110 research outputs found
Hypertension after preeclampsia and relation to the C1114G polymorphism (rs4606) in RGS2 : data from the Norwegian HUNT2 study
Peer reviewe
Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels
The accuracy of genotype imputation depends upon two factors: the sample size of the reference panel and the genetic similarity between the reference panel and the target samples. When multiple reference panels are not consented to combine together, it is unclear how to combine the imputation results to optimize the power of genetic association studies. We compared the accuracy of 9,265 Norwegian genomes imputed from three reference panelsâ1000 Genomes phase 3 (1000G), Haplotype Reference Consortium (HRC), and a reference panel containing 2,201 Norwegian participants from the populationâbased Nord Trøndelag Health Study (HUNT) from lowâpass genome sequencing. We observed that the populationâmatched reference panel allowed for imputation of more populationâspecific variants with lower frequency (minor allele frequency (MAF) between 0.05% and 0.5%). The overall imputation accuracy from the populationâspecific panel was substantially higher than 1000G and was comparable with HRC, despite HRC being 15âfold larger. These results recapitulate the value of populationâspecific reference panels for genotype imputation. We also evaluated different strategies to utilize multiple sets of imputed genotypes to increase the power of association studies. We observed that testing association for all variants imputed from any panel results in higher power to detect association than the alternative strategy of including only one version of each genetic variant, selected for having the highest imputation quality metric. This was particularly true for lower frequency variants (MAF < 1%), even after adjusting for the additional multiple testing burden.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139954/1/gepi22067_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139954/2/gepi22067.pd
Blood pressure changes during 22-year of follow-up in large general population - the HUNT Study, Norway
Common Variants Show Predicted Polygenic Effects on Height in the Tails of the Distribution, Except in Extremely Short Individuals
Peer reviewe
Coding variants in RPL3L and MYZAP increase risk of atrial fibrillation
Source at https://doi.org/10.1038/s42003-018-0068-9. Most sequence variants identified hitherto in genome-wide association studies (GWAS) of atrial fibrillation are common, non-coding variants associated with risk through unknown mechanisms. We performed a meta-analysis of GWAS of atrial fibrillation among 29,502 cases and 767,760 controls from Iceland and the UK Biobank with follow-up in samples from Norway and the US, focusing on low-frequency coding and splice variants aiming to identify causal genes. We observe associations with one missense (ORâ=â1.20) and one splice-donor variant (ORâ=â1.50) in RPL3L, the first ribosomal gene implicated in atrial fibrillation to our knowledge. Analysis of 167 RNA samples from the right atrium reveals that the splice-donor variant in RPL3L results in exon skipping. We also observe an association with a missense variant in MYZAP (ORâ=â1.38), encoding a component of the intercalated discs of cardiomyocytes. Both discoveries emphasize the close relationship between the mechanical and electrical function of the heart
Dissecting the shared genetic basis of migraine and mental disorders using novel statistical tools
Migraine is three times more prevalent in people with bipolar disorder or depression. The relationship between
schizophrenia and migraine is less certain although glutamatergic and serotonergic neurotransmission are implicated in both. A shared genetic basis to migraine and mental disorders has been suggested but previous studies
have reported weak or non-significant genetic correlations and five shared risk loci. Using the largest samples to
date and novel statistical tools, we aimed to determine the extent to which migraineâs polygenic architecture
overlaps with bipolar disorder, depression and schizophrenia beyond genetic correlation, and to identify shared
genetic loci.
Summary statistics from genome-wide association studies were acquired from large-scale consortia for migraine
(n cases = 59 674; n controls = 316 078), bipolar disorder (n cases = 20 352; n controls = 31 358), depression
(n cases = 170 756; n controls = 328 443) and schizophrenia (n cases = 40 675, n controls = 64 643). We applied the bivariate causal mixture model to estimate the number of disorder-influencing variants shared between migraine
and each mental disorder, and the conditional/conjunctional false discovery rate method to identify shared loci.
Loci were functionally characterized to provide biological insights.
Univariate MiXeR analysis revealed that migraine was substantially less polygenic (2.8 K disorder-influencing variants) compared to mental disorders (8100â12 300 disorder-influencing variants). Bivariate analysis estimated that
800 (SD = 300), 2100 (SD = 100) and 2300 (SD = 300) variants were shared between bipolar disorder, depression and
schizophrenia, respectively. There was also extensive overlap with intelligence (1800, SD = 300) and educational attainment (2100, SD = 300) but not height (1000, SD = 100). We next identified 14 loci jointly associated with migraine
and depression and 36 loci jointly associated with migraine and schizophrenia, with evidence of consistent genetic
effects in independent samples. No loci were associated with migraine and bipolar disorder. Functional annotation
mapped 37 and 298 genes to migraine and each of depression and schizophrenia, respectively, including several
novel putative migraine genes such as L3MBTL2, CACNB2 and SLC9B1. Gene-set analysis identified several putative
gene sets enriched with mapped genes including transmembrane transport in migraine and schizophrenia.
Most migraine-influencing variants were predicted to influence depression and schizophrenia, although a minority
of mental disorder-influencing variants were shared with migraine due to the difference in polygenicity. Similar
overlap with other brain-related phenotypes suggests this represents a pool of âpleiotropicâ variants that influence
vulnerability to diverse brain-related disorders and traits. We also identified specific loci shared between migraine and each of depression and schizophrenia, implicating shared molecular mechanisms and highlighting candidate
migraine genes for experimental validation
Hypertension after preeclampsia and relation to the C1114G polymorphism (rs4606) in RGS2: data from the Norwegian HUNT2 study
Meta-Analysis of Gene Level Tests for Rare Variant Association
The vast majority of connections between complex disease and common genetic variants were identified through meta-analysis, a powerful approach that enables large sample sizes while protecting against common artifacts due to population structure, repeated small sample analyses, and/or limitations with sharing individual level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the unit of analysis. Here, we propose and evaluate new approaches for performing meta-analysis of rare variant association tests, including burden tests, weighted burden tests, variable threshold tests and tests that allow variants with opposite effects to be grouped together. We show that our approach retains useful features of single variant meta-analytic approaches and demonstrate its utility in a study of blood lipid levels in âź18,500 individuals genotyped with exome arrays
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