Genetic Design and Statistical Power of Nested Association Mapping in Maize

TLDR

The NAM design simultaneously exploits the advantages of both linkage analysis and association mapping. We investigated the genetic and statistical properties of the nested association mapping (NAM) design in maize to dissect the genetic basis of complex quantitative traits. Using common‑parent‑specific markers and additional high‑density markers, the authors inferred chromosome segment inheritance via linkage and projected linkage disequilibrium information from founders to progenies. Computer simulations showed that NAM enables high‑power, cost‑effective genome scans, precisely identifying 30–79% of simulated QTL in 5000 genotypes, and the strategy should greatly facilitate linking molecular variation with phenotypic variation for complex traits.

Abstract

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.