Germ line variants predispose to both JAK2 V617F clonal hematopoiesis and myeloproliferative neoplasms.

We conducted a genome-wide association study (GWAS) to identify novel predisposition alleles associated with Philadelphia chromosome-negative myeloproliferative neoplasms (MPNs) and JAK2 V617F clonal hematopoiesis in the general population. We recruited a web-based cohort of 726 individuals with polycythemia vera, essential thrombocythemia, and myelofibrosis and 252 637 population controls unselected for hematologic phenotypes. Using a single-nucleotide polymorphism (SNP) array platform with custom probes for the JAK2 V617F mutation (V617F), we identified 497 individuals (0.2%) among the population controls who were V617F carriers. We performed a combined GWAS of the MPN cases plus V617F carriers in the control population (n = 1223) vs the remaining controls who were noncarriers for V617F (n = 252 140). For these MPN cases plus V617F carriers, we replicated the germ line JAK2 46/1 haplotype (rs59384377: odds ratio [OR] = 2.4, P = 6.6 × 10(-89)), previously associated with V617F-positive MPN. We also identified genome-wide significant associations in the TERT gene (rs7705526: OR = 1.8, P = 1.1 × 10(-32)), in SH2B3 (rs7310615: OR = 1.4, P = 3.1 × 10(-14)), and upstream of TET2 (rs1548483: OR = 2.0, P = 2.0 × 10(-9)). These associations were confirmed in a separate replication cohort of 446 V617F carriers vs 169 021 noncarriers. In a joint analysis of the combined GWAS and replication results, we identified additional genome-wide significant predisposition alleles associated with CHEK2, ATM, PINT, and GFI1B All SNP ORs were similar for MPN patients and controls who were V617F carriers. These data indicate that the same germ line variants endow individuals with a predisposition not only to MPN, but also to JAK2 V617F clonal hematopoiesis, a more common phenomenon that may foreshadow the development of an overt neoplasm.

Genetic variants associated with motion sickness point to roles for inner ear development, neurological processes and glucose homeostasis.

Roughly one in three individuals is highly susceptible to motion sickness and yet the underlying causes of this condition are not well understood. Despite high heritability, no associated genetic factors have been discovered. Here, we conducted the first genome-wide association study on motion sickness in 80 494 individuals from the 23andMe database who were surveyed about car sickness. Thirty-five single-nucleotide polymorphisms (SNPs) were associated with motion sickness at a genome-wide-significant level (P < 5 × 10(-8)). Many of these SNPs are near genes involved in balance, and eye, ear and cranial development (e.g. PVRL3, TSHZ1, MUTED, HOXB3, HOXD3). Other SNPs may affect motion sickness through nearby genes with roles in the nervous system, glucose homeostasis or hypoxia. We show that several of these SNPs display sex-specific effects, with up to three times stronger effects in women. We searched for comorbid phenotypes with motion sickness, confirming associations with known comorbidities including migraines, postoperative nausea and vomiting (PONV), vertigo and morning sickness and observing new associations with altitude sickness and many gastrointestinal conditions. We also show that two of these related phenotypes (PONV and migraines) share underlying genetic factors with motion sickness. These results point to the importance of the nervous system in motion sickness and suggest a role for glucose levels in motion-induced nausea and vomiting, a finding that may provide insight into other nausea-related phenotypes like PONV. They also highlight personal characteristics (e.g. being a poor sleeper) that correlate with motion sickness, findings that could help identify risk factors or treatments.

Genome-wide analysis points to roles for extracellular matrix remodeling, the visual cycle, and neuronal development in myopia.

Myopia, or nearsightedness, is the most common eye disorder, resulting primarily from excess elongation of the eye. The etiology of myopia, although known to be complex, is poorly understood. Here we report the largest ever genome-wide association study (45,771 participants) on myopia in Europeans. We performed a survival analysis on age of myopia onset and identified 22 significant associations ([Formula: see text]), two of which are replications of earlier associations with refractive error. Ten of the 20 novel associations identified replicate in a separate cohort of 8,323 participants who reported if they had developed myopia before age 10. These 22 associations in total explain 2.9% of the variance in myopia age of onset and point toward a number of different mechanisms behind the development of myopia. One association is in the gene PRSS56, which has previously been linked to abnormally small eyes; one is in a gene that forms part of the extracellular matrix (LAMA2); two are in or near genes involved in the regeneration of 11-cis-retinal (RGR and RDH5); two are near genes known to be involved in the growth and guidance of retinal ganglion cells (ZIC2, SFRP1); and five are in or near genes involved in neuronal signaling or development. These novel findings point toward multiple genetic factors involved in the development of myopia and suggest that complex interactions between extracellular matrix remodeling, neuronal development, and visual signals from the retina may underlie the development of myopia in humans.

Reducing pervasive false-positive identical-by-descent segments detected by large-scale pedigree analysis.

Analysis of genomic segments shared identical-by-descent (IBD) between individuals is fundamental to many genetic applications, from demographic inference to estimating the heritability of diseases, but IBD detection accuracy in nonsimulated data is largely unknown. In principle, it can be evaluated using known pedigrees, as IBD segments are by definition inherited without recombination down a family tree. We extracted 25,432 genotyped European individuals containing 2,952 father-mother-child trios from the 23andMe, Inc. data set. We then used GERMLINE, a widely used IBD detection method, to detect IBD segments within this cohort. Exploiting known familial relationships, we identified a false-positive rate over 67% for 2-4 centiMorgan (cM) segments, in sharp contrast with accuracies reported in simulated data at these sizes. Nearly all false positives arose from the allowance of haplotype switch errors when detecting IBD, a necessity for retrieving long (>6 cM) segments in the presence of imperfect phasing. We introduce HaploScore, a novel, computationally efficient metric that scores IBD segments proportional to the number of switch errors they contain. Applying HaploScore filtering to the IBD data at a precision of 0.8 produced a 13-fold increase in recall when compared with length-based filtering. We replicate the false IBD findings and demonstrate the generalizability of HaploScore to alternative data sources using an independent cohort of 555 European individuals from the 1000 Genomes project. HaploScore can improve the accuracy of segments reported by any IBD detection method, provided that estimates of the genotyping error rate and switch error rate are available.

Comparison of family history and SNPs for predicting risk of complex disease.

The clinical utility of family history and genetic tests is generally well understood for simple Mendelian disorders and rare subforms of complex diseases that are directly attributable to highly penetrant genetic variants. However, little is presently known regarding the performance of these methods in situations where disease susceptibility depends on the cumulative contribution of multiple genetic factors of moderate or low penetrance. Using quantitative genetic theory, we develop a model for studying the predictive ability of family history and single nucleotide polymorphism (SNP)-based methods for assessing risk of polygenic disorders. We show that family history is most useful for highly common, heritable conditions (e.g., coronary artery disease), where it explains roughly 20%-30% of disease heritability, on par with the most successful SNP models based on associations discovered to date. In contrast, we find that for diseases of moderate or low frequency (e.g., Crohn disease) family history accounts for less than 4% of disease heritability, substantially lagging behind SNPs in almost all cases. These results indicate that, for a broad range of diseases, already identified SNP associations may be better predictors of risk than their family history-based counterparts, despite the large fraction of missing heritability that remains to be explained. Our model illustrates the difficulty of using either family history or SNPs for standalone disease prediction. On the other hand, we show that, unlike family history, SNP-based tests can reveal extreme likelihood ratios for a relatively large percentage of individuals, thus providing potentially valuable adjunctive evidence in a differential diagnosis.

Six novel susceptibility Loci for early-onset androgenetic alopecia and their unexpected association with common diseases.

Androgenetic alopecia (AGA) is a highly heritable condition and the most common form of hair loss in humans. Susceptibility loci have been described on the X chromosome and chromosome 20, but these loci explain a minority of its heritable variance. We conducted a large-scale meta-analysis of seven genome-wide association studies for early-onset AGA in 12,806 individuals of European ancestry. While replicating the two AGA loci on the X chromosome and chromosome 20, six novel susceptibility loci reached genome-wide significance (p = 2.62×10⁻9-1.01×10⁻¹²). Unexpectedly, we identified a risk allele at 17q21.31 that was recently associated with Parkinson's disease (PD) at a genome-wide significant level. We then tested the association between early-onset AGA and the risk of PD in a cross-sectional analysis of 568 PD cases and 7,664 controls. Early-onset AGA cases had significantly increased odds of subsequent PD (OR = 1.28, 95% confidence interval: 1.06-1.55, p = 8.9×10⁻³). Further, the AGA susceptibility alleles at the 17q21.31 locus are on the H1 haplotype, which is under negative selection in Europeans and has been linked to decreased fertility. Combining the risk alleles of six novel and two established susceptibility loci, we created a genotype risk score and tested its association with AGA in an additional sample. Individuals in the highest risk quartile of a genotype score had an approximately six-fold increased risk of early-onset AGA [odds ratio (OR) = 5.78, p = 1.4×10⁻88]. Our results highlight unexpected associations between early-onset AGA, Parkinson's disease, and decreased fertility, providing important insights into the pathophysiology of these conditions.

Comprehensive research synopsis and systematic meta-analyses in Parkinson's disease genetics: The PDGene database.

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of -27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P < 5 × 10(-8)) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P  =  1.3 × 10(-8)). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies.

Genome-wide association analysis identifies 11 risk variants associated with the asthma with hay fever phenotype.

BACKGROUND: To date, no genome-wide association study (GWAS) has considered the combined phenotype of asthma with hay fever. Previous analyses of family data from the Tasmanian Longitudinal Health Study provide evidence that this phenotype has a stronger genetic cause than asthma without hay fever. OBJECTIVE: We sought to perform a GWAS of asthma with hay fever to identify variants associated with having both diseases. METHODS: We performed a meta-analysis of GWASs comparing persons with both physician-diagnosed asthma and hay fever (n = 6,685) with persons with neither disease (n = 14,091). RESULTS: At genome-wide significance, we identified 11 independent variants associated with the risk of having asthma with hay fever, including 2 associations reaching this level of significance with allergic disease for the first time: ZBTB10 (rs7009110; odds ratio [OR], 1.14; P = 4 × 10(-9)) and CLEC16A (rs62026376; OR, 1.17; P = 1 × 10(-8)). The rs62026376:C allele associated with increased asthma with hay fever risk has been found to be associated also with decreased expression of the nearby DEXI gene in monocytes. The 11 variants were associated with the risk of asthma and hay fever separately, but the estimated associations with the individual phenotypes were weaker than with the combined asthma with hay fever phenotype. A variant near LRRC32 was a stronger risk factor for hay fever than for asthma, whereas the reverse was observed for variants in/near GSDMA and TSLP. Single nucleotide polymorphisms with suggestive evidence for association with asthma with hay fever risk included rs41295115 near IL2RA (OR, 1.28; P = 5 × 10(-7)) and rs76043829 in TNS1 (OR, 1.23; P = 2 × 10(-6)). CONCLUSION: By focusing on the combined phenotype of asthma with hay fever, variants associated with the risk of allergic disease can be identified with greater efficiency.

Replicability and robustness of genome-wide-association studies for behavioral traits.

A recent genome-wide-association study of educational attainment identified three single-nucleotide polymorphisms (SNPs) whose associations, despite their small effect sizes (each R (2) ≈ 0.02%), reached genome-wide significance (p < 5 × 10(-8)) in a large discovery sample and were replicated in an independent sample (p < .05). The study also reported associations between educational attainment and indices of SNPs called "polygenic scores." In three studies, we evaluated the robustness of these findings. Study 1 showed that the associations with all three SNPs were replicated in another large (N = 34,428) independent sample. We also found that the scores remained predictive (R (2) ≈ 2%) in regressions with stringent controls for stratification (Study 2) and in new within-family analyses (Study 3). Our results show that large and therefore well-powered genome-wide-association studies can identify replicable genetic associations with behavioral traits. The small effect sizes of individual SNPs are likely to be a major contributing factor explaining the striking contrast between our results and the disappointing replication record of most candidate-gene studies.

Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson's disease.

Although the causes of Parkinson's disease (PD) are thought to be primarily environmental, recent studies suggest that a number of genes influence susceptibility. Using targeted case recruitment and online survey instruments, we conducted the largest case-control genome-wide association study (GWAS) of PD based on a single collection of individuals to date (3,426 cases and 29,624 controls). We discovered two novel, genome-wide significant associations with PD-rs6812193 near SCARB2 (p = 7.6 × 10(-10), OR = 0.84) and rs11868035 near SREBF1/RAI1 (p = 5.6 × 10(-8), OR = 0.85)-both replicated in an independent cohort. We also replicated 20 previously discovered genetic associations (including LRRK2, GBA, SNCA, MAPT, GAK, and the HLA region), providing support for our novel study design. Relying on a recently proposed method based on genome-wide sharing estimates between distantly related individuals, we estimated the heritability of PD to be at least 0.27. Finally, using sparse regression techniques, we constructed predictive models that account for 6%-7% of the total variance in liability and that suggest the presence of true associations just beyond genome-wide significance, as confirmed through both internal and external cross-validation. These results indicate a substantial, but by no means total, contribution of genetics underlying susceptibility to both early-onset and late-onset PD, suggesting that, despite the novel associations discovered here and elsewhere, the majority of the genetic component for Parkinson's disease remains to be discovered.

Serum iron levels and the risk of Parkinson disease: a Mendelian randomization study.

BACKGROUND: Although levels of iron are known to be increased in the brains of patients with Parkinson disease (PD), epidemiological evidence on a possible effect of iron blood levels on PD risk is inconclusive, with effects reported in opposite directions. Epidemiological studies suffer from problems of confounding and reverse causation, and mendelian randomization (MR) represents an alternative approach to provide unconfounded estimates of the effects of biomarkers on disease. We performed a MR study where genes known to modify iron levels were used as instruments to estimate the effect of iron on PD risk, based on estimates of the genetic effects on both iron and PD obtained from the largest sample meta-analyzed to date. METHODS AND FINDINGS: We used as instrumental variables three genetic variants influencing iron levels, HFE rs1800562, HFE rs1799945, and TMPRSS6 rs855791. Estimates of their effect on serum iron were based on a recent genome-wide meta-analysis of 21,567 individuals, while estimates of their effect on PD risk were obtained through meta-analysis of genome-wide and candidate gene studies with 20,809 PD cases and 88,892 controls. Separate MR estimates of the effect of iron on PD were obtained for each variant and pooled by meta-analysis. We investigated heterogeneity across the three estimates as an indication of possible pleiotropy and found no evidence of it. The combined MR estimate showed a statistically significant protective effect of iron, with a relative risk reduction for PD of 3% (95% CI 1%-6%; p = 0.001) per 10 µg/dl increase in serum iron. CONCLUSIONS: Our study suggests that increased iron levels are causally associated with a decreased risk of developing PD. Further studies are needed to understand the pathophysiological mechanism of action of serum iron on PD risk before recommendations can be made.

Web-based, participant-driven studies yield novel genetic associations for common traits.

Despite the recent rapid growth in genome-wide data, much of human variation remains entirely unexplained. A significant challenge in the pursuit of the genetic basis for variation in common human traits is the efficient, coordinated collection of genotype and phenotype data. We have developed a novel research framework that facilitates the parallel study of a wide assortment of traits within a single cohort. The approach takes advantage of the interactivity of the Web both to gather data and to present genetic information to research participants, while taking care to correct for the population structure inherent to this study design. Here we report initial results from a participant-driven study of 22 traits. Replications of associations (in the genes OCA2, HERC2, SLC45A2, SLC24A4, IRF4, TYR, TYRP1, ASIP, and MC1R) for hair color, eye color, and freckling validate the Web-based, self-reporting paradigm. The identification of novel associations for hair morphology (rs17646946, near TCHH; rs7349332, near WNT10A; and rs1556547, near OFCC1), freckling (rs2153271, in BNC2), the ability to smell the methanethiol produced after eating asparagus (rs4481887, near OR2M7), and photic sneeze reflex (rs10427255, near ZEB2, and rs11856995, near NR2F2) illustrates the power of the approach.

Genetic variants associated with breast size also influence breast cancer risk.

BACKGROUND: While some factors of breast morphology, such as density, are directly implicated in breast cancer, the relationship between breast size and cancer is less clear. Breast size is moderately heritable, yet the genetic variants leading to differences in breast size have not been identified. METHODS: To investigate the genetic factors underlying breast size, we conducted a genome-wide association study (GWAS) of self-reported bra cup size, controlling for age, genetic ancestry, breast surgeries, pregnancy history and bra band size, in a cohort of 16,175 women of European ancestry. RESULTS: We identified seven single-nucleotide polymorphisms (SNPs) significantly associated with breast size (p<5.10(-8)): rs7816345 near ZNF703, rs4849887 and (independently) rs17625845 flanking INHBB, rs12173570 near ESR1, rs7089814 in ZNF365, rs12371778 near PTHLH, and rs62314947 near AREG. Two of these seven SNPs are in linkage disequilibrium (LD) with SNPs associated with breast cancer (those near ESR1 and PTHLH), and a third (ZNF365) is near, but not in LD with, a breast cancer SNP. The other three loci (ZNF703, INHBB, and AREG) have strong links to breast cancer, estrogen regulation, and breast development. CONCLUSIONS: These results provide insight into the genetic factors underlying normal breast development and show that some of these factors are shared with breast cancer. While these results do not directly support any possible epidemiological relationships between breast size and cancer, this study may contribute to a better understanding of the subtle interactions between breast morphology and breast cancer risk.

ShoRAH: estimating the genetic diversity of a mixed sample from next-generation sequencing data.

BACKGROUND: With next-generation sequencing technologies, experiments that were considered prohibitive only a few years ago are now possible. However, while these technologies have the ability to produce enormous volumes of data, the sequence reads are prone to error. This poses fundamental hurdles when genetic diversity is investigated. RESULTS: We developed ShoRAH, a computational method for quantifying genetic diversity in a mixed sample and for identifying the individual clones in the population, while accounting for sequencing errors. The software was run on simulated data and on real data obtained in wet lab experiments to assess its reliability. CONCLUSIONS: ShoRAH is implemented in C++, Python, and Perl and has been tested under Linux and Mac OS X. Source code is available under the GNU General Public License at http://www.cbg.ethz.ch/software/shorah.

Parametric analysis of alignment and phylogenetic uncertainty.

To infer a phylogenetic tree from a set of DNA sequences, typically a multiple alignment is first used to obtain homologous bases. The inferred phylogeny can be very sensitive to how the alignment was created. We develop tools for analyzing the robustness of phylogeny to perturbations in alignment parameters in the NW algorithm. Our main tool is parametric alignment, with novel improvements that are of general interest in parametric inference. Using parametric alignment and a Gaussian distribution on alignment parameters, we derive probabilities of optimal alignment summaries and inferred phylogenies. We apply our method to analyze intronic sequences from Drosophila flies. We show that phylogeny estimates can be sensitive to the choice of alignment parameters, and that parametric alignment elucidates the relationship between alignment parameters and reconstructed trees.

Genome-wide association study identifies 48 common genetic variants associated with handedness.

Handedness has been extensively studied because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and the International Handedness Consortium, we conducted a genome-wide association meta-analysis of handedness (N = 1,766,671). We found 41 loci associated (P < 5 × 10-8) with left-handedness and 7 associated with ambidexterity. Tissue-enrichment analysis implicated the CNS in the aetiology of handedness. Pathways including regulation of microtubules and brain morphology were also highlighted. We found suggestive positive genetic correlations between left-handedness and neuropsychiatric traits, including schizophrenia and bipolar disorder. Furthermore, the genetic correlation between left-handedness and ambidexterity is low (rG = 0.26), which implies that these traits are largely influenced by different genetic mechanisms. Our findings suggest that handedness is highly polygenic and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders.

Viral population estimation using pyrosequencing.

The diversity of virus populations within single infected hosts presents a major difficulty for the natural immune response as well as for vaccine design and antiviral drug therapy. Recently developed pyrophosphate-based sequencing technologies (pyrosequencing) can be used for quantifying this diversity by ultra-deep sequencing of virus samples. We present computational methods for the analysis of such sequence data and apply these techniques to pyrosequencing data obtained from HIV populations within patients harboring drug-resistant virus strains. Our main result is the estimation of the population structure of the sample from the pyrosequencing reads. This inference is based on a statistical approach to error correction, followed by a combinatorial algorithm for constructing a minimal set of haplotypes that explain the data. Using this set of explaining haplotypes, we apply a statistical model to infer the frequencies of the haplotypes in the population via an expectation-maximization (EM) algorithm. We demonstrate that pyrosequencing reads allow for effective population reconstruction by extensive simulations and by comparison to 165 sequences obtained directly from clonal sequencing of four independent, diverse HIV populations. Thus, pyrosequencing can be used for cost-effective estimation of the structure of virus populations, promising new insights into viral evolutionary dynamics and disease control strategies.

Inferring Multidimensional Rates of Aging from Cross-Sectional Data.

Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained to be order-isomorphic, the model family is identifiable solely from cross-sectional data provided the distribution of time-independent variation is known. On the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging associated with diseases, mortality, and aging risk factors.

Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder.

Attention deficit/hyperactivity disorder (ADHD) is a highly heritable childhood behavioral disorder affecting 5% of children and 2.5% of adults. Common genetic variants contribute substantially to ADHD susceptibility, but no variants have been robustly associated with ADHD. We report a genome-wide association meta-analysis of 20,183 individuals diagnosed with ADHD and 35,191 controls that identifies variants surpassing genome-wide significance in 12 independent loci, finding important new information about the underlying biology of ADHD. Associations are enriched in evolutionarily constrained genomic regions and loss-of-function intolerant genes and around brain-expressed regulatory marks. Analyses of three replication studies: a cohort of individuals diagnosed with ADHD, a self-reported ADHD sample and a meta-analysis of quantitative measures of ADHD symptoms in the population, support these findings while highlighting study-specific differences on genetic overlap with educational attainment. Strong concordance with GWAS of quantitative population measures of ADHD symptoms supports that clinical diagnosis of ADHD is an extreme expression of continuous heritable traits.

Self-report data as a tool for subtype identification in genetically-defined Parkinson's Disease.

Through a targeted recruitment 23andMe has collected DNA and patient-reported symptoms from more than 10,000 subjects reporting a physician-verified diagnosis of PD. This study evaluated the potential of self-report, web-based questionnaires to rapidly assess disease natural history and symptomology in genetically-defined PD populations. While average age-at-diagnosis was significantly lower in GBA mutation carriers compared to idiopathic PD, or iPD (idiopathic PD, defined as no GBA mutations and no LRRK2 G2019S mutation), there were no significant differences in symptoms. Conversely, LRRK2 G2019S carrier status significantly associated with reporting of milder daily symptoms of lightheadedness and several differences were observed at a false discovery rate < 0.1, including increased reporting of changes in walking as an initial symptom of disease, decreased reporting of lightheadedness upon standing, and milder symptoms related to daily functioning. The subclinical differences in symptoms reported by LRRK2 G2019S carriers suggest differences in underlying pathophysiology and/or disease progression in LRRK2 carriers compared to iPD. Importantly, we confirm previous findings in PD genetic subsets where disease characteristics were ascertained through clinical exam. Overall, these data support the effective use of self-report and genetic data to rapidly analyze information from a large disease population or difficult to identify genetic subgroups.