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      Another Round of “Clue” to Uncover the Mystery of Complex Traits

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          Abstract

          A plethora of genetic association analyses have identified several genetic risk loci. Technological and statistical advancements have now led to the identification of not only common genetic variants, but also low-frequency variants, structural variants, and environmental factors, as well as multi-omics variations that affect the phenotypic variance of complex traits in a population, thus referred to as complex trait architecture. The concept of heritability, or the proportion of phenotypic variance due to genetic inheritance, has been studied for several decades, but its application is mainly in addressing the narrow sense heritability (or additive genetic component) from Genome-Wide Association Studies (GWAS). In this commentary, we reflect on our perspective on the complexity of understanding heritability for human traits in comparison to model organisms, highlighting another round of clues beyond GWAS and an alternative approach, investigating these clues comprehensively to help in elucidating the genetic architecture of complex traits.

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          The mystery of missing heritability: Genetic interactions create phantom heritability.

          O Zuk, E. Hechter, S. R. Sunyaev … (2012)
          Human genetics has been haunted by the mystery of "missing heritability" of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator--that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating "phantom heritability." Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example, 80% of the currently missing heritability for Crohn's disease could be due to genetic interactions, if the disease involves interaction among three pathways. In short, missing heritability need not directly correspond to missing variants, because current estimates of total heritability may be significantly inflated by genetic interactions. Finally, we describe a method for estimating heritability from isolated populations that is not inflated by genetic interactions.
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            Rare-variant association analysis: study designs and statistical tests.

            Seunggeung Lee, Gonçalo R Abecasis, Michael Boehnke … (2014)
            Despite the extensive discovery of trait- and disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants can explain additional disease risk or trait variability. An increasing number of studies are underway to identify trait- and disease-associated rare variants. In this review, we provide an overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests. We present the design and analysis pipeline of rare-variant studies and review cost-effective sequencing designs and genotyping platforms. We compare various gene- or region-based association tests, including burden tests, variance-component tests, and combined omnibus tests, in terms of their assumptions and performance. Also discussed are the related topics of meta-analysis, population-stratification adjustment, genotype imputation, follow-up studies, and heritability due to rare variants. We provide guidelines for analysis and discuss some of the challenges inherent in these studies and future research directions. Copyright © 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
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              Methods of integrating data to uncover genotype-phenotype interactions.

              Marylyn D Ritchie, Emily R. Holzinger, Ruowang Li … (2015)
              Recent technological advances have expanded the breadth of available omic data, from whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic data. A key goal of analyses of these data is the identification of effective models that predict phenotypic traits and outcomes, elucidating important biomarkers and generating important insights into the genetic underpinnings of the heritability of complex traits. There is still a need for powerful and advanced analysis strategies to fully harness the utility of these comprehensive high-throughput data, identifying true associations and reducing the number of false associations. In this Review, we explore the emerging approaches for data integration - including meta-dimensional and multi-staged analyses - which aim to deepen our understanding of the role of genetics and genomics in complex outcomes. With the use and further development of these approaches, an improved understanding of the relationship between genomic variation and human phenotypes may be revealed.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Genes (Basel)
                Journal ID (iso-abbrev): Genes (Basel)
                Journal ID (publisher-id): genes
                Title: Genes
                Publisher: MDPI
                ISSN (Electronic): 2073-4425
                Publication date (Electronic): 25 January 2018
                Publication date Collection: February 2018
                Volume: 9
                Issue: 2
                Electronic Location Identifier: 61
                Affiliations
                [1 ]The Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA; shefali.setiaverma@ 123456pennmedicine.upenn.edu
                [2 ]Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
                Author notes
                [* ]Correspondence: marylyn@ 123456upenn.edu
                Article
                Publisher ID: genes-09-00061
                DOI: 10.3390/genes9020061
                PMC ID: 5852557
                PubMed ID: 29370075
                SO-VID: 18313134-d854-48aa-87f0-8d8579d08b39
                Copyright © © 2018 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 01 November 2017
                Date accepted : 15 January 2018
                Categories
                Subject: Review

                Keywords: complex traits,meta-dimensional analysis,multi-omics datasets,heritability,game of “clue”
                Data availability:
                Keywords: complex traits, meta-dimensional analysis, multi-omics datasets, heritability, game of “clue”

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