Large-scale phenome analysis defines a behavioral signature for Huntington's disease genotype in mice

Abstract

Rapid technological advances for the frequent monitoring of health parameters have raised the intriguing possibility that an individual's genotype could be predicted from phenotypic data alone. Here we used a machine learning approach to analyze the phenotypic effects of polymorphic mutations in a mouse model of Huntington's disease that determine disease presentation and age of onset. The resulting model correlated variation across 3,086 behavioral traits with seven different CAG-repeat lengths in the huntingtin gene (Htt). We selected behavioral signatures for age and CAG-repeat length that most robustly distinguished between mouse lines and validated the model by correctly predicting the repeat length of a blinded mouse line. Sufficient discriminatory power to accurately predict genotype required combined analysis of >200 phenotypic features. Our results suggest that autosomal dominant disease-causing mutations could be predicted through the use of subtle behavioral signatures that emerge in large-scale, combinatorial analyses. Our work provides an open data platform that we now share with the research community to aid efforts focused on understanding the pathways that link behavioral consequences to genetic variation in Huntington's disease.

PeripheralHttsilencing does not ameliorate central signs of disease in the B6.HttQ111/+mouse model of Huntington’s Disease

“…In fact, a recent review suggests that only approximately 3% of preclinical studies in HD research are conducted using knock-in HD mice [40]. New investigations of knock-in models of HD reveal very clear, if subtle, behavioral changes [41,42], as well as extremely consistent molecular changes which are most pronounced in the striatum [16,43]. Power analyses suggest many of these phenotypes provide a robust system in which to test novel therapeutic compounds targeting the earliest phenotypes caused by CAGexpansion in Htt.…”

“…After shipment, mice habituated to local vivarium conditions for at least two weeks prior to every study. For both the washout and dose-response studies, treatment began at three months of 41 age and continued for three weeks. For the dose-response study, mice were sacrificed two days after the final injection as described in the primary manuscript.…”

Precision epidemiology for infectious disease control

“…This realization has resulted in the development of an array of 'deep phenotyping' programs and tools focused on the collection and use of precise, standardized, and comprehensive phenotypic data obtained via wearables, wireless sensors, and other self-reporting tools 5,[75][76][77][78] . Thus far, phenome characterization efforts have focused primarily on noncommunicable diseases, including Huntington's disease 79 , Alzheimer's 80 , sleep apnea 81 , and copy-number-variant-based developmental abnormalities 82 . Some of these data, however, are similarly applicable for the investigation of and response to infectious diseases.…”

Close encounters: Moving along bumps, breaks, and bubbles on expanded trinucleotide tracts

“…MSH2-MSH3 plays roles as a co-factor in BER [94,98,99], transcription-coupled repair (TCR) [136–138] and double strand break repair (DSBR) [137,139,140]. Each of these pathways has been implicated in CAG instability in mice [58,77–79, 81–83, 94], and classically operates with distinct Family A, B, X or Y polymerases, depending on the pathway (Table 2). Does MSH2-MSH3 stimulate them all?…”

“…The somatic changes in the long TNRs are observed in most non-coding genes, but the size of the somatic lengths are small by comparison to the large jumps that occur in early germ cell development. Importantly, both the germ cells and the somatic expansions contribute to toxicity [58–61]. In this regard, the somatic expansion has raised the possiblity that shortening the repeats during life may be therapeutic [62,63].…”