scholarly journals Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

2018 ◽  
Author(s):  
Sandra Silvia Negro ◽  
Emilie Millet ◽  
Delphine Madur ◽  
Cyril Bauland ◽  
Valérie Combes ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Association Studies ◽  
Snp Array ◽  
Genotyping By Sequencing ◽  
Ascertainment Bias ◽  
F1 Hybrids ◽  
Genome Wide Association Studies ◽  
Snp Arrays ◽  
Sequencing Technologies ◽  
Single Quantitative Trait Locus

AbstractBackgroundSingle Nucleotide Polymorphism (SNP) array and re-sequencing technologies have different properties (e.g. calling rate, minor allele frequency profile) and drawbacks (e.g. ascertainment bias). This lead us to study their complementarity and the consequences of using them separately or combined in diversity analyses and Genome-Wide Association Studies (GWAS). We performed GWAS on three traits (grain yield, plant height and male flowering time) measured in 22 environments on a panel of 247 F1 hybrids obtained by crossing 247 diverse dent maize inbred lines with a same flint line. The 247 lines were genotyped using three genotyping technologies (Genotyping-By-Sequencing, Illumina Infinium 50K and Affymetrix Axiom 600K arrays).ResultsThe effects of ascertainment bias of the 50K and 600K arrays were negligible for deciphering global genetic trends of diversity and for estimating relatedness in this panel. We developed an original approach based on linkage disequilibrium (LD) extent in order to determine whether SNPs significantly associated with a trait and that are physically linked should be considered as a single Quantitative Trait Locus (QTL) or several independent QTLs. Using this approach, we showed that the combination of the three technologies, which have different SNP distributions and densities, allowed us to detect more QTLs (gain in power) and potentially refine the localization of the causal polymorphisms (gain in resolution).ConclusionsConceptually different technologies are complementary for detecting QTLs by tagging different haplotypes in association studies. Considering LD, marker density and the combination of different technologies (SNP-arrays and re-sequencing), the genotypic data available were most likely enough to well represent polymorphisms in the centromeric regions, whereas using more markers would be beneficial for telomeric regions.

scholarly journals Japonica Array NEO with Increased Genome-Wide Coverage and Abundant Disease Risk SNPs

2020 ◽  
Author(s):  
Mika Sakurai-Yageta ◽  
Kazuki Kumada ◽  
Chinatsu Gocho ◽  
Satoshi Makino ◽  
Akira Uruno ◽  
...  
Keyword(s):  
Japanese Population ◽  
Disease Risk ◽  
Association Studies ◽  
Snp Array ◽  
Reference Panel ◽  
Genome Wide Association Studies ◽  
Snp Arrays ◽  
Tag Snps ◽  
Genome Wide ◽  
Risk Snps

Abstract Background: Increasing the power of genome-wide association studies in diverse populations is important for understanding the genetic determinants of disease risks, and large-scale genotype data are collected by genome cohort and biobank projects all over the world. In particular, ethnic-specific SNP arrays are becoming more important because the use of universal SNP arrays has some limitations in terms of cost-effectiveness and throughput. As part of the Tohoku Medical Megabank Project, which integrates prospective genome cohorts into a biobank, we have been developing a series of Japonica Arrays for genotyping participants based on reference panels constructed from whole-genome sequence data of the Japanese population.Results: We designed a novel version of the SNP Array for the Japanese population, called Japonica Array NEO, comprising a total of 666,883 SNPs, including tag SNPs of autosomes and X chromosome with pseudoautosomal regions, SNPs of Y chromosome and mitochondria, and known disease risk SNPs. Among them, 654,246 tag SNPs were selected from an expanded reference panel of 3,552 Japanese using pairwise r2 of linkage disequilibrium measures. Moreover, 28,298 SNPs were included for the evaluation of previously identified disease risk SNPs from the literature and databases, and those present in the Japanese population were extracted using the reference panel. The imputation performance of Japonica Array NEO was assessed by genotyping 286 Japanese samples. We found that the imputation quality r2 and INFO score in the minor allele frequency bin >2.5%–5% were >0.9 and >0.8, respectively, and >12 million markers were imputed with an INFO score >0.8. After verification, Japonica Arrays were used to efficiently genotype cohort participants from the sample selection to perform a quality assessment of the raw data; approximately 130,000 genotyping data of >150,000 participants has already been obtained. Conclusions: Japonica Array NEO is a promising tool for genotyping the Japanese population with genome-wide coverage, contributing to the development of genetic risk scores for this population and further identifying disease risk alleles among individuals of East Asian ancestry.

scholarly journals Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Sandra S. Negro ◽  
Emilie J. Millet ◽  
Delphine Madur ◽  
Cyril Bauland ◽  
Valérie Combes ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genotyping By Sequencing ◽  
Snp Arrays ◽  
Trait Loci

scholarly journals Japonica Array NEO with increased genome-wide coverage and abundant disease risk SNPs

2020 ◽  
Author(s):  
Mika Sakurai-Yageta ◽  
Kazuki Kumada ◽  
Chinatsu Gocho ◽  
Satoshi Makino ◽  
Akira Uruno ◽  
...  
Keyword(s):  
Japanese Population ◽  
Disease Risk ◽  
Association Studies ◽  
Snp Array ◽  
Reference Panel ◽  
Genome Wide Association Studies ◽  
Snp Arrays ◽  
Tag Snps ◽  
Genome Wide ◽  
Risk Snps

AbstractBackgroundIncreasing the power of genome-wide association studies in diverse populations is important for understanding the genetic determinants of disease risks, and large-scale genotype data are collected by genome cohort and biobank projects all over the world. In particular, ethnic-specific SNP arrays are becoming more important because the use of universal SNP arrays has some limitations in terms of cost-effectiveness and throughput. As part of the Tohoku Medical Megabank Project, which integrates prospective genome cohorts into a biobank, we have been developing a series of Japonica Arrays for genotyping participants based on reference panels constructed from whole-genome sequence data of the Japanese population.ResultsWe designed a novel version of the SNP Array for the Japanese population, called Japonica Array NEO, comprising a total of 666,883 SNPs, including tag SNPs of autosomes and X chromosome with pseudoautosomal regions, SNPs of Y chromosome and mitochondria, and known disease risk SNPs. Among them, 654,246 tag SNPs were selected from an expanded reference panel of 3,552 Japanese using pairwise r2 of linkage disequilibrium measures. Moreover, 28,298 SNPs were included for the evaluation of previously identified disease risk SNPs from the literature and databases, and those present in the Japanese population were extracted using the reference panel. The imputation performance of Japonica Array NEO was assessed by genotyping 286 Japanese samples. We found that the imputation quality r2 and INFO score in the minor allele frequency bin >2.5%–5% were >0.9 and >0.8, respectively, and >12 million markers were imputed with an INFO score >0.8. After verification, Japonica Arrays were used to efficiently genotype cohort participants from the sample selection to perform a quality assessment of the raw data; approximately 130,000 genotyping data of >150,000 participants has already been obtained.ConclusionsJaponica Array NEO is a promising tool for genotyping the Japanese population with genome-wide coverage, contributing to the development of genetic risk scores for this population and further identifying disease risk alleles among individuals of East Asian ancestry.

scholarly journals Family-Based Quantitative Trait Meta-Analysis Implicates Rare Noncoding Variants in DENND1A in Polycystic Ovary Syndrome

2019 ◽  
Vol 104 (9) ◽  
pp. 3835-3850 ◽  
Author(s):  
Matthew Dapas ◽  
Ryan Sisk ◽  
Richard S Legro ◽  
Margrit Urbanek ◽  
Andrea Dunaif ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Quantitative Trait ◽  
Rare Variants ◽  
Polycystic Ovary ◽  
Association Studies ◽  
Meta Analysis ◽  
Premenopausal Women ◽  
Endocrine Disorders ◽  
Genome Wide Association Studies ◽  
Ovary Syndrome

AbstractContextPolycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5% to15% of this population depending on the diagnostic criteria applied. It is characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology. PCOS is highly heritable, but only a small proportion of this heritability can be accounted for by the common genetic susceptibility variants identified to date.ObjectiveThe objective of this study was to test whether rare genetic variants contribute to PCOS pathogenesis.Design, Patients, and MethodsWe performed whole-genome sequencing on DNA from 261 individuals from 62 families with one or more daughters with PCOS. We tested for associations of rare variants with PCOS and its concomitant hormonal traits using a quantitative trait meta-analysis.ResultsWe found rare variants in DENND1A (P = 5.31 × 10−5, adjusted P = 0.039) that were significantly associated with reproductive and metabolic traits in PCOS families.ConclusionsCommon variants in DENND1A have previously been associated with PCOS diagnosis in genome-wide association studies. Subsequent studies indicated that DENND1A is an important regulator of human ovarian androgen biosynthesis. Our findings provide additional evidence that DENND1A plays a central role in PCOS and suggest that rare noncoding variants contribute to disease pathogenesis.

scholarly journals Fidelity of SNP Array Genotyping Using Epstein Barr Virus-Transformed B-Lymphocyte Cell Lines: Implications for Genome-Wide Association Studies

PLoS ONE ◽  
2009 ◽  
Vol 4 (9) ◽  
pp. e6915 ◽  
Author(s):  
Joshua T. Herbeck ◽  
Geoffrey S. Gottlieb ◽  
Kim Wong ◽  
Roger Detels ◽  
John P. Phair ◽  
...  
Keyword(s):  
Epstein Barr Virus ◽  
B Lymphocyte ◽  
Association Studies ◽  
Snp Array ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Barr Virus ◽  
Genome Wide ◽  
Epstein Barr ◽  
Lymphocyte Cell

scholarly journals Metabolome-scale genome-wide association studies reveal chemical diversity and genetic control of maize specialized metabolites

2018 ◽  
Author(s):  
Zhou Shaoqun ◽  
Karl A. Kremling ◽  
Bandillo Nonoy ◽  
Richter Annett ◽  
Ying K. Zhang ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Citrate Synthase ◽  
Association Studies ◽  
Inbred Lines ◽  
Chemical Diversity ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Single Linkage ◽  
Genome Wide ◽  
Maize Varieties

One Sentence SummaryHPLC-MS metabolite profiling of maize seedlings, in combination with genome-wide association studies, identifies numerous quantitative trait loci that influence the accumulation of foliar metabolites.AbstractCultivated maize (Zea mays) retains much of the genetic and metabolic diversity of its wild ancestors. Non-targeted HPLC-MS metabolomics using a diverse panel of 264 maize inbred lines identified a bimodal distribution in the prevalence of foliar metabolites. Although 15% of the detected mass features were present in >90% of the inbred lines, the majority were found in <50% of the samples. Whereas leaf bases and tips were differentiated primarily by flavonoid abundance, maize varieties (stiff-stalk, non-stiff-stalk, tropical, sweet corn, and popcorn) were differentiated predominantly by benzoxazinoid metabolites. Genome-wide association studies (GWAS), performed for 3,991 mass features from the leaf tips and leaf bases, showed that 90% have multiple significantly associated loci scattered across the genome. Several quantitative trait locus hotspots in the maize genome regulate the abundance of multiple, often metabolically related mass features. The utility of maize metabolite GWAS was demonstrated by confirming known benzoxazinoid biosynthesis genes, as well as by mapping isomeric variation in the accumulation of phenylpropanoid hydroxycitric acid esters to a single linkage block in a citrate synthase-like gene. Similar to gene expression databases, this metabolomic GWAS dataset constitutes an important public resource for linking maize metabolites with biosynthetic and regulatory genes.

scholarly journals Family-based quantitative trait meta-analysis implicates rare noncoding variants in DENND1A in pathogenesis of polycystic ovary syndrome

2018 ◽  
Author(s):  
Matthew Dapas ◽  
Ryan Sisk ◽  
Richard S. Legro ◽  
Margrit Urbanek ◽  
Andrea Dunaif ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Quantitative Trait ◽  
Rare Variants ◽  
Polycystic Ovary ◽  
Association Studies ◽  
Meta Analysis ◽  
Premenopausal Women ◽  
Endocrine Disorders ◽  
Genome Wide Association Studies ◽  
Ovary Syndrome

ABSTRACTPolycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5-15% of this population depending on the diagnostic criteria applied. It is characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. PCOS is a leading risk factor for type 2 diabetes in young women. PCOS is highly heritable, but only a small proportion of this heritability can be accounted for by the common genetic susceptibility variants identified to date. To test the hypothesis that rare genetic variants contribute to PCOS pathogenesis, we performed whole-genome sequencing on DNA from 62 families with one or more daughters with PCOS. We tested for associations of rare variants with PCOS and its concomitant hormonal traits using a quantitative trait meta-analysis. We found rare variants in DENND1A (P=5.31×10−5, Padj=0.019) that were significantly associated with reproductive and metabolic traits in PCOS families. Common variants in DENND1A have previously been associated with PCOS diagnosis in genome-wide association studies. Subsequent studies indicated that DENND1A is an important regulator of human ovarian androgen biosynthesis. Our findings provide additional evidence that DENND1A plays a central role in PCOS and suggest that rare noncoding variants contribute to disease pathogenesis.

scholarly journals Identification, deployment, and transferability of quantitative trait loci from genome-wide association studies in plants

2020 ◽  
Vol 24 ◽  
pp. 100145 ◽  
Author(s):  
Mohsen Mohammadi ◽  
Alencar Xavier ◽  
Travis Beckett ◽  
Savannah Beyer ◽  
Liyang Chen ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Trait Loci

ADDO: a comprehensive toolkit to detect, classify and visualize additive and non-additive quantitative trait loci

2019 ◽  
Vol 36 (5) ◽  
pp. 1517-1521
Author(s):  
Leilei Cui ◽  
Bin Yang ◽  
Nikolas Pontikos ◽  
Richard Mott ◽  
Lusheng Huang
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genetic Effects ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Additive Effects ◽  
A Genome ◽  
Trait Loci ◽  
Additive Genetic Effects

Abstract Motivation During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked. Results We developed ADDO, a highly efficient tool to detect, classify and visualize QTLs with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single-nucleotide polymorphism effects as either additive, partial dominant, dominant or over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 h with 10 CPUs. Analysis of simulated data confirms ADDO’s performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects. Availability and implementation ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Detection of quantitative trait loci in Bos indicus and Bos taurus cattle using genome-wide association studies

2013 ◽  
Vol 45 (1) ◽  
Author(s):  
Sunduimijid Bolormaa ◽  
Jennie E Pryce ◽  
Kathryn E Kemper ◽  
Ben J Hayes ◽  
Yuandan Zhang ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Bos Taurus ◽  
Association Studies ◽  
Bos Indicus ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Trait Loci
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