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Kuffler, L.

Publications and source records attributed to Kuffler, L..

2 recordsLinked to original sources

Imputation of 3D genome structure by genetic-epigenetic interaction modeling in mice

Gene expression is known to be affected by interactions between local genetic variation and DNA accessibility, with the latter organized into three-dimensional chromatin structures. Analyses of these interactions has previously been limited, obscuring their regulatory context, and the extent to which they occur throughout the genome. Here we undertake a genome-scale analysis of these interactions in a genetically diverse population to systematically identify global genetic-epigenetic interaction, and reveal constraints imposed by chromatin structure. We establish the extent and structure of genotype-by-epigenotype interaction using embryonic stem cells derived from Diversity Outbred mice. This mouse population segregates millions of variants from eight inbred founders, enabling precision genetic mapping with extensive genotypic and phenotypic diversity. With 176 samples profiled for genotype, gene expression, and open chromatin, we used regression modeling to infer genetic-epigenetic interactions on a genome-wide scale. Our results demonstrate that statistical interactions between genetic variants and chromatin accessibility are common throughout the genome. We found that these interactions occur within the local area of the affected gene, and that this locality corresponds to topologically associated domains (TADs). The likelihood of interaction was most strongly defined by the three-dimensional (3D) domain structure rather than linear DNA sequence. We show that stable 3D genome structure is an effective tool to guide searches for regulatory elements and, conversely, that regulatory elements in genetically diverse populations provide a means to infer 3D genome structure. We confirmed this finding with CTCF ChIP-seq that revealed strain-specific binding in the inbred founder mice. In stem cells, open chromatin participating in the most significant regression models demonstrated an enrichment for developmental genes and the TAD-forming CTCF binding complex, providing an opportunity for statistical inference of shifting TAD boundaries operating during early development. These findings provide evidence that genetic and epigenetic factors operate within the context of three- dimensional chromatin structure. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/479436v3_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@14d4221org.highwire.dtl.DTLVardef@130a812org.highwire.dtl.DTLVardef@16a6069org.highwire.dtl.DTLVardef@df9d9c_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

Genome-wide association mapping of ethanol sensitivity in the Diversity Outbred mouse population

BackgroundA strong predictor for the development of alcohol use disorders (AUDs) is altered sensitivity to the intoxicating effects of alcohol. Individual differences in the initial sensitivity to alcohol are controlled in part by genetic factors. Mice offer a powerful tool for elucidating the genetic basis of behavioral and physiological traits relevant to AUDs; but conventional experimental crosses have only been able to identify large chromosomal regions rather than specific genes. Genetically diverse, highly recombinant mouse populations allow for the opportunity to observe a wider range of phenotypic variation, offer greater mapping precision, and thus increase the potential for efficient gene identification. MethodsWe have taken advantage of the Diversity Outbred (DO) mouse population to identify and precisely map quantitative trait loci (QTL) associated with ethanol sensitivity. We phenotyped 798 male J:DO mice for three measures of ethanol sensitivity: ataxia, hypothermia, and loss of the righting response. We used high density MEGAMuga and GIGAMuga arrays to obtain genotypes ranging from 77,808 - 143,259 SNPs. In addition, we performed RNA sequencing in striatum to map expression QTLs and to identify gene expression-trait correlations. ResultsWe then applied a systems genetic strategy to identify narrow QTLs and construct the network of correlations that exist between DNA sequence, gene expression values and ethanol-related phenotypes to prioritize our list of positional candidate genes. ConclusionsOur results can be used to identify alleles that contribute to AUDs in humans, elucidate causative biological mechanisms, or assist in the development of novel therapeutic interventions.

genetics↗