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Association mapping approaches for tagging quality traits in maize
 

Association mapping approaches for tagging quality traits in maize

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Association mapping has been widely used to study the genetic basis of complex traits in human and animal systems and is a very efficient and effective method for confirming candidate genes or for ...

Association mapping has been widely used to study the genetic basis of complex traits in human and animal systems and is a very efficient and effective method for confirming candidate genes or for identifying new genes (Altshuler et al., 2008). Association mapping is now being increasingly used in a wide range of plants (Rafalski, 2010), where it appears to be more powerful than in humans or animals (Zhu et al., 2008). Unlike linkage mapping, association mapping can explore all the recombination events and mutations in a given population and with a higher resolution (Yu and Buckler, 2006). However, association mapping has a lower power to detect rare alleles in a population, even those with large effects, than linkage mapping (Hill et al., 2008). Yan et al., (2010) demonstrated that the gene encoding β-carotene hydroxylase 1 (crtRB1) underlies a principal quantitative trait locus associated with β-carotene concentration and conversion in maize kernels has been identified through candidate gene strategy of association mapping.

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  • Association mapping through linkage disequilibrium (LD) analysis is a powerful tool for the dissection of complex agronomic traits and for the identification of alleles that can contribute to the enhancement of a target trait in maize. With the developments of high throughput genotyping techniques and advanced statistical approaches as well as the assembling and characterization of multiple association mapping panels, maize has become the model crop for association analysis (Yan et al., 2011). Association mapping has been widely used to study the genetic basis of complex traits in human and animal systems and is a very efficient and effective method for confirming candidate genes or for identifying new genes (Altshuleret al., 2008). Association mapping is now being increasingly used in a wide range of plants (Rafalski, 2010), where it appears to be more powerful than in humans or animals (Zhu et al., 2008). Unlike linkage mapping, association mapping can explore all the recombination events and mutations in a given population and with a higher resolution (Yu and Buckler, 2006). However, association mapping has a lower power to detect rare alleles in a population, even those with large effects, than linkage mapping (Hill et al., 2008). Yan et al., (2010) demonstrated that the gene encoding β-carotene hydroxylase 1 (crtRB1) underlies a principal quantitative trait locus associated with β-carotene concentration and conversion in maize kernels has been identified through candidate gene strategy of association mapping
  • Genomic technologies for high-throughput genome sequencingand genotyping made it more affordable to obtain a large amount of marker data across a large diversity panel for complex trait dissectionand superior allele mining
  • comparison of linkage analysis with designed mapping populations and association mapping with diverse collections.
  • Association mapping panel constitutes the genotypes sampled for capturing maximum amount of genetic variation.
  • LD blocks are very useful in association mapping when sizes are calculated, which suggest the needs for the minimum number of markers to efficiently cover the genome-wide haplotype blocks in association mapping
  • P value from ANOVA analysis of 2003 data.

Association mapping approaches for tagging quality traits in maize Association mapping approaches for tagging quality traits in maize Presentation Transcript

  • Varalakshmi,B.C
  • What is association mapping? „‟Association genetics‟‟ or „‟association studies,” or „‟linkage disequilibrium mapping” (Oraguzie et al. 2007) Tool to resolve complex trait variation down to the sequence level by exploiting historical and evolutionary recombination events at the population level. (Nordborg & Tavare, 2002; Risch& Merikangas, 1996).
  •  LD mapping detects and locates quantitativetrait loci (QTL) by the strength of the correlationbetween a trait and a marker. Offers greater precision in QTL location thanfamily-based linkage analysis More efficient marker-assisted selection,facilitate gene discovery. Does not require family or pedigree information ,can be applied to a range of experimental and non-experimental populations. Care must be taken during analysis to control forthe increased rate of false positive results arising. (Mackay and Powell, 2007)
  • Why association mapping..?New Resolve trait Identifying noveltool variation down to and superior sequence level alleles Sequencing technologies Sequencing, markedly gene expression reduced the cost Genomic profiling,Technology comparative genomics Annotated genome sequence from model species Harnesses genetic Natural diversity of Diversity natural populations to individual nucleotides Hansen et al.,2001 ; Kraakman et al., 2006
  • Diversity Panel Genomic technologies for high-throughput genome sequencing Zhu et al., 2008
  • Comparison of Association Genetics and Conventional QTL mappingAttribute QTL mapping Association geneticsDetection goal Quantitative trait Quantitative trait locus, i.e., wide nucleotide, i.e., region within specific physically as close as pedigrees within which possible to causative a QTL is located sequence(s)Resolution of Low – moderate High – disequilibriumcausative Trait density linkage maps within small physicalpolymorphism only required regions requiring many markersMarker Moderate Moderate for fewdiscovery costs traits, high for many traits
  • Attribute QTL mapping Association genetics Experimental Defined pedigrees, Unrelated individuals populations e.g., backcross, (“unstructured” for detection F2, RI, three and two populations), large generation numbers of small pedigrees/families, unrelated families half-sib families, etc. Number of 102–low 103 105 for small genomes markers ~109 for required for large genomes genome coverage
  • Linkage analysis and Association Mapping Linkage analysis Association Mapping
  • Advantages of Using Natural Population Broader genetic variations with wider background for marker-trait correlations . Higher resolution mapping ( recombination events) Exploiting historically measured trait data for association. No need for the development of expensive and tedious bi-parental populations (Kraakman 2006 ; Hansen, 2001)
  • RILs V/s Association Mapping Panel Morrell et al., 2011
  • Scheme of association mapping or tagging a geneof interest using germplasm accessions. (Nordborg et al., 2005)
  • Types of Association MappingGenome-wide Association Mapping (GWAS) Comprehensive approach to systematically search the genome for causal genetic variation. Large no of markers are tested for association with complex traits. Prior information regarding candidate gene is not required Works best for a research consortium with complementary expertise & adequate funding.
  • Candidate- gene association mapping Candidate genes selected based on knowledge from mutational analysis, biochemical pathway, or linkage analysis Independent set of random markers needs to be scored to infer genetic relationships. Low cost, hypothesis driven, and trait specific approach but will miss other unknown loci. (Zhu et al., 2010).
  • Principle Of Association Mapping isLinkage disequilibrium (LD) Linkage refers to coinheritance of different loci within a genetic distance on the chromosome. LE is a random association of alleles at different loci and equals the product of allele frequencies within haplotypes. LD is a non-random association of alleles at different loci, describing the condition with non-equal frequency of haplotypes in a population. Oraguzie et al.,2007
  • Concept of LD Linkage disequilibrium also referred as “gametic phase disequilibrium” (GPD) or “gametic disequilibrium” (GLD) first described by Jennings in 1917, and its quantification (D i.e. coefficient of LD) was developed by Lewtonin in 1964. D is the difference between the observed gametic frequencies of haplotypes and the expected gametic haplotype frequencies under linkage equilibrium. D = P AB − PAPB = PAB Pab − PAbPaB Besides D, a various different measures of LD are D, r2, D2, D∗ (Oraguzie ., 2007)
  •  Choosing appropriate LD measures depends on the objective of the study. r2, the square of the correlation coefficient between the two loci. r2 is affected by mutation and recombination D is affected by more mutational histories. The r2 value varies from 0 to 1. The r2 value of equal to 0.1 (10%) or above considered the significant. (Abdurakhmonov and Abdukarimov, 2008)
  • Calculation and visualization of LD: LD triangle and decay plots LD can be calculated using haplotyping algorithms. Maximum likelihood estimate (MLE) using an expectation maximization algorithm. Graphical display of pairwise LD between two loci is useful to estimate the LD patterns measured using a large number of molecular markers. (Abdurakhmonov and Abdukarimov, 2008)
  • Software used for calculation of LD “Graphical overview of linkage disequilibrium” (GOLD) to depict the structure and pattern of LD. “Trait Analysis by aSSociation, Evolution and Linkage” (TASSEL) and PowerMarker
  • The TASSEL generated triangle plot for pairwise LDEach cell represents the comparison of two pairs of marker sites withthe colour codes for the presence of significant LD. (Abdurakhmonov and Abdukarimov,
  • LD decay plot To estimate the size of LD blocks, the r 2 values (alternatively, D can also be used) usually plotted against the genetic (cM) or weighted (bp) distance referred to as a “LD decay plot”. (Abdurakhmonov and Abdukarimov, 2008)
  • Factors affecting LD & association mapping  Mutation and recombination are one of the strong impact factors influencing LD.  Factors Increasing LD: New mutation, mating system (self-pollination), genetic isolation, population structure, relatedness (kinship), genetic drift, admixture, selection (natural, artificial).  Factors Decreasing LD: High recombination and mutation rate, recurrent mutations, outcrossing (Huttley et al., 2005).
  • Need of Association Mapping in MAIZE ? Source of cooking oil, biofuel and animal feed. Model organism for cytogenetics, genetics, genomics, and functional genomics studies. (Strable and Scanlon, 2009). Primary staple food in many African countries. Map-based cloning of QTLs is time consuming and expensive process in Maize . Association mapping can explore all recombination events and mutations in a given population and with a higher resolution . (Yu and Buckler, 2006)
  • Examples of the range of phenotypic variation in maizegermplasm held in the CIMMYT genebank (Dr. SuketoshiTaba).
  • Nested Association Mapping(NAM) Joint linkage and linkage disequilibrium mapping have been proposed as “Fine Mapping’’ approach. (Mott and Flint, 2002; Wu et al., 2002) NAM is currently implemented in maize. Powerful strategy for dissecting the genetic basis of quantitative traits in species with low LD. For other crop species, different genetic designs (e.g., diallel, eight-way cross) could be used to accommodate the level of LD. NAM allows high power, cost effective genome scans, and facilitates to link molecular variation with complex trait variation. (Yu et al., 2008)
  • Nested Association Mapping
  • Population Sample Background Association Candidate Traits References size markers method genesDiverse 97 47 LR+Q ae1, bt2, Kernel Wilson etinbred sh1, sh2, composition al., 2004lines sugary1, & starch waxy1 pasting propertiesDiverse 42 101 LR+Q,G bm3 Forage Lübberstedtinbred LM–Q quality et al.,lines traits 2005Diverse 57 --- Haplotype Sweet taste Tracy etinbred tree Sugary1 al., 2006lines scanningDiverse 281 89 plus MLM crtRB1 Carotenoid Yan et al.,inbred 553 content 2010linesElite 71 --- DGAT Oil Zheng etlines Unknown content & al., 2008 compositionElite 75 --- Case- Y1 Endosperm Palaisa et
  • Application of candidate gene strategy to identify CrtRB1 locus
  • β-carotene biosynthetic pathway Simplified Carotenoid biosynthetic pathway in maize and (Tian et al.,2001).
  • crtRB1 is the target gene Zea mays crtRB1 is the target gene in the present study.translated exons are depicted as black boxes .
  • MethodsGermplasm evaluation Panel 1 (P1): 281 maize inbred lines grown in Urbana, Illinois (USA) in 2002–2005. Panel 2 (P2): 245 diverse maize inbred lines derived from tropical and subtropical adapted maize germplasm. Panel 3 (P3): 55 diverse maize inbred lines derived from temperate-adapted maize germplasm.
  • Carotenoid Quantification HPLC analysis: Extraction of carotenoids for all segregating mapping populations was carried out by HPLC analysis. (Kurilich and Juvik, 1999).Population structure and kinship analysis Population structure and kinship for P1 was estimated using 89 simple sequence repeat (SSR) markers and 553 SNP markers, respectively (Yu et al., 2006). STRUCTURE 2.1 was used to estimate the population structure of P2 and P3 using 46 and 86 SSRs, respectively.
  • Linkage mapping and QTL mapping  crtRB1 was mapped via genetic linkage mapping in a RIL population derived from B73 and BY80415, using the crtRB1 3′TE polymorphism.  QTL analysis in this population was done using QTL Cartographer 2.5 (Wang et al.,2005).
  • Statistical analysis Association analysis was carried out using a mixed model incorporating kinship and population structure as implemented in TASSEL2.1 (Bradbury, et al., 2007). LD analysis was carried out using TASSEL2.1 with the entire sequence of crtRB1; a window size of 50 bp was used to plot the average r2 against the distance.
  • 5′TE allelic series: 1, 397-bp insertion; 2, 206-bp insertion; 3, 0-bpinsertion.InDel4 allelic series: 12-bp or 0-bp insertion.3′TE allelic series: 1, no insertion; carried outinsertion; 3, 1,250-bpP value from association analysis 2, 325-bp using the mixed modelinsertion.incorporating population structure and kinship, using data from 4 differentyears.R2 values from analysis of variance (ANOVA) of data showingpercentage phenotypic variation .
  • Haplotype is shown as linear combination5′TE allele (1, 397-bp insertion; 2, 206-bp insertion; 3, 0-bp insertion),InDel4 allele (12-bp or 0-bp insertion),3′TE allele (1, no insertion; 2, 325-bp insertion; 3, 1,250-bp insertion).
  • Allele-specific crtRB1 effects on biochemical activity andtranscriptional expression.CrtRB1 quantitative RT-PCR from whole kernel at 15 d afterpollination (DAP) and seedling leaf messenger RNA for the sixindicated lines of Zea mays.
  • β-carotene hydroxylase product profiles for the four CRTRB1allozymes expressed in a recombinant E. coli assay systemproducing β-carotene. Genetic variation for each allozyme islisted according to InDel4 and C-terminal (3′TE) differences.
  • Whole genome scan association mapping foroleic acid content  To identify loci with major effect on oleic acid content in maize kernels.  8,590 loci were tested for association with oleic acid content in 553 maize inbreds.  A single locus with major effect on oleic acid was mapped between 380 and 384 cM in the IBM2 neighbors genetic map onchromosome 4 and conWrmed in a biparental population.
  •  Fatty acid desaturase, fad2, idenntified >2 kb from the associated genetic marker, is the most likely candidate gene responsible for the difference in the phenotype. Non-conservative amino acid polymorphism near the active site of fad2 contributes to the effect on oleic acid content. First report on use of a high resolution whole genome scan association mapping.
  • Materials and MethodsWhole genome scan association mapping Single nucleotide polymorphism(SNP) haplotypes at 8,590 genetic loci were genotyped in 553 maize inbred lines. Statistical test for association between haplotypes and the and the embryo oleic acid was performed by STRUCTURE program (Pritchard et al. 2000). LD was computed between the locus of interest and all other loci using r2 (Devlin and Risch 1995).
  • Results
  • Comparison of Low-oleic Acid Content (Lo) AgainstHigh-oleic Acid Content (Ho) Alleles of fad2.Boxes domain regions of the protein sequence.Horizontal grey arrows in both sequences coding region.Vertical bars nucleotide polymorphisms between both alleleshalf-length vertical bars synonymous substitutions.Triangles amino acid substitutionsLines across both sequences deletions and insertions.Black triangle non-conservative amino acid substitution of a leucine by
  • Association mapping of the markers MZA10924, MZA4015, andMZA5102 (top) and linkage disequilibrium (LD) of all markers against theMZA10924 (bottom).vertical scale negative logarithm of the association mapping P-valuestatistics horizontal scale genetic position in cM from Pioneer‟s genetic map.