Rice (Oryza sativa L.) is a staple food crop for people worldwide, and a key goal has been to increase its grain yield. An increasing population that relies on a decreasing level of farmland has rendered the traditional method for the isolation and use of genetic loci in rice breeding unsatisfactory. Recently, the rapid development in next generation sequencing (NGS) has boosted the number of genome sequences for hundreds to thousands of rice varieties. A MutMap strategy and bulk segregation analysis (BSA) has been developed to directly identify candidate genes based on NGS. The genome-wide association analysis (GWAS) has become a commonly used approach toward identifying the genetic loci and candidate genes for several traits that are closely associated with grain yield. The Multi-parent Advanced Generation Inter-Cross population (MAGIC) is introduced here to discuss potential applications for mapping QTLs for rice varietal development. These strategies broaden the capacity of functional gene identification and its application as a complementary method to insert mutants that comprise T-DNA and transposons. High-throughput SNP analysis platforms, such as the SNP array, provide novel strategies for genomic-assisted selections (GAS) for rice genetic improvements. Moreover, accurate genome sequence information enables genome editing for the utilization of key recessive genes that control important agronomic traits. This review summarizes how NGS accelerates rice genetic improvements through the identification and utilization of key functional genes that regulate agronomic traits.