scholarly journals Deconstruction of Vulnerability to Complex Diseases: Enhanced Effect Sizes and Power of Intermediate Phenotypes

2007 ◽  
Vol 7 ◽  
pp. 124-130 ◽  
Author(s):  
David Goldman ◽  
Francesca Ducci
Keyword(s):  
Effect Size ◽  
Complex Disease ◽  
Complex Diseases ◽  
Effect Sizes ◽  
Complex Disorders ◽  
Large Samples ◽  
Intermediate Phenotypes ◽  
Genome Association ◽  
Whole Genome Association ◽  
Disease Loci

The deconstruction of vulnerability to complex disease with the help of intermediate phenotypes, including the heritable and disease-associated endophenotypes, is a legacy of Henri Begleiter. Systematic searches for genes influencing complex disorders, including bipolar disorder, have recently been completed using whole genome association (WGA), identifying a series of validated loci. Using this information, it is possible to compare effect sizes of disease loci discovered in very large samples to the effect sizes of replicated functional loci determining intermediate phenotypes that are of essential interest in psychiatric disorders. It is shown that the genes influencing intermediate phenotypes tend to have a larger effect size. Furthermore, the WGA results reveal that the number of loci of large effect size for complex diseases is limited, and yet multiple functional loci have already been identified for intermediate phenotypes relevant to psychiatric diseases, and without the benefit of WGA.

Mapping complex disease loci in whole-genome association studies

Nature ◽  
2004 ◽  
Vol 429 (6990) ◽  
pp. 446-452 ◽  
Author(s):  
Christopher S. Carlson ◽  
Michael A. Eberle ◽  
Leonid Kruglyak ◽  
Deborah A. Nickerson
Keyword(s):  
Complex Disease ◽  
Association Studies ◽  
Whole Genome ◽  
Genome Association ◽  
Whole Genome Association ◽  
Disease Loci

The Relationship Between Sample Sizes and Effect Sizes in Systematic Reviews in Education

2009 ◽  
Vol 31 (4) ◽  
pp. 500-506 ◽  
Author(s):  
Robert Slavin ◽  
Dewi Smith
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Secondary Mathematics ◽  
Significant Negative Correlation ◽  
Small Sample ◽  
Effect Sizes ◽  
Sample Sizes ◽  
Large Samples ◽  
Small Sample Sizes ◽  
The Relationship

Research in fields other than education has found that studies with small sample sizes tend to have larger effect sizes than those with large samples. This article examines the relationship between sample size and effect size in education. It analyzes data from 185 studies of elementary and secondary mathematics programs that met the standards of the Best Evidence Encyclopedia. As predicted, there was a significant negative correlation between sample size and effect size. The differences in effect sizes between small and large experiments were much greater than those between randomized and matched experiments. Explanations for the effects of sample size on effect size are discussed.

(iii) Whole-genome association studies of complex diseases

2008 ◽  
Vol 22 (4) ◽  
pp. 251-258 ◽  
Author(s):  
Pak C. Sham ◽  
Stacey S. Cherny ◽  
Patrick Y.P. Kao ◽  
You-Qiang Song ◽  
Danny Chan ◽  
...  
Keyword(s):  
Association Studies ◽  
Complex Diseases ◽  
Whole Genome ◽  
Genome Association ◽  
Whole Genome Association

scholarly journals Junction-skipping regulation in complex disease

2019 ◽  
Author(s):  
Ruize Liu ◽  
Juha Karjalainen ◽  
Andrea Byrnes ◽  
Beryl B. Cummings ◽  
Padhraig Gormley ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Complex Disease ◽  
Single Gene ◽  
Complex Diseases ◽  
Mrna Isoforms ◽  
Complex Disorders ◽  
Genomic Variants ◽  
Disease Associations ◽  
Human Disorders ◽  
Credible Set

AbstractMultiple mRNA isoforms can be generated from a single gene locus through alternative splicing. Abnormality in alternative splicing has been linked to many human disorders. Here using RNA-seq data from 48 tissues from GTEx v7 release and summary statistics from GWAS of complex diseases and traits, we present a study to identify genomic variants regulating junction-skipping with the goal to understand their contribution to complex diseases and traits. For each tissue, we found 48 - 575 junction-skipping events regulated by genomic variants. We performed fine-mapping on both the junction-skipping association and 23 complex disease and trait associations and mapped them to 95% credible sets. We found 13 - 279 junction-skipping regulations were mapped to a credible set with ≤5 variants. On the genome-wide scale, we noted a clear disease-tissue specificity. Results from this approach provided critical insights into the functional mechanism of the genetic disease associations and contributed to our understanding of the genetic architecture of human complex disorders.

Clinically Meaningful Change

Methodology ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 97-105
Author(s):  
Rodrigo Ferrer ◽  
Antonio Pardo
Keyword(s):  
Effect Size ◽  
False Negative ◽  
False Negative Rate ◽  
Point Of View ◽  
Skewed Distribution ◽  
Effect Sizes ◽  
False Negatives ◽  
Large Size ◽  
Before And After ◽  
Post Test

Abstract. In a recent paper, Ferrer and Pardo (2014) tested several distribution-based methods designed to assess when test scores obtained before and after an intervention reflect a statistically reliable change. However, we still do not know how these methods perform from the point of view of false negatives. For this purpose, we have simulated change scenarios (different effect sizes in a pre-post-test design) with distributions of different shapes and with different sample sizes. For each simulated scenario, we generated 1,000 samples. In each sample, we recorded the false-negative rate of the five distribution-based methods with the best performance from the point of view of the false positives. Our results have revealed unacceptable rates of false negatives even with effects of very large size, starting from 31.8% in an optimistic scenario (effect size of 2.0 and a normal distribution) to 99.9% in the worst scenario (effect size of 0.2 and a highly skewed distribution). Therefore, our results suggest that the widely used distribution-based methods must be applied with caution in a clinical context, because they need huge effect sizes to detect a true change. However, we made some considerations regarding the effect size and the cut-off points commonly used which allow us to be more precise in our estimates.

scholarly journals Multiple Sclerosis Biomarker Discoveries by Proteomics and Metabolomics Approaches

2021 ◽  
Vol 16 ◽  
pp. 117727192110133
Author(s):  
Ameneh Jafari ◽  
Amirhesam Babajani ◽  
Mostafa Rezaei-Tavirani
Keyword(s):  
Multiple Sclerosis ◽  
Complex Disease ◽  
Biomarker Discovery ◽  
Response To Treatment ◽  
Inflammatory Disorder ◽  
Protein Marker ◽  
Complex Disorders ◽  
New Information ◽  
The Central Nervous System ◽  
Monitoring Treatment

Multiple sclerosis (MS) is an autoimmune inflammatory disorder of the central nervous system (CNS) resulting in demyelination and axonal loss in the brain and spinal cord. The precise pathogenesis and etiology of this complex disease are still a mystery. Despite many studies that have been aimed to identify biomarkers, no protein marker has yet been approved for MS. There is urgently needed for biomarkers, which could clarify pathology, monitor disease progression, response to treatment, and prognosis in MS. Proteomics and metabolomics analysis are powerful tools to identify putative and novel candidate biomarkers. Different human compartments analysis using proteomics, metabolomics, and bioinformatics approaches has generated new information for further clarification of MS pathology, elucidating the mechanisms of the disease, finding new targets, and monitoring treatment response. Overall, omics approaches can develop different therapeutic and diagnostic aspects of complex disorders such as multiple sclerosis, from biomarker discovery to personalized medicine.

scholarly journals Neuroinflammation as a Common Denominator of Complex Diseases (Cancer, Diabetes Type 2, and Neuropsychiatric Disorders)

2021 ◽  
Vol 22 (11) ◽  
pp. 6138
Author(s):  
Serena Asslih ◽  
Odeya Damri ◽  
Galila Agam
Keyword(s):  
Cell Activation ◽  
Neuronal Degeneration ◽  
Complex Diseases ◽  
Diabetes Type 2 ◽  
Neuropsychiatric Disorders ◽  
Diabetes Type ◽  
Common Denominator ◽  
Cancer Type ◽  
Complex Disorders

The term neuroinflammation refers to inflammation of the nervous tissue, in general, and in the central nervous system (CNS), in particular. It is a driver of neurotoxicity, it is detrimental, and implies that glial cell activation happens prior to neuronal degeneration and, possibly, even causes it. The inflammation-like glial responses may be initiated in response to a variety of cues such as infection, traumatic brain injury, toxic metabolites, or autoimmunity. The inflammatory response of activated microglia engages the immune system and initiates tissue repair. Through translational research the role played by neuroinflammation has been acknowledged in different disease entities. Intriguingly, these entities include both those directly related to the CNS (commonly designated neuropsychiatric disorders) and those not directly related to the CNS (e.g., cancer and diabetes type 2). Interestingly, all the above-mentioned entities belong to the same group of “complex disorders”. This review aims to summarize cumulated data supporting the hypothesis that neuroinflammation is a common denominator of a wide variety of complex diseases. We will concentrate on cancer, type 2 diabetes (T2DM), and neuropsychiatric disorders (focusing on mood disorders).

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