Fascin 2, involved in gastric apoptosis Rab-1a 2.5x Ras-related protein, signal transduction, protein transport LRP1 2.3x Low density lipoprotein receptor-related protein 1 LUC7l 2.2x RNA binding protein – regulates myogenesis gclm 2.1x ATP-dependent ligase birc5b 1.7x Apoptosis regulator Dead/h 1.7x ATP-dependent RNA helicase, responsive to low intracellular Zn

  32. Discussion • This is the first study showing dietary modification of gene expression in the gut of tilapia • Identifies candidate genes for quantitative RT-PCR studies to truly quantify changes of expression of specific genes • Use of microarrays allows a much broader survey of gene expression

  33. Functional genomics and gene expression – some case studies • Toxicogenomic response to individual chemicals and mixtures • Effects of persistent toxicants in the diet • Gene expression in macrophages in response to immunogenic stimuli • Gene expression in response to plant feedstuffs in the diet • Transcriptome variation among wild and domesticated fish in response to satiety and stress • Transcriptome variation among life history morphs of lake trout and sockeye salmon

  34. Suppose, the, that by any method, we know the fitness-related genes… How does the genetic composition of a population change in response to selection pressure? • Abiotic environmental stressors • Disease • Introgression of exogenous genes: • Selectively bred or inbred stocks • Interspecific hybrids • Transgenics • Let’s consider some examples of how such experiments might be designed

  35. What genes and mechanisms underlie inbreeding depression? • Which genes are differentially expressed in inbred and outbred half-sibs? • Dominance hypothesis: Inbreeding depression results from expression of deleterious recessive alleles masked in heterozygous state • Overdominance hypothesis: Inbreeding depression results from low levels of heterozygosity genome-wide

  36. What is the genetic architecture of inbreeding depression? • Inferring the effects of segregating alleles at individual loci has proven problematic using classical methods, but mapping may contribute to our understanding • Conifers exhibit high levels of inbreeding depression, especially for embryonic viability and early growth • Remington et al. (2000. Evolution 54:186-1589) screened for QTLs for early survival and early growth in a selfed family of loblolly pine

  37. QTLs for survival and growth in loblolly pine • Two QTLs linked to height growth accounted for 13% of inbreeding depression • One mapped to cad-n1, a lignin biosynthesis mutation • A chlorophyll deficiency mutation, spf, had significant effects upon survival • Results suggest loci affecting inbreeding depression are largely stage-specific • Results challenge hypothesis that inbreeding depression for growth is largely due to alleles of small effect at many loci • Results are family-specific, but provide detail not attainable to date • Mapping allows observation of recombination, size of effects, additivity/dominance, and linkages among traits

  38. What genes and mechanisms underlie heterosis? • Some interspecific hybrids and intraspecific crossbreds express heterosis for valued traits • The molecular basis for heterosis is generally not understood: • Novel molecules (e.g., hemoglobin in centrarchids)? • Novel gene regulation (much more common)? • Expression of which loci is affected by hybridization, and affect valued traits?

  39. Environmental genomics • Advances in basic science: adaptation, physiology, population genetics, quantitative genetics • Advances in understanding of genomics may allow aquaculturists and fisheries managers to minimize potential population-level impacts of their activities. • Wildlife: bovids, porcids, caprids, ovids, galliforms – limited scope of application, so far…

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