KCNQ2 Cures Summit – Patient Family Passion and Involvement

The passion to find answers is inspiring.

I had the good fortune to attend the KCNQ2 Cure Summit 2018. Meeting the patient families and seeing their passion to find answers, brings meaning and urgency to the work I have been doing to develop high-throughput drug screening platforms.

image Credit: Chris Hopkins with permission of KCNQ2 patient family gathering

What can we do to help these families?

The first step I have been doing is to get more understanding.  KCNQ2 cure is a patient organization that strives to raise “research funds for KCNQ2 epileptic encephalopathy, a rare and catastrophic form of epilepsy beginning in the first days of life.” By nature of the name, the foundation focus on how DNA sequence variation in the KCNQ2 gene does or does not causes epilepsy.  For those variations that cause disease (pathogenic), the foundation supports research to find new treatments and therapies. As I previously blogged, KCNQ2 has 21% of the 855 variants in the NCBI database ( as known or suspected to be pathogenic. About another 10% are known to be benign or likely benign. That leaves 69% variants for which we are scratching our head and feeling uncertain, or there nothing known about them.  The challenge to the expecting parent is “What is the likelihood I will be passing on a disease to my child?”

For the expecting parent what becomes important is to know the status of the KCNQ2 gene being passed on to the child. There are two ways to pass the genetic disease to our children. For some of the KCNQ2 variants, they are recessive – both copies of the gene will need to have these recessive pathogenic alleles in order to manifest disease.  It may be the father provides a chromosome where the KCNQ2 gene is defective with a pathogenic variation.  From the mother, her gene copy for KCNQ2 may contain a variant of uncertain significance (a “VUS” allele).  The chance that epilepsy will manifest in the child remains uncertain due to uncertainty of the mother’s VUS allele.

Yet a majority of variants in KCNQ2 are autosomal dominant. Unlike a recessive allele, only one bad copy is needed for the disease to manifest. In these cases, the mutation is almost never detected at high level in the parent.  Instead of being past down by the parent, the pathogenic variant is thought to have occurred by random chance and becomes manifest at conception. These are considered to be de novo variations because they are not detected to be present in one of the chromosomes of either parent – both the mom and dad appear to be homozygous wild-type. In many of these cases, a random mutation occurred when either the Dad’s sperm or the mother’s egg were formed. This happens because replication of DNA is not perfect. There is a very low frequency of DNA replication errors – close to 1 in every 1,000,000,000 base pairs (Pray 2008). That means that each gamete (sperm or egg) has at least 3 errors somewhere in the genome. Thankfully 98.8% of the genome is non-coding, so a change in coding sequence is quite rare. Add in that the genome size is 20,000 genes, it becomes very rare that the KCNQ2 gene picks up a mutation by chance.

What are the chances of that!

image Credit: rosendahl at flikr

Mosaic in the parent

Yet some de novo assigned conditions have much higher frequency than they should. A very interesting phenomenon that was illustrated multiple times by the speakers at the KCNQ2 Cure Summit was the issue of mosaicism. If getting a de novo variation is an ultra rare “roll-of-the-dice,” how is it that a surprising number of the KCNQ2 families with “de novo” origin have more than 1 effected sibling?  That should just not happen at chance if DNA replication is as good as we have measured it to be in laboratory tests.  A big component of the answer to that puzzle turns out to be a phenomenon called mosaicism (Weckhuysen 2012, Milh 2015, Mulkey 2017).  Lets now imagine a situation where the Dad may have pick up a random pathogenic mutation occurring shortly after he was conceived by his parents.  If the error occurred during the 3rd cell division event in utero, then 1/8th of his cells would have the pathogenic variant.  Now as a grown man and expecting father, 1 in 16 of his sperm harbor the pathogenic variant.  There is significant (non-rare) risk he can pass the variant on to more than one of his children.  Yet, because more than 80% of his cells do not have the variant, he remains dosage compensated and unaffected.  His child’s variation appears “de novo,” but it is actually being inherited at a less than mendelian frequency.  Dr. Ingrid Scheffer, who is helping write the definitions of what is epilepsy (Scheffer 2017), gave a presentation suggesting near 8% of the proband Mom and/or Dad on de novo assignments have evidence of mosaicism. She suggest high depth sequencing of exomes should become more important to the clinician in helping them get an accurate diagnosis.

Enhancer/suppressor effect

Another phenomenon that could alter the presentation of a gene’s phenotype is to have a compensating variation somewhere else in the genome.  This is the form of a classic suppressor effects taught in genetics courses. Another gene, usually upstream in the functional pathway, can have a mutation that suppresses the effect of the variant in question – a condition referred to as an epistasis.  For instance, a KCNQ2 variant in one person may lead to a pathogenicity that is quite severe, while another person having the same KCNQ2 variant presents with a less severe phenotype due to a compensating mutation somewhere else in the KCNQ2 signaling pathway.  Conceptually applied, if you had a Gain-of-Function (GOF) variant in KCNQ2 and a Loss of Function (LOF) variants in either SCN1a or STXBP1, this would could counter the bad effect and restore activity towards wild-type behavior.  This effect in ion channels has been recently documented and described (Noebels 2017). Dr Phillip Pearl, the Director of Epilepsy and Clinical Neurophysiology at Boston Children’s Hospital and William G. Lennox Chair and Professor of Neurology at Harvard Medical School, gave a lecture on variant types and how they fall into classes of mild (BNFE) and severe (EE) variant groups.  So, mediating severity will not only be the molecular nature of variation severity in the KCNQ2 gene, but also what is the profile of the background variants for enhancing or suppressing that given variant’s expressivity and penetrance.

If you would like to learn more, see some of the KCNQ2 Cure speaker presentations here

Pray, L. DNA Replication and Causes of Mutation. Nature Education 2008 1(1):214

Scheffer IE et al. ILAE classification of the epilepsies: Position paper of the ILAE Commission for Classificationand Terminology. Epilepsia. 2017 Apr;58(4):512-521. doi: 10.1111/epi.13709. Epub 2017 Mar 8.

Weckhuysen S et al. KCNQ2 encephalopathy: emerging phenotype of a neonatal epileptic encephalopathy. Ann Neurol. 2012 Jan;71(1):15-25. doi: 10.1002/ana.22644.

Milh M et al. Variable clinical expression in patients with mosaicism for KCNQ2 mutations. Am J Med Genet A. 2015 Oct;167A(10):2314-8. doi: 10.1002/ajmg.a.37152. Epub 2015 May 10.

Mulkey SB et al. Neonatal nonepileptic myoclonus is a prominent clinical feature of KCNQ2 gain-of-function variants R201C and R201H. Epilepsia. 2017 Mar;58(3):436-445. doi: 10.1111/epi.13676. Epub 2017 Jan 31.

Noebels J. Precision physiology and rescue of brain ion channel disorders. J Gen Physiol. 2017 May 1;149(5):533-546. doi: 10.1085/jgp.201711759. Epub 2017 Apr 20.

Genome Data Suppliers

When will you decide it is time to give a spit?

It will not take too much effort.  You order a kit, contributed a batch of spittle into a receptacle cup, then send it away for DNA analysis.

The most common utility format for this type of DNA testing is ancestry analysis:

imagecredit: Molly K. McLaughlin for PC Reviews Magazine

Ancestry determinations have been the mainstay for early adoption of DNA sequence analysis technology.  Health testing has lagged, primarily due to regulatory concerns, but with the viewpoint starting to support the individual’s right to know, more people are getting DNA analysis for health impact profiling.  The conservative path to recommend in obtaining health-related DNA information is to consult a doctor. And then perhaps guide them to the service you seek.  Many providers may not be up to speed and you will be helping them navigate the path to preventative genomics utilization in their patient populations.  Be pleased when they respond that this is interesting, but they would like for you to consult with a genetic counselor prior to and after the data comes in.

Direct to Consumer

For the highly adventurous of us who want more than ancestry, you can follow the path taken by Tom Petch at Medgadget. In a detailed story of his endeavor to uncover his preventative genomics potential, Tom used whole-genome analysis kits supplied by Dante. He obtained close to 1 Gig of raw data files and a detailed report covering a range of dispositions. He found out some intriguing idiosyncrasies explaining his predilection to coffee. This was followed by the more puzzling finding of having variants that both increase and decrease his risk of Alzheimer’s.

Having the raw data files is highly intriguing to me.  There is a treasure trove of information in there that will only be released as time ticks away and our understanding of disease biology increases.  Some providers such as Helix offer a plan where you can park your DNA files with them and they will periodically “auto update” you as variant biology upgrades become available.  Veritas is another company offering whole genome analysis but it is not clear from their website I will be handed my VCF files on a flash drive. 

For me, I want to keep the adventure to be heavily under my control.  I want access to the raw data.   I want to identify and catalog the full depth of my variant profile. The variations that don’t fit the norm are likely to number in the 1000’s, and perhaps 10,000’s if we include non-coding.  To get some of my answers on variant pathogenicity, I will use NIH’s Variation Viewer. This will allow me to get many of my variants understood for their significance.  The ClinVar Miner and SNPedia databases might be referenced also if a novel variant is in question.  Another profoundly interesting tool when you are searching for information on a specific variant is Mastermined by Genomenon.  And for even greater detail, I may reach out to my Human Genetics contacts who will have unique insights and database access to get me even greater resolution.  Where I might find some variants are binning to the Uncertain Significance category, I might be motivated enough to make an animal model with the variant in question installed.  With the resulting variant avatar created as an animal model, I will then start a set of functional assays to see if my variant of uncertain significance exhibits a certain significant deviation of function.

Dealing with Uncertain Significance of the Genome

“Risky Business”

Most of us are wired to be risk averse. Yet, I have been giving serious contemplation to the “risky business” of having my whole genome be sequenced as a preventative medicine approach to my healthcare.

What will happen if go there?

I find myself staring into the murky abyss from the edge of the data cliff. A creepy feeling urges in my belly from the depths of my ski-bum days… Should I….


photocredit: Bradly J. Boner for Jackson Hole Magazine

Upon landing, I will need “spoon-my-tracks” to get the data to be interpretable and informative as possible.

photocredit: James Fagedes at Foothill freak

Mental Health

There is plenty of reason to be cautious. A recent publication by the Hasting Center report urges a high level of caution (Johnston 2018).  Although we can be somewhat dismissive of their dismissiveness –  the cost of whole genome sequencing is dramatically dropping and phenotyping technology is rapidly improving – one observation is likely to hold true for a while:

“Given the psychosocial costs of predicting one’s own or one’s child’s future life plans based on uncertain [Genomic] testing results, we think the hope and optimism deserve to be tempered.”

So it is clear there will be quite a bit of uncertainty when one opens the Pandora’s box of the genome, but hope and optimism will remain. Whether there is clearly actionable results, or frustrating uncertainty, the knowledge gained means there are things to be done, platforms to build, and cures to be discovered.

Not If, but When

Caution keeps us safe. But safe for how long? By not “going there” we might be just deluding our selves from the inevitable.  At some point, we will have a deep understanding of the consequence of genome variation.  The first to fall into line will be variants delivering functional consequence on the monogenic side of the spectrum.  These will be the easiest to model and uncover biological consequence because the variant will have clear and sometimes deterministic output on life quality and healthspan. More challenging will be the variants whose effects predominate in polygenic contexts.  These are the more subtle “risk factor” effects where the other variants in one’s genome are the influencers that either enhance or suppress the capacity of the risk factor variant to manifest.  Adding to the challenge of understanding a risk factor is the influence of external factors, such as diet and exercise. Or the more internal factors such as genomic imprinting and gene methylation status.

Yet it is clear where we are heading. Much of the uncertainty will be resolved and we will soon be living in the genome-actionable era where medicine becomes highly personalized to the individual’s variant profile.  For a glimpse of what the future holds, and if you can make the time for an amazing Rob Reid interview of Dr. Robert Green from Harvard, put the headphones on for the following podcast:


1. Johnston J et al. Sequencing Newborns: A Call for Nuanced Use of Genomic Technologies. Hastings Cent Rep. 2018 Jul-Aug;48(2):S2-6.

Properties of Top 20 Epilepsy Genes

Epilepsy Genetics

1 out of 100 persons is living with active epilepsy (Zack and Kobau 2017, WHO 2018). For the subset that can be pinned down to having a genetic cause, there are about 70 or so genes involved in causing the illness (Lindy 2018). Until recently, the frequency of a gene’s association with epilepsy remained unclear. The GeneDX study by the Lindy team is helping bring clarity by analyzing the genetic underpinnings in 8565 patients with active epilepsy.

Positive Cases

From the GeneDX study, we can plot the rank of the top 20 genes in epilepsy (green graph at top of the figure). The SCN1A gene is by far the major source gene for genetically-associated epilepsies. 27% of all the pathogenic variants in the top 20 epilepsy genes are in SCN1A and 24% in all 70 known epilepsy genes. For instance, 322 positive cases occur in SCN1A out of a total of 1181 positive case in the top 20 genes. Applying the data set to the larger populations the 1181 cases of 8565 (13%) as genetically caused, suggest close to 1 of 1000 persons are living with gene-induced epilepsy. For SCN1A, the population estimate is 78,000 individuals in USA or 1.8 Million worldwide living with pathogenic lesions in their SCN1A gene.

Total Variants

Another way to look at the rank is to ask how many variants occur in each gene (Blue graph). Gathering data from NCBI’s Variant Viewer ( There is a large difference in numbers of observed variant per gene. For example, in two genes of similar protein size (SCN1A and TSC2), there is a 2.5 fold difference in their relative numbers of variants.

Pathogenic Variants

In another aspect derived from Variant Viewer, we can look at numbers of variants known to be pathogenic. For the top 20 epilepsy genes, SCN1A comes out on top again but the next gene with high levels of pathogenic variants is MECP2 (purple graph).

Pathogenic vs total

Finally, when we look at the ratio of pathogenic to total variants, we see some interesting findings. MECP2 has major sensitivity by having a high pathogenic variant load. Similarly, UBE3 also harbors a high pathogenic variant ratio. Another gene jumping up for sensitivity is the expected SCN1A gene. But now we also get KCNQ2, CDKL5, STXBP1, SLC2A1, FOXG1, and ARX as variation-sensitive genes.

Favorite genes and an anomaly

A gene for which I hold high passion is STXBP1. This gene is ranked #6 as likely genetic cause of epilepsy. STXBP1 has 47 variants of 349 as pathogenic (13%). Another gene on our list for humanization development is KCNQ2. This gene is at the #2 position for being a frequent cause of epilepsy. KCNQ2 has 855 variants of which 181 are pathogenic so it has a pathogenicity load of 21%. The TSC2 gene has a strange result. There are 145 pathogenic variants out of 3445 total which gives a 4% pathogenicity penetration. Why are there so many non-pathogenic variants for this gene? Perhaps this gene is highly flexible and can tolerate a high level of variant load. There is a slightly higher proportion of synonymous variants at 27%. Population frequency in the measured variant pool exceeds 0.0001 for only 12% of the variants. So, most of the TSC2 alleles are rare. My other suspicion is TSC2 is like BRCA1 gene. There has been the very large size of researchers studying each of these genes. This leads to more patients being examined for variants in TSC2 and BRCA1. The result, these two genes have attained a higher sampling of the variant diversity occuring in the general population.

1. Zack MM and Kobau R. National and State Estimates of the Numbers of Adults and Children with Active Epilepsy – United States, 2015. CDC: MMWR Morb Mortal Wkly Rep. 2017 Aug 11;66(31):821-825. doi: 10.15585/mmwr.mm6631a1.

2. (World Health Organization) Epilepsy (access 8/1/2018)

3. Lindy AS et al. Diagnostic outcomes for genetic testing of 70 genes in 8565 patients with epilepsy and neurodevelopmental disorders. Epilepsia. 2018 May;59(5):1062-1071. doi: 10.1111/epi.14074. Epub 2018 Apr 14.

C. elegans as Fast and Affordable System for Variant Phenotyping

Systems for Functional Studies

A variety of modeling systems can be used to explore variant function. Initially, many researchers turn to a computational approach to aid variant assessments (Eilbeck 2017). A recent bioinformatics study was used to refine the variant classification of voltage-gated sodium channels (KCNQs) for their contributions to epilepsy (Hol 2017). Yet many of the KCNQ variants remained VUS alleles after the bioinformatic refinement, so alternative functional assays are needed to capture full functional assessment. Various techniques applied for obtaining functional data from clinical variants range from bacterial and yeast expression systems to mammalian cell models (Rodríguez-Escudero 2015, Woods 2016). In one example, bacterial expression of recombinant USP6 was used to detect pathological enzymatic activity in one of 18 clinical variants (liu 2016). Recombinant protein assays can be effective for enzymatic genes, but for disease genes with complex interaction phenotypes, expression in bacteria or yeast removes the gene from its native context and prevents the exploration of pathologies that involve these complex interactions. A mouse or rat animal model is the “gold standard” for finding clear phenotypes from a clinical variant, yet the cost and time spent are more than 10x relative to C. elegans animal models. Further, humanized rodent models are expensive to deploy early in drug development (McGonigle 2014). Disease modeling with Induced Pluripotent Stem Cells (iPSC) offers an exciting platform to study clinical variants (Csöbönyeiová 2016), but the removal of the cells from their native context of the intact animal regrettably removes the important effect of a tissue-based environment. 3D cell culturing techniques and organs-on-a-chip can be useful in restoring proper microenvironment context (Breslin 2013), but the ease of use for routine analysis clinical variants has yet to evolve.

Alternative Animal Models

An emerging approach used by the Undiagnosed Disease Network (Wang 2017), a variant observed in humans is homology modeled by installing the same amino acid change at a conserved position in the disease gene homologs. For instance, clinical variants in CACNA1A were installed in zebrafish and Drosophila and pathogenic activities were observed (Luo 2017). In a recent publication using C. elegans, CRISPR was used to install a patient sequence variant suspect of having CLIFAHDD syndrome (Congenital Contractures of the Limbs and Face with Hypotonia and Developmental Delay) due to defects in the NALCN gene (Bend 2016). These authors demonstrated a gain-of-function pathogenicity assignment could be made for a patient V637F sequence variation. Similar results have been observed for variant installs in other C. elegans disease gene orthologs (Sorkaç 2016, Bulger 2017, Prior 2017, Canning 2018, Pierce 2018, Troulinaki 2018). These results reinforce the C. elegans animal model as a good platform for modeling gene function of pathological variants. The fast life-cycle and ease of genome engineering allow direct modeling of pathogenic alleles to occur within a one-month timeline needed to create and analyze the transgenic animals.

1. Eilbeck K et al. Settling the score: variant prioritization and Mendelian disease. Nat Rev Genet. 2017 Oct;18(10):599-612. doi: 10.1038/nrg.2017.52.

2. Hol et al. Comparison and optimization of in silico algorithms for predicting the pathogenicity of sodium channel variants in epilepsy. Epilepsia. 2017 Jul;58(7):1190-1198. doi: 10.1111/epi.13798. Epub 2017 May 18.

3. Rodríguez-Escudero I et al. Yeast-based methods to assess PTEN phosphoinositide phosphatase activity in vivo. Methods. 2015 May;77-78:172-9. doi: 10.1016/j.ymeth.2014.10.020. Epub 2014 Oct 25.

4. Woods NT et al. Functional assays provide a robust tool for the clinical annotation of genetic variants of uncertain significance. NPJ Genom Med. 2016;1. pii: 16001. doi: 10.1038/npjgenmed.2016.1. Epub 2016 Mar 2.

5. Liu YL et al. The impacts of nineteen mutations on the enzymatic activity of USP26. Gene. 2018 Jan 30;641:292-296. doi: 10.1016/j.gene.2017.10.074. Epub 2017 Oct 27.

6. McGonigle P. Animal Models of CNS Disorders. Biochemical Pharmacology 87, no. 1 (January 1, 2014): 140–49. doi:10.1016/j.bcp.2013.06.016.

7. Csöbönyeiová M et al. Recent Advances in iPSC Technologies Involving Cardiovascular and Neurodegenerative Disease Modeling. General Physiology and Biophysics 35, no. 1 (January 2016): 1–12. doi:10.4149/gpb_2015023.

8. Breslin S and O’Driscoll L. Three-Dimensional Cell Culture: The Missing Link in Drug Discovery. Drug Discovery Today 18, no. 5–6 (March 2013): 240–49. doi:10.1016/j.drudis.2012.10.003.

9. Wang J et al. MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome. Am J Hum Genet. 2017 Jun 1;100(6):843-853. doi: 10.1016/j.ajhg.2017.04.010. Epub 2017 May 11.

10. Luo X et al. Clinically severe CACNA1A alleles affect synaptic function and neurodegeneration differentially. PLoS Genet. 2017 Jul 24;13(7):e1006905. doi: 10.1371/journal.pgen.1006905. eCollection 2017 Jul.

11. Bend EG et al. NALCN channelopathies: Distinguishing gain-of-function and loss-of-function mutations. Neurology. 2016 Sep 13;87(11):1131-9. doi: 10.1212/WNL.0000000000003095. Epub 2016 Aug 24.

12. Sorkaç A et al. In Vivo Modelling of ATP1A3 G316S-Induced Ataxia in C. elegans Using CRISPR/Cas9-Mediated Homologous Recombination Reveals Dominant Loss of Function Defects. PLoS One. 2016 Dec 9;11(12):e0167963. doi: 10.1371/journal.pone.0167963.

13. Bulger DA et al. Caenorhabditis elegans DAF-2 as a Model for Human Insulin Receptoropathies.G3 (Bethesda). 2017 Jan 5;7(1):257-268. doi: 10.1534/g3.116.037184.. Available at:

14. Prior H et al. Highly Efficient, Rapid and Co-CRISPR-Independent Genome Editing in Caenorhabditis elegans. G3 (Bethesda). 2017 Nov 6;7(11):3693-3698. doi: 10.1534/g3.117.300216.

15. Canning P et al. CDKL Family Kinases Have Evolved Distinct Structural Features and Ciliary Function. Cell Rep. 2018 Jan 23;22(4):885-894. doi: 10.1016/j.celrep.2017.12.083.

16. Pierce SB et al. De novo mutation in RING1 with epigenetic effects on neurodevelopment. Proc Natl Acad Sci U S A. 2018 Feb 13;115(7):1558-1563. doi: 10.1073/pnas.1721290115.

17. Troulinaki K et al. WAH-1/AIF regulates mitochondrial oxidative phosphorylation in the nematode Caenorhabditis elegans. Cell Death Discov. 2018 Jan 29;4:2. doi: 10.1038/s41420-017-0005-6.

Explosive Growth of Gene Variant Numbers

Genetic Testing

Clinical geneticists have an acute need to understand pathogenicity in genomes of their patients (Figure 1). Cost per human genome has now approached $1000 each (Wetterstrand 2018). This affordable cost is allowing clinicians to start incorporating next-generation sequencing (NGS) technology into the patient diagnosis.

Variant Diversity

The American College of Medical Genetics and Genomics and the Association for Molecular Pathology recommend that variants be classified in five groups (Pathogenic, Likely Pathogenic, Uncertain Significance, Likely Benign, Benign) (Richards 2015). Further, they suggest greater efforts are needed to resolve variants of Uncertain Significance (VUS) into either Pathogenic or Benign status. The re-classification of a VUS is no small task. In a recent publication on the analysis of the clinical variant database (ClinVar), the number of known genetic variants in human disease was reported on 9/9/2016 to be 72,472 (Manolio 2017). One year later (9/21/2017), a survey of ClinVar using an online database viewer reported 359,938 known variants (Henrie 2018). This 4.9-fold increase in the number of known clinical variants over one year reflects the explosive application of whole genome sequencing in clinical diagnostics (Stavropoulos 2015, Ellingford 2016, Volk 2017).

Need for Innovation

The daunting task now is to determine the significance of each new variant. Bioinformatics can provide some insight into variant pathogenicity (Oliver 2015). Unfortunately, the number of VUS alleles has remained close to 40% year-on-year since 2015. As of the July 2018, there are 192,843 identified VUS alleles. With such high numbers, there is a pressing need to quickly correlate genotype to phenotype and determine if VUS alleles are benign or pathogenic (Cox 2015). Model systems that reconstitute mutations in a physiological context are a robust method to demonstrate variant pathogenicity (Eilbeck 2017). Traditionally, mouse models have been used to characterize defective function in VUS alleles, but the expanding universe of clinical variants is overwhelming the current capacity. Higher throughput animal modeling is needed to address the growing demand.

1. Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) Accessed July 12, 2018.

2. Richards S et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015 May;17(5):405-24. doi: 10.1038/gim.2015.30. Epub 2015 Mar 5.

3. Manolio TA et al. Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research. Cell. 2017 Mar 23;169(1):6-12. doi: 10.1016/j.cell.2017.03.005.

4. Henrie A. et al. ClinVar Miner: Demonstrating utility of a Web-based tool for viewing and filtering ClinVar data. Hum Mutat. 2018 May 23. doi: 10.1002/humu.23555.

5. Stavropoulos DJ et al. Whole Genome Sequencing Expands Diagnostic Utility and Improves Clinical Management in Pediatric Medicine. NPJ Genom Med. 2016 Jan 13;1. pii: 15012. doi: 10.1038/npjgenmed.2015.12.

6. Ellingford JM et al. Whole Genome Sequencing Increases Molecular Diagnostic Yield Compared with Current Diagnostic Testing for Inherited Retinal Disease. Ophthalmology. 2016 May;123(5):1143-50. doi: 10.1016/j.ophtha.2016.01.009.

7. Volk AE and Kubisch C. The rapid evolution of molecular genetic diagnostics in neuromuscular diseases. Curr Opin Neurol. 2017 Oct;30(5):523-528. doi: 10.1097/WCO.0000000000000478.

8. Oliver GR et al. Bioinformatics for clinical next generation sequencing. Clin Chem. 2015 Jan;61(1):124-35. doi: 10.1373/clinchem.2014.224360.

9. Cox TC. Utility and limitations of animal models for the functional validation of human sequence variants. Mol Genet Genomic Med. 2015 Sep;3(5):375-82. doi: 10.1002/mgg3.167.

10. Eilbeck K et al. Settling the score: variant prioritization and Mendelian disease. Nat Rev Genet. 2017 Oct;18(10):599-612. doi: 10.1038/nrg.2017.52.

Beginning of the Journey to the End

The purpose of this blog has many uses.  It allows me to create content exploring genome biology in more depth than what can be achieved in a mere 140 characters.  But more ambitiously, I will be embarking on a journey of self-discovery, of the molecular and structural kind.  I plan to acquire my own genome’s data at high-density read depth.  I expect to catalog over 1,000,000 variations from my genome when compared to the norm.   Of these variations, I expect about 1000 will be in the coding sequence of my genes.  About 7% of these will be obviously deleterious to gene function.  Yet, because we are diploids organisms with two copies of every gene, many of these “bad” genes will be backed up by a good copy.  As a result, I will know what is my carrier status for bad alleles. Intriguingly, I will be uncovering which important genes (or hopefully not so important genes) have had two hits that render their phenotypic consequence as defective.