A general dimension of genetic sharing across diverse cognitive traits inferred from molecular data

It has been known since 1904 that, in humans, diverse cognitive traits are positively inter correlated. This forms the basis for the general factor of intelligence (g). Here, we directly test whether there is a partial genetic basis for individual differences in g using data from seven different cognitive tests (N = 11,263 to N = 331,679) and genome-wide autosomal single nucleotide polymorphisms. A genetic g factor accounts for an average of 58.4% (SE = 4.8%) of the genetic variance in the cognitive traits, with the proportion varying widely across traits (range: 9% to 95%). We distill genetic loci that are broadly relevant for many cognitive traits (g) from loci associated specifically with individual cognitive traits. These results contribute to elucidating the etiology of a long-known yet poorly-understood phenomenon, revealing a fundamental dimension of genetic sharing across diverse cognitive traits

Patterns of item nonresponse behaviour to survey questionnaires are systematic and associated with genetic loci

Peer reviewe

Exploratory factor analysis and principal component analysis in clinical studies: Which one should you use?

Factor analysis covers a range of multivariate methods used to explain how underlying factors influence a set of observed variables. When research aims to identify these underlying factors, exploratory factor analysis (EFA) is used. In contrast, when the aim is to test whether a set of observed variables represents the underlying factors, in accordance with an existing conceptual basis, confirmatory factor analysis is performed. EFA has many similarities with a commonly used data reduction technique called principal component analysis (PCA). These similarities, along with using the related terms factor and component interchangeably, contribute to confusion in analysis. The difficulty in identifying the appropriate use of statistical methods and their application and interpretation impacts clinical and research implications (Beavers et al., 2013; Tabachnick & Fidell, 2001). We acknowledge previous articles in nursing journals offering guidance on the use of factor analysis (Gaskin & Happell, 2014; Watson & Thompson, 2006)

Genetic variants that associate with cirrhosis have pleiotropic effects on human traits

Background and AimsCirrhosis is characterized by extensive fibrosis of the liver and is a major cause of liver‐related mortality. Cirrhosis is partially heritable but genetic contributions to cirrhosis have not been systemically explored. Here, we carry out association analyses with cirrhosis in two large biobanks and determine the effects of cirrhosis associated variants on multiple human disease/traits.MethodsWe carried out a genome‐wide association analysis of cirrhosis as a diagnosis in UK BioBank (UKBB; 1088 cases vs. 407 873 controls) and then tested top‐associating loci for replication with cirrhosis in a hospital‐based cohort from the Michigan Genomics Initiative (MGI; 875 cases of cirrhosis vs. 30 346 controls). For replicating variants or variants previously associated with cirrhosis that also affected cirrhosis in UKBB or MGI, we determined single nucleotide polymorphism effects on all other diagnoses in UKBB (PheWAS), common metabolic traits/diseases and serum/plasma metabolites.ResultsUnbiased genome‐wide association study identified variants in/near PNPLA3 and HFE, and candidate variant analysis identified variants in/near TM6SF2, MBOAT7, SERPINA1, HSD17B13, STAT4 and IFNL4 that reproducibly affected cirrhosis. Most affected liver enzyme concentrations and/or aspartate transaminase‐to‐platelet ratio index. PheWAS, metabolic trait and serum/plasma metabolite association analyses revealed effects of these variants on lipid, inflammatory and other processes including new effects on many human diseases and traits.ConclusionsWe identified eight loci that reproducibly associate with population‐based cirrhosis and define their diverse effects on human diseases and traits.See Editorial on Page 281Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153621/1/liv14321_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153621/2/liv14321.pd

Computational and Statistical Approaches for Large-Scale Genome-Wide Association Studies

Over the past decade, genome-wide association studies (GWAS) have proven successful at shedding light on the underlying genetic variations that affect the risk of human complex diseases, which can be translated to novel preventative and therapeutic strategies. My research aims at identifying novel disease-associated genetic variants through large-scale GWAS and developing computational and statistical pipelines and methods to improve power and accuracy of GWAS. Bicuspid aortic valve (BAV) is a congenital heart defect characterized by fusion of two of the normal three leaflets of the aortic valve. As the most common cardiovascular malformation in humans, BAV is moderately heritable and is an important risk factor for valvulopathy and aortopathy, but its genetic origins remain elusive. In Chapter 2, we present the first large-scale GWAS study to identify novel genetic variants associated with BAV. We report association with a non-coding variant 151kb from the gene encoding the cardiac-specific transcription factor, GATA4, and near-significance for p.Ser377Gly in GATA4. We used multiple bioinformatics approaches to demonstrate that the GATA4 gene is a plausible biological candidate. In the subsequent functional follow-up, GATA4 was interrupted by CRISPR-Cas9 in induced pluripotent stem cells from healthy donors. The disruption of GATA4 significantly impaired the transition from endothelial cells into mesenchymal cells, a critical step in heart valve development. Genotype imputation is widely used in GWAS to perform in silico genotyping, leading to higher power to identify novel genetic signals. 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 tests. In Chapter 3, we compared the accuracy of 9,265 Norwegian genomes imputed from three reference panels – 1000 Genomes Phase 3 (1000G), Haplotype Reference Consortium (HRC), and a reference panel containing 2,201 Norwegian participants from the HUNT study with low-pass genome sequencing. We observed that the overall imputation accuracy from the population-specific panel was substantially higher than 1000G and was comparable with HRC, despite HRC being 15-fold larger. We also evaluated different strategies to utilize multiple sets of imputed genotypes to increase the power of association studies. We propose that testing association for all variants imputed from any panel results in higher power to detect association than the alternative strategy of testing only the version of each genetic variant with the highest imputation quality metric. In phenome-wide GWAS by large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly -- producing large type I error rates -- in the analysis of phenotypes with unbalanced case-control ratios. In Chapter 4, we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation (SPA) to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational time and memory cost of generalized mixed model. The computation cost linearly depends on sample size, and hence can be applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 white British European-ancestry samples for 1,403 dichotomous phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144097/1/zhowei_1.pd

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease : a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7.8 million single nucleotide polymorphisms in 37688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1.4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16-36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0 .0035 for intracranial volume, p=0.024 for putamen volume), smoking status (p=0.024), and educational attainment (p=0.038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8.00 x10 -7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Copyright (C) 2019 Elsevier Ltd. All rights reserved.Peer reviewe

Genome-wide association study for detecting autoimmune-disease-associated genetic pattern differences in specific HLA type carriers

The HLA locus variants are one of the strongest genetic predictors for most, if not all, human autoimmune diseases. The HLA locus genes include the antigen-presenting cell surface peptide encoding genes, which form an essential component in the maturation of the T-cell population in the thymus, and their subsequent activation in the periphery. Leveraging the modern population-wide genotype information that capture even the most polymorphic loci, this work sets the aim to design a case-control genome-wide association study (GWAS), that would result in the detection of non-HLA genetic variants that have a statistically different effect on an autoimmune disease in the carriers of certain HLA types, in comparison to the non-carriers. For the purpose of this aim, study groups are assembled based on specific HLA allele doses, so that for 42 HLA allele typesselected for this study there are 42 HLA-specific groups where every individual is a carrier of at least one copy of the HLA allele type. The effect sizes from the summary statistics of the HLA-specific GWASs are compared to a general population GWAS (which is done on all the participants of the Estonian Biobank in this case). The variants are considered relevant to this aim if their effect size is statisticallt different in the HLA-specific groups than they are in the general population GWAS