Statistical omics and more
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Statistical omics and more
Statistical omics and its surroundings, from the viewpoint of a geneticist
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Grant funding: Playing the odds

Yurii Aulchenko's insight:
It feels totally right that "The current system is fairly effective in identi- fying the top 20% of applications... Selection of the best of the best resembles a lottery in its unpredictability, but one that lacks the benefit of being truly random, due to bias" - and lottery is suggested. Seems to be more fair indeed!
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Introduction to Literate Programming

Introduction to Literate Programming | Statistical omics and more | Scoop.it
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Highly recommended by Lennart as a reference for org-model literate programming 
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Detecting variants shared between traits, detecting potential causality from GWAS summary data | PolyOmica

Detecting variants shared between traits, detecting potential causality from GWAS summary data | PolyOmica | Statistical omics and more | Scoop.it
Yurii Aulchenko's insight:
Mini-review of the paper by Pickrell et al. on "Detection and interpretation of shared genetic influences on 42 human traits"
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Integration of summary data from GWAS and eQTL studies | PolyOmica

Integration of summary data from GWAS and eQTL studies | PolyOmica | Statistical omics and more | Scoop.it
Yurii Aulchenko's insight:
Interested in figuring out how knowledge of disease loci may translate into biology? You could now try to do more, using data from #omics #GWAS! - short review of paper of by Zhu et al, 48, 481-487 (2016). 
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PLOS Computational Biology: A Quick Introduction to Version Control with Git and GitHub

PLOS Computational Biology: A Quick Introduction to Version Control with Git and GitHub | Statistical omics and more | Scoop.it

Share Your Code Once you have your files saved in a Git repository, you can share it with your collaborators and the wider scientific community by putting your code online (Fig 3).


Via Chris Upton + helpers, Asela Wijeratne
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Asela Wijeratne's curator insight, February 4, 11:41 AM

Nice overview of version control!

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Genotype Imputation with Millions of Reference Samples: The American Journal of Human Genetics

Genotype Imputation with Millions of Reference Samples: The American Journal of Human Genetics | Statistical omics and more | Scoop.it
We present a genotype imputation method that scales to millions of reference samples. The imputation method, based on the Li and Stephens model and implemented in Beagle v.4.1, is parallelized and memory efficient, making it well suited to multi-core computer processors. It achieves fast, accurate, and memory-efficient genotype imputation by restricting the probability model to markers that are genotyped in the target samples and by performing linear interpolation to impute ungenotyped variants. We compare Beagle v.4.1 with Impute2 and Minimac3 by using 1000 Genomes Project data, UK10K Project data, and simulated data. All three methods have similar accuracy but different memory requirements and different computation times. When imputing 10 Mb of sequence data from 50,000 reference samples, Beagle’s throughput was more than 100× greater than Impute2’s throughput on our computer servers. When imputing 10 Mb of sequence data from 200,000 reference samples in VCF format, Minimac3 consumed 26× more memory per computational thread and 15× more CPU time than Beagle. We demonstrate that Beagle v.4.1 scales to much larger reference panels by performing imputation from a simulated reference panel having 5 million samples and a mean marker density of one marker per four base pairs.
Yurii Aulchenko's insight:

Bravo, Beagle team! (although - almost off-topic! - practically it is very hard to beat the convenience of the Michigan Imputation Server :))

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Revised estimates for the number of human and bacteria cells in the body

Revised estimates for the number of human and bacteria cells in the body | Statistical omics and more | Scoop.it
We critically revisit the ″common knowledge″ that bacteria outnumber human cells by a ratio of at least 10:1 in the human body. We found the total number of bacteria in the ″reference man″ to be 3.9·1013, with an uncertainty (SEM) of 25%, and a variation over the population (CV) of 52%. For human cells we identify the dominant role of the hematopoietic lineage to the total count of body cells (≈90%), and revise past estimates to reach a total of 3.0·1013 human cells in the 70 kg ″reference man″ with 2% uncertainty and 14% CV. Our analysis updates the widely-cited 10:1 ratio, showing that the number of bacteria in our bodies is actually of the same order as the number of human cells. Indeed, the numbers are similar enough that each defecation event may flip the ratio to favor human cells over bacteria.
Yurii Aulchenko's insight:

An interesting review/analysis paper which questions the 'common knowledge' that a we have ten times more bacteria on/in us than the our own cells. The authors make an interesting historical analysis tracing the '10:1' statement back to its origins (back to the beginning of 1970s!); based on analysis of more recent literature, they come to conclusion that a more likely ratio is roughly 1:1. While one could still argue with their estimates, this paper clearly shows that some of 'common knowledge' is not so solid, and it is worth asking yourself what a 'common knowledge' is based upon.

 

Reminds me of a story from Richard Feynman, where "everyone knew that neutron-proton coupling is T", which generated a lot of confusion because at some point it became the piece that did not fit in the new nice theory; but when tracking this common knowledge back in time, and looking in the original paper, it turned out that the data on which this common knowledge was based were rather shaky. Quote: "Had I been a good physicist, when I thought of the original idea ... I would have immediately looked up "how strong do we know it's T? ... Since then I never pay any attention to anything by 'experts'. I calculate everything myself." 

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Diet-induced extinctions in the gut microbiota compound over generations : Nature : Nature Publishing Group

Diet-induced extinctions in the gut microbiota compound over generations : Nature : Nature Publishing Group | Statistical omics and more | Scoop.it
The gut is home to trillions of microorganisms that have fundamental roles in many aspects of human biology, including immune function and metabolism. The reduced diversity of the gut microbiota in Western populations compared to that in populations living traditional lifestyles presents the question of which factors have driven microbiota change during modernization. Microbiota-accessible carbohydrates (MACs) found in dietary fibre have a crucial involvement in shaping this microbial ecosystem, and are notably reduced in the Western diet (high in fat and simple carbohydrates, low in fibre) compared with a more traditional diet. Here we show that changes in the microbiota of mice consuming a low-MAC diet and harbouring a human microbiota are largely reversible within a single generation. However, over several generations, a low-MAC diet results in a progressive loss of diversity, which is not recoverable after the reintroduction of dietary MACs. To restore the microbiota to its original state requires the administration of missing taxa in combination with dietary MAC consumption. Our data illustrate that taxa driven to low abundance when dietary MACs are scarce are inefficiently transferred to the next generation, and are at increased risk of becoming extinct within an isolated population. As more diseases are linked to the Western microbiota and the microbiota is targeted therapeutically, microbiota reprogramming may need to involve strategies that incorporate dietary MACs as well as taxa not currently present in the Western gut.
Yurii Aulchenko's insight:

The bottom-line of this paper probably is that, at least in mice, in laboratory conditions, "Introduction of dietary microbiota-accessible carbohydrates are insufficient to regain ‘lost’ taxa in the absence of their deliberate re-introduction"

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Using populations of human and microbial genomes for organism detection in metagenomes

Identifying causative disease agents in human patients from shotgun metagenomic sequencing (SMS) presents a powerful tool to apply when other targeted diagnostics fail. Numerous technical challenges remain, however, before SMS can move beyond the role of research tool. Accurately separating the known and unknown organism content remains difficult, particularly when SMS is applied as a last resort. The true amount of human DNA that remains in a sample after screening against the human reference genome and filtering nonbiological components left from library preparation has previously been underreported. In this study, we create the most comprehensive collection of microbial and reference-free human genetic variation available in a database optimized for efficient metagenomic search by extracting sequences from GenBank and the 1000 Genomes Project. The results reveal new human sequences found in individual Human Microbiome Project (HMP) samples. Individual samples contain up to 95% human sequence, and 4% of the individual HMP samples contain 10% or more human reads. Left unidentified, human reads can complicate and slow down further analysis and lead to inaccurately labeled microbial taxa and ultimately lead to privacy concerns as more human genome data is collected.

Yurii Aulchenko's insight:

Editor's summary: this is a resource for identifying contaminating human DNA sequences in metagenomic samples, which could potentially be used to identify donors. To find the contaminating human sequences, the researchers compiled a comprehensive 

database of human variation not present in the human reference genome. Applying their database to available data from the Human Microbiome Project, the authors find individual samples containing up to 95% human sequence. 

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NIPD in routine clinical care

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A set of great slides from UCL on NIPD/NIPT

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Frontiers | The diagnosis of infectious diseases by whole genome next generation sequencing: a new era is opening | Frontiers in Cellular and Infection Microbiology

The diagnosis of infectious diseases by whole genome next generation sequencing: a new era is opening
Yurii Aulchenko's insight:

The authors say/conclude that "The question is probably not if, but rather when, WG-NGS will become a routine test in diagnostics of infectious diseases. This development will require improvement in sample preparation, availability of sequencers in central laboratories and validated pipelines for read sorting and taxonomic assignation"

 

Other quote: "Developing a WG-NGS diagnostic pipeline critically relies on two partly interdependent criteria: time to results and database exhaustiveness"

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Identification of a large set of rare complete human knockouts : Nature Genetics : Nature Publishing Group

Identification of a large set of rare complete human knockouts : Nature Genetics : Nature Publishing Group | Statistical omics and more | Scoop.it
Patrick Sulem, Hannes Helgason and colleagues identify homozygous and compound heterozygous loss-of-function variants of minor allele frequency <2% in 7.7% of the genotyped Icelandic population. Under transmission of some of these variants from heterozygous parents provides evidence that they are actually deleterious.
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A contribution to the list of loss-of-function (LOF) variants

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FDA Approves 23andMe's DNA Test, But Not the One It Halted Earlier

FDA Approves 23andMe's DNA Test, But Not the One It Halted Earlier | Statistical omics and more | Scoop.it
FDA's decision to approve 23andMe's Bloom syndrome carrier test is a first step for the online offer of medical genetic testing, but the real regulatory breakthroughs have yet to come....
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Beware of circularity: Evaluating SNV deleteriousness prediction tools - Chloé-Agathe Azencott

If you're working with next-generation sequencing (NGS) human data, chances are at some point you will be interested in automatically determining which of your sequence variants
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Genetic and transcriptional analysis of human host response to healthy gut microbiome

Many studies have demonstrated the importance of the gut microbiome in healthy and disease states. However, establishing the causality of host- microbiome interactions in humans is still challenging. Here, we describe a novel experimental system to define the transcriptional response induced by the microbiome in human cells and to shed light on the molecular mechanisms underlying host-gut microbiome interactions. In primary human colonic epithelial cells, we identified over 6,000 genes that change expression at various time points following co-culturing with the gut microbiome of a healthy individual. The differentially expressed genes are enriched for genes associated with several microbiome-related diseases, such as obesity and colorectal cancer. In addition, our experimental system allowed us to identify 87 host SNPs that show allele-specific expression in 69 genes. Furthermore, for 12 SNPs in 12 different genes, allele-specific expression is conditional on the exposure to the microbiome. Of these 12 genes, eight have been associated with diseases linked to the gut microbiome, specifically colorectal cancer, obesity and type 2 diabetes. Our study demonstrates a scalable approach to study host-gut microbiome interactions and can be used to identify putative mechanisms for the interplay between host genetics and microbiome in health and disease.
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Control of fluxes in metabolic networks

Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.
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I wonder if we could use this in our glycomics works
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No Evidence of a Common DNA Variant Profile Specific to World Class Endurance Athletes

No Evidence of a Common DNA Variant Profile Specific to World Class Endurance Athletes | Statistical omics and more | Scoop.it
There are strong genetic components to cardiorespiratory fitness and its response to exercise training. It would be useful to understand the differences in the genomic profile of highly trained endurance athletes of world class caliber and sedentary controls. An international consortium (GAMES) was established in order to compare elite endurance athletes and ethnicity-matched controls in a case-control study design. Genome-wide association studies were undertaken on two cohorts of elite endurance athletes and controls (GENATHLETE and Japanese endurance runners), from which a panel of 45 promising markers was identified. These markers were tested for replication in seven additional cohorts of endurance athletes and controls: from Australia, Ethiopia, Japan, Kenya, Poland, Russia and Spain. The study is based on a total of 1520 endurance athletes (835 who took part in endurance events in World Championships and/or Olympic Games) and 2760 controls. We hypothesized that world-class athletes are likely to be characterized by an even higher concentration of endurance performance alleles and we performed separate analyses on this subsample. The meta-analysis of all available studies revealed one statistically significant marker (rs558129 at GALNTL6 locus, p = 0.0002), even after correcting for multiple testing. As shown by the low heterogeneity index (I 2 = 0), all eight cohorts showed the same direction of association with rs558129, even though p-values varied across the individual studies. In summary, this study did not identify a panel of genomic variants common to these elite endurance athlete groups. Since GAMES was underpowered to identify alleles with small effect sizes, some of the suggestive leads identified should be explored in expanded comparisons of world-class endurance athletes and sedentary controls and in tightly controlled exercise training studies. Such studies have the potential to illuminate the biology not only of world class endurance performance but also of compromised cardiac functions and cardiometabolic diseases.

Via Dmitry Alexeev
Yurii Aulchenko's insight:

Taking trait into account, the sample size is impressive. No genome-wide significant results are found, which is probably no surprise given complexity of the trait. Would be interesting to look at SNP-heritability and guess the architecture, possibly estimating the sample size needed to get to GW-significance. The data/results should in principle allow to do this. 

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Dmitry Alexeev's curator insight, January 30, 2:19 AM

Largest study so far - we had no chances to have power enough for that - it is just because eilte athletes are elite)

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Imputation-Based Genomic Coverage Assessments of Current Human Genotyping Arrays

Imputation-Based Genomic Coverage Assessments of Current Human Genotyping Arrays | Statistical omics and more | Scoop.it
Microarray single-nucleotide polymorphism genotyping, combined with imputation of untyped variants, has been widely adopted as an efficient means to interrogate variation across the human genome. “Genomic coverage” is the total proportion of genomic variation captured by an array, either by direct observation or through an indirect means such as linkage disequilibrium or imputation. We have performed imputation-based genomic coverage assessments of eight current genotyping arrays that assay from ~0.3 to ~5 million variants. Coverage was determined separately in each of the four continental ancestry groups in the 1000 Genomes Project phase 1 release. We used the subset of 1000 Genomes variants present on each array to impute the remaining variants and assessed coverage based on correlation between imputed and observed allelic dosages. More than 75% of common variants (minor allele frequency > 0.05) are covered by all arrays in all groups except for African ancestry, and up to ~90% in all ancestries for the highest density arrays. In contrast, less than 40% of less common variants (0.01 < minor allele frequency < 0.05) are covered by low density arrays in all ancestries and 50–80% in high density arrays, depending on ancestry. We also calculated genome-wide power to detect variant-trait association in a case-control design, across varying sample sizes, effect sizes, and minor allele frequency ranges, and compare these array-based power estimates with a hypothetical array that would type all variants in 1000 Genomes. These imputation-based genomic coverage and power analyses are intended as a practical guide to researchers planning genetic studies.
Yurii Aulchenko's insight:

Somewhat 'old' (2013) paper, still one of the latest evaluating genomic coverage by different arrays. In contrast to previous works, the comparison is based not on the % of SNPs (at certain MAF) with high enough LD (typically r2>0.8) with the genotyped SNPs, but rather on resulting quality of imputations, which sounds very reasonable approach to me. From figure 2 it follows that for Europeans any genome-wide array has a reasonable (~80% or more) coverage for variants with MAF>0.05; it also looks like the arrays with 700-800k SNPs may provide an optimum between cost and efficiency. I could speculate that in case a larger imputation panel (e.g. HRC) would have been used, the performance would be even better. 

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Why Bioinformatics Analysis Is Not Free

On the costs and necessity of skilled bioinformatics analysis for large-scale sequencing projects.


Via Mel Melendrez-Vallard
Yurii Aulchenko's insight:

Quite typical that a grant is written and millions are planned for experimentation. In a 'good' scenario someone at the end would remember: oh, and we would need to do analysis! - and few % of budget will be squeezed out for 'bioinformatics' - just enough to do some irreproducible research. In many projects I high-throighput projects I have seen, a reasonable 'BI' budget should have been rather 20-30% to ensure good quality and timing, reproducibility and maintainability. 

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FOXO1 couples metabolic activity and growth state in the vascular endothelium : Nature : Nature Publishing Group

FOXO1 couples metabolic activity and growth state in the vascular endothelium : Nature : Nature Publishing Group | Statistical omics and more | Scoop.it
Endothelial cells (ECs) are plastic cells that can switch between growth states with different bioenergetic and biosynthetic requirements. Although quiescent in most healthy tissues, ECs divide and migrate rapidly upon proangiogenic stimulation. Adjusting endothelial metabolism to the growth state is central to normal vessel growth and function, yet it is poorly understood at the molecular level. Here we report that the forkhead box O (FOXO) transcription factor FOXO1 is an essential regulator of vascular growth that couples metabolic and proliferative activities in ECs. Endothelial-restricted deletion of FOXO1 in mice induces a profound increase in EC proliferation that interferes with coordinated sprouting, thereby causing hyperplasia and vessel enlargement. Conversely, forced expression of FOXO1 restricts vascular expansion and leads to vessel thinning and hypobranching. We find that FOXO1 acts as a gatekeeper of endothelial quiescence, which decelerates metabolic activity by reducing glycolysis and mitochondrial respiration. Mechanistically, FOXO1 suppresses signalling by MYC (also known as c-MYC), a powerful driver of anabolic metabolism and growth. MYC ablation impairs glycolysis, mitochondrial function and proliferation of ECs while its EC-specific overexpression fuels these processes. Moreover, restoration of MYC signalling in FOXO1-overexpressing endothelium normalizes metabolic activity and branching behaviour. Our findings identify FOXO1 as a critical rheostat of vascular expansion and define the FOXO1–MYC transcriptional network as a novel metabolic checkpoint during endothelial growth and proliferation.
Yurii Aulchenko's insight:

A very nice experimental work, involving tissue-specific, induced, genetic manipulations in vivo in mice to study organism-level vascular phenotypes and analysis of genetically altered human umbilical vein endothelial cells to study (co)expression and other cellular phenotypes. Quite inspiring and thought-provoking.

 

On a critical side: the statistical analysis is described very shortly as "performed by unpaired, two-tailed Student’s t-test, or non-parametric one-way ANOVA followed by Bonferroni’s multiple comparison test unless mentioned otherwise". I presume that by "non-parametric ANOVA" the authors mean the Kruskal–Wallis test. The number of tests is not explicitly mentioned, and it is unclear whether what we see in the paper are nominal p-values, or these corrected for the (unknown to the reader) number of tests. For many statistical tests, the size of groups compared is very small (less than 10), which brings the question whether the use of asymptotic statistic - either parametric or not - is valid. Exact tests would be preferred for such data.

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Romantic Relationship Satisfaction Moderates the Etiology of Adult Personality - Online First - Springer

Romantic Relationship Satisfaction Moderates the Etiology of Adult Personality - Online First - Springer | Statistical omics and more | Scoop.it

The heritability of major normative domains of personality is well-established, with approximately half the proportion of variance attributed to genetic differences. In the current study, we examine the possibility of gene × environment interaction (G×E) for adult personality using the environmental context of intimate romantic relationship functioning. Personality and relationship satisfaction are significantly correlated phenotypically, but to date no research has examined how the genetic and environmental components of variance for personality differ as a function of romantic relationship satisfaction. Given the importance of personality for myriad outcomes from work productivity to psychopathology, it is vital to identify variables present in adulthood that may affect the etiology of personality. In the current study, quantitative models of G×E were used to determine whether the genetic and environmental influences on personality differ as a function of relationship satisfaction. We drew from a sample of now-adult twins followed longitudinally from adolescence through age 29. All participants completed the Multidimensional Personality Questionnaire (MPQ) and an abbreviated version of the Dyadic Adjustment Scale. Biometric moderation was found for eight of the eleven MPQ scales examined: well-being, social potency, negative emotionality, alienation, aggression, constraint, traditionalism, and absorption. The pattern of findings differed, suggesting that the ways in which relationship quality moderates the etiology of personality may depend on the personality trait.

Yurii Aulchenko's insight:

The question they ask is interesting: in heritability of personality traits, is there (poly)gene by romantic relationship interaction? The answer they give in the abstract is "the pattern of findings differed".

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The Tree of Life: Overselling the Microbiome

The Tree of Life: Overselling the Microbiome | Statistical omics and more | Scoop.it
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Discovered that Jonathan Eisen, who runs the #BadOmics award, also runs the "Overselling Microbiome Award"

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Circulating Cell-Free DNA Enables Noninvasive Diagnosis of Heart Transplant Rejection

Circulating Cell-Free DNA Enables Noninvasive Diagnosis of Heart Transplant Rejection | Statistical omics and more | Scoop.it

Editor's summary: Not all heart transplants succeed, but early detection of organ rejection could spare the patient severe adverse events and graft dysfunction. De Vlaminck et al. devised a noninvasive, sequencing-based method to monitor and predict rejection, relying on the presence of donor DNA in recipient blood plasma. The fraction of donor DNA is naturally elevated 1 day after transplant (because organ transplants are essentially genome transplants), and these levels decline exponentially over the course of the week, if the organ is accepted. The authors noted that patients who rejected their new heart had high levels of donor DNA even months after transplant. In a prospective trial, elevated donor DNA was detected months before the rejection episode, suggesting that such noninvasive analysis tools could be used in lieu of an invasive biopsy, to let doctors know which patients are likely to reject their transplanted organ.

Yurii Aulchenko's insight:

Rather interesting, nice, and kind of obvious - but only after you hear/think about it! - use of cell-free DNA testing for monitoring (risk of) transplant rejection

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Human Genetics Shape the Gut Microbiome: Cell

Human Genetics Shape the Gut Microbiome: Cell | Statistical omics and more | Scoop.it
Host genetics and the gut microbiome can both influence metabolic phenotypes. However, whether host genetic variation shapes the gut microbiome and interacts with it to affect host phenotype is unclear. Here, we compared microbiotas across >1,000 fecal samples obtained from the TwinsUK population, including 416 twin pairs. We identified many microbial taxa whose abundances were influenced by host genetics. The most heritable taxon, the family Christensenellaceae, formed a co-occurrence network with other heritable Bacteria and with methanogenic Archaea. Furthermore, Christensenellaceae and its partners were enriched in individuals with low body mass index (BMI). An obese-associated microbiome was amended with Christensenella minuta, a cultured member of the Christensenellaceae, and transplanted to germ-free mice. C. minuta amendment reduced weight gain and altered the microbiome of recipient mice. Our findings indicate that host genetics influence the composition of the human gut microbiome and can do so in ways that impact host metabolism.
Yurii Aulchenko's insight:

This manuscript analyses heritability of gut microbiome (16S-based OTUs) in the sample of 171 MZ, 245 DZ twins and 143 unrelated individuals. In total, 909 traits (defined as OTUs or taxonomic groupings) were investigated. 63% of traits had A>C; still, on average the largest variance was attributed to random individual environment (E>A+C for 906 out of 909 traits). From 909 investigated traits, 117 had (permutation) p-value <0.05 for significance of the additive component of heritability (ACE model). Only 10 traits met FTD q-value < 0.1.

 

The largest heritability of 0.38 was observed for genus/family Christensenellaceae (although permutation p=0.001 does not look over-convincing three traits/groupings involving "Christensenellaceae" had FDR q<0.1).

 

Interesting that "OTUs with high heritability - A>0.2 - were enriched in lean subjects" (BMI x genes interaction?! - should be testable with available data!)

 

Rather cool - and, to me, convincing - experiments with transfer of stool to germ-free mice; seems that transfer of C. minuta depress the weight gain.


"Stool energy content was significantly higher for the methanogen-positive microbiomes" (seems that these who were "lean" did not manage to extract all energy from food?)


"Heritable" taxons formed a co-occurence network. In discussion the authors mention that "these patterns could derive" from all these taxa being heritable, or some being heritable and other correlated. This question should be addressable with available data - with e.g. bivariate heritability analysis figuring out genetic vs. environmental correlations between traits.

 

Few side notes:
- Box-Cox does not really fix the point mass at zero problem
- "We repeated analysis ... with effects of BMI regressed out": not sure, but would two-step generate potential problem here?

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Open consent, biobanking and data protection law: can open consent be ‘informed’ under the forthcoming data protection regulation?

Open consent, biobanking and data protection law: can open consent be ‘informed’ under the forthcoming data protection regulation? | Statistical omics and more | Scoop.it
This article focuses on whether a certain form of consent used by biobanks – open consent – is compatible with the Proposed Data Protection Regulation. In an open consent procedure, the biobank requests consent once from the data subject for all future research uses of genetic material and data. However, as biobanks process personal data, they must comply with data protection law. Data protection law is currently undergoing reform. The Proposed Data Protection Regulation is the culmination of this reform and, if voted into law, will constitute a new legal framework for biobanking. The Regulation puts strict conditions on consent – in particular relating to information which must be given to the data subject. It seems clear that open consent cannot meet these requirements.
Yurii Aulchenko's insight:

Open consent is probably one of the best - from the viewpoint of researcher - types of consent, and is used by many biobanks. It seems that this may come in odds with the proposed EU Data Protection Regulation. Scary. Life is so difficult!

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