Exact Inference for Hardy-Weinberg Proportions with Missing Genotypes: Single and Multiple Imputation

Integrating whole-genome sequencing with multi-omic data reveals the impact of structural variants on gene regulation in the human brain

Structural variants (SVs), which are genomic rearrangements of more than 50 base pairs, are an important source of genetic diversity and have been linked to many diseases. However, it remains unclear how they modulate human brain function and disease risk. Here we report 170,996 SVs discovered using 1,760 short-read whole genomes from aged adults and individuals with Alzheimer's disease. By applying quantitative trait locus (SV-xQTL) analyses, we quantified the impact of cis-acting SVs on histone modifications, gene expression, splicing and protein abundance in postmortem brain tissues. More than 3,200 SVs were associated with at least one molecular phenotype. We found reproducibility of 65-99% SV-eQTLs across cohorts and brain regions. SV associations with mRNA and proteins shared the same direction of effect in more than 87% of SV-gene pairs. Mediation analysis showed ~8% of SV-eQTLs mediated by histone acetylation and ~11% by splicing. Additionally, associations of SVs with progressive supranuclear palsy identified previously known and novel SVs.

Single nucleotide polymorphisms in PNPLA3, ADAR-1 and IFIH1 are associated with advanced liver fibrosis in patients co-infected with HIV-1//hepatitis C virus

OBJECTIVE

Nonalcoholic fatty liver disease (NAFLD), insulin resistance and liver fibrosis are prevalent in individuals co-infected with HIV type 1 (HIV-1)/hepatitis C virus (HCV), even after HCV eradication. Our aim was to evaluate single nucleotide polymorphisms (SNPs) associated with advanced liver fibrosis in HIV-1/HCV co-infected patients.

DESIGN/METHODS

In a cohort of 102 participants, we genotyped 16 SNPs in 10 genes previously associated with NAFLD and the innate immune response and correlated the genotypes with liver fibrosis and fat accumulation.

RESULTS

Multinomial logistic regression analysis identified three metabolic parameters that were significantly associated with advanced liver fibrosis (stage F3-F4): albumin [odds ratio (OR) 0.80, 95% confidence interval (CI) 0.69-0.91, P = 0.001], percentage of visceral fat area (PVFA) (OR 1.06, 95% CI 1.01-1.12, P = 0.03) and BMI (OR 1.47, 95% CI 1.22-1.77, P < 0.0001). After adjustment for sex, albumin, PVFA and BMI, we found that three SNPs were significantly associated with advanced fibrosis, one each in PNPLA3/rs738409 (P = 0.016), ADAR-1/rs1127313 (P = 0.029) and IFIH1/rs1990760 (P = 0.033).

CONCLUSION

Our results indicate that genotyping for these SNPs can be a useful predictive tool for liver fibrosis progression and liver fat accumulation in patients co-infected with HIV-1/HCV.

Impact of FOXP1 rs2687201 genetic variant on the susceptibility to HCV-related hepatocellular carcinoma in Egyptians

Hepatocellular carcinoma (HCC) constitutes a challenging health problem in Egypt due to the high incidence of hepatitis C virus (HCV) infection. Improved understanding of genetic mechanisms underlying the individual predisposition to HCC will lead to enhancements in the early diagnosis, treatment, and prevention of this disease. Transcription factor forkhead box P1 (FOXP1) is involved in the cellular processes of proliferation, differentiation, metabolism, and longevity. In addition, it has been implicated in hepatic tumorigenesis. The present study explored the association of C/A single-nucleotide polymorphism in the FOXP1 gene (rs2687201) with HCC susceptibility in HCV Egyptian patients. The study included 108 patients with HCV-dependant HCC, 86 HCV patients, and 80- age and gender-matched healthy controls. rs2687201 genotyping was performed by allelic discrimination method using TaqMan real-time PCR assays while FOXP1 gene expression and protein level were determined using qRT-PCR and enzyme-linked immunoassay, respectively. Our results revealed a significant association between FOXP1 rs2687201 and HCC risk where (A) allele was significantly more frequent in patients with HCC compared to controls (odds ratio [OR]: 1.88, 95% confidence interval [CI]: 1.17-3.04, p = 0.01) and to HCV patients (OR: 1.85, 95% CI: 1.62-2.94, p = 0.012). Furthermore, FOXP1 gene and protein expression levels were remarkably higher in (CA + AA) than in CC genotype carriers in a dominant model. The (CA + AA) genotype displayed a significantly shorter overall survival than the CC genotype in HCC patients. In conclusion, FOXP1 gene polymorphism rs2687201 is significantly associated with HCC, but not with HCV infection, in Egyptian patients.

Pseudoreplication in genomics-scale datasets

In genomics-scale datasets, loci are closely packed within chromosomes and hence provide correlated information. Averaging across loci as if they were independent creates pseudoreplication, which reduces the effective degrees of freedom (df') compared to the nominal degrees of freedom, df. This issue has been known for some time, but consequences have not been systematically quantified across the entire genome. Here we measured pseudoreplication (quantified by the ratio df'/df) for a common metric of genetic differentiation (F_ST ) and a common measure of linkage disequilibrium between pairs of loci (r² ). Based on data simulated using models (SLiM and msprime) that allow efficient forward-in-time and coalescent simulations while precisely controlling population pedigrees, we estimated df' and df'/df by measuring the rate of decline in the variance of mean F_ST and mean r² as more loci were used. For both indices, df' increases with N_e and genome size, as expected. However, even for large N_e and large genomes, df' for mean r² plateaus after a few thousand loci, and a variance components analysis indicates that the limiting factor is uncertainty associated with sampling individuals rather than genes. Pseudoreplication is less extreme for F_ST , but df'/df ≤0.01 can occur in datasets using tens of thousands of loci. Commonly-used block-jackknife methods consistently overestimated var(F_ST ), producing very conservative confidence intervals. Predicting df' based on our modeling results as a function of N_e , L, S, and genome size provides a robust way to quantify precision associated with genomics-scale datasets.

Genome-Wide Association Study of Metamizole-Induced Agranulocytosis in European Populations

Agranulocytosis is a rare yet severe idiosyncratic adverse drug reaction to metamizole, an analgesic widely used in countries such as Switzerland and Germany. Notably, an underlying mechanism has not yet been fully elucidated and no predictive factors are known to identify at-risk patients. With the aim to identify genetic susceptibility variants to metamizole-induced agranulocytosis (MIA) and neutropenia (MIN), we conducted a retrospective multi-center collaboration including cases and controls from three European populations. Association analyses were performed using genome-wide genotyping data from a Swiss cohort (45 cases, 191 controls) followed by replication in two independent European cohorts (41 cases, 273 controls) and a joint discovery meta-analysis. No genome-wide significant associations (p < 1 × 10⁻⁷) were observed in the Swiss cohort or in the joint meta-analysis, and no candidate genes suggesting an immune-mediated mechanism were identified. In the joint meta-analysis of MIA cases across all cohorts, two candidate loci on chromosome 9 were identified, rs55898176 (OR = 4.01, 95%CI: 2.41–6.68, p = 1.01 × 10⁻⁷) and rs4427239 (OR = 5.47, 95%CI: 2.81–10.65, p = 5.75 × 10⁻⁷), of which the latter is located in the SVEP1 gene previously implicated in hematopoiesis. This first genome-wide association study for MIA identified suggestive associations with biological plausibility that may be used as a stepping-stone for post-GWAS analyses to gain further insight into the mechanism underlying MIA.

Does Bonferroni correction "rescue" the deviation from Hardy-Weinberg equilibrium?

The application of Bonferroni correction (BC) has been constantly controversial; nevertheless, in forensic population genetics research, it is common to apply it to Hardy-Weinberg equilibrium (HWE) tests referring to multiple loci. This letter aimed to discuss the problems of applying BC to HWE tests involving multiple loci by surveying population genetics research studies published over the last 10 years (2009-2019) from two major forensic genetic journals: Forensic Science International: Genetics (FSIG) and the International Journal of Legal Medicine (IJLM). The results showed that there was no uniform standard of whether to apply BC to HWE tests or not, and researchers commonly did not provide any explanation for the observation of deviations from HWE. Despite its widespread use in population genetics, BC may not guarantee a prudent result due to an irrelevant null hypothesis, reluctance to reject the null hypothesis, different interpretations of identical p-values, and inflated type Ⅱ error. We recommended a notable two-step approach suggested by Waples to evaluate the results of HWE tests: 1) identifying causes of departures from HWE and 2) evaluating the biological consequences of HW departures. In addition, for forensic researchers, we suggested that if a certain degree of deviation from HWE does occur, the first step to take should involve checking the technique and genotyping results carefully rather than recklessly using BC. In conclusion, according to the purpose of forensic population research, applying BC to HWE tests is unnecessary; rather, an unadjusted α should be used. BC does not "rescue" the deviation from HWE. To "rescue" it indeed, directly discussing the possible explanation for each departure from HWE and simply describing what has been done sequentially and why should be enough for readers to reach a reasonable conclusion even without the help of Bonferroni methods.

Efficient statistical inference for a parallel study with missing data by using an exact method

In a parallel group study comparing a new treatment with a standard of care, missing data often occur for various reasons. When the outcome is binary, the data from such studies can be summarized into a 2 × 3 contingency table, with the missing observations in the last column. When the missingness is neither related to the outcome of interest nor related to other outcomes from the study but it is covariate dependent with the sole covariate being treatment, this type of missing data mechanism is considered as missing at random. In 2016, Tian et al.  proposed three statistics to test the hypothesis that the response rate is equivalent for a parallel group study with missing data. The asymptotic limiting distributions of these test statistics were used for statistical inference. However, asymptotic approaches for testing proportions generally do not have satisfactory performance with regard to type I error rate control for a clinical trial with the sample size from small to medium. For this reason, we consider an exact approach based on maximization to provide valid and efficient statistical inference for a parallel group study with missing data. Exact approaches can guarantee the type I error rate and they are computationally feasible in this setting. We conduct extensive numerical studies to compare the performance of the exact approach based on the three statistics for a one-sided hypothesis testing problem. We conclude that the exact approach based on the likelihood ratio statistic is more powerful than the exact approach based on the other two statistics. Two real clinical trial data sets are used to illustrate the application of the proposed exact approach.

Fast score test with global null estimation regardless of missing genotypes

In genome-wide association studies (GWASs) for binary traits (or case-control samples) in the presence of covariates to be adjusted for, researchers often use a logistic regression model to test variants for disease association. Popular tests include Wald, likelihood ratio, and score tests. For likelihood ratio test and Wald test, maximum likelihood estimation (MLE), which requires iterative procedure, must be computed for each single nucleotide polymorphism (SNP). In contrast, the score test only requires MLE under the null model, being lower in computational cost than other tests. Usually, genotype data include missing genotypes because of assay failures. It loses computational efficiency in the conventional score test (CST), which requires null estimation by excluding individuals with missing genotype for each SNP. In this study, we propose two new score tests, called PM1 and PM2, that use a single global null estimator for all SNPs regardless of missing genotypes, thereby enabling faster computation than CST. We prove that PM2 and CST have an equivalent asymptotic power and that the power of PM1 is asymptotically lower than that of PM2. We evaluate the performance of the proposed methods in terms of type I error rates and power by simulation studies and application to real GWAS data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI), confirming our theoretical results. ADNI-GWAS application demonstrated that the proposed score tests improve computational speed about 6–18 times faster than the existing tests, CST, Wald tests and likelihood ratio tests. Our score tests are general and applicable to other regression models.

On the testing of Hardy-Weinberg proportions and equality of allele frequencies in males and females at biallelic genetic markers

Standard statistical tests for equality of allele frequencies in males and females and tests for Hardy‐Weinberg equilibrium are tightly linked by their assumptions. Tests for equality of allele frequencies assume Hardy‐Weinberg equilibrium, whereas the usual chi‐square or exact test for Hardy‐Weinberg equilibrium assume equality of allele frequencies in the sexes. In this paper, we propose ways to break this interdependence in assumptions of the two tests by proposing an omnibus exact test that can test both hypotheses jointly, as well as a likelihood ratio approach that permits these phenomena to be tested both jointly and separately. The tests are illustrated with data from the 1000 Genomes project.

Friends and family: A software program for identification of unrelated individuals from molecular marker data

The identification of related and unrelated individuals from molecular marker data is often difficult, particularly when no pedigree information is available and the data set is large. High levels of relatedness or inbreeding can influence genotype frequencies and thus genetic marker evaluation, as well as the accurate inference of hidden genetic structure. Identification of related and unrelated individuals is also important in breeding programmes, to inform decisions about breeding pairs and translocations. We present Friends and Family, a Windows executable program with a graphical user interface that identifies unrelated individuals from a pairwise relatedness matrix or table generated in programs such as coancestry and genalex. Friends and Family outputs a list of samples that are all unrelated to each other, based on a user-defined relatedness cut-off value. This unrelated data set can be used in downstream analyses, such as marker evaluation or inference of genetic structure. The results can be compared to that of the full data set to determine the effect related individuals have on the analyses. We demonstrate one of the applications of the program: how the removal of related individuals altered the Hardy-Weinberg equilibrium test outcome for microsatellite markers in an empirical data set. Friends and Family can be obtained from https://github.com/DeondeJager/Friends-and-Family.

A genome-wide study of Hardy-Weinberg equilibrium with next generation sequence data

Statistical tests for Hardy–Weinberg equilibrium have been an important tool for detecting genotyping errors in the past, and remain important in the quality control of next generation sequence data. In this paper, we analyze complete chromosomes of the 1000 genomes project by using exact test procedures for autosomal and X-chromosomal variants. We find that the rate of disequilibrium largely exceeds what might be expected by chance alone for all chromosomes. Observed disequilibrium is, in about 60% of the cases, due to heterozygote excess. We suggest that most excess disequilibrium can be explained by sequencing problems, and hypothesize mechanisms that can explain exceptional heterozygosities. We report higher rates of disequilibrium for the MHC region on chromosome 6, regions flanking centromeres and p-arms of acrocentric chromosomes. We also detected long-range haplotypes and areas with incidental high disequilibrium. We report disequilibrium to be related to read depth, with variants having extreme read depths being more likely to be out of equilibrium. Disequilibrium rates were found to be 11 times higher in segmental duplications and simple tandem repeat regions. The variants with significant disequilibrium are seen to be concentrated in these areas. For next generation sequence data, Hardy–Weinberg disequilibrium seems to be a major indicator for copy number variation.

A POWERFUL METHOD FOR INCLUDING GENOTYPE UNCERTAINTY IN TESTS OF HARDY-WEINBERG EQUILIBRIUM

The use of posterior probabilities to summarize genotype uncertainty is pervasive across genotype, sequencing and imputation platforms. Prior work in many contexts has shown the utility of incorporating genotype uncertainty (posterior probabilities) in downstream statistical tests. Typical approaches to incorporating genotype uncertainty when testing Hardy-Weinberg equilibrium tend to lack calibration in the type I error rate, especially as genotype uncertainty increases. We propose a new approach in the spirit of genomic control that properly calibrates the type I error rate, while yielding improved power to detect deviations from Hardy-Weinberg Equilibrium. We demonstrate the improved performance of our method on both simulated and real genotypes.

Testing for Hardy-Weinberg equilibrium at biallelic genetic markers on the X chromosome

Testing genetic markers for Hardy–Weinberg equilibrium (HWE) is an important tool for detecting genotyping errors in large-scale genotyping studies. For markers at the X chromosome, typically the χ² or exact test is applied to the females only, and the hemizygous males are considered to be uninformative. In this paper we show that the males are relevant, because a difference in allele frequency between males and females may indicate HWE not to hold. The testing of markers on the X chromosome has received little attention, and in this paper we lay down the foundation for testing biallelic X-chromosomal markers for HWE. We develop four frequentist statistical test procedures for X-linked markers that take both males and females into account: the χ² test, likelihood ratio test, exact test and permutation test. Exact tests that include males are shown to have a better Type I error rate. Empirical data from the GENEVA project on venous thromboembolism is used to illustrate the proposed tests. Results obtained with the new tests differ substantially from tests that are based on female genotype counts only. The new tests detect differences in allele frequencies and seem able to uncover additional genotyping error that would have gone unnoticed in HWE tests based on females only.