What is missing? The catchy term “missing heritability” refers to a long-standing issue in human genetics that is particularly relevant to common diseases that are thought to have complex genetic architecture. Even though we know several thousands of risk factors for common diseases, the sum of all these risk factors only explains a small proportion of the genetic risk for disease. Where is all the remaining genetic disease risk hidden? A recent publication in PLOS Genetics suggests that known association peaks in genome-wide association studies (GWAS) may harbor more than one risk variant, turning GWAS peaks into mountain ranges. Also, this publication provides an interesting state-of-the art review on the role of common and rare variants with respect to missing heritability. Let’s turn back the clock and start with the decade-old debate on common versus rare variant models of human disease. Continue reading
My untested assumption. Recently, I have boasted quite a bit about the power of the trio design, i.e. the inclusion of patients and parents in the analysis of rare genetic variants. Rare variants, in contrast to monogenic variants that arise de novo, are usually transmitted from unaffected parents and are the big unknown of modern day genetic studies. Much of the missing heritability may be accounted for by rare variants, but identifying these variants from genomic noise is difficult. Power calculations for association studies usually suggest that thousands, if not tens of thousands, of patients are necessary to identify these variants with sufficient statistical certainty, a sample size that the field of epilepsy research may never arrive at. So what about switching to parent-offspring trios? Would this help us? Follow me on a brief statistical journey through the land of rare variants. Continue reading
Mergers and acquisitions. Invariably, genetic research in neurodevelopmental disorders is moving towards joint analyses of large datasets. While the methodology of meta-analysis is well established for genome-wide association studies, the joint analysis of exome datasets comes with many question marks. Now, a recent paper in PLOS Genetics pioneers the field of joint exome data analysis for association studies in autism. This paper highlights some unexpected facets of rare variant analysis. Continue reading
Epic dimensions. 5,000 years ago, human civilization was getting off the ground in Mesopotamia. At some point, the early human pioneers decided to use pictures as letters and human writing was invented. Ox became aleph, which became alpha, which turned into literature, which finally turned into blogging. At around the same time that the Mesopotamian people invented the direct precursor of modern day tweets and text messages, rare genetic variants started spreading through the human population. In fact, all the rare variation that we see in humans today, had probably not been present prior to the chiseling of the first human words. Continue reading
My wrong guesses of 2012. Two weeks ago during a presentation, I had to admit that there is little evidence for a large contribution of recessive or compound heterozygous mutations in epileptic encephalopathies. At the beginning of 2012, I had initially suggested that recessive or compound heterozygous mutation of known neurometabolic disorders could be identified through exome sequencing in sporadic epileptic encephalopathies. However, as of 2013, there is little evidence for this in our data or the data from other consortia. Now, two papers in Cell suggest a significant contribution of recessive mutations in autism including a revival of the “hidden neurometabolic hypothesis”. Continue reading
Twilight zone. Admittedly, Halloween is already a few weeks behind us, but I was reminded of it a week ago when I stumbled across the concept of phantom heritability. And guess what, this concept has already been out there since early 2012 and, scarily enough, we didn’t notice it. So what is this mysterious conspiracy behind phantom heritability? Well, it’s about things out there beyond our understanding and the fact that we might already know more than we think we know. But be warned, if you decide to read this post, your understanding of genetic architecture might be changed forever. And there is no going back. Boo! Continue reading
The flood of variants. Every re-sequencing of a genome leads to many more variants than can be validated with functional assays. Many strategies exist to select the candidate variants. Filtering on criteria might remove all variants so efforts are focused to re-rank the list of variants such that the most promising appear on top. A recent review in Nature Reviews Genetics wants to give users a hand with using the bioinformatics tools available. As a bioinformatician, I find a number of important points missing.
Once again, the flood of rare variants. Deep sequencing studies have revealed an unexpected plethora of rare variants, i.e. genetic variants that can only be found in few or even single individuals. While the genetic architecture of more common genetic variants, so-called Single Nucleotide Polymorphisms (SNPs) is well known through the HapMap project, the role of rare variants identified with recent sequencing studies is difficult to interpret. Basically, for an individual variant it is difficult to establish whether this variant is disease-causing or disease-related based on the frequency in cases. Establishing association at the same level of statistical significance as required for SNPs is difficult given that much larger samples are needed. Furthermore, protein prediction algorithms have their limitations and might not be able to discriminate an accidental from a causal variant, given that every individual might be homozygous or compound homozygous for gene-disrupting variants in at least three genes. We are drowning in a flood of rare variants and cannot distinguish pathological from benign variants very well yet. Continue reading