scholarly journals Identification and Genetic Analysis of Major Gene ST3GAL4 Related to Serum Alkaline Phosphatase in Chickens

2021 ◽  
Author(s):  
Hehe Tang ◽  
Yanchao Ma ◽  
Jianzeng Li ◽  
Zhenzhen Zhang ◽  
Wenting Li ◽  
...  
Keyword(s):  
Alkaline Phosphatase ◽  
Genetic Analysis ◽  
Bone Metabolism ◽  
Growth Traits ◽  
Serum Alkaline Phosphatase ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Important Indicator ◽  
Resource Population ◽  
First Time

Abstract Background: An increasing number of studies have explored disease and growth traits through quantitative trait locus (QTLs) in chickens. Nevertheless, the pathogenic genes underlying the QTL effects remain poorly understood. Alkaline phosphatase (ALP) is a marker of osteoblast maturation and an important indicator of bone metabolism. The change of ALP can reflect the bone metabolism and growth traits of animals to a certain extent. Results: In this study, we identified a SNP site at ST3GAL4 that found by genome-wide association studies (GWAS) in previous studies, and found another 8 SNPs by DNA sequencing. Interestingly, there are 4 SNPs rs475471G>A, rs475533C>T, rs475621A>G, rs475647C>A were completely linked by haplotype analysis, and selected a tag SNP rs475471G>A to analyze the correlation between this SNP and ALP level in Hubbard leg disease population and an F2 chicken resource population produced by Anka and Gushi chickens, and carried out population genetic analysis in 18 chicken breeds. Association analysis showed that this QTL within ST3GAL4 were highly correlated with ALP level, the mutant individuals with genotype AA had the highest ALP level, followed by GA and GG genotypes. The mutant individuals with genotype AA and GA genotype had higher values for body weight (BW), chest width (CW), body slanting length (BSL), pelvis width (PW) at 4-week, the semi-evisceration weight (SEW), evisceration weight (EW) and Leg weight (LW) of AA and GA genotype also higher. The amplification and typing of 4,852 DNA samples from 18 different breeds and the result shown GG genotype mainly existed in egg-type chickens and dual-type chickens, while AA genotype mainly distributed in commercial broilers and F2 resource population. The individuals of AA genotype had the highest ALP and showed better growth performance. This is the first time to report the causal variant of ST3GAL4 gene that located at chromosome 24 related to chicken serum ALP level. Besides, tissue expression analysis used Cobb broiler showed that there were significant differences between different genotypes in spleen and duodenum.Conclusions: This study the first time to determine 9 SNPs within ST3GAL4 were related to ALP in chickens, 4 of them were complete linkage sequence variants, which provide useful information on the mutation of ST3GAL4 and could predict the serum ALP level of chicken early and effectively as a potential molecular breeding marker.

scholarly journals Genome-wide association studies for feedlot and growth traits in cattle1

2011 ◽  
Vol 89 (6) ◽  
pp. 1684-1697 ◽  
Author(s):  
S. Bolormaa ◽  
B. J. Hayes ◽  
K. Savin ◽  
R. Hawken ◽  
W. Barendse ◽  
...  
Keyword(s):  
Growth Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Sequencing of over 100,000 individuals identifies multiple genes and rare variants associated with Crohns disease susceptibility

2021 ◽  
Author(s):  
Aleksejs Sazonovs ◽  
Christine R Stevens ◽  
Guhan R Venkataraman ◽  
Kai Yuan ◽  
Brandon Avila ◽  
...  
Keyword(s):  
Rare Variants ◽  
Disease Risk ◽  
Sequence Data ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Crohns Disease ◽  
Biological Targets ◽  
Genome Wide ◽  
Coding Variants ◽  
First Time

Genome-wide association studies (GWAS) have identified hundreds of loci associated with Crohns disease (CD); however, as with all complex diseases, deriving pathogenic mechanisms from these non-coding GWAS discoveries has been challenging. To complement GWAS and better define actionable biological targets, we analysed sequence data from more than 30,000 CD cases and 80,000 population controls. We observe rare coding variants in established CD susceptibility genes as well as ten genes where coding variation directly implicates the gene in disease risk for the first time.

scholarly journals Extensive impact of low-frequency variants on the phenotypic landscape at population-scale

2019 ◽  
Author(s):  
T. Fournier ◽  
O. Abou Saada ◽  
J. Hou ◽  
J. Peter ◽  
E. Caudal ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Growth Traits ◽  
Association Studies ◽  
Significant Proportion ◽  
Low Frequency ◽  
Population Level ◽  
Initial Population ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies

AbstractGenome-wide association studies (GWAS) allows to dissect the genetic basis of complex traits at the population level1. However, despite the extensive number of trait-associated loci found, they often fail to explain a large part of the observed phenotypic variance2–4. One potential source of this discrepancy could be the preponderance of undetected low-frequency genetic variants in natural populations5,6. To increase the allele frequency of those variants and assess their phenotypic effects at the population level, we generated a diallel panel consisting of 3,025 hybrids, derived from pairwise crosses between a subset of natural isolates from a completely sequenced 1,011 Saccharomyces cerevisiae population. We examined each hybrid across a large number of growth traits, resulting in a total of 148,225 cross/trait combinations. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a significant proportion (29%) is governed by non-additive effects. This is confirmed by the fact that a majority of complete dominance is observed in 25% of the traits. By performing GWAS on the diallel panel, we detected 1,723 significantly associated genetic variants, with 16.3% of them being low-frequency variants in the initial population. These variants, which would not be detected using classical GWAS, explain 21% of the phenotypic variance on average. Altogether, our results demonstrate that low-frequency variants should be accounted for as they contribute to a large part of the phenotypic variation observed in a population.

scholarly journals Joint genetic analysis using variant sets reveals polygenic gene-context interactions

2016 ◽  
Author(s):  
Francesco Paolo Casale ◽  
Danilo Horta ◽  
Barbara Rakitsch ◽  
Oliver Stegle
Keyword(s):  
Genetic Analysis ◽  
Mixed Models ◽  
Association Studies ◽  
Statistical Tests ◽  
Linear Mixed Models ◽  
Genetic Effects ◽  
Alternative Methods ◽  
Genome Wide Association Studies ◽  
Lipid Levels ◽  
Multiple Traits

AbstractJoint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. Our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. We first use simulations to validate iSet before applying the model to the analysis of genotype-environment interactions in an eQTL study. Our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. Finally, we apply iSet to test for sub-group specific genetic effects in human lipid levels in a large human cohort, where we identify a gene-sex interaction for C-reactive protein that is missed by alternative methods.Author summaryGenetic effects on phenotypes can depend on external contexts, including environment. Statistical tests for identifying such interactions are important to understand how individual genetic variants may act in different contexts. Interaction effects can either be studied using measurements of a given phenotype in different contexts, under the same genetic backgrounds, or by stratifying a population into subgroups. Here, we derive a method based on linear mixed models that can be applied to both of these designs. iSet enables testing for interactions between context and sets of variants, and accounts for polygenic effects. We validate our model using simulations, before applying it to the genetic analysis of gene expression studies and genome-wide association studies of human blood lipid levels. We find that modeling interactions with variant sets offers increased power, thereby uncovering interactions that cannot be detected by alternative methods.

scholarly journals Multiple association analysis of loci and candidate genes that regulate body size at three growth stages in Simmental beef cattle

2020 ◽  
Author(s):  
Bingxing An ◽  
Lei Xu ◽  
Jiangwei Xia ◽  
Xiaoqiao Wang ◽  
Jian Miao ◽  
...  
Keyword(s):  
Body Size ◽  
Beef Cattle ◽  
Candidate Genes ◽  
Association Analysis ◽  
Growth Traits ◽  
Association Studies ◽  
Growth Stages ◽  
Genome Wide Association Studies ◽  
Trait Analysis ◽  
Multiple Association

Abstract Background: Body size traits as one of the main breeding selection criteria was widely used to monitor cattle growth and to evaluate the selection response. In this study, body size was defined as body height (BH), body length (BL), hip height (HH), heart size (HS), abdominal size (AS), and cannon bone size (CS). We performed genome-wide association studies (GWAS) of these traits over the course of three growth stages (6, 12 and 18 months after birth) using three statistical models, single-trait GWAS, multi-trait GWAS and LONG-GWAS. The Illumina Bovine HD 770K BeadChip was used to identify genomic single nucleotide polymorphisms (SNPs) in 1217 individuals. Results: In total, 19, 29, and 10 significant SNPs were identified by the three models, respectively. Among these, 21 genes were promising candidate genes, including SOX2, SNRPD1, RASGEF1B, EFNA5, PTBP1, SNX9, SV2C, PKDCC, SYNDIG1, AKR1E2, and PRIM2 identified by single-trait analysis; SLC37A1, LAP3, PCDH7, MANEA, and LHCGR identified by multi-trait analysis; and P2RY1, MPZL1, LINGO2, CMIP, and WSCD1 identified by LONG-GWAS. Conclusions: Multiple association analysis was performed for six growth traits at each growth stage. These findings offer valuable insights for the further investigation of potential genetic mechanism of growth traits in Simmental beef cattle.

scholarly journals Multiple association analysis of loci and candidate genes that regulate body size at three growth stages in Simmental beef cattle

2020 ◽  
Author(s):  
Bingxing An ◽  
Lei Xu ◽  
Jiangwei Xia ◽  
Xiaoqiao Wang ◽  
Jian Miao ◽  
...  
Keyword(s):  
Body Size ◽  
Beef Cattle ◽  
Candidate Genes ◽  
Association Analysis ◽  
Growth Traits ◽  
Association Studies ◽  
Growth Stages ◽  
Genome Wide Association Studies ◽  
Trait Analysis ◽  
Multiple Association

Abstract Background: Body size traits as one of the main breeding selection criteria was widely used to monitor cattle growth and to evaluate the selection response. In this study, body size was defined as body height (BH), body length (BL), hip height (HH), heart size (HS), abdominal size (AS), and cannon bone size (CS). We performed genome-wide association studies (GWAS) of these traits over the course of three growth stages (6, 12 and 18 months after birth) using three statistical models, single-trait GWAS, multi-trait GWAS and LONG-GWAS. The Illumina Bovine HD 770K BeadChip was used to identify genomic single nucleotide polymorphisms (SNPs) in 1217 individuals. Results: In total, 19, 29, and 10 significant SNPs were identified by the three models, respectively. Among these, 21 genes were promising candidate genes, including SOX2, SNRPD1, RASGEF1B, EFNA5, PTBP1, SNX9, SV2C, PKDCC, SYNDIG1, AKR1E2, and PRIM2 identified by single-trait analysis; SLC37A1, LAP3, PCDH7, MANEA, and LHCGR identified by multi-trait analysis; and P2RY1, MPZL1, LINGO2, CMIP, and WSCD1 identified by LONG-GWAS. Conclusions: Multiple association analysis was performed for six growth traits at each growth stage. These findings offer valuable insights for the further investigation of potential genetic mechanism of growth traits in Simmental beef cattle.

Genome scan to detect quantitative trait loci for economically important traits in Holstein cattle using a dense SNP map

2007 ◽  
Vol 2007 ◽  
pp. 6-6
Author(s):  
H.D. Daetwyler ◽  
F.S. Schenkel ◽  
M. Sargolzaei ◽  
J.A.B. Robinson
Keyword(s):  
Linkage Disequilibrium ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Bovine Genome ◽  
Phenotypic Trait ◽  
Important Indicator ◽  
Trait Loci ◽  
Snp Map ◽  
First Time

Quantitative trait loci (QTL) are chromosome regions which are significantly associated with the expression of a phenotypic trait in a particular population. Detection of a QTL is carried out using association with a genetic marker, such as a single nucleotide polymorphism (SNP), which is in linkage disequilibrium (LD) with the QTL. The two main categories of association studies are linkage analyses (LA), which consider LD within families and linkage disequilibrium methods, which make use of LD across an entire population. The recent reduction in genotyping costs has allowed for testing individuals for a large number of SNP. This substantial increase in genotypic data has lead to denser marker distributions on the bovine genome thus potentially increasing the power of QTL detection studies. The objective of this study was to scan the bovine genome to detect QTL for 305 day lactation milk yield (MY), 305 day lactation fat yield (FY), 305 day lactation protein yield (PY), herd life (HL), somatic cell score (SCS), interval from calving to first service in cows (CTFS) and age at first service in heifers (AFS). HL is a measure of longevity measured in the number of lactations a cow stays in the herd. SCS refers to the amount of somatic cells a cow has in her milk and is an important indicator trait for mastitis. CTFS is the period from parturition to first insemination in days and AFS is the age in days at which a heifer was artificially inseminated for the first time. Fertility traits, such as CTFS and AFS, are indicators of reproductive efficiency.

scholarly journals GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI

2019 ◽  
Vol 5 (9) ◽  
pp. eaaw3095 ◽  
Author(s):  
Alexessander Couto Alves ◽  
N. Maneka G. De Silva ◽  
Ville Karhunen ◽  
Ulla Sovio ◽  
Shikta Das ◽  
...  
Keyword(s):  
Genetic Factors ◽  
Developmental Stages ◽  
Growth Traits ◽  
Association Studies ◽  
Child Growth ◽  
Growth Patterns ◽  
Longitudinal Growth ◽  
Genome Wide Association Studies ◽  
Adult Health ◽  
Control Infant

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

Bone metabolism biomarkers in men with advanced prostate cancer (PC) and bone metastases treated with denosumab versus zoledronic acid (ZA).

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 267-267
Author(s):  
Matthew Raymond Smith ◽  
Janet Elizabeth Brown ◽  
Karim Fizazi ◽  
Laurence Klotz ◽  
Gavin M. Marx ◽  
...  
Keyword(s):  
Alkaline Phosphatase ◽  
Treatment Group ◽  
Bone Metastases ◽  
Bone Metabolism ◽  
Proportional Hazards Model ◽  
Serum Alkaline Phosphatase ◽  
Cox Proportional Hazards Model ◽  
Total Serum ◽  
Double Blind ◽  
Increased Risk

267 Background: Denosumab, compared with ZA, has recently demonstrated significant benefits in preventing skeletal-related events (SREs) in a double-blind phase 3 study of men with castration-resistant PC and bone metastases (N=1901). This post hoc analysis was performed to determine if key bone metabolism biomarker levels correlated with time-to-first SRE. Methods: Time-to-Grade ≥2 increase in total serum alkaline phosphatase (ALP) was assessed for correlation with time-to-first SRE. Levels of urinary N-telopeptide (uNTX) and serum bone-specific alkaline phosphatase (BSAP) markers were measured at baseline and study week 13, and correlations with time-to-first SRE were assessed. Covariate analyses were performed using a Cox proportional hazards model, stratified by treatment group or (for baseline analyses) with treatment group as the independent variable. Results: Analysis of the relationship between time-to-Grade ≥2 increase in total ALP and time-to-first SRE demonstrated that Grade ≥2 increases in ALP of PC patients were associated with a higher risk of first SRE (hazard ratio [HR] 1.838, 95% confidence interval [CI] 1.559, 2.167; P<0.0001). In dichotomized analyses (< vs ≥ median), a higher level of uNTX and BSAP (at baseline or week 13) was correlated with an increased risk of first SRE (Table). In baseline covariate analyses, treatment benefit for denosumab was maintained after adjusting for either baseline uNTX (HR 0.818, 95% CI 0.703, 0.951; P=0.0091) or BSAP (HR 0.813, 95% CI 0.700, 0.943; P=0.0061). Conclusions: Higher levels of uNTX or BSAP at baseline or week 13 were associated with worse SRE outcomes in men with advanced PC and bone metastases. Denosumab was more efficacious for preventing or delaying SREs compared with ZA, regardless of bone-related biomarker levels. Relationship of covariate to time-to-first SRE Clinical trial information: NCT00321620. [Table: see text]

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