Genome-wide Association Single-nucleotide Polymorphism Research Articles

Somatic point mutations are enriched in non-coding RNAs with possible regulatory function in breast cancer

Non-coding RNAs (ncRNAs) form a large portion of the mammalian genome. However, their biological functions are poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we analyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancer samples. We identify 1030 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs significantly enrich active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 1030 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other cancers. Using breast cell line related Hi-C data, we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role of candidate ncRNA genes in the activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource (https://www.ihealthe.unsw.edu.au/research) to communicate our results with the research community. Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used to analyze somatic mutations in all cancers.

Human Leukocyte Antigen Association Study Reveals DRB1*04:02 Effects Additional to DRB1*07:01 in Anti-LGI1 Encephalitis.

Background and ObjectivesTo study human leukocyte antigen (HLA) allele associations in anti-leucine–rich glioma-inactivated 1 (LGI1) encephalitis.MethodsA multiethnic cohort of 269 patients with anti-LGI1 encephalitis and 1,359 controls was included. Four-digit HLA sequencing and genome wide association single-nucleotide polymorphism typing imputation (0.99 concordance) were used for HLA typing. Significance of primary and secondary associations was tested using χ2, Fisher exact tests, or logistic regression with the control of population stratification covariates when applicable.ResultsDRB1*07:01 and DQA1*02:01, 2 alleles in strong linkage disequilibrium, were associated with the disease (90% vs 24%, OR = 27.8, p < 10e−50) across ethnicity independent of variation at DRB3 and DQB1, 2 flanking HLA loci. DRB1*07:01 homozygosity was associated with a doubling of risk (OR = 2.1, p = 0.010), suggesting causality. DRB1*07:01 negative subjects were younger (p = 0.003) and more frequently female (p = 0.015). Three patients with malignant thymomas did not carry DRB1*07:01, whereas patients with other tumors had high DRB1*07:01 frequency, suggesting that the presence of tumors other than thymomas may be coincidental and not causal. In both DRB1*07:01 heterozygous individuals and DRB1*07:01 negative subjects, DRB1*04:02 was associated with anti-LGI1 encephalitis, indicating an independent effect of this allele (OR = 6.85, p = 4.57 × 10−6 and OR = 8.93, p = 2.50 × 10−3, respectively). DRB1*04:02 was also independently associated with younger age at onset (β = −6.68, p = 9.78 × 10−3). Major histocompatibility complex peptide-binding predictions using LGI1-derived peptides revealed divergent binding propensities for DRB1*04:02 and DRB1*07:01 alleles, suggesting independent pathogenic mechanisms.DiscussionIn addition to the established primary DRB1*07:01 association in anti-LGI1 encephalitis, we observe a secondary effect of DRB1*04:02 with lower age at onset. Our study provides evidence for secondary effects within HLA locus that correlate with clinical phenotypes in anti-LGI1 encephalitis.

Abstract 825: Genetically determined elevated C-reactive protein levels and chronic inflammation status associated with primary colorectal cancer risk: Mendelian randomization with lifestyle interactions

Abstract Purpose: Immune-related etiologic pathways in the development of colorectal cancer (CRC) have not been convincingly determined and may be confounded by lifestyle factors or reverse causality. We investigated the genetically predicted C-reactive protein (CRP) phenotype in the potential causal pathway of primary CRC risk in postmenopausal women in a Mendelian randomization (MR) framework. Methods: We employed individual-level data of the Women's Health Initiative Database for Genotypes and Phenotypes Study, which consists of 5 genome-wide association (GWA) studies, including 10,142 women, 737 of whom developed primary CRC. We examined 61 GWA single-nucleotide polymorphisms (SNPs) associated with CRP by using weighted/penalized MR weighted-medians and MR gene-environment interactions that allow some relaxation of the strict variable requirements and attenuate the heterogeneous estimates of outlying SNPs. Results: In lifestyle-stratification analyses, genetically determined CRP exhibited its effects on the decreased CRC risk in non-viscerally obese and high-fat diet subgroups. In contrast, genetically driven CRP was associated with an increased risk for CRC in women who smoked 15 or more cigarettes/day, with significant interaction of the gene-smoking relationship. Further, a substantially increased risk of CRC induced by CRP was observed in relatively short-term users (less than 5 years) of estrogen (E)-only and also longer-term users (longer than 5 years) of E plus progestin. Conclusions: Our findings may provide novel evidence on immune-related etiologic pathways connected to CRC risk and suggest the possible use of CRP as a CRC-predictive biomarker in women with particular behaviors and CRP marker-informed interventions to reduce CRC risk. Citation Format: Su Yon Jung, Herbert Yu, Matteo Pellegrini, Jeanette C. Papp, Eric M. Sobel, Zuo-Feng Zhang. Genetically determined elevated C-reactive protein levels and chronic inflammation status associated with primary colorectal cancer risk: Mendelian randomization with lifestyle interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 825.

Hypothesis-independent pathway analysis implicates GABA and acetyl-CoA metabolism in primary open-angle glaucoma and normal-pressure glaucoma.

Primary open-angle glaucoma (POAG) is a leading cause of blindness worldwide. Using genome-wide association single-nucleotide polymorphism data from the Glaucoma Genes and Environment study and National Eye Institute Glaucoma Human Genetics Collaboration comprising 3,108 cases and 3,430 controls, we assessed biologic pathways as annotated in the KEGG database for association with risk of POAG. After correction for genic overlap among pathways, we found 4 pathways, butanoate metabolism (hsa00650), hematopoietic cell lineage (hsa04640), lysine degradation (hsa00310) and basal transcription factors (hsa03022) related to POAG with permuted p<0.001. In addition, the human leukocyte antigen (HLA) gene family was significantly associated with POAG (p<0.001). In the POAG subset with normal-pressure glaucoma (NPG), the butanoate metabolism pathway was also significantly associated (p<0.001) as well as the MAPK and Hedgehog signaling pathways (hsa04010 and hsa04340), glycosaminoglycan biosynthesis-heparan sulfate pathway (hsa00534) and the phenylalanine, tyrosine and tryptophan biosynthesis pathway (hsa0400). The butanoate metabolism pathway overall, and specificallythe aspects of the pathwaythat contribute to GABA and acetyl-CoA metabolism, was the only pathway significantly associated with both POAG and NPG. Collectively these results implicate GABA and acetyl-CoA metabolism in glaucoma pathogenesis, and suggest new potential therapeutic targets.

Comparison of multilevel modeling and the family-based association test for identifying genetic variants associated with systolic and diastolic blood pressure using Genetic Analysis Workshop 18 simulated data.

Identifying genetic variants associated with complex diseases is an important task in genetic research. Although association studies based on unrelated individuals (ie, case-control genome-wide association studies) have successfully identified common single-nucleotide polymorphisms for many complex diseases, these studies are not so likely to identify rare genetic variants. In contrast, family-based association studies are particularly useful for identifying rare-variant associations. Recently, there has been some interest in employing multilevel models in family-based genetic association studies. However, the performance of such models in these studies, especially for longitudinal family-based sequence data, has not been fully investigated. Therefore, in this study, we investigated the performance of the multilevel model in the family-based genetic association analysis and compared it with the conventional family-based association test, by examining the powers and type I error rates of the 2 approaches using 3 data sets from the Genetic Analysis Workshop 18 simulated data: genome-wide association single-nucleotide polymorphism data, sequence data, and rare-variants-only data. Compared with the univariate family-based association test, the multilevel model had slightly higher power to identify most of the causal genetic variants using the genome-wide association single-nucleotide polymorphism data and sequence data. However, both approaches had low power to identify most of the causal single-nucleotide polymorphisms, especially those among the relatively rare genetic variants. Therefore, we suggest a unified method that combines both approaches and incorporates collapsing strategy, which may be more powerful than either approach alone for studying genetic associations using family-based data.

The association of genetic variants of type 2 diabetes with kidney function

Type 2 diabetes is highly prevalent and is the major cause of progressive chronic kidney disease in American Indians. Genome wide association studies identified several loci associated with diabetes but their impact on susceptibility to diabetic complications is unknown. To measure this we studied the association of 18 type 2 diabetes genome wide association single nucleotide polymorphisms (SNPs) with the estimated glomerular filtration rate (eGFR) (MDRD equation) and urine albumin to creatinine ratio in 6,958 individuals in the Strong Heart Study family and cohort participants. Center specific residuals of eGFR and the log urine albumin to creatinine ratio, obtained from linear regression models adjusted for age, sex and body mass index, were regressed onto SNP dosage using variance component in family data and linear regression models in unrelated individuals. Estimates were then combined across centers. Four diabetic loci were associated with eGFR and one locus with the urine albumin to creatinine ratio. A SNP in the WFS1 gene (rs10010131) was associated with higher eGFR in younger individuals and with increased albuminuria. The SNPs of the FTO, KCNJ11 and TCF7L2 genes were associated with lower eGFR, not albuminuria, and were not significant in prospective analyses. Our findings suggest a shared genetic risk for type 2 diabetes, its kidney complications, and a potential role for WFS1 in early onset diabetic nephropathy in American Indian populations.

Merging pharmacometabolomics with pharmacogenomics using ‘1000 Genomes’ single-nucleotide polymorphism imputation

We set out to test the hypothesis that pharmacometabolomic data could be efficiently merged with pharmacogenomic data by single-nucleotide polymorphism (SNP) imputation of metabolomic-derived pathway data on a 'scaffolding' of genome-wide association (GWAS) SNP data to broaden and accelerate 'pharmacometabolomics-informed pharmacogenomic' studies by eliminating the need for initial genotyping and by making broader SNP association testing possible. We previously genotyped 131 tag SNPs for six genes encoding enzymes in the glycine synthesis and degradation pathway using DNA from 529 depressed patients treated with citalopram/escitalopram to pursue a glycine metabolomics 'signal' associated with selective serotonine reuptake inhibitor response. We identified a significant SNP in the glycine dehydrogenase gene. Subsequently, GWAS SNP data were generated for the same patients. In this study, we compared SNP imputation within 200 kb of these same six genes with the results of the previous tag SNP strategy as a rapid strategy for merging pharmacometabolomic and pharmacogenomic data. Imputed genotype data provided greater coverage and higher resolution than did tag SNP genotyping, with a higher average genotype concordance between genotyped and imputed SNP data for '1000 Genomes' (96.4%) than HapMap 2 (93.2%) imputation. Many low P-value SNPs with novel locations within genes were observed for imputed compared with tag SNPs, thus altering the focus for subsequent functional genomic studies. These results indicate that the use of GWAS data to impute SNPs for genes in pathways identified by other 'omics' approaches makes it possible to rapidly and cost efficiently identify SNP markers to 'broaden' and accelerate pharmacogenomic studies.