Patterns of Nonrandom Mating Within and Across 11 Major Psychiatric Disorders

Abstract

Importance: Psychiatric disorders are heritable, polygenic traits, which often share risk alleles and for which nonrandom mating has been suggested. However, despite the potential etiological implications, the scale of nonrandom mating within and across major psychiatric conditions remains unclear.

Objective: To quantify the nature and extent of nonrandom mating within and across a broad range of psychiatric conditions at the population level.

Design, setting, and participants: Population-based cohort using Swedish population registers. Participants were all Swedish residents with a psychiatric diagnosis of interest (attention-deficit/hyperactivity disorder, autism spectrum disorder, schizophrenia, bipolar disorder, major depression, generalized anxiety disorder, agoraphobia, social phobia, obsessive-compulsive disorder, anorexia, or substance abuse), along with their mates. Individuals with select nonpsychiatric disorders (Crohn's disease, type 1 and type 2 diabetes mellitus, multiple sclerosis, or rheumatoid arthritis) were included for comparison. General population samples were also derived and matched 1:5 with each case proband. Inpatient and outpatient diagnostic data were derived from the Swedish National Patient Register (1973-2009), with analyses conducted between June 2014 and May 2015.

Main outcomes and measures: Correlation in the diagnostic status of mates both within and across disorders. Conditional logistic regression was used to quantify the odds of each diagnosis in the mates of cases relative to matched population controls.

Results: Across cohorts, data corresponded to 707 263 unique case individuals, with women constituting 45.7% of the full population. Positive correlations in diagnostic status were evident between mates. Within-disorder correlations were marginally higher (range, 0.11-0.48) than cross-disorder correlations (range, 0.01-0.42). Relative to matched populations, the odds of psychiatric case probands having an affected mate were significantly elevated. Differences in the magnitude of observed relationships were apparent by disorder (odds ratio range, 0.8-11.4). The number of comorbidities in a case proband was associated with the proportion of affected mates. These relationships were not apparent or weaker in magnitude among nonpsychiatric conditions (correlation range, -0.03 to 0.17).

Conclusions and relevance: Nonrandom mating is evident in psychiatric populations both within specific disorders and across the spectrum of psychiatric conditions. This phenomenon may hold important implications for how we understand the familial transmission of these disorders and for psychiatric genetic research.

Figure 1. Within-Disorder and Cross-Disorder Partner Correlations, by Psychiatric Proband Sex, in Restricted Case Samples

The magnitude of the spousal correlations is reflected in the figure’s coloration, with darker boxes indicating stronger correlations between the diagnostic status of the relevant proband (indicated by row labels) and the corresponding diagnostic status of his or her mate (indicated by column labels). pXXX is the opposite-sex partner of a proband with diagnosis XXX. Large figures in each box reflect the correlation for that row or column, with small figures indicating the standard error. Large figures in bold indicate that the correlation is statistically significant (P < .001). Due to unique matched populations and the possibility of multiple pairings per individual, within-disorder correlations may be asymmetric for the same comparison depending on the proband sex. Empty values in ANO and pANO rows and columns reflect confinement of analyses to female probands or partners. ADHD indicates attention-deficit/hyperactivity disorder; AGO, agoraphobia; ANO, anorexia nervosa; ASD, autism spectrum disorder; BIP, bipolar disorder; DEP, major depressive disorder; GAD, generalized anxiety disorder; OCD, obsessive-compulsive disorder; SCZ, schizophrenia; SOC, social phobia; and SUB, substance abuse.

Figure 2. Dot Plot of Odds Ratios Illustrating Mating Patterns Within and Across Major Mental Disorders, by Psychiatric Proband Sex

Plotted points illustrate the increased odds, relative to matched populations, of each individual diagnosis among the opposite-sex partners of case probands (whose sex and diagnosis are labeled on the x-axis). mXXX is a male proband with diagnosis XXX, fXXX is a female proband with diagnosis XXX, and pXXX is the opposite-sex partner of a proband with diagnosis XXX. ADHD indicates attention-deficit/hyperactivity disorder; AGO, agoraphobia; ANO, anorexia nervosa; ASD, autism spectrum disorder; BIP, bipolar disorder; DEP, major depressive disorder; GAD, generalized anxiety disorder; OCD, obsessive-compulsive disorder; SCZ, schizophrenia; SOC, social phobia; and SUB, substance abuse.

Figure 3. Linear Plot Depicting the Relationship of Psychiatric Proband Diagnosis (Total No.) to the Proportion of Partners With a Diagnosis

The proportion of case probands having an affected mate increased linearly with the number of comorbidities in the proband.

Figure 4. Within-Disorder and Cross-Disorder Partner Correlations, by Nonpsychiatric Proband Sex, in Restricted Case Samples

The magnitude of the spousal correlations is reflected in the figure’s coloration, with darker boxes indicating stronger correlations between the diagnostic status of the relevant proband (indicated by row labels) and the corresponding diagnostic status of his or her mate (indicated by column labels). pXXX is the opposite-sex partner of a proband with diagnosis XXX. Large figures in each box reflect the correlation for that row or column, with small figures indicating the standard error. Large figures in bold indicate that the correlation is statistically significant (P < .001). Due to unique matched populations and the possibility of multiple pairings per individual, within-disorder correlations may be asymmetric for the same comparison depending on the proband sex. CD indicates Crohn’s disease; DM1, type I diabetes mellitus; DM2, type 2 diabetes mellitus; MS, multiple sclerosis; and RA, rheumatoid arthritis.