1QTL mappingPaul GeptsPLB152: Plant GeneticsSources• ** Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica 142:169-196• *Francia E, Tacconi G, Crosatti C, Barabaschi D, Bulgarelli D, Dall'Aglio E, Vale G (2005) Marker assisted selection in crop plants. Plant Cell Tissue and Organ Culture 82:317-342 (for this lecture: especially pp. 317-321)• Morgante M, Salamini F (2003) From plant genomics to breeding practice. Curr Opin Biotechnol 14:214-219• Xu YB, McCouch SR, Zhang QF (2005) How can we use genomics to improve cereals with rice as a reference genome? Plant Molec Biol 59:7-26• Varshney RK, Graner A, Sorrells ME (2005) Genomics-assisted breeding for crop improvement. Trends Plant Sci 10:621-630• Kelly JD, Gepts P, Miklas PN, Coyne DP (2003) Tagging and mapping of genes and QTL and molecular marker-assisted selection for traits of economic importance in bean and cowpea. Field Crops Res 82:135–154• Sax K (1923) The association of size differences with seed coat pattern and pigmentation in Phaseolus vulgaris. Genetics 8:552-560Outline• What is a qualitative/quantitative trait?• QTL mapping• Towards MAS: marker-assisted selection2Difference between a qualitative and a quantitative trait• Qualitative:– Presence or absence:• Growth habit: tall vs. dwarf• Pigmentation: pigmented vs. non-pigmented• Disease reaction: resistant vs. susceptible– Generally single gene trait– Markers are qualitative traits• Quantitative:– Quantity:• Tallness• Yield– Generally more than one gene and/or environmental effectsExamples of qualitative traits:see Mendel!Effect of the number of genes on segregationEffect of the magnitude of the environmental effect on segregationFrom Allard 19923Consequence of the nature of segregation of quantitative traits• No one-on-one relationship between phenotype and genotype:– One cannot infer the genotype underlying a phenotype– Uncertainty as to the number of genes and/or magnitude of environmental effects• Solution: proposed by Sax (1923)– “Mendelize” the segregation– Correlate segregation of the quantitative trait with that of qualitative trait, i.e., markers– QTL = quantitative trait locus = gene!Methodology of Sax (1923)A BDADBMA1MB1MA2MB2D: gene for domesticationM: molecular markers 1 & 2xParents:Segregating progeny: subpopulations based on segregation for molecular markers MB1(mostly D ; some D )ABM A1(mostly D ; some D ) vs.ABM A2(D + D ) vs.ABMB1(D + D )ABExample: seed weightSeed weight0246810121416183456789101112Seed weightFrequencySeed weightQTL Mappng Seed Weightin cross Midas x G12873012345678910Phs P SkdhAllele MAllele G4Based on results:• No seed weight gene near Skdhlocus• One or more seed weight genes near Pand Phs; same or different?Mapping informationMethodology of Sax (1923) (contd.)• Repeat previous analysis for set of markers distributed at regular intervals on each chromosome in the genome• Results: i.e. data obtained– minimum number of genes distinguishing the two parents– magnitude of the phenotypic effect of individual genes– total proportion of phenotypic variation in the segregating population for a given trait that can be accounted for in genetic terms– linkage relationships among genes• Same basic approach in human genetics but some modifications: e.g., affected siblings methodMethods to detect QTLs• Single-marker analysis:– Divide population into subpopulations based on allelic segregation of individual loci– Measure phenotypic trait and average in each subpopulation– Determine if statistically significant differences by:• t-test, ANOVA, linear regression (R2)– Disadvantage:• Underestimation of the effect of a linked QTL due to recombination between marker and QTL• Solution: use markers at spacing < 10-15 cM5Methods to detect QTLs• Simple interval mapping:– Analyzes intervals between adjacent markers instead of single markers– Eliminates problems of recombination between marker and QTL– Statistically more powerful• Composite interval mapping– Analyzes intervals between adjacent markers + additional markers unlinked to the interval markers to focus on the interval and eliminate confounding effects from other QTLs:• Elsewhere in the genome: “background noise”• Linked to QTL: biases location of QTLQTL mapping statistics• LOD = logarithm of the odds ratio– (P of observing the data under linkage)/P of observing the same data under no linkage)–Key value: ≥ 3; this means 1000 more likely under linkage• Permutation test:– Shuffle phenotypic data but keep marker data constant– Repeat 1000 times– Determine significance level such that false positives is below a certain level.Reporting QTL data• Generally on linkage maps:– Rectangles:• Encompassing most closely linked markers • Exceeding significance level• “Major” and “minor” QTLs:–R2> 30%: major; <10%: minor• Confidence interval:– Around the max LOD location:Region that corresponds to a decline of 1 LOD from the peak6Methodological concerns• Number of markers:– Approx. every 10 cM Æ total number depends on genetic size of the genome; usually around 100-200• Shared markers:– Try using > 2-3 shared markers per chromosome to allow comparisons among different maps• Size of the mapping population:– Larger populations: >150-200• More accurate mapping of QTLs• Detection of smaller QTLs• Less overestimation of magnitude of QTLs• Confirmation of QTL mapping– Different population from same parents– Near-isogenic linesShort-cuts for gene tagging• Bulked segregantanalysis:– Two bulks of DNA samples from ~ 10 individuals each, differing for the trait of interest– For traits controlled by major genes or QTLs• Trait-based marker analysis:– Select individuals with extreme phenotypes– Only for one trait at a timeTowards marker-assisted selection• MAS = select for linked marker instead of the phenotypic trait– Time savings compared to complex field trials– Elimination of unreliable or unfeasible (e.g., quarantine) phenotypic evaluations– Selection at a different development or growth stage– Gene pyramiding– Limit transfer of linked, undesirable genes (“linkage drag”)– Selection for traits with low heritabilityFrom Francia et al.