Identification of Novel Alzheimer’s Disease Loci Using Sex-Specific Family-Based Association Analysis of Whole-Genome Sequence Data

Abstract

With the advent of whole genome-sequencing (WGS) studies, family-based designs enable sex-specific analysis approaches that can be applied to only affected individuals; tests using family-based designs are attractive because they are completely robust against the effects of population substructure. These advantages make family-based association tests (FBATs) that use siblings as well as parents especially suited for the analysis of late-onset diseases such as Alzheimer's Disease (AD). However, the application of FBATs to assess sex-specific effects can require additional filtering steps, as sensitivity to sequencing errors is amplified in this type of analysis. Here, we illustrate the implementation of robust analysis approaches and additional filtering steps that can minimize the chances of false positive-findings due to sex-specific sequencing errors. We apply this approach to two family-based AD datasets and identify four novel loci (GRID1, RIOK3, MCPH1, ZBTB7C) showing sex-specific association with AD risk. Following stringent quality control filtering, the strongest candidate is ZBTB7C (P inter = 1.83 × 10 −7 ), in which the minor allele of rs1944572 confers increased risk for AD in females and protection in males. ZBTB7C encodes the Zinc Finger and BTB Domain Containing 7C, a transcriptional repressor of membrane metalloproteases (MMP). Members of this MMP family were implicated in AD neuropathology.Alzheimer's disease (AD) is the most common form of dementia worldwide, with a substantial burden for not only patients, but their families, society and the healthcare system. The impact of the disease is expected to increase further by 2050, with a projected 13.9 million Americans to develop AD or related dementias 1 . Like most complex diseases, AD is caused by a mixture of genetic and environmental factors. Early-onset familial AD (monogenic) is caused by rare fully penetrant mutations in APP, PSEN1, PSEN2 genes 2 . The more prevalent form, late-onset (sporadic) AD, is caused by a complex polygenic architecture, including large-effect variants in the APOE gene 3 . Environmental and lifestyle factors also affect the prevalence of the disease, however this domain is less well elucidated to date. Although one of the strongest predictors for AD is age, there are several other risk factors, including race 4 , high blood pressure 5 , brain trauma 6 and sex 7-10 .Not only are women at twofold greater risk than men, the progression of the disease and neurodegeneration is more rapid among women versus men 11,12 . In contrast, men with AD have higher mortality, as compared to women 12,13 . Interactions between sex and APOE ε4 have been previously reported. For instance, Altmann et al. showed that women have greater AD risk in the presence of APOE ε4, and this APOE-related risk in women may

Folate-Dependent Cognitive Impairment Associated With Specific Gene Networks in the Adult Mouse Hippocampus

“…We have demonstrated that folate-deficient mice which exhibit cognitive impairment also have an increase in Tomm40 expression further implicating this gene. Grid1 expression in human females is positively correlated with protection from AD risk (63). Our study with cognitively impaired female mice demonstrated a downregulation of Grid1 (2.9-fold down, P < 0.001; Supplementary Table 3) which also suggests the positive correlation of Grid1 expression and cognitive health.…”

Sex-dependent effect of APOE on Alzheimer's disease and other age-related neurodegenerative disorders

“…As the use of polygenic risk scores (Box 1) is proving beneficial for predicting disease risk (Box 4), the same approach may be applied to sex-specific risk. Furthermore, family-based analytical designs, which robustly recapitulate population structure, can be employed to test for sex-specific disease risk loci (Prokopenko et al, 2020). In addition to the sex-dependent effect described for APOE, other genetic and genomic factors contribute to LOAD in a sex-specific manner, as we review here.…”

“…rs13115400 Female-specific association with LOAD and Aβ42 levels (Deming et al, 2018) MCPH1 rs13259125 Increased risk in males; protection in females (Prokopenko et al, 2020) MPO rs2333227 Increased risk in females, reduced risk in males (Reynolds et al, 1999) NGFR rs2072446 Increased risk in females (Matyi et al, 2017) RELN rs528528 Increased risk in males (Fehér et al, 2015) rs607755…”

“…rs5030732 Reduced risk in females (Xue and Jia, 2006) ZBTB7C rs1944572 Increased risk in females; protection in males (Prokopenko et al, 2020) PD APOE rs429358 Younger age at onset in ε4 females (Buchanan et al, 2007) rs7412 COMT* rs4680 Younger age of onset in males; epistasis with CYP1A1 polymorphism (Klebe et al, 2013;Kumudini et al, 2014) CYP1A1* rs4646903 Younger age of onset in males; epistasis with COMT polymorphism (Kumudini et al, 2014) IL10 rs1800872 Increased risk in females (Li et al, 2012) LRRK2 rs34637584 Increased risk in females (Orr-Urtreger et al, 2007) PON1 rs854571 Male-specific association with PD and Lewy body pathology (Saito et al, 2004) TLR9 rs352140 Increased risk in females (Zhu et al, 2016) ALS APOE rs429358 Increased risk for bulbar-onset ALS in ε4 males (Praline et al, 2011) rs7412…”

Similar Genetic Architecture of Alzheimer’s Disease and Differential APOE Effect Between Sexes

“…However, due to low statistical power to robustly detect sex-specific loci in AD after sample stratification, few studies have investigated sex difference effects through a classical GWAS approach to identify variants associated with the AD diagnosis in case-control cohorts (Nazarian et al, 2019). Other study paradigms such as incorporating familybased association design or leveraging neuropathological features as AD endophenotypes have found sex-specific associations (Deming et al, 2018;Dumitrescu et al, 2019;Prokopenko et al, 2020). In addition to detecting sex-specific loci, some studies identified significant sex-specific predictors for AD phenotypes such as neuropathology (Deming et al, 2018), biomarkers (Dumitrescu et al, 2019), and age at onset using polygenic hazard scores (Fan et al, 2020).…”