DOCSLIB.ORG

Comparing Allele Specific Expression and Local Expression Quantitative

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Comparing Allele Specific Expression and Local Expression Quantitative Khansefid et al. BMC Genomics (2018) 19:793 https://doi.org/10.1186/s12864-018-5181-0 RESEARCHARTICLE Open Access Comparing allele specific expression and local expression quantitative trait loci and the influence of gene expression on complex trait variation in cattle Majid Khansefid1,2* , Jennie E. Pryce2,3, Sunduimijid Bolormaa2, Yizhou Chen4, Catriona A. Millen5, Amanda J. Chamberlain2, Christy J. Vander Jagt2 and Michael E. Goddard1,2 Abstract Background: The mutations changing the expression level of a gene, or expression quantitative trait loci (eQTL), can be identified by testing the association between genetic variants and gene expression in multiple individuals (eQTL mapping), or by comparing the expression of the alleles in a heterozygous individual (allele specific expression or ASE analysis). The aims of the study were to find and compare ASE and local eQTL in 4 bovine RNA-sequencing (RNA-Seq) datasets, validate them in an independent ASE study and investigate if they are associated with complex trait variation. Results: We present a novel method for distinguishing between ASE driven by polymorphisms in cis and parent of origin effects. We found that single nucleotide polymorphisms (SNPs) driving ASE are also often local eQTL and therefore presumably cis eQTL. These SNPs often, but not always, affect gene expression in multiple tissues and, when they do, the allele increasing expression is usually the same. However, there were systematic differences between ASE and local eQTL and between tissues and breeds. We also found that SNPs significantly associated with gene expression (p < 0.001) were likely to influence some complex traits (p < 0.001), which means that some mutations influence variation in complex traits by changing the expression level of genes. Conclusion: We conclude that ASE detects phenomenon that overlap with local eQTL, but there are also systematic differences between the SNPs discovered by the two methods. Some mutations influencing complex traits are actually eQTL and can be discovered using RNA-Seq including eQTL in the genes CAST, CAPN1, LCORL and LEPROTL1. Keywords: RNA-sequencing, Allele specific expression, ASE analysis, eQTL mapping, Genome-wide association study, Genetic variation of complex traits Background so understanding gene expression may improve our Gene expression varies between tissues and individuals knowledge of the genetic architecture of complex traits. and can be measured by counting the number of mRNA In traditional gene expression studies with microar- copies of the gene [1, 2]. The variation in gene expres- rays, it is not possible to discriminate between eQTL sion between individuals can influence their phenotype responsible for changing the expression of a gene on the [3]. Some of this variation in gene expression is genetic, same chromosome (cis eQTL) from mutations influen- cing the expression of a gene on both chromosomes (trans eQTL) [4]. However, it has become common to * Correspondence: [email protected] regard polymorphisms that are close to the gene whose 1 Department of Agriculture and Food Systems, The University of Melbourne, expression they affect as cis eQTL, although a better Parkville, VIC, Australia 2Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, name would be local eQTL [5]. Australia Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Khansefid et al. BMC Genomics (2018) 19:793 Page 2 of 18 High-throughput RNA sequencing with next gener- strains. Here we introduce a novel statistical analysis to ation sequencing technology (RNA-Seq), can be used to distinguish between these two causes of allele specific measure gene expression and to detect heterozygous expression. Our novel method can be used to explain sites in the transcript [6]. If there is a polymorphic site some of the discrepancy between the results from ASE in the transcript, then RNA-Seq can also be used to de- analyses and local eQTL mapping. tect allele specific expression (ASE) or allelic bias in Finding the biological background of how mutations which one allele is expressed more highly than the other. cause variation in a trait is very useful to confirm the To detect ASE, we require a SNP to be in the transcript causality of the statistically significant association be- (tSNP), but the polymorphism causing the difference in tween mutations and phenotypic records. However, expression between the alleles (the driver SNP or dSNP) many of the polymorphisms associated with complex is likely to be in non-coding, regulatory DNA (Fig. 1a). traits (quantitative trait loci or QTL), found by genome An individual that is heterozygous for a cis eQTL should wide association (GWA) studies, are non-coding variants show ASE. Therefore, RNA-Seq data can be used for [8]. Therefore, it seems reasonable to assume that at eQTL mapping of global RNA expression and also for least some of the causal variants that affect complex ASE analysis, and hence it is possible to distinguish cis traits, do so by regulating the expression of genes. If this eQTL from trans eQTL even among local eQTL [7]. is true, we expect that some QTL are actually eQTL. In ASE analysis, the difference in expression of the al- However, only a few QTL have been shown to be eQTL leles is a within individual comparison (Fig. 1b and c), (e.g. in humans the SNP which is located in an enhancer while in local eQTL mapping, the association between of Nitric Oxide Synthase 1 Adaptor Protein gene affects gene expression level and genotypes at polymorphic loci cardiac function by increasing the expression of relies on comparisons between individuals (Fig. 1d). NOS1AP)[3]. Here we examine the overlap between Therefore, the two methods use independent data and QTL for dairy and beef traits and eQTL. combining them should increase power. For the detec- The gene expression profile and imbalance can be dif- tion of cis eQTL, ASE has an advantage over local eQTL ferent across tissues and breeds. Therefore, in our study, because it relies on comparisons within an individual, so RNA-Seq of 4 datasets (45 Angus bull muscle samples, sources of error between individuals, such as environ- 37 Angus bull liver samples and 20 Holstein cow white mental or trans eQTL effects, are eliminated [8]. How- blood cell (WBC) and liver samples) and corresponding ever, this advantage in power is offset by the fact that phased genotypes were used to find local eQTL and only RNA reads that include a heterozygous site are use- measure ASE and PO-ASE within 50 kb of the genes ful for ASE, whereas all reads from a gene are useful for expressed in each tissue. We compared the local eQTL, local eQTL mapping. ASE and PO-ASE measurement in different datasets to Higher expression of one allele compared with the find if the SNP associated with allelic imbalance in ASE other can be due to cis eQTL, but it can also be due to studies and gene expression in eQTL mapping are the parent of origin of allelic expression (PO-ASE, Fig. 1b) same and if so, whether the same allele is associated or imprinting, where maternal or paternal inheritance of with higher expression in both cases. We compared the the allele determines the allelic imbalance ratio [9]. cases of ASE and eQTL found in these 4 datasets to the Thus, before using ASE to distinguish between cis and ASE found in 18 tissues from a single Holstein cow [11], trans local eQTL, we need to know whether ASE is to broaden the range of tissues examined. The SNP as- largely due to cis eQTL (Fig. 1c), or largely due to other sociated with gene expression in each RNA-Seq dataset phenomenon such as parent of origin effects. However, (p < 0.001) and SNP associated with 21 complex traits to date the reported concordance rates between local and a multi-trait test (p < 0.001), detected by GWAS, eQTL mapping and ASE studies have not been very high were compared to test if QTL are also likely to be due to biological and technical factors [10]. Hasin- eQTL. Brumshtein et al. (2014) reported only 15–20% of the The aims of the study were to find and compare ASE local eQTL were found as cis eQTL by ASE analysis in and local eQTL, validate them in an independent ASE mouse adipose tissue [5]. Failure of ASE and local eQTL study and investigate if they are associated with complex analyses to agree could be due to insufficient statistical trait variation. power in one or both analyses, to parent of origin effects or to local trans eQTL. In this paper we examine the ex- Results tent of systematic differences between local eQTL and RNA-Seq quality control ASE. About 80% of the RNA-Seq data passed the quality Distinguishing between ASE due to cis eQTL and par- check (QC) filters. The summary of raw reads, reads ent of origin effects is more complex but more inform- passing QC, aligned paired reads and concordant pair ative in outbred individuals than in crosses of inbred alignment rate (%) of the animals in each RNA-Seq Khansefid et al. BMC Genomics (2018) 19:793 Page 3 of 18 Fig.
Load more

Recommended publications
  • Interpreting Noncoding Genetic Variation in Complex Traits and Human Disease
    REVIEW Interpreting noncoding genetic variation in complex traits and human disease Lucas D Ward1,2 & Manolis Kellis1,2 Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has focused primarily on protein-coding variants, owing to the difficulty of interpreting noncoding mutations. This picture has changed with advances in the systematic annotation of functional noncoding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs and molecular quantitative trait loci all provide complementary information about the function of noncoding sequences. These functional maps can help with prioritizing variants on risk haplotypes, filtering mutations encountered in the clinic and performing systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable data-set integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis and treatment. Understanding the genetic basis of disease can transform medicine ture of the human genome, and its systematic mapping by the HapMap by elucidating relevant biochemical pathways for drug targets and by Project9, allowed single-nucleotide polymorphisms (SNPs) to be used enabling personalized risk assessments1,2. With the evolution of technol- as markers for common haplotypes, which could be genotyped using ogies over the past century, geneticists are no longer limited to studying chip technology. The stage was set for a flood of unbiased, genome-wide Mendelian disorders and can tackle complex phenotypes.
    [Show full text]
  • Genetics in Harry Potter's World: Lesson 2
    Genetics in Harry Potter’s World Lesson 2 • Beyond Mendelian Inheritance • Genetics of Magical Ability 1 Rules of Inheritance • Some traits follow the simple rules of Mendelian inheritance of dominant and recessive genes. • Complex traits follow different patterns of inheritance that may involve multiples genes and other factors. For example, – Incomplete or blended dominance – Codominance – Multiple alleles – Regulatory genes Any guesses on what these terms may mean? 2 Incomplete Dominance • Incomplete dominance results in a phenotype that is a blend of a heterozygous allele pair. Ex., Red flower + Blue flower => Purple flower • If the dragons in Harry Potter have fire-power alleles F (strong fire) and F’ (no fire) that follow incomplete dominance, what are the phenotypes for the following dragon-fire genotypes? – FF – FF’ – F’F’ 3 Incomplete Dominance • Incomplete dominance results in a phenotype that is a blend of the two traits in an allele pair. Ex., Red flower + Blue flower => Purple flower • If the Dragons in Harry Potter have fire-power alleles F (strong fire) and F’ (no fire) that follow incomplete dominance, what are the phenotypes for the following dragon-fire genotypes: Genotypes Phenotypes FF strong fire FF’ moderate fire (blended trait) F’F’ no fire 4 Codominance • Codominance results in a phenotype that shows both traits of an allele pair. Ex., Red flower + White flower => Red & White spotted flower • If merpeople have tail color alleles B (blue) and G (green) that follow the codominance inheritance rule, what are possible genotypes and phenotypes? Genotypes Phenotypes 5 Codominance • Codominance results in a phenotype that shows both traits of an allele pair.
    [Show full text]
  • The Genetics of Complex Traits Programme Course 7.5 Credits Genetiska Mekanismer Bakom Komplexa Egenskaper 8MEA14 Valid From: 2019 Autumn Semester
    DNR LIU-2019-00662 1(5) The genetics of complex traits Programme course 7.5 credits Genetiska mekanismer bakom komplexa egenskaper 8MEA14 Valid from: 2019 Autumn semester Determined by The Board for First and Second Cycle Programmes at the Faculty of Medicine and Health Sciences Date determined 2019-03-07 LINKÖPING UNIVERSITY FACULTY OF MEDICINE AND HEALTH SCIENCES LINKÖPING UNIVERSITY THE GENETICS OF COMPLEX TRAITS FACULTY OF MEDICINE AND HEALTH SCIENCES 2(5) Main field of study Medical Biology Course level Second cycle Advancement level A1X Course offered for Master's Programme in Experimental and Medical Biosciences Entry requirements Degree of Bachelor of Science, 180 ECTS in a major subject area such as medicine, biology, technology/natural sciences, odontology or veterinary medicine. 90 ECTS of courses included in the Bachelor degree should be in subjects such as biochemistry, cell biology, molecular biology, genetics, gene technology, microbiology, immunology, physiology, histology, anatomy or pathology. Documented skills in English corresponding to level 6/B. Intended learning outcomes The student will learn and understand the basis of quantitative genetic techniques, in particular how they pertain to the identification of genes underlying complex traits and diseases. Knowledge and understanding On completion of the course, the student shall be able to: Describe and understand statistical quantitative genetic techniques and how they apply to complex traits. Explain the statistical basis of quantitative traits. Explain and distinguish between linkage and linkage disequilibrium and their uses. Analyse the genetic architecture of different behavioral and disease-related traits. Analyse and understand the theory and steps required to identify the genetic components of a quantitative trait.
    [Show full text]
  • Genetic Analysis of Complex Traits in the Emerging Collaborative Cross
    Downloaded from genome.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press Research Genetic analysis of complex traits in the emerging Collaborative Cross David L. Aylor,1 William Valdar,1,13 Wendy Foulds-Mathes,1,13 Ryan J. Buus,1,13 Ricardo A. Verdugo,2,13 Ralph S. Baric,3,4 Martin T. Ferris,1 Jeff A. Frelinger,4 Mark Heise,1 Matt B. Frieman,4 Lisa E. Gralinski,4 Timothy A. Bell,1 John D. Didion,1 Kunjie Hua,1 Derrick L. Nehrenberg,1 Christine L. Powell,1 Jill Steigerwalt,5 Yuying Xie,1 Samir N.P. Kelada,6 Francis S. Collins,6 Ivana V. Yang,7 David A. Schwartz,7 Lisa A. Branstetter,8 Elissa J. Chesler,2 Darla R. Miller,1 Jason Spence,1 Eric Yi Liu,9 Leonard McMillan,9 Abhishek Sarkar,9 Jeremy Wang,9 Wei Wang,9 Qi Zhang,9 Karl W. Broman,10 Ron Korstanje,2 Caroline Durrant,11 Richard Mott,11 Fuad A. Iraqi,12 Daniel Pomp,1,14 David Threadgill,5,14 Fernando Pardo-Manuel de Villena,1,14 and Gary A. Churchill2,14 1Department of Genetics, University of North Carolina–Chapel Hill, Chapel Hill, North Carolina 27599, USA; 2The Jackson Laboratory, Bar Harbor, Maine 04609, USA; 3Department of Epidemiology, University of North Carolina–Chapel Hill, Chapel Hill, North Carolina 27599, USA; 4Department of Microbiology and Immunology, University of North Carolina–Chapel Hill, Chapel Hill, North Carolina 27599, USA; 5Department of Genetics, North Carolina State University, Raleigh, North Carolina 27695, USA; 6Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
    [Show full text]
  • Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples
    HIGHLIGHTED ARTICLE | INVESTIGATION Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples Daniel A. Skelly,* Narayanan Raghupathy,* Raymond F. Robledo,* Joel H. Graber,*,† and Elissa J. Chesler*,1 *The Jackson Laboratory, Bar Harbor, Maine 04609 and †MDI Biological Laboratory, Bar Harbor, Maine 04609 ORCID IDs: 0000-0002-2329-2216 (D.A.S.); 0000-0002-5642-5062 (E.J.C.) ABSTRACT Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript–trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative “reference” traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression.
    [Show full text]
  • Genetics and Human Traits
    Help Me Understand Genetics Genetics and Human Traits Reprinted from MedlinePlus Genetics U.S. National Library of Medicine National Institutes of Health Department of Health & Human Services Table of Contents 1 Are fingerprints determined by genetics? 1 2 Is eye color determined by genetics? 3 3 Is intelligence determined by genetics? 5 4 Is handedness determined by genetics? 7 5 Is the probability of having twins determined by genetics? 9 6 Is hair texture determined by genetics? 11 7 Is hair color determined by genetics? 13 8 Is height determined by genetics? 16 9 Are moles determined by genetics? 18 10 Are facial dimples determined by genetics? 20 11 Is athletic performance determined by genetics? 21 12 Is longevity determined by genetics? 23 13 Is temperament determined by genetics? 26 Reprinted from MedlinePlus Genetics (https://medlineplus.gov/genetics/) i Genetics and Human Traits 1 Are fingerprints determined by genetics? Each person’s fingerprints are unique, which is why they have long been used as a way to identify individuals. Surprisingly little is known about the factors that influence a person’s fingerprint patterns. Like many other complex traits, studies suggest that both genetic and environmental factors play a role. A person’s fingerprints are based on the patterns of skin ridges (called dermatoglyphs) on the pads of the fingers. These ridges are also present on the toes, the palms of the hands, and the soles of the feet. Although the basic whorl, arch, and loop patterns may be similar, the details of the patterns are specific to each individual. Dermatoglyphs develop before birth and remain the same throughout life.
    [Show full text]
  • A Model and Test for Coordinated Polygenic Epistasis in Complex Traits
    A model and test for coordinated polygenic epistasis in complex traits Brooke Shepparda,1, Nadav Rappoporta,b,1, Po-Ru Lohc,d, Stephan J. Sandersa, Noah Zaitlena,e,f,1,2, and Andy Dahle,f,g,1,2 aDepartment of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143; bBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA 94143; cProgram in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142; dDivision of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115; eDepartment of Neurology, University of California Los Angeles, Los Angeles, CA 90095; fDepartment of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095; and gSection of Genetic Medicine, University of Chicago, Chicago, IL 60637 Edited by Andrew G. Clark, Cornell University, Ithaca, NY, and approved December 2, 2020 (received for review February 19, 2020) Interactions between genetic variants—epistasis—is pervasive in of epistasis. For example, Zuk et al. describe a limiting pathway model systems and can profoundly impact evolutionary adaption, pop- model of human disease that directly implies negative interactions ulation disease dynamics, genetic mapping, and precision medicine between SNPs contributing to different pathways (29). Also, the efforts. In this work, we develop a model for structured polygenic HSP90 community has discussed the possibility of a polygenic epistasis, called coordinated epistasis (CE), and prove that several re- version of chaperon function (30), which would induce coordi- cent theories of genetic architecture fall under the formal umbrella of nated interactions between HSP90 buffer SNPs and exonic mis- CE.
    [Show full text]
  • Five Fundamental Gaps in Nature-Nurture Science Peter J
    University of Massachusetts Boston ScholarWorks at UMass Boston Working Papers on Science in a Changing World Critical and Creative Thinking Program Spring 3-22-2014 Five Fundamental Gaps In Nature-Nurture Science Peter J. Taylor University of Massachusetts Boston, [email protected] Follow this and additional works at: https://scholarworks.umb.edu/cct_sicw Part of the Biostatistics Commons, and the Genetics Commons Recommended Citation Taylor, Peter J., "Five Fundamental Gaps In Nature-Nurture Science" (2014). Working Papers on Science in a Changing World. 3. https://scholarworks.umb.edu/cct_sicw/3 This Article is brought to you for free and open access by the Critical and Creative Thinking Program at ScholarWorks at UMass Boston. It has been accepted for inclusion in Working Papers on Science in a Changing World by an authorized administrator of ScholarWorks at UMass Boston. For more information, please contact [email protected]. Paper # 3-2014 Five Fundamental Gaps in Nature-Nurture Science PETER J. TAYLOR http://scholarworks.umb.edu/cct_sicw/3 Five Fundamental Gaps In Nature-Nurture Science Peter J. Taylor Science in a Changing World graduate track University of Massachusetts, Boston, MA 02125, USA [email protected] Abstract Difficulties identifying causally relevant genetic variants underlying patterns of human variation have been given competing interpretations. The debate is illuminated in this article by drawing attention to the issue of underlying heterogeneity—the possibility that genetic and environmental factors or
    [Show full text]
  • Inferring Relevant Cell Types for Complex Traits by Integrating Genome-Wide Association Studies and Single-Cell RNA Sequencing
    bioRxiv preprint doi: https://doi.org/10.1101/2021.06.09.447805; this version posted June 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. EPIC: inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing Rujin Wang1, Dan-Yu Lin1,2, Yuchao Jiang1,2,3,* 1 Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA. 2 Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA. 3 Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA. To whom correspondence should be addressed: [email protected]. bioRxiv preprint doi: https://doi.org/10.1101/2021.06.09.447805; this version posted June 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. 1 Abstract 2 More than a decade of genome-wide association studies (GWASs) have identified genetic 3 risk variants that are significantly associated with complex traits. Emerging evidence 4 suggests that the function of trait-associated variants likely acts in a tissue- or cell-type- 5 specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types 6 to elucidate disease etiology.
    [Show full text]
  • 8. Pritchardj.Omnigenic Architecture of Human Complex Traits.05012018
    Omnigenic architecture of human complex traits Jonathan Pritchard Departments of Genetics & Biology, & HHMI Stanford University Joint work with Evan Boyle, Yang Li, Xuanyao Liu ok Missing Heritability Workshop, NIH,Henry May 2018Stewart Talks Questions: 1. Why do the lead hits for any given trait contribute so little heritability? 2. Why does so much of the genome contribute to heritability? Example #1: Schizophrenia 108 genome-wide significant loci so far (Ripke 2014) Responsible for ~10% of explained variance (Shi…Pasaniuc 2016) We have estimated that ~half of all SNPs -log(p) have non-zero association effect sizes (unpub) chromosome See key work on polygenic models and heritability by Visscher, Yang, Pasaniuc, Price, and many others Example #2: What about a potentially simpler trait: lipid levels? (LDL, HDL and triglycerides) Monogenic lipid disorders ~2 dozen major effect loci Modified from Dron et al 2016 Common Variation: GWAS of Lipid Levels 57 genome-wide significant loci (Willer et al 2013) Monogenic genes for LDL HDL cHolesterol Total cHolesterol The significant loci only explain ~20% of Heritability of LDL All loci togetHer explain about ~80% (SHi…Pasaniuc 2016) Triglycerides LDL cHolesterol Modified from: Willer et al 2013, Dron et al 2016 For a wide variety of traits and diseases: • Heritability is spread extremely widely across the genome • Genes with trait-relevant functions only contribute a small fraction of the total disease risk • Low frequency-large effect variants often have clearer enrichment in relevant gene sets
    [Show full text]
  • Polygenic Prediction of Complex Human Traits Arxiv:2101.05870V1
    From Genotype to Phenotype: polygenic prediction of complex human traits Timothy G. Raben1, Louis Lello1,2, Erik Widen1, and Stephen D.H. Hsu1,2 1Michigan State University, East Lansing, Michigan, 48824 2Genomic Prediction, North Brunswick, New Jersey, 08902 Abstract Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coro- nary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this arXiv:2101.05870v1 [q-bio.GN] 14 Jan 2021 category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering. A version of this article was prepared for Genomic Prediction of Complex Traits, Springer Nature book series Methods in Molecular Biology. Keywords: genomics, complex trait prediction, PRS, in vitro fertilization, genetic engineering 1 1 Introduction I, on the other hand, knew nothing, except ... physics and mathematics and an ability to turn my hand to new things. — Francis Crick The challenge of decoding the genome has loomed large over biology since the time of Wat- son and Crick.
    [Show full text]
  • Heritability Jointly Explained by Host Genotype and Microbiome:Will Improve Traits Prediction?
    bioRxiv preprint doi: https://doi.org/10.1101/2020.04.25.061226; this version posted April 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Heritability jointly Explained by Host Genotype and Microbiome:Will Improve Traits Prediction? Denis Awany 1, and Emile R. Chimusa 1,? 1Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa. ? Correspondence to Emile R. Chimusa: [email protected] Abstract As we observe the 70th anniversary of the publication by Robertson that formalized the notion of ‘her- itability’, geneticists remain puzzled by the problem of missing/hidden heritability, where heritability estimates from genome-wide association studies (GWAS) fall short of that from twin-based studies. Many possible explanations have been offered for this discrepancy, including existence of genetic vari- ants poorly captured by existing arrays, dominance, epistasis, and unaccounted-for environmental factors; albeit these remain controversial. We believe a substantial part of this problem could be solved or better understood by incorporating the host’s microbiota information in the GWAS model for heritability estimation; ultimately also increasing human traits prediction for clinical utility. This is because, despite empirical observations such as (i) the intimate role of the microbiome in many com- plex human phenotypes, (ii) the overlap between genetic variants associated with both microbiome attributes and complex diseases, and (iii) the existence of heritable bacterial taxa, current GWAS models for heritability estimate do not take into account the contributory role of the microbiome.
    [Show full text]