Mapping Quantitative Trait Loci Two approaches Biparental mapping populations Linkage analysis in unselected progeny of a cross Genome wide association studies (GWAS) Search for associations between markers and phenotypes in a panel of individuals that represent the gene pool of interest

Why map QTL? Understand how genes control quantitative traits (trait dissection) To aid in selection for a phenotype (Marker Assisted Selection = MAS) Starting point for map-based cloning of genes Conifer Genomics Learning Modules http://articles.extension.org/pages/60370/conifer-genomics-learning-modules Developed by Nicholas Wheeler and David Harry for the CTGN

QTL mapping – linkage analysis Select two parents that are genetically diverse for the trait(s) of interest Cross parents and develop a population of segregating progeny Measure phenotypes Score molecular markers Construct a linkage map for the markers Identify QTL that are linked to the markers Observe segregation of genetic markers See if individuals with different marker genotypes have different phenotypes Oregon Wolfe Barley Dom Rec www.barleyworld.org

Advantages of biparental mapping populations Derived from an F1 population, so for unlinked loci, “linkage” equilibrium is obtained with one generation of recombination Effects of QTL are not correlated (confounded) with the effects of unlinked genes in the population Simplicity No more than two alleles per locus For polymorphic loci, p = q = 0.5 (no minor alleles) Note: Other types of pedigreed populations can also be used for linkage mapping Results from multiple mapping populations may be pooled together These approaches may provide additional benefits, but analysis may be more complex

Disadvantages of linkage analysis approach There is often only one opportunity for recombination, so estimates of map distances are imprecise Large populations are needed to obtain rare recombinants (in practice, existing populations are often too small) Alternatively, can random-mate the F2 for several generations before extracting lines, but this requires more time and expense Must develop and maintain mapping populations that are often used solely for experimental purposes e.g., resistant x susceptible (rather than resistant x resistant) additional time and expense Results are specific for each mapping population only 2 alleles per locus are sampled

Common types of mapping populations F2 population Individual plants F2:3 or F2:4 families – families of F3 or F4 plants that trace back to an F2 individual Backcross population Recombinant inbred lines (RILs)** Inbred lines obtained from an F2 population through single seed descent (without selection)  F6 or later generation Doubled haploids (DH)** Derived from male or female gametes of an F1 plant F2 and backcross – advantage is simplicity and short time-frame for development; disadvantage is that they are heterozygous and subject to sampling variation RILs – takes longer, but haplotypes are permanently fixed. DH – system for production in tissue culture must exist. Haplotypes, with no residual variation. **Populations of fixed haplotypes can be efficiently genotyped and permanently maintained

Linkage mapping - steps Genotype the mapping population and prepare data Calculate recombination fractions (RFs) Maximum likelihood estimates of pairwise RFs Group loci into linkage groups Based on maximum allowable RF threshold Apply LOD threshold (e.g., LOD > 3 indicates linkage) Locus ordering Begin with smallest RFs, and add others to minimize total size Highest multipoint LOD among possible orders Computer intensive Multilocus estimation of map distances Apply appropriate mapping function (Haldane, Kosambi)

Mapping functions When RF is small (≤ 15%), RF~cM When RF is larger (>15%), then cM ≥ RF Most mapping software includes map function adjustments Haldane accounts for double crossovers Kosambi also accounts for interference, which reduces chances of double crossovers Source: Conifer Genomics Learning Modules

Software >100 genetic analyses software packages (linkage analysis and QTL mapping) Distinguishing features Computer platform Types of populations that can be handled Mapping methods Price Common examples for linkage maps Mapmaker/EXP JoinMap Common examples for QTL analysis QTL Cartographer R/qtl

Linkage map Oregon Wolfe Barley 175 DH lines 1328 SNP markers 1H 2H 3H 4H 5H 6H 7H Linkage map Oregon Wolfe Barley 175 DH lines 1328 SNP markers Same map order with 82 maternal DH 93 paternal DH Cistué et al. 2011. Theoretical and Applied Genetics 122: 1399–1410.

QTL detection methods Single-marker analysis (SMA) Consider one marker at a time Simple statistical tests are used to compare phenotypes of marker classes (e.g. AA, Aa, aa) Need to make adjustments for multiple tests Does not require a linkage map Can’t tell how close the marker is to the QTL Observed phenotypes for marker classes will depend on the extent of recombination as well as the genotypic effect of the QTL Can’t evaluate interactions among QTLs

QTL detection methods Simple interval mapping (SIM) – most common Test for likelihood of a QTL at many positions (“sliding window”) between two mapped markers Options to use a likelihood approach (best) or regression (faster) Composite interval mapping (CIM) Also uses a “sliding window” QTL in other regions of the genome are used as covariates, so the effects of the QTL of interest can be estimated more precisely

Cercospora zeae-maydis Y32 Q11 1 3 5 7 9 161 F2:3 families 2 replicates, 2 locations 183 polymorphic SSR markers Constructed linkage map 4 QTL identified using CIM 15 F2 recombinants within a major QTL were used for fine mapping Gray leaf spot causes serious yield losses (20 – 60% or up to 100%) in maize worldwide Y32 – resistant tropical inbred form Suwan1 Q11 – susceptible inbred derived from temperate hybrid For fine mapping, selfed and backcrossed progeny of the 15 recombinants were phenotyped Used mapmaker for linkage map (Kosambi mapping function) Used QTL cartographer for QTL analysis, using composite interval mapping

QTL mapping of resistance to gray leaf spot in maize Zhang et al., 2012 From Q11(S) From Y32(R) Disease ratings for GLS disease in four replicates for three genotype classes at the umc1913 marker H2 for GLS resistance = 0.85 QTL-qRgls1 could enhance resistance by 19.7-61.3%