DOI: 10.5061/dryad.dr7sqvb11
title: Genetic modifiers of Huntington’s disease differentially influence motor and cognitive domains

description: Genome-wide association studies (GWAS) of Huntington’s disease (HD) have
 identified six DNA maintenance gene loci (among others) as modifiers and
 implicated a two step-mechanism of pathogenesis: somatic instability of
 the causative HTT CAG repeat with subsequent triggering of neuronal
 damage. The largest studies have been limited to HD individuals with a
 rater-estimated age at motor onset. To capitalize on the wealth of
 phenotypic data in several large HD natural history studies, we have
 performed algorithmic prediction using common motor and cognitive measures
 to predict age at other disease landmarks as additional phenotypes for
 GWAS.  Combined with imputation using the Trans-Omics for
 Precision Medicine reference panel, predictions using integrated measures
 provided objective landmark phenotypes with greater power to detect most
 modifier loci.  Importantly, substantial differences in the
 relative modifier signal across loci, highlighted by comparing common
 modifiers at MSH3 and FAN1, revealed that individual modifier effects can
 act preferentially in the motor or cognitive domains. Individual
 components of the DNA maintenance modifier mechanisms may therefore act
 differentially on the neuronal circuits underlying the corresponding
 clinical measures. In addition, we identified new modifier effects at the
 PMS1 and PMS2 loci and implicated a potential new locus on chromosome 7.
 These findings indicate that broadened discovery and characterization of
 HD genetic modifiers based on additional quantitative or qualitative
 phenotypes offers not only the promise of in-human validated therapeutic
 targets, but also a route to dissecting the mechanisms and cell types
 involved in both the somatic instability and toxicity components of HD
 pathogenesis.
description: Five batches of GWA data sets were
 produced and subsequently imputed using the HRC reference panel.  as
 described previously (Cell 2019, 178, 887). Genotype imputation using the
 TOPMed reference panel was performed similarly. Briefly, the individual
 genotype data set (i.e., GWA1, 2, 3, 4, and 5) comprising subjects with
 genotype call rate greater than 90% and single nucleotide variants (SNVs)
 with call rate &gt; 95% and MAF &gt; 1% was subjected to quality
 control by the “HRC or 1000G Imputation preparation and checking” program.
 Additional QC was performed using the TOPMed Imputation Server, revealing
 genomic regions of 10 MB with at least one sample with call rate &lt;
 50%. Those low call rate samples at certain genomic regions were further
 excluded from genotype imputation. The final QC-passed typed data set was
 used for imputation by the TOPMed Imputation Server. Genomic coordinates
 of TOPMed imputation data were converted to GRCh37/hg19 to make this data
 set directly comparable to the HRC imputation data set. Finally, we
 removed SNVs with imputation r-square &lt; 0.5 in any of the GWA data
 sets, HWE p-value &lt; 1E-6 (excluding chr4:1-5000000 region
 containing HTT), or MAF &lt; 0.1%. For a given phenotype, genome-wide
 association analysis was performed for individuals of European ancestry
 using a mixed effects model with a relationship matrix and a set of
 covariates such as sex, batch, and the first four principal component
 values from genetic ancestry analysis as covariates. For selected
 candidate regions with signiﬁcant association signals, conditional
 analysis was performed using a ﬁxed effect linear model that included one
 or more additional covariates representing the minor allele counts of
 conditioned SNVs. SNVs are expressed as
 chromosome_coordinate_reference-allele_alternative-allele based on the
 GRCh37/hg19 genome assembly. CAG repeat lengths for each subject were
 generated by a standard ABI fragment analysis assay.3; 28 For individuals
 uncovered in the TopMed imputation with HTT CAA-loss and
 CAACAG-duplication haplotypes, whose CAG lengths are systematically
 misestimated by the fragment analysis assay, the pure CAG repeat size was
 determined by MiSeq (Illumina) DNA sequencing analysis using the
 previously reported assay method.