We assembled SNP array data from 29,589 unrelated people and 222 nuclear families genotyped at 490,000–910,000 SNPs from the C andidate Gene A ssociation R esource (CARe), studies at the Children’s Hospital of Philadelphia (CHOP), the African American Breast Cancer Consortium, the African American Prostate Cancer Consortium and the African American Lung Cancer Consortium. To build a recombination map, we used HAPMIX to localize candidate crossover positions15 (link), and implemented a Markov Chain Monte Carlo (MCMC) that used the probability distributions for the positions of the filtered crossovers to infer recombination rates for each of 1.3 million inter-SNP intervals. We also implemented a second MCMC that models each individual’s set of crossovers as a mixture of a Shared (S) Map similar to the European deCODE Map and an African-enriched (AE) Map, and then assigns each individual an “AE phenotype” corresponding to the proportion of their newly detected crossovers assigned to the AE Map. We imputed genotypes at up to three million HapMap2 SNPs8 using MaCH26 (link), and then tested each of these SNPs for association with the AE phenotype and other recombination-related phenotypes. We identified 2,454 candidate African-enriched hotspots with increased recombination rates in the YRI vs. CEU maps, and in the AE vs. S maps, and searched for motifs enriched at these loci, thus identifying a degenerate 17-bp motif. To study the structure of PRDM9, we measured the length of the PRDM9 zinc finger array and genotyped rs6889665 in YRI, CEU and the CARe nuclear families; we also carried out imputation based on 1000 Genomes Project short read data10 (link) to infer the alleles individuals carry, among 29 previously characterized in a sequencing study of PRDM99 (link).
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Living Beings
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Family Group
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Nuclear Family
Nuclear Family
The nuclear family, a core social unit, consists of a pair of adults and their children.
This family structure is a fundamental component of human societies, providing a nurturing environment for the upbringing and development of offspring.
Nuclear families may vary in composition, such as single-parent or blended households, but share the common elements of close emotional and economic ties between the parental figures and their children.
Reseach on the nuclear family can provide insights into social dynamics, child rearing practices, and the evolution of familial structures across cultures and historical contexts.
Studying the nuclear family is an important area of sociological, anthropological, and psychological inquiry.
This family structure is a fundamental component of human societies, providing a nurturing environment for the upbringing and development of offspring.
Nuclear families may vary in composition, such as single-parent or blended households, but share the common elements of close emotional and economic ties between the parental figures and their children.
Reseach on the nuclear family can provide insights into social dynamics, child rearing practices, and the evolution of familial structures across cultures and historical contexts.
Studying the nuclear family is an important area of sociological, anthropological, and psychological inquiry.
Most cited protocols related to «Nuclear Family»
African American
Alleles
Europeans
Genes
Genome
Lung Cancer
Malignant Neoplasm of Breast
Microtubule-Associated Proteins
Negroid Races
Phenotype
Prostate Cancer
Recombination, Genetic
Single Nucleotide Polymorphism
Zinc Fingers
Alcoholic Intoxication, Chronic
Cocaine
DNA Replication
Drug Dependence
Ethanol
Ethics Committees, Research
Genome-Wide Association Study
N-nitrosoiminodiacetic acid
Opioids
To demonstrate the utility of our recombination detection method we conducted association testing between genetic variants in the PRDM9 region (chr5:23007723–24028706) and the “hotspot usage” phenotype described in Coop et al. (2008) [39] (link). Substantial association in this region was also found in Kong et al. (2010) [12] (link) and Hinch (2011) [41] (link). We calculated the same phenotype as Coop et al. (2008), the proportion of crossover events, , that occur in a recombination hotspot for individual (the parent). This value was corrected for the probability that events occur in one of these hotspot regions by chance via simulation.
The accuracy with which is measured increases with the number of crossovers observed for that parent, hence parents with more observed crossovers should be given higher weighting (large nuclear families are advantageous in this situation). We weighted individuals by creating pseudo-counts of hotspot events where is the number of crossover events observed for parent . We then fit a standard Binomial Generalised Linear Model (GLM) with as the response and the genetic dosage at each SNP as the covariate. We then performed a likelihood ratio test between this model of association and the ‘null’ model where no genetic variant is included. Variants were imputed from the 1000 Genomes March 2012 reference panel and filtered such that all variants had and in all cohorts.
The use of the Binomial GLM allows us to leverage parents who are part of typically uninformative meioses, where it is unlikely the majority of crossover events were detected. Such individuals are simply down weighted in our association testing.
The accuracy with which is measured increases with the number of crossovers observed for that parent, hence parents with more observed crossovers should be given higher weighting (large nuclear families are advantageous in this situation). We weighted individuals by creating pseudo-counts of hotspot events where is the number of crossover events observed for parent . We then fit a standard Binomial Generalised Linear Model (GLM) with as the response and the genetic dosage at each SNP as the covariate. We then performed a likelihood ratio test between this model of association and the ‘null’ model where no genetic variant is included. Variants were imputed from the 1000 Genomes March 2012 reference panel and filtered such that all variants had and in all cohorts.
The use of the Binomial GLM allows us to leverage parents who are part of typically uninformative meioses, where it is unlikely the majority of crossover events were detected. Such individuals are simply down weighted in our association testing.
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Genetic Diversity
Genome
Meiosis
Parent
Phenotype
Recombination, Genetic
Individuals in pedigrees obviously share large amounts of their genome IBD. Algorithms that have the ability to exploit IBD sharing in distantly related individuals may also work well on explicitly related individuals. Hence, we also evaluated the accuracy of SHAPEIT2, SLRP, HAPI-UR and Beagle applied to the full cohorts described here, with the full extended pedigrees included. We ran each of the methods using no information regarding relatedness of samples. We calculated SE for each method on the haplotypes of any individual in a pedigree larger than a mother-father-child pedigree, using the Merlin haplotypes as truth.
SHAPEIT2, Beagle and HAPI-UR all provide functionality to phase parent-child duos and mother-father-child trios, by constraining the possible haplotypes to those consistent with the transmitted and untransmitted haplotypes of each parent (the child having each of the parents' transmitted haplotypes). This approach will produce very accurate haplotypes although will return the recombined haplotypes for each parent, rather than the true parental haplotypes. Since only several recombinations occur per chromosome, this is not introducing a substantial amount of error in the context of pre-phasing/imputation but is obviously problematic for researchers wishing to study recombination.
Larger pedigrees could be divided into subsets of duos and trios but often there will exist no subdivision that allows all samples to exploit a parental relationship. For example, a family with two parents and two siblings may be divided into two duos, but partitioning a nuclear family with three children means at least one child will be phased without using parental information. There is no obvious optimum way to partition pedigrees of arbitrary size and structure. We investigated a simple method where we enumerate every possible partitioning of a pedigree into duos/trios and choose the partition that minimises the number of individuals that are not included in a duo/trio (many partitions often share the same minimum in which case one is picked at random). We applied this partitioning to the datasets and then ran Beagle (since it was the next most competitive method) taking the implied duo and trio information into account. We refer to this as the Beagle duo/trio method. Beagle was found to use substantially more memory in this setting (over 150 GB for the GPC cohort) which may be problematic for some researchers. This issue is noted in the Beagle manual and relates to missing data in parent-offspring duos and trios.
On duos and trios this method will agree perfectly with Merlin at sites that Merlin can phase. This introduces a possible confounding effect when using the Merlin haplotypes as the truth, as any errors in the Merlin haplotypes will not be detectable when compared to the Beagle duo/trio method. We show below using simulated data that Merlin is quite sensitive to genotyping error and that this does result in elevated switch errors. For this reason we only consider pedigrees that are more complex than a parent-child duo or father-mother-child trio when comparing methods. Larger pedigrees also give Merlin better ability to remove genotyping errors yielding more accurate validation haplotypes.
SHAPEIT2, Beagle and HAPI-UR all provide functionality to phase parent-child duos and mother-father-child trios, by constraining the possible haplotypes to those consistent with the transmitted and untransmitted haplotypes of each parent (the child having each of the parents' transmitted haplotypes). This approach will produce very accurate haplotypes although will return the recombined haplotypes for each parent, rather than the true parental haplotypes. Since only several recombinations occur per chromosome, this is not introducing a substantial amount of error in the context of pre-phasing/imputation but is obviously problematic for researchers wishing to study recombination.
Larger pedigrees could be divided into subsets of duos and trios but often there will exist no subdivision that allows all samples to exploit a parental relationship. For example, a family with two parents and two siblings may be divided into two duos, but partitioning a nuclear family with three children means at least one child will be phased without using parental information. There is no obvious optimum way to partition pedigrees of arbitrary size and structure. We investigated a simple method where we enumerate every possible partitioning of a pedigree into duos/trios and choose the partition that minimises the number of individuals that are not included in a duo/trio (many partitions often share the same minimum in which case one is picked at random). We applied this partitioning to the datasets and then ran Beagle (since it was the next most competitive method) taking the implied duo and trio information into account. We refer to this as the Beagle duo/trio method. Beagle was found to use substantially more memory in this setting (over 150 GB for the GPC cohort) which may be problematic for some researchers. This issue is noted in the Beagle manual and relates to missing data in parent-offspring duos and trios.
On duos and trios this method will agree perfectly with Merlin at sites that Merlin can phase. This introduces a possible confounding effect when using the Merlin haplotypes as the truth, as any errors in the Merlin haplotypes will not be detectable when compared to the Beagle duo/trio method. We show below using simulated data that Merlin is quite sensitive to genotyping error and that this does result in elevated switch errors. For this reason we only consider pedigrees that are more complex than a parent-child duo or father-mother-child trio when comparing methods. Larger pedigrees also give Merlin better ability to remove genotyping errors yielding more accurate validation haplotypes.
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Child
Chromosomes
Genome
Haplotypes
Memory
Mothers
nf2 Gene
Nuclear Family
Parent
Recombination, Genetic
Sibling
Small Leucine-Rich Proteoglycans
TRIO protein, human
Adolescent
Adult
Alcoholic Intoxication, Chronic
Birth Cohort
BLOOD
Care, Ambulatory
Childbirth
Cognition
Diagnosis
Disease Progression
Ethanol
Ethics Committees, Research
Europeans
Genes
Illicit Drugs
Inpatient
Lobe, Frontal
Mental Disorders
Motivation
Parent
Substance Use
Tobacco Products
Young Adult
Youth
Most recents protocols related to «Nuclear Family»
The cfDNA concentrations were determined as described by Neuberger et al. [22 (link)]. Ahead of the measurements, the linearity, limit of quantification, and limit of detection of the assay were determined. The qPCR assay amplifies DNA in unpurified plasma, which is diluted 1:10 in UltraPure DNase/RNase-Free H2O (Invitrogen, Waltham, MA), targeting a 90 bp fragment of human long interspersing nuclear elements (LINEs) of the repetitive L1PA2 family (5′-TGCCGCAATAAACATACGTG-3′ and 5′-GACCCAGCCATCCCATTAC-3′). Briefly, each sample was measured as a technical triplicate with 5 µL final volume containing 0.66 µL of 1:10 diluted plasma, 0.33 µL primer mix (140 nm final concentration of each primer) and 4 µL of qPCR mix with 0.6 U Velocity Polymerase (Bioline, London, UK), 1.2 × Hifi Buffer (Bioline, London, UK), 0.1 × SYBR Green (Sigma, St. Louis, MO, USA), and 0.3 mM dNTPs (Bioline, London, UK). The qPCR reaction was carried out using a CFX384 Bio-Rad (Bio-Rad, Munich, Germany) cycler with a two-step protocol. The cycling conditions were: initial heat activation at 98 °C for 2 min followed by 33 cycles of 95 °C for 10 s and 64 °C for 10 s with a subsequent melting curve from 70 to 95 °C with 0.5 °C increments for 10 s. The operator measuring the samples was blinded and did not know the allocation of the samples into the control or intervention group.
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Biological Assay
Buffers
Cell-Free DNA
Deoxyribonucleases
Homo sapiens
Nuclear Family
Oligonucleotide Primers
Plasma
Repetitive Region
Ribonucleases
SYBR Green I
Beginning in June 2020, we participated in an International Consortium encompassing research teams from ten countries with a wide range of socioeconomic and cultural realities. The project constituted a comparative cross-country enterprise to investigate how people in diverse contexts understood and responded to government guidelines around the COVID-19 pandemic and what impact these measures had on family life and individual subjectivities. Our team in Chile brought researchers from different disciplines in the social sciences1 . We designed a qualitative longitudinal study to explore how families and family members coped with the challenges posed by COVID-19 during the most critical months of the pandemic. From September 2020 to January 2021, we followed 38 families2 and gathered information on how lockdowns and other health and safety measures impacted their everyday life and how their family and personal relationships changed during this period.
We implemented adaptive strategies to address some of the most pressing challenges of conducting qualitative research during the pandemic (Table1 ). For selecting participants, we used a purposive sampling of families that could constitute information-rich cases to study changes in everyday life during the pandemic (Palinkas et al. 2015 (link)). Families had to have at least one school-age child and reside permanently in one of the regions covered by the study. We also ensured that selected cases represented a range of socioeconomic backgrounds—including households of lower-middle, middle, and upper-middle income3 —and family structures—including families from nuclear, extended, and single-parent households. The recruitment of participants worked through decentralized research teams based in six universities in northern, central, and southern Chile4 . Each team had local knowledge of their regions and vast experience conducting fieldwork in their communities. We contacted gatekeepers, people who belonged to the selected regions and had deep connections with their communities and neighborhoods, to establish contact with local families. Research assistants helped to verify that families met the selection criteria and maintained regular contact with family members throughout the project by phone and the Indeemo app.
5 . Once a family became part of the study, a mobile phone with internet data was provided for contact and participation through the Indeemo app. We used Indeemo as a non-intrusive methodological device for recording multimodal diaries of family trajectories during the pandemic. Considering the difficulties of accessing the field and building trust and rapport with families and individuals, we deployed different techniques that allowed us to connect with people and to gather information gradually, without much intrusion into their daily lives. These techniques included a quantitative questionnaire to draw a sociodemographic profile of each family, two open-ended family interviews to get a qualitative overview of their lives during the pandemic, and one in-depth interview with heads of households to get additional insights relative to their experiences. All these techniques allowed us to build a general understanding of the field and provided contextual information about families and their backgrounds and trajectories. Data production was also adapted to the specific conditions of the participants and subject to their time and technological needs6 .
We implemented adaptive strategies to address some of the most pressing challenges of conducting qualitative research during the pandemic (Table
Methodological Challenges and Adaptive Strategies
| Challenges | Adaptive Strategies |
|---|---|
| Contact with participants | Working through decentralized research teams with local knowledge, fieldwork experience in communities, and research assistants acting as gatekeepers. |
| Gaining access to the field | Adopting a multi-method approach and different techniques to gather data and continuously monitor families and participants. |
| Co-creating a field | Introducing the Indeemo mobile app as an interface for participating and submitting posts within specific tasks. |
| Flexible conditions for participation and fieldwork | Implement a non-intrusive approach that relies heavily on multimodal diaries tailored to the times and requirements of the participants. |
Acclimatization
Child
COVID 19
Family Member
Family Structure
Head of Household
Households
Medical Devices
Menopause
Multimodal Imaging
Pandemics
Safety
Single Parent
Virus
The Stanford Asia–Pacific Program for Hypertension and Insulin Resistance (SAPPHIRe) is a collaborative family study sponsored by the Family Blood Pressure Program of the National Heart, Lung and Blood Institute of the National Institutes of Health. The study was conducted to identify the genetic determinant of hypertension and insulin resistance in participants of Chinese ancestry. The study collected sibling pairs of over 1144 participants from 360 nuclear families who were either concordant or discordant for high blood pressure as previously described [24 (link)]. The definition of high blood pressure was systolic blood pressure > 140 mm Hg and diastolic blood pressure > 90 mm Hg or with two medications for hypertension. Blood pressure in the bottom 30% of age- and sex-adjusted blood pressure distributions was defined as low-normal blood pressure. Individuals with heart, liver, or kidney diseases or chronic diseases such as diabetes or cancer were excluded. The study was approved by the Institutional Review Boards/Research Ethics Review Committee including National Taiwan University Hospital, National Health Research Institutes, Taichung Veterans General Hospital, Taipei Veterans General Hospital, and Tri-Service General Hospital. All participants signed informed consent. All procedures were conducted according to principles outlined in the Declaration of Helsinki. The detailed analysis is shown in Additional file 1 .
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BLOOD
Blood Pressure
Chinese
Diabetes Mellitus
Disease, Chronic
Ethics Committees, Research
Heart
High Blood Pressures
Insulin Resistance
Kidney Diseases
Liver
Lung
Malignant Neoplasms
Pharmaceutical Preparations
Pressure, Diastolic
Systolic Pressure
For this analysis, only data from the last episode of disease syndrome was included if more than one episode had been reported. Using principal component analysis, specifically using the factor effects derived from the first component of household goods, house construction material, source of water supply, source of cooking fuel and sanitation facility [20 (link), 21 (link)], we generated the household wealth index as proxy for socioeconomic status (SES) stratified by county. The wealth index was categorized into quintiles; wealthy households in this study were defined as those whose wealth index was in the fourth or fifth quintile.
All analyses were conducted using Stata 15.1 software (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC). Multivariable survey logistic regression methods were used in the analyses to identify factors that were independently associated with healthcare seeking behaviors for respiratory illness while accounting for the survey design. Variables that were included in each of the multivariable models assessed were site (county where data were collected), sex, age, SES, and level of education of the head of the household. Other variables that were assessed included religion, household size, childbirth order, and family member status (i.e., part of the nuclear family or other relative).
All analyses were conducted using Stata 15.1 software (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC). Multivariable survey logistic regression methods were used in the analyses to identify factors that were independently associated with healthcare seeking behaviors for respiratory illness while accounting for the survey design. Variables that were included in each of the multivariable models assessed were site (county where data were collected), sex, age, SES, and level of education of the head of the household. Other variables that were assessed included religion, household size, childbirth order, and family member status (i.e., part of the nuclear family or other relative).
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Childbirth
Family Member
Head of Household
Households
Nuclear Family
Respiratory Rate
Syndrome
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Child
Family Structure
Mental Disorders
Mothers
Nuclear Family
Only Child
Parent
Self-Evaluation
Sons
Stem, Plant
Wife
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More about "Nuclear Family"
The nuclear family, a fundamental social unit, consists of a pair of adults and their children.
This family structure is a core component of human societies, providing a nurturing environment for the upbringing and development of offspring.
Nuclear families may vary in composition, such as single-parent or blended households, but share the common elements of close emotional and economic ties between the parental figures and their children.
Research on the nuclear family can provide valuable insights into social dynamics, child-rearing practices, and the evolution of familial structures across cultures and historical contexts.
This area of study is an important focus of sociological, anthropological, and psychological inquiry.
Advances in genetic and genomic technologies, such as the HiSeq 2500, HiSeq 2000, Genome Analyzer IIx, and NanoDrop spectrophotometer, have enabled researchers to explore the biological and genetic factors that influence family structures and dynamics.
Additionally, tools like the Illumina sample prep kit, CanineHD BeadChip, HumanOmni1-Quad BeadChip, and PAXgene Blood RNA Kit Handbook have facilitated the analysis of genetic and epigenetic data in the context of nuclear family studies.
By leveraging these cutting-edge technologies and methodologies, researchers can gain a deeper understanding of the complex interplay between biological, social, and cultural factors that shape the nuclear family.
This knowledge can inform policies, interventions, and support systems aimed at strengthening and supporting this core social unit, ultimately benefiting individuals, families, and communities.
This family structure is a core component of human societies, providing a nurturing environment for the upbringing and development of offspring.
Nuclear families may vary in composition, such as single-parent or blended households, but share the common elements of close emotional and economic ties between the parental figures and their children.
Research on the nuclear family can provide valuable insights into social dynamics, child-rearing practices, and the evolution of familial structures across cultures and historical contexts.
This area of study is an important focus of sociological, anthropological, and psychological inquiry.
Advances in genetic and genomic technologies, such as the HiSeq 2500, HiSeq 2000, Genome Analyzer IIx, and NanoDrop spectrophotometer, have enabled researchers to explore the biological and genetic factors that influence family structures and dynamics.
Additionally, tools like the Illumina sample prep kit, CanineHD BeadChip, HumanOmni1-Quad BeadChip, and PAXgene Blood RNA Kit Handbook have facilitated the analysis of genetic and epigenetic data in the context of nuclear family studies.
By leveraging these cutting-edge technologies and methodologies, researchers can gain a deeper understanding of the complex interplay between biological, social, and cultural factors that shape the nuclear family.
This knowledge can inform policies, interventions, and support systems aimed at strengthening and supporting this core social unit, ultimately benefiting individuals, families, and communities.