Detecting the Common and Individual Effects of Rare Variants on Quantitative Traits by Using Extreme Phenotype Sampling

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.

A Cautionary Note on the Effects of Population Stratification Under an Extreme Phenotype Sampling Design

Extreme phenotype sampling (EPS) is a popular study design used to reduce genotyping or sequencing costs. Assuming continuous phenotype data are available on a large cohort, EPS involves genotyping or sequencing only those individuals with extreme phenotypic values. Although this design has been shown to have high power to detect genetic effects even at smaller sample sizes, little attention has been paid to the effects of confounding variables, and in particular population stratification. Using extensive simulations, we demonstrate that the false positive rate under the EPS design is greatly inflated relative to a random sample of equal size or a “case-control”-like design where the cases are from one phenotypic extreme and the controls randomly sampled. The inflated false positive rate is observed even with allele frequency and phenotype mean differences taken from European population data. We show that the effects of confounding are not reduced by increasing the sample size. We also show that including the top principal components in a logistic regression model is sufficient for controlling the type 1 error rate using data simulated with a population genetics model and using 1,000 Genomes genotype data. Our results suggest that when an EPS study is conducted, it is crucial to adjust for all confounding variables. For genetic association studies this requires genotyping a sufficient number of markers to allow for ancestry estimation. Unfortunately, this could increase the costs of a study if sequencing or genotyping was only planned for candidate genes or pathways; the available genetic data would not be suitable for ancestry correction as many of the variants could have a true association with the trait.

Genomewide association study for economic traits in the large yellow croaker with different numbers of extreme phenotypes

A traditional genomewide association study (GWAS) detects genotype-phenotype associations by the vast number of genotyped individuals. This method requires large-scale samples and considerable sequencing costs. Extreme phenotypic sampling proposes make GWAS more cost-efficient and are applied more widely. With extreme phenotypic sampling, we performed a GWAS for n-3 highly unsaturated fatty acids (HUFA) and eviscerated weight (EW) traits in the large yellowcroaker population. Of the 32,249 and 29,748 detected SNPs for the two traits, three candidate regions were found in each trait. Three candidate regions associated with HUFA were known near genes on chromosomes 4 and 11, and three candidate regions were on chromosome 6, and 15 for the EW trait. By combing through our GWAS results and the biological functional analysis of the genes, we suggest that the FABP, DGAT, ATP8B1, FAF2 and CERS2 genes, as well as the IGF2, BORA, CYP1A1, GRTP1 and HOX genes are promising candidate genes for n-3 HUFA and EW, respectively, in the large yellow croaker.Moreover, compared with the different numbers of the extreme phenotypic sampling, we conclude that 60% of the extreme phenotypic subsample can obtain a similar result as GWAS with whole phenotypes. Thus, extreme phenotypic sampling could save 40% of the cost for genotyping and DNA extraction without loss of the candidate regions and functional genes. Our study may provide a basis for further genomic breeding and a reference for others who want to perform GWAS with extreme phenotypes.

Concepts of Genomics in Kidney Transplantation

Purpose of review

Identification of genetic variants to aid in individualized treatment of solid organ allograft recipients would improve graft survival. We will review the current state of knowledge for associations of variants with transplant outcomes.

Recent findings

Many studies have yet to exhibit robust and reproducible results, however, pharmacogenomic studies focusing on cytochrome P450 (CYP) enzymes, transporters and HLA variants have shown strong associations with outcomes and have relevance towards drugs used in transplant. Genome wide association study data for the immunosuppressant tacrolimus have identified multiple variants in the CYP3A5 gene associated with trough concentrations. Additionally, APOL1 variants had been shown to confer risk to the development of end stage renal disease in African Americans.

Summary

The field is rapidly evolving and new technology such as next generation sequencing, along with larger cohorts, will soon be commonly applied in transplantation to understand genetic association with outcomes and personalized medicine.

Densification of Type I Collagen Matrices as a Model for Cardiac Fibrosis

Cardiac fibrosis is a disease state characterized by excessive collagenous matrix accumulation within the myocardium that can lead to ventricular dilation and systolic failure. Current treatment options are severely lacking due in part to the poor understanding of the complexity of molecular pathways involved in cardiac fibrosis. To close this gap, in vitro model systems that recapitulate the defining features of the fibrotic cellular environment are in need. Type I collagen, a major cardiac extracellular matrix protein and the defining component of fibrotic depositions, is an attractive choice for a fibrosis model, but demonstrates poor mechanical strength due to solubility limits. However, plastic compression of collagen matrices is shown to significantly increase its mechanical properties. Here, confined compression of oligomeric, type I collagen matrices is utilized to resemble defining hallmarks seen in fibrotic tissue such as increased collagen content, fibril thickness, and bulk compressive modulus. Cardiomyocytes seeded on compressed matrices show a strong beating abrogation as observed in cardiac fibrosis. Gene expression analysis of selected fibrosis markers indicates fibrotic activation and cardiomyocyte maturation with regard to the existing literature. With these results, a promising first step toward a facile heart-on-chip model is presented to study cardiac fibrosis.