Nanopore-based genome assembly and the evolutionary genomics of basmati rice, bioRxiv, 2018-08-20
ABSTRACTBACKGROUNDThe circum-basmati group of cultivated Asian rice (Oryza sativa) contains many iconic varieties and is widespread in the Indian subcontinent. Despite its economic and cultural importance, a high-quality reference genome is currently lacking, and the group’s evolutionary history is not fully resolved. To address these gaps, we used long-read nanopore sequencing and assembled the genomes of two circum-basmati rice varieties, Basmati 334 and Dom Sufid.RESULTSWe generated two high-quality, chromosome-level reference genomes that represented the 12 chromosomes of Oryza. The assemblies showed a contig N50 of 6.32Mb and 10.53Mb for Basmati 334 and Dom Sufid, respectively. Using our highly contiguous assemblies we characterized structural variations segregating across circum-basmati genomes. We discovered repeat expansions not observed in japonica—the rice group most closely related to circum- basmati—as well as presenceabsence variants of over 20Mb, one of which was a circum- basmati-specific deletion of a gene regulating awn length. We further detected strong evidence of admixture between the circum-basmati and circum-aus groups. This gene flow had its greatest effect on chromosome 10, causing both structural variation and single nucleotide polymorphism to deviate from genome-wide history. Lastly, population genomic analysis of 78 circum-basmati varieties showed three major geographically structured genetic groups (1) BhutanNepal group, (2) IndiaBangladeshMyanmar group, and (3) IranPakistan group.CONCLUSIONAvailability of high-quality reference genomes from nanopore sequencing allowed functional and evolutionary genomic analyses, providing genome-wide evidence for gene flow between circum-aus and circum-basmati, the nature of circum-basmati structural variation, and the presenceabsence of genes in this important and iconic rice variety group.
biorxiv evolutionary-biology 100-200-users 2018Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure, bioRxiv, 2018-08-16
AbstractSNP datasets are high-dimensional, often with thousands to millions of SNPs and hundreds to thousands of samples or individuals. Accordingly, PCA graphs are frequently used to provide a low-dimensional visualization in order to display and discover patterns in SNP data from humans, animals, plants, and microbes—especially to elucidate population structure. Given the popularity of PCA, one might expect that PCA is understood well and applied effectively. However, our literature survey of 125 representative articles that apply PCA to SNP data shows that three choices have usually been made poorly PCA graph, SNP coding, and PCA variant. Our main three recommendations are simple and easily implemented Use PCA biplots, SNP coding 1 for the rare allele and 0 for the common allele, and double-centered PCA (or AMMI1 if main effects are of interest). The ultimate benefit from informed and optimal choices of PCA graph, SNP coding, and PCA variant, is expected to be discovery of more biology, and thereby acceleration of medical, agricultural, and other vital applications.
biorxiv genomics 0-100-users 2018High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution, bioRxiv, 2018-08-16
AbstractTargeted PCR amplification and high-throughput sequencing (amplicon sequencing) of 16S rRNA gene fragments is widely used to profile microbial communities. New long-read sequencing technologies can sequence the entire 16S rRNA gene, but higher error rates have limited their attractiveness when accuracy is important. Here we present a high-throughput amplicon sequencing methodology based on PacBio circular consensus sequencing and the DADA2 sample inference method that measures the full-length 16S rRNA gene with single-nucleotide resolution and a near-zero error rate.In two artificial communities of known composition, our method recovered the full complement of full-length 16S sequence variants from expected community members without residual errors. The measured abundances of intra-genomic sequence variants were in the integral ratios expected from the genuine allelic variants within a genome. The full-length 16S gene sequences recovered by our approach allowed E. coli strains to be correctly classified to the O157H7 and K12 sub-species clades. In human fecal samples, our method showed strong technical replication and was able to recover the full complement of 16S rRNA alleles in several E. coli strains.There are likely many applications beyond microbial profiling for which high-throughput amplicon sequencing of complete genes with single-nucleotide resolution will be of use.
biorxiv microbiology 200-500-users 2018Large-scale whole-genome sequencing of three diverse Asian populations in Singapore, bioRxiv, 2018-08-11
AbstractAsian populations are currently underrepresented in human genetics research. Here we present whole-genome sequencing data of 4,810 Singaporeans from three diverse ethnic groups 2,780 Chinese, 903 Malays, and 1,127 Indians. Despite a medium depth of 13.7×, we achieved essentially perfect (>99.8%) sensitivity and accuracy for detecting common variants and good sensitivity (>89%) for detecting extremely rare variants with <0.1% allele frequency. We found 89.2 million single-nucleotide polymorphisms (SNPs) and 9.1 million small insertions and deletions (INDELs), more than half of which have not been cataloged in dbSNP. In particular, we found 126 common deleterious mutations (MAF>0.01) that were absent in the existing public databases, highlighting the importance of local population reference for genetic diagnosis. We describe fine-scale genetic structure of Singapore populations and their relationship to worldwide populations from the 1000 Genomes Project. In addition to revealing noticeable amounts of admixture among three Singapore populations and a Malay-related novel ancestry component that has not been captured by the 1000 Genomes Project, our analysis also identified some fine-scale features of genetic structure consistent with two waves of prehistoric migration from south China to Southeast Asia. Finally, we demonstrate that our data can substantially improve genotype imputation not only for Singapore populations, but also for populations across Asia and Oceania. These results highlight the genetic diversity in Singapore and the potential impacts of our data as a resource to empower human genetics discovery in a broad geographic region.
biorxiv genetics 100-200-users 2018Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk, Nature Genetics, 2018-08-10
Coding variants represent many of the strongest associations between genotype and phenotype; however, they exhibit inter-individual differences in effect, termed ‘variable penetrance’. Here, we study how cis-regulatory variation modifies the penetrance of coding variants. Using functional genomic and genetic data from the Genotype-Tissue Expression Project (GTEx), we observed that in the general population, purifying selection has depleted haplotype combinations predicted to increase pathogenic coding variant penetrance. Conversely, in cancer and autism patients, we observed an enrichment of penetrance increasing haplotype configurations for pathogenic variants in disease-implicated genes, providing evidence that regulatory haplotype configuration of coding variants affects disease risk. Finally, we experimentally validated this model by editing a Mendelian single-nucleotide polymorphism (SNP) using CRISPRCas9 on distinct expression haplotypes with the transcriptome as a phenotypic readout. Our results demonstrate that joint regulatory and coding variant effects are an important part of the genetic architecture of human traits and contribute to modified penetrance of disease-causing variants.
nature genetics genetics 500+-users 2018One read per cell per gene is optimal for single-cell RNA-Seq, bioRxiv, 2018-08-10
An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget deep sequencing of a few cells or shallow sequencing of many cells? A mathematical framework reveals that, for estimating many important gene properties, the optimal allocation is to sequence at the depth of one read per cell per gene. Interestingly, the corresponding optimal estimator is not the widely-used plugin estimator but one developed via empirical Bayes.
biorxiv bioinformatics 100-200-users 2018