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The use of publicly available sequencing datasets as controls (hereafter, "public controls") in studies of rare variant disease associations has great promise but can increase the risk of false-positive discovery. The specific factors that could contribute to inflated distribution of test statistics have not been systematically examined. Here, we leveraged both public controls, gnomAD v2.1 and several datasets sequenced in our laboratory to systematically investigate factors that could contribute to the false-positive discovery, as measured by λΔ95, a measure to quantify the degree of inflation in statistical significance. Analyses of datasets in this investigation found that 1) the significantly inflated distribution of test statistics decreased substantially when the same variant caller and filtering pipelines were employed, 2) differences in library prep kits and sequencers did not affect the false-positive discovery rate and, 3) joint vs. separate variant-calling of cases and controls did not contribute to the inflation of test statistics. Currently available methods do not adequately adjust for the high false-positive discovery. These results, especially if replicated, emphasize the risks of using public controls for rare-variant association tests in which individual-level data and the computational pipeline are not readily accessible, which prevents the use of the same variant-calling and filtering pipelines on both cases and controls. A plausible solution exists with the emergence of cloud-based computing, which can make it possible to bring containerized analytical pipelines to the data (rather than the data to the pipeline) and could avert or minimize these issues. It is suggested that future reports account for this issue and provide this as a limitation in reporting new findings based on studies that cannot practically analyze all data on a single pipeline.

Like most complex phenotypes, exceptional longevity is thought to reflect a combined influence of environmental (e.g., lifestyle choices, where we live) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity in 801 centenarians (median age at death 104 years) and 914 genetically matched healthy controls. Using these data, we built a genetic model that includes 281 single nucleotide polymorphisms (SNPs) and discriminated between cases and controls of the discovery set with 89% sensitivity and specificity, and with 58% specificity and 60% sensitivity in an independent cohort of 341 controls and 253 genetically matched nonagenarians and centenarians (median age 100 years). Consistent with the hypothesis that the genetic contribution is largest with the oldest ages, the sensitivity of the model increased in the independent cohort with older and older ages (71% to classify subjects with an age at death>102 and 85% to classify subjects with an age at death>105). For further validation, we applied the model to an additional, unmatched 60 centenarians (median age 107 years) resulting in 78% sensitivity, and 2863 unmatched controls with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE that reached irrefutable genome wide significance (posterior probability of association = 1) and replicated in the independent cohort. Removal of this SNP from the model reduced the accuracy by only 1%. Further in-silico analysis suggests that 90% of centenarians can be grouped into clusters characterized by different "genetic signatures" of varying predictive values for exceptional longevity. The correlation between 3 signatures and 3 different life spans was replicated in the combined replication sets. The different signatures may help dissect this complex phenotype into sub-phenotypes of exceptional longevity.

Abstract Background Population stratification can cause spurious associations in a genome-wide association study (GWAS), and occurs when differences in allele frequencies of single nucleotide polymorphisms (SNPs) are due to ancestral differences between cases and controls rather than the trait of interest. Principal components analysis (PCA) is the established approach to detect population substructure using genome-wide data and to adjust the genetic association for stratification by including the top principal components in the analysis. An alternative solution is genetic matching of cases and controls that requires, however, well defined population strata for appropriate selection of cases and controls. Results We developed a novel algorithm to cluster individuals into groups with similar ancestral backgrounds based on the principal components computed by PCA. We demonstrate the effectiveness of our algorithm in real and simulated data, and show that matching cases and controls using the clusters assigned by the algorithm substantially reduces population stratification bias. Through simulation we show that the power of our method is higher than adjustment for PCs in certain situations. Conclusions In addition to reducing population stratification bias and improving power, matching creates a clean dataset free of population stratification which can then be used to build prediction models without including variables to adjust for ancestry. The cluster assignments also allow for the estimation of genetic heterogeneity by examining cluster specific effects.

Background Pediatric cancers are the leading cause of death by disease in children despite improved survival rates overall. The contribution of germline genetic susceptibility to pediatric cancer survivors has not been extensively characterized. We assessed the frequency of pathogenic or likely pathogenic (P/LP) variants in 5451 long-term pediatric cancer survivors from the Childhood Cancer Survivor Study. Methods Exome sequencing was conducted on germline DNA from 5451 pediatric cancer survivors (cases who survived ≥5 years from diagnosis; n = 5105 European) and 597 European cancer-free adults (controls). Analyses focused on comparing the frequency of rare P/LP variants in 237 cancer-susceptibility genes and a subset of 60 autosomal dominant high-to-moderate penetrance genes, for both case-case and case-control comparisons. Results Of European cases, 4.1% harbored a P/LP variant in high-to-moderate penetrance autosomal dominant genes compared with 1.3% in controls (2-sided P  = 3 × 10-4). The highest frequency of P/LP variants was in genes typically associated with adult onset rather than pediatric cancers, including BRCA1/2 , FH , PALB2 , PMS2 , and CDKN2A. A statistically significant excess of P/LP variants, after correction for multiple tests, was detected in patients with central nervous system cancers (NF1 , SUFU , TSC1 , PTCH2), Wilms tumor (WT1 , REST), non-Hodgkin lymphoma (PMS2), and soft tissue sarcomas (SDHB , DICER1 , TP53 , ERCC4 , FGFR3) compared with other pediatric cancers. Conclusion In long-term pediatric cancer survivors, we identified P/LP variants in cancer-susceptibility genes not previously associated with pediatric cancer as well as confirmed known associations. Further characterization of variants in these genes in pediatric cancer will be important to provide optimal genetic counseling for patients and their families. [ABSTRACT FROM AUTHOR]

Abstract Background Imputation of missing genotypes is becoming a very popular solution for synchronizing genotype data collected with different microarray platforms but the effect of ethnic background, subject ascertainment, and amount of missing data on the accuracy of imputation are not well understood. Results We evaluated the accuracy of the program IMPUTE to generate the genotype data of partially or fully untyped single nucleotide polymorphisms (SNPs). The program uses a model-based approach to imputation that reconstructs the genotype distribution given a set of referent haplotypes and the observed data, and uses this distribution to compute the marginal probability of each missing genotype for each individual subject that is used to impute the missing data. We assembled genome-wide data from five different studies and three different ethnic groups comprising Caucasians, African Americans and Asians. We randomly removed genotype data and then compared the observed genotypes with those generated by IMPUTE. Our analysis shows 97% median accuracy in Caucasian subjects when less than 10% of the SNPs are untyped and missing genotypes are accepted regardless of their posterior probability. The median accuracy increases to 99% when we require 0.95 minimum posterior probability for an imputed genotype to be acceptable. The accuracy decreases to 86% or 94% when subjects are African Americans or Asians. We propose a strategy to improve the accuracy by leveraging the level of admixture in African Americans. Conclusion Our analysis suggests that IMPUTE is very accurate in samples of Caucasians origin, it is slightly less accurate in samples of Asians background, but substantially less accurate in samples of admixed background such as African Americans. Sample size and ascertainment do not seem to affect the accuracy of imputation.

Abstract Background One of the challenges of the analysis of pooling-based genome wide association studies is to identify authentic associations among potentially thousands of false positive associations. Results We present a hierarchical and modular approach to the analysis of genome wide genotype data that incorporates quality control, linkage disequilibrium, physical distance and gene ontology to identify authentic associations among those found by statistical association tests. The method is developed for the allelic association analysis of pooled DNA samples, but it can be easily generalized to the analysis of individually genotyped samples. We evaluate the approach using data sets from diverse genome wide association studies including fetal hemoglobin levels in sickle cell anemia and a sample of centenarians and show that the approach is highly reproducible and allows for discovery at different levels of synthesis. Conclusion Results from the integration of Bayesian tests and other machine learning techniques with linkage disequilibrium data suggest that we do not need to use too stringent thresholds to reduce the number of false positive associations. This method yields increased power even with relatively small samples. In fact, our evaluation shows that the method can reach almost 70% sensitivity with samples of only 100 subjects.

PURPOSE: Radiotherapy for childhood cancer is associated with elevated subsequent neoplasm (SN) risk, but the contribution of rare variants in DNA damage response and radiation sensitivity genes to SN risk is unknown. PATIENTS AND METHODS: We conducted whole-exome sequencing in a cohort of childhood cancer survivors originally diagnosed during 1970 to 1986 (mean follow-up, 32.7 years), with reconstruction of doses to body regions from radiotherapy records. We identified patients who developed SN types previously reported to be related to radiotherapy (RT-SNs; eg, basal cell carcinoma [BCC], breast cancer, meningioma, thyroid cancer, sarcoma) and matched controls (sex, childhood cancer type/diagnosis, age, SN location, radiation dose, survival). Conditional logistic regression assessed SN risk associated with potentially protein-damaging rare variants (SnpEff, ClinVar) in 476 DNA damage response or radiation sensitivity genes with exact permutation-based P values using a Bonferroni-corrected significance threshold of P < 8.06 × 10−5. RESULTS: Among 5,105 childhood cancer survivors of European descent, 1,108 (21.7%) developed at least 1 RT-SN. Out-of-field RT-SN risk, excluding BCC, was associated with homologous recombination repair (HRR) gene variants (patient cases, 23.2%; controls, 10.8%; odds ratio [OR], 2.6; 95% CI, 1.7 to 3.9; P = 4.79 × 10−5), most notably but nonsignificantly for FANCM (patient cases, 4.0%; matched controls, 0.6%; P = 9.64 × 10−5). HRR variants were not associated with likely in/near-field RT-SNs, excluding BCC (patient cases, 12.7%; matched controls, 12.9%; P =.92). Irrespective of radiation dose, risk for RT-SNs was also associated with EXO1 variants (patient cases, 1.8%; controls, 0.4%; P = 3.31 × 10−5), another gene implicated in DNA double-strand break repair. CONCLUSION: In this large-scale discovery study, we identified novel associations between RT-SN risk after childhood cancer and potentially protein-damaging rare variants in genes involved in DNA double-strand break repair, particularly HRR. With replication, these results could affect screening recommendations for childhood cancer survivors and risk-benefit assessments of treatment approaches. [ABSTRACT FROM AUTHOR]

Alternative isoform regulation (AIR) vastly increases transcriptome diversity and plays an important role in numerous biological processes and pathologies. However, the detection and analysis of isoform-level differential regulation is difficult, particularly in the face of complex and incompletely-annotated transcriptomes. Here we have used Illumina short-read/high-throughput RNA-Seq to identify 55 genes that exhibit neurally-regulated AIR in the pineal gland, and then used two other complementary experimental platforms to further study and characterize the Ttc8 gene, which is involved in Bardet-Biedl syndrome and non-syndromic retinitis pigmentosa. Use of the JunctionSeq analysis tool led to the detection of several novel exons and splice junctions in this gene, including two novel alternative transcription start sites which were found to display disproportionately strong neurally-regulated differential expression in several independent experiments. These high-throughput sequencing results were validated and augmented via targeted qPCR and long-read Pacific Biosciences SMRT sequencing. We confirmed the existence of numerous novel splice junctions and the selective upregulation of the two novel start sites. In addition, we identified more than 20 novel isoforms of the Ttc8 gene that are co-expressed in this tissue. By using information from multiple independent platforms we not only greatly reduce the risk of errors, biases, and artifacts influencing our results, we also are able to characterize the regulation and splicing of the Ttc8 gene more deeply and more precisely than would be possible via any single platform. The hybrid method outlined here represents a powerful strategy in the study of the transcriptome. [ABSTRACT FROM AUTHOR]

The term neurotranscriptomics is used here to describe genome-wide analysis of neural control of transcriptomes. In this report, next-generation RNA sequencing was using to analyze the effects of neonatal (5-days-of-age) surgical stimulus deprivation on the adult rat pineal transcriptome. In intact animals, more than 3000 coding genes were found to exhibit differential expression (adjusted-p < 0.001) on a night/day basis in the pineal gland (70% of these increased at night, 376 genes changed more than 4-fold in either direction). Of these, more than two thousand genes were not previously known to be differentially expressed on a night/day basis. The night/day changes in expression were almost completely eliminated by neonatal removal (SCGX) or decentralization (DCN) of the superior cervical ganglia (SCG), which innervate the pineal gland. Other than the loss of rhythmic variation, surgical stimulus deprivation had little impact on the abundance of most genes; of particular interest, expression levels of the melatonin-synthesis-related genes Tph1, Gch1, and Asmt displayed little change (less than 35%) following DCN or SCGX. However, strong and consistent changes were observed in the expression of a small number of genes including the gene encoding Serpina1, a secreted protease inhibitor that might influence extracellular architecture. Many of the genes that exhibited night/day differential expression in intact animals also exhibited similar changes following in vitro treatment with norepinephrine, a superior cervical ganglia transmitter, or with an analog of cyclic AMP, a norepinephrine second messenger in this tissue. These findings are of significance in that they establish that the pineal-defining transcriptome is established prior to the neonatal period. Further, this work expands our knowledge of the biological process under neural control in this tissue and underlines the value of RNA sequencing in revealing how neurotransmission influences cell biology. [ABSTRACT FROM AUTHOR]

Background: High-throughput next-generation RNA sequencing has matured into a viable and powerful method for detecting variations in transcript expression and regulation. Proactive quality control is of critical importance as unanticipated biases, artifacts, or errors can potentially drive false associations and lead to flawed results. Results: We have developed the Quality of RNA-Seq Toolset, or QoRTs, a comprehensive, multifunction toolset that assists in quality control and data processing of high-throughput RNA sequencing data. Conclusions: QoRTs generates an unmatched variety of quality control metrics, and can provide cross-comparisons of replicates contrasted by batch, biological sample, or experimental condition, revealing any outliers and/or systematic issues that could drive false associations or otherwise compromise downstream analyses. In addition, QoRTs simultaneously replaces the functionality of numerous other data-processing tools, and can quickly and efficiently generate quality control metrics, coverage counts (for genes, exons, and known/novel splice-junctions), and browser tracks. These functions can all be carried out as part of a single unified data-processing/quality control run, greatly reducing both the complexity and the total runtime of the analysis pipeline. The software, source code, and documentation are available online at http://hartleys.github.io/QoRTs. [ABSTRACT FROM AUTHOR]

Serum bilirubin levels have been associated with polymorphisms in the UGT1A1 promoter in normal populations and in patients with hemolytic anemias, including sickle cell anemia. When hemolysis occurs circulating heme increases, leading to elevated bilirubin levels and an increased incidence of cholelithiasis. We performed the first genome-wide association study (GWAS) of bilirubin levels and cholelithiasis risk in a discovery cohort of 1,117 sickle cell anemia patients. We found 15 single nucleotide polymorphisms (SNPs) associated with total bilirubin levels at the genome-wide significance level (p value <5x10-8). SNPs in UGT1A1, UGT1A3, UGT1A6, UGT1A8 and UGT1A10, different isoforms within the UGT1A locus, were identified (most significant rs887829, p = 9.08x10-25). All of these associations were validated in 4 independent sets of sickle cell anemia patients. We tested the association of the 15 SNPs with cholelithiasis in the discovery cohort and found a significant association (most significant p value 1.15x10-4). These results confirm that the UGT1A region is the major regulator of bilirubin metabolism in African Americans with sickle cell anemia, similar to what is observed in other ethnicities. [ABSTRACT FROM AUTHOR]

Background: Population stratification can cause spurious associations in a genome-wide association study (GWAS), and occurs when differences in allele frequencies of single nucleotide polymorphisms (SNPs) are due to ancestral differences between cases and controls rather than the trait of interest. Principal components analysis (PCA) is the established approach to detect population substructure using genome-wide data and to adjust the genetic association for stratification by including the top principal components in the analysis. An alternative solution is genetic matching of cases and controls that requires, however, well defined population strata for appropriate selection of cases and controls. Results: We developed a novel algorithm to cluster individuals into groups with similar ancestral backgrounds based on the principal components computed by PCA. We demonstrate the effectiveness of our algorithm in real and simulated data, and show that matching cases and controls using the clusters assigned by the algorithm substantially reduces population stratification bias. Through simulation we show that the power of our method is higher than adjustment for PCs in certain situations. Conclusions: In addition to reducing population stratification bias and improving power, matching creates a clean dataset free of population stratification which can then be used to build prediction models without including variables to adjust for ancestry. The cluster assignments also allow for the estimation of genetic heterogeneity by examining cluster specific effects. [ABSTRACT FROM AUTHOR]

Background: One of the challenges of the analysis of pooling-based genome wide association studies is to identify authentic associations among potentially thousands of false positive associations. Results: We present a hierarchical and modular approach to the analysis of genome wide genotype data that incorporates quality control, linkage disequilibrium, physical distance and gene ontology to identify authentic associations among those found by statistical association tests. The method is developed for the allelic association analysis of pooled DNA samples, but it can be easily generalized to the analysis of individually genotyped samples. We evaluate the approach using data sets from diverse genome wide association studies including fetal hemoglobin levels in sickle cell anemia and a sample of centenarians and show that the approach is highly reproducible and allows for discovery at different levels of synthesis. Conclusion: Results from the integration of Bayesian tests and other machine learning techniques with linkage disequilibrium data suggest that we do not need to use too stringent thresholds to reduce the number of false positive associations. This method yields increased power even with relatively small samples. In fact, our evaluation shows that the method can reach almost 70% sensitivity with samples of only 100 subjects. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article “Genetic Signatures of Exceptional Longevity in Humans,” by Paola Sebastiani and colleagues in a July 1, 2010 online issue.

Objective: Zidovudine (ZDV) has been extensively used in pregnant women to prevent vertical transmission of HIV but few studies have evaluated potential mutagenic effects of ZDV during fetal development., Design: Our study investigated clonal hematopoiesis in HIV-exposed uninfected (HEU) newborns, 94 of whom were ZDV-exposed and 91 antiretroviral therapy (ART)-unexposed and matched for potential confounding factors., Methods: Utilizing high depth sequencing and genotyping arrays, we comprehensively examined blood samples collected during the first week after birth for potential clonal hematopoiesis associated with fetal ZDV exposure, including clonal single nucleotide variants (SNVs), small insertions and deletions (indels), and large structural copy number or copy neutral alterations., Results: We observed no statistically significant difference in the number of SNVs and indels per person in ZDV-exposed children (adjusted ratio [95% confidence interval, CI] for expected number of mutations = 0.79 [0.50--1.22], P = 0.3), and no difference in the number of large structural alterations. Mutations in common clonal hematopoiesis driver genes were not found in the study population. Mutational signature analyses on SNVs detected no novel signatures unique to the ZDV-exposed children and the mutational profiles were similar between the two groups., Conclusion: Our results suggest that clonal hematopoiesis at levels detectable in our study is not strongly influenced by in-utero ZDV exposure; however, additional follow-up studies are needed to further evaluate the safety and potential long-term impacts of in-utero ZDV exposure in HEU children as well as better investigate genomic aberrations occurring late in pregnancy., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Genome-wide association studies (GWAS) have identified numerous associations between genetic loci and individual phenotypes; however, relatively few GWAS have attempted to detect pleiotropic associations, in which loci are simultaneously associated with multiple distinct phenotypes. We show that pleiotropic associations can be directly modeled via the construction of simple Bayesian networks, and that these models can be applied to produce single or ensembles of Bayesian classifiers that leverage pleiotropy to improve genetic risk prediction. The proposed method includes two phases: (1) Bayesian model comparison, to identify Single-Nucleotide Polymorphisms (SNPs) associated with one or more traits; and (2) cross-validation feature selection, in which a final set of SNPs is selected to optimize prediction. To demonstrate the capabilities and limitations of the method, a total of 1600 case-control GWAS datasets with two dichotomous phenotypes were simulated under 16 scenarios, varying the association strengths of causal SNPs, the size of the discovery sets, the balance between cases and controls, and the number of pleiotropic causal SNPs. Across the 16 scenarios, prediction accuracy varied from 90 to 50%. In the 14 scenarios that included pleiotropically associated SNPs, the pleiotropic model search and prediction methods consistently outperformed the naive model search and prediction. In the two scenarios in which there were no true pleiotropic SNPs, the differences between the pleiotropic and naive model searches were minimal. To further evaluate the method on real data, a discovery set of 1071 sickle cell disease (SCD) patients was used to search for pleiotropic associations between cerebral vascular accidents and fetal hemoglobin level. Classification was performed on a smaller validation set of 352 SCD patients, and showed that the inclusion of pleiotropic SNPs may slightly improve prediction, although the difference was not statistically significant. The proposed method is robust, computationally efficient, and provides a powerful new approach for detecting and modeling pleiotropic disease loci.

Healthy aging is thought to reflect the combined influence of environmental factors (lifestyle choices) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity (EL) in 1055 centenarians and 1267 controls. Using these data, we built a genetic model that includes 150 single-nucleotide polymorphisms (SNPs) and found that it could predict EL with 77% accuracy in an independent set of centenarians and controls. Further in silico analysis revealed that 90% of centenarians can be grouped into 19 clusters characterized by different combinations of SNP genotypes-or genetic signatures-of varying predictive value. The different signatures, which attest to the genetic complexity of EL, correlated with differences in the prevalence and age of onset of age-associated diseases (e.g., dementia, hypertension, and cardiovascular disease) and may help dissect this complex phenotype into subphenotypes of healthy aging.

Background: Imputation of missing genotypes is becoming a very popular solution for synchronizing genotype data collected with different microarray platforms but the effect of ethnic background, subject ascertainment, and amount of missing data on the accuracy of imputation are not well understood., Results: We evaluated the accuracy of the program IMPUTE to generate the genotype data of partially or fully untyped single nucleotide polymorphisms (SNPs). The program uses a model-based approach to imputation that reconstructs the genotype distribution given a set of referent haplotypes and the observed data, and uses this distribution to compute the marginal probability of each missing genotype for each individual subject that is used to impute the missing data. We assembled genome-wide data from five different studies and three different ethnic groups comprising Caucasians, African Americans and Asians. We randomly removed genotype data and then compared the observed genotypes with those generated by IMPUTE. Our analysis shows 97% median accuracy in Caucasian subjects when less than 10% of the SNPs are untyped and missing genotypes are accepted regardless of their posterior probability. The median accuracy increases to 99% when we require 0.95 minimum posterior probability for an imputed genotype to be acceptable. The accuracy decreases to 86% or 94% when subjects are African Americans or Asians. We propose a strategy to improve the accuracy by leveraging the level of admixture in African Americans., Conclusion: Our analysis suggests that IMPUTE is very accurate in samples of Caucasians origin, it is slightly less accurate in samples of Asians background, but substantially less accurate in samples of admixed background such as African Americans. Sample size and ascertainment do not seem to affect the accuracy of imputation.