Subcortical Reserve Estimated With Polygenic Scores And Its Impact On Cognitive Impairment In Amyotrophic Lateral Sclerosis
Klara Gawor
Prepared under the supervision of dr. Wouter van Rheenen (UMC Utrecht)
The Research Project II report submitted to the Institute for Interdisciplinary Studies, University of Amsterdam
2 Abstract
Some form of cognitive impairment may affect even half of the patients with Amyotrophic Lateral Sclerosis (ALS). Degeneration of subcortical structures was shown to occur in ALS patients, especially in ones with cognitive dysfunction or other symptoms of Frontotemporal Dementia. This research was conducted to test whether the size of the caudate basal ganglia, thalamus, brainstem, nucleus accumbens, amygdala, or hippocampus can differentiate between ALS patients with and without cognitive symptoms measured on the Edinburgh Cognitive and Behavioural ALS Screen scale. To reduce the confounding effect of age and progressive degeneration, we used the genetic proxy of subcortical structures volume estimated with polygenic scores on summary statistics after joint-analysis. We found that subcortical volumes do not increase susceptibility to ALS but might impact the development of cognitive impairment. The globus pallidus was shown to be a predictor of memory and language scores of ALS patients. Putamen and brainstem volumes also explained the variance in memory score. Our results added evidence to the claim that basal ganglia are involved in the development of cognitive impairment in ALS patients. Additionally, we showed that genetic proxies for imaging parameters, calculated as the polygenic score could be a valuable measure in research on neurodegeneration.
3 Introduction
Amyotrophic lateral sclerosis (ALS) is a multi-system neurodegenerative disorder with progressive motor deficits caused by the degeneration of both the upper and lower motor neurons as a primary symptom (van Es et al., 2017, Hardiman et al., 2017). The primary pathogenetic mechanism identified behind ALS is intracellular ubiquitin inclusions with the transactive response DNA binding protein-43 (TDP-43). Even though the pathology is shared among most patients, the clinical course of a disorder (age at onset, site of onset, rate of progression) is highly variable (Takeda et al., 2020). To date, a polygenic genetic architecture with more than 30 genes and risk loci discovered to influence its variability (Brown & Al-Chalabi, 2017; van Rheenen et al., 2019).
The last decade was a period of excessive research on non-motor manifestations in ALS, which lead to surprising results that as much as ~50% of ALS patients exhibit some level of cognitive or behavioral deficits (Montuschi et al., 2015; Phukan et al., 2007). Most
commonly, ALS affects fluency, language, social cognition, executive functions, and verbal memory (Beeldman et al., 2016). These abnormalities range from mild dysfunctions to fully developed dementia in ~6-15% of patients (Consonni et al., 2013; Montuschi et al., 2015; Murphy et al., 2016). The presence of cognitive impairment is linked to faster ALS progression and shorter survival (Elamin et al., 2011; Giordana et al., 2011).
ALS non-motor symptoms partially overlap with ones seen in Frontotemporal Dementia (FTD) (Lomen-Hoerth, 2011). FTD is the most common early-onset dementia (Vieira et al., 2013), which affects behavioral, linguistic, and executive functions and leads to behavioral deficits (Bang et al., 2015). These two types of disorders also share TDP-43 as their major disease protein (Lippa et al., 2000; Neumann et al., 2006). The high similarity between ALS and FTD encouraged many scientists to consider them as belonging to the common spectrum (Bak, 2010; Ferrari et al., 2011; Swinnen & Robberecht, 2014). However, some cognitive impairments identified in ALS patients are not present in FTD. In addition to that, another type of dementia, one that is caused by dysfunction in frontal-subcortical circuits, remarkably resembles the cognitive impairment in ALS. The main symptoms of this dementia include executive and memory problems, behavioral disinhibition, and apathy (Bonelli & Cummings, 2007, 2008). It suggests that comorbidity with FTD may only partially explain non-motor symptoms in ALS and that subcortical structures may play a significant role in this phenomenon.
Pathology causing neurodegeneration in ALS is believed to start in motor and frontal cortices and, in later stages of the disease, to spread via axonal projections to subcortical structures such as the striatum and hippocampus (Verde et al., 2017). Multiple studies have shown the atrophy and shape distortion in basal ganglia, thalamus, accumbens nuclei, and hippocampus (Bede et al., 2014; Finegan et al., 2019; Machts et al., 2015). Interestingly, the study of Bede et al. (2018) revealed that subcortical degeneration is only present in ALS patients with some cognitive or behavioral deficits. Dorsal striatum degeneration is also observed in FTD
4 All this supports the involvement of subcortical structures in the origin of cognitive
impairment in ALS. Nevertheless, the question of why only half of the ALS populations develop behavioral or cognitive problems remains. The brain reserve hypothesis, borrowed from research on Alzheimer's disease, could be one of the answers. Accordingly, the clinical outcome is related to the baseline brain volume of patients (Mortimer, 1997). The bigger are structures the more time is needed for such a degree of degeneration that gives symptoms. The estimated survival of ALS patients is 3–5 years after diagnosis (Brown & Al-Chalabi, 2017). Therefore, patients with higher subcortical volumes could never evolve any cognitive and behavioral impairment.
Testing of the hypothesis of brain reserve on neurodegenerative patients is constricted due to three problems with the assessment of baseline brain structures volume. Firstly, as it was mentioned above, ALS itself causes atrophy of both subcortical and cortical brain structures. Secondly, the median age at the time of onset is 58–63 years in ALS (Kiernan et al., 2011) which means a neuronal loss in the subcortex of patients as a natural result of ageing (Wang et al., 2019). The last restriction is the shortage of magnetic resonance imaging (MRI) data on ALS patients caused by the high costs of neuroimaging as well as the inability of patients to keep the supine position due to swallowing difficulties and reduced vital capacity (supine hypoventilation).
These limitations could be resolved through the recent advances in human genetics technology, such as polygenic scores that assess individuals' genetic predisposition to a particular trait rather than its actual value (Choi et al., 2020). Polygenic scores take into account variation in multiple genetic variants and can be calculated on the results of genome-wide association studies (GWAS). GWAS is an approach to find associations between genetic regions and traits and has already been applied to study a multitude of different features, including cortical and subcortical volumes (Bulik-Sullivan et al., 2015). The high heritability of subcortical structures volumes (Satizabal et al., 2019) should allow for designing an MRI-proxy based on polygenic scores of these structures with sufficient predicting power.
In this study, we created the measure of an individual's predisposition to develop a higher or lower subcortical reserve (MRI-proxy). It was done by assessing the polygenic score using GWAS summary statistics for caudate nucleus, putamen, globus pallidum, accumbens nuclei, thalamus, amygdala, and hippocampus volumes. This measure was then applied to test the possibility that subcortical volume is associated with generally increased susceptibility to develop ALS. Based on the foregoing research results, we hypothesized that there is no such relation. Finally, we investigated our primary hypothesis that ALS patients with low
5 Methodology
Figure 1. The research flow of this study
The information on all employed versions and sources of software is located in
Supplementary Table S1. The simplified representation of the study stages is presented in Figure 1.
Summary Statistics
The GWAS summary statistics on the subcortical and hippocampal volumes, conducted on the 38,851 and 33,536 participants respectively, were obtained from the ENIGMA-CHARGE collaboration website (Hibar et al., 2017; Satizabal et al., 2019). Summary statistics on ALS and were calculated on 27,205 patients diagnosed with ALS and 110,881 controls. The individual-level genetic data for these 138,086 participants were used in subsequent analyses. Both summary statistics and individual-level data were obtained from an international
collaborative genome-wide association study on ALS (unpublished, presented on conference presentation: The Project MinE GWAS Consortium, 2019). Both patients with and without a family history for ALS or dementia were included. All participants were of self-reported European ancestries. Supplementary Table S2 contains the main characteristics of sample strata. The complete method of whole-genome sequencing and the quality control of the genotype data can be found in the published study (Project MinE ALS Sequencing
Consortium, 2018). All summary statistics dataset underwent a standardized quality check to ensure the absence of any ambiguous, multiallelic, duplicated, and low-frequency
(MAF<0.01) single nucleotide polymorphisms (SNPs).
We applied a multi-trait analysis of GWAS (MTAG) to summary statistics on subcortical and hippocampal volumes (Turley et al., 2018). MTAG is a technique for joint analysis of two or more traits that can increase the power of genetics associations for each separate trait. It was shown that polygenic scores calculated on summary statistics after MTAG analysis explain significantly more variance of a trait and improve genetic prediction (Turley et al., 2018).
6 The beta coefficient and p-values resulting from MTAG were used like single-trait GWAS summary statistics.
Heritability and Genetic Correlation
The SNP-based heritability of the subcortical and hippocampal volumes, i.e., the proportion of phenotypic variance explained by common SNPs (Yang et al., 2017), was estimated using commonly employed techniques of LD-Score regression (Bulik-Sullivan et al., 2015) and SumHer (Speed & Balding, 2019). Additionally, LD-Score regression was applied to
calculate SNP-based bivariate genetic correlations (for a review see van Rheenen et al., 2019) between subcortical/hippocampal volumes and ALS. Genetic correlations were not adjusted for multiple testing due to violating the assumption on the independence of measurements. All steps in this part were performed on single-trait summary statistics.
Polygenic Scores
To ensure the robustness of our results, we assessed the polygenic score of subcortical and hippocampal volumes with two different software: PRSice (Euesden et al., 2015) and
sBayesR (Lloyd-Jones et al., 2019). PRSice calculates polygenic scores by summing over all SNPs that meet a certain p-value threshold. Summary statistics during this step were pruned with a clumping procedure implemented in PLINK with the squared correlation threshold (rc2)of 0.1 and window size (wc) of 250 kb. sBayesR is a more advanced technique that utilizes prior Bayesian regression models and LD matrices and was shown to increase the predictive accuracy of polygenic scores (Lloyd-Jones et al., 2019). Polygenic scores were calculated separately for summary statistics with and without the MTAG procedure.
The validation of the polygenic scores was performed on a subsample of 229 ALS patients and 107 controls from the project Mine with assessed volumes of subcortical structures using 3T T1-weighted magnetic resonance imaging (MRI) (for a detailed description of the method, (Tan et al., 2020). We assessed the predictive accuracy of polygenic scores using linear regression controlled for age and sex of participants as well as genetic structure captured with principal components described in the Statistical Analysis section. Finally, we predicted the subcortical and hippocampal volumes for 138,086 participants with known genetic
architecture using a polygenic score calculated with sBayesR on summary statistics after a multi-trait analysis. All estimated values were transformed to follow the standard normal distribution, and Z-scores further from zero than +/-3 have been removed from the analysis. Edinburgh Cognitive and Behavioural ALS Screen (ECAS)
ECAS is proven to be a valid and reliable cognitive test battery designed with motor difficulties of the ALS population in mind (Niven et al., 2015). The cognitive impairment was measured with a Dutch version of Edinburgh Cognitive and Behavioural ALS Screen (ECAS, Bakker et al., 2019) in 471 ALS patients. ECAS measures the participant's
7 visuospatial processing. The test was conducted at the time of patients’ diagnosis by a
neuropsychologist at a tertiary referral motor neuron diseases clinic at the University Medical Center Utrecht.
Statistical Analysis
All statistical analyses were conducted using the generalized linear model framework
implemented in R. P-values obtained in the course of statistical analysis were not adjusted for multiple testing due to violating the assumption on the independence of measurements. The population stratification was accounted for by performing principal component analyses. We selected SNPs in the HapMap release 3 that are nearly uncorrelated with each other by performing a pruning procedure in PLINK (𝑟𝑐2 = 0.5; 𝑤𝑐 = 50 kb), and, calculated
eigenvectors for the first 20 principal components. Subsequently, SNP loadings were calculated using GCTA and used as a PCA projection in regression models testing the association between ALS (Jiang et al., 2019). The eigenvectors were included as covariate also during polygenic score validation and testing of the hypothesis on cognitive impairment. However, as none of the ancestry informative PCA showed to explain variation in such a small dataset, and all patients taken to this analysis were from the Dutch population, we excluded these principal components. Instead, and constructed a genetic relationship matrix (GRM) of the Dutch individuals using LD-pruned SNPs (𝑟𝑐2 = 0.1; 𝑤𝑐 = 50 kb) to capture the cohort-specific population structure. From this GRM, we calculated the first five principal components and included them as covariates.
To investigate the association between predicted subcortical volumes and ALS susceptibility, we performed a logistic regression analysis using the total sample of 138,086 participants for each of the six strata separately. We combined the findings in the inverse-variance weighted fixed-effect meta-analysis using the 'meta' package for R.
The primary analysis of the impact of subcortical volumes on cognitive impairment was con-ducted by creating linear models explaining ECAS scores for fluency, language, executive, memory, and visuospatial function with the MRI-proxy of subcortical structures as independ-ent variables. 471 ALS patiindepend-ents with known ECAS scores and assessed polygenic scores were taken to this analysis. We controlled these models for sex, age, level of education, and co-hort-specific components. Additionally, we generated linear models with only the control variables. The level of education was coded accordingly to the International Standard Classi-fication of Education (ISCED, 2011 version).
Results
The quality-controlled summary statistics for ALS consisted of 7,957,693 SNPs. The
subcortical and hippocampal volume summary statistics had 5,570,023 high-quality SNPs, all of which were present in the ALS GWAS. Manhattan plots of association results are in Supplementary Figure S3.
8 Heritability Estimates
The SNP-based heritability of ALS was estimated to be 0.06 (SE = 0.01) for LDSC and 0.13 (SE = 0.02) for SumHer (see Figure 2). For subcortical structures, the values of heritability ranged from 0.09 (SE = 0.02, LDSC) and 0.14 (SE = 0.03, SumHer) for the amygdala to 0.32 (SE = 0.03, LDSC) and 0.37 (SE = 0.06, SumHer) for the brainstem. Estimating the SNP-based heritability for the globus pallidus volume with LDSC gave 0.16 (SE = 0.021), however, it was not possible to estimate it with SumHer due to the instability of the
algorithm. The SumHer method gave consistently higher values to ones obtained with LDSC. Supplementary Table S3 contains the exact estimated values.
Figure 2. The SNP-based heritability estimates for ALS and eight subcortical volumes using LDSC and SumHer.
Genetics Correlations
We found one negative relation between ALS and hippocampus that reached a significant level of 0.05 (rg = -0.18 p = 0.04), however, it would not survive the correction for multiple comparisons. We observed several cross-subcortical relations. The strongest correlation was detected between the hippocampus and amygdala volumes (rg = 0.669, p = 3.17 x 10-11). Putamen volume was shown to be highly correlated with globus pallidus (rg = 0.551, p = 7.76 x 10-25), caudate nucleus (rg = 0.524, p = 1.82 x 10-19), and the nucleus accumbens (rg = 0.439, p = 4.81 x 10-18). The nucleus accumbens was strongly genetically related to the caudate nucleus (rg = 0.446, p = 6.33 x 10-19). The genetic basis of brainstem was shown to be strongly similar to globus pallidus (rg = 0.477, p = 1.27 x 10-21) and thalamus (rg = 0.493, p = 4.32 x 10-22). The matrix with all genetic correlations for ALS and subcortical structures calculated using LDSC is depicted in Figure 3a; for p-values, see Supplementary Table S4. A network of genetic relations is presented in Figure 3b, with significant correlations depicted as the presence of the edge and their strength as an edge’s width. Globus pallidus and thalamus were shown to be “hubs” in this network, i.e., these nodes are connected with the highest number of other nodes. On the other hand, in this network graph of genetic
9 correlations caudate and brainstem have the fewest, nevertheless strong connections with other structures.
Figure 3. a) Correlation matrix for eight subcortical volumes and ALS disease. The rG values are plotted. Values in bold letters show correlations that reached P-level of 0.006 (α corrected for the number of traits for better visualisation). b) Network of correlation for eight subcortical volumes. The presence of an edge represents the correlations with P-level lower than 0.006, whereas the width represents the strength of correlation (rG).
Polygenic Scores Accuracy
Using summary statistics for each of the subcortical volumes, we generated polygenic predictors and assessed their predictive accuracy in a cohort with MRI and genetic data. The mean age of the population at the time of the MRI scan was 62.03 (9.6) years, and 63.9% of participants were male. The detailed results, including thresholds and the number of SNPs used during the PRSice procedure, are in Supplementary Table S5. Figure 4 plots the
predictive accuracy of calculated polygenic scores. Generally, the sBayesR procedure yielded better estimators than PRSice. Application of PRSice on both raw summary statistics and ones after MTAG analysis gave 11 out of 16 polygenic scores significant (alpha=0.05) whereas using sBayesR 12 out of 16.
Caudate nucleus polygenic score showed the highest accuracy in each of the analyses
explaining up to 8.6% of the structure’s volume calculated with sBayesR on jointly analyzed summary statistics. The polygenic scores for the putamen reached the maximum accuracy of 5.1% and nucleus accumbens of 3%. Polygenic scores for pallidum and brainstem had an accuracy of approximately 2%. The accuracy for thalamus MRI-proxy was assessed to be around 1%. The lowest predictive power of polygenic scores was obtained for the
hippocampus and amygdala volume; the accuracy of their polygenic scores did not exceed 0.5% of the explained variance; therefore, we excluded them from further analysis.
10 Figure 4. The accuracy of polygenic scores estimated on summary statistics of subcortical structures with and without joint-analysis (MTAG) using sBayesR (a) and PRSice (b);
* < 0.01, · < 0.05
Effect of Subcortical Volumes on ALS Susceptibility
To check the potential association between low subcortical volume and susceptibility to ALS, we regressed ALS case-control status on the polygenic MRI predictors within each stratum separately using logistic regression and combined the results in a analysis. The meta-analysis on the impact of brainstem volume reached the p-value of 0.03 with the combined OR for all six strata of 1.02 and 95% CI ranging from 1.00 to 1.03, see the forest plot on Figure 5. This results would not survive the correction for multiple comparisons. No other results reached the level of significance. The forest plots for other subcortical structures are in Supplementary Figure S3.
Figure 5. Forest plot showing the odds ratios (OR) for the association of brainstem MRI-proxy of volume with ALS disease prevalence in six strata. The value of the odds ratio is depicted as black squares, the horizontal line represents the 95% confidence interval. The size of the squares varies with the sample size of each stratum.
11 Effect of Subcortical Volumes on Cognitive Impairment
We checked the hypothesis of the impact of the volume of subcortical structures on developing cognitive impairment in 471 ALS patients, 60.72% of whom were men. The descriptive statistics are presented in Table 1. The mean age of the sample was 64.31 (SD = 10.46) years, and the mean level of education was 3.47 (SD = 1.77) on the ISCED scale, where 0 means pre-primary education and 8 means the second stage of tertiary education. Patients obtained a mean total ECAS score of 103.71 (SD = 15.97) points. For executive function, the mean score was 34.12 (SD = 7.85) points, for verbal fluency 17.31 (SD = 4.77) points, and 25.36 (SD = 2.57) points for the language function. Mean memory and
visuospatial scores were 15.54 (SD = 5.18) and 11.39 (SD = 1.16) points, respectively. The distributions of scores for this sample were plotted and contrasted with the distribution of ECAS scores of 787 healthy controls (60.89% male) in Supplementary Figure S4. The descriptive statistics of the healthy sample are in Supplementary Table S7. The plot of two first principal components from cohort-specific PCA is presented in Supplementary Figure S5.
Table 1. Descriptive statistics of ALS patients age, level of education, and obtained ECAS scores.
The results of linear models regression the polygenic MRI predictors on the five cognitive domains measured by ECAS are presented in Tables 2a-e. The coefficients for sex, age, and level of educations were obtained from the models with exclusively control independent variables. The explained variance for subcortical structures was calculated by subtracting the adjusted R-squared from the model with covariates only from the adjusted R-squared of the full (polygenic scores and covariates) model. Age was negatively associated with scores for each of the cognitive domains. Education was positively related to obtained scores in each domain except for visuospatial. Additionally, being a woman slightly positively impacted the fluency function (Estimate = 1.04, Pval = 0.02), yet negatively the visuospatial function (Estimate = -0.44, Pval = 3.6 x 10-05).
ECAS score Min Max Max
range Median Mean (SD)
Total 44 133 0-136 106 103.71 (15.97) ALS specific 25 99 0-100 79 76.78 (12.37) Language 11 28 0-28 26 25.36 (2.57) Executive 5 48 0-48 36 34.12 (7.85) Fluency 0 24 0-24 20 17.31 (4.77) ALS non-specific 4 36 0-36 28 26.94 (5.56) Memory 0 24 0-24 16 15.54 (5.18) Visuo-spatial 2 12 0-12 12 11.39 (1.16) Age (years) 25 88 - 66 64.31 (10.46) Education (ISCED) 1 8 0-8 3 3.47 (1.77)
12 a. Language
Estimate StdErr t-value P-value ExpVar
Sex -0.0327 0.2320 -0.1408 0.8881 - Age -0.0728 0.0110 -6.6268 9.58E-11 - Education 0.1387 0.0654 2.1198 0.0346 - Caudate 0.1269 0.1116 1.1373 0.2560 0.06% Putamen 0.1625 0.1127 1.4423 0.1499 0.21% Pallidum 0.2324 0.1133 2.0519 0.0407 0.62% Brainstem 0.1402 0.1137 1.2333 0.2181 0.10% Thalamus 0.1224 0.1093 1.1202 0.2632 0.05% Accumbens 0.0614 0.1134 0.5412 0.5886 0.00% b. Executive
Estimate StdErr t-value P-value ExpVar
Sex 1.1663 0.6929 1.6831 0.0930 - Age -0.2251 0.0328 -6.8627 2.18E-11 - Education 0.9504 0.1954 4.8636 1.58E-06 - Caudate -0.0439 0.0511 -0.8589 0.3908 0.00% Putamen -0.0082 0.0517 -0.1591 0.8736 0.00% Pallidum 0.0570 0.0520 1.0968 0.2733 0.04% Brainstem 0.0238 0.0521 0.4564 0.6484 0.00% Thalamus 0.0578 0.0500 1.1558 0.2483 0.07% Accumbens -0.0453 0.0519 -0.8740 0.3826 0.00% c. Fluency
Estimate StdErr t-value P-value ExpVar
Sex 1.0432 0.4470 2.3340 0.0200 - Age -0.0628 0.0212 -2.9655 0.0032 - Education 0.3759 0.1260 2.9820 0.0030 - Caudate 0.1262 0.2152 0.5863 0.5579 0.00% Putamen 0.1396 0.2175 0.6421 0.5211 0.00% Pallidum 0.0423 0.2192 0.1932 0.8469 0.00% Brainstem -0.0853 0.2193 -0.3891 0.6974 0.00% Thalamus 0.0662 0.2109 0.3138 0.7538 0.00% Accumbens 0.0973 0.2184 0.4455 0.6562 0.00%
13 d. Visuo-spatial
Estimate StdErr t-value P-value ExpVar Sex -0.4431 0.1062 -4.1728 3.60E-05 - Age -0.0242 0.0050 -4.8222 1.93E-06 - Education 0.0446 0.0299 1.4907 0.1367 - Caudate -0.0439 0.0511 -0.8589 0.3908 0.00% Putamen -0.0082 0.0517 -0.1591 0.8736 0.00% Pallidum 0.0570 0.0520 1.0968 0.2733 0.04% Brainstem 0.0238 0.0521 0.4564 0.6484 0.00% Thalamus 0.0578 0.0500 1.1558 0.2483 0.07% Accumbens -0.0453 0.0519 -0.8740 0.3826 0.00% e. Memory
Estimate StdErr t-value P-value ExpVar
Sex 0.6931 0.4614 1.5019 0.133795 - Age -0.1252 0.0218 -5.7323 1.79E-08 - Education 0.6470 0.1301 4.9718 9.37E-07 - Caudate 0.1474 0.2221 0.6634 0.5074 0.00% Putamen 0.4964 0.2234 2.2218 0.0268 0.73% Pallidum 0.7641 0.2235 3.4187 0.0007 1.96% Brainstem 0.5522 0.2250 2.4541 0.0145 0.93% Thalamus 0.3312 0.2172 1.5251 0.1279 0.25% Accumbens -0.0495 0.2256 -0.2196 0.8263 0.00%
Table 2. The results of linear regressions on associations between subcortical MRI-proxy of volumes and ECAS scores for five cognitive functions. The coefficient estimates for sex, age, and education are extracted from the linear models with only covariates. The significant relations (α = 0.05) are indicated with a yellow color. ExpVar – percent of the variance of ECAS score explained by
subcortical volume MRI-proxy; calculated by subtraction of adjusted R-squared from the model with only covariates from the adjusted R-squared from the model with both covariates and the subcortical volume MRI-proxy.
In our models, MRI-proxies of putamen (Estimate = 0.50, Pval = 0.027) and brainstem (Estimate = 0.55, Pval = 0.015) were associated with ECAS subscores , of memory function and explained 0.73% and 0.93% of its variance. Additionally, volume of globus pallidus explained 0.62% of language score variance (Estimate = 0.23, Pval = 0.04) and 1.96%
memory score variance (Estimate = 0.76, Pval = 0.0007). The violin plot showing the relation between estimated pallidum volume and memory score is presented on Figure 6. No other relation reached statistical significance.
14 Figure 6. Violin plots depicting relations between the ECAS memory score and MRI-proxy of globus pallidus; cut in 1.5SD length bins. Inside each violin plot, there is a box summarizing range, quartiles and median (black horizontal line).
Discussion
In this study, we created the MRI-proxies for volumes of six subcortical structures using summary statistics after joint-analysis and showed their predicting accuracy to range from 0.87% up to 8.58% when compared to independent MRI measures. By this, we demonstrated that polygenic scores for traits with high SNP-heritability could serve as a decent estimate of an individual’s predispositions and could be used to test the brain-volume hypothesis in neurodegenerative disorders like ALS.
As predicted, we found little evidence for the association between subcortical volumes and developing ALS. No genetic correlation between ALS and each subcortical structure proved to be significant. Additionally, although the p-value of the meta-analysis measuring the relation between brainstem MRI-proxy and ALS phenotype was slightly below 0.05, it did not survive correction for multiple testing (5 regions tested). Furthermore, the very low Odds Ratio (OR = 1.02), high heterogeneity among the strata (𝐼2= 59%), and high sample size (n = 138,086) deem this relation implausible. Volumes of other subcortical structures did not affect the ALS prevalence. This is in line with the previous research showing that the degeneration of the subcortical structures happens not at the beginning of the ALS
development, but during later stages (Verde et al., 2017), and that pathology of subcortical structures in ALS patients without cognitive or behavioral impairment might be limited (Bede et al., 2018). Our results suggest that subcortical structures’ involvement is not significant for the disease etiology and that their high volume cannot prevent it.
Regarding our primary hypothesis, on ALS patients with low subcortical volume having an increased likelihood of developing cognitive impairments, we obtained mixed results. We found that the increase in one standard deviation (SD) of brainstem or putamen MRI-proxy leads to the ~0.5 points increase in memory score (max 24 points). The volume of globus
15 pallidus was shown to be the best predictor of memory function, as for an increase in one SD patient obtain a 0.76-point higher score on memory. Globus pallidus was also the only structure significantly predicting other cognitive functions, namely language. However, the effect was small; the increase of one SD in pallidum volume led to only a 0.23point increase in language score (max 28 points). None of the subcortical structures explained variation in other ALS specific functions.
Memory impairment is considered a non-specific symptom of ALS and is attributed by some researchers solely to executive dysfunction (Phukan et al., 2007; Strong, 2017). Nevertheless, the memory function was not studied extensively in ALS patients, and the existing results show a slightly different picture. The number of memory-impaired ALS patients ranges between 11% (Phukan et al., 2011) and 23% (Raaphorst et al., 2015); the variance in these estimates can be attributed to different criteria of impairment and the fact that some studies measure working-memory, other intermediate-memory, and some long-term memory.
Additionally, the study of (Machts et al., 2015) showed that the executive control deficiencies can only explain a small fraction of memory impairment in ALS patients and that this
impairment is distinct from amnestic mild cognitive impairment (a prodromal stage of Alzheimer’s disease). In our study, the distribution of memory scores for ALS patients was bimodal compared to the distribution of healthy individuals, and the pallidum volume proxy could only explain the variation in memory scores, not in executive scores. All this suggests that memory dysfunction might be a distinct symptom and specific for some ALS patients. More research, on memory function in ALS patients, with clear operationalization of this concept, is needed.
Globus pallidum, which volume was in our study the most promising candidate impacting the cognitive impairment in ALS patients, is classified as a part of the basal ganglia system that integrates information from different cortical areas. Basal ganglia are known for being involved in various cognitive functions, including motor control and procedural learning (for a review, see (Milardi et al., 2019). In the study of Bede 2018, the volume reduction of both left and right globus pallidus was observed in ALS patients with behavioral impairment and patients categorized as ALS-FTD, but not in ALS patients without cognitive and behavioral symptoms. Other studies, like (Riku et al., 2016), showed that patients with FTD or FTD-ALS exhibit higher loss of striatal axon terminals in globus pallidus than patients with pure ALS. Interestingly, globus pallidus is one of the few subcortical structures which seems not to be affected by Alzheimer’s disease (Coupé et al., 2019; Möller et al., 2015). Therefore, there is some evidence for the relation between globus pallidus and cognitive problems in ALS patients; yet it is difficult to explain why pallidum MRI-proxy could predict only memory (and maybe language) functions, and not executive or fluency. Potentially, studying a single structure might not be enough to answer this question, and future studies should focus on exploring circuits affected by ALS.
It may also be necessary, as most of the subcortical structures are not homogenous entities, but consist of several functionally and connectively distinct units, to create MRI-proxies of each of the subregions separately. Although globus pallidus and putamen showed association with memory impairment, another part of basal ganglia, namely the caudate nucleus, did not
16 show any relation with cognition in ALS patients. It is all the more surprising as the caudate nucleus had a high predictive accuracy of MRI-proxy and is believed to be involved in working memory and executive functioning. One explanation is that the variation in the volume of the most prominent parts of a nucleus, like its tail, contributes to MRI-proxy most, whereas, as some studies suggest, it is the head of the caudate nucleus that exhibits
substantial atrophy in ALS-FTD patients (Masuda et al., 2016). Additionally, the subcortex is a difficult target for MRI visualization due to the decreasing signal-to-noise ratio with
increasing depth away from the surface and the small size of nuclei that consist of it (Keuken et al., 2018). These properties can significantly lower spatial resolution and therefore add variance to volumetric assessment. To more precisely target the hypothesis of the subcortical reserve, it may be required to use the 7T MRI scanning, tailored MRI contrasts, and small voxel volumes (Mulder et al., 2019), which increase the precision of mapping.
Another promising candidate for a structure that degeneration could be linked to cognitive impairment in ALS patients is the hippocampus. This area is known for its high contribution to human cognitive abilities (Lisman et al., 2017) its decrease with ageing (O’Shea et al., 2016) as well as in the course of many psychiatric and neurologic disorders (Frodl et al., 2006; Lieberman et al., 2018). Its involvement in the ALS cognitive impairment is suggested, e.g., by the study of Matcht et al. (2018), who showed that the verbal memory of patients from the ALS-FTD spectrum correlated with the hippocampal volumes. Unfortunately, due to the low predictive power of the hippocampal MRI-proxy that we created, we could not
include it in subsequent analyses. Although the hippocampus was estimated to be highly heritable (~70%) by twin heritability studies (den Braber et al., 2013; Kremen et al., 2010) summary statistics used in this research could only explain 14% (LDSC) of hippocampal volume variance. It is slightly less than estimates by (Hibar et al., 2017) and is probably related to the removal of ~2M of loci from the hippocampus dataset we performed to enable joint-analysis of all subcortical summary statistics. A different method for constructing a polygenic score for hippocampus might be necessary to explore its involvement in cognitive impairment of ALS.
We are aware of other limitations of this study. Firstly, our results could be distorted due to the atypical distributions of ECAS scores. Only memory and executive function scores follow the binominal distribution and the distinct pattern of scores between ALS and control
participants. A strong ceiling effect was observed for visuospatial scores and medium for language, which reduced the variability of data. The main limitation of this study was, however, the relatively low population of ALS patients used for testing the hypothesis of subcortical volume impact on cognitive impairment. The MRI-proxy we calculated could, on average, explain only 2% of subcortical volume variations. This fact, combined with the possible small effect of brain reserve on cognitive impairment, limits power and makes our analysis prone to type 2 error. A study with better predictors or a bigger sample size could be necessary to explore the hypothesis of brain reserve in ALS patients fully.
Despite these difficulties, employing the achievements of human genetics seems to be a valuable addition to the research on neurodegeneration. Polygenic scores allow estimating
17 brain properties indirectly, which removes the necessity of correcting for normal-ageing or disease-related atrophy. MRI-proxies, in contrast to standard MRI scans, should be less prone to systemic bias and create the opportunity to approach the brain reserve hypothesis more efficiently. Equipped with these tools, we showed that it is unlikely that subcortical structures volumes influence the development of ALS disorder, and we added evidence of the
involvement of basal ganglia in cognitive impairment of ALS patients. Although it is just the beginning of the research application of genetic proxies, this approach is already showing promising results and is likely to advance the field of neurodegenerative disorders in the future.
18 References
Bak, T. H. (2010). Motor neuron disease and frontotemporal dementia: One, two, or three diseases? Annals of Indian Academy of Neurology, 13(Suppl2), S81–S88. https://doi.org/10.4103/0972-2327.74250
Bakker, L. A., Schröder, C. D., Spreij, L. A., Verhaegen, M., De Vocht, J., Van Damme, P., Veldink, J. H., Visser-Meily, J. M. A., van den Berg, L. H., Nijboer, T. C. W., & van Es, M. A. (2019). Derivation of norms for the Dutch version of the Edinburgh cognitive and behavioral ALS screen. Amyotrophic Lateral Sclerosis & Frontotemporal Degeneration, 20(1–2), 19–27. https://doi.org/10.1080/21678421.2018.1522352
Bang, J., Spina, S., & Miller, B. L. (2015). Non-Alzheimer’s dementia 1. Lancet (London, England), 386(10004), 1672–1682. https://doi.org/10.1016/S0140-6736(15)00461-4
Bede, P., Elamin, M., Byrne, S., McLaughlin, R., Kenna, K., Vajda, A., Pender, N., Bradley, D., & Hardiman, O. (2014). Basal Ganglia Involvement in Amyotrophic Lateral Sclerosis (S26.002). Neurology, 82(10 Supplement).
https://n.neurology.org/content/82/10_Supplement/S26.002
Bede, P., Omer, T., Finegan, E., Chipika, R. H., Iyer, P. M., Doherty, M. A., Vajda, A., Pender, N., McLaughlin, R. L., Hutchinson, S., & Hardiman, O. (2018). Connectivity-based
characterisation of subcortical grey matter pathology in frontotemporal dementia and ALS: A multimodal neuroimaging study. Brain Imaging and Behavior, 12(6), 1696–1707.
https://doi.org/10.1007/s11682-018-9837-9
Beeldman, E., Raaphorst, J., Klein Twennaar, M., de Visser, M., Schmand, B. A., & de Haan, R. J. (2016). The cognitive profile of ALS: A systematic review and meta-analysis update. Journal of Neurology, Neurosurgery, and Psychiatry, 87(6), 611–619. https://doi.org/10.1136/jnnp-2015-310734
Bonelli, R. M., & Cummings, J. L. (2007). Frontal-subcortical circuitry and behavior. Dialogues in Clinical Neuroscience, 9(2), 141–151.
19 Bonelli, R. M., & Cummings, J. L. (2008). Frontal-Subcortical Dementias. The Neurologist, 14(2),
100–107. https://doi.org/10.1097/NRL.0b013e31815b0de2
Brown, R. H., & Al-Chalabi, A. (2017). Amyotrophic Lateral Sclerosis. The New England Journal of Medicine, 377(16), 1602. https://doi.org/10.1056/NEJMc1710379
Bulik-Sullivan, B., Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., ReproGen
Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Duncan, L., Perry, J. R. B., Patterson, N., Robinson, E. B., Daly, M. J., Price, A. L., & Neale, B. M. (2015). An atlas of genetic correlations across human diseases and traits. Nature Genetics, 47(11), 1236–1241. https://doi.org/10.1038/ng.3406
Choi, S. W., Mak, T. S.-H., & O’Reilly, P. F. (2020). Tutorial: A guide to performing polygenic risk score analyses. Nature Protocols, 15(9), 2759–2772. https://doi.org/10.1038/s41596-020-0353-1
Consonni, M., Iannaccone, S., Cerami, C., Frasson, P., Lacerenza, M., Lunetta, C., Corbo, M., & Cappa, S. F. (2013). The cognitive and behavioural profile of amyotrophic lateral sclerosis: Application of the consensus criteria. Behavioural Neurology, 27(2), 143–153.
https://doi.org/10.3233/BEN-2012-110202
Coupé, P., Manjón, J. V., Lanuza, E., & Catheline, G. (2019). Lifespan Changes of the Human Brain In Alzheimer’s Disease. Scientific Reports, 9(1), 3998. https://doi.org/10.1038/s41598-019-39809-8
den Braber, A., Bohlken, M. M., Brouwer, R. M., van ’t Ent, D., Kanai, R., Kahn, R. S., de Geus, E. J. C., Hulshoff Pol, H. E., & Boomsma, D. I. (2013). Heritability of subcortical brain measures: A perspective for future genome-wide association studies. NeuroImage, 83, 98–102.
https://doi.org/10.1016/j.neuroimage.2013.06.027
Elamin, M., Phukan, J., Bede, P., Jordan, N., Byrne, S., Pender, N., & Hardiman, O. (2011). Executive dysfunction is a negative prognostic indicator in patients with ALS without dementia. Neurology, 76(14), 1263–1269. https://doi.org/10.1212/WNL.0b013e318214359f
20 Euesden, J., Lewis, C. M., & O’Reilly, P. F. (2015). PRSice: Polygenic Risk Score software.
Bioinformatics (Oxford, England), 31(9), 1466–1468. https://doi.org/10.1093/bioinformatics/btu848
Ferrari, R., Kapogiannis, D., Huey, E. D., & Momeni, P. (2011). FTD and ALS: A tale of two diseases. Current Alzheimer Research, 8(3), 273–294.
Finegan, E., Li Hi Shing, S., Chipika, R. H., Doherty, M. A., Hengeveld, J. C., Vajda, A., Donaghy, C., Pender, N., McLaughlin, R. L., Hardiman, O., & Bede, P. (2019). Widespread subcortical grey matter degeneration in primary lateral sclerosis: A multimodal imaging study with genetic profiling. NeuroImage: Clinical, 24, 102089.
https://doi.org/10.1016/j.nicl.2019.102089
Frodl, T., Schaub, A., Banac, S., Charypar, M., Jäger, M., Kümmler, P., Bottlender, R., Zetzsche, T., Born, C., Leinsinger, G., Reiser, M., Möller, H.-J., & Meisenzahl, E. M. (2006). Reduced hippocampal volume correlates with executive dysfunctioning in major depression. Journal of Psychiatry and Neuroscience, 31(5), 316–325.
Giordana, M. T., Ferrero, P., Grifoni, S., Pellerino, A., Naldi, A., & Montuschi, A. (2011). Dementia and cognitive impairment in amyotrophic lateral sclerosis: A review. Neurological Sciences: Official Journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 32(1), 9–16. https://doi.org/10.1007/s10072-010-0439-6
Hibar, D. P., Adams, H. H. H., Jahanshad, N., Chauhan, G., Stein, J. L., Hofer, E., Renteria, M. E., Bis, J. C., Arias-Vasquez, A., Ikram, M. K., Desrivières, S., Vernooij, M. W., Abramovic, L., Alhusaini, S., Amin, N., Andersson, M., Arfanakis, K., Aribisala, B. S., Armstrong, N. J., … Ikram, M. A. (2017). Novel genetic loci associated with hippocampal volume. Nature Communications, 8, 13624. https://doi.org/10.1038/ncomms13624
Jiang, L., Zheng, Z., Qi, T., Kemper, K. E., Wray, N. R., Visscher, P. M., & Yang, J. (2019). A resource-efficient tool for mixed model association analysis of large-scale data. Nature Genetics, 51(12), 1749–1755. https://doi.org/10.1038/s41588-019-0530-8
21 Keuken, M. C., Isaacs, B. R., Trampel, R., van der Zwaag, W., & Forstmann, B. U. (2018).
Visualizing the Human Subcortex Using Ultra-high Field Magnetic Resonance Imaging. Brain Topography, 31(4), 513–545. https://doi.org/10.1007/s10548-018-0638-7
Kiernan, M. C., Vucic, S., Cheah, B. C., Turner, M. R., Eisen, A., Hardiman, O., Burrell, J. R., & Zoing, M. C. (2011). Amyotrophic lateral sclerosis. Lancet (London, England), 377(9769), 942–955. https://doi.org/10.1016/S0140-6736(10)61156-7
Kremen, W. S., Prom-Wormley, E., Panizzon, M. S., Eyler, L. T., Fischl, B., Neale, M. C., Franz, C. E., Lyons, M. J., Pacheco, J., Perry, M. E., Stevens, A., Schmitt, J. E., Grant, M. D., Seidman, L. J., Thermenos, H. W., Tsuang, M. T., Eisen, S. A., Dale, A. M., & Fennema-Notestine, C. (2010). Genetic and Environmental Influences on the Size of Specific Brain Regions in Midlife: The VETSA MRI Study. Neuroimage, 49(2), 1213–1223.
https://doi.org/10.1016/j.neuroimage.2009.09.043
Lieberman, J., Girgis, R., Brucato, G., Moore, H., Provenzano, F., Kegeles, L., Javitt, D., Kantrowitz, J., Wall, M., Corcoran, C., Schobel, S., & Small, S. (2018). Hippocampal dysfunction in the pathophysiology of schizophrenia: A selective review and hypothesis for early detection and intervention. Molecular Psychiatry, 23. https://doi.org/10.1038/mp.2017.249
Lippa, C. F., Zhukareva, V., Kawarai, T., Uryu, K., Shafiq, M., Nee, L. E., Grafman, J., Liang, Y., George‐Hyslop, P. H. S., Trojanowski, J. Q., & Lee, V. M.-Y. (2000). Frontotemporal dementia with novel tau pathology and a Glu342Val tau mutation. Annals of Neurology, 48(6), 850–858.
https://doi.org/10.1002/1531-8249(200012)48:6<850::AID-ANA5>3.0.CO;2-V
Lisman, J., Buzsáki, G., Eichenbaum, H., Nadel, L., Rangananth, C., & Redish, A. (2017).
Viewpoints: How the hippocampus contributes to memory, navigation and cognition. Nature Neuroscience, 20, 1434–1447. https://doi.org/10.1038/nn.4661
Lloyd-Jones, L. R., Zeng, J., Sidorenko, J., Yengo, L., Moser, G., Kemper, K. E., Wang, H., Zheng, Z., Magi, R., Esko, T., Metspalu, A., Wray, N. R., Goddard, M. E., Yang, J., & Visscher, P. M. (2019). Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nature Communications, 10(1), 5086. https://doi.org/10.1038/s41467-019-12653-0
22 Lomen-Hoerth, C. (2011). Clinical Phenomenology and Neuroimaging Correlates in ALS-FTD.
Journal of Molecular Neuroscience, 45(3), 656. https://doi.org/10.1007/s12031-011-9636-x Machts, J., Loewe, K., Kaufmann, J., Jakubiczka, S., Abdulla, S., Petri, S., Dengler, R., Heinze, H.-J.,
Vielhaber, S., Schoenfeld, M. A., & Bede, P. (2015). Basal ganglia pathology in ALS is associated with neuropsychological deficits. Neurology, 85(15), 1301–1309.
https://doi.org/10.1212/WNL.0000000000002017
Masuda, M., Senda, J., Watanabe, H., Epifanio, B., Tanaka, Y., Imai, K., Riku, Y., Li, Y., Nakamura, R., Ito, M., Ishigaki, S., Atsuta, N., Koike, H., Katsuno, M., Hattori, N., Naganawa, S., & Sobue, G. (2016). Involvement of the caudate nucleus head and its networks in sporadic amyotrophic lateral sclerosis-frontotemporal dementia continuum. Amyotrophic Lateral Sclerosis & Frontotemporal Degeneration, 17(7–8), 571–579.
https://doi.org/10.1080/21678421.2016.1211151
Milardi, D., Quartarone, A., Bramanti, A., Anastasi, G., Bertino, S., Basile, G. A., Buonasera, P., Pilone, G., Celeste, G., Rizzo, G., Bruschetta, D., & Cacciola, A. (2019). The Cortico-Basal Ganglia-Cerebellar Network: Past, Present and Future Perspectives. Frontiers in Systems Neuroscience, 13. https://doi.org/10.3389/fnsys.2019.00061
Möller, C., Dieleman, N., van der Flier, W. M., Versteeg, A., Pijnenburg, Y., Scheltens, P., Barkhof, F., & Vrenken, H. (2015). More atrophy of deep gray matter structures in frontotemporal dementia compared to Alzheimer’s disease. Journal of Alzheimer’s Disease: JAD, 44(2), 635–647. https://doi.org/10.3233/JAD-141230
Montuschi, A., Iazzolino, B., Calvo, A., Moglia, C., Lopiano, L., Restagno, G., Brunetti, M., Ossola, I., Lo Presti, A., Cammarosano, S., Canosa, A., & Chiò, A. (2015). Cognitive correlates in amyotrophic lateral sclerosis: A population-based study in Italy. Journal of Neurology, Neurosurgery, and Psychiatry, 86(2), 168–173. https://doi.org/10.1136/jnnp-2013-307223 Mortimer, J. A. (1997). Brain reserve and the clinical expression of Alzheimer’s disease. Geriatrics,
23 Mulder, M. J., Keuken, M. C., Bazin, P.-L., Alkemade, A., & Forstmann, B. U. (2019). Size and
shape matter: The impact of voxel geometry on the identification of small nuclei. PLOS ONE, 14(4), e0215382. https://doi.org/10.1371/journal.pone.0215382
Murphy, J., Factor-Litvak, P., Goetz, R., Lomen-Hoerth, C., Nagy, P. L., Hupf, J., Singleton, J., Woolley, S., Andrews, H., Heitzman, D., Bedlack, R. S., Katz, J. S., Barohn, R. J., Sorenson, E. J., Oskarsson, B., Fernandes Filho, J. A. M., Kasarskis, E. J., Mozaffar, T., Rollins, Y. D., … ALS COSMOS. (2016). Cognitive-behavioral screening reveals prevalent impairment in a large multicenter ALS cohort. Neurology, 86(9), 813–820.
https://doi.org/10.1212/WNL.0000000000002305
Neumann, M., Sampathu, D. M., Kwong, L. K., Truax, A. C., Micsenyi, M. C., Chou, T. T., Bruce, J., Schuck, T., Grossman, M., Clark, C. M., McCluskey, L. F., Miller, B. L., Masliah, E.,
Mackenzie, I. R., Feldman, H., Feiden, W., Kretzschmar, H. A., Trojanowski, J. Q., & Lee, V. M.-Y. (2006). Ubiquitinated TDP-43 in Frontotemporal Lobar Degeneration and
Amyotrophic Lateral Sclerosis. Science, 314(5796), 130–133. https://doi.org/10.1126/science.1134108
Niven, E., Newton, J., Foley, J., Colville, S., Swingler, R., Chandran, S., Bak, T. H., & Abrahams, S. (2015). Validation of the Edinburgh Cognitive and Behavioural Amyotrophic Lateral Sclerosis Screen (ECAS): A cognitive tool for motor disorders. Amyotrophic Lateral Sclerosis & Frontotemporal Degeneration, 16(3–4), 172–179.
https://doi.org/10.3109/21678421.2015.1030430
O’Shea, A., Cohen, R., Porges, E. C., Nissim, N. R., & Woods, A. J. (2016). Cognitive Aging and the Hippocampus in Older Adults. Frontiers in Aging Neuroscience, 8.
https://doi.org/10.3389/fnagi.2016.00298
Phukan, J., Elamin, M., Bede, P., Jordan, N., Gallagher, L., Byrne, S., Lynch, C., Pender, N., & Hardiman, O. (2011). The syndrome of cognitive impairment in amyotrophic lateral sclerosis: A population-based study. Journal of Neurology, Neurosurgery, and Psychiatry, 83, 102–108. https://doi.org/10.1136/jnnp-2011-300188
24 Phukan, J., Pender, N. P., & Hardiman, O. (2007). Cognitive impairment in amyotrophic lateral
sclerosis. The Lancet. Neurology, 6(11), 994–1003. https://doi.org/10.1016/S1474-4422(07)70265-X
Project MinE ALS Sequencing Consortium. (2018). Project MinE: Study design and pilot analyses of a large-scale whole-genome sequencing study in amyotrophic lateral sclerosis. European Journal of Human Genetics: EJHG, 26(10), 1537–1546. https://doi.org/10.1038/s41431-018-0177-4
Raaphorst, J., van Tol, M. J., de Visser, M., van der Kooi, A. J., Majoie, C. B., van den Berg, L. H., Schmand, B., & Veltman, D. J. (2015). Prose memory impairment in amyotrophic lateral sclerosis patients is related to hippocampus volume. European Journal of Neurology, 22(3), 547–554. https://doi.org/10.1111/ene.12615
Riku, Y., Watanabe, H., Yoshida, M., Mimuro, M., Iwasaki, Y., Masuda, M., Ishigaki, S., Katsuno, M., & Sobue, G. (2016). Marked Involvement of the Striatal Efferent System in TAR DNA-Binding Protein 43 kDa-Related Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis. Journal of Neuropathology & Experimental Neurology, 75, nlw053. https://doi.org/10.1093/jnen/nlw053
Satizabal, C. L., Adams, H. H. H., Hibar, D. P., White, C. C., Knol, M. J., Stein, J. L., Scholz, M., Sargurupremraj, M., Jahanshad, N., Roshchupkin, G. V., Smith, A. V., Bis, J. C., Jian, X., Luciano, M., Hofer, E., Teumer, A., van der Lee, S. J., Yang, J., Yanek, L. R., … Ikram, M. A. (2019). Genetic architecture of subcortical brain structures in 38,851 individuals. Nature Genetics, 51(11), 1624–1636. https://doi.org/10.1038/s41588-019-0511-y
Speed, D., & Balding, D. J. (2019). SumHer better estimates the SNP heritability of complex traits from summary statistics. Nature Genetics, 51(2), 277–284. https://doi.org/10.1038/s41588-018-0279-5
Strong, M. J. (2017). Revisiting the concept of amyotrophic lateral sclerosis as a multisystems disorder of limited phenotypic expression. Current Opinion in Neurology, 30(6), 599–607. https://doi.org/10.1097/WCO.0000000000000488
25 Swinnen, B., & Robberecht, W. (2014). The phenotypic variability of amyotrophic lateral sclerosis.
Nature Reviews. Neurology, 10(11), 661–670. https://doi.org/10.1038/nrneurol.2014.184 Takeda, T., Kitagawa, K., & Arai, K. (2020). Phenotypic variability and its pathological basis in
amyotrophic lateral sclerosis. Neuropathology, 40(1), 40–56. https://doi.org/10.1111/neup.12606
Tan, H. H. G., Westeneng, H.-J., Burgh, H. K. van der, Es, M. A. van, Bakker, L. A., Veenhuijzen, K. van, Eijk, K. R. van, Eijk, R. P. A. van, Veldink, J. H., & Berg, L. H. van den. (2020). The Distinct Traits of the UNC13A Polymorphism in Amyotrophic Lateral Sclerosis. Annals of Neurology, 88(4), 796–806. https://doi.org/10.1002/ana.25841
Turley, P., Walters, R. K., Maghzian, O., Okbay, A., Lee, J. J., Fontana, M. A., Nguyen-Viet, T. A., Wedow, R., Zacher, M., Furlotte, N. A., Magnusson, P., Oskarsson, S., Johannesson, M., Visscher, P. M., Laibson, D., Cesarini, D., Neale, B. M., & Benjamin, D. J. (2018). Multi-trait analysis of genome-wide association summary statistics using MTAG. Nature Genetics, 50(2), 229–237. https://doi.org/10.1038/s41588-017-0009-4
van Es, M. A., Hardiman, O., Chio, A., Al-Chalabi, A., Pasterkamp, R. J., Veldink, J. H., & van den Berg, L. H. (2017). Amyotrophic lateral sclerosis. Lancet (London, England), 390(10107), 2084–2098. https://doi.org/10.1016/S0140-6736(17)31287-4
van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H., & Wray, N. R. (2019). Genetic correlations of polygenic disease traits: From theory to practice. Nature Reviews. Genetics, 20(10), 567– 581. https://doi.org/10.1038/s41576-019-0137-z
Verde, F., Del Tredici, K., Braak, H., & Ludolph, A. (2017). The multisystem degeneration
amyotrophic lateral sclerosis—Neuropathological staging and clinical translation. Archives Italiennes De Biologie, 155(4), 118–130. https://doi.org/10.12871/00039829201746
Vieira, R. T., Caixeta, L., Machado, S., Silva, A. C., Nardi, A. E., Arias-Carrión, O., & Carta, M. G. (2013). Epidemiology of early-onset dementia: A review of the literature. Clinical Practice and Epidemiology in Mental Health : CP & EMH, 9, 88–95.
26 Wang, Y., Xu, Q., Luo, J., Hu, M., & Zuo, C. (2019). Effects of Age and Sex on Subcortical
Volumes. Frontiers in Aging Neuroscience, 11. https://doi.org/10.3389/fnagi.2019.00259 Yang, J., Zeng, J., Goddard, M. E., Wray, N. R., & Visscher, P. M. (2017). Concepts, estimation and
interpretation of SNP-based heritability. Nature Genetics, 49(9), 1304–1310. https://doi.org/10.1038/ng.3941
Yi, D. S., Bertoux, M., Mioshi, E., Hodges, J. R., & Hornberger, M. (2013). Fronto-striatal atrophy correlates of neuropsychiatric dysfunction in frontotemporal dementia (FTD) and Alzheimer’s disease (AD). Dementia & Neuropsychologia, 7(1), 75–82. https://doi.org/10.1590/S1980-57642013DN70100012
27
Supplementary Figures
Figure S1. Manhattan plots of genome-wide association analysis of eight subcortical volumes and ALS disease (a-i). The X-axis shows chromosomal positions. Y-axis shows -log10 p values. The horizontal line indicates the genome-wide significance threshold (p=5.0×10−8). Figure S2. Forest plots showing the odds ratios (OR) for the association of caudate nucleus, putamen, nucleus accumbens, thalamus and pallidum (a-e). MRI-proxy of volume with ALS disease prevalence in six strata. The value of the odds ratio is depicted as black squares, the horizontal line represents the 95% confidence interval. The size of the squares varies with the sample size of each stratum.
Figure S3. Histogram plots showing the distribution of ECAS scores for ALS patients and healthy controls.
Figure S4. Principal component analysis plots for PCA1 and PCA2 of cohort-specific relations between 471 ALS patients.
28 Figure S1
a. Accumbens nucleus
b. Thalamus
29 d. Globus pallidus
e. Amygdala
30 g. Putamen
h. Brainstem
31 Figure S2
a. Acuumbens nucleus
b. Caudate nucleus
32 d. Putamen
33 Figure S3
34 Figure S4
35
Supplementary Tables
Table S1. List of all software applied in the research. The information on the version, website, authors and the purpose of the software application is given.
Table S2. Sample size and sex distributions for each stratum.
Table S3. SNP-based Heritability Results of SNP-based heritability estimates for subcortical volume and ALS disease (standard error in parentheses)
Table S4. Strength of genetic correlations and corresponding p-values for subcortical volumes and ALS disease. Values in bold letters show significant correlations (α corrected for the number of traits = 0.006).
Table S5. Polygenic scores accuracy estimations calculated with summary statistics after joint-analysis (MTAG) and without joint-analysis (no MTAG) and corresponding p-values. For PRSice additionally, p-value thresholds and the number of SNPs taken into polygenic score were presented. Values in bold letters show significant correlations (α = 0.05). Table S6. Control Population The descriptive statistics of ECAS scores, age and level of education for a sample of 787 controls.
36 Table S1
Software Version Web source Purpose Citation R 3.5.1
www.R-pro-ject.org
Summary statistics quality control; sta-tistical analysis
R Core Team. (2013). R: A language and environment for statistical compu-ting. R Foundation for Statistical Com-puting.
Python 2.7/3.6.1 www.python.org Summary statistics quality control
van Rossum, G. (1995). Python tuto-rial, Technical Report CS-R9526, Cen-trum voor Wiskunde en Informatica (CWI)
PLINK 1.9 www.cog-geno-mics.org/plink2
BED files manipu-lation; clumping, pruning; ancestry-informative PCA
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A.R., Bender, D., Maller, J., Sklar, P., de Bakker, P.I.W., Daly, M.J. & Sham, P.C. (2007) PLINK: a toolset for whole-ge-nome association and population-based linkage analysis. American Journal of Human Genetics, 81 GCTA 1.24 www.complextra- itgeno- mics.com/so-ftware/gcta Ancestry-informa-tive & cohort-spe-cific PCA
Yang, J., Lee, S.H., Goddard, M.E., Visscher, P.M. (2011). GCTA: a tool for Genome-wide Complex Trait Anal-ysis. Am J Hum Genet, 88(1), 76-82. LDSC 1.0.1 www.gi- thub.com/bu-lik/ldsc SNPs Heritability; genetic correlations Bulik-Sullivan, B., et al. (2015). LD Score Regression Distinguishes Con-founding from Polygenicity in Ge-nome-Wide Association Studies. Na-ture Genetics
Bulik-Sullivan, B., et al. (2015). An At-las of Genetic Correlations across Hu-man Diseases and Traits. Nature Gene-tics.
LDAK (SumHer)
5.1 www.dou-gspeed.com/ldak
SNP-heritability Speed, D., Balding, D.J. (2019). Sum-Her better estimates the SNP heritabil-ity of complex traits from summary sta-tistics. Nat Genet 51, 277–284.
MTAG 1.0.0 www.omic- tools.com/mtag-tool Jointly analyzing multiple GWAS summary statistics
Turley, P., Walters, R.K., Maghzian, O., et al. (2018). Multi-trait analysis of genome-wide association summary sta-tistics using MTAG. Nat Genet, 50(2), 229-237.
PRSice 2.3.1 www.prsice.info Polygenic-scores Choi, S.W., & O’Reilly, PF. (2019). PRSice-2: Polygenic Risk Score Soft-ware for Biobank-Scale Data. Giga-Science, 8(7). GCTB (sBay-esR) 2.0 www.cnsgeno- mics.com/so- ftware/gctb/#Ove-rview
Polygenic-scores Lloyd-Jones, Zeng, et al. (2019) Im-proved polygenic prediction by Bayes-ian multiple regression on summary statistics. Nature Communications.
37 Table S2
Stratum Controls (n) Patients (n) % Males
1 11155 2254 46.51% 2 2043 1458 65.64% 3 2555 1701 49.20% 4 42402 3394 45.08% 6 32094 14402 53.62% 7 20632 3996 48.00% Total 110881 27205 49.28%
38 Table S3
39 Table S4
41 Table S6
ECAS score Min Max Max
range Median Mean (SD)
Total 47 134 0-136 112 109.42 (11.58) ALS specific 39 98 0-100 83 80.57 (9.22) Language 15 28 0-28 27 26.19 (1.82) Executive 11 47 0-48 38 35.87 (6.67) Fluency 0 24 0-24 20 18.51 (3.34) ALS non-specific 8 36 0-36 30 28.85 (4.06) Memory 2 24 0-24 18 17.3 (3.73) Visuo-spatial 5 12 0-12 12 11.55 (0.95) Age (years) 24 87 - 67 64.31 (10.46) Education (ISCED) 1 7 0-8 3 3.67 (1.89)
