Cholesterol gallstone formation is influenced by environmental and genetic factors. Cholesterol gallstone susceptible genes (Lith genes) are complex and show polygenic traits. Quantitative trait locus (QTL) analysis in inbred mice is a powerful method for identifying these genetic defects. More than 20 Lith genes were discovered by QTL in inbred mice models. The co-localized, candidate genes responsible for gallstone susceptible QTL can lead to the discovery of pathophysiologic functions of Lith (gallstone) genes. These genetic studies may reveal novel molecular targets for prevention and medical therapy. Presently, the only effective treament for gallstone is cholecystectomy. In the future, new drugs targeting Lith genes can be available not only for the treatment of gallstone disease, but also for "pre-stone" diagnosis.
Animals ; Cholecystectomy ; Cholesterol* ; Diagnosis ; Gallstones* ; Mice ; Molecular Biology* ; Multifactorial Inheritance ; Quantitative Trait Loci

Genetic epidemiology studies have established that the natural variation of gene expression profiles is heritable and has genetic bases. A number of proximal and remote DNA variations, known as expression quantitative trait loci (eQTLs), that are associated with the expression phenotypes have been identified, first in Epstein-Barr virus-transformed lymphoblastoid cell lines and later expanded to other cell and tissue types. Integration of the eQTL information and the network analysis of transcription modules may lead to a better understanding of gene expression regulation. As these network modules have relevance to biological or disease pathways, these findings may be useful in predicting disease susceptibility.
Cell Line ; Disease Susceptibility ; DNA ; Gene Expression Regulation ; Metagenomics ; Molecular Epidemiology ; Phenotype ; Quantitative Trait Loci ; Transcriptome

Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with complex diseases or traits. However, the exact biological functions of these loci are largely unknown. Recent functional annotation indicates that the majority of disease/trait associated loci are concentrated in regulatory DNA of human genome. Expression quantitative trait loci (eQTL) analyses, chromosome conformation capture related methods and genome editing methods (such as CRISPR/Cas9) may facilitate the functional study of these loci. Research on noncoding RNAs and rare variants may improve the functional understanding. These efforts may promise translation of GWAS findings to clinical practices.
Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Quantitative Trait Loci

OBJECTIVETo evaluate the effect and profitability of using the quantitative trait loci (QTL)-linked direct marker (DR marker) in gene-assisted selection (GAS).

METHODSThree populations (100, 200, or 300 sows plus 10 boars within each group) with segregating QTL were simulated stochastically. Five economic traits were investigated, including number of born alive (NBA), average daily gain to 100 kg body weight (ADG), feed conversion ratio (FCR), back fat at 100 kg body weight (BF) and intramuscular fat (IMF). Selection was based on the estimated breeding value (EBV) of each trait. The starting frequencies of the QTL's favorable allele were 0.1, 0.3 and 0.5, respectively. The economic return was calculated by gene flow method.

RESULTSThe selection efficiency was higher than 100% when DR markers were used in GAS for 5 traits. The selection efficiency for NBA was the highest, and the lowest was for ADG whose QTL had the lowest variance. The mixed model applied DR markers and obtained higher extra genetic gain and extra economic returns. We also found that the lower the frequency of the favorable allele of the QTL, the higher the extra return obtained.

CONCLUSIONGAS is an effective selection scheme to increase the genetic gain and the economic returns in pig breeding.

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) suggested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.
Bread ; Chromosome Mapping ; Quantitative Trait Loci ; genetics ; Triticum ; genetics ; growth & development

Quantitative trait loci (QTL) and their additive, dominance and epistatic effects play a critical role in complex trait variation. It is often infeasible to detect multiple interacting QTL due to main effects often being confounded by interaction effects. Positioning interacting QTL within a small region is even more difficult. We present a variance component approach nested in an empirical Bayesian method, which simultaneously takes into account additive, dominance and epistatic effects due to multiple interacting QTL. The covariance structure used in the variance component approach is based on combined linkage disequilibrium and linkage (LDL) information. In a simulation study where there are complex epistatic interactions between QTL, it is possible to simultaneously fine map interacting QTL using the proposed approach. The present method combined with LDL information can efficiently detect QTL and their dominance and epistatic effects, making it possible to simultaneously fine map main and epistatic QTL.
Chromosome Mapping ; Epistasis, Genetic ; Genetic Linkage ; Humans ; Linkage Disequilibrium ; Monte Carlo Method ; Quantitative Trait Loci ; genetics

The alteration of alternative splicing patterns has an effect on the quantification of functional proteins, leading to phenotype variation. The splicing quantitative trait locus (sQTL) is one of the main genetic elements affecting splicing patterns. Here, we report the results of genome-wide sQTLs across 141 strains of Arabidopsis thaliana with publicly available next generation sequencing datasets. As a result, we found 1,694 candidate sQTLs in Arabidopsis thaliana at a false discovery rate of 0.01. Furthermore, among the candidate sQTLs, we found 25 sQTLs that overlapped with the list of previously examined trait-associated single nucleotide polymorphisms (SNPs). In summary, this sQTL analysis provides new insight into genetic elements affecting alternative splicing patterns in Arabidopsis thaliana and the mechanism of previously reported trait-associated SNPs.
Alternative Splicing ; Arabidopsis* ; Dataset ; Phenotype ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci*

BACKGROUND AND OBJECTIVES : The common methods of genetic association analysis are sensitive to population stratification, which may easily lead to a spurious association result. We used a regression approach based for linkage disequilibrium to perform a high resolution genetic association analysis. SUBJECTS AND METHODS : We applied a regression approach that can increase the resolution of quantitative traits that are related with cardiovascular diseases. The population data was composed of 543 males and 876 females without cardiovascular diseases, and it was obtained from a cardiovascular genome center. We used information about linkage disequilibrium between the marker and trait locus, and we added the covariates to model their effects. RESULTS : We found that this regression approach has the merit of analyzing genetic association based on linkage disequilibrium. In the analysis of the male group, the total cholesterol was significantly in linkage disequilibrium with CETP3 (p=0.002), and triglyceride was significantly in linkage disequilibrium with ACE8 (p=0.037), APOA1-1 (p=0.031), APOA5-1 (p=0.001), APOA5-2 (p=0.001) and LIPC4 (p=0.022). HDL-cholesterol was significantly in linkage disequilibrium with ACE7 (p=0.002), ACE8 (p=0.008), ACE10 (p=0.003), APOA5-2 (p=0.022), and MTP1 (p=0.001). In the female group, total cholesterol was significantly associated with APOA5-1 (p=0.020), APOA5-2 (p=0.001), and LIPC1 (p=0.016), and triglyceride was significantly associated with APOA5-1 (p=0.009), APOA5-2 (p=0.001), and CETP5 (p=0.049). LDL-cholesterol was significantly associated with APOA5-2 (p=0.004), and HDL-cholesterol was significantly associated with LIPC1 (p=0.004). CONCLUSION : We used a regression-based method to perform high resolution linkage disequilibrium analysis of a quantitative trait locus that's associated with lipid profiles. This method of using a single marker, as applied in this paper, was well suited for analysis of genetic association. Because of the simplicity, the method can also be easily performed by routine statistical analysis software.
Cardiovascular Diseases ; Cholesterol ; Female ; Genome ; Humans ; Linkage Disequilibrium* ; Male ; Quantitative Trait Loci* ; Triglycerides

To understand genetic patterns of the morphological and physiological traits in flag leaf of barley, a double haploid (DH) population derived from the parents Yerong and Franklin was used to determine quantitative trait loci (QTL) controlling length, width, length/width, and chlorophyll content of flag leaves. A total of 9 QTLs showing significantly additive effect were detected in 8 intervals on 5 chromosomes. The variation of individual QTL ranged from 1.9% to 20.2%. For chlorophyll content expressed as SPAD value, 4 QTLs were identified on chromosomes 2H, 3H and 6H; for leaf length and width, 2 QTLs located on chromosomes 5H and 7H, and 2 QTLs located on chromosome 5H were detected; and for length/width, 1 QTL was detected on chromosome 7H. The identification of these QTLs associated with the properties of flag leaf is useful for barley improvement in breeding programs.
Chlorophyll ; analysis ; Chromosome Mapping ; Hordeum ; genetics ; Phenotype ; Plant Leaves ; genetics ; Quantitative Trait Loci

A method was proposed for the detection of outliers and influential observations in the framework of a mixed linear model, prior to the quantitative trait locus (QTL) mapping analysis. We investigated the impact of outliers on QTL mapping for complex traits in a mouse BXD population, and observed that the dropping of outliers could provide the evidence of additional QTL and epistatic loci affecting the 1stBrain-OB and the 2ndBrain-OB in a cross of the abovementioned population. The results could also reveal a remarkable increase in estimating heritabilities of QTL in the absence of outliers. In addition, simulations were conducted to investigate the detection powers and false discovery rates (FDRs) of QTLs in the presence and absence of outliers. The results suggested that the presence of a small proportion of outliers could increase the FDR and hence decrease the detection power of QTLs. A drastic increase could be obtained in the estimates of standard errors for position, additive and additivex environment interaction effects of QTLs in the presence of outliers.
Animals ; Chromosome Mapping ; methods ; Mice ; Mice, Inbred C57BL ; Mice, Inbred DBA ; Quantitative Trait Loci