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Statistical Human Genetics

Overview of attention for book
Cover of 'Statistical Human Genetics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Statistical Genetic Terminology
  3. Altmetric Badge
    Chapter 2 Identification of Genotype Errors
  4. Altmetric Badge
    Chapter 3 Detecting Pedigree Relationship Errors
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    Chapter 4 Identifying Cryptic Relationships
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    Chapter 5 Estimating Allele Frequencies
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    Chapter 6 Testing Departure from Hardy-Weinberg Proportions
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    Chapter 7 Estimating Disequilibrium Coefficients
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    Chapter 8 Detecting Familial Aggregation
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    Chapter 9 Estimating Heritability from Twin Studies
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    Chapter 10 Estimating Heritability from Nuclear Family and Pedigree Data
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    Chapter 11 Correcting for Ascertainment
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    Chapter 12 Segregation Analysis Using the Unified Model
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    Chapter 13 Design Considerations for Genetic Linkage and Association Studies
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    Chapter 14 Model-Based Linkage Analysis of a Quantitative Trait
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    Chapter 15 Model-Based Linkage Analysis of a Binary Trait
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    Chapter 16 Model-Free Linkage Analysis of a Quantitative Trait
  18. Altmetric Badge
    Chapter 17 Model-Free Linkage Analysis of a Binary Trait
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    Chapter 18 Single Marker Association Analysis for Unrelated Samples
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    Chapter 19 Single Marker Family-Based Association Analysis Conditional on Parental Information
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    Chapter 20 Single Marker Family-Based Association Analysis Not Conditional on Parental Information
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    Chapter 21 Calibrating Population Stratification in Association Analysis
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    Chapter 22 Cross-Phenotype Association Analysis Using Summary Statistics from GWAS
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    Chapter 23 Haplotype Inference
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    Chapter 24 Multi-SNP Haplotype Analysis Methods for Association Analysis
  26. Altmetric Badge
    Chapter 25 The Analysis of Ethnic Mixtures
  27. Altmetric Badge
    Chapter 26 Detecting Multiethnic Rare Variants
  28. Altmetric Badge
    Chapter 27 Identifying Gene Interaction Networks
  29. Altmetric Badge
    Chapter 28 Structural Equation Modeling
  30. Altmetric Badge
    Chapter 29 Mendelian Randomization
  31. Altmetric Badge
    Chapter 30 Preprocessing and Quality Control for Whole-Genome Sequences from the Illumina HiSeq X Platform
  32. Altmetric Badge
    Chapter 31 Processing and Analyzing Human Microbiome Data
Attention for Chapter 29: Mendelian Randomization
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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Chapter title
Mendelian Randomization
Chapter number 29
Book title
Statistical Human Genetics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-7274-6_29
Pubmed ID
Book ISBNs
978-1-4939-7273-9, 978-1-4939-7274-6

Sandeep Grover, Fabiola Del Greco M., Catherine M. Stein, Andreas Ziegler, Grover, Sandeep, Del Greco M., Fabiola, Stein, Catherine M., Ziegler, Andreas


Confounding and reverse causality have prevented us from drawing meaningful clinical interpretation even in well-powered observational studies. Confounding may be attributed to our inability to randomize the exposure variable in observational studies. Mendelian randomization (MR) is one approach to overcome confounding. It utilizes one or more genetic polymorphisms as a proxy for the exposure variable of interest. Polymorphisms are randomly distributed in a population, they are static throughout an individual's lifetime, and may thus help in inferring directionality in exposure-outcome associations. Genome-wide association studies (GWAS) or meta-analyses of GWAS are characterized by large sample sizes and the availability of many single nucleotide polymorphisms (SNPs), making GWAS-based MR an attractive approach. GWAS-based MR comes with specific challenges, including multiple causality. Despite shortcomings, it still remains one of the most powerful techniques for inferring causality.With MR still an evolving concept with complex statistical challenges, the literature is relatively scarce in terms of providing working examples incorporating real datasets. In this chapter, we provide a step-by-step guide for causal inference based on the principles of MR with a real dataset using both individual and summary data from unrelated individuals. We suggest best possible practices and give recommendations based on the current literature.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 234 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 <1%
Canada 1 <1%
Unknown 232 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 22%
Researcher 37 16%
Student > Master 23 10%
Student > Bachelor 17 7%
Professor > Associate Professor 14 6%
Other 47 20%
Unknown 44 19%
Readers by discipline Count As %
Medicine and Dentistry 70 30%
Biochemistry, Genetics and Molecular Biology 50 21%
Agricultural and Biological Sciences 19 8%
Nursing and Health Professions 5 2%
Neuroscience 5 2%
Other 26 11%
Unknown 59 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 July 2021.
All research outputs
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Outputs from Methods in molecular biology
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Outputs of similar age
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Outputs of similar age from Methods in molecular biology
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Altmetric has tracked 23,039,416 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,192 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 421,400 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.