Genetic variation at transcription factor binding sites largely explains phenotypic heritability in maize

We quantified haplotype-specific TF footprints across a pan-genome of 25 maize hybrids and mapped over 200,000 variants, genetic, epigenetic, or both (termed binding quantitative trait loci (bQTL)), linked to cis-element occupancy.

Three lines of evidence support the functional significance of bQTL: (1) coincidence with causative loci that regulate traits, including vgt1, ZmTRE1 and the MITE transposon near ZmNAC111 under drought; (2) bQTL allelic bias is shared between inbred parents and matches chromatin immunoprecipitation sequencing results; and (3) partitioning genetic variation across genomic regions demonstrates that bQTL capture the majority of heritable trait variation across ~72% of 143 phenotypes. Our study provides an auspicious approach to make functional cis-variation accessible at scale for genetic studies and targeted engineering of complex traits.

Over the past two decades, genome-wide association studies (GWAS) have transformed our understanding of the inheritance of many complex traits in important crops such as maize. Several studies have estimated that non-coding variation accounts for about 50% of the additive genetic variance underlying phenotypic diversity in plants. Although identification of functional non-coding variants is advancing with the development of new genomics technologies5, it remains challenging to discern functional variants that impact cis-elements efficiently and at cistrome (defined as the genome-wide set of cis-acting regulatory loci) scale. Knowing which loci to target has become one of the obstacles for trait improvement by targeted genome editing. Scalable methods to construct comprehensive cis-element maps are essential to understand complex transcriptional networks that underlie development, growth and disease. The potential of cis-element maps has been demonstrated by the ENCODE projects that exist for many eukaryotes, including humans. However, genome-wide, high-resolution maps of functional variants are currently lacking in plants8. Despite many successes, GWAS generally suffer from insufficient resolution, which limits the identification of individual causal single-nucleotide polymorphisms (SNPs) or insertions or deletions (INDELs) and cannot provide independent molecular information on the potential function of variants, requiring laborious follow-up analyses of numerous individual loci.