Association Study Of Variants Research Articles

Genopyc: a Python library for investigating the functional effects of genomic variants associated to complex diseases.

Integrative Biomedicl Informatics, Research Program on Biomedical Informatics (IBI - GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF) C/ del Dr. Aiguader 88 Barcelona 08003 Spain.Understanding the genetic basis of complex diseases is one of the main challenges in modern genomics. However, current tools often lack the versatility to efficiently analyze the intricate relationships between genetic variations and disease outcomes. To address this, we introduce Genopyc, a novel Python library designed for comprehensive investigation of how the variants associated to complex diseases affects downstream pathways. Genopyc offers an extensive suite of functions for heterogeneous data mining and visualization, enabling researchers to delve into and integrate biological information from large-scale genomic datasets. In this work, we present the Genopyc library through application to real-world genome wide association studies variants. Using Genopyc to investigate the functional consequences of variants associated to intervertebral disc degeneration enabled a deeper understanding of the potential dysregulated pathways involved in the disease, which can be explored and visualized by exploiting the functionalities featured in the package. Genopyc emerges as a powerful asset for researchers, facilitating the investigation of complex diseases paving the way for more targeted therapeutic interventions. Genopyc is available on pip https://pypi.org/project/genopyc/.The source code of Genopyc is available at https://github.com/freh-g/genopyc. A tutorial notebook is available at https://github.com/freh-g/genopyc/blob/main/tutorials/Genopyc_tutorial_notebook.ipynb. Finally, a detailed documentation is available at: https://genopyc.readthedocs.io/en/latest/.

Genome‐wide association study of variation in cooking time among common bean (Phaseolus vulgaris L.) accessions using Diversity Arrays Technology markers

AbstractStored grains of common bean (Phaseolus vulgaris L.) develop the hard‐to‐cook trait (HTC), which is manifested in a prolonged cooking time, thereby imposing time and energy constraints. The objective of this study was to determine variation in cooking time among common bean genotypes and to identify single nucleotide polymorphism (SNP) markers associated with cooking time.Seeds of 222 common bean accessions sourced from Kenyan institutions were multiplied in the Jomo Kenyatta University of Agriculture and Technology (JKUAT) field in 2019. The freshly harvested seeds and those stored at 35°C and 50% red haricot (RH) for 4 months for accelerated aging were soaked in distilled water for 16 h and evaluated for cooking time using the finger‐pressing method. The accessions were also genotyped to determine variation in SNP markers using Diversity Arrays Technology Sequencing (DArTseq). Genome‐wide association study (GWAS) analysis was conducted to identify SNPs significantly associated with cooking time.The study revealed significant differences (p ≤ 0.05) within and between fresh and aged bean accessions. Fresh seeds had a lower cooking time with a mean of 40.8 min and ranged from 28.1 to 72.2 min, whereas aged seeds had a higher average cooking time of 54.1 min and ranged from 32.1 to 96.3 min. GWAS identified a region in Chromosome 10 to be significantly (p ≤ 0.05) associated with the cooking time of aged seeds. Consequently, two potential candidate genes Phvul.010G038000 and Phvul.010G038100 were revealed. The characterized common bean accessions and the identified SNP markers can be utilized in breeding programs to improve the cooking quality of the common bean.

3D chromatin structure in chondrocytes identifies putative osteoarthritis risk genes.

Genome-wide association studies have identified over 100 loci associated with osteoarthritis risk, but the majority of osteoarthritis risk variants are noncoding, making it difficult to identify the impacted genes for further study and therapeutic development. To address this need, we used a multiomic approach and genome editing to identify and functionally characterize potential osteoarthritis risk genes. Computational analysis of genome-wide association studies and ChIP-seq data revealed that chondrocyte regulatory loci are enriched for osteoarthritis risk variants. We constructed a chondrocyte-specific regulatory network by mapping 3D chromatin structure and active enhancers in human chondrocytes. We then intersected these data with our previously collected RNA-seq dataset of chondrocytes responding to fibronectin fragment, a known osteoarthritis trigger. Integration of the 3 genomic datasets with recently reported osteoarthritis genome-wide association study variants revealed a refined set of putative causal osteoarthritis variants and their potential target genes. One of the putative target genes identified was SOCS2, which was connected to a putative causal variant by a 170-kb loop and is differentially regulated in response to fibronectin fragment. CRISPR-Cas9-mediated deletion of SOCS2 in primary human chondrocytes from 3 independent donors led to heightened expression of inflammatory markers after fibronectin fragment treatment. These data suggest that SOCS2 plays a role in resolving inflammation in response to cartilage matrix damage and provides a possible mechanistic explanation for its influence on osteoarthritis risk. In total, we identified 56 unique putative osteoarthritis risk genes for further research and potential therapeutic development.

Association study of candidate DNA-repair gene variants and acute graft versus host disease in pediatric patients receiving allogeneic hematopoietic stem-cell transplantation

Acute Graft versus Host Disease (aGvHD) grades 2–4 occurs in 15–60% of pediatric patients undergoing allogeneic haematopoietic stem-cell transplantation (allo-HSCT). The collateral damage to normal tissue by conditioning regimens administered prior to allo-HSCT serve as an initial trigger for aGvHD. DNA-repair mechanisms may play an important role in mitigating this initial damage, and so the variants in corresponding DNA-repair protein-coding genes via affecting their quantity and/or function. We explored 51 variants within 17 DNA-repair genes for their association with aGvHD grades 2–4 in 60 pediatric patients. The cumulative incidence of aGvHD 2–4 was 12% (n = 7) in the exploratory cohort. MGMT rs10764881 (G>A) and EXO rs9350 (c.2270C>T) variants were associated with aGvHD 2–4 [Odds ratios = 14.8 (0 events out of 40 in rs10764881 GG group) and 11.5 (95% CI: 2.3–191.8), respectively, multiple testing corrected p ≤ 0.001]. Upon evaluation in an extended cohort (n = 182) with an incidence of aGvHD 2–4 of 22% (n = 40), only MGMT rs10764881 (G>A) remained significant (adjusted HR = 2.05 [95% CI: 1.06–3.94]; p = 0.03) in the presence of other clinical risk factors. Higher MGMT expression was seen in GG carriers for rs10764881 and was associated with higher IC50 of Busulfan in lymphoblastoid cells. MGMT rs10764881 carrier status could predict aGvHD occurrence in pediatric patients undergoing allo-HSCT.

Cap analysis of gene expression (CAGE) and noncoding regulatory elements.

Cap analysis of gene expression (CAGE) was developed to detect the 5' end of RNA. Trapping of the RNA 5'-cap structure enables the enrichment and selective sequencing of complete transcripts. Upscaled high-throughput versions of CAGE have enabled the genome-wide identification of transcription start sites, including transcriptionally active promoters and enhancers. CAGE sequencing can be exploited to draw comprehensive maps of active genomic regulatory elements in a cell type- and activation-specific manner. The cells of the immune system are among the best candidates to be analyzed in humans, since they are easily accessible. In this review, we discuss how CAGE data are instrumental for integrative analyses with quantitative trait loci and omics data, and their usefulness in the mechanistic interpretation of the effects of genetic variations over the entire human genome. Integrating CAGE data with the currently available omics information will contribute to better understanding of the genome-wide association study variants that lie outside of annotated genes, deepening our knowledge on human diseases, and enabling the targeted design of more specific therapeutic interventions.

Polygenic Risk Scores in a Prospective Parkinson's Disease Cohort.

Ethnic-specific genetic risk assessment framework for Parkinson's disease (PD) is lacking for the Asian population. We investigated the association of a polygenic risk score (PRS) with PD incidence in a population-based Asian prospective cohort. Genetic, dietary, and lifestyle information were prospectively collected from 25,646 participants within the Singapore Chinese Health Study cohort. PRS was constructed with Asian-specific and top genome-wide association study variants. The association between PRS and PD incidence was evaluated with multivariable Cox proportional hazard models, Kaplan-Meier survival analysis, and concordance statistics. A total of 333 incident cases were identified after a follow-up period of more than 20 years. Participants with PRS in the top tertile (hazard ratio [HR], 1.81; 95% confidence interval [CI], 1.37-2.39) and middle tertile (HR,1.35; 95% CI,1.00-1.83) are at higher risk of developing PD after adjusting for dietary and lifestyle risk factors, with a shorter time to PD event in a Kaplan-Meier survival analysis (P < 0.001). We identified a PRS that was significantly associated with PD incidence in a prospective Chinese cohort after adjusting for dietary and lifestyle factors. © 2021 International Parkinson and Movement Disorder Society.

Single-Cell Epigenomics and Functional Fine-Mapping of Atherosclerosis GWAS Loci.

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Clinical Evaluation of the Polygenetic Background of Blood Pressure in the Population-Based Setting.

The clinical value of the polygenetic component of blood pressure (BP) is commonly questioned. We evaluated a genetic risk score for BP (BP-GRS858), based on the most recently published genome-wide association studies variants that were significantly associated with either systolic BP or diastolic BP, for prediction of hypertension and cardiovascular end points. The genotyping was performed in 2 urban-based prospective cohorts: the Malmö Diet and Cancer (n=29 295) and the Malmö Preventive Project (n=9367) and a weighted BP-GRS858 based on 858 SNPs was calculated. At baseline, we found a difference of 9.0 mm Hg (systolic BP) and 4.8 mm Hg (diastolic BP) between the top and the bottom quartile of BP-GRS858. In Malmö Preventive Project, the top versus bottom quartile of BP-GRS858 was associated with a doubled risk of incident hypertension (odds ratio, 2.05 [95% CI, 1.75-2.39], P=1.4×10-21), a risk higher than that of body mass index, as evaluated in quartiles. In Malmö Diet and Cancer, significant association was found between the age and sex-adjusted BP-GRS858 and the incidence of total cardiovascular events, stroke, coronary artery disease, heart failure, atrial fibrillation, and total mortality. Most of these associations remained significant after adjusting for traditional risk factors, including hypertension. BP-GRS858 could contribute predictive information regarding future hypertension, with an effect size comparable to other well-known risk factors such as obesity, and predicts cardiovascular events. Given that the exposure to high polygenetic risk starts at birth, we suggest that the BP-GRS858 might be useful to identify children or adolescents who would benefit from early hypertension screening and treatment.

Association Study of Polymorphic Varients of Beta (B) Casein Gene with Milk Production Traits (Lactose, Snf and Density) in Malvi, Nimari, Sahiwal and H.F. Cross Breeds Cow

Background: The most frequently observed forms of beta casein (β-Cn) in dairy cattle are A1 and A2. A2 β-Cn is recognized as the original β-Cn protein because it existed before a mutation caused the appearance of A1 β-Cn in European cattle (Bos taurus) a few thousand years ago. In cattle, beta-casein (CSN2) gene is highly polymorphic with at least 13 genetic variants known until now.Methods: Research work was carried out on 50 Malvi and 50 Nimari, 50 Sahiwal and 50 H F Cross bred cow. In present research work the PCR amplicons of 121bp were digested by restriction endonuclease enzyme DdeI, which recognizes G^AATTC sites. Present association study of polymorphic variants’ showed that the presence of no restriction sites for the enzyme DdeI in both Malvi and Nimari. So one band of 121bp was observed on the gel and such genotype was designated as A2A2 type. Whereas in Sahiwal and H F Crossbred showed two type of genotypes A1A2 and A2A2.Result: The result of RFLP revealed that the gene and genotypic frequencies of β-casein (CSN2) gene for A2A2 was 1.00 for both Malvi and Nimari breed of cattle but 0.00, 0.30 and 0.70 in Sahiwal and 0.00, 0.64 and 0.36 in HF crossbred cattle, respectively. Association study showed that the lactose per cent was significantly higher in Nimari as compared to Malvi and Sahiwal. Among all the four breeds of cattle, significantly lower SNF (%) and density was noticed in Malvi breed of cattle for A2A2 genotype compared to remaining three breeds.

Исследование ассоциации аллельных вариантов гена рецептора лептина с задержкой темпа полового развития мальчиков в околопубертатный период

Исследование ассоциации аллельных вариантов гена рецептора лептина с задержкой темпа полового развития мальчиков в околопубертатный период

30-OR: Whole Genome Sequence Analysis of Type 2 Diabetes Risk in 44,713 Humans of Diverse Ancestry in the TOPMed Study

Risk of developing type 2 diabetes (T2D) is heritable, with over 403 independent genetic variants having been identified primarily through analysis of common variants in European ancestry populations. To further illuminate the genetic architecture of T2D, we conducted a whole genome sequence (WGS) association study (&amp;gt;38x depth) of common, low frequency, and rare variants in 9,663 individuals with T2D and 35,050 controls of diverse ancestry from NHLBI’s Trans-Omics for Precision Medicine (TOPMed) study. We conducted single variant analyses and SKAT tests of rare variants (MAF &amp;lt;1%) annotated with pancreatic islet specific regulatory annotation to define variant sets. In single variant analyses, a total of 15 regions were significantly associated (α=5e-08) with T2D, seven of which are novel associations (in or near ODF2L, LMAN2, KCNV1, CBR1, VLDLR-AS1, LINC01052 and NKX2-5). These new associations were either ancestry specific or rare variants. For instance, a variant not present in other ancestral groups, near KCNV1, was associated with increased risk of T2D (MAF 5.5%, P= 1.57e-08, OR=1.43) in African ancestry. Set based testing of rare variants identified seven regions associated with T2D in at least one ancestry. Six of these were noncoding: in African ancestry CBR1 gene centric variants (cumulative MAC[cMAC]=623, P = 5.85e-09) and an active promoter region in proximity to MRPL46 and MRPS11 (cMAC=3,218, P = 4.89e-07); and in Asian ancestry an enhancer hub containing NXPH3 (cMAC=3,317, P = 3.42e-05) and an active enhancer region linked to RP11-135A1.2 and HES1 (cMAC=57, P = 4.87e-07) were associated with T2D. KLRC4 predicted deleterious coding variants were associated with T2D in the European ancestry group (cMAC=25, P = 3.63e-06). In large scale WGS analyses, we extended our knowledge of the genetic basis of T2D through the inclusion of ancestrally diverse samples, variant typing without imputation, and use of tissue specific regulatory annotation to identify new T2D loci. Disclosure H.M. Highland: None. J. Wessel: Employee; Spouse/Partner; Eli Lilly and Company. A. Manning: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (5U01DK078616-10, K01DK107836)

Genomic and epigenomic mapping of leptin-responsive neuronal populations involved in body weight regulation.

Genome wide association studies (GWAS) in obesity have identified a large number of noncoding loci located near genes expressed in the central nervous system. However, due to the difficulties in isolating and characterizing specific neuronal subpopulations, few obesity-associated SNPs have been functionally characterized. Leptin responsive neurons in the hypothalamus are essential in controlling energy homeostasis and body weight. Here, we combine FACS-sorting of leptin-responsive hypothalamic neuron nuclei with genomic and epigenomic approaches (RNA-seq, ChIP-seq, ATAC-seq) to generate a comprehensive map of leptin-response specific regulatory elements, several of which overlap obesity-associated GWAS variants. We demonstrate the usefulness of our leptin-response neuron regulome, by functionally characterizing a novel enhancer near Socs3, a leptin response-associated transcription factor. We envision our data to serve as a useful resource and a blueprint for functionally characterizing obesity-associated SNPs in the hypothalamus.

High-Resolution Regulatory Maps Connect Vascular Risk Variants to Disease-Related Pathways.

Genetic variant landscape of coronary artery disease is dominated by noncoding variants among which many occur within putative enhancers regulating the expression levels of relevant genes. It is crucial to assign the genetic variants to their correct genes both to gain insights into perturbed functions and better assess the risk of disease. In this study, we generated high-resolution genomic interaction maps (≈750 bases) in aortic endothelial, smooth muscle cells and THP-1 (human leukemia monocytic cell line) macrophages stimulated with lipopolysaccharide using Hi-C coupled with sequence capture targeting 25 429 features, including variants associated with coronary artery disease. We also sequenced their transcriptomes and mapped putative enhancers using chromatin immunoprecipitation with an antibody against H3K27Ac. The regions interacting with promoters showed strong enrichment for enhancer elements and validated several previously known interactions and enhancers. We detected interactions for 727 risk variants obtained by genome-wide association studies and identified novel, as well as established genes and functions associated with cardiovascular diseases. We were able to assign potential target genes for additional 398 genome-wide association studies variants using haplotype information, thereby identifying additional relevant genes and functions. Importantly, we discovered that a subset of risk variants interact with multiple promoters and their expression levels were strongly correlated. In summary, we present a catalog of candidate genes regulated by coronary artery disease-related variants and think that it will be an invaluable resource to further the investigation of cardiovascular pathologies and disease.

Genomic Predictors of Asthma Phenotypes and Treatment Response.

Asthma is a complex respiratory disease considered as the most common chronic condition in children. A large genetic contribution to asthma susceptibility is predicted by the clustering of asthma and allergy symptoms among relatives and the large disease heritability estimated from twin studies, ranging from 55 to 90%. Genetic basis of asthma has been extensively investigated in the past 40 years using linkage analysis and candidate-gene association studies. However, the development of dense arrays for polymorphism genotyping has enabled the transition toward genome-wide association studies (GWAS), which have led the discovery of several unanticipated asthma genes in the last 11 years. Despite this, currently known risk variants identified using many thousand samples from distinct ethnicities only explain a small proportion of asthma heritability. This review examines the main findings of the last 2 years in genomic studies of asthma using GWAS and admixture mapping studies, as well as the direction of studies fostering integrative perspectives involving omics data. Additionally, we discuss the need for assessing the whole spectrum of genetic variation in association studies of asthma susceptibility, severity, and treatment response in order to further improve our knowledge of asthma genes and predictive biomarkers. Leveraging the individual's genetic information will allow a better understanding of asthma pathogenesis and will facilitate the transition toward a more precise diagnosis and treatment.

Association study of variants in genes FTO, SLC6A4, DRD2, BDNF and GHRL with binge eating disorder (BED) in Portuguese women

A population based case-control study was conducted in Portuguese women with overweight/obesity to investigate the possible association of variants in genes FTO, SLC6A4, DRD2, BDNF and GHRL with binge eating disorder (BED). The distribution of seven polymorphisms was evaluated in 31 BED patients and 62 controls. No significant associations were found between polymorphisms and BED. Of interest, a markedly lower frequency of the FTO rs9939609 obesity risk A-allele was found in BED patients (0.290) in relation to the control group (0.402). Contrasting with anorexia nervosa and bulimia nervosa, our data suggest that rs9939609 A-allele has no potential role in BED.

Association Study of ARMC9 Gene Variants with Vogt-Koyanagi-Harada Disease in Japanese Patients

ABSTRACTPurpose: To investigate whether variants in the ARMC9 gene encoding KU-MEL-1 are associated with Vogt-Koyanagi-Harada (VKH) disease in a Japanese population.Methods: We recruited 380 Japanese patients with VKH disease and 744 Japanese healthy controls to genotype seven single-nucleotide polymorphisms (SNPs) in ARMC9. We also performed imputation analysis of the ARMC9 region and 195 imputed SNPs were included in the statistical analysis.Results: We observed an increased frequency of the A allele of rs28690417 in patients compared with controls (P = 0.0097, odds ratio (OR) = 1.46). The A allele had a dominant effect on VKH disease risk (P = 0.011, OR = 1.51). However, these significant differences disappeared after Bonferroni correction (corrected P > 0.05). The remaining 201 SNPs did not show any significant association with disease risk.Conclusions: Our study suggests that ARMC9 variants do not play a critical role in the development of VKH disease.

Recovery of novel association loci in Arabidopsis thaliana and Drosophila melanogaster through leveraging INDELs association and integrated burden test.

Short insertions, deletions (INDELs) and larger structural variants have been increasingly employed in genetic association studies, but few improvements over SNP-based association have been reported. In order to understand why this might be the case, we analysed two publicly available datasets and observed that 63% of INDELs called in A. thaliana and 64% in D. melanogaster populations are misrepresented as multiple alleles with different functional annotations, i.e. where the same underlying variant is represented by inconsistent alignments leading to different variant calls. To address this issue, we have developed the software Irisas to reclassify and re-annotate these variants, which we then used for single-locus tests of association. We also integrated them to predict the functional impact of SNPs, INDELs, and structural variants for burden testing. Using both approaches, we re-analysed the genetic architecture of complex traits in A. thaliana and D. melanogaster. Heritability analysis using SNPs alone explained on average 27% and 19% of phenotypic variance for A. thaliana and D. melanogaster respectively. Our method explained an additional 11% and 3%, respectively. We also identified novel trait loci that previous SNP-based association studies failed to map, and which contain established candidate genes. Our study shows the value of the association test with INDELs and integrating multiple types of variants in association studies in plants and animals.

Comparison of common burden tests for genetic association studies of rare variants.

Common burden tests have different statistical performance in genetic association studies of rare variants. Here, we compare the statistical performance of burden tests, such as CMC, WST, SUM and extension methods, using the computer-simulated datasets of rare variants with different parameters of sample sizes, linkage disequilibrium (LD), and different numbers of mixed non-associated variants. The simulation results showed that the type I error for all methods is near 0.05. When the rare variants had the same direction of effect, the higher LD and the less non-associated variants, the higher the power of these method, except the data adaptive SUM test. When the direction was different, the power was significantly reduced for all methods. The methods that consider the direction yielded larger statistical power than those methods without considering the effect direction, except the strong LD condition. And the larger the sample size, the larger the power. The statistical performance of burden tests is affected by a variety of factors, including the sample size, effect direction of variants, non-associated variants, and LD. Therefore, when choosing the method and setting the collection unit and weight, the prior biological information of genetic variation should be integrated to improve study efficiency.

Abstract 1304: The contribution of rare and low-frequency variants to colorectal cancer heritability

Abstract Studies that estimate complex disease heritability based on genome-wide common SNP array data have shown that a large fraction of heritability is contributed from variants that do not reach genome-wide significance at current genome-wide association study (GWAS) sample sizes. The contribution of rare variants to heritability has yet to be explored for many complex diseases. Despite the decreasing cost of sequencing, it still remains prohibitively expensive to sequence sufficient samples for well-powered genetic association studies of rare variants. However, with increasingly large and denser imputation reference panels it has become feasible to accurately impute variants with minor allele frequencies (MAFs) as low as 0.1%, enabling study of a subset of rare risk variants. In previous work, we used restricted maximum likelihood (REML) to estimate the total additive heritability of colorectal cancer (CRC) based on common SNP array genotypes. Here, we expand this work using imputed genotype data and a larger sample size. We performed whole-genome sequencing of 1,961 CRC cases and 981 controls, and subsequently imputed these haplotypes into 11,895 unrelated CRC cases and 14,659 unrelated controls that are part of the Colorectal Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). We estimated heritability from individual-level imputed genotype data using LD- and MAF-stratified GREML, as implemented in GCTA. In total, we analyzed 17,649,167 imputed genetic variants with minor allele count &amp;gt;3. Published heritability estimates for CRC from family-based studies vary from 12% to 35%. Based on common genotyped SNPs, we previously estimated heritability to be 7.42% (95% CI: 4.71-10.12%) on the underlying liability scale, assuming a population prevalence of 0.004. For imputed genotypes, we estimate the total heritability to be 12.0% (95% CI: 9.65-14.35). Using a likelihood ratio test, we demonstrate a significant contribution of variants with MAF ≤1% to CRC genetic risk (P=0.003). Because of the imperfect imputation accuracy for very rare variants, their contribution is likely higher. These results suggest that with additional sequencing, improved imputation accuracy, and larger GWAS, we should expect to start discovering rare variant associations for CRC risk. Citation Format: Jeroen R. Huyghe, Sai Chen, Hyun M. Kang, Tabitha Harrison, Sonja I. Berndt, Stephane Bézieau, Hermann Brenner, Graham Casey, Andrew T. Chan, Jenny Chang-Claude, Steven J. Gallinger, Stephen B. Gruber, Andrea Gsur, Michael Hoffmeister, Thomas Hudson, Mark A. Jenkins, Loic Le Marchand, Polly A. Newcomb, John D. Potter, Conghui Qu, Martha L. Slattery, Joshua D. Smith, Emily White, Goncalo R. Abecasis, Li Hsu, Deborah A. Nickerson, Ulrike Peters, on behalf of CCFR and GECCO. The contribution of rare and low-frequency variants to colorectal cancer heritability [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1304. doi:10.1158/1538-7445.AM2017-1304

Comparison of single-marker and multi-marker tests in rare variant association studies of quantitative traits.

In genetic association studies of rare variants, low statistical power and potential violations of established estimator properties are among the main challenges of association tests. Multi-marker tests (MMTs) have been proposed to target these challenges, but any comparison with single-marker tests (SMTs) has to consider that their aim is to identify causal genomic regions instead of variants. Valid power comparisons have been performed for the analysis of binary traits indicating that MMTs have higher power, but there is a lack of conclusive studies for quantitative traits. The aim of our study was therefore to fairly compare SMTs and MMTs in their empirical power to identify the same causal loci associated with a quantitative trait. The results of extensive simulation studies indicate that previous results for binary traits cannot be generalized. First, we show that for the analysis of quantitative traits, conventional estimation methods and test statistics of single-marker approaches have valid properties yielding association tests with valid type I error, even when investigating singletons or doubletons. Furthermore, SMTs lead to more powerful association tests for identifying causal genes than MMTs when the effect sizes of causal variants are large, and less powerful tests when causal variants have small effect sizes. For moderate effect sizes, whether SMTs or MMTs have higher power depends on the sample size and percentage of causal SNVs. For a more complete picture, we also compare the power in studies of quantitative and binary traits, and the power to identify causal genes with the power to identify causal rare variants. In a genetic association analysis of systolic blood pressure in the Genetic Analysis Workshop 19 data, SMTs yielded smaller p-values compared to MMTs for most of the investigated blood pressure genes, and were least influenced by the definition of gene regions.