IIIVmrMLM.QEI: An effective tool for indirect detection of QTN-by-environment interactions in genome-wide association studies.

Although 3VmrMLM-MEJA and several indirect indicators have been employed to identify QTN-by-environment interactions (QEIs) in genome-wide association studies (GWAS), there is no convenient, flexible, and accurate method to comprehensively identify QEIs. To address this issue, 3VmrMLM-random was first extended to 3VmrMLM-fixed. Next, the two single-environment QTN detection methods were integrated with trait differences and regression parameters to indirectly detect QEIs.

The integration of quantile regression with 3VmrMLM identifies more QTNs and QTNs-by-environment interactions using SNP and haplotype-based markers.

Current methods used in genome-wide association studies frequently lack power due to their inability to detect heterogeneous associations and rare and multiallelic variants. To address these issues, quantile regression was integrated for the first time with a compressed variance component multi-locus random-SNP-effect mixed linear model (3VmrMLM) to propose q3VmrMLM for detecting heterogeneous quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs), while q3VmrMLM-Hap was designed to identify multiallelic haplotypes and rare variants. In Monte Carlo simulation studies, q3VmrMLM had higher power than 3VmrMLM, SKAT, and iQRAT.

Spatiotemporal patterns of leaf nutrients of wild apples in a wild fruit forest plot in the Ili Valley, China.

Malus sieversii, commonly known as wild apples, represents a Tertiary relict plant species and serves as the progenitor of globally cultivated apple varieties. Unfortunately, wild apple populations are facing significant degradation in localized areas due to a myriad of factors. To gain a comprehensive understanding of the nutrient status and spatiotemporal variations of M.

FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.

Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two.

Identification of QTNs, QTN-by-environment interactions, and their candidate genes for salt tolerance related traits in soybean.

Background: Salt stress significantly reduces soybean yield. To improve salt tolerance in soybean, it is important to mine the genes associated with salt tolerance traits.

Results: Salt tolerance traits of 286 soybean accessions were measured four times between 2009 and 2015.

A multi-locus linear mixed model methodology for detecting small-effect QTLs for quantitative traits in MAGIC, NAM, and ROAM populations.

Although multi-parent populations (MPPs) integrate the advantages of linkage and association mapping populations in the genetic dissection of complex traits and especially combine genetic analysis with crop breeding, it is difficult to detect small-effect quantitative trait loci (QTL) for complex traits in multiparent advanced generation intercross (MAGIC), nested association mapping (NAM), and random-open-parent association mapping (ROAM) populations. To address this issue, here we proposed a multi-locus linear mixed model method, namely mppQTL, to detect QTLs, especially small-effect QTLs, in these MPPs. The new method includes two steps.

Perennial herb diversity contributes more than annual herb diversity to multifunctionality in dryland ecosystems of North-western China.

Background: Considerable attention has been given to how different aspects of biodiversity sustain ecosystem functions. Herbs are a critical component of the plant community of dryland ecosystems, but the importance of different life form groups of herbs is often overlooked in experiments on biodiversity-ecosystem multifunctionality. Hence, little is known about how the multiple attributes of diversity of different life form groups of herbs affect changes to the multifunctionality of ecosystems.

Identification of QTNs, QTN-by-environment interactions, and their candidate genes for grain size traits in main crop and ratoon rice.

Although grain size is an important quantitative trait affecting rice yield and quality, there are few studies on gene-by-environment interactions (GEIs) in genome-wide association studies, especially, in main crop (MC) and ratoon rice (RR). To address these issues, the phenotypes for grain width (GW), grain length (GL), and thousand grain weight (TGW) of 159 accessions of MC and RR in two environments were used to associate with 2,017,495 SNPs for detecting quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) using 3VmrMLM. As a result, 64, 71, 67, 72, 63, and 56 QTNs, and 0, 1, 2, 2, 2, and 1 QEIs were found to be significantly associated with GW in MC (GW-MC), GL-MC, TGW-MC, GW-RR, GL-RR, and TGW-RR, respectively.

Analysis of complete chloroplast genome sequences and insight into the phylogenetic relationships of Ferula L.

Background: Ferula L. is one of the largest and most taxonomically complicated genera as well as being an important medicinal plant resource in the family Apiaceae. To investigate the plastome features and phylogenetic relationships of Ferula and its neighboring genera Soranthus Ledeb.

dQTG.seq: A comprehensive R tool for detecting all types of QTLs using extreme phenotype individuals in bi-parental segregation populations.

Although methodologies and software packages for bulked segregant analysis (BSA) are well established, it is difficult to detect extremely over-dominant and small-effect genes for quantitative traits in F population. To address this issue, we proposed a combinatorial strategy to identify all types of quantitative trait loci (QTLs) using extreme phenotype individuals in F. To popularize this strategy, we developed an R software package dQTG.