Human Heredity最新文献

Objectives: Peripheral blood mononuclear cells (PBMCs) are critical for immunity and participate in multiple human diseases, including rheumatoid arthritis (RA). PhosSNPs are nonsynonymous SNPs influencing protein phosphorylation, thus probably modulate cell signaling and gene expression. We aimed to identify phosSNPs-regulated gene network/pathway potentially significant for RA.

Methods: We collected genome-wide phosSNP genotyping data and transcriptome-wide mRNA expression data from PBMCs of a Chinese sample. We discovered and verified with public datasets differentially expressed genes (DEGs) associated with RA, and replicated RA-associated SNPs in our study sample. We performed a targeted expression quantitative trait locus (eQTL) study on significant phosSNPs and DEGs.

Results: We identified 29 nominally significant eQTL phosSNPs and 83 target genes, and constructed comprehensive regulatory/interaction networks, highlighting the vital effects of two eQTL phosSNPs (rs371513 and rs4824675, FDR <0.05) and four critical node genes (HSPA4, NDUFA2, MRPL15, and ATP5O). Besides, two node/key genes NDUFA2 and ATP5O, regulated by rs371513, were significantly enriched in mitochondrial oxidative phosphorylation pathway. Besides, four pairs of eQTL effects were replicated independently in whole blood and/or transformed fibroblasts.

Conclusions: The findings delineated a potential role of protein phosphorylation and genetic variations in RA and warranted the significant roles of phosSNPs in regulating RA-associated genes expression in PBMCs. The results pointed out the relevance and significance of oxidative phosphorylation pathway to RA.

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.

Many complex diseases are caused by single nucleotide polymorphisms (SNPs), environmental factors, and the interaction between SNPs and environment. Joint tests of the SNP and SNP-environment interaction effects (JMA) and meta-regression (MR) are commonly used to evaluate these SNP-environment interactions. However, these two methods do not consider genetic heterogeneity. We previously presented a random-effect MR, which provided higher power than the MR in datasets with high heterogeneity. However, this method requires group-level data, which sometimes are not available. Given this, we designed this study to evaluate the introduction of the random effects of SNP and SNP-environment interaction into the JMA, and then extended this to the random effect model. Likelihood ratio statistic is applied to test the JMA and the new method we proposed in this paper. We evaluated the null distributions of these tests, and the powers for this method. This method was verified by simulation and was shown to provide similar powers to the random effect meta-regression method (RMR). However, this method only requires study-level data which relaxed the condition of the RMR. Our study suggests that this method is more suitable for finding the association between SNP and diseases in the absence of group-level data.

Background: Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The "rare variant hypothesis" proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale.

Objectives: In this study, we investigated associations between rare variants and 14 cancer types.

Methods: We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG).

Results: We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer).

Conclusions: Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.

It is plausible that variants in the ACE2 and TMPRSS2 genes might contribute to variation in COVID-19 severity and that these could explain why some people become very unwell whereas most do not. Exome sequence data was obtained for 49,953 UK Biobank subjects, of whom 82 had tested positive for SARS-CoV-2 and could be presumed to have severe disease. A weighted burden analysis was carried out using SCOREASSOC to determine whether there were differences between these cases and the other sequenced subjects in the overall burden of rare, damaging variants in ACE2 or TMPRSS2. There were no statistically significant differences in weighted burden scores between cases and controls for either gene. There were no individual DNA sequence variants with a markedly different frequency between cases and controls. Whether there are small effects on severity, or whether there might be rare variants with major effect sizes, would require studies in much larger samples. Genetic variants affecting the structure and function of the ACE2 and TMPRSS2 proteins are not the main explanation for why some people develop severe symptoms in response to infection with SARS-CoV-2. This research was conducted using the UK Biobank Resource.