Genetic Determinants of Cognitive Function and Age-Related Brain Changes

Author: M. Schuur
Submitted: Friday 1st of October 2010 11:07:12 AM
Submitted by: pgmvanoverveld
Language: English
Content type: Learning resource
Educational levels: expert, qc2, qc3

Abstract

Thesis in English with a summary in English and Dutch In this thesis I describe my research on genetic determinants of cognitive function and age-related brain changes. I have used outcomes that are highly heritable as endophenotypes for my studies of Alzheimer’s disease, including cognitive function, Aβ plasma levels and age-related brain changes as visible on magnetic resonance imaging (MRI). Different study-designs were chosen to investigate our research questions including candidate gene studies, genome wide linkage analysis and genome wide association studies. In the following chapter, I will discuss the main findings of this thesis. One of the most extensively studied candidate gene in Alzheimer’s disease is the apolipoprotein E gene (APOE). The ε4 allele of this gene is a well-established determinant of AD with a large effect on disease risk. Based on the hypothesis that cognitive function may be a relevant endophenotype for AD, we studied the relation between APOE and cognitive function in chapter 3. We found that the APOE*ε4 allele was significantly associated with lower test scores on the Adult Verbal Learning Test in individuals older than 50 years of age. This effect of APOE*ε4 was independent of the effect of APOE*ε4 on vascular risk factors and most pronounced on learning ability. Similar to the findings of others, we found that the APOE*ε4 allele has an effect on cognitive function, but that in contrast to AD the effect is relatively small. We focused our gene discovery studies on cognitive function, since this outcome showed the most consistent association to APOE and may therefore be the most promising endophenotype. To explore new susceptibility regions for cognitive functioning without prior assumptions of pathways involved, we conducted a hypothesis-free genome-wide search on a range of cognitive tests. In chapter 4 we present the findings of a non-parametric linkage analyses in the Erasmus Rucphen Family (ERF) Study, which is a family-based study in a genetically isolated population. Since we were targeting genes with a major effect, we selected individuals from the lower extremes of the trait distribution for the linkage analysis. Thresholds for significant and suggestive linkage were estimated by a simulation study. Significant linkage (LOD > 3.78) to cognitive functioning was found on chromosomes 1p13.1, 12q24.33, 19q13.43, 20p13, 21q22.13 and 21q22.3. For the fine-mapping of the region, we used dense genotyping in the regions under the linkage peak in ERF and replicated these findings in a large outbred, population-based cohort, the Rotterdam Study (RS). Finemapping showed significant associations to chromosome 1 (p-value=0.03) and 21 (p-value=0.01) after correction for multiple testing, and association with the latter region on 21q22.13 was replicated in the Rotterdam Study (nominal p-value 0.003). Both fine-mapping and replication pointed to variants within the potassium inwardly-rectifying channel, subfamily J, member 6 gene (KCNJ6). Whereas linkage analysis in the extremes of the distribution specifically targets variants with larger effects, we conducted a genome-wide association study of cognitive function as a continuous outcome in search of common variants with small effects. In chapter 5 we describe a meta-analysis of different genome-wide association studies performed in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. This consortium includes large prospective population-based cohorts. Neuropsychological testing was available for 13 cohorts. In this thesis, we focussed on executive function and processing speed tasks including the Trail Making Test (TMT) parts A and B and the Stroop Color and Word Test in the analyses. All individual studies used their genotyped data to impute to 2.5 million single nucleotide polymorphisms (SNPs). The analyses were performed in Caucasians older than 45 years who were free of dementia and clinical stroke at times of cognitive testing. The most significant finding was found with TMT-B and a SNP on chromosome 18. This SNP was just above the genome-wide significant threshold with a p-value of 6.95*10-8 and located between two plausible candidate genes. We further conducted an exploratory analysis in which we searched for overlap between our findings and the genome-wide association analyses published for AD and schizophrenia. Overlap with previous genome-wide association studies was found for multiple other SNPs with a p-value smaller than 1.0*10-3, of which the sortilin-related-receptor-1 (SORL1), the syntaxin-binding-protein-6 (STXBP6) and the protocadherin-9 (PCDH9) genes are the most interesting genes. The genes in the regions that we identified in this study may provide further insights into the pathways involved in the normal variation of cognition. Our findings, however, await replication, which is currently ongoing. A preliminary comparison between the findings of the genome-wide linkage and association analysis suggests no overlap in genes, which may be expected in light of the mechanisms underlying the methods. Linkage is designed to target rare variants with large effects and association on the other hand is designed to find common variants with moderate effects. Of interest is also that we did not find evidence for a role of APOE, or the recently discovered AD genes, PICALM, CRI and CLU in cognitive function in our genome-wide association analyses. This finding reveals, again as expected, that findings on endophenotypes cannot be translated 1:1 to the disease of interest. Another issue to realize is that tests assess different aspects of cognitive function. Indeed we found that APOE was associated to the Adult Verbal Learning Test in chapter 3 but non-significantly to TMT-A, B or Stroop. Having studied cognitive function as endophenotype, we further studied age-related brain changes as a second group of endophenotypes. We considered plasma Aβ levels as biomarkers for the presence of senile plaques and amyloid angiopathy, and asymptomatic brain lesions on MRI as age-related brain changes. We have focused on lacunar infarcts, white matter lesions (WML), microbleeds and hippocampal atrophy. All are associated with hypertension, stroke, dementia and cognitive impairment, and are also found in healthy elderly. We examined the role of candidate genes involved in blood pressure regulation and in amyloid metabolism. We studied APOE, the renin-angiotensin system (RAS) related genes (Angiotensin, Angiotensin II type 1 Receptor, alpha-Adducin) and the sortilin-related receptor (SORL1) gene. RAS genes are involved in the regulation of blood pressure and salt homeostasis and the RAS proteins have also been implicated in Alzheimer’s disease. Receptors for angiotensin II are present in brain tissue and an increased activation of RAS is seen in AD brains. As already mentioned APOE has consistently been associated with AD and there is increasing evidence that also SORL1 is associated with AD. SORL1 consists of two functional regions, one functioning in the cholesterol pathway and the other in the APP processing pathway. Interestingly, the gene has also been associated to cerebrovascular disease in a previous study [26] and also emerged in our comparative analysis in the genome-wide association study (chapter 5). Third, we studied the association of the three variants within the angiotensin, angiotensin II type 1 receptor and adducin genes (AGT-M235T, AGTR1-C573T and ADD1-Gly460Trp) in the same middleaged hypertensive subset of ERF. Variants in these genes were previously reported in cerebroand cardiovascular disease in relation to circulating levels of plasma Aβ (chapter 10). The AGTM235T TT-genotype was significantly associated with higher levels of plasma Aβ42 (p=0.008) and truncated Aβn42 (p=0.02). The association to Aβ42 remained significant after adjusting for potential First, we studied all five variants in relation to the MRI endophenotypes: volumes of WML and presence of lacunes and microbleeds in a subgroup of the ERF study aged 55 and 75 years with hypertension (chapter 8). WML was present in variable severity in all participants, whereas lacunar infarcts were present in 15.5% and microbleeds in 23.3%. Homozygosity for the APOE ε4 allele was associated with lacunes (OR, 4.8; 95% CI, 1.2-19.3). Individuals carrying two copies of the variant allele of 4 SNPs located at the 3’-end of SORL1 (rs1699102, rs3824968, rs2282649, rs1010159), had an increased risk of microbleeds (highest odds ratio, 6.87; 95% CI, 1.78-26.44), which is suggestive for the hypothesis that the amyloid cascade is involved in the etiology of microbleeds in populations with hypertension. Second, in chapter 9 we studied SORL1 in relation to hippocampal volume and plasma Aβ levels in the same subgroup of the ERF study. Hippocampal volumes were quantitatively measured on MRI and plasma Aβ levels were determined in non-fasting blood samples. We studied the effect of 7 variants within SORL1 that were previously reported in AD. Three variants located near the 3’-end of SORL1 were significantly associated to hippocampal volume. The 3-SNP haplotypes for rs1699102, rs3824968 and rs2282649 (CAT) and for rs3824968, rs2282649 and rs1010159 (ATC) were associated to higher hippocampal volumes when adjusting for multiple testing. We did not find significant associations of single variants with plasma Aβ levels. Taken together, the most interesting finding of our studies may be the associations that were found for SORL1 in various study designs. Our candidate gene analyses showed association of SORL1 with cognition as well as microbleeds and hippocampal volume. SORL1 also emerged in our genomewide association meta-analyses of cognitive function. A word of caution is, however, needed: our candidate gene studies were performed in a small sample size and were restricted to hypertensive individuals. These findings therefore need replication in larger cohorts in the general population. Finally, we conducted two candidate gene studies in Alzheimer’s disease to elucidate the role of two interesting pathways. Iron overload may contribute to the risk of Alzheimer’s disease. We earlier have studied the genes implicated in hemochromatosis in relation to AD. We found an effect of the hemochromatosis gene (HFE) on the age of onset of AD. The HFE-63D mutation was related to an earlier onset in APOE*ε4 carriers, but not to the disease risk. Other groups reported evidence in other variants in hemochromatosis genes HFE-C282Y and -H63D, and transferrin (TF). In the Epistasis Project, with 1757 AD cases and 6295 controls, we studied four variants in two genes of iron metabolism: HFE-C282Y and -H63D, and TF-C2 and -2G/A (chapter 7). We replicated the interactive effect between HFE-282Y and TF-C2 on the risk of AD in Northern Europeans. We also found an interaction between HFE-63HH and TF-2AA, which was markedly modified by age. The interaction between HFE-282Y and TF-C2 has now been replicated twice, in a total of 2313 cases of AD and 7065 control. There are a number of limitations of this study that hamper firm conclusions. confounders and multiple testing. No significant associations were found between AGTR1-C573T or ADD1-Gly460Trp and plasma Aβ. First, both interactions were found mainly or only in Northern Europeans. In fact, there was an absence of a relation between HFE and AD in a Northern Spanish population. From a statistical perspective, the exclusion of the Spanish data is problematic. Although the allele frequencies in Northern Spain differed from those in the Northern Europeans, this does not imply that the relation to AD should be different. A second problem is that although we pooled the data, the numbers are small and as a consequence the study power is low, making the analysis susceptible to false positive findings. We also studied the Cathepsin D gene (CTSD) in relation to AD (chapter 6). CTSD is involved in amyloid precursor protein processing and is therefore considered a candidate for AD. We performed a candidate-gene analysis in the Rotterdam Study, which is a population-based cohort-study (N=7983) and estimated the effect of CTSD variants on the risk of AD. Additionally, we performed a large metaanalysis incorporating our data and previously published data. The T-allele of CTSD rs17571 was associated with an increased risk of AD (p-value 0.007) in the Rotterdam Study. This association was predominantly found in APOE ε4 noncarriers. A meta-analysis of previously published data showed. Besides these genetic studies, in chapter 2 we also performed a classical epidemiological study in which we studied a combination of cardiovascular risk factors as composed in the metabolic syndrome (MetS) in relation to cognition. While type 2 diabetes is known to be associated with poorer cognitive performance, fewer studies have reported on the association of MetS and contributing factors, such as insulin-resistance (HOMA-IR), low adiponectin-, and high C-reactive protein (CRP)- levels. We studied whether these factors are related to cognitive function and which of the MetS components are independently associated. Also this study was performed in the ERF study where extensive data on physical examination, biomedical measurements and neuropsychological testing were available. Linear regression models were used to determine the association between MetS, HOMA-IR, adiponectin levels, CRP, and cognitive test scores. We found that predominantly women with MetS and high HOMA-IR had lower scores on executive function tests (p=0.03 and p=0.009). The most consistent individual component of MetS, contributing to the association with executive test scores was systolic blood pressure. We interpret these results with caution, however, since the design was cross-sectional and with very strict multiple testing adjustment using Bonferroni would result in only borderline significant p-values. Longitudinal studies will be needed to gain insight in the causality of our reported findings and may result in more conclusive findings.

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M. Schuur. Genetic Determinants of Cognitive Function and Age-Related Brain Changes. EUROGENE portal. October 2010. online: http://eurogene.open.ac.uk/content/genetic-determinants-cognitive-function-and-age-related-brain-changes

Keywords

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