Genetic design and statistical power of nested association mapping in maize

Abstract

We investigated the genetic and statistical properties of the nested association mapping (NAM) design currently being implemented in maize (26 diverse founders and 5000 distinct immortal genotypes) to dissect the genetic basis of complex quantitative traits. The NAM design simultaneously exploits the advantages of both linkage analysis and association mapping. We demonstrated the power of NAM for high-power cost-effective genome scans through computer simulations based on empirical marker data and simulated traits with different complexities. With common-parent-specific (CPS) markers genotyped for the founders and the progenies, the inheritance of chromosome segments nested within two adjacent CPS markers was inferred through linkage. Genotyping the founders with additional high-density markers enabled the projection of genetic information, capturing linkage disequilibrium information, from founders to progenies. With 5000 genotypes, 30-79% of the simulated quantitative trait loci (QTL) were precisely identified. By integrating genetic design, natural diversity, and genomics technologies, this new complex trait dissection strategy should greatly facilitate endeavors to link molecular variation with phenotypic variation for various complex traits.

Figure 1.—

Diagram of genome reshuffling between 25 diverse founders and the common parent and the resulting 5000 immortal genotypes. Due to diminishing chances of recombination over short genetic distance and a given number of generations, the genomes of these recombinant inbred lines (RILs) are essentially mosaics of the founder genomes. ×, crossing; ⊗, selfing; SSD, single-seed descent.

Figure 2.—

Diagram of polymorphisms within a pair of CPS markers leading to fine mapping of NAM. (a) Genotyping of both founders and RILs with CPS markers to track the inheritance of chromosome segments that resulted from recent recombination during RIL development; (b) genotyping of founders with high-density SNPs, projecting sequence polymorphism information (biallelic) from founders to RILs, and mapping in high resolution through exploiting both recent and ancient recombination. Black/gray squares, alleles of CPS markers; blue/white squares, same as or different from B73 alleles at random SNPs; color segments, haplotype information from each parent; ×, crossing. Sites enclosed by the vertical bar represent the functional polymorphism.

Figure 3.—

Statistical power of NAM to detect QTL with different genetic effects with 5000 phenotyped RILs. Complete information available for both CPS markers and random markers: (a) q = 20 QTL and (b) q = 50 QTL. Only CPS markers available: (c) q = 20 QTL and (d) q = 50 QTL.

Figure 4.—

Average power and FDR of NAM with different numbers of phenotyped RILs when complete markers are genotyped for RILs. (a) q = 20 QTL and h² = 0.4; (b) q = 50 QTL and h² = 0.4; (c) q = 20 QTL and h² = 0.7; (d) q = 50 QTL and h² = 0.7.

Figure 5.—

Average power and FDR of NAM with different numbers of phenotyped RILs when only CPS markers are genotyped for RILs. (a) q = 20 QTL and h² = 0.4; (b) q = 50 QTL and h² = 0.4; (c) q = 20 QTL and h² = 0.7; (d) q = 50 QTL and h² = 0.7.

Figure 6.—

Comparison of average power and FDR for NAM analysis and traditional linkage analysis of multiple line crosses. Significance threshold was set at α = 10⁻⁷. (a) q = 20 QTL and h² = 0.4; (b) q = 50 QTL and h² = 0.4; (c) q = 20 QTL and h² = 0.7; (d) q = 50 QTL and h² = 0.7. For NAM analysis, SNP information between CPS markers was projected from founders to 5000 RILs and a true positive was counted only if the QTL locus was retained in the final model; for linkage analysis, no projection was done and a unique allele was assumed for each founder and a true positive was counted as long as the locus retained in the final model was located within the region containing a QTL.