Statistical inference for Hardy-Weinberg proportions in the presence of missing genotype information

GPC3 gene expression and allelic discrimination of FZD7 gene in Egyptian patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide, and especially in Egypt. Early diagnosis of HCC greatly improves the survival and prognosis of patients. Low sensitivity and specificity of alpha-fetoprotein (AFP) has led to the demand for novel biomarkers of HCC. The aim of the present study was to evaluate the validity of frizzled-7 (FZD7) and glypican-3 (GPC3) gene expression as potential biomarkers for HCC early diagnosis, and to investigate the association between FZD7 rs2280509 polymorphism and HCC risk.

Materials and methods

Quantification of FZD7 and GPC3 gene expression by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay, and genotyping FZD 7 (rs2280509 SNP) gene polymorphism using RT-PCR.

Results

The current results revealed that FZD7 gene expression had a greater area under the curve (AUC) for identifying HCC than GPC3 gene expression and AFP levels. The combination of the three markers as a panel showed a better diagnostic performance with a greater AUC than any of the single markers alone (p < 0.05). The FZD7 rs2280509 polymorphism (CT) was found to be significantly associated with an increased risk of HCC. The CT genotype and T allele were significantly more prevalent in the HCC group compared to either the cirrhosis (p = 0.03) or control groups (p = 0.0009 and 0.002; respectively).

Conclusion

FZD7 and GPC3 gene expressions have a complementary role in early HCC detection, with a greater diagnostic sensitivity and accuracy than AFP. In addition, FZD7 rs2280509 polymorphism is significantly associated with an increased risk of HCC in the Egyptian population.

A test for deviations from expected genotype frequencies on the X chromosome for sex-biased admixed populations

Genome-wide scans for deviations from expected genotype frequencies, as determined by the Hardy-Weinberg equilibrium (HWE), are commonly applied to detect genotyping errors and deviations from random mating. In contrast to the autosomes, genotype frequencies on the X chromosome do not reach HWE within a single generation. Instead, if allele frequencies in males and females initially differ, they oscillate for a few generations toward equilibrium. Allele frequency differences between the sexes are expected in populations that have experienced recent sex-biased admixture, namely, their male and female founders differed in ancestry. Sex-biased admixture does not allow testing for HWE on X, because deviations are naturally expected, even under random mating (post admixture) and error-free genotyping. In this paper, we develop a likelihood ratio test and a χ² test to detect deviations from expected genotype frequencies on X, beyond natural deviations due to sex-biased admixture. We demonstrate by simulations that our tests are powerful for detecting deviations due to non-random mating, while at the same time they do not reject the null under historical sex-biased admixture and random mating thereafter. We also demonstrate that when applied to 1000 Genomes project populations, our likelihood ratio test rejects fewer SNPs than other tests, but we describe limitations in the interpretation of the results.

rs2267531, a promoter SNP within glypican-3 gene in the X chromosome, is associated with hepatocellular carcinoma in Egyptians

Hepatocellular carcinoma (HCC) is a major health concern in Egypt owing to the high prevalence of hepatitis C virus (HCV) infection. HCC incidence is characterized by obvious male predominance, yet the molecular mechanisms behind this gender bias are still unidentified. Functional variations in X-linked genes have more impact on males than females. Glypican-3 (GPC3) gene, located in the Xq26 region, has lately emerged as being potentially implicated in hepatocellular carcinogenesis. The current study was designed to examine the association of -784 G/C single nucleotide polymorphism (SNP) in GPC3 promoter region (rs2267531) with HCC susceptibility in male and female Egyptian HCV patients. Our results revealed a significant association between GPC3 and HCC risk in both males and females, evidenced by higher C allele and CC/C genotype frequencies in HCC patients when compared to controls. However, no such association was found when comparing HCV patients to controls. Moreover, GPC3 gene and protein expression levels were significantly higher in CC/C than in GG/G genotype carriers in males and females. The CC/C genotype exhibited a significant shorter overall survival than GG/G genotype in HCC patients. In conclusion, GPC3 rs2267531 on the X chromosome is significantly associated with HCC, but not with HCV infection, in the Egyptian population.

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.

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.

Observed and expected frequencies of structural hemoglobin variants in newborn screening surveys in Africa and the Middle East: deviations from Hardy-Weinberg equilibrium

PURPOSE

Our objective was to compare observed and expected genotype proportions from newborn screening surveys of structural hemoglobin variants.

METHODS

We conducted a systematic review of newborn screening surveys of hemoglobins S and C in Africa and the Middle East. We compared observed frequencies to those expected assuming Hardy-Weinberg equilibrium (HWE). Significant deviations were identified by an exact test. The fixation index FIS was calculated to assess excess homozygosity. We compared newborn estimates corrected and uncorrected for HWE deviations using demographic data.

RESULTS

Sixty samples reported genotype counts for hemoglobin variants in Africa and the Middle East. Observed and expected counts matched in 27%. The observed number of sickle cell anemia (SCA) individuals was higher than expected in 42 samples, reaching significance (P < 0.05) in 24. High FIS values were common across the study regions. The estimated total number of newborns with SCA, corrected based on FIS, was 33,261 annual births instead of 24,958 for the 38 samples across sub-Saharan Africa and 1,109 annual births instead of 578 for 12 samples from the Middle East.

CONCLUSION

Differences between observed and expected genotype frequencies are common in surveys of hemoglobin variants in the study regions. Further research is required to identify and quantify factors responsible for such deviations. Estimates based on HWE might substantially underestimate the annual number of SCA-affected newborns (up to one-third in sub-Saharan Africa and one-half in the Middle East).

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

This paper addresses the issue of exact-test based statistical inference for Hardy-Weinberg equilibrium in the presence of missing genotype data. Missing genotypes often are discarded when markers are tested for Hardy-Weinberg equilibrium, which can lead to bias in the statistical inference about equilibrium. Single and multiple imputation can improve inference on equilibrium. We develop tests for equilibrium in the presence of missingness by using both inbreeding coefficients (or, equivalently, χ(2) statistics) and exact p-values. The analysis of a set of markers with a high missing rate from the GENEVA project on prematurity shows that exact inference on equilibrium can be altered considerably when missingness is taken into account. For markers with a high missing rate (>5%), we found that both single and multiple imputation tend to diminish evidence for Hardy-Weinberg disequilibrium. Depending on the imputation method used, 6-13% of the test results changed qualitatively at the 5% level.

Genomic Variants Revealed by Invariably Missing Genotypes in Nelore Cattle

High density genotyping panels have been used in a wide range of applications. From population genetics to genome-wide association studies, this technology still offers the lowest cost and the most consistent solution for generating SNP data. However, in spite of the application, part of the generated data is always discarded from final datasets based on quality control criteria used to remove unreliable markers. Some discarded data consists of markers that failed to generate genotypes, labeled as missing genotypes. A subset of missing genotypes that occur in the whole population under study may be caused by technical issues but can also be explained by the presence of genomic variations that are in the vicinity of the assayed SNP and that prevent genotyping probes from annealing. The latter case may contain relevant information because these missing genotypes might be used to identify population-specific genomic variants. In order to assess which case is more prevalent, we used Illumina HD Bovine chip genotypes from 1,709 Nelore (Bos indicus) samples. We found 3,200 missing genotypes among the whole population. NGS re-sequencing data from 8 sires were used to verify the presence of genomic variations within their flanking regions in 81.56% of these missing genotypes. Furthermore, we discovered 3,300 novel SNPs/Indels, 31% of which are located in genes that may affect traits of importance for the genetic improvement of cattle production.