Human Heredity最新文献

Introduction: Although breast and prostate cancers arise in different organs and are more frequent in the opposite sex, multiple studies have reported an association between their family history. Analysis of single nucleotide polymorphism data, based on distant relatives, has revealed a small positive genetic correlation between these cancers explained by common variants. The estimate of genetic correlation based on close relatives reveals the extent to which shared genetic risks are explained by both common and rare variants. This estimate is unknown for breast and prostate cancer.

Method: We estimated the relative risks, heritability, and genetic correlation of breast cancer and prostate cancer, based on the Minnesota Breast and Prostate Cancer Study, a family study of 141 families ascertained for breast cancer.

Results: Heritability of breast cancer was 0.34 (95% credible interval: 0.23-0.49) and 0.65 (95% credible interval: 0.36-0.97) for prostate cancer, and the genetic correlation was 0.23. In terms of odds ratios, these values correspond to a 1.3 times higher odds of breast cancer among probands, given that the brother has prostate cancer.

Conclusion: This study shows the inherent relation between prostate cancer and breast cancer; an incident of one in a family increases the risk of developing the other. The large difference between estimates of genetic correlation from distant and close relatives, if replicated, suggests that rare variants contribute to the shared genetic risk of breast and prostate cancer. However, the difference could steam from genotype-by-family effects shared between the two types of cancers.

Objective: Based on the HLA profile of Indian Gorkhas, Debnath and Chaudhuri (2006) proposed that Gorkhas are genetically closer to Mongoloids, and they may have originated from Mongolians or Tibetan stocks. However, the major limitation of the earlier study was that Gorkhas comprise 2 broad groups, i.e. Tibeto-Burmans and Indo-Aryans. Besides, Gorkhas have an assemblage of many sociocultural and linguistically distinct populations such as Rai, Magar, Limbu, Tamang, Newar, Bahun, Kami, and so on. Thus, the generalization of the findings on Gorkhas by considering them as a single homogenous population may not be free from biases. Therefore, the present study aims to understand the genetic affinity of a constituent population from the Gorkha community, i.e. Kami, based on HLA polymorphism.

Methods: First field HLA typing was performed among 158 Kami individuals by PCR-SSP methods.

Results: The most frequent genes observed were HLA-A*11, HLA-B*15, HLA-DRB1*15. The frequency of HLA-DRB1*15 reported here is the highest recorded among the North Indian population to date, which is a noteworthy finding of the study. The hierarchical cluster analysis and principal component analysis showed that the Kami population lies within the cluster of the Indian subcontinental population.

Conclusion: The study refutes the earlier proposition of exclusive belongingness of all the Gorkhas to Mongoloids.

Background: Genome-wide association studies (GWAS) were successful in identifying SNPs showing association with disease, but their individual effect sizes are small and require large sample sizes to achieve statistical significance. Methods of post-GWAS analysis, including gene-based, gene-set and polygenic risk scores, combine the SNP effect sizes in an attempt to boost the power of the analyses. To avoid giving undue weight to SNPs in linkage disequilibrium (LD), the LD needs to be taken into account in these analyses.

Objectives: We review methods that attempt to adjust the effect sizes (β-coefficients) of summary statistics, instead of simple LD pruning.

Methods: We subject LD adjustment approaches to a mathematical analysis, recognising Tikhonov regularisation as a framework for comparison.

Results: Observing the similarity of the processes involved with the more straightforward Tikhonov-regularised ordinary least squares estimate for multivariate regression coefficients, we note that current methods based on a Bayesian model for the effect sizes effectively provide an implicit choice of the regularisation parameter, which is convenient, but at the price of reduced transparency and, especially in smaller LD blocks, a risk of incomplete LD correction.

Conclusions: There is no simple answer to the question which method is best, but where interpretability of the LD adjustment is essential, as in research aiming at identifying the genomic aetiology of disorders, our study suggests that a more direct choice of mild regularisation in the correction of effect sizes may be preferable.

Introduction: Studies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association.

Methods: In addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation, and SD. We performed 1,000 simulations for 50 regions of size 3 kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to 2 existing methods: adaptive combination of p values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF).

Results: All outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 < minor allele frequency, MAF > 0.03) compared to very rare variants (MAF <0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to 2 regions found previously associated with IPF including 100 rare variants, we identified 6 polymorphisms with the greatest evidence for influencing the association with IPF.

Discussion: In summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.

Background: Lung cancer is one of the most common malignant tumors, and asbestos exposure was suggested to contribute to a proportion of lung cancer cases. Previous genome-wide gene-environment interaction analysis reported that rs13383928 was associated with asbestos-related lung cancer. However, the mechanism of this association was still unclear.

Methods: In the present study, we retrieved the genotype data from the 1,000 Genomes Project on single-nucleotide polymorphisms (SNPs) surrounding rs13383928 and analyzed the linkage disequilibrium (LD) pattern of this region. Further functional genomics analyses were performed.

Results: The result indicated that no other SNPs were in LD with rs13383928, suggesting that rs13383928 is the causal one. The following dual luciferase assay disclosed that the T allele of rs13383928 presented significantly higher enhancer activity than G in lung cells, thus verifying that this SNP was functional in the lung. Through chromosome conformation capture, the PTH2R (parathyroid hormone 2 receptor)promoter was identified to interact with the segment surrounding rs13383928. By chromatin immunoprecipitation, it was observed that the region spanning rs13383928 could bind transcription factor FOXJ2 (forkhead box J2).

Conclusion: Our functional genomics evidence supports a link between rs13383928 and asbestos-related lung cancer through regulating PTH2R.

Introduction: Liver disease accounts for approximately 2 million deaths per year worldwide. The majority of liver diseases are due to complications of cirrhosis, viral hepatitis, and hepatocellular carcinoma. Increased levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) may indicate liver disease. Moreover, there are additional noninvasive liver fibrosis indices that help to estimate liver damage, including AST-to-ALT ratio, AST-to-platelet ratio index (APRI), fibrosis-4 (FIB-4) score, and nonalcoholic fatty liver disease (NAFLD) fibrosis score. The aims of the present study were to (1) perform an association analysis of the patatin-like phospholipase domain containing 3 (PNPLA3) I148M (rs738409) variant with ALT, AST, and various liver fibrosis indices, and (2) determine whether there are gender-related differences in these associations.

Methods: We obtained demographic, anthropometric, and metabolic phenotypes from Latino adult participants (n = 503, 64% female, 36.4 ± 0.5 years) from the Arizona Insulin Resistance (AIR) registry. SNP genotyping of I148M was performed using the TaqMan allelic discrimination assay. We used linear regression for the association analyses of the genotypes with ALT, AST, and the various liver fibrosis indices. We included genotype, age, body mass index, and alcohol status in the linear regression model.

Results: The variant I148M was in Hardy-Weinberg equilibrium, with genotype distribution: non-risk CC 118, heterozygous CG 246, and risk GG 139. The G allele was significantly associated with increased ALT and AST levels (p = 7.8 × 10-7 and p = 9.7 × 10-6, respectively). Moreover, we showed that the G allele was significantly associated with higher APRI (p = 3.7 × 10-7) and FIB-4 score (p = 4.1 × 10-3). When we analyzed the data by gender, we observed similar significant trends for ALT, AST, and APRI (all, p < 0.01). In females, the G allele was significantly associated with increased FIB-4 score (p = 6.9 × 10-3), which was not observed in the males (p > 0.05). There was no association of the I148M variant with AST/ALT ratio nor NAFLD risk score, whether analyzed in all adults or by gender.

Discussion/conclusion: Our findings provide additional evidence of an association of PNPLA3 I148M with several liver disease biomarkers in male and female Latinos residing in the Southwest of the United States.

Multiple familial diseases in a single patient often present with overlapping symptomatology that confers difficulty in delineating a clinical diagnosis. Pedigree analysis has been a long-standing practice in the field of medical genetics to discover familial diseases. In recent years, whole exome sequencing (WES) has proven to be a useful tool for aiding physicians in diagnosing and understanding disease etiology. This report shows that pedigree analysis and WES are co-dependent processes in establishing diagnoses in a family with 4 different genetic disorders: Birt-Hogg-Dubé Syndrome, RRM2B-related mitochondrial disease, CDC73-related primary hyperparathyroidism, and familial prostate cancer.