Single-cell genomics meets human genetics

Abstract

Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.

Fig. 1∣. Overview of single-cell expression quantitative trait locus studies.

Single-cell studies published in the past 5 years. On the x axis is the date of publication, and on the y-axis is the number of unique individuals considered. The size of the dots represents the average number of cells per individual included in each study (when this number was not reported in this study, we estimated it as the total number of cells divided by the total number of individuals).

Fig. 2∣. Human genetics and single-cell genomics, a 20-year timeline.

Fundamental genomic resources (red), genetic studies (blue), sequencing technologies (yellow) and statistical methods and software (green) have contributed to the current state of single-cell genomics and human genetics, including expression quantitative trait locus (eQTL) mapping studies. References -,, and - are for landmark studies and initiatives, respectively; refs. ,,,,, and - are for technological and statistical advances, respectively; and refs. ,,,,,,- are for eQTL mapping. GWAS, genome-wide association study; HCA, Human Cell Atlas; PRS, polygenic risk score; RNA-seq, RNA sequencing; snRNA-seq, single-nucleus RNA sequencing; TWAS, transcriptome-wide association study.

Fig. 3∣. Types of single-cell expression quantitative trait locus.

Single-cell-resolved expression matched with genotype information allows one to consider different types of expression quantitative trait locus (eQTL) mapping strategies. When mapping cell-type-specific eQTLs, the single-cell resolution is exclusively utilized to more precisely characterize transcriptionally similar cells. Variance eQTLs test for genetic variants associated with cell-to-cell variability of gene expression (versus average expression level). Finally, to map dynamic eQTLs, single cells are ordered along a continuous trajectory, and the test consists in identifying eQTLs, the strength of which is modulated by such a trajectory.

Fig. 4∣. Downstream effect of context-dependent single-cell expression quantitative trait locus.

Identification of the specific contexts in which a disease-associated genetic variant regulates gene expression may ultimately lead to new therapeutic strategies. eQTL, expression quantitative trait locus.