Quantitative and Qualitative Role of Antagonistic Heterogeneity in Genetics of Blood Lipids

Abstract Prevailing strategies in genome-wide association studies (GWAS) mostly rely on principles of medical genetics emphasizing one gene, one function, one phenotype concept. Here, we performed GWAS of blood lipids leveraging a new systemic concept emphasizing complexity of genetic predisposition to such phenotypes. We focused on total cholesterol, low- and high-density lipoprotein cholesterols, and triglycerides available for 29,902 individuals of European ancestry from seven independent studies, men and women combined. To implement the new concept, we leveraged the inherent heterogeneity in genetic predisposition to such complex phenotypes and emphasized a new counter intuitive phenomenon of antagonistic genetic heterogeneity, which is characterized by misalignment of the directions of genetic effects and the phenotype correlation. This analysis identified 37 loci associated with blood lipids but only one locus, FBXO33, was not reported in previous top GWAS. We, however, found strong effect of antagonistic heterogeneity that leaded to profound (quantitative and qualitative) changes in the associations with blood lipids in most, 25 of 37 or 68%, loci. These changes suggested new roles for some genes, which functions were considered as well established such as GCKR, SIK3 (APOA1 locus), LIPC, LIPG, among the others. The antagonistic heterogeneity highlighted a new class of genetic associations emphasizing beneficial and adverse trade-offs in predisposition to lipids. Our results argue that rigorous analyses dissecting heterogeneity in genetic predisposition to complex traits such as lipids beyond those implemented in current GWAS are required to facilitate translation of genetic discoveries into health care.

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The PAGE Study: How Genetic Diversity Improves Our Understanding of the Architecture of Complex Traits

10.1101/188094 ◽

2017 ◽

Cited By ~ 10

Author(s):

Genevieve L Wojcik ◽

Mariaelisa Graff ◽

Katherine K Nishimura ◽

Ran Tao ◽

Jeffrey Haessler ◽

...

Keyword(s):

Complex Traits ◽

Association Studies ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Genome Wide ◽

Size Heterogeneity ◽

Gwas Catalog ◽

Health Applications

Summary/AbstractGenome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of the role of human variation in disease, and hinders the equitable translation of genetic associations into clinical and public health applications. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using strategies designed for analysis of multi-ethnic and admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 38 secondary signals in known loci and 27 novel loci. Our data shows strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts, and insights into clinical implications. We strongly advocate for continued, large genome-wide efforts in diverse populations to reduce health disparities.

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Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

10.1101/2020.05.06.077982 ◽

2020 ◽

Author(s):

Jingshu Wang ◽

Qingyuan Zhao ◽

Jack Bowden ◽

Gilbran Hemani ◽

George Davey Smith ◽

...

Keyword(s):

Risk Factors ◽

Complex Traits ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Genome Wide ◽

Genetic Instruments

Over a decade of genome-wide association studies have led to the finding that significant genetic associations tend to spread across the genome for complex traits. The extreme polygenicity where "all genes affect every complex trait" complicates Mendelian Randomization studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing Mendelian Randomization methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using summary statistics from genome-wide association studies, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, adjust for confounding risk factors, and determine the causal direction. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and the potential pleiotropic pathways.

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Identification of putative effector genes across the GWAS Catalog using molecular quantitative trait loci from 68 tissues and cell types

10.1101/808444 ◽

2019 ◽

Cited By ~ 2

Author(s):

Cong Guo ◽

Karsten B. Sieber ◽

Jorge Esparza-Gordillo ◽

Mark R. Hurle ◽

Kijoung Song ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Complex Traits ◽

Association Studies ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Effector Genes ◽

Genome Wide ◽

Trait Loci ◽

Gwas Catalog

AbstractIdentifying the effector genes from genome-wide association studies (GWAS) is a crucial step towards understanding the biological mechanisms underlying complex traits and diseases. Colocalization of expression and protein quantitative trait loci (eQTL and pQTL, hereafter collectively called “xQTL”) can be effective for mapping associations to genes in many loci. However, existing colocalization methods require full single-variant summary statistics which are often not readily available for many published GWAS or xQTL studies. Here, we present PICCOLO, a method that uses minimum SNP p-values within a locus to determine if pairs of genetic associations are colocalized. This method greatly expands the number of GWAS and xQTL datasets that can be tested for colocalization. We applied PICCOLO to 10,759 genome-wide significant associations across the NHGRI-EBI GWAS Catalog with xQTLs from 28 studies. We identified at least one colocalized gene-xQTL in at least one tissue for 30% of associations, and we pursued multiple lines of evidence to demonstrate that these mappings are biologically meaningful. PICCOLO genes are significantly enriched for biologically relevant tissues, and 4.3-fold enriched for targets of approved drugs.

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An atlas of genetic associations in UK Biobank

10.1101/176834 ◽

2017 ◽

Cited By ~ 18

Author(s):

Oriol Canela-Xandri ◽

Konrad Rawlik ◽

Albert Tenesa

Keyword(s):

Complex Traits ◽

Statistical Power ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Associations ◽

Genome Wide ◽

Related Individuals ◽

Sufficient Statistical Power

ABSTRACTGenome-wide association studies have revealed many loci contributing to the variation of complex traits, yet the majority of loci that contribute to the heritability of complex traits remain elusive. Large study populations with sufficient statistical power are required to detect the small effect sizes of the yet unidentified genetic variants. However, the analysis of huge cohorts, like UK Biobank, is complicated by incidental structure present when collecting such large cohorts. For instance, UK Biobank comprises 107,162 third degree or closer related participants. Traditionally, GWAS have removed related individuals because they comprised an insignificant proportion of the overall sample size, however, removing related individuals in UK Biobank would entail a substantial loss of power. Furthermore, modelling such structure using linear mixed models is computationally expensive, which requires a computational infrastructure that may not be accessible to all researchers. Here we present an atlas of genetic associations for 118 non-binary and 599 binary traits of 408,455 related and unrelated UK Biobank participants of White-British descent. Results are compiled in a publicly accessible database that allows querying genome-wide association summary results for 623,944 genotyped and HapMap2 imputed SNPs, as well downloading whole GWAS summary statistics for over 30 million imputed SNPs from the Haplotype Reference Consortium panel. Our atlas of associations (GeneATLAS,http://geneatlas.roslin.ed.ac.uk) will help researchers to query UK Biobank results in an easy way without the need to incur in high computational costs.

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Abstract 141: APOA1, CETP, and CILP2 Variants Modify the Lipid Response to Fenofibrate: Genetics of Lipid Lowering and Diet Network

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvb.32.suppl_1.a141 ◽

2012 ◽

Vol 32 (suppl_1) ◽

Author(s):

Stella Aslibekyan ◽

Marguerite M Irvin ◽

Alexis C Frazier-Wood ◽

Robert J Straka ◽

Ingrid B Borecki ◽

...

Keyword(s):

Blood Lipids ◽

Association Studies ◽

Meta Analysis ◽

Density Lipoprotein ◽

Lipoprotein Cholesterol ◽

Lipid Lowering ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Lipid Response ◽

Induced Changes

Introduction: Despite the widespread use of fibrates in treatment of dyslipidemia, the observed response is highly heterogeneous, suggesting a role for genetic determinants. Whether replicated variants associated with blood lipids identified by genome wide association studies (GWAS) are also associated with lipid response to fenofibrate is unknown. Objectives: To test if 95 genome-wide significant loci identified in a recent meta-analysis of blood lipids are associated with changes in high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), non-HDL-C, and triglycerides (TG) following 3 weeks of therapy with 160 mg of micronized fenofibrate. Methods: Using data from 861 European American Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) participants, we fit mixed linear models with baseline blood lipids and the post-to-pre fenofibrate treatment ratio of blood lipid levels as outcomes, the corresponding genetic markers from the published meta-analysis as predictors, and age, sex, pedigree, and ancestry as assessed by principal components as covariates. A Bonferroni correction was applied to adjust for multiple comparisons. Least square means were used to report the direction of fenofibrate-induced changes by genotype. Results: We observed statistically significant associations between rs964184 , a variant near the APOA1 gene, and baseline HDL-C (P<0.0001) and baseline TG (P<0.0001), as well as with diminished response to fenofibrate as evidenced by a smaller increase in HDL-C (P<0.0001) and a smaller decrease in TG (P=0.0001) per each copy of the variant allele. Additionally, we report suggestive associations of rs3764261 locus in the CETP gene and the rs10401969 locus in the CILP2 gene with decreased fenofibrate response as measured by changes in LDL-C (P=0.0003 and 0.02, respectively) and non-HDL-C (P=0.004 and 0.005, respectively). Conclusions: We have identified several novel biologically relevant loci associated with baseline blood lipids and fenofibrate-induced changes in blood lipids.

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Genome-wide association studies and contribution to cardiovascular physiology

Physiological Genomics ◽

10.1152/physiolgenomics.00004.2015 ◽

2015 ◽

Vol 47 (9) ◽

pp. 365-375 ◽

Cited By ~ 7

Author(s):

Patricia B. Munroe ◽

Andrew Tinker

Keyword(s):

Complex Traits ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Cardiovascular Disorders ◽

Genetic Associations ◽

Physiological Processes ◽

Genome Wide ◽

Personal Interests

The study of family pedigrees with rare monogenic cardiovascular disorders has revealed new molecular players in physiological processes. Genome-wide association studies of complex traits with a heritable component may afford a similar and potentially intellectually richer opportunity. In this review we focus on the interpretation of genetic associations and the issue of causality in relation to known and potentially new physiology. We mainly discuss cardiometabolic traits as it reflects our personal interests, but the issues pertain broadly in many other disciplines. We also describe some of the resources that are now available that may expedite follow up of genetic association signals into observations on causal mechanisms and pathophysiology.

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Quantifying genetic heterogeneity between continental populations for human height and body mass index

Scientific Reports ◽

10.1038/s41598-021-84739-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jing Guo ◽

Andrew Bakshi ◽

Ying Wang ◽

Longda Jiang ◽

Loic Yengo ◽

...

Keyword(s):

Body Mass Index ◽

Body Mass ◽

Complex Traits ◽

Association Studies ◽

Significant Snps ◽

Complex Trait ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Population Difference ◽

Genome Wide

AbstractGenome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs ($$r_{g}$$ r g ) or genome-wide significant SNPs ($$r_{{g\left( {GWS} \right)}}$$ r g GWS ) for height and body mass index (BMI) in samples of European (EUR; $$n = 49,839$$ n = 49 , 839 ) and African (AFR; $$n = 17,426$$ n = 17 , 426 ) ancestry. The $$\hat{r}_{g}$$ r ^ g between EUR and AFR was 0.75 ($${\text{s}}.{\text{e}}. = 0.035$$ s . e . = 0.035 ) for height and 0.68 ($${\text{s}}.{\text{e}}. = 0.062$$ s . e . = 0.062 ) for BMI, and the corresponding $$\hat{r}_{{g\left( {GWS} \right)}}$$ r ^ g GWS was 0.82 ($${\text{s}}.{\text{e}}. = 0.030$$ s . e . = 0.030 ) for height and 0.87 ($${\text{s}}.{\text{e}}. = 0.064$$ s . e . = 0.064 ) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that $$\hat{r}_{g}$$ r ^ g differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.

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XGMix: Local-Ancestry Inference with Stacked XGBoost

10.1101/2020.04.21.053876 ◽

2020 ◽

Author(s):

Arvind Kumar ◽

Daniel Mas Montserrat ◽

Carlos Bustamante ◽

Alexander Ioannidis

Keyword(s):

Association Studies ◽

Genomic Medicine ◽

Genetic Association Studies ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Local Ancestry ◽

Genome Wide ◽

Health Burdens ◽

Local Ancestry Inference

AbstractGenomic medicine promises increased resolution for accurate diagnosis, for personalized treatment, and for identification of population-wide health burdens at rapidly decreasing cost (with a genotype now cheaper than an MRI and dropping). The benefits of this emerging form of affordable, data-driven medicine will accrue predominantly to those populations whose genetic associations have been mapped, so it is of increasing concern that over 80% of such genome-wide association studies (GWAS) have been conducted solely within individuals of European ancestry [1]. The severe under-representation of the majority of the world’s populations in genetic association studies stems in part from an addressable algorithmic weakness: lack of simple, accurate, and easily trained methods for identifying and annotating ancestry along the genome (local ancestry). Here we present such a method (XGMix) based on gradient boosted trees, which, while being accurate, is also simple to use, and fast to train, taking minutes on consumer-level laptops.

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Widespread allelic heterogeneity in complex traits

10.1101/076984 ◽

2016 ◽

Cited By ~ 3

Author(s):

Farhad Hormozdiari ◽

Anthony Zhu ◽

Gleb Kichaev ◽

Ayellet V. Segrè ◽

Chelsea J.-T. Ju ◽

...

Keyword(s):

Complex Traits ◽

Statistical Power ◽

Association Studies ◽

Density Lipoprotein ◽

Computational Method ◽

Allelic Heterogeneity ◽

Genome Wide Association Studies ◽

New Methods ◽

Genome Wide ◽

Causal Variants

AbstractRecent successes in genome-wide association studies (GWASs) make it possible to address important questions about the genetic architecture of complex traits, such as allele frequency and effect size. One lesser-known aspect of complex traits is the extent of allelic heterogeneity (AH) arising from multiple causal variants at a locus. We developed a computational method to infer the probability of AH and applied it to three GWAS and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4152 loci with strong evidence of AH. The proportion of all loci with identified AH is 4-23% in eQTLs, 35% in GWAS of High-Density Lipoprotein (HDL), and 23% in schizophrenia. For eQTLs, we observed a strong correlation between sample size and the proportion of loci with AH (R2=0.85, P = 2.2e-16), indicating that statistical power prevents identification of AH in other loci. Understanding the extent of AH may guide the development of new methods for fine mapping and association mapping of complex traits.

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Quantifying genetic heterogeneity between continental populations for human height and body mass index

10.1101/839373 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jing Guo ◽

Andrew Bakshi ◽

Ying Wang ◽

Longda Jiang ◽

Loic Yengo ◽

...

Keyword(s):

Body Mass Index ◽

Body Mass ◽

Complex Traits ◽

Association Studies ◽

Significant Snps ◽

Complex Trait ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Population Difference ◽

Genome Wide

AbstractGenome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs (rg) or genome-wide significant SNPs (rg(GWS)) for height and body mass index (BMI) in samples of European (EUR; n = 49,839) and African (AFR; n = 17,426) ancestry. The between EUR and AFR was 0.75 (s. e. = 0.035) for height and 0.68 (s. e. = 0.062) for BMI, and the corresponding was 0.82 (s. e. = 0.030) for height and 0.87 (s. e. = 0.064) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.

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