Quantitative Trait Single Nucleotide Polymorphisms Research Articles

Elucidation of the genetic determination of clutch traits in Chinese local chickens of the Laiwu Black breed

BackgroundEgg laying rate (LR) is associated with a clutch, which is defined as consecutive days of oviposition. The clutch trait can be used as a selection indicator to improve egg production in poultry breeding. However, little is known about the genetic basis of clutch traits. In this study, our aim was to estimate genetic parameters and identify quantitative trait single nucleotide polymorphisms for clutch traits in 399 purebred Laiwu Black chickens (a native Chinese breed) using a genome-wide association study (GWAS).MethodsIn this work, after estimating the genetic parameters of age at first egg, body weight at first egg, LR, longest clutch until 52 week of age, first week when the longest clutch starts, last week when the longest clutch ends, number of clutches, and longest number of days without egg-laying until 52 week of age, we identified single nucleotide polymorphisms (SNPs) and potential candidate genes associated with clutch traits in Laiwu Black chickens. The restricted maximum likelihood method was used to estimate genetic parameters of clutch pattern in 399 Laiwu Black hens, using the GCTA software.ResultsThe results showed that SNP-based heritability estimates of clutch traits ranged from 0.06 to 0.59. Genotyping data were obtained from whole genome re-sequencing data. After quality control, a total of 10,810,544 SNPs remained to be analyzed. The GWAS revealed that 421 significant SNPs responsible for clutch traits were scattered on chicken chromosomes 1–14, 17–19, 21–25, 28 and Z. Among the annotated genes, NELL2, SMYD9, SPTLC2, SMYD3 and PLCL1 were the most promising candidates for clutch traits in Laiwu Black chickens.ConclusionThe findings of this research provide critical insight into the genetic basis of clutch traits. These results contribute to the identification of candidate genes and variants. Genes and SNPs potentially provide new avenues for further research and would help to establish a framework for new methods of genomic prediction, and increase the accuracy of estimated genetic merit for egg production and clutch traits.

An early prediction model for type 2 diabetes mellitus based on genetic variants and nongenetic risk factors in a Han Chinese cohort.

We aimed to construct a prediction model of type 2 diabetes mellitus (T2DM) in a Han Chinese cohort using a genetic risk score (GRS) and a nongenetic risk score (NGRS). A total of 297 Han Chinese subjects who were free from type 2 diabetes mellitus were selected from the Tianjin Medical University Chronic Disease Cohort for a prospective cohort study. Clinical characteristics were collected at baseline and subsequently tracked for a duration of 9 years. Genome-wide association studies (GWASs) were performed for T2DM-related phenotypes. The GRS was constructed using 13 T2DM-related quantitative trait single nucleotide polymorphisms (SNPs) loci derived from GWASs, and NGRS was calculated from 4 biochemical indicators of independent risk that screened by multifactorial Cox regressions. We found that HOMA-IR, uric acid, and low HDL were independent risk factors for T2DM (HR >1; P<0.05), and the NGRS model was created using these three nongenetic risk factors, with an area under the ROC curve (AUC) of 0.678; high fasting glucose (FPG >5 mmol/L) was a key risk factor for T2DM (HR = 7.174, P< 0.001), and its addition to the NGRS model caused a significant improvement in AUC (from 0.678 to 0.764). By adding 13 SNPs associated with T2DM to the GRS prediction model, the AUC increased to 0.892. The final combined prediction model was created by taking the arithmetic sum of the two models, which had an AUC of 0.908, a sensitivity of 0.845, and a specificity of 0.839. We constructed a comprehensive prediction model for type 2 diabetes out of a Han Chinese cohort. Along with independent risk factors, GRS is a crucial element to predicting the risk of type 2 diabetes mellitus.

Genome-wide superior alleles, haplotypes and candidate genes associated with tolerance on sodic-dispersive soils in wheat (Triticum aestivum L.)

Key messageThe pleiotropic SNPs/haplotypes, overlapping genes (metal ion binding, photosynthesis), and homozygous/biallelic SNPs and transcription factors (HTH myb-type and BHLH) hold great potential for improving wheat yield potential on sodic-dispersive soils.Sodic-dispersive soils have multiple subsoil constraints including poor soil structure, alkaline pH and subsoil toxic elemental ion concentration, affecting growth and development in wheat. Tolerance is required at all developmental stages to enhance wheat yield potential on such soils. An in-depth investigation of genome-wide associations was conducted using a field phenotypic data of 206 diverse Focused Identification of Germplasm Strategy (FIGS) wheat lines for two consecutive years from different sodic and non-sodic plots and the exome targeted genotyping by sequencing (tGBS) assay. A total of 39 quantitative trait SNPs (QTSs), including 18 haplotypes were identified on chromosome 1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 5A, 5D, 6B, 7A, 7B, 7D for yield and yield-components tolerance. Among these, three QTSs had common associations for multiple traits, indicating pleiotropism and four QTSs had close associations for multiple traits, within 32.38 Mb. The overlapping metal ion binding (Mn, Ca, Zn and Al) and photosynthesis genes and transcription factors (PHD-, Dof-, HTH myb-, BHLH-, PDZ_6-domain) identified are known to be highly regulated during germination, maximum stem elongation, anthesis, and grain development stages. The homozygous/biallelic SNPs having allele frequency above 30% were identified for yield and crop establishment/plants m−2. These SNPs correspond to HTH myb-type and BHLH transcription factors, brassinosteroid signalling pathway, kinase activity, ATP and chitin binding activity. These resources are valuable in haplotype-based breeding and genome editing to improve yield potential on sodic-dispersive soils.

Abstract LB-175: Genome-wide scan of the effect of cis-eSNPs on gastric cancer in Chinese population

Abstract Background: Previous genome-wide association studies (GWASs) have identified multiple single-nucleotide polymorphisms (SNPs) for gastric cancer risk. However, only a tiny fraction of heritability can be explained. Methods: We performed a comprehensive analysis to evaluate the associations between cis-expression quantitative trait SNPs (eSNPs) and susceptibility to gastric cancer in two GWAS datasets, including 2,631 gastric cancer cases and 4,373 controls. Gene-based and pathway analysis were conducted by the sequence kernel association test (SKAT-C) and the adaptive rank truncated product (ARTP) method, respectively. Results: A total of 314,518 eSNPs were remained after quality control, which mapped to 5,155 genes and 1,077 pathways. We identified six novel eQTL SNPs in unknown loci which can explain approximately 3.56% of the phenotypic variances combined with known GWAS related variants. Function analysis revealed that rs3129206 might affect the susceptibility of gastric cancer via regulating the expression of HSD17B8, while rs6930376 regulating the expression of PSORS1C3. Pathway-based analysis showed a total of seven potential pathways might account for the risk of gastric cancer (Pfdr&amp;lt;0.1). Conclusion: Our findings demonstrate an overview of the associations between eSNPs and gastric cancer risk in Chinese population, which provide insight into genes and pathways that may be biologically relevant for risk of gastric cancer. Citation Format: Jing Ni, Jing Han, Meng Zhu, Guangfu Jin, Zhibin Hu. Genome-wide scan of the effect of cis-eSNPs on gastric cancer in Chinese population [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-175.

Conditional GWAS revealing genetic impacts of lifestyle behaviors on low-density lipoprotein (LDL)

BackgroundAccumulation of LDL cholesterol (LDL-c) within artery walls is strongly associated with the initiation and progression of atherosclerosis development. This complex trait is affected by multifactor involving polygenes, environments, and their interactions. Uncovering genetic architecture of LDL may help to increase the understanding of the genetic mechanism of cardiovascular diseases. MethodsWe used a genetic model to analyze genetic effects including additive, dominance, epistasis, and ethnic interactions for data from the Multi-Ethnic Study of Atherosclerosis (MESA). Three lifestyle behaviors (reading, intentional exercising, smoking) were used as cofactor in conditional models. ResultsWe identified 156 genetic effects of 10 quantitative trait SNPs (QTSs) in base model and three conditional models. The total estimated heritability of these genetic effects was approximately 72.88% in the base model. Five genes (CELSR2, MARK2, ADAMTS12, PFDN4, and MAGI2) have biological functions related to LDL. ConclusionsCompared with the based model LDL, the results in three conditional models revealed that intentional exercising and smoking could have impacts for causing and suppressing some of genetic effects and influence the levels of LDL. Furthermore, these two lifestyles could have different genetic effects for each ethnic group on a specific QTS. As most of the heritability in based model LDL and conditional model LDL|Smk was contributed from epistasis effects, our result indicated that epistasis effects played important roles in determining LDL levels. Our study provided useful insight into the biological mechanisms underlying regulation of LDL and might help in the discovery of novel therapeutic targets for cardiovascular disease.

Dissection of complicate genetic architecture and breeding perspective of cottonseed traits by genome-wide association study

BackgroundCottonseed is one of the most important raw materials for plant protein, oil and alternative biofuel for diesel engines. Understanding the complex genetic basis of cottonseed traits is requisite for achieving efficient genetic improvement of the traits. However, it is not yet clear about their genetic architecture in genomic level. GWAS has been an effective way to explore genetic basis of quantitative traits in human and many crops. This study aims to dissect genetic mechanism seven cottonseed traits by a GWAS for genetic improvement.ResultsA genome-wide association study (GWAS) based on a full gene model with gene effects as fixed and gene-environment interaction as random, was conducted for protein, oil and 5 fatty acids using 316 accessions and ~ 390 K SNPs. Totally, 124 significant quantitative trait SNPs (QTSs), consisting of 16, 21, 87 for protein, oil and fatty acids (palmitic, linoleic, oleic, myristic, stearic), respectively, were identified and the broad-sense heritability was estimated from 71.62 to 93.43%; no QTS-environment interaction was detected for the protein, the palmitic and the oleic contents; the protein content was predominantly controlled by epistatic effects accounting for 65.18% of the total variation, but the oil content and the fatty acids except the palmitic were mainly determined by gene main effects and no epistasis was detected for the myristic and the stearic. Prediction of superior pure line and hybrid revealed the potential of the QTSs in the improvement of cottonseed traits, and the hybrid could achieve higher or lower genetic values compared with pure lines.ConclusionsThis study revealed complex genetic architecture of seven cottonseed traits at whole genome-wide by mixed linear model approach; the identified genetic variants and estimated genetic component effects of gene, gene-gene and gene-environment interaction provide cotton geneticist or breeders new knowledge on the genetic mechanism of the traits and the potential molecular breeding design strategy.

Dissecting genetic architecture of startle response in Drosophila melanogaster using multi-omics information

Startle behavior is important for survival, and abnormal startle responses are related to several neurological diseases. Drosophila melanogaster provides a powerful system to investigate the genetic underpinnings of variation in startle behavior. Since mechanically induced, startle responses and environmental conditions can be readily quantified and precisely controlled. The 156 wild-derived fully sequenced lines of the Drosophila Genetic Reference Panel (DGRP) were used to identify SNPs and transcripts associated with variation in startle behavior. The results validated highly significant effects of 33 quantitative trait SNPs (QTSs) and 81 quantitative trait transcripts (QTTs) directly associated with phenotypic variation of startle response. We also detected QTT variation controlled by 20 QTSs (tQTSs) and 73 transcripts (tQTTs). Association mapping based on genomic and transcriptomic data enabled us to construct a complex genetic network that underlies variation in startle behavior. Based on principles of evolutionary conservation, human orthologous genes could be superimposed on this network. This study provided both genetic and biological insights into the variation of startle response behavior of Drosophila melanogaster, and highlighted the importance of genetic network to understand the genetic architecture of complex traits.

Genomic Prediction of Genotypic Effects with Epistasis and Environment Interactions for Yield-Related Traits of Rapeseed (Brassica napus L.).

Oilseed rape (Brassica napus) is an economically important oil crop, yet the genetic architecture of its complex traits remain largely unknown. Here, genome-wide association study was conducted for eight yield-related traits to dissect the genetic architecture of additive, dominance, epistasis, and their environment interaction. Additionally, the optimal genotype combination and the breeding value of superior line, superior hybrid and existing best line in mapping population were predicted for each trait in two environments based on the predicted genotypic effects. As a result, 17 quantitative trait SNPs (QTSs) were identified significantly for target traits with total heritability varied from 58.47 to 87.98%, most of which were contributed by dominance, epistasis, and environment-specific effects. The results indicated that non-additive effects were large contributions to heritability and epistasis, and also noted that environment interactions were important variants for oilseed breeding. Our study facilitates the understanding of genetic basis of rapeseed yield trait, helps to accelerate rapeseed breading, and also offers a roadmap for precision plant breeding via marker-assisted selection.

A Genome-Wise Association Study of A Quantitative Alcoholism Trait Using A Full Genetic Model Identifies Significant SNP Heritability From Dominance And Epistatic Interactions

Most psychiatric diseases are complex diseases involving polygenes, environment, and their interactions. Although genome-wide association studies (GWASs) revolutionized the genetic dissection of many diseases, only a very small portion of heritability has been accounted for, i.e. there was a remarkable limitation as missing heritability. Here, we demonstrate that perturbations arising from gene-gene interactions, including dominance and epistasis, and conditions of comorbidity substantially influence the complex trait of alcohol dependence symptom count (ADSC). We conducted a GWAS of ADSC by using a mixed liner approach in a full genetic model considering additive, dominance, epistasis and their interactions with ethnicity, as well as conditions of comorbid substance dependences, in the dataset of the Study of Addiction: Genetics and Environment with 3838 subjects. Twenty quantitative trait single nucleotide polymorphisms (QTSs) showed significant associations with ADSC (experiment-wise P < 0.05 based on permutation test), including four in previously reported genes (ADH1C, PKNOX2, CPE, and KCNB2), supporting the overall validity of the analysis. Two QTSs within or near ADH1C showed significant association in a dominance inheritance mode, and increased the phenotype value of ADSC when the effect of comorbid opiate or marijuana dependence was controlled. Significant association was also identified in variants within 4 novel genes (RGS6, FMN1, NRM, and BPTF), 2 noncoding RNA and 2 epistasis loci. QTS rs7616413, located near PTPRG encoding a protein tyrosine phosphatase (PTP) receptor, interacted with rs10090742 within ANGPT1 encoding a PTP, in an additive × additive or dominance × additive manner. The detected QTSs contributed to about 20% of total heritability, in which dominance and epistasis effects accounted for over 50%. In comparison, the original analysis of the dataset using the conventional approach of GWAS failed to detect a genome-wide association with alcohol dependence. Our analysis discloses a much larger number of SNPs or epistasis for a quantitative trait of a psychiatric disorder, and may represent an important stride toward solving the problem of missing heritability. This approach can be employed in GWASs and precision medicine of many other psychiatric disorders such as schizophrenia, major depression et al.

Dissection of genetic architecture of rice plant height and heading date by multiple-strategy-based association studies.

Xieyou9308 is a certified super hybrid rice cultivar with a high grain yield. To investigate its underlying genetic basis of high yield potential, a recombinant inbred line (RIL) population derived from the cross between the maintainer line XieqingzaoB (XQZB) and the restorer line Zhonghui9308 (ZH9308) was constructed for identification of quantitative trait SNPs (QTSs) associated with two important agronomic traits, plant height (PH) and heading date (HD). By re-sequencing of 138 recombinant inbred lines (RILs), a total of ~0.7 million SNPs were identified for the association studies on the PH and HD. Three association mapping strategies (including hypothesis-free genome-wide association and its two complementary hypothesis-engaged ones, QTL-based association and gene-based association) were adopted for data analysis. Using a saturated mixed linear model including epistasis and environmental interaction, we identified a total of 31 QTSs associated with either the PH or the HD. The total estimated heritability across three analyses ranged from 37.22% to 45.63% and from 37.53% to 55.96% for the PH and HD, respectively. In this study we examined the feasibility of association studies in an experimental population (RIL) and identified several common loci through multiple strategies which could be preferred candidates for further research.

An association study revealed substantial effects of dominance, epistasis and substance dependence co-morbidity on alcohol dependence symptom count.

Alcohol dependence is a complex disease involving polygenes, environment and their interactions. Inadequate consideration of these interactions may have hampered the progress on genome-wide association studies of alcohol dependence. By using the dataset of the Study of Addiction: Genetics and Environment with 3838 subjects, we conducted a genome-wide association studies of alcohol dependence symptom count (ADSC) with a full genetic model considering additive, dominance, epistasis and their interactions with ethnicity, as well as conditions of co-morbid substance dependence. Twenty quantitative trait single nucleotide polymorphisms (QTSs) showed highly significant associations with ADSC, including four previously reported genes (ADH1C, PKNOX2, CPE and KCNB2) and the reported intergenic rs1363605, supporting the overall validity of the analysis. Two QTSs within or near ADH1C showed very strong association in a dominance inheritance mode and increased the phenotype value of ADSC when the effect of co-morbid opiate or marijuana dependence was controlled. Highly significant association was also identified in variants within four novel genes (RGS6, FMN1, NRM and BPTF), two non-coding RNA and two epistasis loci. QTS rs7616413, located near PTPRG encoding a protein tyrosine phosphatase receptor, interacted with rs10090742 within ANGPT1 encoding a protein tyrosine phosphatase in an additive × additive or dominance × additive manner. The detected QTSs contributed to about 20 percent of total heritability, in which dominance and epistasis effects accounted for over 50 percent. These results demonstrated that perturbations arising from gene-gene interaction and conditions of co-morbidity substantially influence the genetic architecture of complex trait.