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PubMed2000

The power of genomic control.

Although association analysis is a useful tool for uncovering the genetic underpinnings of complex traits, its utility is diminished by population substructure, which can produce spurious association between phenotype and genotype within population-based samples. Because family-based designs are robust against substructure, they have risen to the fore of association analysis. Yet, if population substructure could be ignored, this robustness can come at the price of power. Unfortunately it is rarely evident when population substructure can be ignored. Devlin and Roeder recently have proposed a method, termed "genomic control" (GC), which has the robustness of family-based designs even though it uses population-based data. GC uses the genome itself to determine appropriate corrections for population-based association tests. Using the GC method, we contrast the power of two study designs, family trios (i.e., father, mother, and affected progeny) versus case-control. For analysis of trios, we use the TDT test. When population substructure is absent, we find GC is always more powerful than TDT; furthermore, contrary to previous results, we show that as a disease becomes more prevalent the discrepancy in power becomes more extreme. When population substructure is present, however, the results are more complex: TDT is more powerful when population substructure is substantial, and GC is more powerful otherwise. We also explore general issues of power and implementation of GC within the case-control setting and find that, economically, GC is at least comparable to and often less expensive than family-based methods. Therefore, GC methods should prove a useful complement to family-based methods for the genetic analysis of complex traits.

Alleles↗
PubMed2005

Quantitative genomics of starvation stress resistance in Drosophila.

BACKGROUND: A major challenge of modern biology is to understand the networks of interacting genes regulating complex traits, and the subset of these genes that affect naturally occurring quantitative genetic variation. Previously, we used P-element mutagenesis and quantitative trait locus (QTL) mapping in Drosophila to identify candidate genes affecting resistance to starvation stress, and variation in resistance to starvation stress between the Oregon-R (Ore) and 2b strains. Here, we tested the efficacy of whole-genome transcriptional profiling for identifying genes affecting starvation stress resistance. RESULTS: We evaluated whole-genome transcript abundance for males and females of Ore, 2b, and four recombinant inbred lines derived from them, under control and starved conditions. There were significant differences in transcript abundance between the sexes for nearly 50% of the genome, while the transcriptional response to starvation stress involved approximately 25% of the genome. Nearly 50% of P-element insertions in 160 genes with altered transcript abundance during starvation stress had mutational effects on starvation tolerance. Approximately 5% of the genome exhibited genetic variation in transcript abundance, which was largely attributable to regulation by unlinked genes. Genes exhibiting variation in transcript abundance among lines did not cluster within starvation resistance QTLs, and none of the candidate genes affecting variation in starvation resistance between Ore and 2b exhibited significant differences in transcript abundance between lines. CONCLUSIONS: Expression profiling is a powerful method for identifying networks of pleiotropic genes regulating complex traits, but the relationship between variation in transcript abundance among lines used to map QTLs and genes affecting variation in quantitative traits is complicated.

Adaptation, Physiological↗
PubMed1998

Sib-pair collection strategies for complex diseases.

When planning an affected sib pair collection for use in a genomewide search for complex trait loci, researchers must ask: (a) Which family structures will yield the most informative pairs? and (b) Should recruitment extend beyond the index sib pair? The optimal collection strategy will depend on the trait's genetic architecture, but this is rarely known for non-Mendelian diseases. In the present report, we study the consequences of collecting only those sib pairs arising from pedigrees with a precisely specified structure as opposed to a strategy that collects all affected sib pairs at random (i.e., blind to the affection status of first-degree relatives). The former approach turns out to be risky because the power of specific pedigree structures can vary dramatically even among models producing identical observable parameters (such as population prevalence and sibling recurrence rate). In contrast, the latter approach typically involves only a modest loss of power as compared with the optimal (but unknowable) design. Further, we compare the strategy of collecting all affected sib pairs at random with the alternative of imposing some modest limitations on family structure (e.g., presence of at least one unaffected sib or parent). The latter approach generally provides some increase in power but entails additional effort to contact and phenotype relatives: the overall merit of imposing such requirements needs to be evaluated in the context of the specific disease to be studied and of the clinical and analytical resources available. In addition, these findings suggest that a further explanation for failure to replicate positive complex trait linkages lies in differences in ascertainment strategy between data sets.

Data Collection↗
PubMed2002

Hypertension as a complex genetic trait.

Pickering first showed that blood pressure distribution is unimodal and that the diagnosis of essential (primary) hypertension is an arbitrary quantitative trait. His family studies suggested that not 1, but many, perhaps 30 or more, gene variations are responsible for raising blood pressure. Two approaches have been used to address the genetics of essential hypertension. Candidate genes have been selected by virtue of their physiologic function, and case-control association studies have been performed. Numerous candidates have been evaluated, genes of the renin-angiotensin-aldosterone system, genes coding for adrenergic receptors, genes coding for proteins regulating endothelial function, and genes involved in signaling have been studied. True to Pickering's prediction, each gene tested thus far seems to exert, at best, a small effect and contradictory studies are common. Linkage studies have been performed to map the loci of genes regulating blood pressure or inducing hypertension. Studies of dizygotic twins and their parents have permitted an identity-by-decent linkage analysis. Studies of affected sibling pairs involve subjects whose parents are generally already dead. Identity-by-state analysis requires a far greater number of pairs for results. Nevertheless, some large linkage studies have now been performed including subjects from Framingham. Log odds (LOD) scores are far removed from actually cloning genes responsible for hypertension. Single nucleotide polymorphisms (SNPs) permit linkage dysequilibrium mapping, which may enable cloning new genes. Thus far, very few new genes have been cloned for any complex genetic disease. The task is daunting but not impossible.

Epithelial Sodium Channels↗
PubMed2003

Confounding, ascertainment bias, and the blind quest for a genetic 'fountain of youth'.

Many promises have been made about the impact of the Human Genome Project on clinical practice and public health, yet despite massively funded efforts over the past decade, little headway has been made in elucidating the specific genetic factors which have major impact on the risk of developing common complex traits. There are two fundamental reasons for this abject failure as follows: 1) studies have been inadequately designed to identify such genetic risk factors; 2) the genetic factors that do exist are individually of small marginal importance, and are characterized by extensive heterogeneity. If 2) is the truth, there is little we can do about it, so we emphasize the importance of 1) in this article, while recognizing that 2) probably is not far from the truth. Genetic studies, in contrast to epidemiological studies, use confounding and ascertainment bias to help identify weak etiologic signal due to genes, since gene mapping is fundamentally a hypothesis-free science. This strategy makes it possible to identify genetic risk factors, but makes it impossible to quantify the size of their effect on risk. Classical epidemiological study designs are of minimal value for gene identification, but may be of use in estimation of the effect size of genetic risk factors once they are identified in more appropriately designed genetic studies. However, if the effects are so weak that we need this strong, systematic ascertainment bias to find them, their relevance to public health may be of questionable immediate value, raising many questions about the rhetoric and promises being made to the public as justification for 'big science' approaches to dissecting the hypothetical role of genes in complex traits.

Animals↗
PubMed2003

Summary report: Missing data and pedigree and genotyping errors.

Genetic epidemiology is faced with mapping complex traits to genes with relatively small effects whose phenotypes may be modulated by temporal factors. To do this, detailed and accurate data must be available on families, perhaps collected over time. The Framingham Heart Study data supplied to Genetic Analysis Workshop 13 (GAW13), along with its simulated counterpart, contain longitudinal measurements and genomic scan data on 2,885 individuals in 330 families, and offer an opportunity to examine data quality and completeness issues as they affect analytical conclusions. Six GAW13 contributions applied methods to deal with missing data, both phenotypic and genotypic, at a single time point and longitudinally, and with possible errors in pedigree structure and genotypes. The methods included missing phenotypic data imputation by Markov chain Monte Carlo sampling, propensity scoring, regression, and adjusted mean values, as well as the assessment of transmission-disequilibrium tests when missing marker data may be allele-specific. Pedigree structural errors were found by genome-wide allele-sharing probabilities, while Mendelian consistent genotype errors were evaluated through likelihoods of double-recombination events. Each of the methods reviewed here offered insights into how to better take advantage of large, time-dependent, familial data sets. However, no one of them dealt with the longitudinal and familial aspects simultaneously. Overall, more consideration needs to be given to the effects that missing data and data errors have on our ability to map complex traits efficiently and accurately.

Cardiovascular Diseases↗
PubMed2004

Temperament types are associated with weak self-construct, elevated distress and emotion-oriented coping in schizophrenia: evidence for a complex vulnerability marker?

Knowledge concerning temperament factors involved in vulnerability to schizophrenia is limited. We hypothesized that temperament and self-variables (emotional distress, coping styles, self-efficacy and self-esteem) might present a complex trait marker for underlying vulnerability to schizophrenia. We sought to (1) assess temperament dimensions and types in schizophrenia patients and healthy controls using the Tridimensional Personality Questionnaire (TPQ), and (2) explore their association with symptom dimensions, emotional distress, coping styles, self-constructs, demographic and background variables. We evaluated 90 consecutively recruited DSM-IV schizophrenia patients and 136 healthy controls matched for gender and age. We found that the harm avoidance (HA) factor was higher, while reward dependence (RD) was lower in schizophrenia patients than in healthy controls. Relationships of negative symptoms to novelty seeking (NS) and general psychopathology with both NS and HA show a confounding relation to self-variables. TPQ temperament types were defined by dichotomization into high and low according to medians of the three TPQ temperament dimensions. The odds ratios for the HA and HA/NS temperament types were significantly higher, while the NS/RD type was lower in schizophrenia patients than in healthy controls. HA/NS, HA/RD and high-HA/NS/RD types revealed higher scores for emotional distress, emotion-oriented coping and lower scores on self-constructs. No links were found between temperament types and schizophrenia symptom dimensions, task and avoidance oriented coping, or demographic and background variables. Thus, our findings strengthen the hypothesis that temperament types, when associated with elevated emotional distress, emotion-oriented coping and weak self-constructs, might represent a complex trait marker for underlying vulnerability to schizophrenia.

Adaptation, Psychological↗
PubMed2004

Haplotypes and SNPs in 13 lipid-relevant genes explain most of the genetic variance in high-density lipoprotein and low-density lipoprotein cholesterol.

Single nucleotide polymorphisms (SNPs) and derived haplotypes within multiple genes may explain genetic variance in complex traits; however, this hypothesis has not been rigorously tested. In an earlier study we analyzed six genes and have now expanded this investigation to include 13. We studied 250 families including 1054 individuals and measured lipid phenotypes. We focused on low-density cholesterol (LDL), high-density cholesterol (HDL) and their ratio (LDL/HDL). A component analysis of the phenotypic variance relying on a standard genetic model' showed that the genetic variance on LDL explained 26%, on HDL explained 38% and on LDL/HDL explained 28% of the total variance, respectively. Genotyping of 93 SNPs in 13 lipid-relevant genes generated 230 haplotypes. The association of haplotypes in all the genes tested explained a major fraction of the genetic phenotypic variance component. For LDL, the association with haplotypes explained 67% and for HDL 58% of the genetic variance relative to the polygenic background. We conclude that these haplotypes explain most of the genetic variance in LDL, HDL and LDL/HDL in these representative German families. An analysis of the contribution to the genetic variance at each locus showed that APOE (50%), CETP (28%), LIPC (9%), APOB (8%) and LDLR (5%) influenced variation in LDL. LIPC (53%), CETP (25%), ABCA1 (10%), LPL (6%) and LDLR (6%) influenced the HDL variance. The LDL/HDL ratio was primarily influenced by APOE (36%), CETP (27%) and LIPC (31%). This expanded analysis substantially increases the explanation of genetic variance on these complex traits.

Cholesterol, HDL↗
PubMed1997

False positive rates in a genomic screen for complex quantitative traits.

We conducted a genomic screen for genes associated with Q1, Q2, and Q3 in 239 nuclear pedigrees from replicate 115, Problem Set 2A. We compared false positive (FP) and true positive (TP) rates for three significance levels and two map densities. Using the 2 cM genetic map and alpha = 0.05 produced the most FP but detected the greatest number of major genes. Following up only 31 plateaus (two or more adjacent markers with significant results) from the 2 cM screen eliminated some FP, but failed to detect MG3 for Q3. Multipoint analysis reduced the number of priority regions from 31 to seven; only two of these regions were TP. Replication of the two-point analysis of plateau markers in replicate 80 detected all of the genes associated with Q1 and Q2, but not Q3. Multipoint analysis in replicate 80 failed to replicate any genes associated with Q1, Q2, or Q3, but "replicated" two FP regions. While FP may be reduced by decreasing map density, considering only plateaus for follow up and decreasing significance levels, such adjustments may also fail to detect weak TP. Multipoint analysis and replication in independent data sets may not be reliable methods of distinguishing FP from TP.

Chromosome Mapping↗
PubMed1998

Susceptibility to tuberculosis as a complex genetic trait: analysis using recombinant congenic strains of mice.

Previous advances in the genetics of infectious diseases derived principally from identification of single genes and their isolated effects on the progression of infection. Modern genetic analysis represents a powerful means of understanding the interplay among different pathways activated in the course of infection, their hierarchy and interactions in terms of the development of an optimal protective strategy. By utilizing both whole-genome scanning of (C3HxC57BL/6)F2 and a set of the recombinant congenic strains, produced by backcrossing B10 onto a C3H background, we demonstrated that susceptibility to tuberculosis is a multigenic trait. We have identified two distinct groups of susceptible mice: one that dies within four to six weeks of infection (supersusceptible) and another that dies within seven to 10 weeks (comparable to the susceptible parental strain). Our preliminary genetic analysis suggests that the susceptibilty of those groups is controlled by different genetic factors. Supersusceptible mice exhibit dramatic lung pathology, not observed in either parental strain, and their survival after infection with virulent Mycobacterium tuberculosis is comparable to that of mice rendered immunodeficient by disruption of essential immune genes. Further genetic and functional analyses of these strains offer possibilities for understanding the control of transmission, preferential growth of the pathogen in the lung, and mechanisms of local and systemic protective immune responses.

Animals↗
PubMed1993

Hominoid heterochromatin: terminal C-bands as a complex genetic trait linking chimpanzee and gorilla.

The genetic relations of the apes have been the source of contention throughout the last decade. A potentially useful suite of phylogenetic characters is the distribution of darkly staining material (heterochromatin) in the chromosomes of the apes. While the precise etiology of this character suite remains unclear, it appears to be fairly easily reconciled to hominoid phylogeny in general. The distribution of heterochromatin at the tips of the chromosomes of gorillas and chimpanzees suggests a phylogenetic association between those two taxa exclusive of humans.

Animals↗
PubMed1999

Simulated data for a complex genetic trait (problem 2 for GAW11): how the model was developed, and why.

This paper describes a simulated data set created as Problem 2 for GAW11. The generating model for Problem 2 involved two different genetic diseases, or "types," in three separate populations. The two-locus (2L) type results from the epistatic interaction of two genetic loci, and the three-allele type, from a single locus with two disease-causing alleles and one normal allele. Each type has two phenotypic forms: Mild and Severe. Both forms are subject to both genetic and environmental influences. The disease occurs in three different hypothetical populations, each with different disease allele frequencies and penetrances. In two populations there is also a fourth locus with an allele that is associated with the 2L type. Misdiagnosis can occur, but only after a family has already been ascertained through > or = 2 "genetically" affected offspring. Finally, the three different populations are studied by four different hypothetical research groups. These groups each have their own ideas about how the disease is inherited and have therefore devised different ascertainment schemes based on those beliefs. Each research group collected 100-family data sets, including data on 300 markers on six chromosomes and measurements on disease status and on the proposed two environmental factors. GAW participants were supplied with 25 random replicates of each data set.

Alleles↗
PubMed1999

Systematic search for disease loci for complex genetic traits: a study based on simulated population data.

Simulated family data were analyzed using one- and two-locus disease models to detect linkage. Regions of interest, found on chromosomes 3 and 5, were then further analyzed to look for evidence of locus interaction and/or genetic heterogeneity. Methods described by Falk [1993] were used to separate families into subsets likely to be genetically homogeneous. Based on the results, it was concluded that there were at least two distinct disease loci, one on chromosome 3 and one on chromosome 5, and that these loci were probably not interacting but were expressing two distinct forms of the disease. The identification of these loci was in agreement with the generating model. However, the analysis did not show any indication of a two-locus form of the disease or detect a disease locus on chromosome 1. This could be due to lack of power and/or too small a sample size for the method of analysis.

Chromosome Mapping↗
PubMed2005

Admixture mapping as a gene discovery approach for complex human traits and diseases.

Admixture mapping (AM) is a special form of conventional meiotic or recombination mapping for disease gene discovery in humans that exploits naturally occurring genetic and phenotypic differences existing in populations between which recent gene flow has occurred. Essentially, mates from two different "parental" populations with different allelic and disease-predisposing mutation profiles will produce "admixed" offspring whose genomes will be mixtures of the genomes associated with the parental populations. Strong linkage disequilibrium (LD) will exist for several generations between neighboring loci of admixed individuals and can be exploited for identifying the genomic location of trait-influencing loci. Although it may be a very clever strategy for identifying genes that influence human traits and diseases, AM can be problematic. We review the foundations, basic strategies, resources, and settings necessary for AM. We conclude that AM has potential in the identification of disease-predisposing loci, but this potential may only exist in a limited number of realistic settings.

Chromosome Mapping↗
PubMed1995

Polygenic disease: methods for mapping complex disease traits.

Improved genotyping technology has made it feasible to use a genetic approach to map genes involved in the etiology of common human diseases. We discuss here recent developments in several different statistical approaches to linkage analysis of these traits, including affected-sib-pair methods, the affected-pedigree-member method, regressive models and linkage-disequilibrium-based approaches. We discuss advantages and disadvantages of the various approaches, as well as factors influencing study design and the ability to detect loci. Statistical methodology in this area is advancing rapidly and will help enable the mapping and cloning of loci involved in susceptibility to common multifactorial diseases.

Autistic Disorder↗
PubMed2002

Linkage disequilibrium and the mapping of complex human traits.

The potential value of haplotypes defined by several single nucleotide polymorphisms has attracted recent interest. With sufficient linkage disequilibrium (LD), haplotypes could be used in association studies to map common alleles that might influence the susceptibility to common diseases, as well as for reconstructing the evolution of the genome. It has been proposed that a globally useful resource need only be based on high frequency variants, identified from a few modest samples. Rapid progress has been made in quantifying the pattern of human LD and haplotypes defined by such common variants within and among populations. However, the quality and utility of the proposed LD-based resource could be seriously compromised if important sampling and analytical factors are overlooked in its design. The LD map should be based on adequately justified criteria defined by sound population genetic principles.

Chromosome Mapping↗