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Review
. 2008 Dec;9(12):911-22.
doi: 10.1038/nrg2415.

Sex-specific genetic architecture of human disease

Carole Ober  1 , Dagan A Loisel, Yoav Gilad
Affiliations
Review

Sex-specific genetic architecture of human disease

Carole Ober et al. Nat Rev Genet. 2008 Dec.

Abstract

Sexual dimorphism in anatomical, physiological and behavioural traits are characteristics of many vertebrate species. In humans, sexual dimorphism is also observed in the prevalence, course and severity of many common diseases, including cardiovascular diseases, autoimmune diseases and asthma. Although sex differences in the endocrine and immune systems probably contribute to these observations, recent studies suggest that sex-specific genetic architecture also influences human phenotypes, including reproductive, physiological and disease traits. It is likely that an underlying mechanism is differential gene regulation in males and females, particularly in sex steroid-responsive genes. Genetic studies that ignore sex-specific effects in their design and interpretation could fail to identify a significant proportion of the genes that contribute to risk for complex diseases.

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Figures

Figure 1
Figure 1. Approximate mean sex steroid levels in plasma in males and females
Variation in steroid levels is shown as percent of the maximum mean testosterone (T) in males and the maximum mean estradiol (E) in females across the life stages. The figure does not show diurnal, cyclic (female), or possible seasonal fluctuations. Female estradiol levels refer to the mean for the mid-follicular phase of the menstrual cycle; estradiol production transiently increases about 5-fold during the pre-ovulatory and luteal phases of the menstrual cycle. Note the drop in levels of all sex steroids at birth and the transient ’minipuberty’ in early infancy. Free testosterone falls more with aging (to approximately 50% of the maximum in 80 year old men) than the total testosterone, which is shown here. Modified from Alonso and Rosenfield 2002, Khosla et al. 1998, Winters et al. 2000.
Figure 2
Figure 2. Models of genotype-sex interactions reflecting genotype effects that differ between males and females. [I'll arrange for ‘B’ to be changed to ‘a’ in this figure]
For any measured phenotype or disease risk (y axes), the genotypic effects may be apparent only in females (red; panels a, d), only in males (blue; panels b, e), or be present in both sexes but with opposite directions of effects (panels c, f). The genotype effects can be additive (panels a-c) or recessive (panels d-f). Other models (e.g., dominant) or interactions (e.g., same direction of effect but differences in magnitude of effect) are not shown. Examples discussed in this review illustrate panel e (relationship between the DD genotype of the angiotensinogen converting enzyme (ACE) and hypertension), panel b (relationship between the DD genotype of ACE and blood pressure), panel d (relationship between the reelin (RELN) rs7341475-GG genotype and schizophrenia), and panel c (relationship between chromosome 4p16.3 SNPs rs3796619 and rs1670533SNPs and recombination rate). Red lines track phenotypic values by genotype in females; blue lines track phenotypic values by genotype in males.
Figure 3
Figure 3. Sex-specific prevalence rates, age of onset, and sex ratios for common sex-skewed diseases
The key for background colors is shown in Figure 1 [I'll ask for the key to be copied to this figure]. a) Cardiovascular disease in the U.S. (from the National Health and Nutrition Examination Survey (NHANES) III 1988−1994). Note the increase in female prevalence rates in the post-menopausal period. b) Asthma in the U.S. from 1998−2006 (Center for Disease Control National Health Interview Survey (CDC NHIS)). Note the increase in female prevalence rates during and following puberty. c) Sex ratios (%female) by mean or median age of onset for autoimmune diseases in the U.S. and Europe,. Note the female skewing at all ages, with the largest skew and number of diseases with onset during and immediately following the reproductive years. T1D, type 1 diabetes; JIA, juvenile idiopathic arthritis; JDM, juvenile dermatomyositis; MS, multiple sclerosis; MG, myasthenia gravis; GD, Grave's disease; SLE, systemic lupus erythematosus; SSc, systemic sclerosis (scleroderma); AD, Addison disease; DM, dermatomyositis/polymyositis; RA, rheumatoid arthritis; TH, thyroiditis; SS, Sjögren's disease.
Figure 4
Figure 4. Sex-specific heritabilities in males and females (data from Pan et al. 200741)
Six quantitative traits with significant sex-specific genetic architecture show differences between males and females in the overall estimates of H2 (e.g., LDL cholesterol, lipoprotein[a], systolic blood pressure) and/or with respect to the best-fitting model (triglycerides, HDL cholesterol, systolic blood pressure, height) are shown.
Figure 5
Figure 5. Strategy for discovering sex-specific eQTLs contributing to sexual dimorphism in disease risk
Red symbols are results for females and blue symbols are results for males. a) Results of genome-wide association study for a disease-associated QTL (such as those shown in Figure 4). Analyses in sex-stratified samples identify an association with SNPs spanning a 50 kb region in females but not in males. b) mRNA expression level by genotype. Using publicly available expression data-, eQTL that reside within the 50 kb region with sex-specific effects on expression levels can be identified. Each copy of the T allele at this eQTL is associated with increased expression in females but has no effect on expression in males (Figure 2a). c) Odds ratios for disease risk by genotype. Validation of a role for the eQTL on disease risk is determined by directly demonstrating a genotype-specific risk for disease in one sex only, in a direction that is consistent with the patterns observed with the associated QTL and eQTL. In this example, each copy of the T allele is associated with increased risk for disease in females. The SNP is not associated with disease risk in males. This model of association is also represented in Figure 2a.
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