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Disparities in the data that underlies clinical genomic interpretation is an acknowledged problem but there is a paucity of data demonstrating it. The National Institutes of Health’sAll of UsResearch Program aims to collect whole genome sequences, electronic health record (EHR) data, surveys and physical measurements for over a million participants of diverse ancestry and varied access to healthcare resources. We grouped participants by computed genetic ancestry and summarized the frequency of pathogenic variation within these groups. The European subgroup showed the highest rate of pathogenic variation (2.1%), with other ancestry groups ranging from 1.04% (East Asian) to 1.87% (‘Other’). Pathogenic variants were most frequently observed in genes related to Breast/Ovarian Cancer, Hypercholesterolemia or Hemochromatosis. Variant frequencies were consistent with gnomAD and some notable exceptions were resolved using gnomAD subsets. We additionally use this data to enrich sets of participants for specific genetic findings and to calculate penetrance. Differences in the frequency of pathogenic variants observed between ancestral groups generally indicate biases of ascertainment, but some may indicate differences in disease prevalence. These analyses are available on theAll of UsResearcher Workbench.

The rapid growth of genomic data has led to a new research paradigm where data are stored centrally in Trusted Research Environments (TREs) such as theAll of UsResearcher Workbench (AoU RW) and the UK Biobank Research Analysis Platform (RAP). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conducted a Genome-Wide Association Study (GWAS) of standard lipid measures on the UKB RAP and AoU RW using two approaches: meta-analysis and pooled analysis. We curated lipid measurements for 37,754All of Usparticipants with whole genome sequence (WGS) data and 190,982 UK Biobank participants with whole exome sequence (WES) data. For the meta-analysis, we performed a GWAS of each cohort in their respective platform and meta-analyzed the results. We separately performed a pooled GWAS on both datasets combined. We identified 490 and 464 significant variants in meta-analysis and pooled analysis, respectively. Comparison of full summary data from both meta-analysis and pooled analysis with an external study showed strong correlation of known loci with lipid levels (R2∼83-97%). Importantly, 90 variants met the significance threshold only in the meta-analysis and 64 variants were significant only in pooled analysis. These method-specific differences may be explained by differences in cohort size, ancestry, and phenotype distributions inAll of Usand UK Biobank. We noted approximately 20% of variants significant in only the pooled analysis or significant in only the meta-analysis were most prevalent in non-European, non-Asian ancestry individuals. Pooled analyses included more variants than meta-analyses. Pooled analysis required about half as many computational steps as meta-analysis. These findings have important implications for both platform implementations and researchers undertaking large-scale cross-cohort analyses, as technical and policy choices lead to cross-cohort analyses generating similar, but not identical results, particularly for non-European ancestral populations.

Background The All of Us Research Program (AoURP, “the program”) is an initiative, sponsored by the National Institutes of Health (NIH), that aims to enroll one million people (or more) across the USA. Through repeated engagement of participants, a research resource is being created to enable a variety of future observational and interventional studies. The program has also committed to genomic data generation and returning important health-related information to participants. Methods Whole-genome sequencing (WGS), variant calling processes, data interpretation, and return-of-results procedures had to be created and receive an Investigational Device Exemption (IDE) from the United States Food and Drug Administration (FDA). The performance of the entire workflow was assessed through the largest known cross-center, WGS-based, validation activity that was refined iteratively through interactions with the FDA over many months. Results The accuracy and precision of the WGS process as a device for the return of certain health-related genomic results was determined to be sufficient, and an IDE was granted. Conclusions We present here both the process of navigating the IDE application process with the FDA and the results of the validation study as a guide to future projects which may need to follow a similar path. Changes to the program in the future will be covered in supplementary submissions to the IDE and will support additional variant classes, sample types, and any expansion to the reportable regions.

Background Individuals with obsessive–compulsive disorder (OCD) frequently have other psychiatric disorders. This study employed latent class analysis (LCA) to explore whether there are underlying clinical constructs that distinguish “OCD-related” subgroups. Methods The study included 450 subjects, case and control probands and their first-degree relatives, and LCA was used to derive empirically based subgroups of 10 disorders: OCD, obsessive–compulsive personality disorder (OCPD), recurrent major depressive disorder (RMDD), separation anxiety disorder, panic disorder or agoraphobia (PD/AG), tic disorders (TD), generalized anxiety disorder (GAD), somatoform disorders (hypochondriasis or body dysmorphic disorder), pathologic skin picking or nail biting (PSP/NB), and eating disorders (EDs). The derived classes were compared on several clinical variables. Results The best fitting model is a four-class structure: minimal disorder, predominant RMDD and GAD, “highly comorbid,” and PD/AG and TD. The nature and number of disorders represented suggests that the first classes are distributed ordinally on a dimension of severity, and the fourth class is qualitatively distinct. Support for this structure is based on the number of disorders, age at onset of OCD, neuroticism, and extraversion. Conclusions In this OCD enriched sample, LCA identified four classes of disorder. These classes appear to conform to two subgroups that may prove useful in investigating the etiology of OCD.

Using a recently developed multipoint parametric method, which tests for linkage in the presence of heterogeneity, we performed a genome-wide search for linkage using the German asthma data. Both dominant and recessive models were assumed in this parametric approach. Identity-by-descent (IBD) sharing for affected sibs was also calculated to help identify an appropriate genetic model and localize the trait locus. The strongest evidence for linkage was on chromosome 6 (p-value = 0.00006) under the dominant model with heterogeneity. Using both linkage and IBD sharing information for D6S422 (36.55 cM) on chromosome 6, we conducted exploratory analyses to locate additional trait loci that might explain the linkage heterogeneity. We found evidence of heterogeneity between D6S422 and D11S4111 based on a test of association (p-value = 0.0015).

We performed a genome scan at an average resolution of 8 cM in 719 Finnish sib pairs with type 2 diabetes. Our strongest results are for chromosome 20, where we observe a weighted maximum LOD score (MLS) of 2.15 at map position 69.5 cM from pter and secondary weighted LOD-score peaks of 2.04 at 56.5 cM and 1.99 at 17.5 cM. Our next largest MLS is for chromosome 11 (MLS = 1.75 at 84.0 cM), followed by chromosomes 2 (MLS = 0.87 at 5.5 cM), 10 (MLS = 0.77 at 75.0 cM), and 6 (MLS = 0.61 at 112.5 cM), all under an additive model. When we condition on chromosome 2 at 8.5 cM, the MLS for chromosome 20 increases to 5.50 at 69.0 cM (P=.0014). An ordered-subsets analysis based on families with high or low diabetes-related quantitative traits yielded results that support the possible existence of disease-predisposing genes on chromosomes 6 and 10. Genomewide linkage-disequilibrium analysis using microsatellite marker data revealed strong evidence of association for D22S423 (P=.00007). Further analyses are being carried out to confirm and to refine the location of these putative diabetes-predisposing genes.

Type 2 diabetes mellitus is a complex disorder encompassing multiple metabolic defects. We report results from an autosomal genome scan for type 2 diabetes-related quantitative traits in 580 Finnish families ascertained for an affected sibling pair and analyzed by the variance components-based quantitative-trait locus (QTL) linkage approach. We analyzed diabetic and nondiabetic subjects separately, because of the possible impact of disease on the traits of interest. In diabetic individuals, our strongest results were observed on chromosomes 3 (fasting C-peptide/glucose: maximum LOD score [MLS] = 3.13 at 53.0 cM) and 13 (body-mass index: MLS = 3.28 at 5.0 cM). In nondiabetic individuals, the strongest results were observed on chromosomes 10 (acute insulin response: MLS = 3.11 at 21.0 cM), 13 (2-h insulin: MLS = 2.86 at 65.5 cM), and 17 (fasting insulin/glucose ratio: MLS = 3.20 at 9.0 cM). In several cases, there was evidence for overlapping signals between diabetic and nondiabetic individuals; therefore we performed joint analyses. In these joint analyses, we observed strong signals for chromosomes 3 (body-mass index: MLS = 3.43 at 59.5 cM), 17 (empirical insulin-resistance index: MLS = 3.61 at 0.0 cM), and 19 (empirical insulin-resistance index: MLS = 2.80 at 74.5 cM). Integrating genome-scan results from the companion article by Ghosh et al., we identify several regions that may harbor susceptibility genes for type 2 diabetes in the Finnish population.

The planum temporale (PT) has been of interest because of (1) its consistent left greater than right asymmetry among right-handed and most left-handed normal individuals; and (2) its relation to language, another variable shown to be highly left-lateralized in normal subjects. Individuals with neurodevelopmental disorders have been reported to show abnormal PT asymmetry (either reversed or absent asymmetry). Several studies have been conducted measuring the PT on MRI scans, although the results do not always concur. We review some of these studies and discuss methodological differences between them. Additionally, we propose a method that has proved to be highly reliable for the measurement of both temporal PT and its parietal extension (PT+).

Periconceptual folate supplementation has been found to prevent the occurrence of many neural tube defects (NTDs). Consequently, genetic variation in folate metabolism genes is expected to contribute to the risk for neural tube defects. Methionine synthase catalyzes the vitamin B 12 -dependent conversion of homocysteine and 5-methyltetrahydrofolate to methionine and tetrahydrofolate. The observation that homocysteine and vitamin B 12 levels are independent predictors of NTD risk suggested that methionine synthase could be a candidate gene for NTDs. To assess the role of the MS gene in NTDs, we performed high-resolution physical mapping of the MS locus, isolated highly polymorphic markers linked to the MS gene, and tested for an association between specific MS alleles and NTDs. We mapped the MS gene to a position between 909 and 913 cR 10000 on chromosome 1 by radiation hybrid mapping. Polymorphic markers D1S1567 and D1S1568 map to locations no more than 900 and 194 kb from the MS gene, respectively. The segregation of these polymorphic markers was measured in 85 Irish NTD families. No allele of either marker showed a significant association with NTDs using the transmission disequilibrium test. A lack of association was also observed for the D1919G missense mutation within the gene. Our results suggest that inherited variation in the MS gene does not contribute to NTD risk in this population.

The purpose of this study was to assess 65 pedigrees ascertained through a Bipolar I (BPI) proband for evidence of linkage, using nonparametric methods in a genome-wide scan and for possible parent of origin effect using several analytical methods. We identified 15 loci with nominally significant evidence for increased allele sharing among affected relative pairs. Eight of these regions, at 8q24, 18q22, 4q32, 13q12, 4q35, 10q26, 2p12, and 12q24, directly overlap with previously reported evidence of linkage to bipolar disorder. Five regions at 20p13, 2p22, 14q23, 9p13, and 1q41 are within several Mb of previously reported regions. We report our findings in rank order and the top five markers had an NPL>2.5. The peak finding in these regions were D8S256 at 8q24, NPL 3.13; D18S878 at 18q22, NPL 2.90; D4S1629 at 4q32, NPL 2.80; D2S99 at 2p12, NPL 2.54; and D13S1493 at 13q12, NPL 2.53. No locus produced statistically significant evidence for linkage at the genome-wide level. The parent of origin effect was studied and consistent with our previous findings, evidence for a locus on 18q22 was predominantly from families wherein the father or paternal lineage was affected. There was evidence consistent with paternal imprinting at the loci on 13q12 and 1q41.

We analyzed data from the German Asthma Genetics Group with three methods that utilize pedigree-specific nonparametric linkage scores to facilitate the search for multiple independent and interacting susceptibility loci. The three methods included a conditional analysis, logistic regression, and neural networks. Although there were differences, the three methods identified many of the same susceptibility loci. The most consistent evidence was provided for loci on chromosomes 1, 2, 6, 9, and 15. Both the conditional and the logistic regression analyses suggested an epistatic relationship between loci on chromosomes 2 and 9. The logistic regression analysis further revealed evidence for locus heterogeneity between loci on chromosomes 6 and 15. Finally, the neural network analysis identified a potential locus on chromosome 17 that was not identified in the other analyses. © 2001 Wiley-Liss, Inc.

We are conducting a genome scan at an average resolution of 10 centimorgans (cM) for type 2 diabetes susceptibility genes in 716 affected sib pairs from 477 Finnish families. To date, our best evidence for linkage is on chromosome 20 with potentially separable peaks located on both the long and short arms. The unweighted multipoint maximum logarithm of odds score (MLS) was 3.08 on 20p (location, x̂ = 19.5 cM) under an additive model, whereas the weighted MLS was 2.06 on 20q ( x̂ = 57 cM, recurrence risk, λ̂ s = 1.25, P = 0.009). Weighted logarithm of odds scores of 2.00 ( x̂ = 69.5 cM, P = 0.010) and 1.92 ( x̂ = 18.5 cM, P = 0.013) were also observed. Ordered subset analyses based on sibships with extreme mean values of diabetes-related quantitative traits yielded sets of families who contributed disproportionately to the peaks. Two-hour glucose levels in offspring of diabetic individuals gave a MLS of 2.12 ( P = 0.0018) at 9.5 cM. Evidence from this and other studies suggests at least two diabetes-susceptibility genes on chromosome 20. We have also screened the gene for maturity-onset diabetes of the young 1, hepatic nuclear factor 4-a ( HNF-4 α) in 64 affected sibships with evidence for high chromosomal sharing at its location on chromosome 20q. We found no evidence that sequence changes in this gene accounted for the linkage results we observed.

In the first reported positive result from a genome scan for non-insulin-dependent diabetes mellitus (NIDDM), Hanis et al. found significant evidence of linkage for NIDDM on chromosome 2q37 and named the putative disease locus NIDDM1 (Hanis et al. 1996. Nat. Genet. 13:161-166). Their total sample was comprised of 440 Mexican-American affected sib-pairs from 246 sibships. The strongest evidence for linkage was at marker D2S125 and best estimates of lambdas (risk to siblings of probands/population prevalence) using this marker were 1.37 under an additive model and 1.36 under a multiplicative model. We examined this chromosomal region using linkage analysis in a Finnish sample comprised of 709 affected sib-pairs from 472 sibships. We excluded this region in our sample (multipoint logarithm of odds score /= 1.37. We discuss possible reasons why linkage to 2q37 was not found and conclude that this region is unlikely to be playing a major role in NIDDM susceptibility in the Finnish Caucasian population.

Large-scale genotyping is required to generate dense identity-by-descent maps to map genes for human complex disease. In some studies the number of genotypes needed can approach or even exceed 1 million. Generally, linkage and linkage disequilibrium analyses depend on clear allele identification and subsequent allele frequency estimation. Accurate grouping or categorization of each allele in the sample (allele calling or binning) is therefore an absolute requirement. Hence, a genotyping system that can reliably achieve this is necessary. In the case of affected sib-pair analysis without parents, the need for accurate allele calling is even more critical. We describe methods that permit precise sizing of alleles across multiple gels using the fluorescence-based, Applied Biosystems (ABI) genotyping technology and discuss ways to reduce genotyping error rates. Using database utilities, we show how to minimize intergel allele size variation, to combine data effectively from different models of ABI sequencing machines, and automatically bin alleles. The final data can then be converted into a format ready for analysis by statistical genetic packages such as MENDEL.