@article {114, title = {Genetics of 35 blood and urine biomarkers in the UK Biobank.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 02}, pages = {185-194}, abstract = {

Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build {\textquoteright}multi-PRS{\textquoteright} models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.

}, keywords = {Biological Specimen Banks, Biomarkers, Cardiovascular Diseases, Diabetes Mellitus, Type 2, DNA Copy Number Variations, Genetic Pleiotropy, HLA Antigens, Humans, Linkage Disequilibrium, Liver-Specific Organic Anion Transporter 1, Mendelian Randomization Analysis, Polymorphism, Single Nucleotide, Proteins, Renal Insufficiency, Chronic, Serine Endopeptidases, United Kingdom}, issn = {1546-1718}, doi = {10.1038/s41588-020-00757-z}, author = {Sinnott-Armstrong, Nasa and Tanigawa, Yosuke and Amar, David and Mars, Nina and Benner, Christian and Aguirre, Matthew and Venkataraman, Guhan Ram and Wainberg, Michael and Ollila, Hanna M and Kiiskinen, Tuomo and Havulinna, Aki S and Pirruccello, James P and Qian, Junyang and Shcherbina, Anna and Rodriguez, Fatima and Assimes, Themistocles L and Agarwala, Vineeta and Tibshirani, Robert and Hastie, Trevor and Ripatti, Samuli and Pritchard, Jonathan K and Daly, Mark J and Rivas, Manuel A} } @article {98, title = {Genome-wide association study reveals a novel locus for asthma with severe exacerbations in diverse populations.}, journal = {Pediatr Allergy Immunol}, volume = {32}, year = {2021}, month = {2021 Jan}, pages = {106-115}, abstract = {

BACKGROUND: Severe asthma exacerbations are a major cause of asthma morbidity and increased healthcare costs. Several studies have shown racial and ethnic differences in asthma exacerbation rates. We aimed to identify genetic variants associated with severe exacerbations in two high-risk populations for asthma.

METHODS: A genome-wide association study of asthma in children and youth with severe exacerbations was performed in 1283 exacerbators and 2027 controls without asthma of Latino ancestry. Independent suggestive variants (P <= 5 {\texttimes} 10 ) were selected for replication in 448 African Americans exacerbators and 595 controls. Case-only analyses were performed comparing the exacerbators with additional 898 Latinos and 524 African Americans asthma patients without exacerbations, while adjusting by treatment category as a proxy of asthma severity. We analyzed the functionality of associated variants with in silico methods and by correlating genotypes with methylation levels in whole blood in a subset of 473 Latinos.

RESULTS: We identified two genome-wide significant associations for susceptibility to asthma with severe exacerbations, including a novel locus located at chromosome 2p21 (rs4952375, odds ratio = 1.39, P = 3.8 {\texttimes} 10 ), which was also associated with asthma exacerbations in a case-only analysis (odds ratio = 1.25, P = 1.95 {\texttimes} 10 ). This polymorphism is an expression quantitative trait locus of the long intergenic non-protein coding RNA 1913 (LINC01913) in lung tissues (P = 1.3 {\texttimes} 10 ) and influences methylation levels of the protein kinase domain-containing cytoplasmic (PKDCC) gene in whole-blood cells (P = 9.8 {\texttimes} 10 ).

CONCLUSION: We identified a novel susceptibility locus for severe asthma exacerbations in Hispanic/Latino and African American youths with functional effects in gene expression and methylation status of neighboring genes.

}, issn = {1399-3038}, doi = {10.1111/pai.13337}, author = {Herrera-Luis, Esther and Espuela-Ortiz, Antonio and Lorenzo-Diaz, Fabian and Keys, Kevin L and Mak, Angel C Y and Eng, Celeste and Huntsman, Scott and Villar, Jes{\'u}s and Rodriguez-Santana, Jose R and Burchard, Esteban G and Pino-Yanes, Maria} } @article {131, title = {Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks.}, journal = {Nat Commun}, volume = {12}, year = {2021}, month = {2021 01 13}, pages = {350}, abstract = {

Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30\%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

}, keywords = {Biological Specimen Banks, Cardiovascular Diseases, Causality, Computer Simulation, Gene Regulatory Networks, Genetic Pleiotropy, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Mendelian Randomization Analysis, Models, Theoretical, Multifactorial Inheritance, Phenotype, Risk Factors}, issn = {2041-1723}, doi = {10.1038/s41467-020-20516-2}, author = {Amar, David and Sinnott-Armstrong, Nasa and Ashley, Euan A and Rivas, Manuel A} } @article {139, title = {GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background.}, journal = {Elife}, volume = {10}, year = {2021}, month = {2021 Feb 15}, abstract = {

Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits-urate, IGF-1, and testosterone-with better-understood biology than most other complex traits. We find that many of the most significant hits are readily interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.

}, issn = {2050-084X}, doi = {10.7554/eLife.58615}, author = {Sinnott-Armstrong, Nasa and Naqvi, Sahin and Rivas, Manuel and Pritchard, Jonathan K} } @article {75, title = {Genetic diagnoses in epilepsy: The impact of dynamic exome analysis in a pediatric cohort.}, journal = {Epilepsia}, volume = {61}, year = {2020}, month = {2020 02}, pages = {249-258}, abstract = {

OBJECTIVE: We evaluated the yield of systematic analysis and/or reanalysis of whole exome sequencing (WES) data from a cohort of well-phenotyped pediatric patients with epilepsy and suspected but previously undetermined genetic etiology.

METHODS: We identified and phenotyped 125 participants with pediatric epilepsy. Etiology was unexplained at the time of enrollment despite clinical testing, which included chromosomal microarray (57 patients), epilepsy gene panel (n = 48), both (n = 28), or WES (n = 8). Clinical epilepsy diagnoses included developmental and epileptic encephalopathy (DEE), febrile infection-related epilepsy syndrome, Rasmussen encephalitis, and other focal and generalized epilepsies. We analyzed WES data and compared the yield in participants with and without prior clinical genetic testing.

RESULTS: Overall, we identified pathogenic or likely pathogenic variants in 40\% (50/125) of our study participants. Nine patients with DEE had genetic variants in recently published genes that had not been recognized as epilepsy-related at the time of clinical testing (FGF12, GABBR1, GABBR2, ITPA, KAT6A, PTPN23, RHOBTB2, SATB2), and eight patients had genetic variants in candidate epilepsy genes (CAMTA1, FAT3, GABRA6, HUWE1, PTCHD1). Ninety participants had concomitant or subsequent clinical genetic testing, which was ultimately explanatory for 26\% (23/90). Of the 67 participants whose molecular diagnoses were "unsolved" through clinical genetic testing, we identified pathogenic or likely pathogenic variants in 17 (25\%).

SIGNIFICANCE: Our data argue for early consideration of WES with iterative reanalysis for patients with epilepsy, particularly those with DEE or epilepsy with intellectual disability. Rigorous analysis of WES data of well-phenotyped patients with epilepsy leads to a broader understanding of gene-specific phenotypic spectra as well as candidate disease gene identification. We illustrate the dynamic nature of genetic diagnosis over time, with analysis and in some cases reanalysis of exome data leading to the identification of disease-associated variants among participants with previously nondiagnostic results from a variety of clinical testing strategies.

}, keywords = {Adolescent, Adult, Age of Onset, Brain Diseases, Child, Child, Preschool, Chromosomes, Human, Cohort Studies, Epilepsy, Epilepsy, Generalized, Exome, Female, Genetic Testing, Genetic Variation, Humans, Infant, Male, Microarray Analysis, Phenotype, Whole Exome Sequencing, Young Adult}, issn = {1528-1167}, doi = {10.1111/epi.16427}, author = {Rochtus, Anne and Olson, Heather E and Smith, Lacey and Keith, Louisa G and El Achkar, Christelle and Taylor, Alan and Mahida, Sonal and Park, Meredith and Kelly, McKenna and Shain, Catherine and Rockowitz, Shira and Rosen Sheidley, Beth and Poduri, Annapurna} } @article {107, title = {Genome-Wide Analysis Reveals Mucociliary Remodeling of the Nasal Airway Epithelium Induced by Urban PM.}, journal = {Am J Respir Cell Mol Biol}, volume = {63}, year = {2020}, month = {2020 08}, pages = {172-184}, abstract = {

Air pollution particulate matter <2.5 μm (PM) exposure is associated with poor respiratory outcomes. Mechanisms underlying PM-induced lung pathobiology are poorly understood but likely involve cellular and molecular changes to the airway epithelium. We extracted and chemically characterized the organic and water-soluble components of air pollution PM samples, then determined the whole transcriptome response of human nasal mucociliary airway epithelial cultures to a dose series of PM extracts. We found that PM organic extract (OE), but not water-soluble extract, elicited a potent, dose-dependent transcriptomic response from the mucociliary epithelium. Exposure to a moderate OE dose modified the expression of 424 genes, including activation of aryl hydrocarbon receptor signaling and an IL-1 inflammatory program. We generated an OE-response gene network defined by eight functional enrichment groups, which exhibited high connectivity through , , and . This OE exposure also robustly activated a mucus secretory expression program (>100 genes), which included transcriptional drivers of mucus metaplasia ( and ). Exposure to a higher OE dose modified the expression of 1,240 genes and further exacerbated expression responses observed at the moderate dose, including the mucus secretory program. Moreover, the higher OE dose significantly increased the / gel-forming mucin expression ratio and strongly downregulated ciliated cell expression programs, including key ciliating cell transcription factors (e.g., and ). Chronic OE stimulation induced mucus metaplasia-like remodeling characterized by increases in MUC5AC secretory cells and MUC5AC mucus secretions. This epithelial remodeling may underlie poor respiratory outcomes associated with high PM exposure.

}, issn = {1535-4989}, doi = {10.1165/rcmb.2019-0454OC}, author = {Montgomery, Michael T and Sajuthi, Satria P and Cho, Seung-Hyun and Everman, Jamie L and Rios, Cydney L and Goldfarbmuren, Katherine C and Jackson, Nathan D and Saef, Benjamin and Cromie, Meghan and Eng, Celeste and Medina, Vivian and Elhawary, Jennifer R and Oh, Sam S and Rodriguez-Santana, Jose and Vladar, Eszter K and Burchard, Esteban G and Seibold, Max A} } @article {62, title = {Genome sequencing analysis identifies Epstein-Barr virus subtypes associated with high risk of nasopharyngeal carcinoma.}, journal = {Nat Genet}, volume = {51}, year = {2019}, month = {2019 Jul}, pages = {1131-1136}, abstract = {

Epstein-Barr virus (EBV) infection is ubiquitous worldwide and is associated with multiple cancers, including nasopharyngeal carcinoma (NPC). The importance of EBV viral genomic variation in NPC development and its striking epidemic in southern China has been poorly explored. Through large-scale genome sequencing of 270 EBV isolates and two-stage association study of EBV isolates from China, we identify two non-synonymous EBV variants within BALF2 that are strongly associated with the risk of NPC (odds ratio (OR) = 8.69, P = 9.69 {\texttimes} 10 for SNP 162476_C; OR = 6.14, P = 2.40 {\texttimes} 10 for SNP 163364_T). The cumulative effects of these variants contribute to 83\% of the overall risk of NPC in southern China. Phylogenetic analysis of the risk variants reveals a unique origin in Asia, followed by clonal expansion in NPC-endemic regions. Our results provide novel insights into the NPC endemic in southern China and also enable the identification of high-risk individuals for NPC prevention.

}, issn = {1546-1718}, doi = {10.1038/s41588-019-0436-5}, author = {Xu, Miao and Yao, Youyuan and Chen, Hui and Zhang, Shanshan and Cao, Su-Mei and Zhang, Zhe and Luo, Bing and Liu, Zhiwei and Li, Zilin and Xiang, Tong and He, Guiping and Feng, Qi-Sheng and Chen, Li-Zhen and Guo, Xiang and Jia, Wei-Hua and Chen, Ming-Yuan and Zhang, Xiao and Xie, Shang-Hang and Peng, Roujun and Chang, Ellen T and Pedergnana, Vincent and Feng, Lin and Bei, Jin-Xin and Xu, Rui-Hua and Zeng, Mu-Sheng and Ye, Weimin and Adami, Hans-Olov and Lin, Xihong and Zhai, Weiwei and Zeng, Yi-Xin and Liu, Jianjun} } @article {41, title = {Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics.}, journal = {Bioinformatics}, volume = {35}, year = {2019}, month = {2019 Jul 15}, pages = {2495-2497}, abstract = {

SUMMARY: Large biobanks linking phenotype to genotype have led to an explosion of genetic association studies across a wide range of phenotypes. Sharing the knowledge generated by these resources with the scientific community remains a challenge due to patient privacy and the vast amount of data. Here, we present Global Biobank Engine (GBE), a web-based tool that enables exploration of the relationship between genotype and phenotype in biobank cohorts, such as the UK Biobank. GBE supports browsing for results from genome-wide association studies, phenome-wide association studies, gene-based tests and genetic correlation between phenotypes. We envision GBE as a platform that facilitates the dissemination of summary statistics from biobanks to the scientific and clinical communities.

AVAILABILITY AND IMPLEMENTATION: GBE currently hosts data from the UK Biobank and can be found freely available at biobankengine.stanford.edu.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bty999}, author = {McInnes, Gregory and Tanigawa, Yosuke and DeBoever, Chris and Lavertu, Adam and Olivieri, Julia Eve and Aguirre, Matthew and Rivas, Manuel A} } @article {56, title = {GRIK5 Genetically Regulated Expression Associated with Eye and Vascular Phenomes: Discovery through Iteration among Biobanks, Electronic Health Records, and Zebrafish.}, journal = {Am J Hum Genet}, volume = {104}, year = {2019}, month = {2019 Mar 07}, pages = {503-519}, abstract = {

Although the use of model systems for studying the mechanism of mutations that have a large effect is common, we highlight here the ways that zebrafish-model-system studies of a gene, GRIK5, that contributes to the polygenic liability to develop eye diseases have helped to illuminate a mechanism that implicates vascular biology in eye disease. A gene-expression prediction derived from a reference transcriptome panel applied to BioVU, a large electronic health record (EHR)-linked biobank at Vanderbilt University Medical Center, implicated reduced GRIK5 expression in diverse eye diseases. We tested the function of GRIK5 by depletion of its ortholog in zebrafish, and we observed reduced blood vessel numbers and integrity in the eye and increased vascular permeability. Analyses of EHRs in >2.6 million Vanderbilt subjects revealed significant comorbidity of eye and vascular diseases (relative risks 2-15); this comorbidity was confirmed in 150 million individuals from a large insurance claims dataset. Subsequent studies in >60,000 genotyped BioVU participants confirmed the association of reduced genetically predicted expression of GRIK5 with comorbid vascular and eye diseases. Our studies pioneer an approach that allows a rapid iteration of the discovery of gene-phenotype relationships to the primary genetic mechanism contributing to the pathophysiology of human disease. Our findings also add dimension to the understanding of the biology driven by glutamate receptors such as GRIK5 (also referred to as GLUK5 in protein form) and to mechanisms contributing to human eye diseases.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.01.017}, author = {Unlu, Gokhan and Gamazon, Eric R and Qi, Xinzi and Levic, Daniel S and Bastarache, Lisa and Denny, Joshua C and Roden, Dan M and Mayzus, Ilya and Breyer, Max and Zhong, Xue and Konkashbaev, Anuar I and Rzhetsky, Andrey and Knapik, Ela W and Cox, Nancy J} } @article {61, title = {Genetic diversity in populations across Latin America: implications for population and medical genetic studies.}, journal = {Curr Opin Genet Dev}, volume = {53}, year = {2018}, month = {2018 12}, pages = {98-104}, abstract = {

Hispanic/Latino (H/L) populations, although linked by culture and aspects of shared history, reflect the complexity of history and migration influencing the Americas. The original settlement by indigenous Americans, followed by postcolonial admixture from multiple continents, has yielded localized genetic patterns. In addition, numerous H/L populations appear to have signatures of pre-colonization and post-colonization bottlenecks, indicating that tens of millions of H/Ls may harbor signatures of founder effects today. Based on both population and medical genetic findings we highlight the extreme differentiation across the Americas, providing evidence for why H/Ls should not be considered a single population in modern human genetics. We highlight the need for additional sampling of understudied H/L groups, and ramifications of these findings for genomic medicine in one-tenth of the world{\textquoteright}s population.

}, keywords = {African Continental Ancestry Group, European Continental Ancestry Group, Genetic Variation, Genetics, Medical, Genetics, Population, Genome, Human, Genomics, Humans, Latin America}, issn = {1879-0380}, doi = {10.1016/j.gde.2018.07.006}, author = {Belbin, Gillian M and Nieves-Col{\'o}n, Maria A and Kenny, Eimear E and Moreno-Estrada, Andres and Gignoux, Christopher R} } @article {46, title = {The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies.}, journal = {J Am Stat Assoc}, volume = {112}, year = {2017}, month = {2017}, pages = {64-76}, abstract = {

It is of substantial interest to study the effects of genes, genetic pathways, and networks on the risk of complex diseases. These genetic constructs each contain multiple SNPs, which are often correlated and function jointly, and might be large in number. However, only a sparse subset of SNPs in a genetic construct is generally associated with the disease of interest. In this article, we propose the generalized higher criticism (GHC) to test for the association between an SNP set and a disease outcome. The higher criticism is a test traditionally used in high-dimensional signal detection settings when marginal test statistics are independent and the number of parameters is very large. However, these assumptions do not always hold in genetic association studies, due to linkage disequilibrium among SNPs and the finite number of SNPs in an SNP set in each genetic construct. The proposed GHC overcomes the limitations of the higher criticism by allowing for arbitrary correlation structures among the SNPs in an SNP-set, while performing accurate analytic -value calculations for any finite number of SNPs in the SNP-set. We obtain the detection boundary of the GHC test. We compared empirically using simulations the power of the GHC method with existing SNP-set tests over a range of genetic regions with varied correlation structures and signal sparsity. We apply the proposed methods to analyze the CGEM breast cancer genome-wide association study. Supplementary materials for this article are available online.

}, issn = {0162-1459}, doi = {10.1080/01621459.2016.1192039}, author = {Barnett, Ian and Mukherjee, Rajarshi and Lin, Xihong} } @article {18, title = {Genetic effects on gene expression across human tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {204-213}, abstract = {

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

}, keywords = {Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci}, issn = {1476-4687}, doi = {10.1038/nature24277}, author = {Battle, Alexis and Brown, Christopher D and Engelhardt, Barbara E and Montgomery, Stephen B} } @article {32, title = {Genetic identification of a common collagen disease in puerto ricans via identity-by-descent mapping in a health system.}, journal = {Elife}, volume = {6}, year = {2017}, month = {2017 09 12}, abstract = {

Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic Bio biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene, , with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2\% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease.

}, keywords = {Adolescent, Adult, Aged, Child, Collagen Diseases, Female, Fibrillar Collagens, Genotype, Heterozygote, Hispanic Americans, Homozygote, Humans, Male, Middle Aged, Molecular Epidemiology, Multigene Family, Musculoskeletal Diseases, New York City, Pedigree, Whole Genome Sequencing, Young Adult}, issn = {2050-084X}, doi = {10.7554/eLife.25060}, author = {Belbin, Gillian Morven and Odgis, Jacqueline and Sorokin, Elena P and Yee, Muh-Ching and Kohli, Sumita and Glicksberg, Benjamin S and Gignoux, Christopher R and Wojcik, Genevieve L and Van Vleck, Tielman and Jeff, Janina M and Linderman, Michael and Schurmann, Claudia and Ruderfer, Douglas and Cai, Xiaoqiang and Merkelson, Amanda and Justice, Anne E and Young, Kristin L and Graff, Misa and North, Kari E and Peters, Ulrike and James, Regina and Hindorff, Lucia and Kornreich, Ruth and Edelmann, Lisa and Gottesman, Omri and Stahl, Eli Ea and Cho, Judy H and Loos, Ruth Jf and Bottinger, Erwin P and Nadkarni, Girish N and Abul-Husn, Noura S and Kenny, Eimear E} } @article {19, title = {A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.}, journal = {Genet Epidemiol}, volume = {41}, year = {2017}, month = {2017 Nov}, pages = {620-635}, abstract = {

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 {\texttimes} 10 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 {\texttimes} 10 ). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases.

}, keywords = {Biomarkers, Body Mass Index, Computational Biology, Diabetes Mellitus, Type 2, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Models, Genetic, Models, Statistical, Polymorphism, Single Nucleotide, Risk, Stochastic Processes}, issn = {1098-2272}, doi = {10.1002/gepi.22058}, author = {He, Liang and Zhbannikov, Ilya and Arbeev, Konstantin G and Yashin, Anatoliy I and Kulminski, Alexander M} }