Recent advances and challenges of rare variant association analysis in the biobank sequencing era

Abstract

Causal variants for rare genetic diseases are often rare in the general population. Rare variants may also contribute to common complex traits and can have much larger per-allele effect sizes than common variants, although power to detect these associations can be limited. Sequencing costs have steadily declined with technological advancements, making it feasible to adopt whole-exome and whole-genome profiling for large biobank-scale sample sizes. These large amounts of sequencing data provide both opportunities and challenges for rare-variant association analysis. Herein, we review the basic concepts of rare-variant analysis methods, the current state-of-the-art methods in utilizing variant annotations or external controls to improve the statistical power, and particular challenges facing rare variant analysis such as accounting for population structure, extremely unbalanced case-control design. We also review recent advances and challenges in rare variant analysis for familial sequencing data and for more complex phenotypes such as survival data. Finally, we discuss other potential directions for further methodology investigation.

ProxyTyper: Generation of Proxy Panels for Privacy-aware Outsourcing of Genotype Imputation

“…where 𝑤 𝑎 (1) , 𝑤 𝑎,𝑏 (2) , 𝑤 𝑎,𝑏,𝑐 (3) denote the randomly selected variant-specific weights for calculating the hash for 1 st , 2 nd , and 3 rd degree combinations for the variants at 𝑎, 𝑏, 𝑐. To decrease computational complexity, the variants are constrained to a maximum distance, i.e., |𝑖 − 𝑎| < 𝑛 𝑣𝑖𝑐 .…”

“…The 3 rows correspond to 1 st , 2 nd , and 3 rd degree interactions of variants at 𝑎, 𝑏, and 𝑐 for variants in (2𝑛 𝑣𝑖𝑐 + 1) vicinity of variant 𝑖. 𝑤 𝑖,𝑎 (1) , 𝑤 𝑖,𝑎,𝑏 (2) , and 𝑤 𝑖,𝑎,𝑏,𝑐…”

“…are the binary weights (specific to variant 𝑖) that add the corresponding component into hash. 𝜙 𝑖,𝑎 (1) , 𝜙 𝑖,𝑎,𝑏 (2) , and 𝜙 𝑖,𝑎,𝑏,𝑐…”

“…Random Selection of Weights. For each weight in the hash calculation, i.e., 𝑤 𝑖,𝑎 (1) , 𝑤 𝑖,𝑎,𝑏 (2) , 𝑤 𝑖,𝑎,𝑏,𝑐 (3) , we use a Bernoulli random variable:…”

“…Decreasing cost of whole genome sequencing and genotyping brought about a massive increase in the number of personal genomes [1]. Starting with the initial population-scale and biobank sequencing [2] projects such as The HapMap Consortium [3,4], The 1000 Genomes Project [5] and the population scale projects such as UK Biobank, Genomics England, Trans-omics for precision medicine (TOPMed) [6], and AllofUs research program [7], millions of personal genomes are available. Local sequencing efforts from underrepresented populations which are vital for increasing diversity in research [8][9][10] and for inclusion of underrepresented populations [11,12].…”