How many diseases does it take to map a gene with SNPs?

Identifying disease modifying genes in multiple sclerosis

Evidence is mounting that genetic variation influences not only susceptibility to multiple sclerosis (MS), but also its course and severity. Identification of disease modifying genes, however, poses unique challenges, especially on how to classify the course and outcome of the disease in ways that may be relevant to analysis of biological factors that might be influenced by genes. The power of the statistical approaches to detect small effects of individual genes in complex disorders such as MS is problematic, and approaches to estimate power must be appropriate for the data. Nonetheless, using contemporary schemes of classification, genetic variants that influence disease course have been found; in fact, a small number have been confirmed to influence disease course in two or more independent studies. This review addresses strategies relevant to identification of disease modifying genes in MS, and summarizes and critically evaluates the current state of knowledge in this area.

Rheumatoid arthritis. The genetic components

Rheumatoid arthritis is a multifactorial disease, with genetic, environmental, and stochastic components to its susceptibility. The search for susceptibility genes is still in progress. Preliminary results suggest the involvement of multiple genes, each with relatively modest effect. Genes within the major histocompatibility complex appear to have the strongest influence on disease susceptibility.

The genetic architecture of quantitative traits

Phenotypic variation for quantitative traits results from the segregation of alleles at multiple quantitative trait loci (QTL) with effects that are sensitive to the genetic, sexual, and external environments. Major challenges for biology in the post-genome era are to map the molecular polymorphisms responsible for variation in medically, agriculturally, and evolutionarily important complex traits; and to determine their gene frequencies and their homozygous, heterozygous, epistatic, and pleiotropic effects in multiple environments. The ease with which QTL can be mapped to genomic intervals bounded by molecular markers belies the difficulty in matching the QTL to a genetic locus. The latter requires high-resolution recombination or linkage disequilibrium mapping to nominate putative candidate genes, followed by genetic and/or functional complementation and gene expression analyses. Complete genome sequences and improved technologies for polymorphism detection will greatly advance the genetic dissection of quantitative traits in model organisms, which will open avenues for exploration of homologous QTL in related taxa.

Role of NQO1, MPO and CYP2E1 genetic polymorphisms in the susceptibility to childhood acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer. The genetic factors underlying the susceptibility to this disease remain elusive. The enzymes CYP2E1, MPO and NQO1 are involved in the biotransformation of a variety of xenobiotics present in organic solvents, tobacco smoke, drugs, plastic derivatives and pesticides. They also control the level of the oxidative stress by catalyzing the formation of free radicals or by protecting cells from their deleterious effect. DNA variants in the corresponding genes have been associated with an increased susceptibility to different adult cancers, including hematologic malignancies. To investigate whether they represent risk-modifying factors in childhood ALL, we conducted a case-control study involving 174 patients and 337 controls, both of French-Canadian origin. We found that carriers of the CYP2E1*5 variant were at 2.8-fold higher risk of ALL (95%CI, 1.2-6.4) and that NQO1 alleles *2 and *3 contributed to the risk of ALL as well (OR = 1.7, 95%CI, 1.2-2.4). No such association was found with MPO alone. However, when the wild-type MPO allele was considered together with the CYP2E1 and NQO1 risk-elevating genotypes, the risk of ALL was increased further (OR = 5.4, 95%CI, 1.2-23.4) suggesting a combined effect. We also found a gene-gene interaction between the GSTM1 null genotype and NQO1 mutant alleles. It is therefore plausible that exposure to xenobiotics metabolized by these enzymes play a role in the etiology of childhood ALL.

The distribution of long range admixture linkage disequilibrium in an African-American population

OBJECTIVES

To better understand the effect of admixture on long range linkage disequilibrium (LD), we characterized extended LD in gene-rich regions of an African-American population.

METHODS

Approximately 290 cM of chromosomes 1, 3, 6, 11-17, 20 and 22 were scanned using 109 polymorphic microsatellite markers spaced an average of 3 cM apart. Disequilibrium between loci (D') was based on maximum-likelihood estimates of haplotype frequencies computed for 200 unrelated African Americans.

RESULTS

Mean D' values were highest on chromosomes 6p23-p21.3 (D' = 0.33) and 15p22.2-p25.3 (D' = 0.34), and lowest on chromosome 12p11.2-q14 (D' = 0.21). Overall, the variance in LD among chromosomes accounted for approximately two-thirds of the total LD variance. Of the 434 locus pairs spaced between 0.3 and 38.7 cM apart, there was no detectable correlation between LD and recombination distance and a weak negative correlation between LD and physical distance (r(s) = -0.12; p = 0.031). For the 192 intrachromosomal locus pairs where allele frequency data were available from the Centre d'Etude du Polymorphisme humain (CEPH), we found a statistically significant positive correlation between LD and the allelic frequency differences (delta) between the African-American study population and Caucasian reference CEPH population (r(s) = 0.53; p < 0.0001). The correlation between LD and both recombination and physical distance was markedly increased for locus pairs with high delta levels.

CONCLUSIONS

Our results suggest that recent Caucasian admixture maintains a high level of long range LD in African Americans on a genomic scale, and selected markers with large African American/Caucasian delta levels may be useful in association studies.

Understanding quantitative genetic variation

Until recently, it was impracticable to identify the genes that are responsible for variation in continuous traits, or to directly observe the effects of their different alleles. Now, the abundance of genetic markers has made it possible to identify quantitative trait loci (QTL)--the regions of a chromosome or, ideally, individual sequence variants that are responsible for trait variation. What kind of QTL do we expect to find and what can our observations of QTL tell us about how organisms evolve? The key to understanding the evolutionary significance of QTL is to understand the nature of inherited variation, not in the immediate mechanistic sense of how genes influence phenotype, but, rather, to know what evolutionary forces maintain genetic variability.

Large upward bias in estimation of locus-specific effects from genomewide scans

The primary goal of a genomewide scan is to estimate the genomic locations of genes influencing a trait of interest. It is sometimes said that a secondary goal is to estimate the phenotypic effects of each identified locus. Here, it is shown that these two objectives cannot be met reliably by use of a single data set of a currently realistic size. Simulation and analytical results, based on variance-components linkage analysis as an example, demonstrate that estimates of locus-specific effect size at genomewide LOD score peaks tend to be grossly inflated and can even be virtually independent of the true effect size, even for studies on large samples when the true effect size is small. However, the bias diminishes asymptotically. The explanation for the bias is that the LOD score is a function of the locus-specific effect-size estimate, such that there is a high correlation between the observed statistical significance and the effect-size estimate. When the LOD score is maximized over the many pointwise tests being conducted throughout the genome, the locus-specific effect-size estimate is therefore effectively maximized as well. We argue that attempts at bias correction give unsatisfactory results, and that pointwise estimation in an independent data set may be the only way of obtaining reliable estimates of locus-specific effect-and then only if one does not condition on statistical significance being obtained. We further show that the same factors causing this bias are responsible for frequent failures to replicate initial claims of linkage or association for complex traits, even when the initial localization is, in fact, correct. The findings of this study have wide-ranging implications, as they apply to all statistical methods of gene localization. It is hoped that, by keeping this bias in mind, we will more realistically interpret and extrapolate from the results of genomewide scans.

Accessing genetic variation: genotyping single nucleotide polymorphisms

Understanding the relationship between genetic variation and biological function on a genomic scale is expected to provide fundamental new insights into the biology, evolution and pathophysiology of humans and other species. The hope that single nucleotide polymorphisms (SNPs) will allow genes that underlie complex disease to be identified, together with progress in identifying large sets of SNPs, are the driving forces behind intense efforts to establish the technology for large-scale analysis of SNPs. New genotyping methods that are high throughput, accurate and cheap are urgently needed for gaining full access to the abundant genetic variation of organisms.

Prefrontal neurons and the genetics of schizophrenia

This article reviews prefrontal cortical biology as it relates to pathophysiology and genetic risk for schizophrenia. Studies of prefrontal neurocognition and functional neuroimaging of prefrontal information processing consistently reveal abnormalities in patients with schizophrenia. Abnormalities of prefrontal information processing also are found in unaffected individuals who are genetically at risk for schizophrenia, suggesting that genetic polymorphisms affecting prefrontal function may be susceptibility alleles for schizophrenia. One such candidate is a functional polymorphism in the catechol-o-methyl transferase (COMT) gene that markedly affects enzyme activity and that appears to uniquely impact prefrontal dopamine. The COMT genotype predicts performance on prefrontal executive cognition and working memory tasks. Functional magnetic resonance imaging confirms that COMT genotype affects prefrontal physiology during working memory. Family-based association studies have revealed excessive transmission to schizophrenic offspring of the allele (val) related to poorer prefrontal function. These various data provide convergent evidence that the COMT val allele increases risk for schizophrenia by virtue of its effect on dopamine-mediated prefrontal information processing-the first plausible mechanism for a genetic effect on normal human cognition and risk for mental illness.

Rethinking genetic strategies to study complex diseases

Understanding the genetic basis of complex diseases is turning out to be difficult, prompting a widespread (re-)evaluation of the relevant issues. 'Forward' and 'reverse' genetics strategies have been applied arguably in a manner only suitable for much simpler diseases. It would now be beneficial to pay detailed attention to experimental design, and to increase study scales dramatically. Ultimately, this would lead to completely hypothesis-free, truly comprehensive, multi-platform investigations. Such studies would maximize the chances of finding data patterns indicative of real etiology, although many aspects of complex disease causation might simply be too intricate and inconsistent to ever be deciphered. Therefore, considerable technology development is an immediate priority, along with parallel advances in bioinformatics and biostatistics systems aimed at discriminating between marginal signals and background noise within extremely large, diverse and complex data sets. Community standards and open data sharing will be essential ingredients for success in this exciting 21st-century challenge.

Prospects for association-based fine mapping of a susceptibility gene for a complex disease

The potential of association studies for fine-mapping loci with common disease susceptibility alleles for complex genetic diseases in outbred populations is unclear. For a battery of tightly linked anonymous genetic markers spanning a candidate region centered around a disease locus, simulation methods based on a coalescent process with mutation, recombination, and genetic drift were used to study the spatial distribution of markers with large noncentrality parameters in a case-control study design. Simulations with a disease allele at intermediate frequency, presumably representing an old mutation, tend to exhibit the largest noncentrality parameter values at markers near the disease locus. In contrast, simulations with a disease allele at low frequency, presumably representing a young mutation, often exhibit the largest noncentrality parameter values at markers scattered over the candidate region. In the former case, sample sizes or marker densities sufficient to detect association are likely to lead to useful localization, whereas, in the latter case, localization of the disease locus within the candidate region is much less likely, regardless of the sample size or density of the map. The simulations suggest that for a single marker analysis, the simple strategy of choosing the marker with smallest associated P value to begin a laboratory search for the disease locus performs adequately for a common disease allele.

Candidate gene case-control association studies: advantages and potential pitfalls

There is increasing information on the importance of genetic polymorphisms in human genes. Polymorphisms occur on average once every 500-1000 base pairs in the human genome and are useful in the identification of genes involved in human disease. Some genetic polymorphisms have functionally significant effects on the gene product and are the most useful type of polymorphism in disease association studies while others are simply useful markers. There are two main approaches using polymorphisms in the identification of genes involved in polygenic diseases. The first involves examining inheritance patterns for genetic polymorphisms in family studies and the second case-control studies which compare genotype frequencies for candidate disease genes in unrelated individuals with the disease and healthy controls. Use of family studies is generally the preferred approach but this is only feasible if the genetic component of the disease is relatively strong, DNA samples are available from other family members and the disease is relatively easy to diagnose and is not stigmatized. Population case-control studies are useful both as an alternative and an adjunct to family studies. When performing case-control studies factors such as study design, methods for recruitment of cases and controls, functional significance of polymorphisms chosen for study and statistical analysis of data require close attention to ensure that only genuine associations are detected. To illustrate some potential problems in the design and interpretation of association studies, some specific examples of association studies on drug response and on disease susceptibility involving receptor genes, cytochrome P450 and other xenobiotic metabolizing enzyme genes and immune system genes including TNF-alpha, IL-10 and the IL-4 receptor are discussed.

SNP analysis to dissect human traits

The analysis of complex human diseases has been spurred by the number of published genomic sequence variants - many identified in the course of sequencing the human genome. But, to be useful for genetic analysis, variants have to be mapped accurately, their frequencies in various populations determined, and automated high-throughput assay techniques developed. Recently proposed methods address these issues: the use of 'reduced representation shotgun' methods for more efficient detection of single nucleotide polymorphisms (SNPs), the employment of high-throughput genotyping techniques, the development of SNP maps that incorporate information about linkage disequilibrium, and the use of SNPs in identifying susceptibility genes for common illnesses.

Putting the search for genes in perspective

The sequencing of the human genome has been heralded by both the mass media and scientists as a breakthrough that will allow the detection of individuals at increased risk for common diseases and the tailoring of drugs to an individual's genetic profile in order to prevent disease. Sequencing is likely to benefit those at risk of developing rare diseases in which inherited mutations in a single gene play a major causal role. In the vast majority of people with common diseases, however, genotypes at many different loci, as well as environmental exposures, must be simultaneously present before disease appears. Elucidating the genes involved will prove elusive. In addition to the large number, different combinations account for a particular disease. Most of the genotypes that contribute to the constellation of necessary genes are uncommon and will be difficult to find. Common genotypes may confer susceptibility but will be weak predictors of disease. Because of the difficulty of discovering genes for common diseases, designing therapies will also prove difficult. More attention to environmental risk factors for particular diseases will have greater yield than a genetic search, but this too will be difficult because of environmental-genetic and other interactions. The search for risk factors for particular diseases neglects the political and social milieu in which individuals swim or sink and in which all diseases occur.

Quantitative-trait-locus analysis of body-mass index and of stature, by combined analysis of genome scans of five Finnish study groups

In recent years, many genomewide screens have been performed, to identify novel loci predisposing to various complex diseases. Often, only a portion of the collected clinical data from the study subjects is used in the actual analysis of the trait, and much of the phenotypic data is ignored. With proper consent, these data could subsequently be used in studies of common quantitative traits influencing human biology, and such a reanalysis method would be further justified by the nonbiased ascertainment of study individuals. To make our point, we report here a quantitative-trait-locus (QTL) analysis of body-mass index (BMI) and stature (i.e., height), with genotypic data from genome scans of five Finnish study groups. The combined study group was composed of 614 individuals from 247 families. Five study groups were originally ascertained in genetic studies on hypertension, obesity, osteoarthritis, migraine, and familial combined hyperlipidemia. Most of the families are from the Finnish Twin Cohort, which represents a population-wide sample. In each of the five genome scans, approximately 350 evenly spaced markers were genotyped on 22 autosomes. In analyzing the genotype data by a variance-component method, we found, on chromosome 7pter (maximum multipoint LOD score of 2.91), evidence for QTLs affecting stature, and a second locus, with suggestive evidence for linkage to stature, was detected on chromosome 9q (maximum multipoint LOD score of 2.61). Encouragingly, the locus on chromosome 7 is supported by the data reported by Hirschhorn et al. (in this issue), who used a similar method. We found no evidence for QTLs affecting BMI.

Chromosome 21: from sequence to applications

Last year we celebrated the sequencing of the entire long arm of human chromosome 21. This achievement now provides unprecedented opportunities to understand the molecular pathophysiology of trisomy 21, elucidate the mechanisms of all monogenic disorders of chromosome 21, and discover genes and functional sequence variations that predispose to common complex disorders. All these steps require the functional analysis of gene products and the determination of the sequence variation of this chromosome.

The power to detect linkage disequilibrium with quantitative traits in selected samples

Results from power studies for linkage detection have led to many ongoing and planned collections of phenotypically extreme nuclear families. Given the great expense of collecting these families and the imminent availability of a dense diallelic marker map, the families are likely to be used in allelic-association as well as linkage studies. However, optimal selection strategies for linkage may not be equally powerful for association. We examine the power to detect linkage disequilibrium for quantitative traits after phenotypic selection. The results encompass six selection strategies that are in widespread use, including single selection (two designs), affected sib pairs, concordant and discordant pairs, and the extreme-concordant and -discordant design. Selection of sibships on the basis of one extreme proband with high or low trait scores provides as much power as discordant sib pairs but requires the screening and phenotyping of substantially fewer initial families from which to select. Analysis of the role of allele frequencies within each selection design indicates that common trait alleles generally offer the most power, but similarities between the marker- and trait-allele frequencies are much more important than the trait-locus frequency alone. Some of the most widespread selection designs, such as single selection, yield power gains only when both the marker and quantitative trait loci (QTL) are relatively rare in the population. In contrast, discordant pairs and the extreme-proband design provide power for the broadest range of QTL-marker-allele frequency differences. Overall, proband selection from either tail provides the best balance of power, robustness, and simplicity of ascertainment for family-based association analysis.

Issues concerning association studies for fine mapping a susceptibility gene for a complex disease

The usefulness of association studies for fine mapping loci with common susceptibility alleles for complex genetic diseases in outbred populations is unclear. We investigate this issue for a battery of tightly linked anonymous genetic markers spanning a candidate region centered around a disease locus, and study the joint behavior of chi-square statistics used to discover and to localize the disease locus. We used simulation methods based on a coalescent process with mutation, recombination, and genetic drift to examine the spatial distribution of markers with large noncentrality parameters in a case-control study design. Simulations with a disease allele at intermediate frequency, presumably representing an old mutation, tend to exhibit the largest noncentrality parameter values at markers near the disease locus. In contrast, simulations with a disease allele at low frequency, presumably representing a young mutation, often exhibit the largest noncentrality parameter values at markers scattered over the candidate region. In the former cases, sample sizes or marker densities sufficient to detect association are likely to lead to useful localization, whereas, in the latter case, localization of the disease locus within the candidate region is much less likely, regardless of the sample size or density of the map. The effects of increasing sample size or marker density are also investigated. Based upon a single marker analysis, we find that a simple strategy of choosing the marker with the smallest associated P value to begin a laboratory search for the disease locus performs adequately for a common disease allele. We also investigated a strategy of pooling nearby sites to form multiple allele markers. Using multiple degree of freedom chi-square tests for two or three nearby sites, we found no clear advantage of this form of pooling over a single marker analysis. Genet. Epidemiol. 20:432-457, 2001. Published by Wiley-Liss, 2001.

Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology

Gregor Mendel was either clever or lucky enough to study traits of simple inheritance in his pea plants; however, many plant characters of interest to modern geneticists are decidedly complex. Understanding the genetic basis of such complex, or quantitative, traits requires a combination of modern molecular genetic techniques and powerful statistical methods. These approaches have begun to give us insight into understanding the evolution of complex traits both in crops and in wild plants.

A follow-up linkage study supports evidence for a bipolar affective disorder locus on chromosome 21q22

Evidence for linkage between bipolar affective disorder (BP) and 21q22 was first reported by our group in a single large pedigree with a lod score of 3.41 with the PFKL locus. In a subsequent study, with denser marker coverage in 40 multiplex BP pedigrees, we reported supporting evidence with a two-point lod score of 2.76 at the D21S1260 locus, about 6 cM proximal to PFKL. For cost-efficiency, the individuals genotyped in that study comprised a subset of our large pedigree sample. To augment our previous analysis, we now report a follow-up study including a larger sample set with an additional 331 typed individuals from the original 40 families, improved marker coverage, and an additional 16 pedigrees. The analysis of all 56 pedigrees (a total of 862 genotyped individuals vs. the 372 genotyped previously), the largest multigenerational BP pedigree sample reportedly analyzed to date, supports our previous results, with a two-point lod score of 3.56 with D21S1260. The 16 new pedigrees analyzed separately gave a maximum two-point lod score of 1.89 at D21S266, less than 1 cM proximal to D21S1260. Our results are consistent with a putative BP locus on 21q22.

Multiple sclerosis: genomic rewards

A large body of immunologic, epidemiologic, and genetic data indicate that tissue injury in multiple sclerosis (MS) results from an abnormal immune response to one or more myelin antigens that develops in genetically susceptible individuals after exposure to an as-yet undefined causal agent. The genetic component of MS etiology is believed to result from the action of several genes of moderate effect. The incomplete penetrance of MS susceptibility alleles probably reflects interactions with other genes, post transcriptional regulatory mechanisms, and significant nutritional and environmental influences. Equally significant, it is also likely that genetic heterogeneity exists, meaning that specific genes influence susceptibility and pathogenesis in some affects but not in others. Results in multiplex MS families confirm the genetic importance of the MHC region in conferring susceptibility of MS. Susceptibility may be mediated by the class II genes themselves (DR, DQ or both), related to the known function of these molecules in the normal immune response, e.g. antigen binding and presentation and T cell repertoire determination. The possibility that other genes in the MHC or the telomeric region of the MHC are responsible for the observed genetic effect cannot be excluded. The data also indicate that although the MHC region plays a significant role in MS susceptibility, much of the genetic effect in MS remains to be explained. Some loci may be involved in the initial pathogenic events, while others could influence the development and progression of the disease. The past few years have seen real progress in the development of laboratory and analytical approaches to study non-Mendelian complex genetic disorders and in defining the pathological basis of demyelination, setting the stage for the final characterization of the genes involved in MS susceptibility and pathogenesis. Their identification and characterization is likely to define the basic etiology of the disease, improve risk assessment and influence therapeutics.

Exploiting human genetic variation in drug discovery and development

'The right drug for the right patient at the right time'Is this a realistic goal for today's pharmaceutical industry and tomorrow's medical practitioner? Or merely an over-simplistic refrain that can only ever be an unfulfilled dream? Here we discuss the reality behind the dream and illustrate how the analysis of genetic variation is a complex science that has the capacity to make significant contributions to drug discovery and development strategies. An understanding of the impact of human variation must be a central consideration in the future practice of pharmaceutical R&D.

Association study designs for complex diseases

Assessing the association between DNA variants and disease has been used widely to identify regions of the genome and candidate genes that contribute to disease. However, there are numerous examples of associations that cannot be replicated, which has led to skepticism about the utility of the approach for common conditions. With the discovery of massive numbers of genetic markers and the development of better tools for genotyping, association studies will inevitably proliferate. Now is the time to consider critically the design of such studies, to avoid the mistakes of the past and to maximize their potential to identify new components of disease.

The genetics of rheumatoid arthritis and the need for animal models to find and understand the underlying genes

The causes of rheumatoid arthritis (RA) are largely unknown. However, RA is most probably a multifactorial disease with contributions from genetic and environmental factors. Searches for genes that influence RA have been conducted in both human and experimental model materials. Both types of study have confirmed the polygenic inheritance of the disease. It has become clear that the features of RA complicate the human genetic studies. Animal models are therefore valuable tools for identifying genes and determining their pathogenic role in the disease. This is probably the fastest route towards unravelling the pathogenesisis of RA and developing new therapies.

Complex genetic diseases: controversy over the Croesus code

The polarization of views on how best to exploit new information from the Human Genome Project for medicine reflects our ignorance of the genetic architecture underlying common diseases: are susceptibility alleles common or rare, neutral or deleterious, few or many? Single-nucleotide polymorphism (SNP) technology is almost in place to dissect such diseases and to create a personalized medicine, but success is critically dependent on the biology and "Nature to be commanded must be obeyed" (Francis Bacon, 1620, Novum Organum).

Genetic epidemiology. Approaches to the genetic analysis of rheumatoid arthritis

The basis of susceptibility to rheumatoid arthritis (RA) is complex, comprising genetic and environmental susceptibility factors. We have reviewed the available approaches to the investigation of the genetic basis of complex diseases and how these are being applied to RA. Affected-sibling-pair methods for nonparametric linkage analysis, linkage-disequilibrium-based approaches, transmission disequilibrium testing, and disease-association studies are discussed. The pros, cons, and limitations of the approaches are considered and are illustrated by examples from the literature about rheumatoid arthritis.

Two loci on chromosomes 2 and X for premature coronary heart disease identified in early- and late-settlement populations of Finland

Coronary heart disease (CHD) is a complex disorder constituting a major health problem in Western societies. To assess the genetic background of CHD, we performed a genomewide linkage scan in two study samples from the genetically isolated population of Finland. An initial study sample consisted of family material from the northeastern part of Finland, settled by a small number of founders approximately 300 years ago. A second study sample originated from the southwestern region of Finland, settled approximately 2,000 years ago. Families were ascertained through probands exhibiting premature CHD, defined as >50% stenosis of at least two coronary arteries at a young age, as verified by coronary angiography. Both study samples and the pooled data set provided evidence for linkage in two chromosomal regions. A region on chromosome 2q21.1-22 yielded two-point LOD scores of 3.2, 1.9, and 3.7, in the affected sib-pair (ASP) analyses of the northeastern, southwestern, and pooled study samples. The corresponding multipoint maximum-likelihood scores (MLSs) for these three study samples were 2.4, 1.3, and 3.0. In addition, a region on chromosome Xq23-26 resulted in two-point LOD scores of 1.9, 3.5, and 2.9 and in multipoint MLSs of 3.4, 3.1, and 2.5, respectively. In conclusion, this study identifies two loci likely to contribute to premature CHD: one on chromosome 2q21.1-22 and another on chromosome Xq23-26.

Genes and addiction

Drug addiction, like all psychiatric disorders, is defined solely in behavioural terms. For example, addiction can be considered a loss of control over drug-taking, or compulsive drug-seeking and -taking despite horrendous consequences. Abnormal behaviours are a consequence of aberrant brain function, which means that it is a tangible goal to identify the biological underpinnings of addiction. The genetic basis of addiction encompasses two broad areas of enquiry. One of these is the identification of genetic variation in humans that partly determines susceptibility to addiction. The other is the use of animal models to investigate the role of specific genes in mediating the development of addiction. Whereas recent advances in this latter effort are heartening, a major challenge remains: to understand how the many genes implicated in rodent models interact to yield as complex a phenotype as addiction.