Allelic penetrance approach as a tool to model two-locus interaction in complex binary traits

SSTR4, Childhood Adversity, Self-efficacy and Suicide Risk in Alcoholics

Background

Patients with alcohol dependence (AD) are known to develop poor social skills, to report a higher number of adverse childhood experiences (ACEs) and to attempt suicide more frequently than the general population. The background for the association between ACEs and a higher risk of suicide still remains understudied. SSTR4 rs2567608 is a functional polymorphism of the gene for somatostatin receptor subtype 4, predominantly found in the CA1 hippocampus area and involved in memory formation. We hypothesize that the functional polymorphism SSTR4 rs2567608, general self-efficacy, and adverse childhood experiences influence the risk of suicide attempt in patients with AD.

Methodology

176 patients with AD and 127 healthy controls were interviewed regarding 13 categories of ACEs and assessed with the General Self-Efficacy Scale. Genotyping for the SSTR4 rs2567608 polymorphism was performed according to the manufacturer’s standard PCR protocol.

Results

Patients with AD and the controls did not differ significantly according to the SSTR4 rs2567608 genotype and allele frequencies. Lower general self-efficacy, higher number of ACEs, and the SSTR4 rs2567608 TT genotype increased the risk of suicide attempt in patients with AD, and it persisted significant only in male patients with AD.

Conclusions

Our study supports previous findings on ACEs and general self-efficacy association with a risk for suicide. Additionally, we suggest that patients with AD of the SSTR4 rs2567608 TT genotype may be more vulnerable to ACEs and at a higher risk of suicide attempt.

Identification of the Plasmodium berghei resistance locus 9 linked to survival on chromosome 9

Background

One of the main causes of mortality from severe malaria in Plasmodium falciparum infections is cerebral malaria (CM). An important host genetic component determines the susceptibility of an individual to develop CM or to clear the infection and become semi-immune. As such, the identification of genetic loci associated with susceptibility or resistance may serve to modulate disease severity.

Methodology

The Plasmodium berghei mouse model for experimental cerebral malaria (ECM) reproduces several disease symptoms seen in human CM, and two different phenotypes, a susceptible (FVB/NJ) and a resistant mouse strain (DBA/2J), were examined.

Results

FVB/NJ mice died from infection within ten days, whereas DBA/2J mice showed a gender bias: males survived on average nineteen days and females either died early with signs of ECM or survived for up to three weeks. A comparison of brain pathology between FVB/NJ and DBA/2J showed no major differences with regard to brain haemorrhages or the number of parasites and CD3+ cells in the microvasculature. However, significant differences were found in the peripheral blood of infected mice: For example resistant DBA/2J mice had significantly higher numbers of circulating basophils than did FVB/NJ mice on day seven. Analysis of the F2 offspring from a cross of DBA/2J and FVB/NJ mice mapped the genetic locus of the underlying survival trait to chromosome 9 with a Lod score of 4.9. This locus overlaps with two previously identified resistance loci (char1 and pymr) from a blood stage malaria model.

Conclusions

Survival best distinguishes malaria infections between FVB/NJ and DBA/2J mice. The importance of char1 and pymr on chromosome 9 in malaria resistance to P. berghei was confirmed. In addition there was an association of basophil numbers with survival.

The Rise and Fall of the Common Disease–Common Variant (CD–CV) Hypothesis: How the Sickle Cell Disease Paradigm Led Us All Astray (Or Did It?)

The common disease–common variant (CD–CV) hypothesis requires an explanation for the origin of the variation observed, since substantial neutral, but not deleterious, variation, that is, several alleles each at moderate to high frequency, can be maintained at any gene/locus by mutation. It is argued here that the guiding principle, not always stated, has been balancing selection, influenced by the wellestablished cases of deleterious alleles maintained through heterozygous advantage in the face of strong malarial selection against normal alleles. It is further argued that, although balanced polymorphisms have indeed arisen and reduced population loss through infectious disease, the history of balance in other contexts should have prevented acceptance of any hypothesis that generalized such a specific mechanism. Finally, it is suggested that in the present state of knowledge no single hypothesis for the genetical contribution to common disorders is justifiable.

Causal models for investigating complex genetic disease: II. what causal models can tell us about penetrance for additive, heterogeneity, and multiplicative two-locus models

BACKGROUND/AIMS

Statistical geneticists commonly use certain two-locus penetrance models because these models are familiar and mathematically tractable. We investigate whether and under what circumstances these two-locus penetrance models correspond to models of causation.

METHODS

We describe a sufficient component cause model for a hypothetical disease with two genetic causes. We then use the potential outcomes framework to determine the expected two-locus penetrances from this causal model and contrast them with commonly used two-locus penetrance models (additive, heterogeneity, and multiplicative penetrance models, as formulated by Risch [Am J Hum Genet 1990;46:222-228]).

RESULTS

Conventional additive and multiplicative models can correspond to any two-locus causal model only when certain very specific algebraic relationships hold. The heterogeneity model corresponds to a two-locus causal model only if the model stipulates that no disease cases are caused by the combined presence of the causal genotypes at both loci (i.e. only when there is no causal gene-gene interaction). Hence the heterogeneity model provides a valid test of the null hypothesis of no gene-gene interaction, whereas the additive and multiplicative models do not.

CONCLUSION

We suggest that causal principles should provide the basis for statistical modeling in genetics.

Causal models for investigating complex disease: I. A primer

BACKGROUND/AIMS

To illustrate the utility of causal models for research in genetic epidemiology and statistical genetics. Causal models are increasingly applied in risk factor epidemiology, economics, and health policy, but seldom used in statistical genetics or genetic epidemiology. Unlike the statistical models usually used in genetic epidemiology, causal models are explicitly formulated in terms of cause and effect relationships occurring at the individual level.

METHODS

We describe two causal models, the sufficient component cause model and the potential outcomes model, and show how key concepts in genetic epidemiology, including penetrance, phenocopies, genetic heterogeneity, etiologic heterogeneity, gene-gene interaction, and gene-environment interaction, can be framed in terms of these causal models. We also illustrate how potential outcomes models can provide insight into the potential for confounding and bias in the measurement of causal effects in genetic studies.

RESULTS

Our analysis illustrates how causal models can elucidate the relationships among underlying causal mechanisms and measures obtained from statistical analysis of observed data.

CONCLUSION

Causal models can enhance research aimed at identifying causal genes.

Detecting gene-gene interactions that underlie human diseases

Following the identification of several disease-associated polymorphisms by genome-wide association (GWA) analysis, interest is now focusing on the detection of effects that, owing to their interaction with other genetic or environmental factors, might not be identified by using standard single-locus tests. In addition to increasing the power to detect associations, it is hoped that detecting interactions between loci will allow us to elucidate the biological and biochemical pathways that underpin disease. Here I provide a critical survey of the methods and related software packages currently used to detect the interactions between genetic loci that contribute to human genetic disease. I also discuss the difficulties in determining the biological relevance of statistical interactions.

Malaria liver stage susceptibility locus identified on mouse chromosome 17 by congenic mapping

Host genetic variants are known to confer resistance to Plasmodium blood stage infection and to control malaria severity both in humans and mice. This work describes the genetic mapping of a locus for resistance to liver stage parasite in the mouse. First, we show that decreased susceptibility to the liver stage of Plasmodium berghei in the BALB/c mouse strain is attributable to intra-hepatic factors and impacts on the initial phase of blood stage infection. We used QTL mapping techniques to identify a locus controlling this susceptibility phenotype (LOD score 4.2) on mouse chromosome 17 (belr1 locus). Furthermore, analysis of congenic mouse strains delimited the belr1 locus boundaries distally to the H2 region. Quantification of parasites in the liver of infected congenic mice strongly suggested that the belr1 locus represents a genetic factor controlling the expansion of P. berghei in the hepatic tissue. The mapping of belr1 locus raises the hypothesis that host gene variation is able to control the progression of Plasmodium liver stage infection and opens the possibility that the human genomic region orthologue to belr1 may contain genes that confer resistance to the human malaria liver stage.