Genetic mapping is a powerful tool for identifying genes and biological processes underlying traits influenced by inheritance, including human diseases. This review discusses the intellectual foundations of genetic mapping for Mendelian and complex traits, lessons from linkage analysis of Mendelian diseases and genome-wide association studies (GWAS) of common diseases, and challenges ahead. By the early 1900s, geneticists understood that Mendel's laws of inheritance underlie gene transmission in diploid organisms. They recognized that most naturally occurring trait variation involves multiple genes and non-genetic factors. Although these insights applied to humans, it took much of the century to develop practical tools for discovering genes contributing to human diseases. Starting in the 1980s, the use of naturally occurring DNA variation as markers led to the discovery of thousands of genes for rare Mendelian diseases. However, this approach proved unsuccessful for common diseases like diabetes, heart disease, and cancer. A new approach to genetic mapping has yielded progress in mapping loci influencing susceptibility to common diseases. However, most genes and mutations underlying these findings remain undefined. Genetic mapping by linkage and association involves locating genes underlying phenotypes based on correlation with DNA variation. Linkage analysis, developed by Sturtevant in 1913, involves crosses between parents varying at a Mendelian trait and many polymorphic variants. In the 1970s, the ability to clone and sequence DNA enabled tying genetic linkage maps to DNA sequences, leading to molecular cloning of genes responsible for Mendelian traits. Linkage analysis in humans was initially limited due to small family sizes and few genetic markers. Progress in identifying genes contributing to human traits was limited to studies of biological candidates. In 1980, Botstein proposed using naturally occurring DNA sequence polymorphisms as markers to create a human genetic map. The feasibility of genetic mapping in humans was demonstrated with the localization of Huntington disease in 1983. Genetic association studies, which compare frequencies of genetic variants among affected and unaffected individuals, have emerged as a possible path forward. These studies have revealed associations between genetic variants and diseases, but have faced challenges such as false positives due to population structure. A systematic genome-wide approach to association studies was proposed in the mid-1990s, focusing on common variants as a mapping tool. The International HapMap Project was launched in 2002 to characterize SNP frequencies and local LD patterns across the human genome. This project has provided a catalog of common SNPs and has shown that most common SNPs are strongly correlated with nearby proxies. Massively parallel genotyping has enabled the genotyping of hundreds of thousands of SNPs at high accuracy and low cost. Copy-number variation (CNV) is another type of genetic variation that has been observed in the human genome. Statistical analysis of genetic data has revealed that common variants typically have modest effect sizes. However, some variants have larger effects,Genetic mapping is a powerful tool for identifying genes and biological processes underlying traits influenced by inheritance, including human diseases. This review discusses the intellectual foundations of genetic mapping for Mendelian and complex traits, lessons from linkage analysis of Mendelian diseases and genome-wide association studies (GWAS) of common diseases, and challenges ahead. By the early 1900s, geneticists understood that Mendel's laws of inheritance underlie gene transmission in diploid organisms. They recognized that most naturally occurring trait variation involves multiple genes and non-genetic factors. Although these insights applied to humans, it took much of the century to develop practical tools for discovering genes contributing to human diseases. Starting in the 1980s, the use of naturally occurring DNA variation as markers led to the discovery of thousands of genes for rare Mendelian diseases. However, this approach proved unsuccessful for common diseases like diabetes, heart disease, and cancer. A new approach to genetic mapping has yielded progress in mapping loci influencing susceptibility to common diseases. However, most genes and mutations underlying these findings remain undefined. Genetic mapping by linkage and association involves locating genes underlying phenotypes based on correlation with DNA variation. Linkage analysis, developed by Sturtevant in 1913, involves crosses between parents varying at a Mendelian trait and many polymorphic variants. In the 1970s, the ability to clone and sequence DNA enabled tying genetic linkage maps to DNA sequences, leading to molecular cloning of genes responsible for Mendelian traits. Linkage analysis in humans was initially limited due to small family sizes and few genetic markers. Progress in identifying genes contributing to human traits was limited to studies of biological candidates. In 1980, Botstein proposed using naturally occurring DNA sequence polymorphisms as markers to create a human genetic map. The feasibility of genetic mapping in humans was demonstrated with the localization of Huntington disease in 1983. Genetic association studies, which compare frequencies of genetic variants among affected and unaffected individuals, have emerged as a possible path forward. These studies have revealed associations between genetic variants and diseases, but have faced challenges such as false positives due to population structure. A systematic genome-wide approach to association studies was proposed in the mid-1990s, focusing on common variants as a mapping tool. The International HapMap Project was launched in 2002 to characterize SNP frequencies and local LD patterns across the human genome. This project has provided a catalog of common SNPs and has shown that most common SNPs are strongly correlated with nearby proxies. Massively parallel genotyping has enabled the genotyping of hundreds of thousands of SNPs at high accuracy and low cost. Copy-number variation (CNV) is another type of genetic variation that has been observed in the human genome. Statistical analysis of genetic data has revealed that common variants typically have modest effect sizes. However, some variants have larger effects,