Healthcare
18 columns
100 rows
8 formats

Family History and Genetic Risk

This dataset provides a comprehensive record of family health history and genetic risk assessments, including detailed information on first- and second-degree relatives, their medical conditions, age of onset, and personalized preventive care recommendations. It enables healthcare professionals and researchers to identify hereditary disease risks and optimize screening strategies for individuals based on both family history and genetic analysis.

last updated 2 Nov 2025
by GoMask

The brief that made it

Hereditary disease risk analysis for clinical decision support

Sample rows

preview · 8 of 100 rows · all 18 columns

individual_idstring	relative_relationshipstring	condition_age_of_onsetinteger	condition_statusstring	individual_first_namestring	individual_last_namestring	individual_date_of_birthdate	relative_idstring	relative_first_namestring	relative_last_namestring	relative_degreeinteger	relative_date_of_birthdate	condition_namestring	genetic_risk_assessedboolean	genetic_risk_scorefloat	genetic_risk_categorystring	screening_recommendationstring	assessment_datedate
IND001	mother	55	active	Samantha	Crawford	1978-07-14	REL001	Margaret	Crawford	1	1950-03-22	Type 2 Diabetes	true	0.76	moderate	Annual blood glucose screening	2023-10-09
IND002	father	47	active	Isaac	Nguyen	1991-11-02	REL002	Daniel	Nguyen	1	1962-12-15	Hypertension	true	0.53	moderate	Annual blood pressure check	2023-09-18
IND003	aunt	52	resolved	Ava	Perez	1982-04-25	REL003	Maria	Santos	2	1957-08-10	Breast Cancer	true	0.85	high	Early and frequent mammography	2023-12-04
IND004	father	59	deceased_due_to_condition	Michael	Johnson	1971-06-05	REL004	Edward	Johnson	1	1946-04-09	Coronary Heart Disease	true	0.92	high	Cardiac stress testing recommended	2023-08-11
IND005	sibling	8	active	Priya	Sharma	1989-10-18	REL005	Anjali	Sharma	1	1993-02-12	Asthma	true	0.42	moderate	Pulmonary function tests every 2 years	2023-11-17
IND006	mother	65	active	Benjamin	Taylor	1974-12-28	REL006	Helen	Taylor	1	1948-06-25	Osteoporosis	true	0.33	low	Bone density scan every 5 years	2023-07-21
IND007	uncle	62	deceased_due_to_condition	Fatima	Alvarez	1993-03-14	REL007	Carlos	Alvarez	2	1960-09-20	Colorectal Cancer	true	0.65	moderate	Colonoscopy every 5 years	2023-10-03
IND008	sibling	19	active	Oliver	Smith	1980-01-29	REL008	Matthew	Smith	1	1982-10-07	Epilepsy	true	0.22	low	Neurological assessment every 3 years	2023-09-22
IND009	grandparent	76	active	Chloe	Lee	1997-05-09	REL009	Hannah	Lee	2	1939-02-17	Alzheimer's Disease	true	0.57	moderate	Cognitive screening after age 60	2023-10-28
IND010	mother	49	active	David	Kim	1985-08-23	REL010	Eun	Kim	1	1956-05-05	Rheumatoid Arthritis	true	0.29	low	Joint screening every 5 years	2023-08-01
IND011	sibling	34	active	Eric	Baker	1979-04-03	REL011	Linda	Baker	1	1982-07-16	Multiple Sclerosis	true	0.48	moderate	Neurological check every 2 years	2023-09-14
IND012	father	13	active	Mina	Patel	1995-06-12	REL012	Raj	Patel	1	1964-11-30	Type 1 Diabetes	true	0.81	high	Annual endocrine screening	2023-12-11
IND013	grandparent	70	resolved	Lucas	White	1973-09-14	REL013	Evelyn	White	2	1932-11-04	Parkinson's Disease	true	0.39	low	Annual neurological assessment after 50	2023-08-29
IND014	sibling	12	active	Julia	Martinez	1990-02-17	REL014	Gabriel	Martinez	1	1993-06-23	Celiac Disease	true	0.27	low	Annual celiac antibody screening	2023-10-17
IND015	mother	58	deceased_due_to_condition	Henry	Williams	1986-12-05	REL015	Susan	Williams	1	1958-08-21	Breast Cancer	true	0.95	high	Genetic counseling and breast MRI	2023-11-03
IND016	uncle	68	active	Sofia	Garcia	1977-03-27	REL016	Luis	Garcia	2	1952-05-13	Prostate Cancer	true	0.37	low	PSA screening after age 50	2023-09-25
IND017	father	65	active	Jack	King	1978-11-10	REL017	Robert	King	1	1951-07-09	Colon Cancer	true	0.73	moderate	Colonoscopy every 5 years	2023-07-31
IND018	grandparent	81	deceased_due_to_condition	Amelia	Brown	1996-05-01	REL018	Grace	Brown	2	1940-01-17	Stroke	true	0.44	moderate	Annual cardiovascular screening	2023-11-22
IND019	sibling	6	active	Khalid	Hassan	1987-02-24	REL019	Sara	Hassan	1	1990-09-06	Asthma	true	0.34	low	Pulmonary screening every 3 years	2023-10-25
IND020	aunt	50	resolved	Emily	Chen	1992-08-03	REL020	Lily	Chen	2	1967-04-18	Ovarian Cancer	true	0.68	moderate	Annual pelvic ultrasound	2023-09-05

What the 100 rows show

from the 100-row sample

Sibling (relative relationship) stands out: mean condition_age_of_onset is 14.2, against 57.9 for the rest.

57median condition_age_of_onset
3genetic risk categories
0.90median genetic_risk_score

Mean condition_age_of_onset by relative_relationship100 rows

condition_age_of_onset100 rows, in bands of 10

Median 57, from 2 to 87.

condition_status100 rows · 3 values

active65
deceased_due_to_condition18
resolved17

18 columns by typefrom the column list below

string 11
integer 2
float 1
date 3
boolean 1

Columns

18 columns in four groups

blueprint · 18 columns
column	type	description	example
Text 11 columns
`individual_id`	string	Unique identifier for the individual whose family history is being recordedunique	IND001
`individual_first_name`	string	First name of the individual	Samantha
`individual_last_name`	string	Last name of the individual	Crawford
`relative_id`	string	Unique identifier for the relativeunique	REL001
`relative_first_name`	string	First name of the relativeoptional	Margaret
`relative_last_name`	string	Last name of the relativeoptional	Crawford
`relative_relationship`	string	Relationship of the relative to the individual (e.g., mother, father, sibling, grandparent, aunt, uncle)10 values	mother
`condition_name`	string	Name of the medical condition or disease diagnosed in the relative	Type 2 Diabetes
`condition_status`	string	Current status of the condition (e.g., active, resolved, deceased due to condition)active · resolved · deceased_due_to_condition · optional	active
`genetic_risk_category`	string	Risk category based on genetic assessment (e.g., low, moderate, high)low · moderate · high · optional	moderate
`screening_recommendation`	string	Recommended preventive care or hereditary disease screening based on family history and genetic riskoptional	Annual pelvic ultrasound
Numbers 3 columns
`relative_degree`	integer	Degree of relationship (1 for first-degree, 2 for second-degree)1 to 2	1
`condition_age_of_onset`	integer	Age at which the condition was first diagnosed in the relative0 or more · optional	55
`genetic_risk_score`	float	Calculated genetic risk score for the individual (higher values indicate greater risk)0 or more · optional	0.76
Dates and times 3 columns
`individual_date_of_birth`	date	Date of birth of the individual	1978-07-14
`relative_date_of_birth`	date	Date of birth of the relativeoptional	1950-03-22
`assessment_date`	date	Date when the genetic risk assessment and screening recommendation were madeoptional	2023-10-09
True or false 1 column
`genetic_risk_assessed`	boolean	Indicates whether a genetic risk assessment has been performed for the individual	true

Use it for

A healthcare dashboard
Condition_age_of_onset by relative_relationship and a breakdown of condition_status. Excel, Power BI or Tableau.
Why do the 20 sibling rows have a mean condition_age_of_onset of 14.2?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Individuals100
IND00155mother
IND00247father
IND00352aunt
A software demo
Believable individuals with individual_first_name, individual_last_name and individual_date_of_birth to fill a screen in front of a buyer.

This dataset100 rows18 columns

Yours1,000 rows18 columns

blueprint · family-history-and-genetic-risk

Behind this dataset

Same schema. As many rows as you need.

These 100 rows came out of a blueprint — 18 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Relationship: mother, father, sibling, grandparent, aunt, uncle
Condition with ICD-10 or SNOMED code
Age of onset for condition
Living or deceased status
Age at death and cause if deceased
Three-generation pedigree for genetic counseling
Red flags: early-onset cancer, multiple affected relatives
Risk calculation for hereditary syndromes (BRCA, Lynch)

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: family-history-and-genetic-risk

What should your data show?

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Sample rows

What the 100 rows show

Columns

Use it for

A healthcare dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?