Sibling recurrence risk ratio as a measure of genetic effect: caveat emptor!

Age-dependent phenotypes of ovarian endometriomas

Purpose

To analyze the characteristics of the ovarian endometrioma (OE) across the life span of a woman. In the past, the OE has traditionally been viewed as a single, monolithic disease. Today, there are emerging data indicating that OE phenotypes differ according to the age of the woman.

Method

A narrative review of original articles on OE indexed by PubMed.

Results

When appearing in infancy and early adolescence, OE may be the consequence of endometrial cells retrogradely shed with neonatal uterine bleeding. The post-menarcheal variant, manifesting itself during full adolescence, is singularly frequent in the presence of vaginal or uterine outflow obstructive anomalies. The typical and most frequent adult phenotype is characterized by increasing fibrosis and a tendency to progress; its mere presence exerts a detrimental effect on the surrounding healthy ovarian tissue. In postmenopause, an old lesion may be reactivated in the presence of exogenous or endogenous estrogens, or even be produced ex novo; rarely, it can spread to a variety of organs and structures and even degenerate causing malignancies.

Conclusions

Given the existence of these variants, it is important to agree on management guidelines that take into consideration these different phenotypes.

Risk Model Assessment in Early-Onset and Adult-Onset Schizophrenia Using Neurological Soft Signs

Age at onset is one of the most important clinical features of schizophrenia that could indicate greater genetic loadings. Neurological soft signs (NSS) are considered as a potential endophenotype for schizophrenia. However, the association between NSS and different age-onset schizophrenia still remains unclear. We aimed to compare risk model in patients with early-onset schizophrenia (EOS) and adult-onset schizophrenia (AOS) with NSS. This study included 262 schizophrenia patients, 177 unaffected first-degree relatives and 243 healthy controls. We estimated the discriminant abilities of NSS models for early-onset schizophrenia (onset age < 20) and adult-onset schizophrenia (onset age ≥ 20) using three data mining methods: artificial neural networks (ANN), decision trees (DT) and logistic regression (LR). We then assessed the magnitude of NSS performance in EOS and AOS families. For the four NSS subscales, the NSS performance were greater in EOS and AOS families compared with healthy individuals. More interestingly, there were significant differences found between patients' families and control group in the four subscales of NSS. These findings support the potential for neurodevelopmental markers to be used as schizophrenia vulnerability indicators. The NSS models had higher discriminant abilities for EOS than for AOS. NSS were more accurate in distinguishing EOS patients from healthy controls compared to AOS patients. Our results support the neurodevelopmental hypothesis that EOS has poorer performance of NSS than AOS. Hence, poorer NSS performance may be imply trait-related NSS feature in EOS.

Improving risk assessment and familial aggregation of age at onset in schizophrenia using minor physical anomalies and craniofacial measures

Age at onset is the most important feature of schizophrenia that could indicate its origin. Minor physical anomalies (MPAs) characterize potential marker indices of disturbances in early neurodevelopment. However, the association between MPAs and age at onset of schizophrenia is still unclear. We aimed to compare risk assessment and familial aggregation in patients with early-onset schizophrenia (EOS) and adult-onset schizophrenia (AOS) with MPAs and craniofacial measures.We estimated the risk assessment of MPAs among patients with EOS (n = 68), patients with AOS (n = 183), nonpsychotic relatives (n = 147), and healthy controls (n = 241) using 3 data-mining algorithms. In addition, we assessed the magnitude of familial aggregation of MPAs with respect to the age at onset of schizophrenia.The performance of EOS was superior to that of AOS, with discrimination accuracies of 89% and 76%, respectively. Combined MPA scores as the risk assessment were significantly higher in all schizophrenia subgroups and the nonpsychotic relatives of EOS patients than in the healthy controls. The recurrence risk ratio for familial aggregation of the MPA scores of EOS families (odds ratio 9.27) was substantially higher than that of AOS families (odds ratio 2.47).The results highlight that EOS improves risk assessment and has a severe magnitude of familial aggregation of MPAs. These findings indicate that EOS might result from a stronger genetic susceptibility to neurodevelopmental deficits.

Multiple sclerosis genetics

Familial aggregation and the studies of twins indicate that heredity contributes to multiple sclerosis (MS) risk. Immunologic studies of leukocyte antigens subsequently followed by gene-mapping techniques identified the primary MS susceptibility locus to be within the major histocompatibility complex (MHC). The primary risk allele is HLA-DRB1*15, although other alleles of this gene also influence MS susceptibility. Other genes within the MHC also contribute to MS susceptibility. Genome-wide association studies have identified over 50 additional common variants of genes across the genome. Estimates suggest that there may be as many as 200 genes involved in MS susceptibility. In addition to these common polymorphisms, studies have identified several rare risk alleles in some families. Interestingly, the majority of the genes identified have known immunologic functions and many contribute to the risk of inheriting other autoimmune diseases. Genetic variants in the vitamin D metabolic pathway have also been identified. That vitamin D contributes to MS susceptibility as both an environmental as well as genetic risk factor underscores the importance of this metabolic pathway in disease pathogenesis. Current efforts are focused on understanding how the myriad of genetic risk alleles interact within networks to influence MS risk at family level as well as within populations.

The genetic aspects of multiple sclerosis

The epidemiology of multiple sclerosis has been extensively investigated and two features have consistently emerged: marked geographical variation in prevalence and substantial familial clustering. At first sight, geographic variation would seem to imply an environmental cause for the disease, while familial clustering would seem to suggest that genetic factors have the predominant etiological effect. However, given that geographic variation in prevalence could result from variation in the frequency of genetic risk alleles and that familial clustering might result from shared environmental exposure rather than shared genetic risk alleles, it is clear that these crude inferences are unreliable. Epidemiologists have been resourceful in their attempts to resolve this apparent conflict between “nurture and nature” and have employed a whole variety of sophisticated methods to try and untangle the etiology of multiple sclerosis. The body of evidence that has emerged from these efforts has formed the foundation for decades of research seeking to identify relevant genes and this is the obvious place to start any consideration of the genetics of multiple sclerosis.

The clinical relevance of genetic susceptibility to osteoarthritis

Osteoarthritis is a major musculoskeletal cause of disability in the elderly, but current therapeutic approaches are insufficient to prevent initiation and progression of the disease. Genetic studies in humans have identified molecules involved in signalling cascades that are important for the pathology of the joint components. These include the bone morphogenetic protein (BMP) signalling, the wingless-type signalling and the thyroid pathway as well as apoptotic-related molecules. There is emerging evidence indicating that inflammatory molecules related to cytokine production, prostaglandin and arachidonic acid metabolism are also involved in susceptibility to osteoarthritis. All of these pathways are likely targets for pharmacological intervention. Genetic variation also affects pain due to osteoarthritis highlighting molecular mechanisms for pain relief. Moreover, combinations of genetic markers can be used to identify individuals at high risk of osteoarthritis and risk of total joint arthroplasty failure, which should facilitate the application of preventive and disease management strategies.

Heritability of audiometric shape parameters and familial aggregation of presbycusis in an elderly Flemish population

This study describes the heritability of audiometric shape parameters and the familial aggregation of different types of presbycusis in a healthy, otologically screened population between 50 and 75 years old. About 342 siblings of 64 families (average family-size: 5.3) were recruited through population registries. Audiometric shape was mathematically quantified by objective parameters developed to measure size, slope, concavity, percentage of frequency-dependent and frequency-independent hearing loss and Bulge Depth. The heritability of each parameter was calculated using a variance components model. Logistic regression models were used to estimate the odds ratios (ORs). Estimates of sibling recurrence risk ratios (lambda(s)) are also provided. Heritability estimates were generally higher compared to previous studies. ORs and lambda(s) for the parameters Total Hearing Loss (size), Uniform Hearing Loss (percentage of frequency-dependent hearing loss) and Bulge Depth suggest a higher heredity for severe types of presbycusis compared to moderate or mild types. Our results suggest that the separation of the parameter 'Total Hearing Loss' into the two parameters 'Uniform Hearing Loss' and 'Non-uniform Hearing Loss' could lead to the discovery of different genetic subtypes of presbycusis. The parameter 'Bulge Depth', instead of 'Concavity', seemed to be an important parameter for classifying subjects into 'susceptible' or 'resistant' to societal or intensive environmental exposure.

The contribution of genes to osteoarthritis

Osteoarthritis (OA) is the most prevalent form of arthritis in the elderly. A large body of evidence, including familial aggregation and classic twin studies, indicates that primary OA has a strong hereditary component that is likely polygenic in nature. Traits related to OA, such as longitudinal changes in cartilage volume and progression of radiographic features, are also under genetic control. In recent years several linkage analyses and candidate gene studies have been performed and unveiled some of the specific genes involved in disease risk, such as FRZB and GDF5. This article discusses the impact that future genome-wide association scans can have on our understanding of the pathogenesis of OA and on identifying individuals at high risk for developing severe OA.

The contribution of genes to osteoarthritis

Osteoarthritis (OA) is the most prevalent form of arthritis in the elderly. A large body of evidence, including familial aggregation and classic twin studies, indicates that primary OA has a strong hereditary component that is likely polygenic in nature. Furthermore, traits related to OA, such as longitudinal changes in cartilage volume and progression of radiographic features, are also under genetic control. In recent years, several linkage analysis and candidate gene studies have been performed and have unveiled some of the specific genes involved in disease risk, such as FRZB and GDF5. The authors discuss the impact that future genome-wide association scans can have on our understanding of the pathogenesis of OA and on identifying individuals at high risk for developing severe OA.

Population-based estimate of sibling risk for preterm birth, preterm premature rupture of membranes, placental abruption and pre-eclampsia

Background

Adverse pregnancy outcomes, such as preterm birth, preeclampsia and placental abruption, are common, with acute and long-term complications for both the mother and infant. Etiologies underlying such adverse outcomes are not well understood. As maternal and fetal genetic factors may influence these outcomes, we estimated the magnitude of familial aggregation as one index of possible heritable contributions.

Using the Missouri Department of Health's maternally-linked birth certificate database, we performed a retrospective population-based cohort study of births (1989–1997), designating an individual born from an affected pregnancy as the proband for each outcome studied. We estimated the increased risk to siblings compared to the population risk, using the sibling risk ratio, λ_s, and sibling-sibling odds ratio (sib-sib OR), for the adverse pregnancy outcomes of preterm birth, preterm premature rupture of membranes (PPROM), placental abruption, and pre-eclampsia.

Results

Risk to siblings of an affected individual was elevated above the population prevalence of a given disorder, as indicated by λ_S (λ_S (95% CI): 4.3 (4.0–4.6), 8.2 (6.5–9.9), 4.0 (2.6–5.3), and 4.5 (4.4–4.8), for preterm birth, PPROM, placental abruption, and pre-eclampsia, respectively). Risk to siblings of an affected individual was similarly elevated above that of siblings of unaffected individuals, as indicated by the sib-sib OR (sib-sib OR adjusted for known risk factors (95% CI): 4.2 (3.9–4.5), 9.6 (7.6–12.2), 3.8 (2.6–5.5), 8.1 (7.5–8.8) for preterm birth, PPROM, placental abruption, and pre-eclampsia, respectively).

Conclusion

These results suggest that the adverse pregnancy outcomes of preterm birth, PPROM, placental abruption, and pre-eclampsia aggregate in families, which may be explained in part by genetics.

The complex genetics of multiple sclerosis: pitfalls and prospects

The genetics of complex disease is entering a new and exciting era. The exponentially growing knowledge and technological capabilities emerging from the human genome project have finally reached the point where relevant genes can be readily and affordably identified. As a result, the last 12 months has seen a virtual explosion in new knowledge with reports of unequivocal association to relevant genes appearing almost weekly. The impact of these new discoveries in Neuroscience is incalculable at this stage but potentially revolutionary. In this review, an attempt is made to illuminate some of the mysteries surrounding complex genetics. Although focused almost exclusively on multiple sclerosis all the points made are essentially generic and apply equally well, with relatively minor addendums, to any other complex trait, neurological or otherwise.

Association mapping of complex diseases in linked regions: estimation of genetic effects and feasibility of testing rare variants

Association mapping in linked regions is a current major approach for the identification of genes for complex diseases. Loci contributing to linkage, even with small values of sibling recurrence risk (lambda(s)), may be equivalent to substantial underlying genetic effects for association studies. For disease alleles with a frequency as low as 1%, highly reliable association studies (80% power for significance level alpha=10(-6)) require only 277, 781, and 1289 families or cases and controls for loci detected with lambda(s) of 1.5, 1.1, and 1.05, respectively, under a multiplicative genetic model. Under alternative models, provided epistatic effects are minor, larger achievable sample sizes will provide sufficient power to map almost any disease gene that may have initially contributed to linkage.

Multiplex relative risk and estimation of the number of loci underlying an inherited disease

Knowledge of the number of causative loci is necessary to estimate the power of mapping studies of complex diseases. In the present article, we reexamine a theory developed by Risch and its implications for estimating the number L of causative loci affecting a complex inherited disease. We first show that methods based on Risch's analysis can produce estimates of L that are inconsistent with the observed population prevalence of the disease. We demonstrate this point by showing that the maximum-likelihood estimate for L produced by the method of Farrall and Holder for cleft lip/cleft palate data is not consistent with the prevalence under the multiplicative model. We show how to incorporate disease prevalence and develop a maximum-likelihood method for estimating L that uses the entire distribution of numbers of affected individuals in families containing an affected individual. This method avoids the potential inconsistencies of the Risch method and has greater precision. We apply our method to data on cleft lip/cleft palate and schizophrenia.

Gene finding strategies

Both linkage and association methods have been used to localise and identify genes related to behaviour and other complex traits. The linkage approach (parametric or non-parametric) can be used for whole genome screens to localise genes of unknown function. The parametric linkage approach is very effective for locating single-gene disorders and is usually based on large family pedigrees. The non-parametric method is useful to detect quantitative trait loci (QTLs) for complex traits and was originally developed for sib pair analyses. Genetic association studies are most often used to test the association of alleles at a candidate gene with a disease or with levels of a quantitative trait. Allelic association between a trait and a marker can be studied in a case-control design, but because of possible problems due to population stratification, within-family designs have been proposed as the optimal test for association.