A cross-trait study of lung cancer and its related respiratory diseases based on large-scale exome sequencing population

Background

Genome-wide association studies (GWASs) explain the genetic susceptibility between diseases and common variants. Nevertheless, with the appearance of large-scale sequencing profiles, we could explore the rare coding variants in disease pathogenesis.

Methods

We estimated the genetic correlation of nine respiratory diseases and lung cancer in the UK Biobank (UKB) by linkage disequilibrium score regression (LDSC). Then, we performed exome-wide association studies at single-variant level and gene-level for lung cancer and lung cancer-related respiratory diseases using the whole-exome sequencing (WES) data of 427,934 European participants. Cross-trait meta-analysis was conducted by association analysis based on subsets (ASSET) to identify the pleiotropic variants, while in-silico functional analysis was performed to explore their function. Causal mediation analysis was used to explore whether these pleiotropic variants lead to lung cancer is mediated by affecting the chronic respiratory diseases.

Results

Five respiratory diseases [emphysema, pneumonia, asthma, chronic obstructive pulmonary disease (COPD), and fibrosis] were genetically correlated with lung cancer. We identified 102 significant independent variants at single-variant levels for lung cancer and five lung cancer-related diseases. 15:78590583:G>A (missense variant in CHRNA5) was shared in lung cancer, emphysema, and COPD. Meanwhile, 14 significant genes and 87 suggestive genes were identified in gene-based association tests, including HSD3B7 (lung cancer), SRSF2 (pneumonia), TNXB (asthma), TERT (fibrosis), MOSPD3 (emphysema). Based on the cross-trait meta-analysis, we detected 145 independent pleiotropic variants. We further identified abundant pathways with significant enrichment effects, demonstrating that these pleiotropic genes were functional. Meanwhile, the proportion of mediation effects of these variants ranged from 6 to 23 (emphysema: 23%; COPD: 20%; pneumonia: 20%; fibrosis: 7%; asthma: 6%) through these five respiratory diseases to the incidence of lung cancer.

Conclusions

The identified shared genetic variants, genes, biological pathways, and potential intermediate causal pathways provide a basis for further exploration of the relationship between lung cancer and respiratory diseases.

Identification of novel rare variants for anxiety: an exome-wide association study in the UK Biobank

BACKGROUND

Rare variants are believed to play a substantial role in the genetic architecture of mental disorders, particularly in coding regions. However, limited evidence supports the impact of rare variants on anxiety.

METHODS

Using whole-exome sequencing data from 200,643 participants in the UK Biobank, we investigated the contribution of rare variants to anxiety. Firstly, we computed genetic risk score (GRS) of anxiety utilizing genotype data and summary data from a genome-wide association study (GWAS) on anxiety disorder. Subsequently, we identified individuals within the lowest 50% GRS, a subgroup more likely to carry pathogenic rare variants. Within this subgroup, we classified individuals with the highest 10% 7-item Generalized Anxiety Disorder scale (GAD-7) score as cases (N = 1869), and those with the lowest 10% GAD-7 score were designated as controls (N = 1869). Finally, we conducted gene-based burden tests and single-variant association analyses to assess the relationship between rare variants and anxiety.

RESULTS

Totally, 47,800 variants with MAF ≤0.01 were annotated as non-benign coding variants, consisting of 42,698 nonsynonymous SNVs, 489 nonframeshift substitution, 236 frameshift substitution, 617 stop-gain and 40 stop-loss variants. After variation aggregation, 5066 genes were included in gene-based association analysis. Totally, 11 candidate genes were detected in burden test, such as RNF123 (PBonferroni adjusted = 3.40 × 10-6), MOAP1(PBonferroni adjusted = 4.35 × 10-4), CCDC110 (PBonferroni adjusted = 5.83 × 10-4). Single-variant test detected 9 rare variants, such as rs35726701(RNF123)(PBonferroni adjusted = 3.16 × 10-10) and rs16942615(CAMTA2) (PBonferroni adjusted = 4.04 × 10-4). Notably, RNF123, CCDC110, DNAH2, and CSKMT gene were identified in both tests.

CONCLUSIONS

Our study identified novel candidate genes for anxiety in protein-coding regions, revealing the contribution of rare variants to anxiety.

Evaluating the role of rare genetic variation in sleep duration

OBJECTIVES

To explore the roles of rare and high-impact variants in sleep duration.

DESIGN

Based on the recently released UK Biobank 200k exome dataset, an exome-wide association study was conducted to detect rare variants (minor allele frequency <0.01) contributing to sleep duration. Variant annotations were performed by the software tool ANNOVAR. Gene-based burden tests of sleep duration were conducted by the SKAT R-package. After quality control, 137,047 subjects were included in this study. CAUSALdb database was used to explore the related mental traits of identified genes.

RESULTS

We detected 730,572 variants with MAF < 1%, including 3873 frameshift variants, 3977 nonframeshift variants, 449,632 nonsynonymous variants, 1293 startloss variants, 10,254 stopgain variants, 413 stoploss variants, 261,130 synonymous variants, and 3102 variants are annotated as unknown. The burden test of exonic variants detected two exome-wide significant associations for sleep duration including TMIE at 3p21.31 (P_{Bonferroni adjusted} = 0.015) and ZIC2 at 13q32.3 (P_{Bonferroni adjusted} = 0.047). There are only nonsynonymous contained in TMIE; as for ZIC2, we detected 2 annotations of variants: nonsynonymous (P_{Bonferroni adjusted} =2.04 × 10^-4) and nonframeshift (P_{Bonferroni adjusted} =0.85). TMIE and ZIC2 were reported to be associated with several mental traits, such as chronotype, depression, and brain natriuretic peptide in published study.

CONCLUSION

This study reported 2 novel candidate genes for a sleep duration, supporting the roles of rare genetic variants in the regulation of sleep duration.

Genetic variants in nuclear DNA along with environmental factors modify mitochondrial DNA copy number: a population-based exome-wide association study

Background

Mitochondrial DNA (mtDNA) copy number has been found associated with multiple diseases, including cancers, diabetes and so on. Both environmental and genetic factors could affect the copy number of mtDNA. However, limited study was available about the relationship between genetic variants and mtDNA copy number. What’s more, most of previous studies considered only environmental or genetic factors. Therefore, it’s necessary to explore the genetic effects on mtDNA copy number with the consideration of PM_2.5 exposure and smoking.

Results

A multi-center population-based study was performed with 301 subjects from Zhuhai, Wuhan and Tianjin. Personal 24-h PM_2.5 exposure levels, smoking and mtDNA copy number were evaluated. The Illumina Human Exome BeadChip, which contained 241,305 single nucleotide variants, was used for genotyping. The association analysis was conducted in each city and meta-analysis was adopted to combine the overall effect among three cities. Seven SNPs showed significant association with mtDNA copy number with P value less than 1.00E-04 after meta-analysis. The following joint analysis of our identified SNPs showed a significant allele-dosage association between the number of variants and mtDNA copy number (P = 5.02 × 10^− 17). Further, 11 genes were identified associated with mtDNA copy number using gene-based analysis with a P value less than 0.01.

Conclusion

This study was the first attempt to evaluate the genetic effects on mtDNA copy number with the consideration of personal PM_2.5 exposure level. Our findings could provide more evidences that genetic variants played important roles in modulating the copy number of mtDNA.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5142-7) contains supplementary material, which is available to authorized users.

Identification of six novel susceptibility loci for dyslipidemia using longitudinal exome-wide association studies in a Japanese population

Recent genome-wide association studies have identified various dyslipidemia-related genetic variants. However, most studies were conducted in a cross-sectional manner. We thus performed longitudinal exome-wide association studies of dyslipidemia in a Japanese population. We used ~244,000 genetic variants and clinical data of 6022 Japanese individuals who had undergone annual health checkups for several years. After quality control, the association of dyslipidemia-related phenotypes with 24,691 single nucleotide polymorphisms (SNPs) was tested using the generalized estimating equation model. In total, 82 SNPs were significantly (P < 2.03 × 10^-6) associated with dyslipidemia phenotypes. Of these SNPs, four (rs74416240 of TCHP, rs925368 of GIT2, rs7969300 of ATXN2, and rs12231744 of NAA25) and two (rs34902660 of SLC17A3 and rs1042127 of CDSN) were identified as novel genetic determinants of hypo-HDL- and hyper-LDL-cholesterolemia, respectively. A replication study using the cross-sectional data of 8310 Japanese individuals showed the association of the six identified SNPs with dyslipidemia-related traits.

Two novel susceptibility loci for type 2 diabetes mellitus identified by longitudinal exome-wide association studies in a Japanese population

Recent genome-wide association studies identified genetic variants that confer susceptibility to type 2 diabetes mellitus (T2DM). However, few longitudinal genome-wide association studies of this metabolic disorder have been reported to date. Therefore, we performed a longitudinal exome-wide association study of T2DM, using 24,579 single nucleotide polymorphisms (SNPs) and repeated measurements from 6022 Japanese individuals. The generalized estimating equation model was applied to test relations of SNPs to three T2DM-related parameters: prevalence of T2DM, fasting plasma glucose level, and blood glycosylated hemoglobin content. Three SNPs that passed quality control were significantly (P<2.26×10^-7) associated with two of the three T2DM-related parameters in additive and recessive models. Of the three SNPs, rs6414624 in EVC and rs78338345 in GGA3 were novel susceptibility loci for T2DM. In the present study, the SNP of GGA3 was predicted to be a genetic variant whose minor allele frequency has recently increased in East Asia.

An exome-wide association study

BACKGROUND

Bicuspid aortic valve is the most common cardiovascular congenital malformation affecting 2% of the general population. The incidence of life-threatening complications, the high heritability, and familial clustering rates support the interest in identifying risk or protective genetic factors. The main objective of the present study was to identify population-based genetic variation associated with bicuspid aortic valve and concomitant ascending aortic dilation.

MATERIALS AND METHODS

A cross-sectional exome-wide association study was conducted in 565 Spanish cases and 484 controls. Single-marker and gene-based association analyses enriched for low frequency and rare genetic variants were performed on this discovery stage cohort and for the subsets of cases with and without ascending aortic dilation. Discovery-stage association signals and additional markers indirectly associated with bicuspid aortic valve, were genotyped in a replication cohort that comprised 895 Caucasian cases and 1483 controls.

RESULTS

Although none of the association signals were consistent across series, the involvement of HMCN2 in calcium metabolism and valve degeneration caused by calcium deposit, and a nominal but not genome-wide significant association, supported it as an interesting gene for follow-up studies on the genetic susceptibility to bicuspid aortic valve.

CONCLUSIONS

The absence of a genome-wide significant association signal shows this valvular malformation may be more genetically complex than previously believed. Exhaustive phenotypic characterization, even larger datasets, and collaborative efforts are needed to detect the combination of rare variants conferring risk which, along with specific environmental factors, could be causing the development of this disease.