Prioritized candidate causal haplotype blocks in plant genome-wide association studies.

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA's results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement.

Edit at will: Genotype independent plant transformation in the era of advanced genomics and genome editing.

The combination of advanced genomics, genome editing and plant transformation biology presents a powerful platform for basic plant research and crop improvement. Together these advances provide the tools to identify genes as targets for direct editing as single base pair changes, deletions, insertions and site specific homologous recombination. Recent breakthrough technologies using morphogenic regulators in plant transformation creates the ability to introduce reagents specific toward their identified targets and recover stably transformed and/or edited plants which are genotype independent. These technologies enable the possibility to alter a trait in any variety, without genetic disruption which would require subsequent extensive breeding, but rather to deliver the same variety with one trait changed. Regulatory issues regarding this technology will predicate how broadly these technologies will be implemented. In addition, education will play a crucial role for positive public acceptance. Taken together these technologies comprise a platform for advanced breeding which is an imperative for future world food security.

Genetic Architecture of a Rice Nested Association Mapping Population.

Describing the genetic diversity in the gene pool of crops will provide breeders with novel resources for varietal improvement. Nested Association Mapping (NAM) populations are uniquely suited for characterizing parental diversity through the shuffling and fixation of parental haplotypes. Here, we describe a set of 1879 rice NAM lines created through the selfing and single-seed descent of F1 hybrids derived from elite IR64 indica crossed with 10 diverse tropical japonica lines. Genotyping data indicated tropical japonica alleles were captured at every queried locus despite the presence of segregation distortion factors. Several distortion loci were mapped, both shared and unique, among the 10 populations. Using two-point and multi-point genetic map calculations, our datasets achieved the ∼1500 cM expected map size in rice. Finally, we highlighted the utility of the NAM lines for QTL mapping, including joint analysis across the 10 populations, by confirming known QTL locations for the trait days to heading.

Constructing linkage maps in the genomics era with MapDisto 2.0.

MOTIVATION: Genotyping by sequencing (GBS) generates datasets that are challenging to handle by current genetic mapping software with graphical interface. Geneticists need new user-friendly computer programs that can analyze GBS data on desktop computers. This requires improvements in computation efficiency, both in terms of speed and use of random-access memory (RAM). RESULTS: MapDisto v.2.0 is a user-friendly computer program for construction of genetic linkage maps. It includes several new major features: (i) handling of very large genotyping datasets like the ones generated by GBS; (ii) direct importation and conversion of Variant Call Format (VCF) files; (iii) detection of linkage, i.e. construction of linkage groups in case of segregation distortion; (iv) data imputation on VCF files using a new approach, called LB-Impute. Features i to iv operate through inclusion of new Java modules that are used transparently by MapDisto; (v) QTL detection via a new R/qtl graphical interface. AVAILABILITY AND IMPLEMENTATION: The program is available free of charge at mapdisto.free.fr. CONTACT: mapdisto@gmail.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Imputing Genotypes in Biallelic Populations from Low-Coverage Sequence Data.

Low-coverage next-generation sequencing methodologies are routinely employed to genotype large populations. Missing data in these populations manifest both as missing markers and markers with incomplete allele recovery. False homozygous calls at heterozygous sites resulting from incomplete allele recovery confound many existing imputation algorithms. These types of systematic errors can be minimized by incorporating depth-of-sequencing read coverage into the imputation algorithm. Accordingly, we developed Low-Coverage Biallelic Impute (LB-Impute) to resolve missing data issues. LB-Impute uses a hidden Markov model that incorporates marker read coverage to determine variable emission probabilities. Robust, highly accurate imputation results were reliably obtained with LB-Impute, even at extremely low (<1×) average per-marker coverage. This finding will have implications for the design of genotype imputation algorithms in the future. LB-Impute is publicly available on GitHub at https://github.com/dellaporta-laboratory/LB-Impute.

Flexible and scalable genotyping-by-sequencing strategies for population studies.

BACKGROUND: Many areas critical to agricultural production and research, such as the breeding and trait mapping in plants and livestock, require robust and scalable genotyping platforms. Genotyping-by-sequencing (GBS) is a one such method highly suited to non-human organisms. In the GBS protocol, genomic DNA is fractionated via restriction digest, then reduced representation is achieved through size selection. Since many restriction sites are conserved across a species, the sequenced portion of the genome is highly consistent within a population. This makes the GBS protocol highly suited for experiments that require surveying large numbers of markers within a population, such as those involving genetic mapping, breeding, and population genomics. We have modified the GBS technology in a number of ways. Custom, enzyme specific adaptors have been replaced with standard Illumina adaptors compatible with blunt-end restriction enzymes. Multiplexing is achieved through a dual barcoding system, and bead-based library preparation protocols allows for in-solution size selection and eliminates the need for columns and gels. RESULTS: A panel of eight restriction enzymes was selected for testing on B73 maize and Nipponbare rice genomic DNA. Quality of the data was demonstrated by identifying that the vast majority of reads from each enzyme aligned to restriction sites predicted in silico. The link between enzyme parameters and experimental outcome was demonstrated by showing that the sequenced portion of the genome was adaptable by selecting enzymes based on motif length, complexity, and methylation sensitivity. The utility of the new GBS protocol was demonstrated by correctly mapping several in a maize F2 population resulting from a B73×Country Gentleman test cross. CONCLUSIONS: This technology is readily adaptable to different genomes, highly amenable to multiplexing and compatible with over forty commercially available restriction enzymes. These advancements represent a major improvement in genotyping technology by providing a highly flexible and scalable GBS that is readily implemented for studies on genome-wide variation.

Identification of the maize gravitropism gene lazy plant1 by a transposon-tagging genome resequencing strategy.

Since their initial discovery, transposons have been widely used as mutagens for forward and reverse genetic screens in a range of organisms. The problems of high copy number and sequence divergence among related transposons have often limited the efficiency at which tagged genes can be identified. A method was developed to identity the locations of Mutator (Mu) transposons in the Zea mays genome using a simple enrichment method combined with genome resequencing to identify transposon junction fragments. The sequencing library was prepared from genomic DNA by digesting with a restriction enzyme that cuts within a perfectly conserved motif of the Mu terminal inverted repeats (TIR). Paired-end reads containing Mu TIR sequences were computationally identified and chromosomal sequences flanking the transposon were mapped to the maize reference genome. This method has been used to identify Mu insertions in a number of alleles and to isolate the previously unidentified lazy plant1 (la1) gene. The la1 gene is required for the negatively gravitropic response of shoots and mutant plants lack the ability to sense gravity. Using bioinformatic and fluorescence microscopy approaches, we show that the la1 gene encodes a cell membrane and nuclear localized protein. Our Mu-Taq method is readily adaptable to identify the genomic locations of any insertion of a known sequence in any organism using any sequencing platform.