20 October 2016
19 October 2016
Genetic epilepsy and developmental disorders in non-affected parents are sometimes de novo disorders, but are also sometimes caused by mosaicism in a parent, which means that the parent has one kind of DNA in some cells and a different kind in other cells. As a result, the risk factor genes may evade detection by normal DNA tests that only look at one, easy to access site. These variants can make their way a child's DNA in vitro after initial fertilization of the egg giving rise to a zygote.
This is discussed in ASHG 2016 Conference Presentations 295, 296 and 297.
We describe the discovery of genetic and phenotypic associations with “nail biting,” technically known as onychophagia. Over 180,000 participants who consented to research in the 23andMe customer base responded to the question “How often do you bite your nails”; 37% reported biting their nails and 7% said they bite very often. Consistent with the literature, “nail biting” was correlated with “conscientiousness” and “neuroticism” of our five dimensional personality questionnaires. Individuals who become nervous easily or are moody report a higher frequency of nail biting.
Our genome-wide scan identified 21 significant associations (p < 5e-8) with nail biting. We identified a loss of function variant (rs117612447, p=4.6e-22) in KRT31, a keratin gene involved in hair and nail formation, and a variant (rs10876505, p=5.5e-9) near HOXC13, a gene linked to nail and hair developmental disorders. Six of the identified loci (rs713843, p=4.2e-26; rs35754740, p=4.8e-11; rs4776970, p=7.4e-11; rs4775313, p=8.4e-11; rs62264775, p=9.4e-9; rs149994299, p=2e- 8) were also associated with BMI in the same direction. Five of the identified loci (rs1442883, p=3.8e-19; rs8095324, p=1.7e-13; rs7837754, p=3.3e-12; rs7411445 [NEGR1], p=8.6e-10; rs2977694 [CSMD1], p=7.2e-8) were also associated with “sweet tooth," but in the different directions. The NEGR1 and CSMD1 regions that have been previously implicated in psychiatric disorders. We also identified variants near GRIN2A in 16p13.2 (rs2014151, p=6e-19) and near NRG1 (rs13255543, p=5.7e-13). Mutations in these two regions have previously been linked to diseases such as autism, schizophrenia, and bipolar disorder.
We estimated a positive genetic correlation between nail biting and BMI (LD score rg=0.17, p=1.46e-14). We found a near-zero genetic correlation between nail biting and sweet tooth. Although they shared many associations, the effects from those pleiotropic loci are not in the same direction. Overall, our findings revealed genetic contributions to nail biting. They also point to a possible connection between nail biting, BMI, and taste perception, which is interesting in light of prior findings that BMI GWASes implicate neural regulations; personality factors such as anxiety and the ability to cope with stress have been discovered to change hormones and act on taste. Our study may provide molecular evidence for neural mechanisms underlying personality and taste.C. Tian, J. Tung, and D. Hinds of 23andme "Genome- and phenome-wide study of “nail biting”: Not just a habit." ASHG Conference Presentation 276 (October 2016)/
Your genes have an impact about seven times as great as your socio-economic status on weight gain among people in the United Kingdom. But, note that the U.K. may lack the extreme deprivations found in other places and thus have less variation due to socio-economic status than the U.S. or less developed countries.
There are 69 known genetic risk factors for obesity that have been reduced to a genetic risk score. On average, each allele in the score that is present increases the weight of a 5'8" person by 0.737 pounds (in theory an average difference of 50.853 pounds between with no risk alleles, and someone with all of the risk alleles for a 5'8" person). The weight gain associated with the genetic risk is 7% higher than average for someone in the bottom half of the socio-economic scale, and is 7% lower than average in someone in the top half of the socio-economic scale. (The combined 14% difference from top to bottom is about 1/7th of the total.)
Statement of Purpose: Susceptibility to obesity in today’s environment has a strong genetic component. However, little is known about how genetic susceptibility interacts with modern environments and behaviours to predispose some individuals to obesity whilst others remain slim. Social deprivation is associated with a higher risk of obesity but it is not known if it accentuates genetic susceptibility to obesity. Previous gene-obesogenic environment studies have been limited by the need to perform meta-analyses of many heterogeneous studies and studies have not necessarily corrected for statistical artefacts such as different variances between groups (heteroscedasticity). We aimed to use 120,000 individuals from the UK Biobank study to test the hypothesis that objective measures of relative deprivation in the UK accentuate genetic susceptibility to obesity.
Methods: We used the Townsend deprivation index (TDI) as a measure of deprivation and a 69-variant genetic risk score (GRS) as a measure of genetic susceptibility to obesity. We tested the association of the genetic risk score with BMI in high and low socioeconomic groups and tested for interactions (using the continuous TDI as an exposure measure). To test the specificity of any apparent interactions we repeated analyses using a simulated environment (that was correlated with BMI in the same way as TDI) as an interaction term and using randomly selected groups of individuals of different BMIs.
Results: We found evidence of gene-environment interactions with TDI (Pinteraction=3x10-10). Within the 50% of most deprived individuals, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73m tall. In contrast, within the 50% of least deprived individuals carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. When we used a simulated environment or randomly selected groups of individuals to be of different BMIs, we observed only nominal evidence of apparent interaction, (simulated environment Pinteraction = 0.04; randomly selected groups: Pinteraction=9x10-4) suggesting the interaction was specific to TDI.
Conclusions: Our findings provide evidence that social deprivation accentuates the genetic predisposition to obesity.
J. Tyrrell, et al., "Evidence for body mass index gene x environment interaction using 120,000 individuals from the UK Biobank study." ASHG Conference Presentation 41 (October 2016).
Autism Spectrum Disorder (ASD) cases can be clustered into two types based upon the risk factor genes involved. This division coincides with a distinctions between severe symptoms and less severe symptoms. The two clusters appear to have distinct causes. Basically, it appears that there are two different conditions that happen to have symptoms that resemble each other.
Knowing what causes a particular individual's ASD could be critical in figuring out what kind of therapies or symptom management strategies are likely to work best for a particular individual.
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disease primarily characterized by deficits in verbal communication, impaired social interaction and repetitive behaviors. It exemplifies profound clinical heterogeneity, which poses challenges in diagnosis and treatment. Genetic studies have pointed to hundreds of presumptive causative or susceptibility genes in ASD, making it difficult to find common underlying pathogenic mechanisms and suggesting that multiple different genetic etiologies for ASDs influence a continuum of traits.
Deep phenotyping analysis allowed for re-categorization of genetic variants. Our previous analysis suggested the existence of two significant subgroups within the existing ASD classification. To investigate this hypothesis in greater detail we have performed in-depth analysis using phenotypic and genetic data from Autism Genetic Resource Exchange (AGRE) and Autism Genome Project (AGP). Our initial findings on both phenotypic and genetic data (1,262 cases and 2,521 controls using familial transmission disequilibrium test) suggest existence of two groups that range in severity. Findings were replicated in a validation dataset. Genetic risk scores (GRS) were used to sum up the total effect of several single-nucleotide polymorphisms characteristic of the two clusters. The high discriminatory ability of the genetic risk score to define cluster 1 from cluster 2 case group at different combinations of sensitivity and specificity was assessed and clearly demonstrates strong signal with AUC being 0.74. There is a significant signal differentiating the 2 clusters relying on non-genetic risk factors and even greater signal when using both non-genetic risk factors and GRS. The detection and validation of the two groups allowed us focus on convergence of findings at the pathway level. ASD heterogeneity was leveraged via large scale pathway analysis within those two categories, which led to identification of a driver gene set across significant pathways. The significant pathways in cluster 1 (severe, affected = 300) include autoimmune disease, vitamin B6 metabolism, whereas in cluster 2 (non-severe, affected = 921) included oxytocin signaling pathway, WNT signaling pathway and glutamatergic synapses (all at P < 0.001). We envision that systematic study of all genomic pathways obtained given a set of redefined categories will yield profound findings for ASD even in the absence of strong individual variant information.S. Smieszek and J.L. Haines., "Autism redefined: Genomic pathway approach to autism spectrum disorder." ASHG Conference Presentation 33 (October 2016).