Challenges in Rare Variant Association Studies for Complex Kidney Traits: CFHR5 and IgA Nephropathy

Abstract

4. Cheema BS, Chan D, Fahey P, Atlantis E: Effect of progressive resistance training on measures of skeletal muscle hypertrophy, muscular strength and health-related quality of life in patients with chronic kidney disease: A systematic review and meta-analysis. These findings led to a significant refinement of the disease pathogenesis model and provided novel clues about the disease geoepidemiology. [10][11][12] Similarly, the genetic interaction between variants in PLA2R1 and HLA arising from GWAS solidified the pathogenesis model for membranous nephropathy, highlighting the antigen-HLA interplay as central to the disease process. 13 Despite this progress, however, a large portion of the genetic contribution to many complex traits remains unexplained, including traits for which very large GWAS meta-analyses have already been performed. This issue has been identified as the missing heritability problem. The missing heritability has many potential explanations, including the possibility that low-frequency variants substantially contribute to the inherited risk of disease. Such rare alleles are not well captured on popular microarray genotyping platforms and thus, have been www.jasn.org EDITORIALS largely ignored by traditional GWASs. However, rare alleles can be accurately detected by direct DNA sequencing. Rapid progress in sequencing technology now enables investigations of the role of rare genetic variants in complex traits through sequence-based association studies. Although the jury is still out on whether such studies can account for the missing heritability, this design has already been deployed for several complex disorders, and we can certainly expect its broader application to kidney traits in the near future. Sequence-based association studies present substantial challenges that relate to the detection, analysis, and interpretation of rare variants. Unless sample sizes or variant effect sizes are very large, these studies are usually limited by low statistical power. Moreover, the requisite multiple test corrections are poorly understood, creating difficulties in the interpretation of findings. In GWASs, the standard approach involves a single-variant test with the significance threshold of 531028 that accounts for the estimated number of common haplotype blocks in the genome. 14 Given sufficient sample size, a similar approach can also be applied to rare variants, but its power is inversely related to allelic frequency. For example, to achieve 80% power to detect a disease association for a rare variant with a frequency of 1:1000 (0.1%) and an odds ratio of 2.0, one would require .60,000 patients (and an equal number of controls) to detect a statistically significant association for a disease with a population prevalence of 5%. In addition, single-variant tests on the basis of standard regression methods might not be accurate if the number of subjects with the variant is very small. Numerous alternative strategies have, therefore, been proposed to circumvent these issues. Most strategies aggregate var...

Perspectives on the Genomics of HSP Beyond Mendelian Inheritance

“…Systematic identification of rare variant associations are usually limited by low statistical power unless sample sizes or variant effect sizes are very large ( 45 ). To illustrate, >60,000 cases (and an equal number of controls) would be necessary to detect a disease association for a rare variant (0.1% frequency) with an odds ratio of 2.0 for a disease with a 5% population prevalence ( 45 ). Fortunately, powerful study designs can alleviate the sample size requirement to more reasonable numbers ( 46 ).…”

“…Fortunately, powerful study designs can alleviate the sample size requirement to more reasonable numbers ( 46 ). One approach that can be explored in HSPs is the gene-based variant burden test which collapses the number of minor alleles into one genetic score (gene), thus reducing multiple testing and increasing power ( 45 , 47 ). One successful example of this approach was the identification of a new ALS gene, TBK1 , in 2,869 sporadic ALS patients ( 35 ).…”