Tourette syndrome research highlights from 2023.

In this, the tenth annual update for the F1000Research Tics collection, we summarize research reports from 2023 on Gilles de la Tourette syndrome and other tic disorders. The authors welcome article suggestions and thoughtful feedback from readers.

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.

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.

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.

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.

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.

Aeolus winds impact on volcanic ash early warning systems for aviation.

Forecasting volcanic ash atmospheric pathways is of utmost importance for aviation. Volcanic ash can interfere with aircraft navigational instruments and can damage engine parts. Early warning systems, activated after volcanic eruptions can alleviate the impacts on aviation by providing forecasts of the volcanic ash plume dispersion. The quality of these short-term forecasts is subject to the accuracy of the meteorological wind fields used for the initialization of regional models. Here, we use wind profiling data from the first high spectral resolution lidar in space, Aeolus, to examine the impact of measured wind fields on regional NWP and subsequent volcanic ash dispersion forecasts, focusing on the case of Etna's eruption on March 2021. The results from this case study demonstrate a significant improvement of the volcanic ash simulation when using Aeolus-assimilated meteorological fields, with differences in wind speed reaching up to 8 m/s when compared to the control run. When comparing the volcanic ash forecast profiles with downwind surface-based aerosol lidar observations, the modeled field is consistent with the measurements only when Aeolus winds are assimilated. This result clearly demonstrates the potential of Aeolus and highlights the necessity of future wind profiling satellite missions for improving volcanic ash forecasting and hence aviation safety.

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.

Polygenic risk score-based phenome-wide association study identifies novel associations for Tourette syndrome.

Tourette Syndrome (TS) is a complex neurodevelopmental disorder characterized by vocal and motor tics lasting more than a year. It is highly polygenic in nature with both rare and common previously associated variants. Epidemiological studies have shown TS to be correlated with other phenotypes, but large-scale phenome wide analyses in biobank level data have not been performed to date. In this study, we used the summary statistics from the latest meta-analysis of TS to calculate the polygenic risk score (PRS) of individuals in the UK Biobank data and applied a Phenome Wide Association Study (PheWAS) approach to determine the association of disease risk with a wide range of phenotypes. A total of 57 traits were found to be significantly associated with TS polygenic risk, including multiple psychosocial factors and mental health conditions such as anxiety disorder and depression. Additional associations were observed with complex non-psychiatric disorders such as Type 2 diabetes, heart palpitations, and respiratory conditions. Cross-disorder comparisons of phenotypic associations with genetic risk for other childhood-onset disorders (e.g.: attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and obsessive-compulsive disorder [OCD]) indicated an overlap in associations between TS and these disorders. ADHD and ASD had a similar direction of effect with TS while OCD had an opposite direction of effect for all traits except mental health factors. Sex-specific PheWAS analysis identified differences in the associations with TS genetic risk between males and females. Type 2 diabetes and heart palpitations were significantly associated with TS risk in males but not in females, whereas diseases of the respiratory system were associated with TS risk in females but not in males. This analysis provides further evidence of shared genetic and phenotypic architecture of different complex disorders.

Enhancing neuroimaging genetics through meta-analysis for Tourette syndrome (ENIGMA-TS): A worldwide platform for collaboration.

Tourette syndrome (TS) is characterized by multiple motor and vocal tics, and high-comorbidity rates with other neuropsychiatric disorders. Obsessive compulsive disorder (OCD), attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASDs), major depressive disorder (MDD), and anxiety disorders (AXDs) are among the most prevalent TS comorbidities. To date, studies on TS brain structure and function have been limited in size with efforts mostly fragmented. This leads to low-statistical power, discordant results due to differences in approaches, and hinders the ability to stratify patients according to clinical parameters and investigate comorbidity patterns. Here, we present the scientific premise, perspectives, and key goals that have motivated the establishment of the Enhancing Neuroimaging Genetics through Meta-Analysis for TS (ENIGMA-TS) working group. The ENIGMA-TS working group is an international collaborative effort bringing together a large network of investigators who aim to understand brain structure and function in TS and dissect the underlying neurobiology that leads to observed comorbidity patterns and clinical heterogeneity. Previously collected TS neuroimaging data will be analyzed jointly and integrated with TS genomic data, as well as equivalently large and already existing studies of highly comorbid OCD, ADHD, ASD, MDD, and AXD. Our work highlights the power of collaborative efforts and transdiagnostic approaches, and points to the existence of different TS subtypes. ENIGMA-TS will offer large-scale, high-powered studies that will lead to important insights toward understanding brain structure and function and genetic effects in TS and related disorders, and the identification of biomarkers that could help inform improved clinical practice.

Reconstructing SNP allele and genotype frequencies from GWAS summary statistics.

The emergence of genome-wide association studies (GWAS) has led to the creation of large repositories of human genetic variation, creating enormous opportunities for genetic research and worldwide collaboration. Methods that are based on GWAS summary statistics seek to leverage such records, overcoming barriers that often exist in individual-level data access while also offering significant computational savings. Such summary-statistics-based applications include GWAS meta-analysis, with and without sample overlap, and case-case GWAS. We compare performance of leading methods for summary-statistics-based genomic analysis and also introduce a novel framework that can unify usual summary-statistics-based implementations via the reconstruction of allelic and genotypic frequencies and counts (ReACt). First, we evaluate ASSET, METAL, and ReACt using both synthetic and real data for GWAS meta-analysis (with and without sample overlap) and find that, while all three methods are comparable in terms of power and error control, ReACt and METAL are faster than ASSET by a factor of at least hundred. We then proceed to evaluate performance of ReACt vs an existing method for case-case GWAS and show comparable performance, with ReACt requiring minimal underlying assumptions and being more user-friendly. Finally, ReACt allows us to evaluate, for the first time, an implementation for calculating polygenic risk score (PRS) for groups of cases and controls based on summary statistics. Our work demonstrates the power of GWAS summary-statistics-based methodologies and the proposed novel method provides a unifying framework and allows further extension of possibilities for researchers seeking to understand the genetics of complex disease.

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.

Variants in clock genes could be associated with lower risk of type 2 diabetes in an elderly Greek population.

OBJECTIVES: Recent evidence has linked circadian rhythm dysregulation to an increased risk of metabolic disorders. This study explores a potential association between variation in genes regulating the endogenous circadian timing system (clock genes) and the risk of type 2 diabetes (T2D) in a sample of Greek elderly people. STUDY DESIGN: Variants within and upstream or downstream of PPARA, PPARD, CLOCK/TMEM165, PER1, PER2 and PER3 genes were genotyped in 716 individuals with T2D (A) and 569 normoglycemic controls (B), and allele frequencies were compared between the groups in a case control study design. MAIN OUTCOME MEASURES: Samples were genotyped on Illumina Human PsychArray. Permutation test analysis was implemented to determine statistical significance. To avoid the possibility of subjects with prediabetes being included in the control group, people with HbA1c <5.7% and fasting glucose <100 mg/dl comprised group C (n = 393), for whom a separate analysis was performed (secondary analysis). RESULTS: A protective role against T2D was identified for 14 variants in the PPARA gene. The rs7291444, rs36125344, rs6008384 in PKDREJ, located upstream of PPARA, and rs2859389 in UTS2, located upstream of PER3, demonstrated a protective role against T2D in both analyses. In contrast, rs6744132, located between HES6 and PER2, was positively correlated with T2D risk. Only in the secondary analysis, rs2278637 in VAMP2, located downstream of PER1, and rs11943456 in CLOCK/TMEM165 were found to confer protection against T2D. In a recessive model analysis of all groups, PPARD variants exhibited a protective role against disease. CONCLUSIONS: Our findings suggest a possible implication of clock genes in T2D susceptibility. Further studies are needed to clarify the mechanisms that connect circadian rhythm dysfunction and T2D pathogenesis.

Candidate Genes and Pathways Associated with Gilles de la Tourette Syndrome-Where Are We?

Gilles de la Tourette syndrome (GTS) is a childhood-onset neurodevelopmental and -psychiatric tic-disorder of complex etiology which is often comorbid with obsessive-compulsive disorder (OCD) and/or attention deficit hyperactivity disorder (ADHD). Twin and family studies of GTS individuals have shown a high level of heritability suggesting, that genetic risk factors play an important role in disease etiology. However, the identification of major GTS susceptibility genes has been challenging, presumably due to the complex interplay between several genetic factors and environmental influences, low penetrance of each individual factor, genetic diversity in populations, and the presence of comorbid disorders. To understand the genetic components of GTS etiopathology, we conducted an extensive review of the literature, compiling the candidate susceptibility genes identified through various genetic approaches. Even though several strong candidate genes have hitherto been identified, none of these have turned out to be major susceptibility genes yet.

Oxidative Stress Genes in Diabetes Mellitus Type 2: Association with Diabetic Kidney Disease.

Diabetic type 2 patients compared to nondiabetic patients exhibit an increased risk of developing diabetic kidney disease (DKD), the leading cause of end-stage renal disease. Hyperglycemia, hypertension, oxidative stress (OS), and genetic background are some of the mechanisms and pathways implicated in DKD pathogenesis. However, data on OS pathway susceptibility genes show limited success and conflicting or inconclusive results. Our study is aimed at exploring OS pathway genes and variants which could be associated with DKD. We recruited 121 diabetes mellitus type 2 (DM2) patients with DKD (cases) and 220 DM2, non-DKD patients (control) of Greek origin and performed a case-control association study using genome-wide association data. PLINK and EIGENSOFT were used to analyze the data. Our results indicate 43 single nucleotide polymorphisms with their 21 corresponding genes on the OS pathway possibly contributing or protecting from DKD: SPP1, TPO, TTN, SGO2, NOS3, PDLIM1, CLU, CCS, GPX4, TXNRD2, EPHX2, MTL5, EPX, GPX3, ALOX12, IPCEF1, GSTA, OXR1, GPX6, AOX1, and PRNP. Therefore, a genetic OS background might underlie the complex pathogenesis of DKD in DM2 patients.

Investigating Shared Genetic Basis Across Tourette Syndrome and Comorbid Neurodevelopmental Disorders Along the Impulsivity-Compulsivity Spectrum.

BACKGROUND: Tourette syndrome (TS) is often found comorbid with other neurodevelopmental disorders across the impulsivity-compulsivity spectrum, with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) as most prevalent. This points to the possibility of a common etiological thread along an impulsivity-compulsivity continuum. METHODS: Investigating the shared genetic basis across TS, ADHD, ASD, and OCD, we undertook an evaluation of cross-disorder genetic architecture and systematic meta-analysis, integrating summary statistics from the latest genome-wide association studies (93,294 individuals, 6,788,510 markers). RESULTS: As previously identified, a common unifying factor connects TS, ADHD, and ASD, while TS and OCD show the highest genetic correlation in pairwise testing among these disorders. Thanks to a more homogeneous set of disorders and a targeted approach that is guided by genetic correlations, we were able to identify multiple novel hits and regions that seem to play a pleiotropic role for the specific disorders analyzed here and could not be identified through previous studies. In the TS-ADHD-ASD genome-wide association study single nucleotide polymorphism-based and gene-based meta-analysis, we uncovered 13 genome-wide significant regions that host single nucleotide polymorphisms with a high posterior probability for association with all three studied disorders (m-value > 0.9), 11 of which were not identified in previous cross-disorder analysis. In contrast, we also identified two additional pleiotropic regions in the TS-OCD meta-analysis. Through conditional analysis, we highlighted genes and genetic regions that play a specific role in a TS-ADHD-ASD genetic factor versus TS-OCD. Cross-disorder tissue specificity analysis implicated the hypothalamus-pituitary-adrenal gland axis in TS-ADHD-ASD. CONCLUSIONS: Our work underlines the value of redefining the framework for research across traditional diagnostic categories.

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.

Genetic variation in CARD8, a gene coding for an NLRP3 inflammasome-associated protein, alters the genetic risk for diabetic nephropathy in the context of type 2 diabetes mellitus.

BACKGROUND: Approximately one third of type 2 diabetes mellitus (T2DM) cases present with diabetic nephropathy (DN), the leading cause of end-stage renal disease. Inflammation plays an important role in T2DM disease and DN pathogenesis. NLRP3 inflammasomes are complexes that regulate interleukin-1B (IL-1B) and IL-18 secretion, both involved in inflammatory responses. Activation of NLRP3 is associated with DN onset and progression. Here, we explore whether DN is associated with variants in genes encoding key members of the NLRP3 inflammasome pathway. METHODS: Using genome-wide association data, we performed a pilot case-control association study, between 101 DN-T2DM and 185 non-DN-T2DM cases from the Hellenic population across six NLRP3 inflammasome pathway genes. RESULTS: Three common CARD8 variants confer decreased risk for DN, namely rs11665831 (OR = 0.62, p = 0.016), rs11083925 (OR = 0.65, p = 0.021), and rs2043211 (OR = 0.66, p = 0.026), independent of sex or co-inheritance with an IL-1B variant. CONCLUSION: CARD8 acts as an NLRP3, NF-κB and caspase-1 inhibitor; perhaps, alterations in the cross-talk between CARD8, NF-κB, and NLRP3, which could affect the pro-inflammatory environment in T2DM, render diabetic carriers of certain common CARD8 variants potentially less likely to develop T2DM-related pro-inflammatory responses followed by DN. These preliminary, yet novel, observations will require validation in larger cohorts from several ethnic groups.

The distinctive geographic patterns of common pigmentation variants at the OCA2 gene.

Oculocutaneous Albinism type 2 (OCA2) is a gene of great interest because of genetic variation affecting normal pigmentation variation in humans. The diverse geographic patterns for variant frequencies at OCA2 have been evident but have not been systematically investigated, especially outside of Europe. Here we examine population genetic variation in and near the OCA2 gene from a worldwide perspective. The very different patterns of genetic variation found across world regions suggest strong selection effects may have been at work over time. For example, analyses involving the variants that affect pigmentation of the iris argue that the derived allele of the rs1800407 single nucleotide polymorphism, which produces a hypomorphic protein, may have contributed to the previously demonstrated positive selection in Europe for the enhancer variant responsible for light eye color. More study is needed on the relationships of the genetic variation at OCA2 to variation in pigmentation in areas beyond Europe.