Structure-informed clustering for population stratification in association studies.

BACKGROUND: Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. RESULTS: To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. CONCLUSIONS: CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes.

Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites replicated using independent exposure data. We found causal associations between accumbens volume and plasma protease c1 inhibitor as well as strong association between putamen volume and Agouti signaling protein. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.

Myasthenia gravis genome-wide association study implicates AGRN as a risk locus.

BACKGROUND: Myasthenia gravis (MG) is a rare autoimmune disorder affecting the neuromuscular junction (NMJ). Here, we investigate the genetic architecture of MG via a genome-wide association study (GWAS) of the largest MG data set analysed to date. METHODS: We performed GWAS meta-analysis integrating three different data sets (total of 1401 cases and 3508 controls). We carried out human leucocyte antigen (HLA) fine-mapping, gene-based and tissue enrichment analyses and investigated genetic correlation with 13 other autoimmune disorders as well as pleiotropy across MG and correlated disorders. RESULTS: We confirmed the previously reported MG association with TNFRSF11A (rs4369774; p=1.09×10-13, OR=1.4). Furthermore, gene-based analysis revealed AGRN as a novel MG susceptibility gene. HLA fine-mapping pointed to two independent MG loci: HLA-DRB1 and HLA-B. MG onset-specific analysis reveals differences in the genetic architecture of early-onset MG (EOMG) versus late-onset MG (LOMG). Furthermore, we find MG to be genetically correlated with type 1 diabetes (T1D), rheumatoid arthritis (RA), late-onset vitiligo and autoimmune thyroid disease (ATD). Cross-disorder meta-analysis reveals multiple risk loci that appear pleiotropic across MG and correlated disorders. DISCUSSION: Our gene-based analysis identifies AGRN as a novel MG susceptibility gene, implicating for the first time a locus encoding a protein (agrin) that is directly relevant to NMJ activation. Mutations in AGRN have been found to underlie congenital myasthenic syndrome. Our results are also consistent with previous studies highlighting the role of HLA and TNFRSF11A in MG aetiology and the different risk genes in EOMG versus LOMG. Finally, we uncover the genetic correlation of MG with T1D, RA, ATD and late-onset vitiligo, pointing to shared underlying genetic mechanisms.

Integrating Linguistics, Social Structure, and Geography to Model Genetic Diversity within India.

India represents an intricate tapestry of population substructure shaped by geography, language, culture, and social stratification. Although geography closely correlates with genetic structure in other parts of the world, the strict endogamy imposed by the Indian caste system and the large number of spoken languages add further levels of complexity to understand Indian population structure. To date, no study has attempted to model and evaluate how these factors have interacted to shape the patterns of genetic diversity within India. We merged all publicly available data from the Indian subcontinent into a data set of 891 individuals from 90 well-defined groups. Bringing together geography, genetics, and demographic factors, we developed Correlation Optimization of Genetics and Geodemographics to build a model that explains the observed population genetic substructure. We show that shared language along with social structure have been the most powerful forces in creating paths of gene flow in the subcontinent. Furthermore, we discover the ethnic groups that best capture the diverse genetic substructure using a ridge leverage score statistic. Integrating data from India with a data set of additional 1,323 individuals from 50 Eurasian populations, we find that Indo-European and Dravidian speakers of India show shared genetic drift with Europeans, whereas the Tibeto-Burman speaking tribal groups have maximum shared genetic drift with East Asians.

TeraPCA: a fast and scalable software package to study genetic variation in tera-scale genotypes.

MOTIVATION: Principal Component Analysis is a key tool in the study of population structure in human genetics. As modern datasets become increasingly larger in size, traditional approaches based on loading the entire dataset in the system memory (Random Access Memory) become impractical and out-of-core implementations are the only viable alternative. RESULTS: We present TeraPCA, a C++ implementation of the Randomized Subspace Iteration method to perform Principal Component Analysis of large-scale datasets. TeraPCA can be applied both in-core and out-of-core and is able to successfully operate even on commodity hardware with a system memory of just a few gigabytes. Moreover, TeraPCA has minimal dependencies on external libraries and only requires a working installation of the BLAS and LAPACK libraries. When applied to a dataset containing a million individuals genotyped on a million markers, TeraPCA requires <5 h (in multi-threaded mode) to accurately compute the 10 leading principal components. An extensive experimental analysis shows that TeraPCA is both fast and accurate and is competitive with current state-of-the-art software for the same task. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are both available at https://github.com/aritra90/TeraPCA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic history of the population of Crete.

The medieval history of several populations often suffers from scarcity of contemporary records resulting in contradictory and sometimes biased interpretations by historians. This is the situation with the population of the island of Crete, which remained relatively undisturbed until the Middle Ages when multiple wars, invasions, and occupations by foreigners took place. Historians have considered the effects of the occupation of Crete by the Arabs (in the 9th and 10th centuries C.E.) and the Venetians (in the 13th to the 17th centuries C.E.) to the local population. To obtain insights on such effects from a genetic perspective, we studied representative samples from 17 Cretan districts using the Illumina 1 million or 2.5 million arrays and compared the Cretans to the populations of origin of the medieval conquerors and settlers. Highlights of our findings include (1) small genetic contributions from the Arab occupation to the extant Cretan population, (2) low genetic contribution of the Venetians to the extant Cretan population, and (3) evidence of a genetic relationship among the Cretans and Central, Northern, and Eastern Europeans, which could be explained by the settlement in the island of northern origin tribes during the medieval period. Our results show how the interaction between genetics and the historical record can help shed light on the historical record.

Neuropathology-driven Whole-genome Sequencing Study Points to Novel Candidate Genes for Healthy Brain Aging.

PURPOSE: Understanding the healthy brain aging process is key to uncover the mechanisms that lead to pathologic age-related neurodegeneration, including progression to Alzheimer disease (AD). We aimed to address the issue of pathologic heterogeneity that often underlies a clinical AD diagnosis. METHODS: We performed a deep whole-genome sequencing study aiming to identify variants that are associated specifically with healthy brain aging. PATIENTS: We examined samples from the community-based longitudinal Vienna Transdanubian Aging study comparing neuropathologically "healthy" aging in individuals above 80 years of age with pure AD patients of the same age. RESULTS: Focusing on potentially functional variants, we discovered a single variant (rs10149146) that lies on the autophagy-associated TECPR2 gene and was carried by 53.6% of the "healthy" brain elderly individuals (15/28). An additional nonsynonymous variant on the CINP gene (encoding a cell cycle checkpoint protein) was also found in 46% of healthy controls. Both variants are absent from all AD cases. TECPR2 and CINP appear to be "partner" genes in terms of regulation and their associated transcription factors have been previously implicated in AD and neurodegeneration. CONCLUSIONS: Our study underlines the strength of neuropathology-driven definitions in genetic association studies and points to a potentially neuroprotective effect of key molecules of autophagy and cell cycle control.

European Multicentre Tics in Children Studies (EMTICS): protocol for two cohort studies to assess risk factors for tic onset and exacerbation in children and adolescents.

Genetic predisposition, autoimmunity and environmental factors [e.g. pre- and perinatal difficulties, Group A Streptococcal (GAS) and other infections, stress-inducing events] might interact to create a neurobiological vulnerability to the development of tics and associated behaviours. However, the existing evidence for this relies primarily on small prospective or larger retrospective population-based studies, and is therefore still inconclusive. This article describes the design and methodology of the EMTICS study, a longitudinal observational European multicentre study involving 16 clinical centres, with the following objectives: (1) to investigate the association of environmental factors (GAS exposure and psychosocial stress, primarily) with the onset and course of tics and/or obsessive-compulsive symptoms through the prospective observation of at-risk individuals (ONSET cohort: 260 children aged 3-10 years who are tic-free at study entry and have a first-degree relative with a chronic tic disorder) and affected individuals (COURSE cohort: 715 youth aged 3-16 years with a tic disorder); (2) to characterise the immune response to microbial antigens and the host's immune response regulation in association with onset and exacerbations of tics; (3) to increase knowledge of the human gene pathways influencing the pathogenesis of tic disorders; and (4) to develop prediction models for the risk of onset and exacerbations of tic disorders. The EMTICS study is, to our knowledge, the largest prospective cohort assessment of the contribution of different genetic and environmental factors to the risk of developing tics in putatively predisposed individuals and to the risk of exacerbating tics in young individuals with chronic tic disorders.

Ancestry inference of 96 population samples using microhaplotypes.

Microhaplotypes have become a new type of forensic marker with a great ability to identify and deconvolute mixtures because massively parallel sequencing (MPS) allows the alleles (haplotypes) of the multi-SNP loci to be determined directly for an individual. As originally defined, a microhaplotype locus is a short segment of DNA with two or more SNPs defining three or more haplotypes. The length is short enough, less than about 300 bp, that the read length of current MPS technology can produce a phase-known sequence of each chromosome of an individual. As part of the discovery phase of our studies, data on 130 microhaplotype loci with estimates of haplotype frequency data on 83 populations have been published. To provide a better picture of global allele frequency variation, we have now tested 13 more populations for 65 of the microhaplotype loci from among those with higher levels of inter-population gene frequency variation, including 8 loci not previously published. These loci provide clear distinctions among 6 biogeographic regions and provide some information distinguishing up to 10 clusters of populations.

Variant Ranker: a web-tool to rank genomic data according to functional significance.

BACKGROUND: The increasing volume and complexity of high-throughput genomic data make analysis and prioritization of variants difficult for researchers with limited bioinformatics skills. Variant Ranker allows researchers to rank identified variants and determine the most confident variants for experimental validation. RESULTS: We describe Variant Ranker, a user-friendly simple web-based tool for ranking, filtering and annotation of coding and non-coding variants. Variant Ranker facilitates the identification of causal variants based on novelty, effect and annotation information. The algorithm implements and aggregates multiple prediction algorithm scores, conservation scores, allelic frequencies, clinical information and additional open-source annotations using accessible databases via ANNOVAR. The available information for a variant is transformed into user-specified weights, which are in turn encoded into the ranking algorithm. Through its different modules, users can (i) rank a list of variants (ii) perform genotype filtering for case-control samples (iii) filter large amounts of high-throughput data based on user custom filter requirements and apply different models of inheritance (iv) perform downstream functional enrichment analysis through network visualization. Using networks, users can identify clusters of genes that belong to multiple ontology categories (like pathways, gene ontology, disease categories) and therefore expedite scientific discoveries. We demonstrate the utility of Variant Ranker to identify causal genes using real and synthetic datasets. Our results indicate that Variant Ranker exhibits excellent performance by correctly identifying and ranking the candidate genes CONCLUSIONS: Variant Ranker is a freely available web server on http://paschou-lab.mbg.duth.gr/Software.html . This tool will enable users to prioritise potentially causal variants and is applicable to a wide range of sequencing data.

Maritime route of colonization of Europe.

The Neolithic populations, which colonized Europe approximately 9,000 y ago, presumably migrated from Near East to Anatolia and from there to Central Europe through Thrace and the Balkans. An alternative route would have been island hopping across the Southern European coast. To test this hypothesis, we analyzed genome-wide DNA polymorphisms on populations bordering the Mediterranean coast and from Anatolia and mainland Europe. We observe a striking structure correlating genes with geography around the Mediterranean Sea with characteristic east to west clines of gene flow. Using population network analysis, we also find that the gene flow from Anatolia to Europe was through Dodecanese, Crete, and the Southern European coast, compatible with the hypothesis that a maritime coastal route was mainly used for the migration of Neolithic farmers to Europe.

Genetic association signal near NTN4 in Tourette syndrome.

Tourette syndrome (TS) is a neurodevelopmental disorder with a complex genetic etiology. Through an international collaboration, we genotyped 42 single nucleotide polymorphisms (p < 10(-3) ) from the recent TS genomewide association study (GWAS) in 609 independent cases and 610 ancestry-matched controls. Only rs2060546 on chromosome 12q22 (p = 3.3 × 10(-4) ) remained significant after Bonferroni correction. Meta-analysis with the original GWAS yielded the strongest association to date (p = 5.8 × 10(-7) ). Although its functional significance is unclear, rs2060546 lies closest to NTN4, an axon guidance molecule expressed in developing striatum. Risk score analysis significantly predicted case-control status (p = 0.042), suggesting that many of these variants are true TS risk alleles.

Epigenome-Wide Association Study of Tic Disorders.

Tic disorders are moderately heritable common psychiatric disorders that can be highly troubling, both in childhood and in adulthood. In this study, we report results obtained in the first epigenome-wide association study (EWAS) of tic disorders. The subjects are participants in surveys at the Netherlands Twin Register (NTR) and the NTR biobank project. Tic disorders were measured with a self-report version of the Yale Global Tic Severity Scale Abbreviated version (YGTSS-ABBR), included in the 8th wave NTR data collection (2008). DNA methylation data consisted of 411,169 autosomal methylation sites assessed by the Illumina Infinium HumanMethylation450 BeadChip Kit (HM450k array). Phenotype and DNA methylation data were available in 1,678 subjects (mean age = 41.5). No probes reached genome-wide significance (p < 1.2 × 10(-7)). The strongest associated probe was cg15583738, located in an intergenic region on chromosome 8 (p = 1.98 × 10(-6)). Several of the top ranking probes (p < 1 × 10(-4)) were in or nearby genes previously associated with neurological disorders (e.g., GABBRI, BLM, and ADAM10), warranting their further investigation in relation to tic disorders. The top significantly enriched gene ontology (GO) terms among higher ranking methylation sites included anatomical structure morphogenesis (GO:0009653, p = 4.6 × 10-(15)) developmental process (GO:0032502, p = 2.96 × 10(-12)), and cellular developmental process (GO:0048869, p = 1.96 × 10(-12)). Overall, these results provide a first insight into the epigenetic mechanisms of tic disorders. This first study assesses the role of DNA methylation in tic disorders, and it lays the foundations for future work aiming to unravel the biological mechanisms underlying the architecture of this disorder.

Support of the histaminergic hypothesis in Tourette syndrome: association of the histamine decarboxylase gene in a large sample of families.

BACKGROUND: Gilles de la Tourette Syndrome is a neurodevelopmental disorder that is caused by the interaction of environment with a complex genetic background. The genetic etiology of the disorder remains, so far, elusive, although multiple promising leads have been recently reported. The recent implication of the histamine decarboxylase (HDC) gene, the key enzyme in histamine production, raises the intriguing hypothesis of a possible role of histaminergic dysfunction leading to TS onset. METHODS: Following up on the finding of a nonsense mutation in a single family with TS, we investigated variation across the HDC gene for association with TS. As a result of a collaborative international effort, we studied a large sample of 520 nuclear families originating from seven European populations (Greek, Hungarian, Italian, Polish, German, Albanian, Spanish) as well as a sample collected in Canada. RESULTS AND CONCLUSIONS: Interrogating 12 tagging SNPs (tSNP) across the HDC region, we find strong over-transmission of alleles at two SNPs (rs854150 and rs1894236) in the complete sample, as well as a statistically significant associated haplotypes. Analysis of individual populations also reveals signals of association in the Canadian, German and Italian samples. Our results provide strong support for the histaminergic hypothesis in TS etiology and point to a possible role of histamine pathways in neuronal development.

Multi-organ imaging-derived polygenic indexes for brain and body health.

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

The distribution and most recent common ancestor of the 17q21 inversion in humans.

The polymorphic inversion on 17q21, sometimes called the microtubular associated protein tau (MAPT) inversion, is an approximately 900 kb inversion found primarily in Europeans and Southwest Asians. We have identified 21 SNPs that act as markers of the inverted, i.e., H2, haplotype. The inversion is found at the highest frequencies in Southwest Asia and Southern Europe (frequencies of approximately 30%); elsewhere in Europe, frequencies vary from < 5%, in Finns, to 28%, in Orcadians. The H2 inversion haplotype also occurs at low frequencies in Africa, Central Asia, East Asia, and the Americas, though the East Asian and Amerindian alleles may be due to recent gene flow from Europe. Molecular evolution analyses indicate that the H2 haplotype originally arose in Africa or Southwest Asia. Though the H2 inversion has many fixed differences across the approximately 900 kb, short tandem repeat polymorphism data indicate a very recent date for the most recent common ancestor, with dates ranging from 13,600 to 108,400 years, depending on assumptions and estimation methods. This estimate range is much more recent than the 3 million year age estimated by Stefansson et al. in 2005.

Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes.

Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2,994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes. We found causal associations between amygdala volume and granzyme A as well as association between accumbens volume and plasma protease c1 inhibitor. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.

Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation.

Complex disorders are caused by a combination of genetic, environmental and lifestyle factors, and their prevalence can vary greatly across different populations. The extent to which genetic risk, as identified by Genome Wide Association Study (GWAS), correlates to disease prevalence in different populations has not been investigated systematically. Here, we studied 14 different complex disorders and explored whether polygenic risk scores (PRS) based on current GWAS correlate to disease prevalence within Europe and around the world. A clear variation in GWAS-based genetic risk was observed based on ancestry and we identified populations that have a higher genetic liability for developing certain disorders. We found that for four out of the 14 studied disorders, PRS significantly correlates to disease prevalence within Europe. We also found significant correlations between worldwide disease prevalence and PRS for eight of the studied disorders with Multiple Sclerosis genetic risk having the highest correlation to disease prevalence. Based on current GWAS results, the across population differences in genetic risk for certain disorders can potentially be used to understand differences in disease prevalence and identify populations with the highest genetic liability. The study highlights both the limitations of PRS based on current GWAS but also the fact that in some cases, PRS may already have high predictive power. This could be due to the genetic architecture of specific disorders or increased GWAS power in some cases.

PheWAS and cross-disorder analysis reveal genetic architecture, pleiotropic loci and phenotypic correlations across 11 autoimmune disorders.

Introduction: Autoimmune disorders (ADs) are a group of about 80 disorders that occur when self-attacking autoantibodies are produced due to failure in the self-tolerance mechanisms. ADs are polygenic disorders and associations with genes both in the human leukocyte antigen (HLA) region and outside of it have been described. Previous studies have shown that they are highly comorbid with shared genetic risk factors, while epidemiological studies revealed associations between various lifestyle and health-related phenotypes and ADs. Methods: Here, for the first time, we performed a comparative polygenic risk score (PRS) - Phenome Wide Association Study (PheWAS) for 11 different ADs (Juvenile Idiopathic Arthritis, Primary Sclerosing Cholangitis, Celiac Disease, Multiple Sclerosis, Rheumatoid Arthritis, Psoriasis, Myasthenia Gravis, Type 1 Diabetes, Systemic Lupus Erythematosus, Vitiligo Late Onset, Vitiligo Early Onset) and 3,254 phenotypes available in the UK Biobank that include a wide range of socio-demographic, lifestyle and health-related outcomes. Additionally, we investigated the genetic relationships of the studied ADs, calculating their genetic correlation and conducting cross-disorder GWAS meta-analyses for the observed AD clusters. Results: In total, we identified 508 phenotypes significantly associated with at least one AD PRS. 272 phenotypes were significantly associated after excluding variants in the HLA region from the PRS estimation. Through genetic correlation and genetic factor analyses, we identified four genetic factors that run across studied ADs. Cross-trait meta-analyses within each factor revealed pleiotropic genome-wide significant loci. Discussion: Overall, our study confirms the association of different factors with genetic susceptibility for ADs and reveals novel observations that need to be further explored.

Tourette syndrome research highlights from 2022.

This is the ninth yearly article in the Tourette Syndrome Research Highlights series, summarizing selected research reports from 2022 relevant to Tourette syndrome. The authors briefly summarize reports they consider most important or interesting.