scholarly journals Genetic Similarity Assessment of Twin-Family Populations by Custom-Designed Genotyping Array

2019 ◽  
Vol 22 (4) ◽  
pp. 210-219 ◽  
Author(s):  
Jeffrey J. Beck ◽  
Jouke-Jan Hottenga ◽  
Hamdi Mbarek ◽  
Casey T. Finnicum ◽  
Erik A. Ehli ◽  
...  
Keyword(s):  
Genetic Similarity ◽  
Meta Analysis ◽  
Principal Component ◽  
European Population ◽  
Population Variation ◽  
Genotype Data ◽  
Genome Wide ◽  
Midwestern Usa ◽  
Fixation Indices ◽  
Gwa Studies

AbstractTwin registries often take part in large collaborative projects and are major contributors to genome-wide association (GWA) meta-analysis studies. In this article, we describe genotyping of twin-family populations from Australia, the Midwestern USA (Avera Twin Register), the Netherlands (Netherlands Twin Register), as well as a sample of mothers of twins from Nigeria to assess the extent, if any, of genetic differences between them. Genotyping in all cohorts was done using a custom-designed Illumina Global Screening Array (GSA), optimized to improve imputation quality for population-specific GWA studies. We investigated the degree of genetic similarity between the populations using several measures of population variation with genotype data generated from the GSA. Visualization of principal component analysis (PCA) revealed that the Australian, Dutch and Midwestern American populations exhibit negligible interpopulation stratification when compared to each other, to a reference European population and to globally distant populations. Estimations of fixation indices (FST values) between the Australian, Midwestern American and Netherlands populations suggest minimal genetic differentiation compared to the estimates between each population and a genetically distinct cohort (i.e., samples from Nigeria genotyped on GSA). Thus, results from this study demonstrate that genotype data from the Australian, Dutch and Midwestern American twin-family populations can be reasonably combined for joint-genetic analysis.

Principals about principal components in statistical genetics

2018 ◽  
Vol 20 (6) ◽  
pp. 2200-2216 ◽  
Author(s):  
Fentaw Abegaz ◽  
Kridsadakorn Chaichoompu ◽  
Emmanuelle Génin ◽  
David W Fardo ◽  
Inke R König ◽  
...  
Keyword(s):  
Principal Components ◽  
Rare Variants ◽  
Association Studies ◽  
Meta Analysis ◽  
Principal Component ◽  
Statistical Genetics ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Study Results

Abstract Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.

scholarly journals Three genes associated with anterior and posterior cruciate ligament injury

2021 ◽  
Vol 2 (6) ◽  
pp. 414-421
Author(s):  
Stuart K. Kim ◽  
Condor Nguyen ◽  
Andrew L. Avins ◽  
Geoffrey D. Abrams
Keyword(s):  
Candidate Genes ◽  
Posterior Cruciate Ligament ◽  
Genetic Markers ◽  
Cruciate Ligament ◽  
Meta Analysis ◽  
Uk Biobank ◽  
Genome Wide ◽  
The Uk ◽  
Pcl Injury ◽  
Gwa Studies

Aims The aim of this study was to screen the entire genome for genetic markers associated with risk for anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) injury. Methods Genome-wide association (GWA) analyses were performed using data from the Kaiser Permanente Research Board (KPRB) and the UK Biobank. ACL and PCL injury cases were identified based on electronic health records from KPRB and the UK Biobank. GWA analyses from both cohorts were tested for ACL and PCL injury using a logistic regression model adjusting for sex, height, weight, age at enrolment, and race/ethnicity using allele counts for single nucleotide polymorphisms (SNPs). The data from the two GWA studies were combined in a meta-analysis. Candidate genes previously reported to show an association with ACL injury in athletes were also tested for association from the meta-analysis data from the KPRB and the UK Biobank GWA studies. Results There was a total of 2,214 cases of ACL and PCL injury and 519,869 controls within the two cohorts, with three loci demonstrating a genome-wide significant association in the meta-analysis: INHBA, AEBP2, and LOC101927869. Of the eight candidate genes previously studied in the literature, six were present in the current dataset, and only COL3A1 (rs1800255) showed a significant association (p = 0.006). Conclusion Genetic markers in three novel loci in this study and one previously-studied candidate gene were identified as potential risk factors for ACL and PCL injury and deserve further validation and investigation of molecular mechanisms. Cite this article: Bone Jt Open 2021;2(6):414–421.

scholarly journals Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine

2015 ◽  
Author(s):  
Padhraig Gormley ◽  
Verneri Anttila ◽  
Bendik S Winsvold ◽  
Priit Palta ◽  
Tonu Esko ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Vascular Dysfunction ◽  
Meta Analysis ◽  
Nucleotide Polymorphisms ◽  
Susceptibility Loci ◽  
Single Nucleotide ◽  
Full Sample ◽  
Muscle Tissues ◽  
Genome Wide ◽  
Gwa Studies

Migraine is a debilitating neurological disorder affecting around 1 in 7 people worldwide, but its molecular mechanisms remain poorly understood. Some debate exists over whether migraine is a disease of vascular dysfunction, or a result of neuronal dysfunction with secondary vascular changes. Genome-wide association (GWA) studies have thus far identified 13 independent loci associated with migraine. To identify new susceptibility loci, we performed the largest genetic study of migraine to date, comprising 59,674 cases and 316,078 controls from 22 GWA studies. We identified 45 independent single nucleotide polymorphisms (SNPs) significantly associated with migraine risk (P < 5 x 10-8) that map to 38 distinct genomic loci, including 28 loci not previously reported and the first locus identified on chromosome X. Furthermore, a subset analysis for migraine without aura (MO) identified seven of the same loci as from the full sample, whereas no loci reached genome-wide significance in the migraine with aura (MA) subset. In subsequent computational analyzes, the identified loci showed enrichment for genes expressed in vascular and smooth muscle tissues, consistent with a predominant theory of migraine that highlights vascular etiologies.

Genome-Wide Association Studies Identify Two Novel Loci Conferring Susceptibility to Diabetic Retinopathy in Japanese Patients with Type 2 Diabetes

2021 ◽  
Author(s):  
Minako Imamura ◽  
Atsushi Takahashi ◽  
Masatoshi Matsunami ◽  
Momoko Horikoshi ◽  
Minoru Iwata ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Association Studies ◽  
Meta Analysis ◽  
Japanese Patients ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Stage 1

Abstract Several reports have suggested that genetic susceptibility contributes to the development and progression of diabetic retinopathy. We aimed to identify genetic loci that confer susceptibility to diabetic retinopathy in Japanese patients with type 2 diabetes. We analysed 5 790 508 single nucleotide polymorphisms (SNPs) in 8880 Japanese patients with type 2 diabetes, 4839 retinopathy cases and 4041 controls, as well as 2217 independent Japanese patients with type 2 diabetes, 693 retinopathy cases, and 1524 controls. The results of these two genome-wide association studies (GWAS) were combined with an inverse variance meta-analysis (Stage-1), followed by de novo genotyping for the candidate SNP loci (p &lt; 1.0 × 10−4) in an independent case–control study (Stage-2, 2260 cases and 723 controls). After combining the association data (Stage-1 and -2) using meta-analysis, the associations of two loci reached a genome-wide significance level: rs12630354 near STT3B on chromosome 3, p = 1.62 × 10−9, odds ratio (OR) = 1.17, 95% confidence interval (CI) 1.11–1.23, and rs140508424 within PALM2 on chromosome 9, p = 4.19 × 10−8, OR = 1.61, 95% CI 1.36–1.91. However, the association of these two loci were not replicated in Korean, European, or African American populations. Gene-based analysis using Stage-1 GWAS data identified a gene-level association of EHD3 with susceptibility to diabetic retinopathy (p = 2.17 × 10−6). In conclusion, we identified two novel SNP loci, STT3B and PALM2, and a novel gene, EHD3, that confers susceptibility to diabetic retinopathy; however, further replication studies are required to validate these associations.

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