High throughput crop genome genotyping by a combination of pool next generation sequencing and haplotype-based data processing

BackgroundThe identification of environmentally specific alleles and the observation of evolutional processes is a goal of conservation genomics. By generational changes of allele frequencies in populations, questions regarding effective population size, gene flow, drift, and selection can be addressed. The observation of such effects often is a trade-off of costs and resolution, when a decent sample of genotypes should be genotyped for many loci. Pool genotyping approaches can derive a high resolution and precision in allele frequency estimation, when high coverage sequencing is utilized. Still, pool high coverage pool sequencing of big genomes comes along with high costs.ResultsHere we present a reliable method to estimate a barley population’s allele frequency at low coverage sequencing. Three hundred genotypes were sampled from a barley backcross population to estimate the entire population’s allele frequency. The allele frequency estimation accuracy and yield were compared for three next generation sequencing methods. To reveal accurate allele frequency estimates on a low coverage sequencing level, a haplotyping approach was performed. Low coverage allele frequency of positional connected single polymorphisms were aggregated to a single haplotype allele frequency, resulting in two to 271 times higher depth and increased precision. We compared different haplotyping tactics, showing that gene and chip marker-based haplotypes perform on par or better than simple contig haplotype windows. The comparison of multiple pool samples and the referencing against an individual sequencing approach revealed whole genome pool resequencing having the highest correlation to individual genotyping (up to 0.97), while transcriptomics and genotyping by sequencing indicated higher error rates and lower correlations.ConclusionUsing the proposed method allows to identify the allele frequency of populations with high accuracy at low cost. This is particularly interesting for conservation genomics in species with big genomes, like barley or wheat. Whole genome low coverage resequencing at 10x coverage can deliver a highly accurate estimation of the allele frequency, when a loci-based haplotyping approach is applied. Using annotated haplotypes allows to capitalize from biological background and statistical robustness.

CRISPR-based knockin mutagenesis of the pioneer transcription factor FOXA1; optimization of strategies for multi-allelic proteins in cancer cells

Precise genome engineering of living cells has been revolutionized by the introduction of the highly specific and easily programmable properties of CRISPR-Cas9 technology. This has greatly accelerated research into human health and has facilitated the discovery of novel therapeutics. CRISPR-Cas9 is most widely employed for its ability to inactivate, or knockout, specific genes, but can be also used to introduce subtle site-specific substitutions of DNA sequences that can lead to changes in the amino acid composition of proteins. Despite the proven success of CRISPR-based knockin strategies of genes in typical diploid cells (i.e. cells containing two sets of chromosomes), precise editing of cancer cells, that typically have unstable genomes and multiple copies of chromosomes, is more challenging and not adequately addressed in the literature. Herein we detail our methodology for replacing endogenous proteins with intended knockin mutants in polyploid cancer cells and discuss our experimental design, screening strategy, and facile allele-frequency estimation methodology. As proof of principle, we performed genome editing of specific amino acids within the pioneer transcription factor FOXA1, a critical component of estrogen and androgen receptor signaling, in MCF-7 breast cancer cells. We confirm proper levels of mutant FOXA1 protein expression and intended amino acids substitutions via western blotting and mass spectrometry. In addition, we show that mutant allele-frequency estimation is easily achieved by TOPO cloning combined with allele-specific PCR, which we later confirmed by next-generation RNA-sequencing. Typically, there are 4 - 5 copies (alleles) of FOXA1 in breast cancer cells making the editing of this protein inherently challenging. As a result, most studies that focus on FOXA1 mutants rely on ectopic overexpression of FOXA1 from a plasmid. Therefore, we provide an optimized methodology for replacing endogenous wildtype FOXA1 with precise knockin mutants to enable the systematic analysis of its molecular mechanisms within the appropriate physiological context.

On a Unifying ‘Reverse’ Regression for Robust Association Studies and Allele Frequency Estimation with Related Individuals

For genetic association studies with related individuals, standard linear mixed-effect model is the most popular approach. The model treats a complex trait (phenotype) as the response variable while a genetic variant (genotype) as a covariate. An alternative approach is to reverse the roles of phenotype and genotype. This class of tests includes quasi-likelihood based score tests. In this work, after reviewing these existing methods, we propose a general, unifying ‘reverse’ regression framework. We then show that the proposed method can also explicitly adjust for potential departure from Hardy–Weinberg equilibrium. Lastly, we demonstrate the additional flexibility of the proposed model on allele frequency estimation, as well as its connection with earlier work of best linear unbiased allele-frequency estimator. We conclude the paper with supporting evidence from simulation and application studies.