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Mapping complex traits in diseases of the hair and skin.

The past decade has witnessed the ascendance of human genetics in modern medicine, and at the forefront of this movement is the identification of genetic factors underlying inherited diseases. The methods of genetic mapping and positional cloning have made the discovery of genes with alleles that cause simple Mendelian diseases commonplace. The elucidation of the genetic basis of such disorders has vitalized both human genetics and the entire medical community as the field has gained prominence. The fact remains, however, that diseases resulting from the action of alleles of a single gene comprise only a minor percentage of traits that are medically relevant to humanity. The majority of these are multifactorial "complex traits", which result from the aggregate contribution of an unknown number of genes interacting with each other and with the environment. The current challenge has become one of parlaying successes in the mapping of Mendelian diseases into the discovery of genes whose alleles predispose the development of a complex disease. In light of this challenge, this review summarizes the methods and addresses some of the central issues of complex trait mapping, while using examples from dermatologically-relevant complex traits such as psoriasis and alopecia. Additionally, current technical and theoretical advances as well as the potential impact of the Human Genome Project will be discussed.

Animals↗

Genetic analysis of late-onset type 2 diabetes in a mouse model of human complex trait.

Type 2 diabetes is a complex trait with both genes and environmental factors contributing to susceptibility. Except for rare subtypes with monogenic inheritance, the genetic basis of type 2 diabetes is unknown because of the complex and heterogeneous nature of the disease. By using the NSY mouse, an inbred mouse model of type 2 diabetes, we genetically dissected late-onset type 2 diabetes and demonstrated age-dependent changes in the genetic control of type 2 diabetes as well as polygenic inheritance. Three major loci (Nidd1nsy, Nidd2nsy, Nidd3nsy) were mapped on mouse chromosomes (Chr) 11, 14, and 6, respectively. The existence of a fourth locus (Nidd4nsy) with an age-dependent effect was suggested by longitudinal, but not cross-sectional, analysis of linkage data. Nidd1nsy and Nidd4nsy appear to affect insulin secretion, whereas Nidd2nsy and Nidd3nsy appear to affect insulin sensitivity. A locus on Chr 6 was significantly linked to epididymal fat weight. A candidate disease gene (Tcf2) on Chr 11, encoding hepatic nuclear factor-1beta, was shown to have a rare sequence variant in the DNA binding domain in the model. The mouse model we used will serve as a useful model for future studies on the etiology of late-onset polygenic type 2 diabetes in humans.

Adipose Tissue↗

Animal genetic models for complex traits of physical capacity.

Animal genetic models for complex traits of physical capacity. Exerc. Sport Sci. Rev., Vol. 29, No. 1, pp 7-14, 2001. Understanding the genetic basis for variance in complex physical traits such as aerobic capacity has become an attainable goal. A starting point is the development or identification of animal genetic models that contrast the low and high values for the trait of interest. Genes that cause natural trait variation can ultimately be determined from animal models via genetic linkage.

Animals↗

Genetic and environmental interactions outweigh mitonuclear coevolution for complex traits in Drosophila.

The interdependent relationship between mitochondrial and nuclear genomes is a powerful model for understanding how epistasis shapes the architecture and evolution of complex traits. Once considered a neutral marker, mitochondrial DNA variation is now recognized as critical to phenotypic evolution because of its epistatic interactions and history of coevolution with the nuclear genome. A central challenge in evolutionary genetics is to quantify the relative importance of stabilizing and directional selection shaping complex trait distributions within and among species. Both can act on interacting and/or co-evolving genes contributing to quantitative traits, but resolving their relative roles is complicated by the complex architecture of most traits. Here, we use a panel of 90 Drosophila mitonuclear genotypes to quantify the relative contributions of mitochondrial, nuclear, and environmental variation and their interactions to four metabolically demanding complex traits. We sample both within-species and between-species mitochondrial variation and observe stronger interaction effects attributable to within-species variation, consistent with stabilizing selection maintaining mitonuclear function. Additionally, culturing the flies on a mitochondrial Complex I inhibitor, rotenone, reveals significant genotype x environment (G×E and G×G×E) interaction effects, providing insight into how genetic variation can be maintained across changing environments. Our results have broader implications in medicine, where mitochondrial DNA donors with longer purifying selection histories may be safer for mitochondrial replacement therapies.

Journal Article↗

Interval mapping of quantitative trait loci employing correlated trait complexes.

An approach to increase the resolution power of interval mapping of quantitative trait (QT) loci is proposed, based on analysis of correlated trait complexes. For a given set of QTs, the broad sense heritability attributed to a QT locus (QTL) (say, A/a) is an increasing function of the number of traits. Thus, for some traits x and y, H(xy)2(A/a) > or = H(x)2(A/a). The last inequality holds even if y does not depend on A/a at all, but x and y are correlated within the groups AA, Aa and aa due to nongenetic factors and segregation of genes from other chromosomes. A simple relationship connects H2 (both in single trait and two-trait analysis) with the expected LOD value, ELOD = -1/2N log(1-H2). Thus, situations could exist that from the inequality H(xy)2(A/a) > or = H(x)2(A/a) a higher resolution is provided by the two-trait analysis as compared to the single-trait analysis, in spite of the increased number of parameters. Employing LOD-score procedure to simulated backcross data, we showed that the resolution power of the QTL mapping model can be elevated if correlation between QTs is taken into account. The method allows us to test numerous biologically important hypotheses concerning manifold effects of genomic segments on the defined trait complex (means, variances and correlations).

Analysis of Variance↗

Replication of linkage studies of complex traits: an examination of variation in location estimates.

In linkage studies, independent replication of positive findings is crucial in order to distinguish between true positives and false positives. Recently, the following question has arisen in linkage studies of complex traits: at what distance do we reject the hypothesis that two location estimates in a genomic region represent the same gene? Here we attempt to address this question. Sampling distributions for location estimates were constructed by computer simulation. The conditions for simulation were chosen to reflect features of "typical" complex traits, including incomplete penetrance, phenocopies, and genetic heterogeneity. Our findings, which bear on what is considered a replication in linkage studies of complex traits, suggest that, even with relatively large numbers of multiplex families, chance variation in the location estimate is substantial. In addition, we report evidence that, for the conditions studied here, the standard error of a location estimate is a function of the magnitude of the expected LOD score.

Chromosome Mapping↗

A twin-pronged attack on complex traits.

Before one starts the hunt for quantitative trait loci (QTLs) for a complex trait, it is necessary to show that the trait is genetically influenced. This evidence is most likely to come from the classical twin study--the demonstration that monozygotic twins are more similar for the trait than dizygotic twins. The strengths and weaknesses of twin studies are discussed, and it is suggested that, far from becoming irrelevant with advances in molecular biology, they can improve the efficiency of QTL detection and play an important role in unravelling developmental genetic mechanisms.

Diseases in Twins↗

Improved power of sib-pair linkage analysis using measures of complex trait dynamics.

The influence of epistasis on a quantitative trait can reduce the power of linkage analysis to identify the underlying loci. In the present study, we simulated a complex trait derived from a dynamic one-locus gene expression system with epistasis arising from feedback regulation and tested the power of sib-pair linkage analysis methods for detecting the underlying quantitative trait locus (QTL). Using this simple genetic architecture, we demonstrate that the power of sib-pair linkage analysis can be greatly improved if measures of complex trait dynamics are considered.

Computer Simulation↗

High-resolution genetic mapping of complex traits.

Positional cloning requires high-resolution genetic mapping. To plan a positional cloning project, one needs to know how many informative meioses will be required to narrow the search for a disease gene to an acceptably small region. For a simple Mendelian trait studied with linkage analysis, the answer is straightforward. In this paper, we address the situation of a complex trait studied with affected-relative-pair methods. We derive mathematical formulas for the size of an appropriate confidence region, as a function of the relative risk attributable to the gene. Using these results, we provide graphs showing the number of relative pairs required to narrow the gene hunt to an interval of a given size. For example, we show that localizing a gene to 1 cM requires a median of 200 sib pairs for a locus causing a fivefold increased risk to an offspring and 700 sib pairs for a locus causing a twofold increased risk. We discuss the implications of these results for the positional cloning of genes underlying complex traits.

Alleles↗

Genetic dissection of complex traits.

Medical genetics was revolutionized during the 1980s by the application of genetic mapping to locate the genes responsible for simple Mendelian diseases. Most diseases and traits, however, do not follow simple inheritance patterns. Genetics have thus begun taking up the even greater challenge of the genetic dissection of complex traits. Four major approaches have been developed: linkage analysis, allele-sharing methods, association studies, and polygenic analysis of experimental crosses. This article synthesizes the current state of the genetic dissection of complex traits--describing the methods, limitations, and recent applications to biological problems.

Alleles↗

Mapping of complex traits by single-nucleotide polymorphisms.

Molecular geneticists are developing the third-generation human genome map with single-nucleotide polymorphisms (SNPs), which can be assayed via chip-based microarrays. One use of these SNP markers is the ability to locate loci that may be responsible for complex traits, via linkage/linkage-disequilibrium analysis. In this communication, we describe a semiparametric method for combined linkage/linkage-disequilibrium analysis using SNP markers. Asymptotic results are obtained for the estimated parameters, and the finite-sample properties are evaluated via a simulation study. We also applied this technique to a simulated genome-scan experiment for mapping a complex trait with two major genes. This experiment shows that separate linkage and linkage-disequilibrium analyses correctly detected the signals of both major genes; but the rates of false-positive signals seem high. When linkage and linkage-disequilibrium signals were combined, the analysis yielded much stronger and clearer signals for the presence of two major genes than did two separate analyses.

Biosensing Techniques↗

Efficient, robust, and unified method for mapping complex traits (I): two-point linkage analysis.

The completion of a preliminary human genome map and development of molecular methods have enabled researchers to assay a large number of polymorphic markers that are evenly spaced along the entire human genome. Among many applications, marker data are valuable for mapping complex traits through linkage or linkage-disequilibrium analysis, the former of which is the focus of this paper, the first in a series on this subject. Formalizing the concept and computation for linkage analysis, Elston and Stewart [1971; Human Heredity 21:523-542] introduced a likelihood function to capture relevant genetic information and a recursive algorithm for computing the likelihood function. However, the computing burden is prohibitive in processing complex pedigrees. Since that fundamental development, improving the computational algorithm and extending the method has been a dynamic area of research. The primary objective of this communication is to introduce a semiparametric method for linkage analysis. It is a particularly suitable approach with desirable properties for mapping complex traits that may be binary, continuous, and partially observed (i.e., censored). It incorporates candidate genes, environmental factors, and their interactions with the putative gene and is expected to be robust and efficient in comparison with likelihood-based methods. The properties of the estimates have been studied in finite samples with a limited simulation study. This method is illustrated with an application to family data contributed to the Breast Cancer Consortium.

Algorithms↗

The future of path analysis, segregation analysis, and combined models for genetic dissection of complex traits.

Appropriate combined models are discussed for the analysis of complex traits. It is argued that combined models may be necessary for optimally extracting the information from family studies. It is further argued that, especially as we face genes with much smaller effects, our ability to find these genes will depend on how precisely and accurately we are able to model the interrelationships. We need these newer models and methods for optimally extracting the information from family data, and we also need to reorient ourselves as to how we interpret the very information extracted. It is projected that path and segregation analysis, as seen in terms of combined models, will be useful in the new millennium.

Chromosome Segregation↗

Expanded Chromatin Accessibility Mapping Explains Genetic Variation Associated with Complex Traits in Liver.

Genome-wide association studies (GWAS) have identified thousands of loci associated with a variety of common, complex human traits. Recent efforts have focused on characterizing chromatin accessibility to discover regulatory elements that modify the expression of nearby genes, suggesting that trait associations are mediated through changes in gene regulation. Genetic variants associated with differences in chromatin accessibility, known as chromatin accessibility quantitative trait loci (caQTLs), are established contributors to gene expression differences, providing mechanistic hypotheses for signals identified by GWAS. Using the assay for transposase-accessible chromatin with sequencing (ATAC-seq), we assessed chromatin accessibility in 189 diverse human liver samples, identifying over two million accessible chromatin regions enriched for gene regulatory features and, in 175 of these samples, over 14,000 caQTLs. Focusing subsequently on liver-relevant complex traits, we obtained publicly available blood lipids GWAS data and identified 157 loci where caQTLs, expression quantitative trait loci (eQTLs), and GWAS signals colocalized. This generated specific molecular hypotheses about regulatory elements, affected genes, and, in some cases, implicated transcription factors. Finally, we enumerated the set of blood lipid trait signals that lack an obvious proposed mechanism beyond catalogs of liver caQTLs and eQTLs. After integrating 10 multi-omic QTL regulatory mechanism datasets whilst considering limitations in statistical power, we found that approximately 20% of blood lipid GWAS signals lacked a statistical link to a proposed mechanism. Our results demonstrate the value of integrating multiple genomic datasets to improve understanding of GWAS signals, while emphasizing the need for additional experimental approaches to fully characterize complex trait associations.

Journal Article↗

Population genomics in Sardinia: a novel approach to hunt for genomic combinations underlying complex traits and diseases.

The availability of highly polymorphic markers permits testing whether complex traits and diseases result from genomic interactions between nonallelic normal variants at separate loci. Such variants may be identified by deviations from the expected distributions of alleles at a high number of polymorphic loci, when individuals with the phenotype of interest are compared to normal controls of the same breeding unit, provided that both groups share the same remote ancestry and had no ancestors in common for the last three to four generations. The circumstances needed for such studies are ideally met on the island of Sardinia. The recurrent finding of the same type of association in separate breeding units between the phenotype of interest and a given genotype should allow a distinction between true genetic identity by descent and randomly occurring identities, as these will be obviously different in separate breeding units. The availability of several breeding units located in sharply different ecological environments will permit assessment of the role of nature/nurture factors in the degree of manifestation of each newly discovered genotype/phenotype association. A pilot study to evaluate the proposed strategy has been carried out in the Sardinian village of Carloforte, a community of about 8,000 individuals who have remained genetically homogeneous. Fifty-five control samples have been genotyped with six tetranucleotide microsatellites and with a subset of the 400 markers contained in the ABI PRISM linkage mapping panel, version 2. The allele frequencies for these microsatellite markers have been determined for these 55 individuals and compared to those from a random sampling of subsets of these 55 persons. For the six tetranucleotide microsatellites, a subset of as few as 20 people displayed the same allele frequency distributions as observed with the original 55 unrelated individuals. In conclusion, when samples are chosen from the same breeding unit, the number of individuals sufficient to draw the genomic profile of an isolated population can be relatively small. Likewise, the number of probands with the phenotype of interest can be even smaller when they are ascertained with the same genealogical criteria as the normal controls. By comparing the genomic profile of the probands to a fraction of the control samples within each of several separate breeding units of common remote ancestry, the search for genotype/phenotype association for mono- and multifactorial traits and diseases should be simplified and yield unequivocal results.

Aged↗

Production of congenic mouse strains carrying NOD-derived diabetogenic genetic intervals: an approach for the genetic dissection of complex traits.

Insulin-dependent (Type 1) diabetes (IDD) in the NOD mouse is inherited as a complex polygenic trait making the identification of susceptibility genes difficult. Currently none of the non-MHC IDD susceptibility genes in NOD have been identified. In this paper we describe the congenic mouse approach that we are using for the dissection of complex traits, such as IDD. We produced a series of six congenic strains carrying NOD-derived diabetogenic genomic intervals, which were previously identified by linkage analysis, on a resistant background. These congenic strains were produced for the purpose of characterizing the function of each of these genes, alone and in combinations, in IDD pathogenesis and to allow fine mapping of the NOD IDD susceptibility genes. Histological examination of pancreata from 6 to 8-month-old congenic mice reveals that intervals on Chromosomes (Chrs) 1 and 17, but not 3, 6, and 11, contain NOD-derived genes that can increase the trafficking of mononuclear cells into the pancreas. Insulitis was observed only very rarely, even in older congenic mice, indicating that multiple genes are required for this phenotype. These results demonstrate the utility of this congenic approach for the study of complex genetic traits.

Animals↗

Simple scaling laws control the genetic architectures of human complex traits.

Genome-wide association studies have revealed that the genetic architectures of complex traits vary widely, including in terms of the numbers, effect sizes, and allele frequencies of significant hits. However, at present we lack a principled way of understanding the similarities and differences among traits. Here, we describe a probabilistic model that combines the effects of mutation, drift, and stabilizing selection at individual sites with a genome-scale model of phenotypic variation. In this model, the architecture of a trait arises from the distribution of selection coefficients of mutations and from two scaling parameters. We fit this model for 95 highly polygenic quantitative traits of different kinds from the UK Biobank. Notably, we infer that all these traits have fairly similar, though not identical, distributions of selection coefficients. This similarity suggests that differences in architectures of highly polygenic traits arise mainly from the two scaling parameters: the mutational target size and heritability per site, which vary by orders of magnitude among traits. When these two scale factors are accounted for, we find that the architectures of all 95 traits are very similar.

Humans↗

Linkage strategies for genetically complex traits. I. Multilocus models.

In order to investigate linkage detection strategies for genetically complex traits, multilocus models of inheritance need to be specified. Here, two types of multilocus model are described: (1) a multiplicative model, representing epistasis (interaction) among loci, and (2) an additive model, which is shown to closely approximate genetic heterogeneity, which is characterized by no interlocus interaction. A ratio lambda R of risk for type R relatives that is compared with population prevalence is defined. For a single-locus model, lambda R - 1 decreases by a factor of two with each degree of relationship. The same holds true for an additive multilocus model. For a multiplicative (epistasis) model, lambda R - 1 decreases more rapidly than by a factor of two with degree of relationship. Examination of lambda R values for various classes of relatives can potentially suggest the presence of multiple loci and epistasis. For example, data for schizophrenia suggest multiple loci in interaction. It is shown in the second paper of this series that lambda R is the critical parameter in determining power to detect linkage by using affected relative pairs.

Genetic Linkage↗