Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data

ACMGA: a reference-free multiple-genome alignment pipeline for plant species

BACKGROUND

The short-read whole-genome sequencing (WGS) approach has been widely applied to investigate the genomic variation in the natural populations of many plant species. With the rapid advancements in long-read sequencing and genome assembly technologies, high-quality genome sequences are available for a group of varieties for many plant species. These genome sequences are expected to help researchers comprehensively investigate any type of genomic variants that are missed by the WGS technology. However, multiple genome alignment (MGA) tools designed by the human genome research community might be unsuitable for plant genomes.

RESULTS

To fill this gap, we developed the AnchorWave-Cactus Multiple Genome Alignment (ACMGA) pipeline, which improved the alignment of repeat elements and could identify long (> 50 bp) deletions or insertions (INDELs). We conducted MGA using ACMGA and Cactus for 8 Arabidopsis (Arabidopsis thaliana) and 26 Maize (Zea mays) de novo assembled genome sequences and compared them with the previously published short-read variant calling results. MGA identified more single nucleotide variants (SNVs) and long INDELs than did previously published WGS variant callings. Additionally, ACMGA detected significantly more SNVs and long INDELs in repetitive regions and the whole genome than did Cactus. Compared with the results of Cactus, the results of ACMGA were more similar to the previously published variants called using short-read. These two MGA pipelines identified numerous multi-allelic variants that were missed by the WGS variant calling pipeline.

CONCLUSIONS

Aligning de novo assembled genome sequences could identify more SNVs and INDELs than mapping short-read. ACMGA combines the advantages of AnchorWave and Cactus and offers a practical solution for plant MGA by integrating global alignment, a 2-piece-affine-gap cost strategy, and the progressive MGA algorithm.

Genome-wide association study identifies a gene responsible for temperature-dependent rice germination

Environment is an important determinant of agricultural productivity; therefore, crops have been bred with traits adapted to their environment. It is assumed that the physiology of seed germination is optimised for various climatic conditions. Here, to understand the genetic basis underlying seed germination, we conduct a genome-wide association study considering genotype-by-environment interactions on the germination rate of Japanese rice cultivars under different temperature conditions. We find that a 4 bp InDel in one of the 14-3-3 family genes, GF14h, preferentially changes the germination rate of rice under optimum temperature conditions. The GF14h protein constitutes a transcriptional regulatory module with a bZIP-type transcription factor, OREB1, and a florigen-like protein, MOTHER OF FT AND TFL 2, to control the germination rate by regulating abscisic acid (ABA)-responsive genes. The GF14h loss-of-function allele enhances ABA signalling and reduces the germination rate. This allele is found in rice varieties grown in the northern area and in modern cultivars of Japan and China, suggesting that it contributes to the geographical adaptation of rice. This study demonstrates the complicated molecular system involved in the regulation of seed germination in response to temperature, which has allowed rice to be grown in various geographical locations.

Hydrophilic But Not Hydrophobic Surfactant Protein Genetic Variants Are Associated With Severe Acute Respiratory Syncytial Virus Infection in Children

Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infection-related hospitalization in the first year of life. Surfactant dysfunction is central to pathophysiologic mechanisms of various pulmonary diseases including RSV. We hypothesized that RSV severity is associated with single nucleotide polymorphisms (SNPs) of surfactant proteins (SPs). We prospectively enrolled 405 RSV-positive children and divided them into moderate and severe RSV disease. DNA was extracted and genotyped for sixteen specific SP gene SNPs. SP-A1 and A2 haplotypes were assigned. The association of RSV severity with SP gene SNPs was investigated by multivariate logistic regression. A likelihood ratio test was used to test the goodness of fit between two models (one with clinical and demographic data alone and another that included genetic variants). p ≤ 0.05 denotes statistical significance. A molecular dynamics simulation was done to determine the impact of the SFTPA2 rs1965708 on the SP-A behavior under various conditions. Infants with severe disease were more likely to be younger, of lower weight, and exposed to household pets and smoking, as well as having co-infection on admission. A decreased risk of severe RSV was associated with the rs17886395_C of the SFTPA2 and rs2243639_A of the SFTPD, whereas an increased risk was associated with the rs1059047_C of the SFTPA1. RSV severity was not associated with SNPs of SFTPB and SFTPC. An increased risk of severe RSV was associated with the 1A0 genotype of SFTPA2 in its homozygous or heterozygous form with 1A3. A molecular dynamic simulation study of SP-A variants that differ in amino acid 223, an important amino acid change (Q223K) between 1A0 and 1A3, showed no major impact on the behavior of these two variants except for higher thermodynamic stability of the K223 variant. The likelihood ratio test showed that the model with multi-allelic variants along with clinical and demographic data was a better fit to predict RSV severity. In summary, RSV severity was associated with hydrophilic (but not with hydrophobic) SPs gene variants. Collectively, our findings show that SP gene variants may play a key role in RSV infection and have a potential role in prognostication.

prewas: data pre-processing for more informative bacterial GWAS

While variant identification pipelines are becoming increasingly standardized, less attention has been paid to the pre-processing of variants prior to their use in bacterial genome-wide association studies (bGWAS). Three nuances of variant pre-processing that impact downstream identification of genetic associations include the separation of variants at multiallelic sites, separation of variants in overlapping genes, and referencing of variants relative to ancestral alleles. Here we demonstrate the importance of these variant pre-processing steps on diverse bacterial genomic datasets and present prewas, an R package, that standardizes the pre-processing of multiallelic sites, overlapping genes, and reference alleles before bGWAS. This package facilitates improved reproducibility and interpretability of bGWAS results. prewas enables users to extract maximal information from bGWAS by implementing multi-line representation for multiallelic sites and variants in overlapping genes. prewas outputs a binary SNP matrix that can be used for SNP-based bGWAS and will prevent the masking of minor alleles during bGWAS analysis. The optional binary gene matrix output can be used for gene-based bGWAS, which will enable users to maximize the power and evolutionary interpretability of their bGWAS studies. prewas is available for download from GitHub.