Modeling familial predictors of proband outcomes in neurogenetic disorders: initial application in XYY syndrome

Kathleen E Wilson,Catherine Mankiw,Armin Raznahan,Jonathan Blumenthal,Ari M Fish,Erin Torres,Liv Clasen,Anastasia Xenophontos,Allysa Warling,Ethan Whitman

doi:10.1186/s11689-021-09360-7

Abstract

BackgroundDisorders of gene dosage can significantly increase risk for psychopathology, but outcomes vary greatly amongst carriers of any given chromosomal aneuploidy or sub-chromosomal copy number variation (CNV). One potential path to advance precision medicine for neurogenetic disorders is modeling penetrance in probands relative to observed phenotypes in their non-carrier relatives. Here, we seek to advance this general analytic framework by developing new methods in application to XYY syndrome—a sex chromosome aneuploidy that is known to increase risk for psychopathology.MethodsWe analyzed a range of cognitive and behavioral domains in XYY probands and their non-carrier family members (n = 58 families), including general cognitive ability (FSIQ), as well as continuous measures of traits related to autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Proband and relative scores were compared using covariance, regression and cluster analysis. Comparisons were made both within and across traits.ResultsProband scores were shifted away from family scores with effect sizes varying between 0.9 and 2.4 across traits. Only FSIQ and vocabulary scores showed a significant positive correlation between probands and their non-carrier relatives across families (R2 ~ 0.4). Variability in family FSIQ also cross-predicted variability in proband ASD trait severity. Cluster analysis across all trait-relative pairings revealed that variability in parental psychopathology was more weakly coupled to their XYY versus their euploid offspring.ConclusionsWe present a suite of generalizable methods for modeling variable penetrance in aneuploidy and CNV carriers using family data. These methods update estimates of phenotypic penetrance for XYY and suggest that the predictive utility of family data is likely to vary for different traits and different gene dosage disorders.Trial registrationsClinicalTrials.govNCT00001246, “89-M-0006: Brain Imaging of Childhood Onset Psychiatric Disorders, Endocrine Disorders and Healthy Controls.” Date of registry: 01 October 1989.

Highlights

Disorders of gene dosage can significantly increase risk for psychopathology, but outcomes vary greatly amongst carriers of any given chromosomal aneuploidy or sub-chromosomal copy number variation (CNV)
Our current study develops and implements new analytic tools to address these open questions using data from XYY syndrome as proof of principle first application, the approaches presented are explicitly designed to be generalizable to any aneuploidy or CNV disorder
Given the extensive phenotypic variation observed in individuals with aneuploidies and pathogenic CNVs, there is a need to build models that will allow for a better prediction of outcomes in individual carriers

Summary

Introduction

Disorders of gene dosage can significantly increase risk for psychopathology, but outcomes vary greatly amongst carriers of any given chromosomal aneuploidy or sub-chromosomal copy number variation (CNV). Disorders of gene dosage, ranging from aneuploidies to copy number variations (CNVs), are increasingly recognized as high-impact genetic risk factors for neuropsychiatric disease [1]. There is strikingly high variability within carriers of any given aneuploidy [XYY [3]] or CNV [16p11.2 deletion [4], 22q11.2 deletion [5]], especially when carriers are identified through populationbased versus clinical sampling frameworks [6, 7]. The broad range of outcomes within aneuploidy and CNV disorders poses complex questions regarding the sources of phenotypic variation, and frustrates personalized medicine approaches by making it difficult to predict outcomes in new cases. There has been growing interest in addressing this challenge by using family-based study designs to improve the prediction of penetrance for individual carriers, and to better understand sources of outcome variance across carriers

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