Meta-Analysis of Common and Rare Variants

2018 ◽  
pp. 73-88 ◽  
Author(s):  
Kyriaki Michailidou
Keyword(s):  
Rare Variants ◽  
Meta Analysis

scholarly journals Examining the effect of mitochondrial DNA variants on blood pressure in two Finnish cohorts

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jaakko Laaksonen ◽  
Pashupati P. Mishra ◽  
Ilkka Seppälä ◽  
Leo-Pekka Lyytikäinen ◽  
Emma Raitoharju ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Mitochondrial Dna ◽  
Genome Wide Association Study ◽  
Rare Variants ◽  
Meta Analysis ◽  
Noncommunicable Diseases ◽  
Nucleotide Polymorphisms ◽  
Genetic Determinants ◽  
Genome Wide ◽  
Mitochondrial Dna Variants

AbstractHigh blood pressure (BP) is a major risk factor for many noncommunicable diseases. The effect of mitochondrial DNA single-nucleotide polymorphisms (mtSNPs) on BP is less known than that of nuclear SNPs. We investigated the mitochondrial genetic determinants of systolic, diastolic, and mean arterial BP. MtSNPs were determined from peripheral blood by sequencing or with genome-wide association study SNP arrays in two independent Finnish cohorts, the Young Finns Study and the Finnish Cardiovascular Study, respectively. In total, over 4200 individuals were included. The effects of individual common mtSNPs, with an additional focus on sex-specificity, and aggregates of rare mtSNPs grouped by mitochondrial genes were evaluated by meta-analysis of linear regression and a sequence kernel association test, respectively. We accounted for the predicted pathogenicity of the rare variants within protein-encoding and the tRNA regions. In the meta-analysis of 87 common mtSNPs, we did not observe significant associations with any of the BP traits. Sex-specific and rare-variant analyses did not pinpoint any significant associations either. Our results are in agreement with several previous studies suggesting that mtDNA variation does not have a significant role in the regulation of BP. Future studies might need to reconsider the mechanisms thought to link mtDNA with hypertension.

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 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 Trans-ethnic meta-analysis of rare variants in sequencing association studies

Biostatistics ◽  
2019 ◽  
Author(s):  
Jingchunzi Shi ◽  
Michael Boehnke ◽  
Seunggeun Lee
Keyword(s):  
Rare Variants ◽  
Sequence Data ◽  
Association Studies ◽  
Meta Analysis ◽  
Error Rates ◽  
Efficient Estimation ◽  
Gene Region ◽  
Type I ◽  
Test Statistic ◽  
Different Populations

Summary Trans-ethnic meta-analysis is a powerful tool for detecting novel loci in genetic association studies. However, in the presence of heterogeneity among different populations, existing gene-/region-based rare variants meta-analysis methods may be unsatisfactory because they do not consider genetic similarity or dissimilarity among different populations. In response, we propose a score test under the modified random effects model for gene-/region-based rare variants associations. We adapt the kernel regression framework to construct the model and incorporate genetic similarities across populations into modeling the heterogeneity structure of the genetic effect coefficients. We use a resampling-based copula method to approximate asymptotic distribution of the test statistic, enabling efficient estimation of p-values. Simulation studies show that our proposed method controls type I error rates and increases power over existing approaches in the presence of heterogeneity. We illustrate our method by analyzing T2D-GENES consortium exome sequence data to explore rare variant associations with several traits.

scholarly journals Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci

2016 ◽  
Vol 48 (10) ◽  
pp. 1162-1170 ◽  
Author(s):  
Chunyu Liu ◽  
◽  
Aldi T Kraja ◽  
Jennifer A Smith ◽  
Jennifer A Brody ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Rare Variants ◽  
Meta Analysis ◽  
Metabolic Trait ◽  
Trait Loci

Meta-Analysis of Rare Variants

2015 ◽  
pp. 215-226 ◽  
Author(s):  
Ioanna Tachmazidou ◽  
Eleftheria Zeggini
Keyword(s):  
Rare Variants ◽  
Meta Analysis

scholarly journals 4258Re-evaluating the genetic contribution of monogenic dilated cardiomyopathy

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
F Mazzarotto ◽  
U Tayal ◽  
R Buchan ◽  
W Midwinter ◽  
A Wilk ◽  
...  
Keyword(s):  
Dilated Cardiomyopathy ◽  
Rare Variants ◽  
Diagnostic Yield ◽  
Meta Analysis ◽  
Diagnostic Testing ◽  
Reference Population ◽  
Population Variation ◽  
Disease Genes ◽  
Specific Patient ◽  
Background Population

Abstract Background Dilated cardiomyopathy (DCM) is genetically heterogeneous, with >100 purported disease genes tested in clinical laboratories. However, many genes were originally identified based on candidate-gene studies that did not adequately account for background population variation. Here we define the frequency of rare variation in 2538 DCM patients across protein-coding regions of 56 commonly tested genes and compare this to both 912 confirmed healthy controls and a reference population of 60,706 individuals. Purpose To identify clinically interpretable genes robustly associated with dominant monogenic DCM. Methods We used the TruSight Cardio sequencing panel to evaluate the burden of rare variants in 56 putative DCM genes in 1040 DCM patients and 912 healthy volunteers processed with identical sequencing and bioinformatics pipelines. We further aggregated data from 1498 DCM patients sequenced in diagnostic laboratories and the ExAC database for replication and meta-analysis. Results Specific variant classes in TTN, DSP, MYH7 and LMNA were associated with DCM in all comparisons. Variants in BAG3, TNNT2, TPM1, NEXN and VCL were significantly enriched specific patient subsets, with the last 3 genes likely contributing primarily to early-onset forms of DCM. Overall, rare variants in these 9 genes potentially explained 19–26% of cases. Whilst the absence of a significant excess in other genes cannot preclude a role in disease, such genes have limited diagnostic value since novel variants will be uninterpretable and therefore non-actionable, and their diagnostic yield is minimal. Conclusion In the largest sequenced DCM cohort yet described, we observe robust disease association only with a limited number of genes, highlighting their importance in DCM and translating into high interpretability in diagnostic testing. The other genes evaluated have limited value in diagnostic testing in DCM. This data will contribute to community gene curation efforts, and will reduce erroneous and inconclusive findings in diagnostic testing. Acknowledgement/Funding Wellcome Trust (107469/Z/15/Z), BHF (SP/10/10/28431), MRC (MR/M003191/1), Fondation Leducq (11-CVD01), Italian Ministry of Health (RF-2013-02356787)

scholarly journals Bayesian mixture model for clustering rare-variant effects in human genetic studies

2021 ◽  
Author(s):  
Guhan Ram Venkataraman ◽  
Yosuke Tanigawa ◽  
Matti Pirinen ◽  
Manuel A Rivas
Keyword(s):  
Mixture Model ◽  
Rare Variant ◽  
Rare Variants ◽  
Meta Analysis ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Multiple Phenotypes ◽  
Aggregation Techniques ◽  
Bayesian Mixture ◽  
The Uk

Rare-variant aggregate analysis from exome and whole genome sequencing data typically summarizes with a single statistic the signal for a gene or the unit that is being aggre- gated. However, when doing so, the effect profile within the unit may not be easily characterized across one or multiple phenotypes. Here, we present an approach we call Multiple Rare-Variants and Phenotypes Mixture Model (MRPMM), which clusters rare variants into groups based on their effects on the multivariate phenotype and makes statistical inferences about the properties of the underlying mixture of genetic effects. Using summary statistic data from a meta-analysis of exome sequencing data of 184,698 individuals in the UK Biobank across 6 populations, we demonstrate that our mixture model can identify clusters of variants responsible for significantly disparate effects across a multivariate phenotype; we study three lipid and three renal traits separately. The method is able to estimate (1) the proportion of non-null variants, (2) whether variants with the same predicted consequence in one gene behave similarly, (3) whether variants across genes share effect profiles across the multivariate phenotype, and (4) whether different annotations differ in the magnitude of their effects. As rare-variant data and aggregation techniques become more common, this method can be used to ascribe further meaning to association results.

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