Special Issue "Complex Genetic Loci"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: 31 May 2018

Special Issue Editor

Guest Editor
Dr. Santiago Rodriguez

MRC Integrative Epidemiology Unit,School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove Bristol BS8 2BN, UK
Website | E-Mail
Interests: Human Genetics; Genetic Epidemiology; Population Genetics; Molecular Genetics; Complex genetic loci; Complex human traits

Special Issue Information

Dear Colleagues,

The study of genetic variation is key to understand genes, genetics and genomics. In previous decades, genetic analyses have been successful in many areas. One of these areas is the understanding of the influence of genetics on “complex traits”, i.e., phenotypes that are not a direct reflection of genotypes. Current genetic studies are mainly focussed on binary genetic variants such as single nucleotide polymorphisms (SNPs) and mutations. However, genomes contain many other types of genetic variants. The use of these “complex loci” in genetics is currently underexplored compared to binary genetic variants.

In this Special Issue, we welcome reviews, new methodologies and original articles covering aspects of complex loci relevant to genes, genetics and genomics. These include, but are not limited to, copy number variants (telomere length variation, mitochondrial DNA copy number, etc.), repeat polymorphisms (microsatellites, minisatellites, etc.), insertion-deletion variants, chromosomal abnormalities (irregular karyotype, structural modifications of chromosomes, etc.), transposable elements (LINEs, SINES, etc.). This Special Issue has a special emphasis on the relation between complex loci and complex traits, although studies relating complex loci to Mendelian traits are also welcome.

Dr. Santiago Rodriguez
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Genetic variation

  • Complex loci

  • Copy number variants

  • Telomere length

  • Microsatellites

  • Minisatellites

  • Transposable elements

  • Chromosomal abnormalities

  • Complex traits

  • Mendelian traits

Published Papers (6 papers)

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Research

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Open AccessArticle MHC-Dependent Mate Selection within 872 Spousal Pairs of European Ancestry from the Health and Retirement Study
Genes 2018, 9(1), 53; doi:10.3390/genes9010053
Received: 15 November 2017 / Revised: 9 January 2018 / Accepted: 15 January 2018 / Published: 22 January 2018
PDF Full-text (2790 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Disassortative mating refers to the phenomenon in which individuals with dissimilar genotypes and/or phenotypes mate with one another more frequently than would be expected by chance. Although the existence of disassortative mating is well established in plant and animal species, the only documented
[...] Read more.
Disassortative mating refers to the phenomenon in which individuals with dissimilar genotypes and/or phenotypes mate with one another more frequently than would be expected by chance. Although the existence of disassortative mating is well established in plant and animal species, the only documented example of negative assortment in humans involves dissimilarity at the major histocompatibility complex (MHC) locus. Previous studies investigating mating patterns at the MHC have been hampered by limited sample size and contradictory findings. Inspired by the sparse and conflicting evidence, we investigated the role that the MHC region played in human mate selection using genome-wide association data from 872 European American spouses from the Health and Retirement Study (HRS). First, we treated the MHC region as a whole, and investigated genomic similarity between spouses using three levels of genomic variation: single-nucleotide polymorphisms (SNPs), classical human leukocyte antigen (HLA) alleles (both four-digit and two-digit classifications), and amino acid polymorphisms. The extent of MHC dissimilarity between spouses was assessed using a permutation approach. Second, we investigated fine scale mating patterns by testing for deviations from random mating at individual SNPs, HLA genes, and amino acids in HLA molecules. Third, we assessed how extreme the spousal relatedness at the MHC region was compared to the rest of the genome, to distinguish the MHC-specific effects from genome-wide effects. We show that neither the MHC region, nor any single SNPs, classic HLA alleles, or amino acid polymorphisms within the MHC region, were significantly dissimilar between spouses relative to non-spouse pairs. However, dissimilarity in the MHC region was extreme relative to the rest of genome for both spousal and non-spouse pairs. Despite the long-standing controversy, our analyses did not support a significant role of MHC dissimilarity in human mate choice. Full article
(This article belongs to the Special Issue Complex Genetic Loci)
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Open AccessArticle Using Y-Chromosomal Haplogroups in Genetic Association Studies and Suggested Implications
Genes 2018, 9(1), 45; doi:10.3390/genes9010045
Received: 23 October 2017 / Revised: 16 January 2018 / Accepted: 16 January 2018 / Published: 22 January 2018
PDF Full-text (1478 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Y-chromosomal (Y-DNA) haplogroups are more widely used in population genetics than in genetic epidemiology, although associations between Y-DNA haplogroups and several traits, including cardiometabolic traits, have been reported. In apparently homogeneous populations defined by principal component analyses, there is still Y-DNA haplogroup variation
[...] Read more.
Y-chromosomal (Y-DNA) haplogroups are more widely used in population genetics than in genetic epidemiology, although associations between Y-DNA haplogroups and several traits, including cardiometabolic traits, have been reported. In apparently homogeneous populations defined by principal component analyses, there is still Y-DNA haplogroup variation which will result from population history. Therefore, hidden stratification and/or differential phenotypic effects by Y-DNA haplogroups could exist. To test this, we hypothesised that stratifying individuals according to their Y-DNA haplogroups before testing for associations between autosomal single nucleotide polymorphisms (SNPs) and phenotypes will yield difference in association. For proof of concept, we derived Y-DNA haplogroups from 6537 males from two epidemiological cohorts, Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 5080; 816 Y-DNA SNPs) and the 1958 Birth Cohort (n = 1457; 1849 Y-DNA SNPs), and studied the robust associations between 32 SNPs and body mass index (BMI), including SNPs in or near Fat Mass and Obesity-associated protein (FTO) which yield the strongest effects. Overall, no association was replicated in both cohorts when Y-DNA haplogroups were considered and this suggests that, for BMI at least, there is little evidence of differences in phenotype or SNP association by Y-DNA structure. Further studies using other traits, phenome-wide association studies (PheWAS), other haplogroups and/or autosomal SNPs are required to test the generalisability and utility of this approach. Full article
(This article belongs to the Special Issue Complex Genetic Loci)
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Open AccessArticle Copy Number Variation in SOX6 Contributes to Chicken Muscle Development
Genes 2018, 9(1), 42; doi:10.3390/genes9010042
Received: 31 October 2017 / Revised: 11 January 2018 / Accepted: 12 January 2018 / Published: 17 January 2018
PDF Full-text (2273 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Copy number variations (CNVs), which cover many functional genes, are associated with complex diseases, phenotypic diversity and traits that are economically important to raising chickens. The sex-determining region Y-box 6 (Sox6) plays a key role in fast-twitch muscle fiber differentiation of
[...] Read more.
Copy number variations (CNVs), which cover many functional genes, are associated with complex diseases, phenotypic diversity and traits that are economically important to raising chickens. The sex-determining region Y-box 6 (Sox6) plays a key role in fast-twitch muscle fiber differentiation of zebrafish and mice, but it is still unknown whether SOX6 plays a role in chicken skeletal muscle development. We identified two copy number polymorphisms (CNPs) which were significantly related to different traits on the genome level in chickens by AccuCopy® and CNVplex® analyses. Notably, five white recessive rock (CN = 1, CN = 3) variant individuals and two Xinghua (CN = 3) variant individuals contain a CNP13 (chromosome5: 10,500,294–10,675,531) which overlaps with SOX6. There is a disordered region in SOX6 proteins 265–579 aa coded by a partial CNV overlapping region. A quantitative real-time polymerase chain reaction showed that the expression level of SOX6 mRNA was positively associated with CNV and highly expressed during the skeletal muscle cell differentiation in chickens. After the knockdown of the SOX6, the expression levels of IGFIR1, MYF6, SOX9, SHOX and CCND1 were significantly down-regulated. All of them directly linked to muscle development. These results suggest that the number of CNVs in the CNP13 is positively associated with the expression level of SOX6, which promotes the proliferation and differentiation of skeletal muscle cells by up-regulating the expression levels of the muscle-growth-related genes in chickens as in other animal species. Full article
(This article belongs to the Special Issue Complex Genetic Loci)
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Open AccessArticle Identification of the Ovine Keratin-Associated Protein 26-1 Gene and Its Association with Variation in Wool Traits
Genes 2017, 8(9), 225; doi:10.3390/genes8090225
Received: 11 July 2017 / Revised: 1 September 2017 / Accepted: 6 September 2017 / Published: 13 September 2017
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Abstract
Keratin-associated proteins (KAPs) are structural components of wool and hair fibres, and are believed to play a role in defining the physico-mechanical properties of the wool fibre. In this study, the putative ovine homologue of the human KAP26-1 gene (KRTAP26-1) was
[...] Read more.
Keratin-associated proteins (KAPs) are structural components of wool and hair fibres, and are believed to play a role in defining the physico-mechanical properties of the wool fibre. In this study, the putative ovine homologue of the human KAP26-1 gene (KRTAP26-1) was sequenced and four variants (named A–D) were identified. The sequences shared some identity with each other and with other KRTAPs, but they had the greatest similarity with the human KRTAP26-1 sequence. This suggests they represent different variants of ovine KRTAP26-1. The association of these KRTAP26-1 variants with wool traits was investigated in the 383 Merino-Southdown cross sheep. The presence of B was associated (p < 0.05) with an increase in mean fibre diameter (MFD), mean fibre curvature, and prickle factor (PF). The presence of C was found to be associated (p < 0.05) with an increase in wool yield (Yield) and mean staple length (MSL), and a decrease in MFD, fibre diameter standard deviation (FDSD), and PF. The results suggest that sheep with C have, on average, higher wool quality. These results may be useful in the future development of breeding programs based on decreasing wool MFD and FDSD, or on increasing wool MSL. Full article
(This article belongs to the Special Issue Complex Genetic Loci)
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Review

Jump to: Research

Open AccessReview Another Round of “Clue” to Uncover the Mystery of Complex Traits
Genes 2018, 9(2), 61; doi:10.3390/genes9020061
Received: 1 November 2017 / Revised: 19 December 2017 / Accepted: 15 January 2018 / Published: 25 January 2018
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Abstract
A plethora of genetic association analyses have identified several genetic risk loci. Technological and statistical advancements have now led to the identification of not only common genetic variants, but also low-frequency variants, structural variants, and environmental factors, as well as multi-omics variations that
[...] Read more.
A plethora of genetic association analyses have identified several genetic risk loci. Technological and statistical advancements have now led to the identification of not only common genetic variants, but also low-frequency variants, structural variants, and environmental factors, as well as multi-omics variations that affect the phenotypic variance of complex traits in a population, thus referred to as complex trait architecture. The concept of heritability, or the proportion of phenotypic variance due to genetic inheritance, has been studied for several decades, but its application is mainly in addressing the narrow sense heritability (or additive genetic component) from Genome-Wide Association Studies (GWAS). In this commentary, we reflect on our perspective on the complexity of understanding heritability for human traits in comparison to model organisms, highlighting another round of clues beyond GWAS and an alternative approach, investigating these clues comprehensively to help in elucidating the genetic architecture of complex traits. Full article
(This article belongs to the Special Issue Complex Genetic Loci)
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Open AccessReview A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis
Genes 2017, 8(12), 347; doi:10.3390/genes8120347
Received: 27 September 2017 / Revised: 16 November 2017 / Accepted: 21 November 2017 / Published: 27 November 2017
PDF Full-text (896 KB) | HTML Full-text | XML Full-text
Abstract
Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for
[...] Read more.
Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example. Full article
(This article belongs to the Special Issue Complex Genetic Loci)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Tentative Title: Whole Exome Sequencing identifies new host genomic susceptibility factors in empyema caused by Streptococcus pneumoniae in children
Authors: 
Antonio Salas Ellacuriaga (University of Santiago de Compostela)

 

Tentative Title: Strategies for Identifying Putative Quantitative Trait Genes in Model Animals
Authors: Akira Ishikawa (Nagoya University, Japan)
Abstract:Large numbers of quantitative trait loci (QTLs) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty of identifying causal quantitative trait genes (QTGs) for typical QTLs with relatively small phenotypic effects. For QTG identification,a standard approachbased on DNA sequencinghas been long and widely used in combination with gene expression analysisin model animals.However, the standard approach doesnotalways allow the identification of a single candidate gene for a QTL of interest, because it is rare to narrow a target genomic region of the QTLdownto a very small region harboring only one gene. An alternativeapproach uses a combination of gene expression analysisandstatistical causal inference tests. This combined approach can dramatically reduce the number of candidate genes and can provide causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I provide overviews of the standard and combined approaches and discuss the usefulness of the combined approach using my successful report as an example.

 

Tentative Title: MHC-Dependent Mate Selection within the Health and Retirement Study
Authors: Zhen Qiao, Joseph Powell, David M. Evans
Abstract: Disassortative mating refers to the phenomenon in which individuals with dissimilar genotypes and/or phenotypes mate with one another more frequently than would be expected by chance. Although the existence of disassortative mating is well established in plant and animal species, the only documented example of negative assortment in humans involves dissimilarity at the Major Histocompatability (MHC) locus. Unfortunately previous studies investigating mating patterns at the MHC have been hampered by limited sample size and contradictory findings. Inspired by the sparse and conflicting evidence, we investigated the role that the MHC region played in human mate selection using genome-wide association data from 597 European American spouses from the Health and Retirement Study (HRS). First we treated the MHC region as a whole, and investigated genomic similarity between spouses using three levels of genomic variation: SNPs, classical HLA alleles (both 4-digit and 2-digit classifications), and amino acid polymorphisms. The extent of MHC dissimilarity between spouses was assessed using a permutation approach. Second, we investigated fine scale mating patterns by testing for deviations from random mating at individual SNPs, HLA genes, and amino acids in HLA molecules. Third, we assessed how extreme the spousal relatedness at the MHC region was compared to the rest of the genome, to distinguish the MHC-specific effects from genome-wide effects. The multi-locus relatedness analyses showed no significant difference in MHC relatedness between spouses and non-spouse pairs at either the level of SNPs (relatedness coefficient, R=0.013, one-sided p=0.638) or classical MHC alleles (R=-0.025, p=0.258); however, analyses based on HLA amino acid polymorphisms revealed stronger evidence of dissimilarity (R=-0.046, p=0.095). In terms of fine mapping, we did not find evidence of excess dissimilarity between spousal pairs at individual SNPs, classical HLA genes or amino acids after adjustment for multiple testing. However, the MHC dissimilarity among spouses was extreme relative to the rest of genome (i.e. only 1.6% of the randomly sampled genomic windows exhibited more extreme dissimilarity than the MHC region), but was as extreme in opposite-sex non-spousal pairs, and therefore the dissimilarity could not be attributed to mate selection. Despite the long-standing controversy, our analyses did not support a significant role of MHC dissimilarity in human mate choice.
 
Tentative Title: Another Round of CLUE to Uncover the Mystery of Complex Trait Heritability
Authors: Shefali Setia Verma, Marylyn D. Ritchie
Abstract: Several genetic loci have been identified for common complex diseases through plethora of statistical and machine learning approaches. Technological and statistical advancements have now led to the identification of not only common genetic variations but also low frequency genetic variants, structural variants, environmentalal factors, as well as multi-omic variations that affect the phenotypic variance among population, thus referred to as heritability. The concept of heritability of complex traits has been studied for many years, but its application is mainly in addressing narrow sense heritability (or additive heritability) from Genome-Wide Association Studies. In this review, we reflect our perspective on the complexity of understanding heritability for human traits in comparison to model organisms, highlighting another round of clues that could help in elucidating the genetic architecture of complex traits.
 
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