Individual-level Genotype Data Research Articles

Association of NTRK2 gene with suicidality: a meta-analysis.

Previous studies have shown that genes in brain development pathways may have important roles in affecting risk of suicidal behaviors, with our previous meta-analysis supporting a role of the brain-derived neurotrophic factor (BDNF) gene. NTRK2 is a gene that encodes the neurotrophic receptor tyrosine kinase 2, which is a receptor for BDNF. In the current study, we aim to examine the potential association between NTRK2 single nucleotide polymorphism (SNPs) and suicidal ideation/behaviors. We first conducted a literature search using keywords like 'NTRK2', 'TRKB', and 'suicid*' to identify papers on NTRK2 SNPs and suicidal ideation/behaviors. In addition, we have individual-level genotype data for all the identified SNPs in literature search. We used the R meta package to perform meta-analyses on both the genotype count and the allele count data. Moreover, we performed meta-analyses on specific haplotypes within each haplotype block. Following our literature search and meta-analyses on 20 NTRK2 SNPs across up to 8467 samples, we found three SNPs, rs10868235 [N = 5,318, odds ratio (OR) = 1.34, P = 0.02], rs1867283 (N = 5,134, OR = 0.73, P = 0.04), and rs1147198 (N = 5,132, OR = 1.36, P = 0.03) to be nominally associated with suicidal attempts. Those three findings, however, did not survive multiple-testing corrections. Also, none of the haplotype blocks showed significant involvement in suicidality. Our results suggest that the NTRK2 gene may not have a major role in suicidality. Future efforts, however, should explore gene-gene interaction and pathway analyses.

Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions.

Polygenic risk scores (PRS) aim to predict a trait from genetic information, relying on common genetic variants with low to medium effect sizes. As genotype data are high-dimensional in nature, it is crucial to develop methods that can be applied to large-scale data (large and large ). Many PRS tools aggregate univariate summary statistics from genome-wide association studies into a single score. Recent advancements allow simultaneous modeling of variant effects from individual-level genotype data. In this context, we introduced snpboost, an algorithm that applies statistical boosting on individual-level genotype data to estimate PRS via multivariable regression models. By processing variants iteratively in batches, snpboost can deal with large-scale cohort data. Having solved the technical obstacles due to data dimensionality, the methodological scope can now be broadened-focusing on key objectives for the clinical application of PRS. Similar to most methods in this context, snpboost has, so far, been restricted to quantitative and binary traits. Now, we incorporate more advanced alternatives-targeted to the particular aim and outcome. Adapting the loss function extends the snpboost framework to further data situations such as time-to-event and count data. Furthermore, alternative loss functions for continuous outcomes allow us to focus not only on the mean of the conditional distribution but also on other aspects that may be more helpful in the risk stratification of individual patients and can quantify prediction uncertainty, for example, median or quantile regression. This work enhances PRS fitting across multiple model classes previously unfeasible for this data type.

The GenoPred pipeline: a comprehensive and scalable pipeline for polygenic scoring.

Polygenic scoring is an approach for estimating an individual's likelihood of a given outcome. Polygenic scores are typically calculated from genome-wide association study (GWAS) summary statistics and individual-level genotype data for the target sample. Going from genotype to interpretable polygenic scores involves many steps and there are many methods available, limiting the accessibility of polygenic scores for research and clinical application. Additional challenges exist for studies in ancestrally diverse populations. We have implemented the leading polygenic scoring methodologies within an easy-to-use pipeline called GenoPred. Here, we present the GenoPred pipeline, an easy-to-use, high-performance, reference-standardized, and reproducible workflow for polygenic scoring. It requires minimal inputs and offers various configuration options to cater to a range of use cases. GenoPred implements a comprehensive set of analyses, including genotype and GWAS quality control, target sample ancestry inference, polygenic score file generation using a range of leading methods, and target sample scoring. GenoPred standardizes the polygenic scoring process using reference genetic data, providing interpretable polygenic scores. The pipeline is applicable to GWAS and targets data from any population within the reference, facilitating studies of diverse ancestry. GenoPred is a Snakemake pipeline with associated Conda software environments, ensuring reproducibility. We apply the pipeline to UK Biobank data demonstrating the pipeline's simplicity, efficiency, and performance. The GenoPred pipeline provides a novel resource for polygenic scoring, integrating a range of complex processes within an easy-to-use framework. GenoPred widens access to the leading polygenic scoring methodology and their application to studies of diverse ancestry. Freely available on the web at https://github.com/opain/GenoPred.

GAUSS: a summary-statistics-based R package for accurate estimation of linkage disequilibrium for variants, Gaussian imputation, and TWAS analysis of cosmopolitan cohorts.

As the availability of larger and more ethnically diverse reference panels grows, there is an increase in demand for ancestry-informed imputation of genome-wide association studies (GWAS), and other downstream analyses, e.g. fine-mapping. Performing such analyses at the genotype level is computationally challenging and necessitates, at best, a laborious process to access individual-level genotype and phenotype data. Summary-statistics-based tools, not requiring individual-level data, provide an efficient alternative that streamlines computational requirements and promotes open science by simplifying the re-analysis and downstream analysis of existing GWAS summary data. However, existing tools perform only disparate parts of needed analysis, have only command-line interfaces, and are difficult to extend/link by applied researchers. To address these challenges, we present Genome Analysis Using Summary Statistics (GAUSS)-a comprehensive and user-friendly R package designed to facilitate the re-analysis/downstream analysis of GWAS summary statistics. GAUSS offers an integrated toolkit for a range of functionalities, including (i) estimating ancestry proportion of study cohorts, (ii) calculating ancestry-informed linkage disequilibrium, (iii) imputing summary statistics of unobserved variants, (iv) conducting transcriptome-wide association studies, and (v) correcting for "Winner's Curse" biases. Notably, GAUSS utilizes an expansive, multi-ethnic reference panel consisting of 32953 genomes from 29 ethnic groups. This panel enhances the range and accuracy of imputable variants, including the ability to impute summary statistics of rarer variants. As a result, GAUSS elevates the quality and applicability of existing GWAS analyses without requiring access to subject-level genotypic and phenotypic information. The GAUSS R package, complete with its source code, is readily accessible to the public via our GitHub repository at https://github.com/statsleelab/gauss. To further assist users, we provided illustrative use-case scenarios that are conveniently found at https://statsleelab.github.io/gauss/, along with a comprehensive user guide detailed in Supplementary Text S1.

Gene-based association tests in family samples using GWAS summary statistics.

Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.

Searching across-cohort relatives in 54,092 GWAS samples via encrypted genotype regression.

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier.

A high-resolution haplotype-resolved Reference panel constructed from the China Kadoorie Biobank Study.

Precisionmedicine depends on high-accuracy individual-level genotype data. However, the whole-genome sequencing (WGS) is still not suitable for gigantic studies due to budget constraints. It is particularly important to construct highly accurate haplotype reference panel for genotype imputation. In this study, we used 10000 samples with medium-depth WGS to construct a reference panel that wenamed theCKB reference panel. By imputing microarray datasets, it showed that the CKB panel outperformed compared panels in terms of both the number of well-imputed variants and imputation accuracy. In addition, we have completed the imputation of 100 706 microarrays with the CKB panel, and the after-imputed data is the hitherto largest whole genome data of the Chinese population. Furthermore, in the GWAS analysis of real phenotype height, the number of tested SNPs tripled and the number of significant SNPs doubled after imputation. Finally, we developed an online server for offering free genotype imputation service based on the CKB reference panel (https://db.cngb.org/imputation/). We believe that the CKB panel is of great value for imputing microarray or low-coverage genotype data of Chinese population, and potentially mixed populations. The imputation-completed 100 706 microarray data are enormous and precious resources of population genetic studies for complex traits and diseases.

Association between ovarian reserve and spontaneous miscarriage and their shared genetic architecture.

Can potential mechanisms involved in the likely concurrence of diminished ovarian reserve (DOR) and miscarriage be identified using genetic data? Concurrence between ovarian reserve and spontaneous miscarriage was observed, and may be attributed to shared genetic risk loci enriched in antigen processing and presentation and autoimmune disease pathways. Previous studies have shown that lower serum anti-Müllerian hormone (AMH) levels are associated with increased risk of embryo aneuploidy and spontaneous miscarriage, although findings have not been consistent across all studies. A recent meta-analysis suggested that the association between DOR and miscarriage may not be causal, but rather a result of shared underlying causes such as clinical conditions or past exposure. Motivated by this hypothesis, we conducted the present analysis to explore the concurrence between DOR and miscarriage, and to investigate potential mechanisms using genetic data. Three data sources were used in the study: the clinical IVF data were retrospectively collected from an academically affiliated Reproductive Medicine Center (17786 cycles included); the epidemiological data from the UK Biobank (UKB), which is a large-scale, population-based, prospective cohort study (35 316 white women included), were analyzed; and individual-level genotype data from the UKB were extracted for further analysis. There were three modules of analysis. First, clinical IVF data were used to test the association between ovarian reserve biomarkers and the subsequent early spontaneous miscarriage risk. Second, the UKB data were used to test the association of spontaneous miscarriage history and early menopause. Third, individual-level genotype data from the UKB were analyzed to identify specific pleiotropic genes which affect the development of miscarriage and menopause. In the analysis of clinical IVF data, the risk of early spontaneous miscarriage was 1.57 times higher in the group with AMH < 1.1 ng/ml group (P < 0.001), 1.62 times for antral follicular count <5 (P < 0.001), and 1.39 times for FSH ≥10 mIU/ml (P < 0.001) in comparison with normal ovarian reserve groups. In the analysis of UKB data, participants with a history of three or more miscarriages had a one-third higher risk of experiencing early menopause (odds ratio: 1.30, 95% CI 1.13-1.49, P < 0.001), compared with participants without spontaneous miscarriage history. We identified 158 shared genetic risk loci that affect both miscarriage and menopause, which enrichment analysis showed were involved in antigen processing and presentation and autoimmune disease pathways. The analyses of the UKB data were restricted to participants of European ancestry, as 94.6% of the cohort were of white ethnicity. Further studies are needed in non-white populations. Additionally, maternal age at the time of spontaneous miscarriage was not available in the UKB cohort, therefore we adjusted for age at baseline assessment in the models instead. It is known that miscarriage rate in IVF is higher compared to natural conception, highlighting a need for caution when generalizing our findings from the IVF cohort to the general population. Our findings have implications for IVF clinicians in terms of patient counseling on the prognosis of IVF treatment, as well as for genetic counseling regarding miscarriage. Our results highlight the importance of further research on the shared genetic architecture and common pathophysiological basis of DOR and miscarriage, which may lead to new therapeutic opportunities. This work was supported by the Hunan Youth Science and Technology Innovation Talent Project (2020RC3060), the International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program, YJ20220220), the fellowship of China Postdoctoral Science Foundation (2022M723564), and the Natural Science Foundation of Hunan Province, China (2023JJ41016). This work has been accepted for poster presentation at the 39th Annual Meeting of ESHRE, Copenhagen, Denmark, 25-28 June 2023 (Poster number: P-477). The authors declare no conflict of interest. N/A.

P-477 Bidirectional association between ovarian reserve and spontaneous miscarriage: findings from the clinical IVF data to epidemiological evidence and genetic links

Abstract Study question To determine whether the association between ovarian reserve and spontaneous miscarriage risk is bidirectional and caused by shared genetic risk loci. Summary answer Bidirectional association between ovarian reserve and spontaneous miscarriage was observed. This may be attributed to five shared risk genes (SYCP2L, TP63, GSPT1, RIF1, and PRRC2A). What is known already Previous studies have shown that women with lower serum anti-Müllerian hormone (AMH) had an increased risk of embryo aneuploidy or spontaneous miscarriage risk, compared to women with higher AMH concentration, although not all studies agree. The most recent meta-analysis postulated that the association between diminished ovarian reserve (DOR) and miscarriage may be not causal, but that the two occurrences merely share a common cause, such as other systematic clinical conditions or past exposure. Inspired by this hypothesis, we conducted the present analyses to clarify this bidirectional association between DOR and miscarriage and further elucidate plausible mechanisms by using genetic data. Study design, size, duration There were four data sources. 1) The Clinical IVF data were retrospectively collected from an academically affiliated Reproductive Medicine Center (17,786 cycles included). 2) We further analyzed data from the UK Biobank (UKB) which is a large-scale, population-based, prospective cohort study (35,316 white women included). 3) The GWAS summary statistics for age at natural menopause (ANM) and spontaneous miscarriage were obtained from published GWAS studies. 4) The individual-level genotype data were also extracted from UKB. Participants/materials, setting, methods There were four analysis modules, 1) IVF data to test the association of ovarian reserve and miscarriage risk by using logistic regression models, 2) UKB data to test the association of spontaneous miscarriage history and ANM by using linear regression models, 3) GWAS summary data to test the overall genetic enrichment between miscarriage and ANM by plotting Q-Q plot, and 4) individual-level genotype data to identify specific shared genes by using ‘pleio’ R package. Main results and the role of chance In the analysis of clinical data, the risk of miscarriage was 1.5 (95% CI: 1.22-1.85, p &amp;lt; 0.001) times in the group with AMH&amp;lt;1.1 ng/ml, compared to the group with AMH &amp;gt; =1.1 ng/ml after adjustment. In the analysis of UK Biobank data, one more spontaneous miscarriage history would lead to 0.02 (95% CI: 0.00-0.03, p = 0.032) years earlier in the standardized ANM after adjustment. Participants with &amp;gt; =2 spontaneous miscarriage history would have 0.05 (95% CI:0.01-0.08, p = 0.011) years earlier in standardized ANM, compared with participants with &amp;lt; =1 spontaneous miscarriage. In the analysis of GWAS summary statistics, we found that there were 11,736,532 single nucleotide polymorphisms (SNPs) tested by both GWAS studies of ANM and miscarriage. In the stratified conditional Q-Q plot, successive leftward deflections did not show which suggested that an overall genetic overlap between miscarriage and ANM was not observed. In the analysis of the individual-level genotype data, we identified five shared genetic risk loci that affect both miscarriage and menopause. They were all novel risk genes for spontaneous miscarriage. Of these five genes, SYCP2L, TP63, GSPT1, and RIF1 were associated with congenital malformations and PRRC2A was associated with autoimmune disease, which are factors that possibly play a role in the occurrence of miscarriage. Limitations, reasons for caution The UKB epidemiology data and genetic data were restricted to participants of European ancestry. Further studies are needed in non-white populations. Second, the predictive value of GWAS summary data for miscarriage may be limited which results in a null finding in the conditional Q-Q plot. Wider implications of the findings Our findings could be of interest to IVF clinicians in patient counseling regarding the prognosis of IVF treatment and genetic counseling for miscarriage. Our findings encourage further research focus on the shared genetic architecture and common pathophysiological basis of diminished ovarian reserve and miscarriage which may bring new therapeutic opportunities. Trial registration number not applicable

A systematical association analysis of 25 common virus infection and genetic susceptibility of COVID-19 infection

ObjectivesPrevious studies identified a number of diseases were associated with 2019 coronavirus disease (COVID-19). However, the associations between these diseases related viral infections and COVID-19 remains unknown now. MethodsIn this study, we utilized single nucleotide polymorphisms (SNPs) related to COVID-19 from genome-wide association study (GWAS) and individual-level genotype data from the UK biobank to calculate polygenic risk scores (PRS) of 487,409 subjects for eight COVID-19 clinical phenotypes. Then, multiple logistic regression models were established to assess the correlation between serological measurements (positive/negative) of 25 viruses and the PRS of eight COVID-19 clinical phenotypes. And we performed stratified analyses by age and gender. ResultsIn whole population, we identified 12 viruses associated with the PRS of COVID-19 clinical phenotypes, such as VZV seropositivity for Varicella Zoster Virus (Unscreened/Exposed_Negative: β = 0.1361, P = 0.0142; Hospitalized/Unscreened: β = 0.1167, P = 0.0385) and MCV seropositivity for Merkel Cell Polyomavirus (Unscreened/Exposed_Negative: β = −0.0614, P = 0.0478). After age stratification, we identified seven viruses associated with the PRS of eight COVID-19 clinical phenotypes in the age < 65 years group. After gender stratification, we identified five viruses associated with the PRS of eight COVID-19 clinical phenotypes in the women group. ConclusionOur study findings suggest that the genetic susceptibility to different COVID-19 clinical phenotypes is associated with the infection status of various common viruses.

Combined effects of inflammation and coronavirus disease 2019 (COVID-19) on the risks of anxiety and depression: A cross-sectional study based on UK Biobank.

Infection-induced perturbation of immune homeostasis could promote psychopathology. Psychiatric sequelae have been observed after previous coronavirus outbreaks. However, limited studies were conducted to explore the potential interaction effects of inflammation and coronavirus disease 2019 (COVID-19) on the risks of anxiety and depression. In this study, first, polygenic risk scores (PRS) were calculated for eight COVID-19 clinical phenotypes using individual-level genotype data from the UK Biobank. Then, linear regression models were developed to assess the effects of COVID-19 PRS, C-reactive protein (CRP), systemic immune inflammation index (SII), and their interaction effects on the Generalized Anxiety Disorder-7 (GAD-7, 104 783 individuals) score and the Patient Health Questionnaire-9 (PHQ-9, 104 346 individuals) score. Several suggestive interactions between inflammation factors and COVID-19 clinical phenotypes were detected for PHQ-9 score, such as CRP/SII × Hospitalized/Not_Hospitalized in women group and CRP × Hospitalized/Unscreened in age >65 years group. For GAD-7 score, we also found several suggestive interactions, such as CRP × Positive/Unscreened in the age ≤65 years group. Our results suggest that not only COVID-19 and inflammation have important effects on anxiety and depression but also the interactions of COVID-19 and inflammation have serious risks for anxiety and depression.

Evaluating the Genetic Effects of Gut Microbiota on the Development of Neuroticism and General Happiness: A Polygenic Score Analysis and Interaction Study Using UK Biobank Data.

Limited efforts have been invested in exploring the interaction effects between genetic factors and gut microbiota on neuroticism and general happiness. The polygenic risk scores (PRS) of gut microbiota were calculated from individual-level genotype data of the UK Biobank cohort. Linear regression models were then used to assess the associations between individual PRS of gut microbiota and mental traits and interaction analysis was performed by PLINK2.0. KOBAS-i was used to conduct gene ontology (GO) enrichment analysis of the identified genes. We observed suggestive significant associations between neuroticism and PRS for the genus Bifidobacterium (rank-normal transformation, RNT) (beta = -1.10, P = 4.16 × 10-3) and the genus Desulfovibrio (RNT) (beta = 0.54, P = 7.46 × 10-3). PRS for the genus Bifidobacterium (hurdle binary, HB) (beta = 1.99, P = 5.24 × 10-3) and the genus Clostridium (RNT) (beta = 1.26, P = 9.27 × 10-3) were found to be suggestive positively associated with general happiness. Interaction analysis identified several significant genes that interacted with gut microbiota, such as RORA (rs575949009, beta = -45.00, P = 1.82 × 10-9) for neuroticism and ASTN2 (rs36005728, beta = 19.15, P = 3.37 × 10-8) for general happiness. Our study results support the genetic effects of gut microbiota on the development of neuroticism and general happiness.

Abstract P038: Transcriptome-wide association study identifies novel genes associated with bladder cancer risk

Abstract Background: Genome-wide association studies (GWAS) have identified several common germline variants associated with bladder cancer risk. However, the genetic architecture of bladder cancer risk is still largely unknown. Therefore, we conducted a transcriptome-wide association study (TWAS) to identify gene expression patterns associated with bladder cancer risk. Methods: Individual-level genotype data from bladder cancer cases and controls was accessed from dbGaP (phs000346.v2.p2). We included 3,630 muscle invasive and non-muscle invasive bladder cancer cases and 3,356 controls from the Spanish Bladder Cancer Study (1,145 cases and 1,083 controls) and the New England Bladder Cancer Study (Maine and Vermont, 2,485 cases and 2,273 controls) with imputed genotype data for 22 million single-nucleotide polymorphisms (SNPs). We used PrediXcan to predict gene expression in whole blood using reference data from the Depression Genes and Networks (DGN). Covariate-adjusted logistic regression was used to estimate the associations between gene expression and bladder cancer risk. Results were validated using data from 280 non-muscle invasive bladder cancer cases and 353 controls of the New Hampshire Bladder Cancer Study. Results: We identified five genes associated with bladder cancer risk, including a previously reported locus (1p13.3: GSTM1) and four other loci (4p16.3: FAM53A, 7q22.3: RP11-325F22.2, 8p23.1: RP11-297N6.4, 11q24.1: JHY). Among them, higher predicted expression of GSTM1 in whole blood was associated with lower risk of bladder cancer (Discovery dataset OR = 0.78, 95% CI = 0.67, 0.92; Validation dataset OR = 0.58, 95% CI = 0.34, 0.98). Higher predicted expression of FAM53A (Discovery OR = 1.34, 95% CI = 1.08, 1.68; Validation OR = 3.79, 95% CI = 1.76, 8.29), RP11-325F22.2 (Discovery OR = 1.34, 95% CI = 1.09, 1.65; Validation OR = 2.11, 95% CI = 1.05, 4.30), RP11-297N6.4 (Discovery OR = 1.78, 95% CI = 1.22, 2.60; Validation OR = 6.32, 95% CI = 1.74, 23.34) and JHY (Discovery OR = 1.94, 95% CI = 1.30, 2.91; Validation OR = 6.78, 95% CI = 1.85, 25.76) in whole blood was associated with higher risk of bladder cancer. Conclusion: Our TWAS identified five genes associated with bladder cancer risk: GSTM1, a previously reported locus associated with bladder cancer risk, is related to metabolic detoxification. Four novel loci were identified: FAM53A (related to neural tube development and was previously reported to be related to breast cancer progression), JHY (related to axoneme structure), RP11-325F22.2 (lincRNA), and RP11-297N6.4 (lncRNA). These loci require further validation and may help to reveal the underlying mechanism of the relationship between gene expression and bladder cancer risk. Citation Format: Siting Li, Jiang Gui, Margaret R. Karagas, Michael N. Passarelli. Transcriptome-wide association study identifies novel genes associated with bladder cancer risk. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P038.

An unbiased kinship estimation method for genetic data analysis

Accurate estimate of relatedness is important for genetic data analyses, such as heritability estimation and association mapping based on data collected from genome-wide association studies. Inaccurate relatedness estimates may lead to biased heritability estimations and spurious associations. Individual-level genotype data are often used to estimate kinship coefficient between individuals. The commonly used sample correlation-based genomic relationship matrix (scGRM) method estimates kinship coefficient by calculating the average sample correlation coefficient among all single nucleotide polymorphisms (SNPs), where the observed allele frequencies are used to calculate both the expectations and variances of genotypes. Although this method is widely used, a substantial proportion of estimated kinship coefficients are negative, which are difficult to interpret. In this paper, through mathematical derivation, we show that there indeed exists bias in the estimated kinship coefficient using the scGRM method when the observed allele frequencies are regarded as true frequencies. This leads to negative bias for the average estimate of kinship among all individuals, which explains the estimated negative kinship coefficients. Based on this observation, we propose an unbiased estimation method, UKin, which can reduce kinship estimation bias. We justify our improved method with rigorous mathematical proof. We have conducted simulations as well as two real data analyses to compare UKin with scGRM and three other kinship estimating methods: rGRM, tsGRM, and KING. Our results demonstrate that both bias and root mean square error in kinship coefficient estimation could be reduced by using UKin. We further investigated the performance of UKin, KING, and three GRM-based methods in calculating the SNP-based heritability, and show that UKin can improve estimation accuracy for heritability regardless of the scale of SNP panel.

TrustGWAS: A full-process workflow for encrypted GWAS using multi-key homomorphic encryption and pseudorandom number perturbation.

The statistical power of genome-wide association studies (GWASs) is affected by the effective sample size. However, the privacy and security concerns associated with individual-level genotype data pose great challenges for cross-institutional cooperation. The full-process cryptographic solutions are in demand but havenot been covered, especially the essential principal-component analysis (PCA). Here, we present TrustGWAS, a complete solution for secure, large-scale GWAS, recapitulating gold standard results against PLINK without compromising privacy and supporting basic PLINK steps including quality control, linkage disequilibrium pruning, PCA, chi-square test, Cochran-Armitage trend test, covariate-supported logistic regression and linear regression, and their sequential combinations. TrustGWAS leverages pseudorandom number perturbations for PCA and multiparty scheme of multi-key homomorphic encryption for all other modules. TrustGWAS can evaluate 100,000 individuals with 1 million variants and complete QC-LD-PCA-regression workflow within 50 h. We further successfully discover gene loci associated with fasting blood glucose, consistent with the findings of the ChinaMAP project.

Control for population stratification in genetic association studies based on GWAS summary statistics.

Over the past years, genome-wide association studies (GWAS) have generated a wealth of new information. Summary data from many GWAS are now publicly available, promoting the development of many statistical methods for association studies based on GWAS summary statistics, which avoids the increasing challenges associated with individual-level genotype and phenotype data sharing. However, for population-based association studies such as GWAS, it has been long recognized that population stratification can seriously confound association results. For large GWAS, it is very likely that there exist population stratification and cryptic relatedness, which will result in inflated Type I error in association testing. Although many methods have been developed to control for population stratification, only two of these approaches can be used to control population stratification without individual-level data: one is based on genomic control (GC) and the other one is based on linkage disequilibrium score regression (LDSC). However, the performance of these two approaches is currently unknown. In this study, we use extensive simulation studies including populations with subpopulations, spatially structured populations, and populations with cryptic relatedness to compare the performance of these two approaches to control for population stratification using only GWAS summary statistics without individual-level data. Data sets from the genetic analysis workshop 19and UK Biobank are also used to evaluate these two approaches. We demonstrate that the intercept of LDSC can be used as a more accurate correction factor than GC. The results from this study will provide very useful information for researchers using GWAS summary statistics while trying to control for population stratification.

Transcriptome-wide association study of coronary artery disease identifies novel susceptibility genes

The majority of risk loci identified by genome-wide association studies (GWAS) are in non-coding regions, hampering their functional interpretation. Instead, transcriptome-wide association studies (TWAS) identify gene-trait associations, which can be used to prioritize candidate genes in disease-relevant tissue(s). Here, we aimed to systematically identify susceptibility genes for coronary artery disease (CAD) by TWAS. We trained prediction models of nine CAD-relevant tissues using EpiXcan based on two genetics-of-gene-expression panels, the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) and the Genotype-Tissue Expression (GTEx). Based on these prediction models, we imputed gene expression of respective tissues from individual-level genotype data on 37,997 CAD cases and 42,854 controls for the subsequent gene-trait association analysis. Transcriptome-wide significant association (i.e. P < 3.85e−6) was observed for 114 genes. Of these, 96 resided within previously identified GWAS risk loci and 18 were novel. Stepwise analyses were performed to study their plausibility, biological function, and pathogenicity in CAD, including analyses for colocalization, damaging mutations, pathway enrichment, phenome-wide associations with human data and expression-traits correlations using mouse data. Finally, CRISPR/Cas9-based gene knockdown of two newly identified TWAS genes, RGS19 and KPTN, in a human hepatocyte cell line resulted in reduced secretion of APOB100 and lipids in the cell culture medium. Our CAD TWAS work (i) prioritized candidate causal genes at known GWAS loci, (ii) identified 18 novel genes to be associated with CAD, and iii) suggested potential tissues and pathways of action for these TWAS CAD genes.

Ancestral genomic contributions to complex traits in contemporary Europeans

The contemporary European genetic makeup formed in the last 8,000 years when local Western Hunter-Gatherers (WHGs) mixed with incoming Anatolian Neolithic farmers and Pontic Steppe pastoralists.1-3 This encounter combined genetic variants with distinct evolutionary histories and, together with new environmental challenges faced by the post-Neolithic Europeans, unlocked novel adaptations.4 Previous studies inferred phenotypes in these source populations, using either a few single loci5-7 or polygenic scores based on genome-wide association studies,8-10 and investigated the strength and timing of natural selection on lactase persistence or height, among others.6,11,12 However, how ancient populations contributed to present-day phenotypic variation is poorly understood. Here, we investigate how the unique tiling of genetic variants inherited from different ancestral components drives the complex traits landscape of contemporary Europeans and quantify selection patterns associated with these components. Using matching individual-level genotype and phenotype data for 27 traits in the Estonian biobank13 and genotype data directly from the ancient source populations, we quantify the contributions from each ancestry to present-day phenotypic variation in each complex trait. We find substantial differences in ancestry for eye and hair color, body mass index, waist/hip circumferences, and their ratio, height, cholesterol levels, caffeine intake, heart rate, and age at menarche. Furthermore, we find evidence for recent positive selection linked to four of these traits and, in addition, sleep patterns and blood pressure. Our results show that these ancient components were differentiated enough to contribute ancestry-specific signatures to the complex trait variability displayed by contemporary Europeans.

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

A summary-statistics-based approach to examine the role of serotonin transporter promoter tandem repeat polymorphism in psychiatric phenotypes

In genetic studies of psychiatric disorders in the pre-genome-wide association study (GWAS) era, one of the most commonly studied loci is the serotonin transporter (SLC6A4) promoter polymorphism, a 43-base-pair insertion/deletion polymorphism in the promoter region (5-HTTLPR). The genetic association signals between 5-HTTLPR and psychiatric phenotypes, however, have been inconsistent across many studies. Since the polymorphism cannot be tested via available SNP arrays, we had previously proposed an efficient machine learning algorithm to predict the genotypes of 5-HTTLPR based on the genotypes of eight nearby SNPs, which requires access to individual-level genotype and phenotype data. To utilize the advantage of publicly available GWAS summary statistics obtained from studies with very large sample sizes, we develop a GWAS summary-statistics-based approach for testing the variable number of tandem repeat (VNTR) associations with various phenotypes. We first cross-verify the accuracy of the summary-statistics-based approach for 61 phenotypes in the UK Biobank. Since we observed a strong similarity between the predicted individual-level 5-HTTLPR genotype-based approach and the summary-statistics-based approach, we applied our method to the available neurobehavioral GWAS summary statistics data obtained from large-scale GWAS. We found no genome-wide significant evidence for association between 5-HTTLPR and any of the neurobehavioral traits. We did observe, however, genome-wide significant evidence for association between this locus and human adult height, BMI, and total cholesterol. Our summary-statistics-based approach provides a systematic way to examine the role of VNTRs and related types of genetic polymorphisms in disease risk and trait susceptibility of phenotypes for which large-scale GWAS summary statistics data are available.